Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050
Status PubMed-not-MEDLINE Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection
Typ dokumentu časopisecké články, přehledy
PubMed
39069976
PubMed Central
PMC11274290
DOI
10.1021/acsphyschemau.4c00003
Knihovny.cz E-zdroje
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
In the last quarter-century, the field of molecular dynamics (MD) has undergone a remarkable transformation, propelled by substantial enhancements in software, hardware, and underlying methodologies. In this Perspective, we contemplate the future trajectory of MD simulations and their possible look at the year 2050. We spotlight the pivotal role of artificial intelligence (AI) in shaping the future of MD and the broader field of computational physical chemistry. We outline critical strategies and initiatives that are essential for the seamless integration of such technologies. Our discussion delves into topics like multiscale modeling, adept management of ever-increasing data deluge, the establishment of centralized simulation databases, and the autonomous refinement, cross-validation, and self-expansion of these repositories. The successful implementation of these advancements requires scientific transparency, a cautiously optimistic approach to interpreting AI-driven simulations and their analysis, and a mindset that prioritizes knowledge-motivated research alongside AI-enhanced big data exploration. While history reminds us that the trajectory of technological progress can be unpredictable, this Perspective offers guidance on preparedness and proactive measures, aiming to steer future advancements in the most beneficial and successful direction.
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Ciccotti G.; Dellago C.; Ferrario M.; Hernández E. R.; Tuckerman M. E. Molecular simulations: past, present, and future (a Topical Issue in EPJB). Eur. Phys. J. B 2022, 95, 1–12. 10.1140/epjb/s10051-021-00249-x. DOI
Frenkel D.; Smit B.. Underst. Mol. Simul. from Algorithms to Appl.. Third Ed.; Elsevier, 2023; pp 1–728, DOI: 10.1016/C2009-0-63921-0. DOI
Gupta C.; Sarkar D.; Tieleman D. P.; Singharoy A. The ugly, bad, and good stories of large-scale biomolecular simulations. Curr. Opin. Struct. Biol. 2022, 73, 102338.10.1016/j.sbi.2022.102338. PubMed DOI
Hollingsworth S. A.; Dror R. O. Molecular Dynamics Simulation for All. Neuron 2018, 99, 1129–1143. 10.1016/j.neuron.2018.08.011. PubMed DOI PMC
Ahmed M.; Maldonado A. M.; Durrant J. D. From Byte to Bench to Bedside: Molecular Dynamics Simulations and Drug Discovery. BMC Biol. 2023, 21, 1–4. 10.1186/s12915-023-01791-z. PubMed DOI PMC
Sever R. We need a plan D. Nat. Methods 2023, 20, 473–474. 10.1038/s41592-023-01817-y. PubMed DOI
Duan Y.; Kollman P. A. Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science 1998, 282, 740–744. 10.1126/science.282.5389.740. PubMed DOI
Berger O.; Edholm O.; Jähnig F. Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature. Biophys. J. 1997, 72, 2002–2013. 10.1016/S0006-3495(97)78845-3. PubMed DOI PMC
Javanainen M.; Heftberger P.; Madsen J. J.; Miettinen M. S.; Pabst G.; Ollila O. H. Quantitative Comparison against Experiments Reveals Imperfections in Force Fields’ Descriptions of POPC–Cholesterol Interactions. J. Chem. Theory Comput. 2023, 19, 6342–6352. 10.1021/acs.jctc.3c00648. PubMed DOI PMC
Karki S.; Javanainen M.; Rehan S.; Tranter D.; Kellosalo J.; Huiskonen J. T.; Happonen L.; Paavilainen V. Molecular view of ER membrane remodeling by the Sec61/TRAP translocon. EMBO Rep 2023, 24, e5791010.15252/embr.202357910. PubMed DOI PMC
Javanainen M.; Karki S.; Tranter D.; Biriukov D.; Paavilainen V. O.. The Sec61/TRAP Translocon Scrambles Lipids. bioRxiv, November 23, 2023, ver. 1. 10.1101/2023.11.23.568215 (accessed 2024-03-04). DOI
Ingólfsson H. I.; Melo M. N.; Van Eerden F. J.; Arnarez C.; Lopez C. A.; Wassenaar T. A.; Periole X.; De Vries A. H.; Tieleman D. P.; Marrink S. J. Lipid organization of the plasma membrane. J. Am. Chem. Soc. 2014, 136, 14554–14559. 10.1021/ja507832e. PubMed DOI
Lorent J. H.; Levental K. R.; Ganesan L.; Rivera-Longsworth G.; Sezgin E.; Doktorova M.; Lyman E.; Levental I. Plasma membranes are asymmetric in lipid unsaturation, packing and protein shape. Nat. Chem. Biol. 2020, 16, 644–652. 10.1038/s41589-020-0529-6. PubMed DOI PMC
Doktorova M.; Symons J. L.; Zhang X.; Wang H.-Y.; Schlegel J.; Lorent J. H.; Heberle F. A.; Sezgin E.; Lyman E.; Levental K. R.; Levental I.. Cell Membranes Sustain Phospholipid Imbalance Via Cholesterol Asymmetry. bioRxiv, July 31, 2023, ver. 1. 10.1101/2023.07.30.551157 (accessed 2024-03-04). DOI
Biriukov D.; Javanainen M. Efficient Simulations of Solvent Asymmetry Across Lipid Membranes Using Flat-Bottom Restraints. J. Chem. Theory Comput. 2023, 19, 6332–6341. 10.1021/acs.jctc.3c00614. PubMed DOI PMC
Bock L. V.; Gabrielli S.; Kolár̃ M. H.; Grubmüller H. Simulation of Complex Biomolecular Systems: The Ribosome Challenge. Annu. Rev. Biophys. 2023, 52, 361–390. 10.1146/annurev-biophys-111622-091147. PubMed DOI
Stevens J. A.; Grünewald F.; van Tilburg P. A.; König M.; Gilbert B. R.; Brier T. A.; Thornburg Z. R.; Luthey-Schulten Z.; Marrink S. J. Molecular dynamics simulation of an entire cell. Front. Chem. 2023, 11, 1106495.10.3389/fchem.2023.1106495. PubMed DOI PMC
Perilla J. R.; Schulten K. Physical properties of the HIV-1 capsid from all-atom molecular dynamics simulations. Nat. Commun. 2017, 8, 1–10. 10.1038/ncomms15959. PubMed DOI PMC
Tarasova E.; Nerukh D. All-Atom Molecular Dynamics Simulations of Whole Viruses. J. Phys. Chem. Lett. 2018, 9, 5805–5809. 10.1021/acs.jpclett.8b02298. PubMed DOI
Pezeshkian W.; Grünewald F.; Narykov O.; Lu S.; Arkhipova V.; Solodovnikov A.; Wassenaar T. A.; Marrink S. J.; Korkin D. Molecular architecture and dynamics of SARS-CoV-2 envelope by integrative modeling. Structure 2023, 31, 492–503.e7. 10.1016/j.str.2023.02.006. PubMed DOI
Behler J.; Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 2007, 98, 146401.10.1103/PhysRevLett.98.146401. PubMed DOI
Smith J. S.; Nebgen B.; Lubbers N.; Isayev O.; Roitberg A. E. Less is more: Sampling chemical space with active learning. J. Chem. Phys. 2018, 148, 241733.10.1063/1.5023802. PubMed DOI
Schütt K. T.; Kessel P.; Gastegger M.; Nicoli K. A.; Tkatchenko A.; Müller K. R. SchNetPack: A Deep Learning Toolbox for Atomistic Systems. J. Chem. Theory Comput. 2019, 15, 448–455. 10.1021/acs.jctc.8b00908. PubMed DOI
Gao X.; Ramezanghorbani F.; Isayev O.; Smith J. S.; Roitberg A. E. TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials. J. Chem. Inf. Model. 2020, 60, 3408–3415. 10.1021/acs.jcim.0c00451. PubMed DOI
Noé F.; Tkatchenko A.; Müller K. R.; Clementi C. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 2020, 71, 361–390. 10.1146/annurev-physchem-042018-052331. PubMed DOI
Doerr S.; Majewski M.; Pérez A.; Krämer A.; Clementi C.; Noe F.; Giorgino T.; De Fabritiis G. TorchMD: A Deep Learning Framework for Molecular Simulations. J. Chem. Theory Comput. 2021, 17, 2355–2363. 10.1021/acs.jctc.0c01343. PubMed DOI PMC
Glielmo A.; Husic B. E.; Rodriguez A.; Clementi C.; Noé F.; Laio A. Unsupervised Learning Methods for Molecular Simulation Data. Chem. Rev. 2021, 121, 9722–9758. 10.1021/acs.chemrev.0c01195. PubMed DOI PMC
Mahmud M.; Kaiser M. S.; McGinnity T. M.; Hussain A. Deep Learning in Mining Biological Data. Cognit. Comput. 2021, 13, 1–33. 10.1007/s12559-020-09773-x. PubMed DOI PMC
Fan F. J.; Shi Y. Effects of data quality and quantity on deep learning for protein-ligand binding affinity prediction. Bioorg. Med. Chem. 2022, 72, 117003.10.1016/j.bmc.2022.117003. PubMed DOI
Ketkaew R.; Luber S. DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space. J. Chem. Inf. Model. 2022, 62, 6352–6364. 10.1021/acs.jcim.2c00883. PubMed DOI
Unsleber J. P.; Grimmel S. A.; Reiher M. Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks. J. Chem. Theory Comput. 2022, 18, 5393–5409. 10.1021/acs.jctc.2c00193. PubMed DOI
Jackson N. E.; Savoie B. M.; Statt A.; Webb M. A. Introduction to Machine Learning for Molecular Simulation. J. Chem. Theory Comput. 2023, 19, 4335–4337. 10.1021/acs.jctc.3c00735. PubMed DOI
Zhang J.; Chen D.; Xia Y.; Huang Y. P.; Lin X.; Han X.; Ni N.; Wang Z.; Yu F.; Yang L.; Yang Y. I.; Gao Y. Q. Artificial Intelligence Enhanced Molecular Simulations. J. Chem. Theory Comput. 2023, 19, 4338–4350. 10.1021/acs.jctc.3c00214. PubMed DOI
Lee J.; et al. CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J. Chem. Theory Comput. 2016, 12, 405–413. 10.1021/acs.jctc.5b00935. PubMed DOI PMC
Thallmair S.; Javanainen M.; Fábián B.; Martinez-Seara H.; Marrink S. J. Nonconverged Constraints Cause Artificial Temperature Gradients in Lipid Bilayer Simulations. J. Phys. Chem. B 2021, 125, 9537–9546. 10.1021/acs.jpcb.1c03665. PubMed DOI PMC
Joseph S.; Aluru N. R. Pumping of confined water in carbon nanotubes by rotation-translation coupling. Phys. Rev. Lett. 2008, 101, 064502.10.1103/PhysRevLett.101.064502. PubMed DOI
Bonthuis D. J.; Falk K.; Kaplan C. N.; Horinek D.; Berker A. N.; Bocquet L.; Netz R. R. Comment on ”Pumping of confined water in carbon nanotubes by rotation-translation coupling. Phys. Rev. Lett. 2010, 105, 064502.10.1103/PhysRevLett.105.209401. PubMed DOI
Shaw D. E.; et al. Anton 3: Twenty Microseconds of Molecular Dynamics Simulation before Lunch. Int. Conf. High Perform. Comput. Networking, Storage Anal. SC 2021, 1–11. 10.1145/3458817.3487397. DOI
Kim H.; Fábián B.; Hummer G. Neighbor List Artifacts in Molecular Dynamics Simulations. J. Chem. Theory Comput. 2023, 19, 8919–8929. 10.1021/acs.jctc.3c00777. PubMed DOI PMC
Biriukov D.; Wang H. W.; Rampal N.; Tempra C.; Kula P.; Neuefeind J. C.; Stack A. G.; Předota M. The ”good,” the ”bad,” and the ”hidden” in neutron scattering and molecular dynamics of ionic aqueous solutions. J. Chem. Phys. 2022, 156, 194505.10.1063/5.0093643. PubMed DOI
Sandoval-Perez A.; Pluhackova K.; Böckmann R. A. Critical Comparison of Biomembrane Force Fields: Protein-Lipid Interactions at the Membrane Interface. J. Chem. Theory Comput. 2017, 13, 2310–2321. 10.1021/acs.jctc.7b00001. PubMed DOI
Javanainen M.; Martinez-Seara H.; Vattulainen I. Excessive aggregation of membrane proteins in the Martini model. PLoS One 2017, 12, e018793610.1371/journal.pone.0187936. PubMed DOI PMC
Yoo J.; Aksimentiev A. New tricks for old dogs: Improving the accuracy of biomolecular force fields by pair-specific corrections to non-bonded interactions. Phys. Chem. Chem. Phys. 2018, 20, 8432–8449. 10.1039/C7CP08185E. PubMed DOI PMC
Meyer T.; D’Abramo M.; Hospital A.; Rueda M.; Ferrer-Costa C.; Pérez A.; Carrillo O.; Camps J.; Fenollosa C.; Repchevsky D.; Gelpí J. L.; Orozco M. MoDEL (Molecular Dynamics Extended Library): A Database of Atomistic Molecular Dynamics Trajectories. Structure 2010, 18, 1399–1409. 10.1016/j.str.2010.07.013. PubMed DOI
Hospital A.; Andrio P.; Cugnasco C.; Codo L.; Becerra Y.; Dans P. D.; Battistini F.; Torres J.; Gõni R.; Orozco M.; Gelpí J. L. BIGNASim: A NoSQL database structure and analysis portal for nucleic acids simulation data. Nucleic Acids Res. 2016, 44, D272–D278. 10.1093/nar/gkv1301. PubMed DOI PMC
Kiirikki A. M.; et al. Overlay databank unlocks data-driven analyses of biomolecules for all. Nat. Commun. 2024, 15, 1136.10.1038/s41467-024-45189-z. PubMed DOI PMC
Knapp B.; Ospina L.; Deane C. M. Avoiding False Positive Conclusions in Molecular Simulation: The Importance of Replicas. J. Chem. Theory Comput. 2018, 14, 6127–6138. 10.1021/acs.jctc.8b00391. PubMed DOI
Jumper J.; et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021, 596, 583–589. 10.1038/s41586-021-03819-2. PubMed DOI PMC
McBride J. M.; Polev K.; Abdirasulov A.; Reinharz V.; Grzybowski B. A.; Tlusty T. AlphaFold2 can predict single-mutation effects on structure and phenotype. Phys. Rev. Lett. 2023, 131, 218401.10.1103/PhysRevLett.131.218401. PubMed DOI
Chakravarty D.; Porter L. L. AlphaFold2 fails to predict protein fold switching. Protein Sci. 2022, 31, e435310.1002/pro.4353. PubMed DOI PMC
Terwilliger T. C.; Liebschner D.; Croll T. I.; Williams C. J.; McCoy A. J.; Poon B. K.; Afonine P. V.; Oeffner R. D.; Richardson J. S.; Read R. J.; Adams P. D. AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. Nat. Methods 2024, 21, 110–116. 10.1038/s41592-023-02087-4. PubMed DOI PMC
Lane T. J. Protein structure prediction has reached the single-structure frontier. Nat. Methods 2023, 20, 170–173. 10.1038/s41592-022-01760-4. PubMed DOI PMC
Vani B. P.; Aranganathan A.; Wang D.; Tiwary P. AlphaFold2-RAVE: From Sequence to Boltzmann Ranking. J. Chem. Theory Comput. 2023, 19, 4351–4354. 10.1021/acs.jctc.3c00290. PubMed DOI PMC
Kmiecik S.; Gront D.; Kolinski M.; Wieteska L.; Dawid A. E.; Kolinski A. Coarse-Grained Protein Models and Their Applications. Chem. Rev. 2016, 116, 7898–7936. 10.1021/acs.chemrev.6b00163. PubMed DOI
Marx D.; Hutter J.. Ab Initio Molecular Dynamics Basic Theory and Advanced Methods; Cambridge University Press, 2009; pp 1–567.10.1017/CBO9780511609633 DOI
Souza P. C.; et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 2021, 18, 382–388. 10.1038/s41592-021-01098-3. PubMed DOI
Yamada T.; Miyazaki Y.; Harada S.; Kumar A.; Vanni S.; Shinoda W. Improved Protein Model in SPICA Force Field. J. Chem. Theory Comput. 2023, 19, 8967–8977. 10.1021/acs.jctc.3c01016. PubMed DOI
Klein F.; Soñora M.; Helene Santos L.; Nazareno Frigini E.; Ballesteros-Casallas A.; Rodrigo Machado M.; Pantano S. The SIRAH force field: A suite for simulations of complex biological systems at the coarse-grained and multiscale levels. J. Struct. Biol. 2023, 215, 107985.10.1016/j.jsb.2023.107985. PubMed DOI
Vácha R.; Frenkel D. Relation between molecular shape and the morphology of self-assembling aggregates: A simulation study. Biophys. J. 2011, 101, 1432–1439. 10.1016/j.bpj.2011.07.046. PubMed DOI PMC
Sukeník L.; Mukhamedova L.; Procházková M.; Škubník K.; Plevka P.; Vácha R. Cargo Release from Nonenveloped Viruses and Virus-like Nanoparticles: Capsid Rupture or Pore Formation. ACS Nano 2021, 15, 19233–19243. 10.1021/acsnano.1c04814. PubMed DOI
Molteni C.; Parrinello M. Glucose in aqueous solution by first principles molecular dynamics. J. Am. Chem. Soc. 1998, 120, 2168–2171. 10.1021/ja973008q. DOI
MacKerell A. D.; et al. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 1998, 102, 3586–3616. 10.1021/jp973084f. PubMed DOI
Goetz R.; Lipowsky R. Computer simulations of bilayer membranes: Self-assembly and interfacial tension. J. Chem. Phys. 1998, 108, 7397–7409. 10.1063/1.476160. DOI
Wang T.; He X.; Li M.; Shao B.; Liu T. Y. AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics. Sci. Data 2023, 10, 1–12. 10.1038/s41597-023-02465-9. PubMed DOI PMC
Shim K. S.; Greskamp B.; Towles B.; Edwards B.; Grossman J. P.; Shaw D. E. The Specialized High-Performance Network on Anton 3. Proc. - Int. Symp. High-Performance Comput. Archit. 2022, 1211–1223. 10.1109/HPCA53966.2022.00092. DOI
Kozinsky B.; Musaelian A.; Johansson A.; Batzner S. Scaling the Leading Accuracy of Deep Equivariant Models to Biomolecular Simulations of Realistic Size. Proc. Int. Conf. High Perform. Comput. Networking, Storage Anal. SC 2023 2023, 1–12. 10.1145/3581784.3627041. DOI
Csizi K. S.; Reiher M. Universal QM/MM approaches for general nanoscale applications. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2023, 13, e165610.1002/wcms.1656. DOI
Raghavan B.; Paulikat M.; Ahmad K.; Callea L.; Rizzi A.; Ippoliti E.; Mandelli D.; Bonati L.; De Vivo M.; Carloni P. Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations. J. Chem. Inf. Model. 2023, 63, 3647–3658. 10.1021/acs.jcim.3c00557. PubMed DOI PMC
Kamerlin S. C.; Warshel A. At the dawn of the 21st century: Is dynamics the missing link for understanding enzyme catalysis. Proteins Struct. Funct. Bioinforma. 2010, 78, 1339–1375. 10.1002/prot.22654. PubMed DOI PMC
Kontkanen O. V.; Biriukov D.; Futera Z. Reorganization free energy of copper proteins in solution, in vacuum, and on metal surfaces. J. Chem. Phys. 2022, 156, 175101.10.1063/5.0085141. PubMed DOI
Rzepiela A. J.; Louhivuori M.; Peter C.; Marrink S. J. Hybrid simulations: Combining atomistic and coarse-grained force fields using virtual sites. Phys. Chem. Chem. Phys. 2011, 13, 10437–10448. 10.1039/c0cp02981e. PubMed DOI
Kar P.; Feig M. Hybrid All-Atom/Coarse-Grained Simulations of Proteins by Direct Coupling of CHARMM and PRIMO Force Fields. J. Chem. Theory Comput. 2017, 13, 5753–5765. 10.1021/acs.jctc.7b00840. PubMed DOI PMC
Liu Y.; De Vries A. H.; Barnoud J.; Pezeshkian W.; Melcr J.; Marrink S. J. Dual Resolution Membrane Simulations Using Virtual Sites. J. Phys. Chem. B 2020, 124, 3944–3953. 10.1021/acs.jpcb.0c01842. PubMed DOI PMC
Lolicato F.; et al. Disulfide bridge-dependent dimerization triggers FGF2 membrane translocation into the extracellular space. eLife 2024, 12, RP88579.10.7554/eLife.88579.2. PubMed DOI PMC
Hess B.; Scheek R. M. Orientation restraints in molecular dynamics simulations using time and ensemble averaging. J. Magn. Reson. 2003, 164, 19–27. 10.1016/S1090-7807(03)00178-2. PubMed DOI
Igaev M.; Kutzner C.; Bock L. V.; Vaiana A. C.; Grubmüller H. Automated cryo-EM structure refinement using correlation-driven molecular dynamics. eLife 2019, 8, e4354210.7554/eLife.43542. PubMed DOI PMC
Reith D.; Meyer H.; Müller-Plathe F. CG-OPT: A software package for automatic force field design. Comput. Phys. Commun. 2002, 148, 299–313. 10.1016/S0010-4655(02)00562-3. DOI
Krämer A.; Hülsmann M.; Köddermann T.; Reith D. Automated parameterization of intermolecular pair potentials using global optimization techniques. Comput. Phys. Commun. 2014, 185, 3228–3239. 10.1016/j.cpc.2014.08.022. DOI
Wang L. P.; Martinez T. J.; Pande V. S. Building force fields: An automatic, systematic, and reproducible approach. J. Phys. Chem. Lett. 2014, 5, 1885–1891. 10.1021/jz500737m. PubMed DOI PMC
Betz R. M.; Walker R. C. Paramfit: Automated optimization of force field parameters for molecular dynamics simulations. J. Comput. Chem. 2015, 36, 79–87. 10.1002/jcc.23775. PubMed DOI
Zahariev F.; De Silva N.; Gordon M. S.; Windus T. L.; Pérez García M. ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data. J. Chem. Inf. Model. 2017, 57, 391–396. 10.1021/acs.jcim.6b00654. PubMed DOI
Sauceda H. E.; Gastegger M.; Chmiela S.; Müller K. R.; Tkatchenko A. Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields. J. Chem. Phys. 2020, 153, 124109.10.1063/5.0023005. PubMed DOI
Befort B. J.; Defever R. S.; Tow G. M.; Dowling A. W.; Maginn E. J. Machine Learning Directed Optimization of Classical Molecular Modeling Force Fields. J. Chem. Inf. Model. 2021, 61, 4400–4414. 10.1021/acs.jcim.1c00448. PubMed DOI
Unke O. T.; Chmiela S.; Sauceda H. E.; Gastegger M.; Poltavsky I.; Schütt K. T.; Tkatchenko A.; Müller K. R. Machine Learning Force Fields. Chem. Rev. 2021, 121, 10142–10186. 10.1021/acs.chemrev.0c01111. PubMed DOI PMC
Unke O. T.; Chmiela S.; Gastegger M.; Schütt K. T.; Sauceda H. E.; Müller K. R. SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects. Nat. Commun. 2021, 12, 1–14. 10.1038/s41467-021-27504-0. PubMed DOI PMC
Yu Y.; Krämer A.; Venable R. M.; Simmonett A. C.; Mackerell A. D.; Klauda J. B.; Pastor R. W.; Brooks B. R. Semi-automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion. J. Chem. Theory Comput. 2021, 17, 1562–1580. 10.1021/acs.jctc.0c01326. PubMed DOI PMC
Empereur-Mot C.; Pedersen K. B.; Capelli R.; Crippa M.; Caruso C.; Perrone M.; Souza P. C. T.; Marrink S. J.; Pavan G. M. Automatic Optimization of Lipid Models in the Martini Force Field Using SwarmCG. J. Chem. Inf. Model. 2023, 63, 3827.10.1021/acs.jcim.3c00530. PubMed DOI PMC
Chmiela S.; Vassilev-Galindo V.; Unke O. T.; Kabylda A.; Sauceda H. E.; Tkatchenko A.; Müller K. R. Accurate global machine learning force fields for molecules with hundreds of atoms. Sci. Adv. 2023, 9, eadf08710.1126/sciadv.adf0873. PubMed DOI PMC
Illarionov A.; et al. Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions. J. Am. Chem. Soc. 2023, 145, 23620–23629. 10.1021/jacs.3c07628. PubMed DOI PMC
Wu S.; Yang X.; Zhao X.; Li Z.; Lu M.; Xie X.; Yan J. Applications and Advances in Machine Learning Force Fields. J. Chem. Inf. Model. 2023, 63, 6972–6985. 10.1021/acs.jcim.3c00889. PubMed DOI
Chen G.; Inizan T. J.; Plé T.; Lagardère L.; Piquemal J.-P.; Maday Y.. Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach. ChemRxiv, December 22, 2023, ver. 1.10.26434/chemrxiv-2023-nz8hc (accessed 2024-03-04). PubMed DOI
Bonomi M.; Barducci A.; Parrinello M. Reconstructing the equilibrium boltzmann distribution from well-tempered metadynamics. J. Comput. Chem. 2009, 30, 1615–1621. 10.1002/jcc.21305. PubMed DOI
Kikutsuji T.; Mori Y.; Okazaki K. I.; Mori T.; Kim K.; Matubayasi N. Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI). J. Chem. Phys. 2022, 156, 154108.10.1063/5.0087310. PubMed DOI
Yao S.; Van R.; Pan X.; Park J. H.; Mao Y.; Pu J.; Mei Y.; Shao Y. Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations. RSC Adv. 2023, 13, 4565–4577. 10.1039/D2RA08180F. PubMed DOI PMC
Baiardi A.; Christandl M.; Reiher M. Quantum Computing for Molecular Biology. ChemBioChem. 2023, 24, e20230012010.1002/cbic.202300120. PubMed DOI
Feng S.; Park S.; Choi Y. K.; Im W. CHARMM-GUI Membrane Builder: Past, Current, and Future Developments and Applications. J. Chem. Theory Comput. 2023, 19, 2161–2185. 10.1021/acs.jctc.2c01246. PubMed DOI PMC
Kroon P. C.; Grunewald F.; Barnoud J.; van Tilburg M.; Souza P. C. T.; Wassenaar T. A.; Marrink S. J. Martinize2 and Vermouth: Unified Framework for Topology Generation. eLife 2023, 12, RP90627.10.7554/eLife.90627.1. DOI
Huang J.; Rauscher S.; Nawrocki G.; Ran T.; Feig M.; De Groot B. L.; Grubmüller H.; MacKerell A. D. CHARMM36m: An improved force field for folded and intrinsically disordered proteins. Nat. Methods 2017, 14, 71–73. 10.1038/nmeth.4067. PubMed DOI PMC
Tian C.; Kasavajhala K.; Belfon K. A.; Raguette L.; Huang H.; Migues A. N.; Bickel J.; Wang Y.; Pincay J.; Wu Q.; Simmerling C. Ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. J. Chem. Theory Comput. 2020, 16, 528–552. 10.1021/acs.jctc.9b00591. PubMed DOI
Salomon-Ferrer R.; Case D. A.; Walker R. C. An overview of the Amber biomolecular simulation package. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2013, 3, 198–210. 10.1002/wcms.1121. DOI
Brooks B. R.; et al. CHARMM: The biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545–1614. 10.1002/jcc.21287. PubMed DOI PMC
Abraham M. J.; Murtola T.; Schulz R.; Páll S.; Smith J. C.; Hess B.; Lindahl E. Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1–2, 19–25. 10.1016/j.softx.2015.06.001. DOI
Phillips J. C.; et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J. Chem. Phys. 2020, 153, 44130.10.1063/5.0014475. PubMed DOI PMC
Eastman P.; Swails J.; Chodera J. D.; McGibbon R. T.; Zhao Y.; Beauchamp K. A.; Wang L. P.; Simmonett A. C.; Harrigan M. P.; Stern C. D.; Wiewiora R. P.; Brooks B. R.; Pande V. S. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput. Biol. 2017, 13, e100565910.1371/journal.pcbi.1005659. PubMed DOI PMC
Bowers K. J.; Chow D. E.; Xu H.; Dror R. O.; Eastwood M. P.; Gregersen B. A.; Klepeis J. L.; Kolossvary I.; Moraes M. A.; Sacerdoti F. D.; Salmon J. K.; Shan Y.; Shaw D. E.. Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters. Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, New York, 2007, 43–43. 10.1109/sc.2006.54 DOI
Hilpert C.; Beranger L.; Souza P. C.; Vainikka P. A.; Nieto V.; Marrink S. J.; Monticelli L.; Launay G. Facilitating CG Simulations with MAD: The MArtini Database Server. J. Chem. Inf. Model. 2023, 63, 702–710. 10.1021/acs.jcim.2c01375. PubMed DOI
Schmitt S.; Kanagalingam G.; Fleckenstein F.; Froescher D.; Hasse H.; Stephan S. Extension of the MolMod Database to Transferable Force Fields. J. Chem. Inf. Model. 2023, 63, 7148–7158. 10.1021/acs.jcim.3c01484. PubMed DOI
Boothroyd S.; Wang L. P.; Mobley D. L.; Chodera J. D.; Shirts M. R. Open Force Field Evaluator: An Automated, Efficient, and Scalable Framework for the Estimation of Physical Properties from Molecular Simulation. J. Chem. Theory Comput. 2022, 18, 3566–3576. 10.1021/acs.jctc.1c01111. PubMed DOI PMC
Hospital A.; Battistini F.; Soliva R.; Gelpí J. L.; Orozco M. Surviving the deluge of biosimulation data. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2020, 10, e144910.1002/wcms.1449. DOI
Marx V. The big challenges of big data. Nature 2013, 498, 255–260. 10.1038/498255a. PubMed DOI
Stephens Z. D.; Lee S. Y.; Faghri F.; Campbell R. H.; Zhai C.; Efron M. J.; Iyer R.; Schatz M. C.; Sinha S.; Robinson G. E. Big data: Astronomical or genomical?. PLoS Biol. 2015, 13, e100219510.1371/journal.pbio.1002195. PubMed DOI PMC
Wilson S. L.; Way G. P.; Bittremieux W.; Armache J. P.; Haendel M. A.; Hoffman M. M. Sharing biological data: why, when, and how. FEBS Lett. 2021, 595, 847–863. 10.1002/1873-3468.14067. PubMed DOI PMC
Berman H.; Henrick K.; Nakamura H. Announcing the worldwide Protein Data Bank. Nat. Struct. Biol. 2003, 10, 980.10.1038/nsb1203-980. PubMed DOI
Domański J.; Stansfeld P. J.; Sansom M. S.; Beckstein O. Lipidbook: A public repository for force-field parameters used in membrane simulations. J. Membr. Biol. 2010, 236, 255–258. 10.1007/s00232-010-9296-8. PubMed DOI
Newport T. D.; Sansom M. S.; Stansfeld P. J. The MemProtMD database: A resource for membrane-embedded protein structures and their lipid interactions. Nucleic Acids Res. 2019, 47, D390–D397. 10.1093/nar/gky1047. PubMed DOI PMC
Rodríguez-Espigares I.; et al. GPCRmd uncovers the dynamics of the 3D-GPCRome. Nat. Methods 2020, 17, 777–787. 10.1038/s41592-020-0884-y. PubMed DOI
Suarez-Leston F.; Calvelo M.; Tolufashe G. F.; Muñoz A.; Veleiro U.; Porto C.; Bastos M.; Piñeiro Á.; Garcia-Fandino R. SuPepMem: A database of innate immune system peptides and their cell membrane interactions. Comput. Struct. Biotechnol. J. 2022, 20, 874–881. 10.1016/j.csbj.2022.01.025. PubMed DOI PMC
Bateman A.; et al. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2023, 51, D523–D531. 10.1093/nar/gkac1052. PubMed DOI PMC
Hoch J. C.; et al. Biological Magnetic Resonance Data Bank. Nucleic Acids Res. 2023, 51, D368–D376. 10.1093/nar/gkac1050. PubMed DOI PMC
Baek M.; et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 2021, 373, 871–876. 10.1126/science.abj8754. PubMed DOI PMC
Feig M.; Abdullah M.; Johnsson L.; Pettitt B. M. Large scale distributed data repository: Design of a molecular dynamics trajectory database. Futur. Gener. Comput. Syst. 1999, 16, 101–110. 10.1016/S0167-739X(99)00039-4. DOI
Tai K.; Murdock S.; Wu B.; Ng M. H.; Johnston S.; Fangohr H.; Cox S. J.; Jeffreys P.; Essex J. W.; Sansom M. S. BioSimGrid: Towards a worldwide repository for biomolecular simulations. Org. Biomol. Chem. 2004, 2, 3219–3221. 10.1039/b411352g. PubMed DOI
van der Kamp M. W.; Schaeffer R. D.; Jonsson A. L.; Scouras A. D.; Simms A. M.; Toofanny R. D.; Benson N. C.; Anderson P. C.; Merkley E. D.; Rysavy S.; Bromley D.; Beck D. A.; Daggett V. Dynameomics: A Comprehensive Database of Protein Dynamics. Structure 2010, 18, 423–435. 10.1016/j.str.2010.01.012. PubMed DOI PMC
Abraham M. J.; et al. Sharing Data from Molecular Simulations. J. Chem. Inf. Model. 2019, 59, 4093–4099. 10.1021/acs.jcim.9b00665. PubMed DOI
Hildebrand P. W.; Rose A. S.; Tiemann J. K. Bringing Molecular Dynamics Simulation Data into View. Trends Biochem. Sci. 2019, 44, 902–913. 10.1016/j.tibs.2019.06.004. PubMed DOI
Abriata L. A.; Lepore R.; Dal Peraro M. About the need to make computational models of biological macromolecules available and discoverable. Bioinformatics 2020, 36, 2952–2954. 10.1093/bioinformatics/btaa086. PubMed DOI
Bekker G. J.; Kawabata T.; Kurisu G. The Biological Structure Model Archive (BSM-Arc): an archive for in silico models and simulations. Biophys. Rev. 2020, 12, 371–375. 10.1007/s12551-020-00632-5. PubMed DOI PMC
Antila H. S.; Kav B.; Miettinen M. S.; Martinez-Seara H.; Jungwirth P.; Ollila O. H. Emerging Era of Biomolecular Membrane Simulations: Automated Physically-Justified Force Field Development and Quality-Evaluated Databanks. J. Phys. Chem. B 2022, 126, 4169–4183. 10.1021/acs.jpcb.2c01954. DOI
Tiemann J. K. S.; Szczuka M.; Bouarroudj L.; Oussaren M.; Garcia S.; Howard R. J.; Delemotte L.; Lindahl E.; Baaden M.; Lindorff-Larsen K.; Chavent M.; Poulain P. MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations. eLife 2023, 12, RP90061.10.7554/eLife.90061.1. DOI
Musen M. A. Without appropriate metadata, data-sharing mandates are pointless. Nature 2022, 609, 222.10.1038/d41586-022-02820-7. PubMed DOI
Antila H. S.; M. Ferreira T.; Ollila O. H.; Miettinen M. S. Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics. J. Chem. Inf. Model. 2021, 61, 938–949. 10.1021/acs.jcim.0c01299. PubMed DOI PMC
Beltrán D.; Hospital A.; Gelpí J. L.; Orozco M. A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort. Nucleic Acids Res. 2024, 52, D393.10.1093/nar/gkad991. PubMed DOI PMC
Amaro R. E.; Mulholland A. J. A Community Letter Regarding Sharing Biomolecular Simulation Data for COVID-19. J. Chem. Inf. Model. 2020, 60, 2653–2656. 10.1021/acs.jcim.0c00319. PubMed DOI
Mulholland A. J.; Amaro R. E. COVID19 - Computational Chemists Meet the Moment. J. Chem. Inf. Model. 2020, 60, 5724–5726. 10.1021/acs.jcim.0c01395. PubMed DOI
Wilkinson M. D.; et al. Comment: The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 2016, 3, 1–9. 10.1038/sdata.2016.18. PubMed DOI PMC
Bonomi M.; et al. Promoting transparency and reproducibility in enhanced molecular simulations. Nat. Methods 2019, 16, 670–673. 10.1038/s41592-019-0506-8. PubMed DOI
Porubsky V. L.; Goldberg A. P.; Rampadarath A. K.; Nickerson D. P.; Karr J. R.; Sauro H. M. Best Practices for Making Reproducible Biochemical Models. Cell Syst. 2020, 11, 109–120. 10.1016/j.cels.2020.06.012. PubMed DOI PMC
Gabelica M.; Bojčić R.; Puljak L. Many researchers were not compliant with their published data sharing statement: a mixed-methods study. J. Clin. Epidemiol. 2022, 150, 33–41. 10.1016/j.jclinepi.2022.05.019. PubMed DOI
Elofsson A.; Hess B.; Lindahl E.; Onufriev A.; van der Spoel D.; Wallqvist A. Ten simple rules on how to create open access and reproducible molecular simulations of biological systems. PLoS Comput. Biol. 2019, 15, e100664910.1371/journal.pcbi.1006649. PubMed DOI PMC
Merz K. M.; Amaro R.; Cournia Z.; Rarey M.; Soares T.; Tropsha A.; Wahab H. A.; Wang R. Editorial: Method and Data Sharing and Reproducibility of Scientific Results. J. Chem. Inf. Model. 2020, 60, 5868–5869. 10.1021/acs.jcim.0c01389. PubMed DOI
Solomon G. C.; Zhang J. Z.; Cuk T. ACS Physical Chemistry Au: A Journal Celebrating Open Science across the Broad Horizons of Physical Chemistry. ACS Phys. Chem. Au 2021, 1, 1–2. 10.1021/acsphyschemau.1c00040. PubMed DOI PMC
Wong-ekkabut J.; Karttunen M. The good, the bad and the user in soft matter simulations. Biochim. Biophys. Acta - Biomembr. 2016, 1858, 2529–2538. 10.1016/j.bbamem.2016.02.004. PubMed DOI
Linton J. D. Research: All journals need to correct errors. Nature 2013, 504, 33.10.1038/504033d. PubMed DOI
Allison D. B.; Brown A. W.; George B. J.; Kaiser K. A. Reproducibility: A tragedy of errors. Nature 2016, 530, 27–29. 10.1038/530027a. PubMed DOI PMC
Van Noorden R. More than 10,000 research papers were retracted in 2023 - a new record. Nature 2023, 624, 479–481. 10.1038/d41586-023-03974-8. PubMed DOI
Liverpool L. AI intensifies fight against ’paper mills’ that churn out fake research. Nature 2023, 618, 222–223. 10.1038/d41586-023-01780-w. PubMed DOI
Gapsys V.; Hahn D. F.; Tresadern G.; Mobley D. L.; Rampp M.; De Groot B. L. Pre-Exascale Computing of Protein-Ligand Binding Free Energies with Open Source Software for Drug Design. J. Chem. Inf. Model. 2022, 62, 1172–1177. 10.1021/acs.jcim.1c01445. PubMed DOI PMC
Kyro G. W.; Brent R. I.; Batista V. S. HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction. J. Chem. Inf. Model. 2023, 63, 1947–1960. 10.1021/acs.jcim.3c00251. PubMed DOI
Dutta P.; Jain D.; Gupta R.; Rai B. Deep learning models for the estimation of free energy of permeation of small molecules across lipid membranes. Digit. Discovery 2023, 2, 189–201. 10.1039/D2DD00119E. DOI
Kabelka I.; Vácha R. Advances in Molecular Understanding of α-Helical Membrane-Active Peptides. Acc. Chem. Res. 2021, 54, 2196–2204. 10.1021/acs.accounts.1c00047. PubMed DOI
Deb R.; Kabelka I.; PÅibyl J.; Vácha R.. De novo design of peptides that form transmembrane barrel pores killing antibiotic resistant bacteria. bioRxiv, May 9, 2022, ver. 1.10.1101/2022.05.09.491086 (accessed 2024-03-04). DOI
Fonseca G.; Poltavsky I.; Tkatchenko A. Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields Under the Microscope. J. Chem. Theory Comput. 2023, 19, 8706–8717. 10.1021/acs.jctc.3c00985. PubMed DOI PMC
Perez S.; et al. Glycosaminoglycans: What Remains To Be Deciphered?. JACS Au 2023, 3, 628–656. 10.1021/jacsau.2c00569. PubMed DOI PMC
Vallet S. D.; Berthollier C.; Ricard-Blum S. The glycosaminoglycan interactome 2.0. Am. J. Physiol. - Cell Physiol. 2022, 322, C1271–C1278. 10.1152/ajpcell.00095.2022. PubMed DOI
Riopedre-Fernandez M.; Biriukov D.; Dračínský M.; Martinez-Seara H. Hyaluronan-arginine enhanced and dynamic interaction emerges from distinctive molecular signature due to electrostatics and side-chain specificity. Carbohydr. Polym. 2024, 325, 121568.10.1016/j.carbpol.2023.121568. PubMed DOI
Ives C. M.; Singh O.; D’Andrea S.; Fogarty C. A.; Harbison A. M.; Satheesan A.; Tropea B.; Fadda E.. Restoring Protein Glycosylation with GlycoShape. bioRxiv, December 11, 2023. 10.1101/2023.12.11.571101 (accessed 2024-03-04). DOI
Ricard-Blum S.; Perez S. Glycosaminoglycan interaction networks and databases. Curr. Opin. Struct. Biol. 2022, 74, 102355.10.1016/j.sbi.2022.102355. PubMed DOI
Casalino L.; Gaieb Z.; Goldsmith J. A.; Hjorth C. K.; Dommer A. C.; Harbison A. M.; Fogarty C. A.; Barros E. P.; Taylor B. C.; Mclellan J. S.; Fadda E.; Amaro R. E. Beyond shielding: The roles of glycans in the SARS-CoV-2 spike protein. ACS Cent. Sci. 2020, 6, 1722–1734. 10.1021/acscentsci.0c01056. PubMed DOI PMC
Bubeck S.; Chandrasekaran V.; Eldan R.; Gehrke J.; Horvitz E.; Kamar E.; Lee P.; Lee Y. T.; Li Y.; Lundberg S.; Nori H.; Palangi H.; Ribeiro M. T.; Zhang Y.. Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv, April 13, 2023, ver. 5. 10.48550/arXiv.2303.12712 (accessed 2024-03-04). DOI