Hybrid computational methods combining experimental information with molecular dynamics
Tài liệu tham khảo
Seffernick, 2020, Hybrid methods for combined experimental and computational determination of protein structure, J Chem Phys, 153
Ziegler, 2021, Advances in integrative structural biology: towards understanding protein complexes in their cellular context, Comput Struct Biotechnol J, 19, 214, 10.1016/j.csbj.2020.11.052
Braitbard, 2019, Integrative structure modeling: overview and assessment, Annu Rev Biochem, 113, 10.1146/annurev-biochem-013118-111429
Bonomi, 2017, Principles of protein structural ensemble determination, Curr Opin Struct Biol, 42, 106, 10.1016/j.sbi.2016.12.004
Rose, 2017, The RCSB protein data bank: integrative view of protein, gene and 3D structural information, Nucleic Acids Res, 45, D271
Ziemianowicz, 2022, New opportunities in integrative structural modeling, Curr Opin Struct Biol, 77, 10.1016/j.sbi.2022.102488
Burley, 2017, PDB-Dev: a prototype system for depositing integrative/hybrid structural models, Structure, 25, 1317, 10.1016/j.str.2017.08.001
Schneidman-Duhovny, 2014, Uncertainty in integrative structural modeling, Curr Opin Struct Biol, 28, 96, 10.1016/j.sbi.2014.08.001
Bonomi, 2019, Determination of protein structural ensembles using cryo-electron microscopy, Curr Opin Struct Biol, 56, 37, 10.1016/j.sbi.2018.10.006
Sali, 2021, From integrative structural biology to cell biology, J Biol Chem, 296, 10.1016/j.jbc.2021.100743
Jeschke, 2022, Integration of nanometer-range label-to-label distances and their distributions into modelling approaches, Biomolecules, 12, 1369, 10.3390/biom12101369
Srivastava, 2020, Integrative/hybrid modeling approaches for studying biomolecules, J Mol Biol, 432, 2846, 10.1016/j.jmb.2020.01.039
Schlick, 2021, Biomolecular modeling and simulation: a prospering multidisciplinary field, Annu Rev Biophys, 50, 1, 10.1146/annurev-biophys-091720-102019
Nerenberg, 2018, New developments in force fields for biomolecular simulations, Curr Opin Struct Biol, 49, 129, 10.1016/j.sbi.2018.02.002
Hénin, 2022, Enhanced sampling methods for molecular dynamics simulations [Article v1.0], Living J Comput Mol Sci, 4, 10.33011/livecoms.4.1.1583
Orioli, 2020, How to learn from inconsistencies: integrating molecular simulations with experimental data, Prog Mol Biol Transl Sci, 170, 123, 10.1016/bs.pmbts.2019.12.006
Shekhar, 2021, CryoFold: determining protein structures and data-guided ensembles from cryo-EM density maps, Matter, 4, 3195, 10.1016/j.matt.2021.09.004
Robertson, 2019, NMR-assisted protein structure prediction with MELDxMD, Proteins, 36, D402
Fajardo, 2019, Assessment of chemical-crosslink-assisted protein structure modeling in CASP13, Proteins Struct Funct Bioinform, 87, 1283, 10.1002/prot.25816
Larsen, 2020, Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution, PLoS Comput Biol, 16, 10.1371/journal.pcbi.1007870
Mondal, 2022, Modelling peptide–protein complexes: docking, simulations and machine learning, QRB Discov, 3, 10.1017/qrd.2022.14
Lubecka, 2022, A coarse-grained approach to NMR-data-assisted modeling of protein structures, J Comput Chem, 43, 2047, 10.1002/jcc.27003
Mondal, 2022, Structure determination of protein-peptide complexes from NMR chemical shift data using MELD, bioRxiv
Sala, 2019, Protein structure prediction assisted with sparse NMR data in CASP13, Proteins Struct Funct Bioinform, 87, 1315, 10.1002/prot.25837
Mondal, 2021, Simultaneous assignment and structure determination of proteins from sparsely labeled NMR datasets, Front Mol Biosci, 8, 10.3389/fmolb.2021.774394
Czaplewski, 2021, Recent developments in data-assisted modeling of flexible proteins, Front Mol Biosci, 8, 10.3389/fmolb.2021.765562
Bonomi, 2016, Metainference: a Bayesian inference method for heterogeneous systems, Sci Adv, 2, 10.1126/sciadv.1501177
Piersimoni, 2022, Cross-linking mass spectrometry for investigating protein conformations and protein–protein interactions—a method for all seasons, Chem Rev, 122, 7500, 10.1021/acs.chemrev.1c00786
Bottaro, 2020, Integrating NMR and simulations reveals motions in the UUCG tetraloop, Nucleic Acids Res, 48, 10.1093/nar/gkaa399
Gaalswyk, 2018, The emerging role of physical modeling in the future of structure determination, Curr Opin Struct Biol, 49, 145, 10.1016/j.sbi.2018.03.005
Rieping, 2005, Inferential structure determination, Science, 309, 303, 10.1126/science.1110428
Habeck, 2005, Bayesian inference applied to macromolecular structure determination, Phys Rev E, 72, 10.1103/PhysRevE.72.031912
Perez, 2016, Blind protein structure prediction using accelerated free-energy simulations, Sci Adv, 2, 10.1126/sciadv.1601274
Noé, 2009, Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations, Proc Natl Acad Sci U S A, 106, 19011, 10.1073/pnas.0905466106
Pande, 2010, Everything you wanted to know about Markov State Models but were afraid to ask, Methods, 52, 99, 10.1016/j.ymeth.2010.06.002
Shirts, 2008, Statistically optimal analysis of samples from multiple equilibrium states, J Chem Phys, 129
Yagi, 2022, Weight average approaches for predicting dynamical properties of biomolecules, Curr Opin Struct Biol, 72, 88, 10.1016/j.sbi.2021.08.008
Ge, 2018, Model selection using BICePs: a Bayesian approach for force field validation and parameterization, J Phys Chem B, 122, 5610, 10.1021/acs.jpcb.7b11871
Bottaro, 2020, Integrating molecular simulation and experimental data: a Bayesian/maximum entropy reweighting approach, Methods Mol Biol, 2112, 219, 10.1007/978-1-0716-0270-6_15
Hummer, 2015, Bayesian ensemble refinement by replica simulations and reweighting, J Chem Phys, 143
Różycki, 2011, SAXS ensemble refinement of ESCRT-III CHMP3 conformational transitions, Structure, 19, 109, 10.1016/j.str.2010.10.006
Biehn, 2021, Protein structure prediction with mass spectrometry data, Annu Rev Phys Chem, 73, 1, 10.1146/annurev-physchem-082720-123928
Gomes, 2022, Integrative conformational ensembles of Sic1 using different initial pools and optimization methods, Front Mol Biosci, 9, 10.3389/fmolb.2022.910956
Matsunaga, 2018, Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning, eLife, 7, 10.7554/eLife.32668
Götz, 2022, A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories, Nat Commun, 13, 5402, 10.1038/s41467-022-33023-3
Saurabh, 2023, Single-photon smFRET. I: theory and conceptual basis, Biophys Rep, 3
Sengupta, 2019, Automated Markov state models for molecular dynamics simulations of aggregation and self-assembly, J Chem Phys, 150
Chang, 2022, Deciphering the folding mechanism of proteins G and L and their mutants, J Am Chem Soc, 144, 14668, 10.1021/jacs.2c04488
Copperman, 2020, Accelerated estimation of long-timescale kinetics from weighted ensemble simulation via non-Markovian “Microbin” analysis, J Chem Theor Comput, 16, 6763, 10.1021/acs.jctc.0c00273
Hamilton, 2022, Fuzzy supertertiary interactions within PSD-95 enable ligand binding, eLife, 11, 10.7554/eLife.77242
Dawson, 2022, Shape shifting: the multiple conformational substates of the PTEN N-terminal PIP2-binding domain, Protein Sci, 31, 10.1002/pro.4308
Jumper, 2021, Highly accurate protein structure prediction with AlphaFold, Nature, 583, 10.1038/s41586-021-03819-2
Stein, 2022, SPEACH_AF: sampling protein ensembles and conformational heterogeneity with Alphafold2, PLoS Comput Biol, 18, 10.1371/journal.pcbi.1010483
Agam, 2022, Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins, bioRxiv
Trewhella, 2022, A round-robin approach provides a detailed assessment of biomolecular small-angle scattering data reproducibility and yields consensus curves for benchmarking, Acta Crystallogr D, 78, 1315, 10.1107/S2059798322009184
Hamilton, 2020, Reporting on the future of integrative structural biology ORAU workshop, Front Biosci, 43
Gutmanas, 2015, NMR exchange format: a unified and open standard for representation of NMR restraint data, Nat Struct Mol Biol, 22, 433, 10.1038/nsmb.3041
Berman, 2021, Synergies between the protein data bank and the community, Nat Struct Mol Biol, 28, 400, 10.1038/s41594-021-00586-6
Valentini, 2015, SASBDB, a repository for biological small-angle scattering data, Nucleic Acids Res, 43, D357, 10.1093/nar/gku1047
Lerner, 2021, FRET-based dynamic structural biology: challenges, perspectives and an appeal for open-science practices, eLife, 10, 10.7554/eLife.60416
Vallat, 2021, New system for archiving integrative structures, Acta Crystallogr D, 77, 1486, 10.1107/S2059798321010871
Sali, 2015, Outcome of the first wwPDB hybrid/integrative methods task force workshop, Structure, 23, 1156, 10.1016/j.str.2015.05.013
Hancock, 2022, Integration of software tools for integrative modeling of biomolecular systems, J Struct Biol, 214, 10.1016/j.jsb.2022.107841
Anscombe, 1973, Graphs in statistical analysis, Am Stat, 27, 17
Vallat, 2018, Development of a prototype system for archiving integrative/hybrid structure models of biological macromolecules, Structure, 26, 894, 10.1016/j.str.2018.03.011
Lazar, 2020, PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins, Nucleic Acids Res, 49, D404, 10.1093/nar/gkaa1021
Roel-Touris, 2019, Less is more: coarse-grained integrative modeling of large biomolecular assemblies with HADDOCK, J Chem Theor Comput, 15, 6358, 10.1021/acs.jctc.9b00310
Roel-Touris, 2020, Coarse-grained (hybrid) integrative modeling of biomolecular interactions, Comput Struct Biotechnol J, 18, 1182, 10.1016/j.csbj.2020.05.002
Chen, 2017, Data-driven coarse graining of large biomolecular structures, PLoS One, 12
Buitrago, 2021, Impact of DNA methylation on 3D genome structure, Nat Commun, 12, 3243, 10.1038/s41467-021-23142-8
Cheng, 2020, Exploring chromosomal structural heterogeneity across multiple cell lines, eLife, 9, 10.7554/eLife.60312
Shinkai, 2020, PHi-C: deciphering Hi-C data into polymer dynamics, NAR Genom Bioinform, 2
Zheng, 2020, FreeHi-C simulates high-fidelity Hi-C data for benchmarking and data augmentation, Nat Methods, 17, 37, 10.1038/s41592-019-0624-3
Itoh, 2021, Liquid-like chromatin in the cell: what can we learn from imaging and computational modeling?, Curr Opin Struct Biol, 71, 123, 10.1016/j.sbi.2021.06.004
Feig, 2019, Whole-cell models and simulations in molecular detail, Annu Rev Cell Dev Biol, 35, 191, 10.1146/annurev-cellbio-100617-062542
Raveh, 2021, Bayesian metamodeling of complex biological systems across varying representations, Proc Natl Acad Sci U S A, 118, 10.1073/pnas.2104559118
Mu, 2021, Recent force field strategies for intrinsically disordered proteins, J Chem Inf Model, 61, 1037, 10.1021/acs.jcim.0c01175
Rahman, 2020, Comparison and evaluation of force fields for intrinsically disordered proteins, J Chem Inf Model, 60, 4912, 10.1021/acs.jcim.0c00762
Saikia, 2021, Integrative structural dynamics probing of the conformational heterogeneity in synaptosomal-associated protein 25, Cell Rep Phys Sci, 2
Crehuet, 2019, Bayesian-maximum-entropy reweighting of IDP ensembles based on NMR chemical shifts, Entropy, 21, 898, 10.3390/e21090898
Ding, 2021, Integrating an enhanced sampling method and small-angle X-ray scattering to study intrinsically disordered proteins, Front Mol Biosci, 8, 10.3389/fmolb.2021.621128
Gomes, 2020, Conformational ensembles of an intrinsically disordered protein consistent with NMR, SAXS, and single-molecule FRET, J Am Chem Soc, 142, 15697, 10.1021/jacs.0c02088
Thomasen, 2022, Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins, Biochem Soc Trans, 50, 541, 10.1042/BST20210499