Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship
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Taiwo Temitope Ogunjobi 1 * , Ijeoma Chineme Okorie 2 , Chimaobi Divine Gigam-Ozuzu 3 , Jumoke Victoria Olorunleke 4 , Felix Iyanu Ogunleye 5 , Emmanuella Osaruese Irimoren 6 , Dorcas Oyedolapo Atanda 7 , Adaobi Mary-Ann Okafor 8 , Chinyere Eucharia Agbo 8 , Favour Onasokhare Okunbi 9 , Otoh Dayo Umoren 10 , Adoyi Daniel Adidu 11 , Emmanuel Oluwadamilare Ojo 12
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1 Department of Biochemistry, Faculty of Basic Medical Sciences, Ladoke Akintola University of Technology, Ogbomosho, Oyo State, NIGERIA2 Dapartment of Mathematics Sciences, Faculty of Science, NDA, Kaduna, Kaduna State, NIGERIA3 Department of Plant Science and Biotechnology, School of Natural and Applied, Science, University of Port Harcourt, Choba, Rivers State, NIGERIA4 Department of Pharmaceutical Microbiology, College of Pharmacy, Obafemi Awolowo University, Ile-Ife, Osun State, NIGERIA5 Department of Biomedical Technology, School of Basic Medical Science, Federal University of Technology Akure, Akure, Ondo State, NIGERIA6 Department of Anatomy, Faculty of Basic Medical Sciences, University of Benin, Benin City, Edo State, NIGERIA7 Department of Biochemistry, Faculty of Sciences, Lagos State University, Ojo, Lagos State, NIGERIA8 Department of Nutrition and Dietetics, Faculty of Agriculture, University of Nigeria Nsukka, Nsukka, Enugu State, NIGERIA9 Department of Microbiology, Faculty of Biological Sciences, Mountain Top University, Pakuro, Ogun State, NIGERIA10 Department of Biological Sciences, Faculty of Science, National Open University of Nigeria, Abuja, NIGERIA11 Department of Pharmacology and Therapeutics, Faculty of Basic Medical Sciences, University of Ibadan, Ibadan, NIGERIA12 Department of Biochemistry, Faculty of Sciences, Obafemi Awolowo University, Ile-Ife, Osun State, NIGERIA* Corresponding Author
Abstract
Protein analysis has been completely transformed by the swift growth of bioinformatics, which has improved protein structure prediction, simulated interactions, and clarified functional interactions. To improve our knowledge of proteomics, this review carefully examines the application of diverse bioinformatics methods in protein analysis. We evaluate computational methods such as molecular dynamics simulations and machine learning algorithms critically, with an emphasis on their applicability to modeling protein-protein interactions and protein tertiary structure prediction. Our findings show that these methods are useful for predicting protein functions and interactions, which are important for drug discovery and development. We also talk about the important implications of these developments for our knowledge of complex biological systems and disease mechanisms at the molecular level. This review also provides insights into the existing and future potential of bioinformatics tools, emphasizing their vital role in revolutionizing protein analysis. We additionally offer future strategies to improve our knowledge and management of complex disorders, particularly highlighting the need for integrated, multi-scale approaches and additional research on underrepresented proteins.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Review Article
EUR J SUSTAIN DEV RES, Volume 9, Issue 3, 2025, Article No: em0298
https://doi.org/10.29333/ejosdr/16340
Publication date: 01 Jul 2025
Online publication date: 05 May 2025
Article Views: 208
Article Downloads: 93
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- Ali, A. M., Atmaj, J., Van Oosterwijk, N., Groves, M. R., & Dömling, A. (2019). Stapled peptides inhibitors: A new window for target drug discovery. Computational and Structural Biotechnology Journal, 17, 263-281. https://doi.org/10.1016/j.csbj.2019.01.012
- AlQuraishi, M. (2020). A watershed moment for protein structure prediction. Nature, 577(7792), 627-628. https://doi.org/10.1038/d41586-019-03951-0
- Bajpai, A. K., Davuluri, S., Tiwary, K., Narayanan, S., Oguru, S., Basavaraju, K., Dayalan, D., Thirumurugan, K., & Acharya, K. K. (2019). How helpful are the protein-protein interaction databases and which ones? bioRxiv. https://doi.org/10.1101/566372
- Ballard, Z., Brown, C., Madni, A. M., & Ozcan, A. (2021). Machine learning and computation-enabled intelligent sensor design. Nature Machine Intelligence, 3(7), 556-565. https://doi.org/10.1038/s42256-021-00360-9
- Ban, X., Lahiri, P., Dhoble, A. S., Li, D., Gu, Z., Li, C., Cheng, L., Hong, Y., Li, Z., & Kaustubh, B. (2019). Evolutionary stability of salt bridges hints its contribution to stability of proteins. Computational and Structural Biotechnology Journal, 17, 895-903. https://doi.org/10.1016/j.csbj.2019.06.022
- Behl, A., & Mishra, P. (2018). Three-dimensional structure of Plasmodium falciparum knob associated heat shock protein 40 predicted by homology modeling method. The Pharma Innovation Journal, 7, 202-205.
- Bekker, G.-J., Araki, M., Oshima, K., Okuno, Y., & Kamiya, N. (2020). Exhaustive search of the configurational space of heat-shock protein 90 with its inhibitor by multicanonical molecular dynamics based dynamic docking. Journal of Computational Chemistry, 41(17), 1606-1615. https://doi.org/10.1002/jcc.26203
- Bergenholm, D., Liu, G., Holland, P., & Nielsen, J. (2018). Reconstruction of a global transcriptional regulatory network for control of lipid metabolism in yeast by using chromatin immunoprecipitation with lambda exonuclease digestion. mSystems, 3(4). https://doi.org/10.1128/msystems.00215-17
- Bolyen, E., Ram Rideout, J., Chase, J., Anders Pitman, T., Shiffer, A., Mercurio, W., Dillon, M. R., & Caporaso,J. G. (2018). An introduction to applied bioinformatics: A free, open, and interactive text. Journal of Open Source Education, 1(5), 27. https://doi.org/10.21105/jose.00027
- Cava, C, & Castiglioni, I. (2020). Integration of molecular docking and in vitro studies: A powerful approach for drug discovery in breast cancer. Applied Sciences, 10(19), 6981. https://doi.org/10.3390/app10196981
- Cetin, B., Song, G. J., & O’Leary, S. E. (2020). Heterogeneous dynamics of protein-RNA interactions across transcriptome-derived messenger RNA populations. Journal of the American Chemical Society, 142(51), 21249-21253. https://doi.org/10.1021/jacs.0c09841
- Cezar, H. M., Canuto, S., & Coutinho, K. (2020). DICE: A Monte Carlo code for molecular simulation including the configurational bias Monte Carlo method. Journal of Chemical Information and Modeling, 60(7), 3472-3788. https://doi.org/10.1021/acs.jcim.0c00077
- Chao, F.-A., & Byrd, R. A. (2018). Protein dynamics revealed by NMR relaxation methods. Emerging Topics in Life Sciences, 2(1), 93-105. https://doi.org/10.1042/etls20170139
- Chen, K.-H., Wang, T.-F., & Hu, Y.-J. (2019). Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme. BMC Bioinformatics, 20(1). https://doi.org/10.1186/s12859-019-2907-1
- Chen, M., Lin, X., Lu, W., Schafer, N. P., Onuchic, J. N., & Wolynes, P. G. (2018). Template-guided protein structure prediction and refinement using optimized folding landscape force fields. Journal of Chemical Theory and Computation, 14(11), 6102-6116. https://doi.org/10.1021/acs.jctc.8b00683
- Cheung, N. J., & Yu, W. (2018). De novo protein structure prediction using ultra-fast molecular dynamics simulation. PLoS ONE, 13(11), e0205819. https://doi.org/10.1371/journal.pone.0205819
- Crosara, K. T. B., Moffa, E. B., Xiao, Y., & Siqueira, W. L. (2018). Merging in-silico and in vitro salivary protein complex partners using the STRING database: A tutorial. Journal of Proteomics, 171, 87-94. https://doi.org/10.1016/j.jprot.2017.08.002
- Davis, R. G., Park, H.-M., Kim, K., Greer, J. B., Fellers, R. T., LeDuc, R. D., Romanova, E. V., Rubakhin, S. S., Zombeck, J. A., Wu, C., Yau, P. M., Gao, P., van Nispen, A. J., Patrie, S. M., Thomas, P. M., Sweedler, J. V., Rhodes, J. S., & Kelleher, N. L. (2018). Top-down proteomics enables comparative analysis of brain proteoforms between mouse strains. Analytical Chemistry, 90(6), 3802-3810. https://doi.org/10.1021/acs.analchem.7b04108
- de Medeiros, A. D., Capobiango, N. P., da Silva, J. M., da Silva, L. J., da Silva, C. B., & dos Santos Dias, D. C. F. (2020). Interactive machine learning for soybean seed and seedling quality classification. Scientific Report, 10(1). https://doi.org/10.1038/s41598-020-68273-y
- De Paris, R., Vahl Quevedo, C., Ruiz, D. D., Gargano, F., de Souza, O. N. (2018). A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model. BMC Bioinformatics, 19(1). https://doi.org/10.1186/s12859-018-2222-2
- Defoort, J., Van de Peer, Y., & Carretero-Paulet, L. (2019). The evolution of gene duplicates in angiosperms and the impact of protein-protein interactions and the mechanism of duplication. Genome Biology and Evolution. https://doi.org/10.1093/gbe/evz156
- Fang, G., Annis, I. E., Elston-Lafata, J., & Cykert, S. (2019). Applying machine learning to predict real-world individual treatment effects: Insights from a virtual patient cohort. Journal of the American Medical Informatics Association, 26(10), 977-988. https://doi.org/10.1093/jamia/ocz036
- Frezza, E., & Lavery, R. (2019). Internal coordinate normal mode analysis: A strategy to predict protein conformational transitions. The Journal of Physical Chemistry B, 123(6), 1294-1301. https://doi.org/10.1021/acs.jpcb.8b11913
- Fukuda, H., & Tomii, K. (2020). DeepECA: An end-to-end learning framework for protein contact prediction from a multiple sequence alignment. BMC Bioinformatics, 21(1). https://doi.org/10.1186/s12859-019-3190-x
- Gao, M., Zhou, H., & Skolnick, J. (2019). DESTINI: A deep-learning approach to contact-driven protein structure prediction. Scientific Reports, 9, 3514. https://doi.org/10.1038/s41598-019-40314-1
- Gebert, D., Jehn, J., & Rosenkranz, D. (2019). Widespread selection for extremely high and low levels of secondary structure in coding sequences across all domains of life. Open Biology, 9(5). https://doi.org/10.1098/rsob.190020
- Gemovic, B., Sumonja, N., Davidovic, R., Perovic, V., & Veljkovic, N. (2019). Mapping of protein-protein interactions: Web-based resources for revealing interactomes. Current Medicinal Chemistry, 26(21), 3890-3891. https://doi.org/10.2174/0929867325666180214113704
- Gouthami, K, Veeraraghavan, V, Rahdar, A, Bilal, M, Shah, A, Rai, V., Gurumurthy, D. M., Ferreira, L. F. R., Amrico-Pinheiro, J. H. P., Murari, S. K., Kalia, S., & Mulla, S. I. (2022). WITHDRAWN: Molecular docking used as an advanced tool to determine novel compounds on emerging infectious diseases: A systematic review. Progress in Biophysics and Molecular Biology. https://doi.org/10.1016/j.pbiomolbio.2022.10.001
- Hao, W., Wang, Y., & Liang, W. (2018). Slice-based building facade reconstruction from 3D point clouds. International Journal of Remote Sensing, 39(20), 6587-6606. https://doi.org/10.1080/01431161.2018.1463113
- Harada, R. (2018). Simple, yet efficient conformational sampling methods for reproducing/predicting biologically rare events of proteins. Bulletin of the Chemical Society of Japan, 91(9), 1436-1450. https://doi.org/10.1246/bcsj.20180170
- Hiranuma, N., Park, H., Baek, M., Anishchenko, I., Dauparas, J., & Baker, D. (2021). Improved protein structure refinement guided by deep learning based accuracy estimation. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-21511-x
- Houkes, W., & Zwart, S. D. (2019). Transfer and templates in scientific modelling. Studies in History and Philosophy of Science, 77, 93-100. https://doi.org/10.1016/j.shpsa.2017.11.003
- Huang, L.-C, Ross, K. E., Baffi, T. R., Drabkin, H., Kochut, K. J., Ruan, Z., D’Eustachio, P., McSkimming, D., Arighi, C., Chen, C., Natale, D. A., Smith, C., Gaudet, P., Newton, A. C., Wu, C., & Kannan, N. (2018). Integrative annotation and knowledge discovery of kinase post-translational modifications and cancer-associated mutations through federated protein ontologies and resources. Scientific Reports, 8, 6518. https://doi.org/10.1038/s41598-018-24457-1
- Jang, W. D., Lee, S. M., Kim, H. U., & Lee, S. Y. (2020). Systematic and comparative evaluation of software programs for template-based modeling of protein structures. Biotechnology Journal, 15(6). https://doi.org/10.1002/biot.201900343
- Jha, K., & Saha, S. (2020). Amalgamation of 3D structure and sequence information for protein-protein interaction prediction. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-75467-x
- Jia, K., & Jernigan, R. L. (2021). New amino acid substitution matrix brings sequence alignments into agreement with structure matches. Proteins, 89(6), 671-682. https://doi.org/10.1002/prot.26050
- Jiang, M., Li, Z., Bian, Y., & Wei, Z. (2019). A novel protein descriptor for the prediction of drug binding sites. BMC Bioinformatics, 20(1). https://doi.org/10.1186/s12859-019-3058-0
- Jin, S., Chen, M., Chen, X., Bueno, C., Lu, W., Schafer, N. P., Lin, X., Onuchic, J. N., & Wolynes, P. G. (2020). Protein structure prediction in CASP13 using AWSEM-suite. Journal of Chemical Theory and Computation, 16(6), 3977-3988. https://doi.org/10.1021/acs.jctc.0c00188
- Kandathil, S. M., Greener, J. G., Lau, A. M., & Jones, D. T. (2022). Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins. PNAS, 119(4). https://doi.org/10.1073/pnas.2113348119
- Karasev, D., Sobolev, B., Lagunin, A., Filimonov, D., & Poroikov, V. (2019). Prediction of protein–ligand interaction based on the positional similarity scores derived from amino acid sequences. International Journal of Molecular Sciences, 21(1), 24. https://doi.org/10.3390/ijms21010024
- Khalatbari, L., Kangavari, M. R., Hosseini, S., Yin, H., & Cheung, N.-M. (2019). MCP: A multi-component learning machine to predict protein secondary structure. Computers in Biology and Medicine, 110, 144-155. https://doi.org/10.1016/j.compbiomed.2019.04.040
- Kim, J.-Y., & Chung, H. S. (2020). Disordered proteins follow diverse transition paths as they fold and bind to a partner. Science, 368(6496), 1253-1257. https://doi.org/10.1126/science.aba3854
- Kondra, S., Sarkar, T., Raghavan, V., & Xu, W. (2021). Development of a TSR-based method for protein 3-D structural comparison with its applications to protein classification and motif discovery. Frontiers in Chemistry, 8. https://doi.org/10.3389/fchem.2020.602291
- Kotowski, K., Smolarczyk, T., Roterman-Konieczna, I., & Stapor, K. (2021). ProteinUnet–An efficient alternative to SPIDER3-single for sequence-based prediction of protein secondary structures. Journal of Computational Chemistry, 42(1), 50-59. https://doi.org/10.1002/jcc.26432
- Kuhlman, B., & Bradley, P. (2019). Advances in protein structure prediction and design. Nature Reviews Molecular Cell Biology, 20(11), 681-697. https://doi.org/10.1038/s41580-019-0163-x
- Kumari, P., Nath, A., & Chaube, R. Identification of human drug targets using machine-learning algorithms. Computers in Biology and Medicine, 56, 175-181. https://doi.org/10.1016/j.compbiomed.2014.11.008
- Kwon, Y., Shin, W.-H., Ko, J., & Lee, J. (2020). AK-score: Accurate protein-ligand binding affinity prediction using an ensemble of 3D-convolutional neural networks. International Journal of Molecular Sciences, 21(22), 8424. https://doi.org/10.3390/ijms21228424
- Larke, M. S. C., Schwessinger, R., Nojima, T., Telenius, J., Beagrie, R. A., Downes, D. J., Oudelaar, M., Truch, J., Graham, B., Bender, M. A., Proudfoot, N. J., Higgs, D. R., & Hughes, J. R. (2021). Enhancers predominantly regulate gene expression during differentiation via transcription initiation. Molecular Cell, 81(5), 983-997.e7. https://doi.org/10.1016/j.molcel.2021.01.002
- Lensink, M. F., Velankar, S., Baek, M., Heo, L., Seok, C., & Wodak, S. J. (2018). The challenge of modeling protein assemblies: the CASP12-CAPRI experiment. Proteins, 86(S1), 257-273. https://doi.org/10.1002/prot.25419
- Li, C., Cesbron, F., Oehler, M., Brunner, M., & Höfer, T. (2018). Frequency modulation of transcriptional bursting enables sensitive and rapid gene regulation. Cell Systems, 6(4), 409-423.e11. https://doi.org/10.1016/j.cels.2018.01.012
- Li, Z., Huang, Q., Chen, X., Wang, Y., Li, J., Xie, Y., Dai, Z., & Zou, X. (2020). Identification of drug-disease associations using information on molecular structures and clinical symptoms via deep convolutional neural network. Frontiers in Chemistry, 7. https://doi.org/10.3389/fchem.2019.00924
- Lin, H.-N., & Hsu, W.-L. (2020). GSAlign: An efficient sequence alignment tool for intra-species genomes. BMC Genomics, 21(1). https://doi.org/10.1186/s12864-020-6569-1
- Lin, T.-T., Yang, L.-Y., Lu, I.-H., Cheng, W.-C., Hsu, Z.-R., Chen, S.-H., & Lin, C.-Y. (2021). AI4AMP: An antimicrobial peptide predictor using physicochemical property-based encoding method and deep learning. mSystems, 6(6). https://doi.org/10.1128/msystems.00299-21
- Liu, Z., Miller, D., Li, F., Liu, X., & Levy, S. F. (2020). A large accessory protein interactome is rewired across environments. Elife, 9. https://doi.org/10.7554/elife.62365
- Lu, J., Chen, D., Wang, G., Kiritsis, D., & Torngren, M. (2022). Model-based systems engineering tool-chain for automated parameter value selection. IEEE Transactions on Systems, Man, and Cybernetics, 52(4), 2333-2347. https://doi.org/10.1109/tsmc.2020.3048821
- Lu, W., Bueno, C., Schafer, NP., Moller, J., Jin, S., Chen, X., Chen, M., Gu, X., de Pablo, J. J., & Wolynes, P. G. (2020). OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations. bioRxiv. https://doi.org/10.1101/2020.09.07.285759
- Makigaki, S, & Ishida, T. (2019). Sequence alignment using machine learning for accurate template-based protein structure prediction. bioRxiv. https://doi.org/10.1101/711945
- Mao, W., Ding, W., Xing, Y., & Gong, H. (2019). AmoebaContact and GDFold as a pipeline for rapid de novo protein structure prediction. Nature Machine Intelligence, 2(1), 25-33. https://doi.org/10.1038/s42256-019-0130-4
- Marino, V., & Dell’Orco, D. (2019). Evolutionary-conserved allosteric properties of three neuronal calcium sensor proteins. Frontiers in Molecular Neuroscience, 12. https://doi.org/10.3389/fnmol.2019.00050
- Masrati, G., Landau, M., Ben-Tal, N., Lupas, A., Kosloff, M., & Kosinski J. (2021). Integrative structural biology in the era of accurate structure prediction. Journal of Molecular Biology, 433(20), 167127. https://doi.org/10.1016/j.jmb.2021.167127
- Milanetti, E., Trandafir, A. G., Alba, J., Raimondo, D., & D’Abramo, M. (2018). Efficient and accurate modeling of conformational transitions in proteins: The case of c-src kinase. The Journal of Physical Chemistry B, 122(38), 8853-8860. https://doi.org/10.1021/acs.jpcb.8b07155
- Miller, E, Murphy, R, Sindhikara, D, Borrelli, K, Grisewood, M, Ranalli, F, Dixon, S., Jerome, S., Boyles, N., Day, T., Ghanakota, P., Mondal, S., Rafi, S. B., Troast, D. M., Abel, R., & Friesner, R. (2020). A reliable and accurate solution to the induced fit docking problem for protein-ligand binding. ChemRxiv. https://doi.org/10.26434/chemrxiv.11983845.v1
- Mohamed, E. M., Mousa, H. M., & Keshk, A. E. (2018). Comparative analysis of multiple sequence alignment tools. International Journal of Computer Science and Information Technologies, 10(8), 24-30. https://doi.org/10.5815/ijitcs.2018.08.04
- Mrozek, D., Suwała, M., & Małysiak-Mrozek, B. (2019). High-throughput and scalable protein function identification with Hadoop and Map-only pattern of the MapReduce processing model. Knowledge and Information Systems, 60(1), 145-178. https://doi.org/10.1007/s10115-018-1245-3
- Mucaki, E. J., Zhao, J. Z. L., Lizotte, D. J., & Rogan, P. K. (2019). Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning. Signal Transduction and Targeted Therapy, 4(1). https://doi.org/10.1038/s41392-018-0034-5
- Mura, C., Veretnik, S., & Bourne, P. E. (2019). The Urfold: Structural similarity just above the superfold level? Protein Science, 28(12), 2119-2126. https://doi.org/10.1002/pro.3742
- Nardo, A. E., Añón, M. C., & Parisi, G. (2018). Large-scale mapping of bioactive peptides in structural and sequence space. PLoS ONE, 13(1), e0191063. https://doi.org/10.1371/journal.pone.0191063
- Nguyen, M. Q., von Buchholtz, L. J., Reker, A. N., Ryba, N. J. P., & Davidson, S. (2021). Single nucleus transcriptomic analysis of human dorsal root ganglion neurons. bioRxiv. https://doi.org/10.1101/2021.07.02.450845
- Orbán-Németh, Z., Beveridge, R., Hollenstein, D. M., Rampler, E., Stranzl, T., Hudecz, O., Doblmann, J., Schlögwlhofer, P., & Mechtler, K. (2018). Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data. Nature Protocols, 13(3), 478-494. https://doi.org/10.1038/nprot.2017.146
- Peker, N., Garcia-Croes, S., Dijkhuizen, B., Wiersma, H. H., van Zanten, E., Wisselink, G., Friedrich, A. W., Kooistra-Smid, M., Sinha, B., Rossen, J. W. A., & Couto, N. (2019). A comparison of three different bioinformatics analyses of the 16S-23S rRNA encoding region for bacterial identification. Frontiers in Microbiology, 10. https://doi.org/10.3389/fmicb.2019.00620
- Rigoldi, F., Donini, S., Redaelli, A., Parisini, E., & Gautieri A. (2018). Review: Engineering of thermostable enzymes for industrial applications. APL Bioengineering, 2(1). https://doi.org/10.1063/1.4997367
- Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., & Fergus, R. (2019). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. bioRxiv. https://doi.org/10.1101/622803
- Rodrigues, C. H. M., Myung, Y., Pires, D. E. V., & Ascher, D. B. (2019). mCSM-PPI2: predicting the effects of mutations on protein-protein interactions. Nucleic Acids Research, 47(W1), W338-W344. https://doi.org/10.1093/nar/gkz383
- Runthala, A., & Chowdhury, S. (2019). Refined template selection and combination algorithm significantly improves template-based modeling accuracy. Journal of Bioinformatics and Computational Biology,17(02), 1950006. https://doi.org/10.1142/s0219720019500069
- Sakhaee, N., & Wilson, M. C. (2021). Information extraction framework to build legislation network. Artificial Intelligence and Law, 29(1), 35-58. https://doi.org/10.1007/s10506-020-09263-3
- Salinas, V. H., & Ranganathan, R. (2018). Coevolution-based inference of amino acid interactions underlying protein function. Elife, 7. https://doi.org/10.7554/elife.34300
- Sartor, R. C., Noshay, J., Springer, N. M., & Briggs, S. P. (2019). Identification of the expressome by machine learning on omics data. PNAS, 116(36), 18119-18125. https://doi.org/10.1073/pnas.1813645116
- Schönherr, R., Rudolph, J. M., & Redecke, L. (2018). Protein crystallization in living cells. Journal of Biological Chemistry, 399(7), 751-772. https://doi.org/10.1515/hsz-2018-0158
- Seath, C. P., Trowbridge, A. D., Muir, T. W., & MacMillan, D. W. C. (2021). Reactive intermediates for interactome mapping. Chemical Society Review, 50(5), 2911-2926. https://doi.org/10.1039/d0cs01366h
- Seidl, P., Renz, P., Dyubankova, N., Neves, P., Verhoeven, J., Wegner, J. K., Segler, M., Hochreiter, S., & Klambauer, G. (2022). Improving few- and zero-shot reaction template prediction using modern Hopfield networks. Journal of Chemical Information and Modeling, 62(9), 2111-2120. https://doi.org/10.1021/acs.jcim.1c01065
- Sejdiu, B. I., & Tieleman, D. P. (2021). ProLint: A web-based framework for the automated data analysis and visualization of lipid–protein interactions. Nucleic Acids Research, 49(W1), W544-W550. https://doi.org/10.1093/nar/gkab409
- Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Zidek, A., Nelson, A. W. R., Bridgland, A., Penedones, H., Petersen, S., Simonyan, K., Crossan, S., Kohli, P., Jones, D. T., Silver, D., Kavukcuoglu, K., & Hassabis, D. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706-710. https://doi.org/10.1038/s41586-019-1923-7
- Siebenmorgen, T., & Zacharias, M. (2020). Efficient refinement and free energy scoring of predicted protein-protein complexes using replica exchange with repulsive scaling. Journal of Chemical Information and Modeling, 60(11), 5552-5562. https://doi.org/10.1021/acs.jcim.0c00853
- Singh, P., & Singh, N. (2021). Role of data mining techniques in bioinformatics. International Journal of Applied Research in Bioinformatics, 11(1), 51-60. https://doi.org/10.4018/ijarb.2021010106
- Song, J., Li, F., Takemoto, K., Haffari, G., Akutsu, T., Chou, K.-C, & Webb, G. I. (2018). PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. Journal of Theoretical Biology, 443, 125-137. https://doi.org/10.1016/j.jtbi.2018.01.023
- Souza, P. C. T., Limongelli, V., Wu, S., Marrink, S. J., & Monticelli, L. (2021). Perspectives on high-throughput ligand/protein docking with Martini MD simulations. Frontiers in Molecular Biosciences, 8. https://doi.org/10.3389/fmolb.2021.657222
- Stacey, R. G., Skinnider, M. A., Chik, J. H. L., & Foster, L. J. (2018). Context-specific interactions in literature-curated protein interaction databases. BMC Genomics, 19(1). https://doi.org/10.1186/s12864-018-5139-2
- Sun, P., Tan, X., Guo, S., Zhang, J., Sun, B., Du, N., Wang, H., & Sun, H. (2018). Protein function prediction using function associations in protein–protein interaction network. IEEE Access, 6, 30892-30902. https://doi.org/10.1109/access.2018.2806478
- Tong, Q., Xue, L., Lv, J., Wang, Y., & Ma, Y. (2018). Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface. Faraday Discuss, 211, 31-43. https://doi.org/10.1039/c8fd00055g
- Truong, D. T., & Li, M. S. (2018). Probing the binding affinity by jarzynski’s nonequilibrium binding free energy and rupture time. The Journal of Physical Chemistry B, 122(17), 4693-4699. https://doi.org/10.1021/acs.jpcb.8b02137
- van Beusekom, B., Joosten, K., Hekkelman, M. L., Joosten, R. P., &Perrakis, A. (2018). Homology-based loop modeling yields more complete crystallographic protein structures. IUCrJ, 5(5), 585-594. https://doi.org/10.1107/s2052252518010552
- Vignani, R., Liò, P., & Scali, M. (2019). How to integrate wet lab and bioinformatics procedures for wine DNA admixture analysis and compositional profiling: Case studies and perspectives. PLoS ONE, 14(2), e0211962. https://doi.org/10.1371/journal.pone.0211962
- Volkov, M., Turk, J.-A., Drizard, N., Martin, N., Hoffmann, B., Gaston-Mathé, Y., & Rognan, D. (2022). On the frustration to predict binding affinities from protein–ligand structures with deep neural networks. Journal of Medicinal Chemistry, 65(11), 7946-7958. https://doi.org/10.1021/acs.jmedchem.2c00487
- Wang, H., & Yang, W. (2019). Toward building protein force fields by residue-based systematic molecular fragmentation and neural network. Journal of Chemical Theory and Computation, 15(2), 1409-1417. https://doi.org/10.1021/acs.jctc.8b00895
- Wang, T., Qiao, Y., Ding, W., Mao, W., Zhou, Y., & Gong, H. (2019). Improved fragment sampling for ab initio protein structure prediction using deep neural networks. Nature Machine Intelligence, 1(8), 347-355. https://doi.org/10.1038/s42256-019-0075-7
- Watanabe, G., Eimura, H., Abbott, N. L., & Kato T. (2020). Biomolecular binding at aqueous interfaces of Langmuir monolayers of bioconjugated amphiphilic mesogenic molecules: A molecular dynamics study. Langmuir, 36(41), 12281-12287. https://doi.org/10.1021/acs.langmuir.0c02191
- Weng, G., Gao, J., Wang, Z., Wang, E., Hu, X., Yao, X., Cao, D., & Hou, T. (2020). Comprehensive evaluation of fourteen docking programs on protein–peptide complexes. Journal of Chemical Theory and Computation, 16(6), 3959-3969. https://doi.org/10.1021/acs.jctc.9b01208
- Wojtowicz, W. M., Vielmetter, J., Fernandes, R. A., Siepe, D. H., Eastman, C. L., Chisholm, G. B., Cox, S., Klock, H., Anderson, P. W., Rue, S. M., Miller, J. J., Glaser, S. M., Bragstad, M. L., Vance, J., Lam, A. W., Lesley, S. A., Zinn, K., & Garcia, K. C. (2020). A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions. Cell, 182(4), 1027-1043.e17. https://doi.org/10.1016/j.cell.2020.07.025
- Xu, X., Yan, C., & Zou, X. (2018a). MDockPeP: An ab-initio protein-peptide docking server. Journal of Computational Chemistry, 39(28), 2409-2413. https://doi.org/10.1002/jcc.25555
- Xu, Y., Wang, S., Hu, Q., Gao, S., Ma, X., Zhang, W., Shen, Y., Chen, F., Lai, L., & Pei, J. (2018b). CavityPlus: A web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction. Nucleic Acids Research, 46(W1), W374-W379. https://doi.org/10.1093/nar/gky380
- Yan, L., Sun, W., Lu, Z., & Fan, L. (2020). Metagenomic next-generation sequencing (mNGS) in cerebrospinal fluid for rapid diagnosis of Tuberculosis meningitis in HIV-negative population. International Journal of Infectious Diseases, 96,270-275. https://doi.org/10.1016/j.ijid.2020.04.048
- Yerneni, S., Khan, I. K., Wei, Q., & Kihara, D. (2018). IAS: Interaction specific GO term associations for predicting protein-protein interaction networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(4), 1247-1258. https://doi.org/10.1109/tcbb.2015.2476809
- Yim, S., Yu, H., Jang, D., & Lee, D. (2018). Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations. BMC Systems Biology, 12(S1). https://doi.org/10.1186/s12918-018-0535-4
- Yu, C.-H., Qin, Z., Martin-Martinez, F. J., & Buehler, M. J. (2019). A self-consistent sonification method to translate amino acid sequences into musical compositions and application in protein design using artificial intelligence. ACS Nano, 13(7), 7471-7482. https://doi.org/10.1021/acsnano.9b02180
- Zhang, C., Zheng, W., Freddolino, P. L., & Zhang, Y. (2018). MetaGO: Predicting gene ontology of non-homologous proteins through low-resolution protein structure prediction and protein-protein network mapping. Journal of Molecular Biology, 430(15), 2256-265. https://doi.org/10.1016/j.jmb.2018.03.004
- Zhang, M. M., Beno, B. R., Huang, R. Y.-C., Adhikari, J., Deyanova, E. G., Li, J., Chen, G., & gross, M. L. (2019a). An integrated approach for determining a protein–protein binding interface in solution and an evaluation of hydrogen–deuterium exchange kinetics for adjudicating candidate docking models. Analytical Chemistry, 91(24), 15709-15017. https://doi.org/10.1021/acs.analchem.9b03879
- Zhang, P., Shen, L., & Yang, W. (2019b). Solvation free energy calculations with quantum mechanics/molecular mechanics and machine learning models. The Journal of Physical Chemistry B, 123(4), 901-908. https://doi.org/10.1021/acs.jpcb.8b11905
- Zhang, Y., Aryee, A. N. A., & Simpson, B. K. (2020). Current role of in silico approaches for food enzymes. Current Opinion in Food Science, 31, 63-70. https://doi.org/10.1016/j.cofs.2019.11.003
- Zhao, B., Katuwawala, A., Oldfield, C. J., Dunker, A. K., Faraggi, E., Gsponer, J., Kloczkowski, A., Malhis, N., Mirdita, M., Obradovic, Z., Söding, J., Steinegger, M., Zhou, Y., & Kurgan, L. (2021a). DescribePROT: Database of amino acid-level protein structure and function predictions. Nucleic Acids Research, 49(D1), D298-D308. https://doi.org/10.1093/nar/gkaa931
- Zhao, L., Ciallella, H. L., Aleksunes, L. M., Zhu, H. (2020). Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discovery Today, 25(9), 1624-1638. https://doi.org/10.1016/j.drudis.2020.07.005
- Zhao, T., Liu, J., Zeng, X., Wang, W., Li, S., Zang, T., Peng, J., & Yang, Y. (2021b). Prediction and collection of protein-metabolite interactions. Briefings in Bioinformatics, 22(5). https://doi.org/10.1093/bib/bbab014
- Zheng, W., Wuyun, Q., Li, Y., Mortuza, S. M., Zhang, C., Pearce, R., Ruan, J., & Zhang, Y. (2019). Detecting distant-homology protein structures by aligning deep neural-network based contact maps. PLOS Computational Biology, 15(10), e1007411. https://doi.org/10.1371/journal.pcbi.1007411
- Zhou, J., Panaitiu, A. E., & Grigoryan, G. (2020). A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures. PNAS, 17(2), 1059-1068. https://doi.org/10.1073/pnas.1908723117
- Zhou, P., Wang, J., Wang, M., Hou, J., Lu, J. R., & Xu, H. (2019). Amino acid conformations control the morphological and chiral features of the self-assembled peptide nanostructures: Young investigators perspective. Journal of Colloid and Interface Science, 548, 244-254. https://doi.org/10.1016/j.jcis.2019.04.019
How to cite this article
APA
Ogunjobi, T. T., Okorie, I. C., Gigam-Ozuzu, C. D., Olorunleke, J. V., Ogunleye, F. I., Irimoren, E. O., Atanda, D. O., Okafor, A. M.-A., Agbo, C. E., Okunbi, F. O., Umoren, O. D., Adidu, A. D., & Ojo, E. O. (2025). Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship. European Journal of Sustainable Development Research, 9(3), em0298. https://doi.org/10.29333/ejosdr/16340
Vancouver
Ogunjobi TT, Okorie IC, Gigam-Ozuzu CD, Olorunleke JV, Ogunleye FI, Irimoren EO, et al. Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship. EUR J SUSTAIN DEV RES. 2025;9(3):em0298. https://doi.org/10.29333/ejosdr/16340
AMA
Ogunjobi TT, Okorie IC, Gigam-Ozuzu CD, et al. Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship. EUR J SUSTAIN DEV RES. 2025;9(3), em0298. https://doi.org/10.29333/ejosdr/16340
Chicago
Ogunjobi, Taiwo Temitope, Ijeoma Chineme Okorie, Chimaobi Divine Gigam-Ozuzu, Jumoke Victoria Olorunleke, Felix Iyanu Ogunleye, Emmanuella Osaruese Irimoren, Dorcas Oyedolapo Atanda, Adaobi Mary-Ann Okafor, Chinyere Eucharia Agbo, Favour Onasokhare Okunbi, Otoh Dayo Umoren, Adoyi Daniel Adidu, and Emmanuel Oluwadamilare Ojo. "Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship". European Journal of Sustainable Development Research 2025 9 no. 3 (2025): em0298. https://doi.org/10.29333/ejosdr/16340
Harvard
Ogunjobi, T. T., Okorie, I. C., Gigam-Ozuzu, C. D., Olorunleke, J. V., Ogunleye, F. I., Irimoren, E. O., . . . Ojo, E. O. (2025). Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship. European Journal of Sustainable Development Research, 9(3), em0298. https://doi.org/10.29333/ejosdr/16340
MLA
Ogunjobi, Taiwo Temitope et al. "Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship". European Journal of Sustainable Development Research, vol. 9, no. 3, 2025, em0298. https://doi.org/10.29333/ejosdr/16340