Comprehensive Physiology Wiley Online Library
For Librarians For Authors Editors About

Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research

Michael T. Zimmermann

10.1002/cphy.c190047

Source: Volume 12, Issue 2, April 2022

Published online: March 2022

Full Article on Wiley Online Library



Abstract

Hundreds of human membrane proteins form channels that transport necessary ions and compounds, including drugs and metabolites, yet details of their normal function or how function is altered by genetic variants to cause diseases are often unknown. Without this knowledge, researchers are less equipped to develop approaches to diagnose and treat channelopathies. High‐resolution computational approaches such as molecular modeling enable researchers to investigate channelopathy protein function, facilitate detailed hypothesis generation, and produce data that is difficult to gather experimentally. Molecular modeling can be tailored to each physiologic context that a protein may act within, some of which may currently be difficult or impossible to assay experimentally. Because many genomic variants are observed in channelopathy proteins from high‐throughput sequencing studies, methods with mechanistic value are needed to interpret their effects. The eminent field of structural bioinformatics integrates techniques from multiple disciplines including molecular modeling, computational chemistry, biophysics, and biochemistry, to develop mechanistic hypotheses and enhance the information available for understanding function. Molecular modeling and simulation access 3D and time‐dependent information, not currently predictable from sequence. Thus, molecular modeling is valuable for increasing the resolution with which the natural function of protein channels can be investigated, and for interpreting how genomic variants alter them to produce physiologic changes that manifest as channelopathies. © 2022 American Physiological Society. Compr Physiol 12:3141‐3166, 2022.

Figure 1. Figure 1. Structural biology enables 3D interpretation of ion channel function. We use an example of a channelopathy gene, KCNK9, which encodes a homodimeric K+ ion channel transporter. (A) We show the protein sequence annotated in different ways using data from UniProt 247 and the MESSA webserver 49. The amino acids are numbered and the sequence colored by amino acid type with hydrophobic amino acids highlighted in yellow, acidic in red, and basic in blue. The second line shows sequence‐based prediction of secondary structures. Next, sequence‐based predictions of transmembrane helices. Finally, we show the localization annotation from UniProt. We underline residues predicted to be disordered by the DisEmbl program 145. Reproduced, with permission, from UniProt C, 2019 247; Cong Q and Grishin NV, 2012 49. (B) We show the dimer structure in a typical cartoon representation and each monomer colored green or blue and the trans‐membrane region marked by grey lines. The amino acid residues of KCNK9 that form the K+ ion selectivity filter are shown in a ball‐and‐stick representation with carbon atoms from each monomer colored green or blue, oxygen red, and nitrogen dark blue. From the model, we can better understand the biochemistry and mechanics of function, compared to only knowing these regions of sequence are pore‐forming. The four most likely positions for ions within the channel are predicted and shown as black spheres in (C) the same orientation, and (D) rotated by 90°.
Figure 2. Figure 2. Diversity of channelopathy structures. Representatives of the diversity of channelopathy structures were chosen from major branches of the phylogenetic tree (Figure 3), and that have most of the protein experimentally resolved or where most of the protein can be modeled (indicated with blue or purple dot). KCNQ1 (purple dot) is an example of a protein where a molecular model can be generated due to known functional and biochemical relationships across its protein family, but that are not evident from purely sequence‐based homology detection (blue dots). Many channelopathy proteins function as multimers. One monomer from each protein has its solvent‐accessible surface shown in transparent white. Beta‐strands are colored yellow, alpha‐helices on the extracellular side are colored dark teal, and alpha‐helices on the intracellular side purple. Trans‐membrane (TM) helices are colored tan. TM helix positions were predicted using hidden Markov models 129 and cellular locations are taken from sequence‐based predictions 170. Ligands are shown as thick sticks colored by atom type and ions as black spheres.
Figure 3. Figure 3. Phylogenetic tree of channelopathy proteins colored by 3D experimental data availability. (A) We show the unrooted tree generated from channelopathy protein sequences and colored by the fraction of amino acids in the protein that either have been solved in 3D experimentally or have a homologous experimental structure available, which can be used to generate a 3D model. The 11 proteins shown in detail (Figure 2) are indicated by gold asterisks. (B) We show the maximum identity across any region of each channelopathy protein.
Figure 4. Figure 4. Maximum coverage of channelopathy proteins by existing 3D structures. The median of 0.7 is marked by an orange line. This histogram summarizes the data shown in the channelopathy phylogeny (Figure 3B).
Figure 5. Figure 5. Detailed coverage summary of channelopathy proteins by existing 3D structures. We show each instance of a channelopathy protein with a sequence‐based relationship to an existing experimental 3D structure as a point, colored by sequence identity. The (A) length of the template's overlap with the channelopathy protein (number of amino acids) and (B) the fraction of the channelopathy protein covered indicate the extent with which molecular modeling can extend existing data to enable 3D structure‐based assessment of channelopathy proteins.
Figure 6. Figure 6. Most channelopathy proteins can be modeled in 3D using homology‐based methods. We combine the data about template coverage to show the fraction of amino acids across all channelopathy proteins that are accessible to 3D modeling through homology‐based methods. Each point is an individual channelopathy protein. At each of the five levels of sequence homology, we show the fraction of amino acids that can be modeled in 3D. We used a violin plot to show the density of data at each level of amino acid coverage. Finally, we binned the amino acid coverage into ten levels and used a heatmap to indicate the number of channelopathy proteins within each bin. The right‐most bin (98%) primarily contains human experimental structures; few channelopathy proteins have been experimentally solved. Most channelopathy proteins, at least in part, can be modeled in 3D using homology‐based methods.
Figure 7. Figure 7. Types of protein visual representations emphasize different protein features. This figure shows the HIV‐1 protease, 1T3R 235, from the PDB 29, in the same orientation and under seven different visualizations from five commonly used software tools. Each software has its own visual style. We show a comparison between default and straightforward visual representations (preset options) using: (A) QuteMol 238 showing atomic spheres, (B) Discovery Studio Visualizer 33 showing cartoons through the protein backbone and bound inhibitor, (C) Chimera 186 showing the same but colored by the two monomers or surface properties, (D) visual molecular dynamics (VMD) 100 showing a similar cartoon view but with different extent of smoothing through the backbone, and (E) PyMol 192 with a tube through the protein backbone scaled in thickness and color by apparent mobility (B‐factors) in the crystallographic structure. (F) Beyond the whole‐protein view, the representation used for the atoms themselves can convey a different amount of information. For example, The PKMIGGIGGFIKVR peptide from HIV‐1 protease is shown in four representations: i, cartoon with hydrogen bonds; ii, backbone nonhydrogen atoms in ball‐and‐stick; iii, all nonhydrogen atoms; iv, cartoon for the backbone and nonhydrogen atoms of side chains.
Figure 8. Figure 8. KCNJ11 tetramer in complex with ATP and ABCC8. The experimental tetramer is shown (A) in two orientations, colored by monomer, and with the molecular surface shown. One monomer has a transparent surface and shows the cartoon representation as shown previously (Figure 1). (B) KCNJ11 monomers for close interactions with ABCC8 in 1:1 stoichiometry. (C) From an inward‐facing view, KCNJ11 trans‐membrane helices pack next to ABCC8 trans‐membrane helices (e.g., light green to dark green), but from an outward‐facing view, the monomer‐monomer relationship is shifted (e.g., light‐green to gray). (D) The ATP binding site is composed of residues from two adjacent KCNJ11 monomers, with residues from ABCC8 close by and supporting.
Figure 9. Figure 9. Charge segregation in channelopathy proteins is critical for assembly and function. We show three channelopathy proteins using APBS electrostatic calculations and a combination of surface and cartoon representations. Cartoons are colored as previously (Figure 2). Surface color scale is the same in all panels and indicates the degree of electrostatic charge. The importance of electrostatic complementarity between monomers is visually apparent. (A) AQP2 is a homotetramer and we show two monomers in their electrostatic surface, and two in cartoon representation. Three orientations show the protein from the perspective of the (i) intracellular‐facing, (ii) membrane‐plane, and (iii) extracellular‐facing sides. (B) GABRG2 is a homopentamer and we show two monomers in their electrostatic surface and three in cartoons. For clarity, the membrane‐plane view, we show a second view that has only the two monomers' surfaces shown, to better view the positive interior. (C) (i) GRIA2 is a homotetramer and we show one monomer in electrostatic surface and in two views. (ii) We show the trans‐membrane regions from an intracellular perspective to view the wide inner membrane cavity. The surfaces of the remaining three monomers are colored tan.
Figure 10. Figure 10. Genomic diversity among channelopathy proteins. We show (A‐C) KCNQ1, a model based on protein family similarity and with very low sequence‐based homology, and (D‐F) GRIN2A, an experimentally determined structure. Because the KCNQ1 model is primarily embedded in the membrane, we show a top‐down view, while GRIN2A has a bulky extracellular domain, so we show it from a side profile. GRIN2A and KCNQ1 are from two ends of the spectrum between many experimental measures informing the model, and few, respectively. Yet, we believe both models are highly useful for generating structure‐based hypotheses. (A, D) We place a sphere at each residue position where there is an observed genomic variant affecting the encoded amino acid. Spheres are colored by their disease context. KCNQ1 has many germline disease genes, while GRIN2A has few. Those occurring in GRIN2A do so primarily at two specific regions—at the top of the transmembrane domain and within the core of the middle domain. Variants observed somatically in cancer follow the same pattern. However, KCNQ1 has few variant sites in the currently healthy adult population, while GRIN2A has them throughout the entire structure. (B, E) Most of these variants are rare, but both proteins have a subset of variants seen in up to one in a thousand people. (C, F) We used a structure‐based algorithm to predict stability changes for each genomic variant. Rare and disease variation is more likely to be destabilizing.
Figure 11. Figure 11. Distribution of pathogenic variants and VUS across channelopathy proteins. Each channelopathy protein is plotted based on the number of ClinVar variants of uncertain significance (VUS; plus one to better accommodate log‐scale) and (likely) pathogenic variants. Coloring is by the total number of ClinVar variants per protein, including benign. Proteins with at least 200 distinct variants are labeled. KCNQ1 and GRIN2A, shown as previously (Figure 10), are among the labeled proteins.
Figure 12. Figure 12. Construction of an Elastic Network Model. (A) We use the HIV‐1 protease as an illustrative example (similar to Figure 7). We show the overall structure of the homodimer colored by crystallographic B‐factors. (B) Spheres mark the Cα positions. Any reference point from each residue can be used, but Cα is the most common. (C) To construct an ENM, we first start with a given residue (red sphere) and connect all residues within a cutoff. This procedure is repeated for all residues until we have constructed (D) an ENM.
Figure 13. Figure 13. ENM modes of motion inform about KCNQ1 mechanism. We use the example of KCNQ1, a model based on protein family similarity and with very low sequence‐based homology, but which can be used to gain insight into potential mechanisms. Reproduced, with permission, from Paquin A, et al., 2018 181; Smith JA, et al., 2007 228.


Figure 1. Structural biology enables 3D interpretation of ion channel function. We use an example of a channelopathy gene, KCNK9, which encodes a homodimeric K+ ion channel transporter. (A) We show the protein sequence annotated in different ways using data from UniProt 247 and the MESSA webserver 49. The amino acids are numbered and the sequence colored by amino acid type with hydrophobic amino acids highlighted in yellow, acidic in red, and basic in blue. The second line shows sequence‐based prediction of secondary structures. Next, sequence‐based predictions of transmembrane helices. Finally, we show the localization annotation from UniProt. We underline residues predicted to be disordered by the DisEmbl program 145. Reproduced, with permission, from UniProt C, 2019 247; Cong Q and Grishin NV, 2012 49. (B) We show the dimer structure in a typical cartoon representation and each monomer colored green or blue and the trans‐membrane region marked by grey lines. The amino acid residues of KCNK9 that form the K+ ion selectivity filter are shown in a ball‐and‐stick representation with carbon atoms from each monomer colored green or blue, oxygen red, and nitrogen dark blue. From the model, we can better understand the biochemistry and mechanics of function, compared to only knowing these regions of sequence are pore‐forming. The four most likely positions for ions within the channel are predicted and shown as black spheres in (C) the same orientation, and (D) rotated by 90°.


Figure 2. Diversity of channelopathy structures. Representatives of the diversity of channelopathy structures were chosen from major branches of the phylogenetic tree (Figure 3), and that have most of the protein experimentally resolved or where most of the protein can be modeled (indicated with blue or purple dot). KCNQ1 (purple dot) is an example of a protein where a molecular model can be generated due to known functional and biochemical relationships across its protein family, but that are not evident from purely sequence‐based homology detection (blue dots). Many channelopathy proteins function as multimers. One monomer from each protein has its solvent‐accessible surface shown in transparent white. Beta‐strands are colored yellow, alpha‐helices on the extracellular side are colored dark teal, and alpha‐helices on the intracellular side purple. Trans‐membrane (TM) helices are colored tan. TM helix positions were predicted using hidden Markov models 129 and cellular locations are taken from sequence‐based predictions 170. Ligands are shown as thick sticks colored by atom type and ions as black spheres.


Figure 3. Phylogenetic tree of channelopathy proteins colored by 3D experimental data availability. (A) We show the unrooted tree generated from channelopathy protein sequences and colored by the fraction of amino acids in the protein that either have been solved in 3D experimentally or have a homologous experimental structure available, which can be used to generate a 3D model. The 11 proteins shown in detail (Figure 2) are indicated by gold asterisks. (B) We show the maximum identity across any region of each channelopathy protein.


Figure 4. Maximum coverage of channelopathy proteins by existing 3D structures. The median of 0.7 is marked by an orange line. This histogram summarizes the data shown in the channelopathy phylogeny (Figure 3B).


Figure 5. Detailed coverage summary of channelopathy proteins by existing 3D structures. We show each instance of a channelopathy protein with a sequence‐based relationship to an existing experimental 3D structure as a point, colored by sequence identity. The (A) length of the template's overlap with the channelopathy protein (number of amino acids) and (B) the fraction of the channelopathy protein covered indicate the extent with which molecular modeling can extend existing data to enable 3D structure‐based assessment of channelopathy proteins.


Figure 6. Most channelopathy proteins can be modeled in 3D using homology‐based methods. We combine the data about template coverage to show the fraction of amino acids across all channelopathy proteins that are accessible to 3D modeling through homology‐based methods. Each point is an individual channelopathy protein. At each of the five levels of sequence homology, we show the fraction of amino acids that can be modeled in 3D. We used a violin plot to show the density of data at each level of amino acid coverage. Finally, we binned the amino acid coverage into ten levels and used a heatmap to indicate the number of channelopathy proteins within each bin. The right‐most bin (98%) primarily contains human experimental structures; few channelopathy proteins have been experimentally solved. Most channelopathy proteins, at least in part, can be modeled in 3D using homology‐based methods.


Figure 7. Types of protein visual representations emphasize different protein features. This figure shows the HIV‐1 protease, 1T3R 235, from the PDB 29, in the same orientation and under seven different visualizations from five commonly used software tools. Each software has its own visual style. We show a comparison between default and straightforward visual representations (preset options) using: (A) QuteMol 238 showing atomic spheres, (B) Discovery Studio Visualizer 33 showing cartoons through the protein backbone and bound inhibitor, (C) Chimera 186 showing the same but colored by the two monomers or surface properties, (D) visual molecular dynamics (VMD) 100 showing a similar cartoon view but with different extent of smoothing through the backbone, and (E) PyMol 192 with a tube through the protein backbone scaled in thickness and color by apparent mobility (B‐factors) in the crystallographic structure. (F) Beyond the whole‐protein view, the representation used for the atoms themselves can convey a different amount of information. For example, The PKMIGGIGGFIKVR peptide from HIV‐1 protease is shown in four representations: i, cartoon with hydrogen bonds; ii, backbone nonhydrogen atoms in ball‐and‐stick; iii, all nonhydrogen atoms; iv, cartoon for the backbone and nonhydrogen atoms of side chains.


Figure 8. KCNJ11 tetramer in complex with ATP and ABCC8. The experimental tetramer is shown (A) in two orientations, colored by monomer, and with the molecular surface shown. One monomer has a transparent surface and shows the cartoon representation as shown previously (Figure 1). (B) KCNJ11 monomers for close interactions with ABCC8 in 1:1 stoichiometry. (C) From an inward‐facing view, KCNJ11 trans‐membrane helices pack next to ABCC8 trans‐membrane helices (e.g., light green to dark green), but from an outward‐facing view, the monomer‐monomer relationship is shifted (e.g., light‐green to gray). (D) The ATP binding site is composed of residues from two adjacent KCNJ11 monomers, with residues from ABCC8 close by and supporting.


Figure 9. Charge segregation in channelopathy proteins is critical for assembly and function. We show three channelopathy proteins using APBS electrostatic calculations and a combination of surface and cartoon representations. Cartoons are colored as previously (Figure 2). Surface color scale is the same in all panels and indicates the degree of electrostatic charge. The importance of electrostatic complementarity between monomers is visually apparent. (A) AQP2 is a homotetramer and we show two monomers in their electrostatic surface, and two in cartoon representation. Three orientations show the protein from the perspective of the (i) intracellular‐facing, (ii) membrane‐plane, and (iii) extracellular‐facing sides. (B) GABRG2 is a homopentamer and we show two monomers in their electrostatic surface and three in cartoons. For clarity, the membrane‐plane view, we show a second view that has only the two monomers' surfaces shown, to better view the positive interior. (C) (i) GRIA2 is a homotetramer and we show one monomer in electrostatic surface and in two views. (ii) We show the trans‐membrane regions from an intracellular perspective to view the wide inner membrane cavity. The surfaces of the remaining three monomers are colored tan.


Figure 10. Genomic diversity among channelopathy proteins. We show (A‐C) KCNQ1, a model based on protein family similarity and with very low sequence‐based homology, and (D‐F) GRIN2A, an experimentally determined structure. Because the KCNQ1 model is primarily embedded in the membrane, we show a top‐down view, while GRIN2A has a bulky extracellular domain, so we show it from a side profile. GRIN2A and KCNQ1 are from two ends of the spectrum between many experimental measures informing the model, and few, respectively. Yet, we believe both models are highly useful for generating structure‐based hypotheses. (A, D) We place a sphere at each residue position where there is an observed genomic variant affecting the encoded amino acid. Spheres are colored by their disease context. KCNQ1 has many germline disease genes, while GRIN2A has few. Those occurring in GRIN2A do so primarily at two specific regions—at the top of the transmembrane domain and within the core of the middle domain. Variants observed somatically in cancer follow the same pattern. However, KCNQ1 has few variant sites in the currently healthy adult population, while GRIN2A has them throughout the entire structure. (B, E) Most of these variants are rare, but both proteins have a subset of variants seen in up to one in a thousand people. (C, F) We used a structure‐based algorithm to predict stability changes for each genomic variant. Rare and disease variation is more likely to be destabilizing.


Figure 11. Distribution of pathogenic variants and VUS across channelopathy proteins. Each channelopathy protein is plotted based on the number of ClinVar variants of uncertain significance (VUS; plus one to better accommodate log‐scale) and (likely) pathogenic variants. Coloring is by the total number of ClinVar variants per protein, including benign. Proteins with at least 200 distinct variants are labeled. KCNQ1 and GRIN2A, shown as previously (Figure 10), are among the labeled proteins.


Figure 12. Construction of an Elastic Network Model. (A) We use the HIV‐1 protease as an illustrative example (similar to Figure 7). We show the overall structure of the homodimer colored by crystallographic B‐factors. (B) Spheres mark the Cα positions. Any reference point from each residue can be used, but Cα is the most common. (C) To construct an ENM, we first start with a given residue (red sphere) and connect all residues within a cutoff. This procedure is repeated for all residues until we have constructed (D) an ENM.


Figure 13. ENM modes of motion inform about KCNQ1 mechanism. We use the example of KCNQ1, a model based on protein family similarity and with very low sequence‐based homology, but which can be used to gain insight into potential mechanisms. Reproduced, with permission, from Paquin A, et al., 2018 181; Smith JA, et al., 2007 228.
References
 1.Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E. GROMACS: High performance molecular simulations through multi‐level parallelism from laptops to supercomputers. SoftwareX 1–2: 19‐25, 2015. https://www.sciencedirect.com/science/article/pii/S2352711015000059.
 2.Ackerman MJ. Genetic purgatory and the cardiac channelopathies: Exposing the variants of uncertain/unknown significance issue. Heart Rhythm 12: 2325‐2331, 2015.
 3.Agrahari AK, Sneha P, George Priya Doss C, Siva R, Zayed H. A profound computational study to prioritize the disease‐causing mutations in PRPS1 gene. Metab Brain Dis 33: 589‐600, 2018.
 4.Ahern CA, Payandeh J, Bosmans F, Chanda B. The hitchhiker's guide to the voltage‐gated sodium channel galaxy. J Gen Physiol 147: 1‐24, 2016.
 5.Albers B. Molecular Biology of the Cell. New York: Garland Science, 2017.
 6.Alders M, Bikker H, Christiaans I. Long QT syndrome. In: Adam MP, Ardinger HH, Pagon RA, Wallace SE, Bean LJH, Mirzaa G, Amemiya A, editors. GeneReviews®. Seattle (WA), 1993. https://pubmed.ncbi.nlm.nih.gov/20301308/
 7.Allen JP. Recent innovations in membrane‐protein structural biology. F1000Res 8, 2019. DOI: 10.12688/f1000research.16234.1.
 8.Allen MP, Tildesley DJ. Computer Simulation of Liquids. Oxford: OUP, 2017.
 9.Almen MS, Nordstrom KJ, Fredriksson R, Schioth HB. Mapping the human membrane proteome: A majority of the human membrane proteins can be classified according to function and evolutionary origin. BMC Biol 7: 50, 2009.
 10.Andreeva A, Howorth D, Chothia C, Kulesha E, Murzin AG. SCOP2 prototype: A new approach to protein structure mining. Nucleic Acids Res 42: D310‐D314, 2014.
 11.Anfinsen CB, Haber E. Studies on the reduction and re‐formation of protein disulfide bonds. J Biol Chem 236: 1361‐1363, 1961.
 12.Arantes PR, Polêto MD, Pedebos C, Ligabue‐Braun R. Making‐it‐rain: Cloud‐based molecular simulations for everyone. Zenodo 61 (10): 4852‐4856, 2021.
 13.Ardevol A, Tribello GA, Ceriotti M, Parrinello M. Probing the unfolded configurations of a beta‐hairpin using sketch‐map. J Chem Theory Comput 11: 1086‐1093, 2015.
 14.Aydinkal RM, Sercinoglu O, Ozbek P. ProSNEx: A web‐based application for exploration and analysis of protein structures using network formalism. Nucleic Acids Res 47: W471‐W476, 2019.
 15.Bahar I, Jernigan R, Dill K. Protein Actions: Principles and Modeling. New York: Garland Science, 2017.
 16.Bahar I, Lezon TR, Bakan A, Shrivastava IH. Normal mode analysis of biomolecular structures: Functional mechanisms of membrane proteins. Chem Rev 110: 1463‐1497, 2010.
 17.Bahar I, Rader AJ. Coarse‐grained normal mode analysis in structural biology. Curr Opin Struct Biol 15: 586‐592, 2005.
 18.Bakan A, Dutta A, Mao W, Liu Y, Chennubhotla C, Lezon TR, Bahar I. Evol and ProDy for bridging protein sequence evolution and structural dynamics. Bioinformatics 30: 2681‐2683, 2014.
 19.Bakan A, Meireles LM, Bahar I. ProDy: Protein dynamics inferred from theory and experiments. Bioinformatics 27: 1575‐1577, 2011.
 20.Baker NA, Sept D, Joseph S, Holst MJ, McCammon JA. Electrostatics of nanosystems: Application to microtubules and the ribosome. Proc Natl Acad Sci U S A 98: 10037‐10041, 2001.
 21.Barley MH, Turner NJ, Goodacre R. Improved descriptors for the quantitative structure‐activity relationship modeling of peptides and proteins. J Chem Inf Model 58: 234‐243, 2018.
 22.Becker KG, Barnes KC, Bright TJ, Wang SA. The genetic association database. Nat Genet 36: 431‐432, 2004.
 23.Behere SP, Weindling SN. Inherited arrhythmias: The cardiac channelopathies. Ann Pediatr Cardiol 8: 210‐220, 2015.
 24.Bendahhou S, Cummins TR, Kula RW, Fu YH, Ptacek LJ. Impairment of slow inactivation as a common mechanism for periodic paralysis in DIIS4‐S5. Neurology 58: 1266‐1272, 2002.
 25.Benkert P, Kunzli M, Schwede T. QMEAN server for protein model quality estimation. Nucleic Acids Res 61 (10): 4852‐4856, 2009.
 26.Benson NC, Daggett V. Dynameomics: Large‐scale assessment of native protein flexibility. Protein Sci 17: 2038‐2050, 2008.
 27.Bera I, Payghan PV. Use of molecular dynamics simulations in structure‐based drug discovery. Curr Pharm Des 25: 3339‐3349, 2019.
 28.Berjanskii M, Zhou J, Liang Y, Lin G, Wishart DS. Resolution‐by‐proxy: A simple measure for assessing and comparing the overall quality of NMR protein structures. J Biomol NMR 53: 167‐180, 2012.
 29.Berman HM, Bhat TN, Bourne PE, Feng Z, Gilliland G, Weissig H, Westbrook J. The Protein Data Bank and the challenge of structural genomics. Nat Struct Biol 7 (Suppl): 957‐959, 2000.
 30.Bhattacharya D, Cheng J. De novo protein conformational sampling using a probabilistic graphical model. Sci Rep 5: 16332, 2015.
 31.Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, Kiefer F, Cassarino TG, Bertoni M, Bordoli L, Schwede T. SWISS‐MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42: W252‐W258, 2014.
 32.Bienert S, Waterhouse A, de Beer TA, Tauriello G, Studer G, Bordoli L, Schwede T. The SWISS‐MODEL Repository‐new features and functionality. Nucleic Acids Res 45: D313‐D319, 2017.
 33.BIOVIA. Dassault Systèmes BIOVIA, Discovery Studio Modeling Environment, Release 2017. San Diego: Dassault Systèmes, 2017.
 34.Blackburn PR, Tischer A, Zimmermann MT, Kemppainen JL, Sastry S, Knight Johnson AE, Cousin MA, Boczek NJ, Oliver G, Misra VK, Gavrilova RH, Lomberk G, Auton M, Urrutia R, Klee EW. A novel Kleefstra Syndrome‐associated variant that affects the conserved TPLX motif within the Ankyrin repeat of EHMT1 Leads to abnormal protein folding. J Biol Chem 292: 3866‐3876, 2017.
 35.Botan A, Favela‐Rosales F, Fuchs PFJ, Javanainen M, Kanduč M, Kulig W, Lamberg A, Loison C, Lyubartsev A, Miettinen MS, Monticelli L, Määttä J, Ollila OHS, Retegan M, Róg T, Santuz H, Tynkkynen J. Toward atomistic resolution structure of phosphatidylcholine headgroup and glycerol backbone at different ambient conditions. J Phys Chem B 119: 15075‐15088, 2015.
 36.Botu V, Batra R, Chapman J, Ramprasad R. Machine learning force fields: Construction, validation, and outlook. J Phys Chem C 121: 511‐522, 2017.
 37.Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE. Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the 2006 ACM/IEEE conference on Supercomputing. Tampa, Florida: ACM, 2006, p. 746.
 38.Bowman GR. A tutorial on building markov state models with MSMBuilder and coarse‐graining them with BACE. Methods Mol Biol 1084: 141‐158, 2014.
 39.Bowman JD, Lindert S. Molecular dynamics and umbrella sampling simulations elucidate differences in troponin C isoform and mutant hydrophobic patch exposure. J Phys Chem B 122: 7874‐7883, 2018.
 40.Brooks BR, Brooks CL 3rd, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner AR, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor RW, Post CB, Pu JZ, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York DM, Karplus M. CHARMM: The biomolecular simulation program. J Comput Chem 30: 1545‐1614, 2009.
 41.Bruccoleri RE, Karplus M. Conformational sampling using high‐temperature molecular dynamics. Biopolymers 29: 1847‐1862, 1990.
 42.Campuzano O, Allegue C, Fernandez A, Iglesias A, Brugada R. Determining the pathogenicity of genetic variants associated with cardiac channelopathies. Sci Rep 5: 7953, 2015.
 43.Case DA, Cheatham TE 3rd, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ. The Amber biomolecular simulation programs. J Comput Chem 26: 1668‐1688, 2005.
 44.Ceriotti M, Tribello GA, Parrinello M. From the cover: Simplifying the representation of complex free‐energy landscapes using sketch‐map. Proc Natl Acad Sci U S A 108: 13023‐13028, 2011.
 45.Chandonia JM, Fox NK, Brenner SE. SCOPe: Classification of large macromolecular structures in the structural classification of proteins‐extended database. Nucleic Acids Res 47: D475‐D481, 2019.
 46.Chavent M, Duncan AL, Sansom MS. Molecular dynamics simulations of membrane proteins and their interactions: From nanoscale to mesoscale. Curr Opin Struct Biol 40: 8‐16, 2016.
 47.Chen CW, Lin MH, Liao CC, Chang HP, Chu YW. iStable 2.0: Predicting protein thermal stability changes by integrating various characteristic modules. Comput Struct Biotechnol J 18: 622‐630, 2020.
 48.Cino EA, Choy WY, Karttunen M. Comparison of secondary structure formation using 10 different force fields in microsecond molecular dynamics simulations. J Chem Theory Comput 8: 2725‐2740, 2012.
 49.Cong Q, Grishin NV. MESSA: MEta‐server for protein sequence analysis. BMC Biol 10: 82, 2012.
 50.consortium P. Promoting transparency and reproducibility in enhanced molecular simulations. Nat Methods 16: 670‐673, 2019.
 51.da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44‐57, 2009.
 52.da Huang W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37: 1‐13, 2009.
 53.Dana JM, Gutmanas A, Tyagi N, Qi G, O'Donovan C, Martin M, Velankar S. SIFTS: Updated structure integration with function, taxonomy and sequences resource allows 40‐fold increase in coverage of structure‐based annotations for proteins. Nucleic Acids Res 47: D482‐D489, 2019.
 54.Das A, Gur M, Cheng MH, Jo S, Bahar I, Roux B. Exploring the conformational transitions of biomolecular systems using a simple two‐state anisotropic network model. PLoS Comput Biol 10: e1003521, 2014.
 55.Daura X, Mark AE, Van Gunsteren WF. Parametrization of aliphatic CHn united atoms of GROMOS96 force field. J Comput Chem 19: 535‐547, 1998.
 56.de Groot BL, Grubmuller H. Water permeation across biological membranes: Mechanism and dynamics of aquaporin‐1 and GlpF. Science 294: 2353‐2357, 2001.
 57.de Jong DH, Singh G, Bennett WF, Arnarez C, Wassenaar TA, Schafer LV, Periole X, Tieleman DP, Marrink SJ. Improved parameters for the martini coarse‐grained protein force field. J Chem Theory Comput 9: 687‐697, 2013.
 58.Defesche JC. Low‐density lipoprotein receptor—its structure, function, and mutations. Semin Vasc Med 4: 5‐11, 2004.
 59.Deserno M, Holm C. How to mesh up Ewald sums. I. A theoretical and numerical comparison of various particle mesh routines. J Chem Phys 109: 7678‐7693, 1998.
 60.Divakar S, Hariharan S. 3D‐QSAR studies on Plasmodium falciparam proteins: A mini‐review. Comb Chem High Throughput Screen 18: 188‐198, 2015.
 61.Dong YW, Liao ML, Meng XL, Somero GN. Structural flexibility and protein adaptation to temperature: Molecular dynamics analysis of malate dehydrogenases of marine molluscs. Proc Natl Acad Sci U S A 115: 1274‐1279, 2018.
 62.Dror RO, Dirks RM, Grossman JP, Xu H, Shaw DE. Biomolecular simulation: A computational microscope for molecular biology. Annu Rev Biophys 41: 429‐452, 2012.
 63.Dyson HJ, Wright PE. Intrinsically unstructured proteins and their functions. Nat Rev Mol Cell Biol 6: 197‐208, 2005.
 64.Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG. A smooth particle mesh Ewald method. J Chem Phys 103: 8577‐8593, 1995.
 65.Eswar N, Webb B, Marti‐Renom MA, Madhusudhan MS, Eramian D, Shen MY, Pieper U, Sali A. Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics 2006 Oct; Chapter 5: Unit-5.6, 2006.
 66.Evans P, McCoy A. An introduction to molecular replacement. Acta Crystallogr D Biol Crystallogr 64: 1‐10, 2008.
 67.Ewen JP, Gattinoni C, Thakkar FM, Morgan N, Spikes HA, Dini D. A comparison of classical force‐fields for molecular dynamics simulations of lubricants. Materials (Basel) 9, 2016, p. 651.
 68.Falck E, Rog T, Karttunen M, Vattulainen I. Lateral diffusion in lipid membranes through collective flows. J Am Chem Soc 130: 44‐45, 2008.
 69.Federici G, Soddu S. Variants of uncertain significance in the era of high‐throughput genome sequencing: A lesson from breast and ovary cancers. J Exp Clin Cancer Res 39: 46, 2020.
 70.Feng Y, Kloczkowski A, Jernigan RL. Four‐body contact potentials derived from two protein datasets to discriminate native structures from decoys. Proteins 68: 57‐66, 2007.
 71.Fernandez‐Falgueras A, Sarquella‐Brugada G, Brugada J, Brugada R, Campuzano O. Cardiac channelopathies and sudden death: Recent clinical and genetic advances. Biology (Basel) 6, 2017, p. 7.
 72.Ferreiro DU, Hegler JA, Komives EA, Wolynes PG. Localizing frustration in native proteins and protein assemblies. Proc Natl Acad Sci U S A 104: 19819‐19824, 2007.
 73.Gabanyi MJ, Adams PD, Arnold K, Bordoli L, Carter LG, Flippen‐Andersen J, Gifford L, Haas J, Kouranov A, McLaughlin WA, Micallef DI, Minor W, Shah R, Schwede T, Tao YP, Westbrook JD, Zimmerman M, Berman HM. The Structural Biology Knowledgebase: A portal to protein structures, sequences, functions, and methods. J Struct Funct Genom 12: 45‐54, 2011.
 74.Gaudet P, Michel PA, Zahn‐Zabal M, Britan A, Cusin I, Domagalski M, Duek PD, Gateau A, Gleizes A, Hinard V, Rech de Laval V, Lin J, Nikitin F, Schaeffer M, Teixeira D, Lane L, Bairoch A. The neXtProt knowledgebase on human proteins: 2017 update. Nucleic Acids Res 45: D177‐D182, 2017.
 75.Giollo M, Martin AJ, Walsh I, Ferrari C, Tosatto SC. NeEMO: A method using residue interaction networks to improve prediction of protein stability upon mutation. BMC Genomics 15 (Suppl 4): S7, 2014.
 76.Glykos NM. Software news and updates. Carma: A molecular dynamics analysis program. J Comput Chem 27: 1765‐1768, 2006.
 77.Goodsell DS, Autin L, Olson AJ. Illustrate: Software for biomolecular illustration. Structure 27: 1716.e1711‐1720.e1711, 2019.
 78.Goossens K, De Winter H. Molecular dynamics simulations of membrane proteins: An overview. J Chem Inf Model 58: 2193‐2202, 2018.
 79.Grant BJ, Rodrigues AP, ElSawy KM, McCammon JA, Caves LS. Bio3d: An R package for the comparative analysis of protein structures. Bioinformatics 22: 2695‐2696, 2006.
 80.Grant WP, Ahnert SE. Modular decomposition of protein structure using community detection. J Complex Netw 7: 101‐113, 2018.
 81.Grouleff J, Irudayam SJ, Skeby KK, Schiott B. The influence of cholesterol on membrane protein structure, function, and dynamics studied by molecular dynamics simulations. Biochim Biophys Acta 1848: 1783‐1795, 2015.
 82.Group CC. MOE: Molecular Operating Environment.
 83.Guntas G, Purbeck C, Kuhlman B. Engineering a protein‐protein interface using a computationally designed library. Proc Natl Acad Sci U S A 107: 19296‐19301, 2010.
 84.Guvench O, MacKerell AD Jr. Comparison of protein force fields for molecular dynamics simulations. Methods Mol Biol 443: 63‐88, 2008.
 85.Guy AJ, Irani V, Richards JS, Ramsland PA. BioStructMap: A Python tool for integration of protein structure and sequence‐based features. Bioinformatics 34: 3942‐3944, 2018.
 86.Hamelberg D, Mongan J, McCammon JA. Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules. J Chem Phys 120: 11919‐11929, 2004.
 87.Han W, Cheng RC, Maduke MC, Tajkhorshid E. Water access points and hydration pathways in CLC H+/Cl‐ transporters. Proc Natl Acad Sci U S A 111: 1819‐1824, 2014.
 88.Hardy DJ, Wu Z, Phillips JC, Stone JE, Skeel RD, Schulten K. Multilevel summation method for electrostatic force evaluation. J Chem Theory Comput 11: 766‐779, 2015.
 89.Harrigan MP, Sultan MM, Hernandez CX, Husic BE, Eastman P, Schwantes CR, Beauchamp KA, McGibbon RT, Pande VS. MSMBuilder: Statistical models for biomolecular dynamics. Biophys J 112: 10‐15, 2017.
 90.Hedger G, Koldso H, Chavent M, Siebold C, Rohatgi R, MSP S. Cholesterol interaction sites on the transmembrane domain of the Hedgehog signal transducer and class F G protein‐coupled receptor smoothened. Structure 27: 549.e542‐559.e542, 2019.
 91.Hinard V, Britan A, Rougier JS, Bairoch A, Abriel H, Gaudet P. ICEPO: The ion channel electrophysiology ontology. Database (Oxford) 2016, 2016.
 92.Hollingsworth SA, Dror RO. Molecular dynamics simulation for All. Neuron 99: 1129‐1143, 2018.
 93.Hollup SM, Salensminde G, Reuter N. WEBnm@: A web application for normal mode analyses of proteins. BMC Bioinformatics 6: 52, 2005.
 94.Hooft RW, Vriend G, Sander C, Abola EE. Errors in protein structures. Nature 381: 272, 1996.
 95.Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E. PhosphoSitePlus, 2014: Mutations, PTMs and recalibrations. Nucleic Acids Res 43: D512‐D520, 2015.
 96.Hospital A, Goni JR, Orozco M, Gelpi JL. Molecular dynamics simulations: Advances and applications. Adv Appl Bioinform Chem 8: 37‐47, 2015.
 97.Hou P, Eldstrom J, Shi J, Zhong L, McFarland K, Gao Y, Fedida D, Cui J. Inactivation of KCNQ1 potassium channels reveals dynamic coupling between voltage sensing and pore opening. Nat Commun 8: 1730, 2017.
 98.Hsin J, Gumbart J, Trabuco LG, Villa E, Qian P, Hunter CN, Schulten K. Protein‐induced membrane curvature investigated through molecular dynamics flexible fitting. Biophys J 97: 321‐329, 2009.
 99.Huang J, MacKerell AD Jr. CHARMM36 all‐atom additive protein force field: Validation based on comparison to NMR data. J Comput Chem 34: 2135‐2145, 2013.
 100.Humphrey W, Dalke A, Schulten K. VMD: Visual molecular dynamics. J Mol Graph 14: 33‐38, 27‐38, 1996.
 101.Husic BE, Pande VS. Markov state models: From an art to a science. J Am Chem Soc 140: 2386‐2396, 2018.
 102.Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: A free tool to discover chemistry for biology. J Chem Inf Model 52: 1757‐1768, 2012.
 103.Jo S, Kim T, Iyer VG, Im W. CHARMM‐GUI: A web‐based graphical user interface for CHARMM. J Comput Chem 29: 1859‐1865, 2008.
 104.Jo S, Lim JB, Klauda JB, Im W. CHARMM‐GUI Membrane Builder for mixed bilayers and its application to yeast membranes. Biophys J 97: 50‐58, 2009.
 105.Jorgensen WL, Tirado‐Rives J. The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc 110: 1657‐1666, 1988.
 106.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Zidek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera‐Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nature 596: 583‐589, 2021.
 107.Jurrus E, Engel D, Star K, Monson K, Brandi J, Felberg LE, Brookes DH, Wilson L, Chen J, Liles K, Chun M, Li P, Gohara DW, Dolinsky T, Konecny R, Koes DR, Nielsen JE, Head‐Gordon T, Geng W, Krasny R, Wei GW, Holst MJ, McCammon JA, Baker NA. Improvements to the APBS biomolecular solvation software suite. Protein Sci 27: 112‐128, 2018.
 108.Kallberg M, Margaryan G, Wang S, Ma J, Xu J. RaptorX server: A resource for template‐based protein structure modeling. Methods Mol Biol 1137: 17‐27, 2014.
 109.Kandathil SM, Garza‐Fabre M, Handl J, Lovell SC. Improved fragment‐based protein structure prediction by redesign of search heuristics. Sci Rep 8: 13694, 2018.
 110.Kandel S, Larsen AB, Wagner JE, Jain A, Vaidehi N. GNEIMO‐Fixman: An accurate torsional molecular dynamics simulation method for studying biomolecular dynamics. Biophys J 106: 404a, 2014.
 111.Kano S, Yuan M, Cardarelli RA, Maegawa G, Higurashi N, Gaval‐Cruz M, Wilson AM, Tristan C, Kondo MA, Chen Y, Koga M, Obie C, Ishizuka K, Seshadri S, Srivastava R, Kato TA, Horiuchi Y, Sedlak TW, Lee Y, Rapoport JL, Hirose S, Okano H, Valle D, O'Donnell P, Sawa A, Kai M. Clinical utility of neuronal cells directly converted from fibroblasts of patients for neuropsychiatric disorders: Studies of lysosomal storage diseases and channelopathy. Curr Mol Med 15: 138‐145, 2015.
 112.Karamzadeh R, Karimi‐Jafari MH, Sharifi‐Zarchi A, Chitsaz H, Salekdeh GH, Moosavi‐Movahedi AA. Machine learning and network analysis of molecular dynamics trajectories reveal two chains of Red/Ox‐specific residue interactions in human protein disulfide isomerase. Sci Rep 7: 3666, 2017.
 113.Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP, Gauthier LD, Brand H, Solomonson M, Watts NA, Rhodes D, Singer‐Berk M, England EM, Seaby EG, Kosmicki JA, Walters RK, Tashman K, Farjoun Y, Banks E, Poterba T, Wang A, Seed C, Whiffin N, Chong JX, Samocha KE, Pierce‐Hoffman E, Zappala Z, O'Donnell‐Luria AH, Minikel EV, Weisburd B, Lek M, Ware JS, Vittal C, Armean IM, Bergelson L, Cibulskis K, Connolly KM, Covarrubias M, Donnelly S, Ferriera S, Gabriel S, Gentry J, Gupta N, Jeandet T, Kaplan D, Llanwarne C, Munshi R, Novod S, Petrillo N, Roazen D, Ruano‐Rubio V, Saltzman A, Schleicher M, Soto J, Tibbetts K, Tolonen C, Wade G, Talkowski ME, Genome Aggregation Database C, Neale BM, Daly MJ, MacArthur DG. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581: 434‐443, 2020.
 114.Karplus M, Kuriyan J. Molecular dynamics and protein function. Proc Natl Acad Sci U S A 102: 6679‐6685, 2005.
 115.Karplus M, McCammon JA. Molecular dynamics simulations of biomolecules. Nat Struct Biol 9: 646‐652, 2002.
 116.Katritch V, Abagyan R. GPCR agonist binding revealed by modeling and crystallography. Trends Pharmacol Sci 32: 637‐643, 2011.
 117.Kellogg EH, Leaver‐Fay A, Baker D. Role of conformational sampling in computing mutation‐induced changes in protein structure and stability. Proteins 79: 830‐838, 2011.
 118.Khelashvili G, Grossfield A, Feller SE, Pitman MC, Weinstein H. Structural and dynamic effects of cholesterol at preferred sites of interaction with rhodopsin identified from microsecond length molecular dynamics simulations. Proteins 76: 403‐417, 2009.
 119.Kiefer F, Arnold K, Kunzli M, Bordoli L, Schwede T. The SWISS‐MODEL repository and associated resources. Nucleic Acids Res 37: D387‐D392, 2009.
 120.Kim JB. Channelopathies. Korean J Pediatr 57: 1‐18, 2014.
 121.Kim RY, Yau MC, Galpin JD, Seebohm G, Ahern CA, Pless SA, Kurata HT. Atomic basis for therapeutic activation of neuronal potassium channels. Nat Commun 6: 8116, 2015.
 122.Klee EW, Zimmermann MT. Molecular modeling of LDLR aids interpretation of genomic variants. J Mol Med (Berl) 97: 533‐540, 2019.
 123.Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A. Coarse‐grained protein models and their applications. Chem Rev 116: 7898‐7936, 2016.
 124.Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, Fan H, Shen H, Way GP, Greene CS, Liu Y, Akbani R, Feng B, Donehower LA, Miller C, Shen Y, Karimi M, Chen H, Kim P, Jia P, Shinbrot E, Zhang S, Liu J, Hu H, Bailey MH, Yau C, Wolf D, Zhao Z, Weinstein JN, Li L, Ding L, Mills GB, Laird PW, Wheeler DA, Shmulevich I, Cancer Genome Atlas Research N, Monnat RJ Jr, Xiao Y, Wang C. Genomic and molecular landscape of DNA damage repair deficiency across the cancer genome Atlas. Cell Rep 23: 239.e236‐254 e236, 2018.
 125.Knudsen M, Wiuf C. The CATH database. Hum Genomics 4: 207‐212, 2010.
 126.Kolinski A. Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 51: 349‐371, 2004.
 127.Konc J, Miller BT, Stular T, Lesnik S, Woodcock HL, Brooks BR, Janezic D. ProBiS‐CHARMMing: Web interface for prediction and optimization of ligands in protein binding sites. J Chem Inf Model 55: 2308‐2314, 2015.
 128.Koukos PI, Glykos NM. Grcarma: A fully automated task‐oriented interface for the analysis of molecular dynamics trajectories. J Comput Chem 34: 2310‐2312, 2013.
 129.Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J Mol Biol 305: 567‐580, 2001.
 130.Kutzner C, Pall S, Fechner M, Esztermann A, de Groot BL, Grubmuller H. More bang for your buck: Improved use of GPU nodes for GROMACS 2018. J Comput Chem 40: 2418‐2431, 2019.
 131.Landrum MJ, Chitipiralla S, Brown GR, Chen C, Gu B, Hart J, Hoffman D, Jang W, Kaur K, Liu C, Lyoshin V, Maddipatla Z, Maiti R, Mitchell J, O'Leary N, Riley GR, Shi W, Zhou G, Schneider V, Maglott D, Holmes JB, Kattman BL. ClinVar: Improvements to accessing data. Nucleic Acids Res 48: D835‐D844, 2020.
 132.Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, Maglott DR. ClinVar: Public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res 42: D980‐D985, 2014.
 133.Larsen AB, Wagner JR, Kandel S, Salomon‐Ferrer R, Vaidehi N, Jain A. GneimoSim: A modular internal coordinates molecular dynamics simulation package. J Comput Chem 35: 2245‐2255, 2014.
 134.Laskowski RA. PDBsum: Summaries and analyses of PDB structures. Nucleic Acids Res 29: 221‐222, 2001.
 135.Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM. AQUA and PROCHECK‐NMR: Programs for checking the quality of protein structures solved by NMR. J Biomol NMR 8: 477‐486, 1996.
 136.Laskowski RA, Swindells MB. LigPlot+: Multiple ligand‐protein interaction diagrams for drug discovery. J Chem Inf Model 51: 2778‐2786, 2011.
 137.Lazniewska J, Weiss N. Glycosylation of voltage‐gated calcium channels in health and disease. Biochim Biophys Acta Biomembr 1859: 662‐668, 2017.
 138.Lee EH, Hsin J, Sotomayor M, Comellas G, Schulten K. Discovery through the computational microscope. Structure 17: 1295‐1306, 2009.
 139.Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, Wei S, Buckner J, Jeong JC, Qi Y, Jo S, Pande VS, Case DA, Brooks CL 3rd, MacKerell AD Jr, Klauda JB, Im W. CHARMM‐GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput 12: 405‐413, 2016.
 140.Lemmon G, Meiler J. Rosetta Ligand docking with flexible XML protocols. Methods Mol Biol 819: 143‐155, 2012.
 141.Lezon TR, Bahar I. Constraints imposed by the membrane selectively guide the alternating access dynamics of the glutamate transporter GltPh. Biophys J 102: 1331‐1340, 2012.
 142.Li H, Chang YY, Lee JY, Bahar I, Yang LW. DynOmics: Dynamics of structural proteome and beyond. Nucleic Acids Res 45: W374‐W380, 2017.
 143.Li J, Wen PC, Moradi M, Tajkhorshid E. Computational characterization of structural dynamics underlying function in active membrane transporters. Curr Opin Struct Biol 31: 96‐105, 2015.
 144.Lindahl E, Sansom MS. Membrane proteins: Molecular dynamics simulations. Curr Opin Struct Biol 18: 425‐431, 2008.
 145.Linding R, Jensen LJ, Diella F, Bork P, Gibson TJ, Russell RB. Protein disorder prediction: Implications for structural proteomics. Structure 11: 1453‐1459, 2003.
 146.Lindorff‐Larsen K, Maragakis P, Piana S, Eastwood MP, Dror RO, Shaw DE. Systematic validation of protein force fields against experimental data. PLoS One 7: e32131, 2012.
 147.Liwo A, Baranowski M, Czaplewski C, Golas E, He Y, Jagiela D, Krupa P, Maciejczyk M, Makowski M, Mozolewska MA, Niadzvedtski A, Oldziej S, Scheraga HA, Sieradzan AK, Slusarz R, Wirecki T, Yin Y, Zaborowski B. A unified coarse‐grained model of biological macromolecules based on mean‐field multipole‐multipole interactions. J Mol Model 20: 2306, 2014.
 148.Lopes PE, Guvench O, MacKerell AD Jr. Current status of protein force fields for molecular dynamics simulations. Methods Mol Biol 1215: 47‐71, 2015.
 149.Lopez‐Blanco JR, Aliaga JI, Quintana‐Orti ES, Chacon P. iMODS: Internal coordinates normal mode analysis server. Nucleic Acids Res 42: W271‐W276, 2014.
 150.Lorenzen K, Wichmann C, Tavan P. Including the dispersion attraction into structure‐adapted fast multipole expansions for MD simulations. J Chem Theory Comput 10: 3244‐3259, 2014.
 151.Loussouarn G, Baro I, Escande D. KCNQ1 K+ channel‐mediated cardiac channelopathies. Methods Mol Biol 337: 167‐183, 2006.
 152.Lu H, Isralewitz B, Krammer A, Vogel V, Schulten K. Unfolding of titin immunoglobulin domains by steered molecular dynamics simulation. Biophys J 75: 662‐671, 1998.
 153.Lu H, Schulten K. Steered molecular dynamics simulations of force‐induced protein domain unfolding. Proteins 35: 453‐463, 1999.
 154.Lueck JD, Mackey AL, Infield DT, Galpin JD, Li J, Roux B, Ahern CA. Atomic mutagenesis in ion channels with engineered stoichiometry. elife 5, 2016, p. e18976.
 155.Luo W, Zhang C, Jiang YH, Brouwer CR. Systematic reconstruction of autism biology from massive genetic mutation profiles. Sci Adv 4: e1701799, 2018.
 156.Lutz B, Sinner C, Bozic S, Kondov I, Schug A. Native structure‐based modeling and simulation of biomolecular systems per mouse click. BMC Bioinformatics 15: 292, 2014.
 157.Lyubartsev AP, Rabinovich AL. Force field development for lipid membrane simulations. Biochim Biophys Acta Biomembr 1858: 2483‐2497, 2016.
 158.Madadkar‐Sobhani A, Guallar V. PELE web server: Atomistic study of biomolecular systems at your fingertips. Nucleic Acids Res 41: W322‐W328, 2013.
 159.Manna M, Kulig W, Javanainen M, Tynkkynen J, Hensen U, Muller DJ, Rog T, Vattulainen I. How to minimize artifacts in atomistic simulations of membrane proteins, whose crystal structure is heavily engineered: Beta(2)‐adrenergic receptor in the spotlight. J Chem Theory Comput 11: 3432‐3445, 2015.
 160.Marrink SJ, Corradi V, Souza PCT, Ingolfsson HI, Tieleman DP, Sansom MSP. Computational modeling of realistic cell membranes. Chem Rev 119: 6184‐6226, 2019.
 161.Marrink SJ, Tieleman DP. Perspective on the martini model. Chem Soc Rev 42: 6801‐6822, 2013.
 162.Martinez L, Andrade R, Birgin EG, Martinez JM. PACKMOL: A package for building initial configurations for molecular dynamics simulations. J Comput Chem 30: 2157‐2164, 2009.
 163.Martin‐Garcia F, Papaleo E, Gomez‐Puertas P, Boomsma W, Lindorff‐Larsen K. Comparing molecular dynamics force fields in the essential subspace. PLoS One 10: e0121114, 2015.
 164.McWilliam H, Li W, Uludag M, Squizzato S, Park YM, Buso N, Cowley AP, Lopez R. Analysis tool web services from the EMBL‐EBI. Nucleic Acids Res 41: W597‐W600, 2013.
 165.Medovoy D, Perozo E, Roux B. Multi‐ion free energy landscapes underscore the microscopic mechanism of ion selectivity in the KcsA channel. Biochim Biophys Acta 1858: 1722‐1732, 2016.
 166.Meyer T, D'Abramo M, Hospital A, Rueda M, Ferrer‐Costa C, Perez A, Carrillo O, Camps J, Fenollosa C, Repchevsky D, Gelpi JL, Orozco M. MoDEL (Molecular Dynamics Extended Library): A database of atomistic molecular dynamics trajectories. Structure 18: 1399‐1409, 2010.
 167.Michaud‐Agrawal N, Denning EJ, Woolf TB, Beckstein O. MDAnalysis: A toolkit for the analysis of molecular dynamics simulations. J Comput Chem 32: 2319‐2327, 2011.
 168.Miller BT, Singh RP, Klauda JB, Hodoscek M, Brooks BR, Woodcock HL 3rd. CHARMMing: A new, flexible web portal for CHARMM. J Chem Inf Model 48: 1920‐1929, 2008.
 169.Mishra SK, Jernigan RL. Protein dynamic communities from elastic network models align closely to the communities defined by molecular dynamics. PLoS One 13: e0199225, 2018.
 170.Mitchell A, Chang HY, Daugherty L, Fraser M, Hunter S, Lopez R, McAnulla C, McMenamin C, Nuka G, Pesseat S, Sangrador‐Vegas A, Scheremetjew M, Rato C, Yong SY, Bateman A, Punta M, Attwood TK, Sigrist CJ, Redaschi N, Rivoire C, Xenarios I, Kahn D, Guyot D, Bork P, Letunic I, Gough J, Oates M, Haft D, Huang H, Natale DA, Wu CH, Orengo C, Sillitoe I, Mi H, Thomas PD, Finn RD. The InterPro protein families database: The classification resource after 15 years. Nucleic Acids Res 43: D213‐D221, 2015.
 171.Montelione GT. The Protein Structure Initiative: achievements and visions for the future. F1000 Biol Rep 4 (7), 2012. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3318194/.
 172.Monticelli L, Tieleman DP. Force fields for classical molecular dynamics. Methods Mol Biol 924: 197‐213, 2013. DOI: 10.1007/978-1-62703-017-5_8
 173.Morales A, Hershberger RE. Variants of uncertain significance: Should we revisit how they are evaluated and disclosed? Circ Genom Precis Med 11: e002169, 2018.
 174.Niu B, Scott AD, Sengupta S, Bailey MH, Batra P, Ning J, Wyczalkowski MA, Liang WW, Zhang Q, McLellan MD, Sun SQ, Tripathi P, Lou C, Ye K, Mashl RJ, Wallis J, Wendl MC, Chen F, Ding L. Protein‐structure‐guided discovery of functional mutations across 19 cancer types. Nat Genet, 2016.
 175.Oliver GR, Zimmermann MT, Klee EW, Urrutia RA. “The molecule's the thing:” The promise of molecular modeling and dynamic simulations in aiding the prioritization and interpretation of genomic testing results. F1000Res 5: 766, 2016.
 176.Ovchinnikov V, Karplus M. Analysis and elimination of a bias in targeted molecular dynamics simulations of conformational transitions: Application to calmodulin. J Phys Chem B 116: 8584‐8603, 2012.
 177.Páll S, Abraham MJ, Kutzner C, Hess B, Lindahl E. Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, p. 3‐27. https://link.springer.com/chapter/10.1007/978‐3‐319‐15976‐8_1
 178.Pande VS, Beauchamp K, Bowman GR. Everything you wanted to know about Markov State Models but were afraid to ask. Methods 52: 99‐105, 2010.
 179.Pang YP. Low‐mass molecular dynamics simulation for configurational sampling enhancement: More evidence and theoretical explanation. Biochem Biophys Rep 4: 126‐133, 2015.
 180.Pantsar T, Rissanen S, Dauch D, Laitinen T, Vattulainen I, Poso A. Assessment of mutation probabilities of KRAS G12 missense mutants and their long‐timescale dynamics by atomistic molecular simulations and Markov state modeling. PLoS Comput Biol 14: e1006458, 2018.
 181.Paquin A, Ye D, Tester DJ, Kapplinger JD, Zimmermann MT, Ackerman MJ. Even pore‐localizing missense variants at highly conserved sites in KCNQ1‐encoded Kv7.1 channels may have wild‐type function and not cause type 1 long QT syndrome: Do not rely solely on the genetic test company's interpretation. HeartRhythm Case Rep 4: 37‐44, 2018.
 182.Parra RG, Schafer NP, Radusky LG, Tsai MY, Guzovsky AB, Wolynes PG, Ferreiro DU. Protein Frustratometer 2: A tool to localize energetic frustration in protein molecules, now with electrostatics. Nucleic Acids Res 44: W356‐W360, 2016.
 183.Parton DL, Grinaway PB, Hanson SM, Beauchamp KA, Chodera JD. Ensembler: Enabling high‐throughput molecular simulations at the superfamily scale. PLoS Comput Biol 12: e1004728, 2016.
 184.Patra M, Karttunen M, Hyvonen MT, Falck E, Lindqvist P, Vattulainen I. Molecular dynamics simulations of lipid bilayers: Major artifacts due to truncating electrostatic interactions. Biophys J 84: 3636‐3645, 2003.
 185.Patra M, Karttunen M, Hyvönen MT, Falck E, Vattulainen I. Lipid bilayers driven to a wrong lane in molecular dynamics simulations by subtle changes in long‐range electrostatic interactions. J Phys Chem B 108: 4485‐4494, 2004.
 186.Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. UCSF Chimera—A visualization system for exploratory research and analysis. J Comput Chem 25: 1605‐1612, 2004.
 187.Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K. Scalable molecular dynamics with NAMD. J Comput Chem 26: 1781‐1802, 2005.
 188.Pieper U, Webb BM, Dong GQ, Schneidman‐Duhovny D, Fan H, Kim SJ, Khuri N, Spill YG, Weinkam P, Hammel M, Tainer JA, Nilges M, Sali A. ModBase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res 42: D336‐D346, 2014.
 189.Plimpton S. Fast parallel algorithms for short‐range molecular dynamics. J Comput Phys 117: 1‐19, 1995.
 190.Ponder JW, Case DA. Force fields for protein simulations. Adv Protein Chem 66: 27‐85, 2003.
 191.Poroca DR, Pelis RM, Chappe VM. ClC channels and transporters: Structure, physiological functions, and implications in human chloride channelopathies. Front Pharmacol 8: 151, 2017.
 192.PyMol The PyMOL Molecular Graphics System. Version 1.9.0. Schrödinger, LLC. https://pymol.org/2/support.html?
 193.Qi R, Wei G, Ma B, Nussinov R. Replica exchange molecular dynamics: A practical application protocol with solutions to common problems and a peptide aggregation and self‐assembly example. Methods Mol Biol 1777: 101‐119, 2018.
 194.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (http://www.R‐project.org), 2019.
 195.Ramakrishnan N, Bradley RP, Tourdot RW, Radhakrishnan R. Biophysics of membrane curvature remodeling at molecular and mesoscopic lengthscales. J Phys Condens Matter 30: 273001, 2018.
 196.Raveh B, London N, Schueler‐Furman O. Sub‐angstrom modeling of complexes between flexible peptides and globular proteins. Proteins 78: 2029‐2040, 2010.
 197.Redfern OC, Harrison A, Dallman T, Pearl FM, Orengo CA. CATHEDRAL: A fast and effective algorithm to predict folds and domain boundaries from multidomain protein structures. PLoS Comput Biol 3: e232, 2007.
 198.Ribeiro JV, Bernardi RC, Rudack T, Stone JE, Phillips JC, Freddolino PL, Schulten K. QwikMD—Integrative molecular dynamics toolkit for novices and experts. Sci Rep 6: 26536, 2016.
 199.Riniker S, van Gunsteren WF. Mixing coarse‐grained and fine‐grained water in molecular dynamics simulations of a single system. J Chem Phys 137: 044120, 2012.
 200.Robertson A, Luttmann E, Pande VS. Effects of long‐range electrostatic forces on simulated protein folding kinetics. J Comput Chem 29: 694‐700, 2008.
 201.Robustelli P, Piana S, Shaw DE. Developing a molecular dynamics force field for both folded and disordered protein states. Proc Natl Acad Sci U S A 115: E4758‐E4766, 2018.
 202.Rodrigues CH, Pires DE, Ascher DB. DynaMut: Predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res 46: W350‐W355, 2018.
 203.Rojas CV, Wang JZ, Schwartz LS, Hoffman EP, Powell BR, Brown RH Jr. A Met‐to‐Val mutation in the skeletal muscle Na+ channel alpha‐subunit in hyperkalaemic periodic paralysis. Nature 354: 387‐389, 1991.
 204.Rosenberg NA, Huang L, Jewett EM, Szpiech ZA, Jankovic I, Boehnke M. Genome‐wide association studies in diverse populations. Nat Rev Genet 11: 356‐366, 2010.
 205.Roy A, Kucukural A, Zhang Y. I‐TASSER: A unified platform for automated protein structure and function prediction. Nat Protoc 5: 725‐738, 2010.
 206.Rui H, Artigas P, Roux B. The selectivity of the Na(+)/K(+)‐pump is controlled by binding site protonation and self‐correcting occlusion. elife 5, 2016.
 207.Sagui C, Darden T. Multigrid methods for classical molecular dynamics simulations of biomolecules. J Chem Phys 114: 6578‐6591, 2001.
 208.Saito H, Watabe N, Nitta T, Takahashi S, Sakamoto K. Motion artifacts on chest computed tomogram: Full scan versus half scan. Nihon Igaku Hoshasen Gakkai Zasshi 54: 340‐344, 1994.
 209.Sanders M, Lawlor JMJ, Li X, Schuen JN, Millard SL, Zhang X, Buck L, Grysko B, Uhl KL, Hinds D, Stenger CL, Morris M, Lamb N, Levy H, Bupp C, Prokop JW. Genomic, transcriptomic, and protein landscape profile of CFTR and cystic fibrosis. Hum Genet 140: 423‐439, 2021.
 210.Sankar K, Jia K, Jernigan RL. Knowledge‐based entropies improve the identification of native protein structures. Proc Natl Acad Sci U S A 114: 2928‐2933, 2017.
 211.Sankar K, Mishra SK, Jernigan RL. Comparisons of protein dynamics from experimental structure ensembles, molecular dynamics ensembles, and coarse‐grained elastic network models. J Phys Chem B 122: 5409‐5417, 2018.
 212.Schlitter J, Engels M, Krüger P, Jacoby E, Wollmer A. Targeted molecular dynamics simulation of conformational change‐application to the T ↔ R transition in insulin. Mol Simul 10: 291‐308, 1993.
 213.Schott‐Verdugo S, Gohlke H. PACKMOL‐Memgen: A simple‐to‐use, generalized workflow for membrane‐protein‐lipid‐bilayer system building. J Chem Inf Model 59: 2522‐2528, 2019.
 214.Schrödinger L. Schrödinger Release 2019‐4: Maestro. New York, NY: Schrödinger, LLC, 2021.
 215.Schuler LD, Daura X, Van Gunsteren WF. An improved GROMOS96 force field for aliphatic hydrocarbons in the condensed phase. J Comput Chem 22: 1205‐1218, 2001.
 216.Schwartz PJ, Ackerman MJ, Antzelevitch C, Bezzina CR, Borggrefe M, Cuneo BF, Wilde AAM. Inherited cardiac arrhythmias. Nat Rev Dis Primers 6: 58, 2020.
 217.Scott H, Panin VM. The role of protein N‐glycosylation in neural transmission. Glycobiology 24: 407‐417, 2014.
 218.Seeber M, Felline A, Raimondi F, Mariani S, Fanelli F. WebPSN: A web server for high‐throughput investigation of structural communication in biomacromolecules. Bioinformatics 31: 779‐781, 2015.
 219.Sellis D, Vlachakis D, Vlassi M. Gromita: A fully integrated graphical user interface to gromacs 4. Bioinform Biol Insights 3: 99‐102, 2009.
 220.Seo S, Kim MK. KOSMOS: A universal morph server for nucleic acids, proteins and their complexes. Nucleic Acids Res 40: W531‐W536, 2012.
 221.Sercinoglu O, Ozbek P. gRINN: A tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations. Nucleic Acids Res 46: W554‐W562, 2018.
 222.Seshasayee AS. High‐temperature unfolding of a trp‐cage mini‐protein: A molecular dynamics simulation study. Theor Biol Med Model 2: 7, 2005.
 223.Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Soding J, Thompson JD, Higgins DG. Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7: 539, 2011.
 224.Silva LA, Correia JCG. GEMS‐Pack: A graphical user interface for the packmol program. J Chem Inf Model 60 (2): 439‐443, 2019.
 225.Simoncini D, Berenger F, Shrestha R, Zhang KY. A probabilistic fragment‐based protein structure prediction algorithm. PLoS One 7: e38799, 2012.
 226.Simons KT, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 268: 209‐225, 1997.
 227.Skinner JR, Winbo A, Abrams D, Vohra J, Wilde AA. Channelopathies that lead to sudden cardiac death: Clinical and genetic aspects. Heart Lung Circ 28: 22‐30, 2019.
 228.Smith JA, Vanoye CG, George AL Jr, Meiler J, Sanders CR. Structural models for the KCNQ1 voltage‐gated potassium channel. Biochemistry 46: 14141‐14152, 2007.
 229.Southgate L, Machado RD, Graf S, Morrell NW. Molecular genetic framework underlying pulmonary arterial hypertension. Nat Rev Cardiol 17: 85‐95, 2020.
 230.Spillane J, Kullmann DM, Hanna MG. Genetic neurological channelopathies: Molecular genetics and clinical phenotypes. J Neurol Neurosurg Psychiatry 87: 37‐48, 2016.
 231.Straube J, Huang BE, Cao KL. DynOmics to identify delays and co‐expression patterns across time course experiments. Sci Rep 7: 40131, 2017.
 232.Studer G, Biasini M, Schwede T. Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane). Bioinformatics 30: i505‐i511, 2014.
 233.Su CC, Lyu M, Morgan CE, Bolla JR, Robinson CV, Yu EW. A 'Build and Retrieve' methodology to simultaneously solve cryo‐EM structures of membrane proteins. Nat Methods 18: 69‐75, 2021.
 234.Sumbalova L, Stourac J, Martinek T, Bednar D, Damborsky J. HotSpot Wizard 3.0: Web server for automated design of mutations and smart libraries based on sequence input information. Nucleic Acids Res 46: W356‐W362, 2018.
 235.Surleraux DL, Tahri A, Verschueren WG, Pille GM, de Kock HA, Jonckers TH, Peeters A, De Meyer S, Azijn H, Pauwels R, de Bethune MP, King NM, Prabu‐Jeyabalan M, Schiffer CA, Wigerinck PB. Discovery and selection of TMC114, a next generation HIV‐1 protease inhibitor. J Med Chem 48: 1813‐1822, 2005.
 236.Tajkhorshid E, Nollert P, Jensen MO, Miercke LJ, O'Connell J, Stroud RM, Schulten K. Control of the selectivity of the aquaporin water channel family by global orientational tuning. Science 296: 525‐530, 2002.
 237.Tan D, Piana S, Dirks RM, Shaw DE. RNA force field with accuracy comparable to state‐of‐the‐art protein force fields. Proc Natl Acad Sci U S A 115: E1346‐E1355, 2018.
 238.Tarini M, Cignoni P, Montani C. Ambient occlusion and edge cueing to enhance real time molecular visualization. IEEE Trans Vis Comput Graph 12: 1237‐1244, 2006.
 239.Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N, Boutselakis H, Cole CG, Creatore C, Dawson E, Fish P, Harsha B, Hathaway C, Jupe SC, Kok CY, Noble K, Ponting L, Ramshaw CC, Rye CE, Speedy HE, Stefancsik R, Thompson SL, Wang S, Ward S, Campbell PJ, Forbes SA. COSMIC: The catalogue of somatic mutations in cancer. Nucleic Acids Res 47: D941‐D947, 2019.
 240.The UPC. UniProt: The universal protein knowledgebase. Nucleic Acids Res 45: D158‐D169, 2017.
 241.Tillman TS, Cascio M. Effects of membrane lipids on ion channel structure and function. Cell Biochem Biophys 38: 161‐190, 2003.
 242.Tirion MM. Large amplitude elastic motions in proteins from a single‐parameter, atomic analysis. Phys Rev Lett 77: 1905‐1908, 1996.
 243.Tiwari SP, Fuglebakk E, Hollup SM, Skjaerven L, Cragnolini T, Grindhaug SH, Tekle KM, Reuter N. WEBnm@ v2.0: Web server and services for comparing protein flexibility. BMC Bioinformatics 15: 427, 2014.
 244.Tribello GA, Ceriotti M, Parrinello M. Using sketch‐map coordinates to analyze and bias molecular dynamics simulations. Proc Natl Acad Sci U S A 109: 5196‐5201, 2012.
 245.Tripathi S, Dsouza NR, Urrutia R, Zimmermann MT. Structural bioinformatics enhances mechanistic interpretation of genomic variation, demonstrated through the analyses of 935 distinct RAS family mutations. Bioinformatics, 2020.
 246.Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Zidek A, Bridgland A, Cowie A, Meyer C, Laydon A, Velankar S, Kleywegt GJ, Bateman A, Evans R, Pritzel A, Figurnov M, Ronneberger O, Bates R, Kohl SAA, Potapenko A, Ballard AJ, Romera‐Paredes B, Nikolov S, Jain R, Clancy E, Reiman D, Petersen S, Senior AW, Kavukcuoglu K, Birney E, Kohli P, Jumper J, Hassabis D. Highly accurate protein structure prediction for the human proteome. Nature 596: 590‐596, 2021.
 247.UniProt C. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res 47: D506‐D515, 2019.
 248.Vaidehi N, Jain A. Internal coordinate molecular dynamics: A foundation for multiscale dynamics. J Phys Chem B 119: 1233‐1242, 2015.
 249.van der Kamp MW, Schaeffer RD, Jonsson AL, Scouras AD, Simms AM, Toofanny RD, Benson NC, Anderson PC, Merkley ED, Rysavy S, Bromley D, Beck DA, Daggett V. Dynameomics: A comprehensive database of protein dynamics. Structure 18: 423‐435, 2010.
 250.Van Durme J, Delgado J, Stricher F, Serrano L, Schymkowitz J, Rousseau F. A graphical interface for the FoldX forcefield. Bioinformatics 27: 1711‐1712, 2011.
 251.van Loo KMJ, Becker AJ. Transcriptional regulation of channelopathies in genetic and acquired epilepsies. Front Cell Neurosci 13: 587, 2019.
 252.Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD Jr. CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields. J Comput Chem 31: 671‐690, 2010.
 253.Vardanyan V, Pongs O. Coupling of voltage‐sensors to the channel pore: A comparative view. Front Pharmacol 3: 145, 2012.
 254.Vicatos S, Rychkova A, Mukherjee S, Warshel A. An effective coarse‐grained model for biological simulations: Recent refinements and validations. Proteins 82: 1168‐1185, 2014.
 255.Vijayabaskar MS, Vishveshwara S. Comparative analysis of thermophilic and mesophilic proteins using Protein Energy Networks. BMC Bioinformatics 11 (Suppl 1): S49, 2010.
 256.Vincent GM. The long QT syndrome. Indian Pacing Electrophysiol J 2: 127‐142, 2002.
 257.Wako H, Endo S. Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank. Biophys Rev 9: 877‐893, 2017.
 258.Wang CK, Lamothe SM, Wang AW, Yang RY, Kurata HT. Pore‐ and voltage sensor‐targeted KCNQ openers have distinct state‐dependent actions. J Gen Physiol 150: 1722‐1734, 2018.
 259.Wang J, Wang W, Kollman PA, Case DA. Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25: 247‐260, 2006.
 260.Wang Y, Geer LY, Chappey C, Kans JA, Bryant SH. Cn3D: Sequence and structure views for Entrez. Trends Biochem Sci 25: 300‐302, 2000.
 261.Wieninger SA, Ullmann GM. CoMoDo: Identifying dynamic protein domains based on covariances of motion. J Chem Theory Comput 11: 2841‐2854, 2015.
 262.Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez‐Beltran A, Gray AJ, Groth P, Goble C, Grethe JS, Heringa J, t Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca‐Serra P, Roos M, van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3: 160018, 2016.
 263.Willard L, Ranjan A, Zhang H, Monzavi H, Boyko RF, Sykes BD, Wishart DS. VADAR: A web server for quantitative evaluation of protein structure quality. Nucleic Acids Res 31: 3316‐3319, 2003.
 264.Wong‐Ekkabut J, Karttunen M. The good, the bad and the user in soft matter simulations. Biochim Biophys Acta 1858: 2529‐2538, 2016.
 265.Wright PE, Dyson HJ. Intrinsically disordered proteins in cellular signalling and regulation. Nat Rev Mol Cell Biol 16: 18‐29, 2015.
 266.Xiang Z. Advances in homology protein structure modeling. Curr Protein Pept Sci 7: 217‐227, 2006.
 267.Yang L, Song G, Jernigan RL. Protein elastic network models and the ranges of cooperativity. Proc Natl Acad Sci U S A 106: 12347‐12352, 2009.
 268.Yue WW. From structural biology to designing therapy for inborn errors of metabolism. J Inherit Metab Dis 39: 489‐498, 2016.
 269.Zarrineh P, Fierro AC, Sanchez‐Rodriguez A, De Moor B, Engelen K, Marchal K. COMODO: An adaptive coclustering strategy to identify conserved coexpression modules between organisms. Nucleic Acids Res 39: e41, 2011.
 270.Zhang L, Zhang Z, Jasa J, Li D, Cleveland RO, Negahban M, Jerusalem A. Molecular dynamics simulations of heterogeneous cell membranes in response to uniaxial membrane stretches at high loading rates. Sci Rep 7: 8316, 2017.
 271.Zhao W, Rog T, Gurtovenko AA, Vattulainen I, Karttunen M. Atomic‐scale structure and electrostatics of anionic palmitoyloleoylphosphatidylglycerol lipid bilayers with Na+ counterions. Biophys J 92: 1114‐1124, 2007.
 272.Zheng W, Brooks BR, Thirumalai D. Low‐frequency normal modes that describe allosteric transitions in biological nanomachines are robust to sequence variations. Proc Natl Acad Sci U S A 103: 7664‐7669, 2006.
 273.Zheng W, Brooks BR, Thirumalai D. Allosteric transitions in the chaperonin GroEL are captured by a dominant normal mode that is most robust to sequence variations. Biophys J 93: 2289‐2299, 2007.
 274.Zimmermann MT, Jia K, Jernigan RL. Ribosome mechanics informs about mechanism. J Mol Biol 428: 802‐810, 2016.
 275.Zimmermann MT, Kloczkowski A, Jernigan RL. MAVENs: Motion analysis and visualization of elastic networks and structural ensembles. BMC Bioinformatics 12: 264, 2011.
 276.Zimmermann MT, Urrutia R, Cousin MA, Oliver GR, Klee EW. Assessing human genetic variations in glucose transporter SLC2A10 and their role in altering structural and functional properties. Front Genet 9: 276, 2018.
 277.Zimmermann MT, Urrutia R, Oliver GR, Blackburn PR, Cousin MA, Bozeck NJ, Klee EW. Molecular modeling and molecular dynamic simulation of the effects of variants in the TGFBR2 kinase domain as a paradigm for interpretation of variants obtained by next generation sequencing. PLoS One 12: e0170822, 2017.
 278.Zimmermann MT, Williams MM, Klee EW, Lomberk GA, Urrutia R. Modeling post‐translational modifications and cancer‐associated mutations that impact the heterochromatin protein 1alpha‐importin alpha heterodimers. Proteins 87: 904‐916, 2019.
 279.Zoete V, Cuendet MA, Grosdidier A, Michielin O. SwissParam: A fast force field generation tool for small organic molecules. J Comput Chem 32: 2359‐2368, 2011.

Contact Editor

Submit a note to the editor about this article by filling in the form below.

* Required Field

Article Title: Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research
 

How to Cite

Michael T. Zimmermann. Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research. Compr Physiol 2022, 12: 3141-3166. doi: 10.1002/cphy.c190047