Learning molecular machines by machine learning

Year 2025, Volume: 6 Issue: 2, 100 - 120, 30.07.2025

Rumeysa Hilal Çelik , Hacı Aslan Onur İşcil , Ecem Bulut , Saliha Ece Acuner

https://doi.org/10.55696/ejset.1620495

Abstract

Proteins, often referred to as molecular machines, are essential biomolecules that perform a wide range of cellular functions, typically by forming complexes. Understanding their three-dimendional (3D) structures is key to deciphering their functions. However, a significant gap exists between the vast number of known protein sequences and the relatively limited number of experimentally determined protein structures. Unraveling the mechanisms of protein folding remains a central challenge in understanding the sequence-structure/dynamics-function relationship. In recent years, machine learning (ML) has become a transformative tool across many scientific fields, and structural biology is no exception. Proteins have benefited substantially from advances in artificial intelligence (AI), as numerous ML-based methods have emerged for modeling the structures of both individual proteins and their complexes. Recent breakthrough in ML have marked a major leap forward in tackling the protein folding problem. ML-based AI algorithms for protein structure prediction —most notably AlphaFold—use protein sequence information to accurately predict 3D structures of monomers and multimeric protein complexes, achieving unprecedented levels of precision. Following the success of AlphaFold, recognized with the 2024 Nobel Prize in Chemistry, researchers worldwide have intensified efforts to leverage AI for unraveling complex biological challenges—from drug discovery to protein-protein interactions. This review highlights ML-based approaches, with a primary focus on AlphaFold and its derivatives, while also covering other notable methods such as the hybrid deep-learning based RoseTTAFold and protein language model-based ESMFold. These tools have diverse applications in protein structure modeling and significantly advance our understanding of the intricate relationships between sequence, structure, dynamics, and function. While ML-based methods still face limitations in certain cases —such as membrane proteins, which are underrepresented in experimental structural databases, or antibody–antigen interactions, which involve highly diverse and difficult-to-model hypervariable regions—advances in computational techniques and the incorporation of new experimental data are steadily improving the accuracy of these algorithms in tackling such challenges. Overall, the implementation of ML in the study of molecular machines represents a promising direction, with the potential to bridge the sequence-structure gap and address longstanding questions in structural biology and medicine.

Keywords

machine learning , artificial intelligence , protein structure prediction , protein folding , computational structural biology

References

• F.S. Collins, F.S., M. Morgan, and A. Patrinos, "The Human Genome Project: lessons from large-scale biology". Science, 300(5617): p. 286-290, 2003. https://www.science.org/doi/10.1126/science.1084564
•     E.S. Lander, et al., "Initial sequencing and analysis of the human genome". Nature, 409(6822): p. 860-921, 2001. https://doi.org/10.1038/35057062
•     A.J. de Koning, et al., "Repetitive elements may comprise over two-thirds of the human genome". PLoS Genetics, 7(12): p. e1002384, 2011. https://doi.org/10.1371/journal.pgen.1002384
•     A. Zanghellini, et al., "New algorithms and an in silico benchmark for computational enzyme design". Protein Science, 15(12): p. 2785-2794, 2006. https://doi.org/10.1110/ps.062353106
•     G. Langer, et al., "Automated macromolecular model building for X-ray crystallography using ARP/wARP version 7". Nature Protocols, 3(7): p. 1171-1179 2008. https://doi.org/10.1038/nprot.2008.91
•     D. Wishart, "NMR spectroscopy and protein structure determination: applications to drug discovery and development". Current Pharmaceutical Biotechnology, 6(2): p. 105-120, 2005. https://doi.org/10.2174/1389201053642367
•     Q. Li, and C. Kang, "A practical perspective on the roles of solution NMR spectroscopy in drug discovery". Molecules, 25(13): p. 2974, 2020. https://doi.org/10.3390/molecules25132974
•     C.R. Matthews, "Pathways of protein folding". Annual Review of Biochemistry, 62(Volume 62, 1993): p. 653-683, 1993. https://doi.org/10.1146/annurev.bi.62.070193.003253
•     C. Frieden, S.D. Hoeltzli, and I.J. Ropson, "NMR and protein folding: Equilibrium and stopped‐flow studies". Protein Science, 2(12): p. 2007-2014, 1993. https://doi.org/10.1002/pro.5560021202
•   A.R. Fersht, and V. Daggett, "Protein folding and unfolding at atomic resolution". Cell, 108(4): p. 573-582, 2002. https://doi.org/10.1016/S0092-8674(02)00620-7
•   X. Benjin, and L. Ling, "Developments, applications, and prospects of cryo‐electron microscopy". Protein Science, 29(4): p. 872-882, 2020. https://doi.org/10.1002/pro.3805
•   P. Cossio, "Need for cross-validation of single particle cryo-EM". Journal of Chemical Information and Modeling, 60(5): p. 2413-2418, 2020. https://doi.org/10.1021/acs.jcim.9b01121
•   F.M. Richards, "Areas, volumes, packing, and protein structure". Annual Review of Biophysics, 6(Volume 6, 1977): p. 151-176, 1977. https://doi.org/10.1146/annurev.bb.06.060177.001055
•   P.Y. Chou, and G.D. Fasman, "Empirical predictions of protein conformation". Annual review of biochemistry, 47(1): p. 251-276, 1978. https://doi.org/10.1146/annurev.bi.47.070178.001343
•   A.C. Anderson, "The process of structure-based drug design". Chemistry & Biology, 10(9): p. 787-797, 2003. https://doi.org/10.1016/j.chembiol.2003.09.002
•   A. Schneuing, et al., "Structure-based drug design with equivariant diffusion models". Nature Computational Science, 4(12): p. 899-909, 2024. https://doi.org/10.1038/s43588-024-00737-x
•   T.J. Lane, "Protein structure prediction has reached the single-structure frontier". Nature Methods, 20(2): p. 170-173, 2023. https://doi.org/10.1038/s41592-022-01760-4
•   P. Aloy, and R.B. Russell, "Structural systems biology: modelling protein interactions". Nature Reviews Molecular Cell Biology, 7(3): p. 188-197, 2006. https://doi.org/10.1038/nrm1859
•   C.M. Dobson, "Protein folding and misfolding". Nature, 426(6968): p. 884-890, 2003. https://doi.org/10.1038/nature02261
•   K. Vollmayr-Lee, "Introduction to molecular dynamics simulations". American Journal of Physics, 88(5): p. 401-422, 2020. https://doi.org/10.1119/10.0000654
•   J.G. Greener, et al., "A guide to machine learning for biologists". Nature Reviews Molecular Cell Biology, 23(1): p. 40-55, 2022. https://doi.org/10.1038/s41580-021-00407-0
•   Z. Qin, Q. Yu, and M.J. Buehler, "Machine learning model for fast prediction of the natural frequencies of protein molecules". RSC Advances, 10(28): p. 16607-16615, 2020. https://doi.org/10.1039/C9RA04186A
•   S. Wang, et al., "Accurate de novo prediction of protein contact map by ultra-deep learning model". PLoS Computational Biology, 13(1): p. e1005324, 2017. https://doi.org/10.1371/journal.pcbi.1005324
•   M. Baek, "Accurate prediction of protein structures and interactions using a three-track neural network". Science, 373(6557): p. 871-876, 2021. https://doi.org/10.1126/science.abj8754
•   F. Pucci, M. Schwersensky, and M. Rooman, "Artificial intelligence challenges for predicting the impact of mutations on protein stability". Current Opinion in Structural Biology, 72: p. 161-168, 2022. https://doi.org/10.1016/j.sbi.2021.11.001
•   S. Navarro, and S. Ventura, "Computational methods to predict protein aggregation". Current Opinion in Structural Biology, 73: p. 102343, 2022. https://doi.org/10.1016/j.sbi.2022.102343
•   M. Duran-Frigola, M. Cigler, and G.E. Winter, "Advancing targeted protein degradation via multiomics profiling and artificial intelligence". Journal of the American Chemical Society, 145(5): p. 2711-2732, 2023. https://doi.org/10.1021/jacs.2c11098
•   A. Dhakal, et al., "Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions". Briefings in Bioinformatics, 23(1): p. bbab476, 2022. https://doi.org/10.1093/bib/bbab476
•   F. Cui, et al., "Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data". Proteomics, 22(8): p. 2100197, 2022. https://doi.org/10.1002/pmic.202100197
•   D. Ovek, et al., "Artificial intelligence based methods for hot spot prediction". Current Opinion in Structural Biology, 72: p. 209-218, 2022. https://doi.org/10.1016/j.sbi.2021.11.003
•   S. Vishnoi, et al., "Artificial intelligence and machine learning for protein toxicity prediction using proteomics data". Chemical Biology & Drug Design, 96(3): p. 902-920, 2020. https://doi.org/10.1111/cbdd.13701
•   K. Prasad, and V. Kumar, "Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2". Current Research in Pharmacology and Drug Discovery, 2: p. 100042, 2021. https://doi.org/10.1016/j.crphar.2021.100042
•   K.-K. Mak, and M.R. Pichika, "Artificial intelligence in drug development: present status and future prospects". Drug Discovery Today, 24(3): p. 773-780, 2019. https://doi.org/10.1016/j.drudis.2018.11.014
•   N. Nagarajan, et al., "Application of computational biology and artificial intelligence technologies in cancer precision drug discovery". BioMed Research International, 2019(1): p. 8427042, 2019. https://doi.org/10.1155/2019/8427042
•   J. Söding, A. Biegert, and A.N. Lupas, "The HHpred interactive server for protein homology detection and structure prediction". Nucleic Acids Research, 33(suppl_2): p. W244-W248, 2005. https://doi.org/10.1093/nar/gki408
•   C. Lambert, et al., "ESyPred3D: Prediction of proteins 3D structures". Bioinformatics, 18(9): p. 1250-1256, 2002. https://doi.org/10.1093/bioinformatics/18.9.1250
•   C.-C. Chen, J.-K. Hwang, and J.-M. Yang, "(PS)2: protein structure prediction server". Nucleic Acids Research, 34(suppl_2): p. W152-W157, 2006. https://doi.org/10.1093/nar/gkl187
•   S. Wu, and Y. Zhang, "LOMETS: a local meta-threading-server for protein structure prediction". Nucleic Acids Research, 35(10): p. 3375-3382, 2007. https://doi.org/10.1093/nar/gkm251
•   H. Zhou, and J. Skolnick, "Ab initio protein structure prediction using chunk-TASSER". Biophysical Journal, 93(5): p. 1510-1518, 2007. https://doi.org/10.1529/biophysj.107.109959
•   D. B. Roche, et al., "The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction". Nucleic Acids Research, 39(suppl_2): p. W171-W176, 2011. https://doi.org/10.1093/nar/gkr184
•   D. Xu, and Y. Zhang, "Ab initio protein structure assembly using continuous structure fragments and optimized knowledge‐based force field". Proteins: Structure, Function, and Bioinformatics, 80(7): p. 1715-1735, 2012. https://doi.org/10.1002/prot.24065
•   T.-T. Huang, et al., "(PS)2: protein structure prediction server version 3.0". Nucleic acids research, 43(W1): p. W338-W342, 2015. https://doi.org/10.1093/nar/gkv454
•   J. Yang, and Y. Zhang, "I-TASSER server: new development for protein structure and function predictions". Nucleic Acids Research, 43(W1): p. W174-W181, 2015. https://doi.org/10.1093/nar/gkv342
•   C. Combet, et al., "Geno3D: automatic comparative molecular modelling of protein". Bioinformatics, 18(1): p. 213-214, 2002. https://doi.org/10.1093/bioinformatics/18.1.213
•   L. A. Kelley, et al., "The Phyre2 web portal for protein modeling, prediction and analysis". Nature Protocols, 10(6): p. 845-858, 2015. https://doi.org/10.1038/nprot.2015.053
•   M. P. Jacobson, et al., "A hierarchical approach to all‐atom protein loop prediction". Proteins: Structure, Function, and Bioinformatics, 55(2): p. 351-367, 2004. https://doi.org/10.1002/prot.10613
•   T. Schwede, et al., "SWISS-MODEL: an automated protein homology-modeling server". Nucleic Acids Research, 31(13): p. 3381-3385, 2003. https://doi.org/10.1093/nar/gkg520
•   M. Källberg, et al., "Template-based protein structure modeling using the RaptorX web server". Nature Protocols, 7(8): p. 1511-1522, 2012. https://doi.org/10.1038/nprot.2012.085
•   M. Nielsen, et al., "CPHmodels-3.0—remote homology modeling using structure-guided sequence profiles". Nucleic Acids Research, 38(suppl_2): p. W576-W581, 2010. https://doi.org/10.1093/nar/gkq535
•   Y. Song, et al., "High-resolution comparative modeling with RosettaCM". Structure, 21(10): p. 1735-1742, 2013. https://doi.org/10.1016/j.str.2013.08.005
•   B. Webb, and A. Sali, "Comparative protein structure modeling using MODELLER". Current Protocols in Bioinformatics, 54(1): p. 5.6. 1-5.6. 37, 2016. https://doi.org/10.1002/cpbi.3
•   J. L. Klepeis, and C.A. Floudas, "ASTRO-FOLD: a combinatorial and global optimization framework for ab initio prediction of three-dimensional structures of proteins from the amino acid sequence". Biophysical Journal, 85(4): p. 2119-2146, 2003. https://doi.org/10.1016/S0006-3495(03)74640-2
•   S. Raman, et al., "Structure prediction for CASP8 with all‐atom refinement using Rosetta". Proteins: Structure, Function, and Bioinformatics, 77(S9): p. 89-99, 2009. https://doi.org/10.1002/prot.22540
•   L.-H. Hung, et al., "PROTINFO: new algorithms for enhanced protein structure predictions". Nucleic Acids Research, 33(suppl_2): p. W77-W80, 2005. https://doi.org/10.1093/nar/gki403
•   S. Montgomerie, et al., "PROTEUS2: a web server for comprehensive protein structure prediction and structure-based annotation". Nucleic Acids Research, 36(suppl_2): p. W202-W209, 2008. https://doi.org/10.1093/nar/gkn255
•   C.-C. Chen, J.-K. Hwang, and J.-M. Yang, "(PS)2-v2: template-based protein structure prediction server". BMC Bioinformatics, 10: p. 1-13, 2009. https://doi.org/10.1186/1471-2105-10-366
•   Z. Wang, J. Eickholt, and J. Cheng, "MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8". Bioinformatics, 26(7): p. 882-888, 2010. https://doi.org/10.1093/bioinformatics/btq058
•   R. Grünberg, M. Nilges, and J. Leckner, "Biskit—a software platform for structural bioinformatics". Bioinformatics, 23(6): p. 769-770, 2007. https://doi.org/10.1093/bioinformatics/btl655
•   N. Hiranuma, et al., "Improved protein structure refinement guided by deep learning based accuracy estimation". Nature Communications, 12(1): p. 1340, 2021. https://doi.org/10.1038/s41467-021-21511-x
•   J. Jumper, et al., "Highly accurate protein structure prediction with AlphaFold". Nature, 596(7873): p. 583-589, 2021. https://doi.org/10.1038/s41586-021-03819-2
•   Y. Xia, et al., "Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning". Communications Biology, 6(1): p. 1221, 2023. https://doi.org/10.1038/s42003-023-05610-7
•   J. Abramson, et al., "Accurate structure prediction of biomolecular interactions with AlphaFold 3". Nature, 630(8016): p. 493-500, 2024. https://doi.org/10.1038/s41586-024-07487-w
•   J. A. Ruffolo, J. Sulam, and J.J. Gray, "Antibody structure prediction using interpretable deep learning". Patterns, 3(2), 2022. https://doi.org/10.1016/j.patter.2021.100406
•   R. Wu, et al., "High-resolution de novo structure prediction from primary sequence". BioRxiv, p. 2022.07. 21.500999, 2022. https://doi.org/10.1101/2022.07.21.500999
•   T. L. Vincent, P.J. Green, and D.N. Woolfson, "LOGICOIL—multi-state prediction of coiled-coil oligomeric state". Bioinformatics, 29(1): p. 69-76, 2013. https://doi.org/10.1093/bioinformatics/bts648
•   C. Li, et al., "Computational characterization of parallel dimeric and trimeric coiled-coils using effective amino acid indices". Molecular BioSystems, 11(2): p. 354-360, 2015. https://doi.org/10.1039/C4MB00569D
•   C. Savojardo, P. Fariselli, and R. Casadio, "BETAWARE: a machine-learning tool to detect and predict transmembrane beta-barrel proteins in prokaryotes". Bioinformatics, 29(4): p. 504-505, 2013. https://doi.org/10.1093/bioinformatics/bts728
•   M. Delorenzi, and T. Speed, "An HMM model for coiled-coil domains and a comparison with PSSM-based predictions". Bioinformatics, 18(4): p. 617-625, 2002. https://doi.org/10.1093/bioinformatics/18.4.617
•   L. Bartoli, et al., "CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information". Bioinformatics, 25(21): p. 2757-2763, 2009. https://doi.org/10.1093/bioinformatics/btp539
•   O. J. Rackham, et al., "The evolution and structure prediction of coiled coils across all genomes". Journal of Molecular Biology, 403(3): p. 480-493, 2010. https://doi.org/10.1016/j.jmb.2010.08.032
•   J. Martin, J.-F. Gibrat, and F. Rodolphe, "Analysis of an optimal hidden Markov model for secondary structure prediction". BMC Structural Biology, 6: p. 1-20, 2006. https://doi.org/10.1186/1472-6807-6-25
•   O. Lund, et al., "CPH models 2.0: X3M a computer program to extract 3D models". Casp Conference, 2002. [Online]. Available: https://sid.ir/paper/571181/en
•   A. V. McDonnell, et al., "Paircoil2: improved prediction of coiled coils from sequence". Bioinformatics, 22(3): p. 356-358, 2006. https://doi.org/10.1093/bioinformatics/bti797
•   J. Trigg, et al., "Multicoil2: predicting coiled coils and their oligomerization states from sequence in the twilight zone". PLoS One, 6(8): p. e23519, 2011. https://doi.org/10.1371/journal.pone.0023519
•   C. T. Armstrong, et al., "SCORER 2.0: an algorithm for distinguishing parallel dimeric and trimeric coiled-coil sequences". Bioinformatics, 27(14): p. 1908-1914, 2011. https://doi.org/10.1093/bioinformatics/btr299
•   X. Wang, Y. Zhou, and R. Yan, "AAFreqCoil: a new classifier to distinguish parallel dimeric and trimeric coiled coils". Molecular BioSystems, 11(7): p. 1794-1801, 2015. https://doi.org/10.1039/c5mb00119f
•   B.-W. Kim, et al., "ACCORD: an assessment tool to determine the orientation of homodimeric coiled-coils". Scientific Reports, 7(1): p. 43318, 2017. https://doi.org/10.1038/srep43318
•   D. Simm, K. Hatje, and M. Kollmar, "Waggawagga: comparative visualization of coiled-coil predictions and detection of stable single α-helices (SAH domains)". Bioinformatics, 31(5): p. 767-769, 2014. https://doi.org/10.1093/bioinformatics/btu700
•   C. W. Wood, and D.N. Woolfson, "CC Builder 2.0: Powerful and accessible coiled‐coil modeling". Protein Science, 27(1): p. 103-111, 2018. https://doi.org/10.1002/pro.3279
•   H. M. Geertz‐Hansen, et al., "Cofactory: Sequence‐based prediction of cofactor specificity of Rossmann folds". Proteins: Structure, Function, and Bioinformatics, 82(9): p. 1819-1828, 2014. https://doi.org/10.1002/prot.24536
•   V. D. T. Tran, et al., "A graph-theoretic approach for classification and structure prediction of transmembrane β-barrel proteins". BMC Genomics, 13: p. 1-18, 2012. https://doi.org/10.1186/1471-2164-13-S2-S5
•   J. A. Cuff, et al., "JPred: a consensus secondary structure prediction server". Bioinformatics (Oxford, England), 14(10): p. 892-893, 1998. https://doi.org/10.1093/bioinformatics/14.10.892
•   C. Cole, J.D. Barber, and G.J. Barton, "The Jpred 3 secondary structure prediction server". Nucleic Acids Research, 36(suppl_2): p. W197-W201, 2008. https://doi.org/10.1093/nar/gkn238
•   A. Drozdetskiy, et al., "JPred4: a protein secondary structure prediction server". Nucleic Acids Research, 43(W1): p. W389-W394, 2015. https://doi.org/10.1093/nar/gkv332
•   G. Karypis, "YASSPP: better kernels and coding schemes lead to improvements in protein secondary structure prediction". Proteins: Structure, Function, and Bioinformatics, 64(3): p. 575-586, 2006. https://doi.org/10.1002/prot.21036
•   R. Adamczak, A. Porollo, and J. Meller, "Combining prediction of secondary structure and solvent accessibility in proteins". Proteins: Structure, Function, and Bioinformatics, 59(3): p. 467-475, 2005. https://doi.org/10.1002/prot.20441
•   L. J. McGuffin, K. Bryson, and D.T. Jones, "The PSIPRED protein structure prediction server". Bioinformatics, 16(4): p. 404-405, 2000. https://doi.org/10.1093/bioinformatics/16.4.404
•   A. Yaseen, and Y. Li, "Context-based features enhance protein secondary structure prediction accuracy". Journal of Chemical Information and Modeling, 54(3): p. 992-1002, 2014. https://doi.org/10.1021/ci400647u
•   C. Fang, Y. Shang, and D. Xu, "MUFOLD‐SS: New deep inception‐inside‐inception networks for protein secondary structure prediction". Proteins: Structure, Function, and Bioinformatics, 86(5): p. 592-598, 2018. https://doi.org/10.1002/prot.25487
•   F. Bettella, D. Rasinski, and E.W. Knapp, "Protein secondary structure prediction with SPARROW". Journal of Chemical Information and Modeling, 52(2): p. 545-556, 2012. https://doi.org/10.1021/ci200321u
•   G. Pollastri, and A. McLysaght, "Porter: a new, accurate server for protein secondary structure prediction". Bioinformatics, 21(8): p. 1719-1720, 2005. https://doi.org/10.1093/bioinformatics/bti203
•   R. Heffernan, et al., "Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility". Bioinformatics, 33(18): p. 2842-2849, 2017. https://doi.org/10.1093/bioinformatics/btx218
•   K. Lin, et al., "A simple and fast secondary structure prediction method using hidden neural networks". Bioinformatics, 21(2): p. 152-159, 2005. https://doi.org/10.1093/bioinformatics/bth487
•   P. Kountouris, and J.D. Hirst, "Prediction of backbone dihedral angles and protein secondary structure using support vector machines". BMC Bioinformatics, 10: p. 1-14, 2009. https://doi.org/10.1186/1471-2105-10-437
•   T. Zhou, N. Shu, and S. Hovmöller, "A novel method for accurate one-dimensional protein structure prediction based on fragment matching". Bioinformatics, 26(4): p. 470-477, 2010. https://doi.org/10.1093/bioinformatics/btp679
•   A. Fiser, and A. Sali, "ModLoop: automated modeling of loops in protein structures". Bioinformatics, 19(18): p. 2500-2501, 2003. https://doi.org/10.1093/bioinformatics/btg362
•   M. Kumar, et al., "BhairPred: prediction of β-hairpins in a protein from multiple alignment information using ANN and SVM techniques". Nucleic Acids Research, 33(suppl_2): p. W154-W159, 2005. https://doi.org/10.1093/nar/gki588
•   M. Soori, B. Arezoo, and R. Dastres, "Artificial intelligence, machine learning and deep learning in advanced robotics, a review". Cognitive Robotics, 3: p. 54-70, 2023. https://doi.org/10.1016/j.cogr.2023.04.001
•   C. Janiesch, P. Zschech, and K. Heinrich, "Machine learning and deep learning". Electronic Markets, 31(3): p. 685-695, 2021. https://doi.org/10.1007/s12525-021-00475-2
• I. H. Sarker, "Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions". SN Computer Science, 2(6): p. 1-20, 2021. https://doi.org/10.1007/s42979-021-00815-1
• F. Noé, G. De Fabritiis, and C. Clementi, "Machine learning for protein folding and dynamics". Current Opinion in Structural Biology, 60: p. 77-84, 2020. https://doi.org/10.1016/j.sbi.2019.12.005
• A.W. Senior, A.W., et al., "Improved protein structure prediction using potentials from deep learning". Nature, 577(7792): p. 706-710, 2020. https://doi.org/10.1038/s41586-019-1923-7
• A.H.-W. Yeh, et al., "De novo design of luciferases using deep learning". Nature, 614(7949): p. 774-780, 2023. https://www.nature.com/articles/s41586-023-05696-3#citeas
• T. Tsaban, et al., "Harnessing protein folding neural networks for peptide–protein docking". Nature Communications, 13(1): p. 176, 2022. https://www.nature.com/articles/s41467-021-27838-9#citeas
• A. Jussupow, and V.R. Kaila, "Effective molecular dynamics from neural network-based structure prediction models". Journal of Chemical Theory and Computation, 19(7): p. 1965-1975, 2023. https://doi.org/10.1021/acs.jctc.2c01027
• A. G. Murzin, et al., "SCOP: A structural classification of proteins database for the investigation of sequences and structures". Journal of Molecular Biology, 247(4): p. 536-540, 1995. https://doi.org/10.1006/jmbi.1995.0159
• P. K. Srivastava, et al., "HMM-ModE–Improved classification using profile hidden Markov models by optimising the discrimination threshold and modifying emission probabilities with negative training sequences". BMC Bioinformatics, 8: p. 1-17, 2007. https://doi.org/10.1186/1471-2105-8-104
• A. K. Mandle, P. Jain, and S.K. Shrivastava, "Protein structure prediction using support vector machine". International Journal on Soft Computing, 3(1): p. 67, 2012.
• C. Cortes, and V. Vapnik, "Support-vector networks". Machine Learning, 20(3): p. 273-297, 1995. https://doi.org/10.1007/BF00994018
• Y. Zhang, and J. Skolnick, "TM-align: a protein structure alignment algorithm based on the TM-score". Nucleic Acids Research, 33(7): p. 2302-2309, 2005. https://doi.org/10.1093/nar/gki524
• Y. Qin, et al., "Deep learning methods for protein structure prediction". MedComm–Future Medicine, 3(3): p. e96, 2024. https://doi.org/10.1002/mef2.96
• R. Heffernan, et al., "Single‐sequence‐based prediction of protein secondary structures and solvent accessibility by deep whole‐sequence learning". Journal of Computational Chemistry, 39(26): p. 2210-2216, 2018. https://doi.org/10.1002/jcc.25534
• X.-M. Zhang, et al., "Graph neural networks and their current applications in bioinformatics". Frontiers in Genetics, 12: p. 690049, 2021. https://doi.org/10.3389/fgene.2021.690049
• S. Indolia, et al., "Conceptual understanding of convolutional neural network-a deep learning approach". Procedia Computer Science, 132: p. 679-688, 2018. https://doi.org/10.1016/j.procs.2018.05.069
• M. Torrisi, G. Pollastri, and Q. Le, "Deep learning methods in protein structure prediction". Computational and Structural Biotechnology Journal, 18: p. 1301-1310, 2020. https://doi.org/10.1016/j.csbj.2019.12.011
• S. Wang, J. Ma, and J. Xu, "AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields". Bioinformatics, 32(17): p. i672-i679, 2016. https://doi.org/10.1093/bioinformatics/btw446
• D. T. Jones, and S.M. Kandathil, "High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features". Bioinformatics, 34(19): p. 3308-3315, 2018. https://doi.org/10.1093/bioinformatics/bty341
• Y. Zhang, et al., "Prodconn-protein design using a convolutional neural network". Biophysical Journal, 118(3): p. 43a-44a, 2020. https://doi.org/10.1002/prot.25868
• F. Ju, et al., "CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction". Nature Communications, 12(1): p. 2535, 2021. https://www.nature.com/articles/s41467-021-22869-8#citeas
• X. Cao, et al., "PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture". Briefings in Bioinformatics, 22(6): p. bbab203, 2021. https://doi.org/10.1093/bib/bbab203
• S. Skansi, "Autoencoders" in Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence, p. 153-163, 2018. https://doi.org/10.1007/978-3-319-73004-2_8
• U. Manzoor, and Z. Halim, "Protein encoder: An autoencoder-based ensemble feature selection scheme to predict protein secondary structure". Expert Systems with Applications, 213: p. 119081, 2023. https://doi.org/10.1016/j.eswa.2022.119081
• H. Li, Q. Lyu, and J. Cheng, "A template-based protein structure reconstruction method using deep autoencoder learning". Journal of Proteomics & Bioinformatics, 9(12): p. 306, 2016. https://doi.org/10.4172/jpb.1000419
• P. Manisha, and S. Gujar, "Generative Adversarial Networks (GANs): What it can generate and what it cannot?" arXiv preprint arXiv:1804.00140, 2018. https://doi.org/10.48550/arXiv.1804.00140
• H. Yang, et al., "GANcon: protein contact map prediction with deep generative adversarial network". IEEE Access, 8: p. 80899-80907, 2020. https://ieeexplore.ieee.org/document/9082609/citations#citations
• M. Madani, et al., "CGAN-Cmap: protein contact map prediction using deep generative adversarial neural networks". BioRxiv, p. 2022.07. 26.501607, 2022. https://doi.org/10.1101/2022.07.26.501607
• Y. Yang, et al., "Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks". BMC Bioinformatics, 22: p. 1-17, 2021. https://doi.org/10.1186/s12859-021-04101-y
• J. Hanson, et al., "Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks". Bioinformatics, 33(5): p. 685-692, 2017. https://doi.org/10.1093/bioinformatics/btw678
• C. Zhao, T. Liu, and Z. Wang, "PANDA2: protein function prediction using graph neural networks". NAR Genomics and Bioinformatics, 4(1): p. lqac004, 2022. https://doi.org/10.1093/nargab/lqac004
• X. Zeng, et al., "GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs". BMC Genomics, 25(1): p. 406, 2024. https://doi.org/10.1186/s12864-024-10299-x
• X. Guo, et al., "Generating tertiary protein structures via interpretable graph variational autoencoders". Bioinformatics Advances, 1(1): p. vbab036, 2021. https://doi.org/10.1093/bioadv/vbab036
• B. Jing, et al., "Eigenfold: Generative protein structure prediction with diffusion models". ArXiv preprint, arXiv:2304.02198, 2023. https://doi.org/10.48550/arXiv.2304.02198
• J. L. Watson, et al., "De novo design of protein structure and function with RFdiffusion". Nature, 620(7976): p. 1089-1100, 2023. https://doi.org/10.1038/s41586-023-06415-8
• Y. Xiao, et al., "Proteingpt: Multimodal llm for protein property prediction and structure understanding". ArXiv preprint, arXiv:2408.11363, 2024. https://doi.org/10.48550/arXiv.2408.11363
• L. Lv, et al., "ProLLaMA: A Protein Language Model for Multi-Task Protein Language Processing". ArXiv preprint, arXiv:2402.16445, 2024.https://doi.org/10.48550/arXiv.2402.16445
• R. Rao, et al., "Evaluating protein transfer learning with TAPE". Advances in Neural Information Processing Systems, 32, 2019. https://pmc.ncbi.nlm.nih.gov/articles/PMC7774645/
• N. Brandes, et al., "ProteinBERT: a universal deep-learning model of protein sequence and function". Bioinformatics, 38(8): p. 2102-2110, 2022. https://doi.org/10.1093/bioinformatics/btac020
• A. Elnaggar, et al., "Prottrans: Toward understanding the language of life through self-supervised learning". IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10): p. 7112-7127, 2021. https://doi.org/10.1109/tpami.2021.3095381
• Z. Lin, et al., "Evolutionary-scale prediction of atomic-level protein structure with a language model". Science, 379(6637): p. 1123-1130, 2023. https://doi.org/10.1126/science.ade2574
• X. Fang, et al., "Helixfold-single: Msa-free protein structure prediction by using protein language model as an alternative". ArXiv preprint, arXiv:2207.13921, 2022. https://doi.org/10.1038/s42256-023-00721-6
• X. Pan, et al., "Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks". BMC Genomics, 19: p. 1-11, 2018. https://doi.org/10.1186/s12864-018-4889-1
• Y. Chen, G. Chen, and C.Y.-C. Chen, "MFTrans: A multi-feature transformer network for protein secondary structure prediction". International Journal of Biological Macromolecules, 267: p. 131311, 2024. https://doi.org/10.1016/j.ijbiomac.2024.131311
• Y. Duan, et al., "A point‐charge force field for molecular mechanics simulations of proteins based on condensed‐phase quantum mechanical calculations". Journal of Computational Chemistry, 24(16): p. 1999-2012, 2003. https://doi.org/10.1002/jcc.10349
• D. S. Marks, et al., "Protein 3D structure computed from evolutionary sequence variation". PLoS One, 6(12): p. e28766, 2011. https://doi.org/10.1371/journal.pone.0028766
• M. AlQuraishi, "End-to-end differentiable learning of protein structure". Cell Systems, 8(4): p. 292-301. e3, 2019. https://doi.org/10.1016/j.cels.2019.03.006
• J. Lyons, et al., "Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network". Journal of Computational Chemistry, 35(28): p. 2040-2046, 2014. https://doi.org/10.1002/jcc.23718
• Z. Guo, et al., "Diffusion models in bioinformatics and computational biology". Nature Reviews Bioengineering, 2(2): p. 136-154, 2024. https://doi.org/10.1038/s44222-023-00114-9
• S. Nakata, Y. Mori, and S. Tanaka, "End-to-end protein–ligand complex structure generation with diffusion-based generative models". BMC Bioinformatics, 24(1): p. 233, 2023. https://doi.org/10.1186/s12859-023-05354-5
• S. L. Lisanza, et al., "Multistate and functional protein design using RoseTTAFold sequence space diffusion". Nature Biotechnology, p. 1-11, 2024. https://doi.org/10.1038/s41587-024-02395-w
• Q. Zhang, et al., "Scientific large language models: A survey on biological & chemical domains". ACM Computing Surveys, 57(6): p. 1-38, 2025. https://doi.org/10.1145/3715318
• K. Sargsyan, and C. Lim, "Using protein language models for protein interaction hot spot prediction with limited data". BMC Bioinformatics, 25(1): p. 115, 2024. https://doi.org/10.1186/s12859-024-05737-2
• S. Gelman, et al., "Biophysics-based protein language models for protein engineering". BioRxiv, p. 2024.03. 15.585128, 2025. https://doi.org/10.1101/2024.03.15.585128
• B. Hu, et al., "Protein language models and structure prediction: Connection and progression". ArXiv preprint, arXiv:2211.16742, 2022. https://doi.org/10.48550/arXiv.2211.16742
• J. Jänes, and P. Beltrao, "Deep learning for protein structure prediction and design—progress and applications". Molecular Systems Biology, 20(3): p. 162-169, 2024. https://doi.org/10.1038/s44320-024-00016-x
• Y. Luo, et al., "MutaPLM: Protein language modeling for mutation explanation and engineering". Advances in Neural Information Processing Systems, 37: p. 79783-79818, 2024.
• M. Heinzinger, et al., "Bilingual language model for protein sequence and structure". NAR Genomics and Bioinformatics, 6(4): p. lqae150, 2024. https://doi.org/10.1093/nargab/lqae150
• D. Medina-Ortiz, et al., "Protein language models and machine learning facilitate the identification of antimicrobial peptides". International Journal of Molecular Sciences, 25(16): p. 8851, 2024. https://doi.org/10.3390/ijms25168851
• B. Jing, B. Berger, and T. Jaakkola, "AlphaFold meets flow matching for generating protein ensembles". ArXiv preprint, arXiv:2402.04845, 2024. https://doi.org/10.48550/arXiv.2402.04845
• D. Liu, et al., "Assessing protein model quality based on deep graph coupled networks using protein language model". Briefings in Bioinformatics, 25(1): p. bbad420, 2024. https://doi.org/10.1093/bib/bbad420
• Y. Si, and C. Yan, "Protein language model-embedded geometric graphs power inter-protein contact prediction". Elife, 12: p. RP92184, 2024. https://doi.org/10.7554/eLife.92184.2
• S. Sledzieski, et al., "Democratizing protein language models with parameter-efficient fine-tuning". Proceedings of the National Academy of Sciences, 121(26): p. e2405840121, 2024. https://doi.org/10.1073/pnas.2405840121
• W. Liu, et al., "PLMSearch: Protein language model powers accurate and fast sequence search for remote homology". Nature Communications, 15(1): p. 2775, 2024. https://doi.org/10.1038/s41467-024-46808-5
• Y. Liu, and B. Tian, "Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning". Briefings in Bioinformatics, 25(1): p. bbad488, 2024. https://doi.org/10.1093/bib/bbad488
• R. Roche, et al., "EquiPNAS: improved protein–nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks". Nucleic Acids Research, 52(5): p. e27-e27, 2024. https://doi.org/10.1093/nar/gkae039
• I. Barrios-Núñez, et al., "Decoding functional proteome information in model organisms using protein language models". NAR Genomics and Bioinformatics, 6(3): p. lqae078, 2024. https://doi.org/10.1093/nargab/lqae078
• I. Pudžiuvelytė, et al., "TemStaPro: protein thermostability prediction using sequence representations from protein language models". Bioinformatics, 40(4): p. btae157, 2024. https://doi.org/10.1093/bioinformatics/btae157
• A. N. Lupas, et al., "The breakthrough in protein structure prediction". Biochemical Journal, 478(10): p. 1885-1890, 2021. https://doi.org/10.1042/bcj20200963
• P. Mamoshina, et al., "Applications of deep learning in biomedicine". Molecular Pharmaceutics, 13(5): p. 1445-1454, 2016. https://doi.org/10.1021/acs.molpharmaceut.5b00982
• S. Ruder, "An overview of multi-task learning in deep neural networks". ArXiv preprint, arXiv:1706.05098, 2017. https://doi.org/10.48550/arXiv.1706.05098
• C. Cao, et al., "Deep learning and its applications in biomedicine". Genomics, Proteomics & Bioinformatics, 16(1): p. 17-32, 2018. https://doi.org/10.1016/j.gpb.2017.07.003
• G. Monteiro da Silva, et al., "High-throughput prediction of protein conformational distributions with subsampled AlphaFold2". Nature Communications, 15(1): p. 2464, 2024. https://doi.org/10.1038/s41467-024-46715-9
• Q. Zhang, et al., "Application of the Alphafold2 protein prediction algorithm based on artificial intelligence". Journal of Theory and Practice of Engineering Science, 4(02): p. 58-65, 2024. https://doi.org/10.53469/jtpes.2024.04(02).09
• R. Evans, et al., "Protein complex prediction with AlphaFold-Multimer". Biorxiv, p. 2021.10. 04.463034, 2021. https://doi.org/10.1101/2021.10.04.463034
• A.-R. Kim, et al., "Enhanced protein-protein interaction discovery via AlphaFold-Multimer". BioRxiv, p. 2024.02. 19.580970, 2024. https://doi.org/10.1101/2024.02.19.580970
• M. Edich, et al., "The impact of AlphaFold2 on experimental structure solution". Faraday Discussions, 240: p. 184-195, 2022. https://doi.org/10.1039/D2FD00072E
• M. L. Hekkelman, et al., "AlphaFill: enriching AlphaFold models with ligands and cofactors". Nature Methods, 20(2): p. 205-213, 2023. https://doi.org/10.1038/s41592-022-01685-y
• J. Yang, et al., "Improved protein structure prediction using predicted interresidue orientations". Proceedings of the National Academy of Sciences, 117(3): p. 1496-1503, 2020. https://doi.org/10.1073/pnas.1914677117
• J. Ingraham, et al., "Learning protein structure with a differentiable simulator". International Conference on Learning Representations, 2018.
• R. Das, and D. Baker, "Macromolecular modeling with rosetta". Annual Review Biochemistry, 77(1): p. 363-382, 2008. https://doi.org/10.1146/annurev.biochem.77.062906.171838
• A. Leaver-Fay, et al., "ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules in methods in enzymology". Elsevier, p. 545-574, 2011. https://doi.org/10.1016/b978-0-12-381270-4.00019-6
• J. K. Leman, et al., "Macromolecular modeling and design in Rosetta: recent methods and frameworks". Nature Methods, 17(7): p. 665-680, 2020. https://doi.org/10.1038/s41592-020-0848-2
• R. F. Alford, et al., "The Rosetta all-atom energy function for macromolecular modeling and design". Journal of Chemical Theory and Computation, 13(6): p. 3031-3048, 2017. https://doi.org/10.1021/acs.jctc.7b00125
• P. Barth, J. Schonbrun, and D. Baker, "Toward high-resolution prediction and design of transmembrane helical protein structures". Proceedings of the National Academy of Sciences, 104(40): p. 15682-15687, 2007. https://doi.org/10.1073/pnas.0702515104
• E. H. Kellogg, A. Leaver‐Fay, and D. Baker, "Role of conformational sampling in computing mutation‐induced changes in protein structure and stability". Proteins: Structure, Function, and Bioinformatics, 79(3): p. 830-838, 2011. https://doi.org/10.1002/prot.22921
• S. Liu, K. Wu, and C. Chen, "Obtaining protein foldability information from computational models of AlphaFold2 and RoseTTAFold". Computational and Structural Biotechnology Journal, 20: p. 4481-4489, 2022. https://doi.org/10.1016/j.csbj.2022.08.034
• M. Baek, et al., "Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA". Nature Methods, 21(1): p. 117-121, 2024. https://doi.org/10.1038/s41592-023-02086-5
• I. Marchal, "RoseTTAFold expands to all-atom for biomolecular prediction and design". Nature Biotechnology, 42(4): p. 571-571, 2024. https://doi.org/10.1038/s41587-024-02211-5
• R. Krishna, et al., "Generalized biomolecular modeling and design with RoseTTAFold All-Atom". Science, 384(6693): p. eadl2528, 2024. https://doi.org/10.1126/science.adl2528
• D. F. Burke, et al., "Towards a structurally resolved human protein interaction network". Nature Structural & Molecular Biology, 30(2): p. 216-225, 2023. https://doi.org/10.1038/s41594-022-00910-8
• Z. Peng, et al., "Protein structure prediction in the deep learning era". Current Opinion in Structural Biology, 77: p. 102495, 2022. https://doi.org/10.1016/j.sbi.2022.102495
• B. Shor, and D. Schneidman-Duhovny, "CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2". Nature Methods, 21(3): p. 477-487, 2024. https://doi.org/10.1038/s41592-024-02174-0
• T. C. Terwilliger, et al., "AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination". Nature Methods, 21(1): p. 110-116, 2024. https://doi.org/10.1038/s41592-023-02087-4
• C.-X. Peng, et al., "Recent advances and challenges in protein structure prediction". Journal of Chemical Information and Modeling, 64(1): p. 76-95, 2023. https://doi.org/10.1021/acs.jcim.3c01324
• M. Schauperl, and R.A. Denny, "AI-based protein structure prediction in drug discovery: impacts and challenges". Journal of Chemical Information and Modeling, 62(13): p. 3142-3156, 2022. https://doi.org/10.1021/acs.jcim.2c00026
• J. Zhu, and P. Lu, "Computational design of transmembrane proteins". Current Opinion in Structural Biology, 74: p. 102381, 2022. https://doi.org/10.1016/j.sbi.2022.102381
• L. Zheng, et al., "MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics". Briefings in Bioinformatics, 25(2): p. bbae006, 2024. https://doi.org/10.1093/bib/bbae006
• T. Beuming, et al., "Are deep learning structural models sufficiently accurate for free-energy calculations? Application of FEP+ to AlphaFold2-predicted structures". Journal of Chemical Information and Modeling, 62(18): p. 4351-4360, 2022. https://doi.org/10.1021/acs.jcim.2c00796
• V. Scardino, J.I. Di Filippo, and C.N. Cavasotto, "How good are AlphaFold models for docking-based virtual screening?". Iscience, 26(1), 2023. https://doi.org/10.1016/j.isci.2022.105920
• M. L. Fernández-Quintero, et al., "Challenges in antibody structure prediction". MAbs Taylor & Francis, 15, 2023. https://doi.org/10.1080/19420862.2023.2175319
• J. Adolf-Bryfogle, et al., "RosettaAntibodyDesign (RAbD): A general framework for computational antibody design". PLoS Computational Biology, 14(4): p. e1006112, 2018. https://doi.org/10.1371/journal.pcbi.1006112