KaydedilenlerDerginizi ekleyin

Dergiler / Eurasian Journal of Science Engineering and Technology / 2025 / Volume: 6 Issue: 2

Learning molecular machines by machine learning

Rumeysa Hilal Çelik(NIGDE UNIVERSITY)0000-0002-9146-3953
Hacı Aslan Onur İşcil0000-0003-2359-2267
Ecem Bulut0009-0008-4698-7505
Saliha Ece Acuner(ISTANBUL MEDENIYET UNIVERSITY)0000-0003-0336-0645
Sayfa
—
Yayın Tarihi
2025-07-30
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Proteins, often referred to as molecular machines, are essential biomolecules that perform a wide range of cellular functions, typically by forming complexes.
📊
Temel Bulgular & Veri:Understanding their three-dimendional (3D) structures is key to deciphering their functions.
💡
Sonuç & Katkı: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.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:2024 Verileri
⚙️Değişkenler:machine learning · artificial intelligence · protein structure prediction

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.

🔖

Bu Makaleye Atıf Yap

Çelik, R. H., İşcil, H. A. O., Bulut, E., Acuner, S. E. (2025). Learning molecular machines by machine learning. Eurasian Journal of Science Engineering and Technology, Volume: 6 Issue: 2. https://doi.org/10.55696/ejset.1620495

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (200)

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. C.M. Dobson, "Protein folding and misfolding". Nature, 426(6968): p. 884-890, 2003. https://doi.org/10.1038/nature02261
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. 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
  70. 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
  71. 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
  72. 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
  73. 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
  74. 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
  75. 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
  76. 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
  77. 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
  78. 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
  79. 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
  80. 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
  81. 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
  82. 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
  83. 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
  84. 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
  85. 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
  86. 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
  87. 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
  88. 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
  89. 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
  90. 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
  91. 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
  92. 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
  93. 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
  94. 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
  95. 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
  96. 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
  97. 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
  98. 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
  99. 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
  100. 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
  101. 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
  102. 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
  103. 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
  104. 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
  105. 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
  106. 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
  107. 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
  108. 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.
  109. C. Cortes, and V. Vapnik, "Support-vector networks". Machine Learning, 20(3): p. 273-297, 1995. https://doi.org/10.1007/BF00994018
  110. 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
  111. 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
  112. 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
  113. 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
  114. 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
  115. 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
  116. 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
  117. 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
  118. 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
  119. 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
  120. 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
  121. 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
  122. 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
  123. 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
  124. 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
  125. 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
  126. 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
  127. 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
  128. 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
  129. 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
  130. 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
  131. 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
  132. 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
  133. 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
  134. 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
  135. 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
  136. 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/
  137. 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
  138. 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
  139. 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
  140. 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
  141. 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
  142. 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
  143. 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
  144. 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
  145. 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
  146. 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
  147. 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
  148. 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
  149. 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
  150. 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
  151. 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
  152. 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
  153. 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
  154. 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
  155. Y. Luo, et al., "MutaPLM: Protein language modeling for mutation explanation and engineering". Advances in Neural Information Processing Systems, 37: p. 79783-79818, 2024.
  156. 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
  157. 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
  158. 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
  159. 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
  160. 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
  161. 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
  162. 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
  163. 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
  164. 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
  165. 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
  166. 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
  167. 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
  168. 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
  169. 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
  170. 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
  171. 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
  172. 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
  173. 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
  174. 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
  175. 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
  176. 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
  177. 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
  178. J. Ingraham, et al., "Learning protein structure with a differentiable simulator". International Conference on Learning Representations, 2018.
  179. 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
  180. 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
  181. 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
  182. 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
  183. 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
  184. 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
  185. 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
  186. 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
  187. 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
  188. 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
  189. 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
  190. 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
  191. 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
  192. 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
  193. 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
  194. 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
  195. 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
  196. 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
  197. 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
  198. 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
  199. M. L. Fernández-Quintero, et al., "Challenges in antibody structure prediction". MAbs Taylor & Francis, 15, 2023. https://doi.org/10.1080/19420862.2023.2175319
  200. 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