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Dixit R, Khambhati K, Supraja KV, Singh V, Lederer F, Show PL, Awasthi MK, Sharma A, Jain R. Application of machine learning on understanding biomolecule interactions in cellular machinery. BIORESOURCE TECHNOLOGY 2023; 370:128522. [PMID: 36565819 DOI: 10.1016/j.biortech.2022.128522] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.
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Affiliation(s)
- Rewati Dixit
- Waste Treatment Laboratory, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Haus-khas, New Delhi 110016, India
| | - Khushal Khambhati
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana 382715, Gujarat, India
| | - Kolli Venkata Supraja
- Waste Treatment Laboratory, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Haus-khas, New Delhi 110016, India
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana 382715, Gujarat, India
| | - Franziska Lederer
- Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Bautzner landstrasse 400, 01328 Dresden, Germany
| | - Pau-Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India; Department of Chemical and Environmental Engineering, University of Nottingham, Malaysia, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
| | - Abhinav Sharma
- Institute Theory of Polymers, Leibniz Institute for Polymer Research, Hohe Strasse 6, 01069 Dresden, Germany
| | - Rohan Jain
- Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Bautzner landstrasse 400, 01328 Dresden, Germany.
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Pai PP, Mondal S. MOWGLI: prediction of protein-MannOse interacting residues With ensemble classifiers usinG evoLutionary Information. J Biomol Struct Dyn 2015; 34:2069-83. [PMID: 26457920 DOI: 10.1080/07391102.2015.1106978] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Proteins interact with carbohydrates to perform various cellular interactions. Of the many carbohydrate ligands that proteins bind with, mannose constitute an important class, playing important roles in host defense mechanisms. Accurate identification of mannose-interacting residues (MIR) may provide important clues to decipher the underlying mechanisms of protein-mannose interactions during infections. This study proposes an approach using an ensemble of base classifiers for prediction of MIR using their evolutionary information in the form of position-specific scoring matrix. The base classifiers are random forests trained by different subsets of training data set Dset128 using 10-fold cross-validation. The optimized ensemble of base classifiers, MOWGLI, is then used to predict MIR on protein chains of the test data set Dtestset29 which showed a promising performance with 92.0% accurate prediction. An overall improvement of 26.6% in precision was observed upon comparison with the state-of-art. It is hoped that this approach, yielding enhanced predictions, could be eventually used for applications in drug design and vaccine development.
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Affiliation(s)
- Priyadarshini P Pai
- a Department of Biological Sciences , Birla Institute of Technology and Science-Pilani , K.K. Birla Goa Campus, Near NH17 Bypass Road, Zuarinagar , Goa 403726 , India
| | - Sukanta Mondal
- a Department of Biological Sciences , Birla Institute of Technology and Science-Pilani , K.K. Birla Goa Campus, Near NH17 Bypass Road, Zuarinagar , Goa 403726 , India
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Abstract
Ligand binding is required for many proteins to function properly. A large number of bioinformatics tools have been developed to predict ligand binding sites as a first step in understanding a protein's function or to facilitate docking computations in virtual screening based drug design. The prediction usually requires only the three-dimensional structure (experimentally determined or computationally modeled) of the target protein to be searched for ligand binding site(s), and Web servers have been built, allowing the free and simple use of prediction tools. In this chapter, we review the underlying concepts of the methods used by various tools, and discuss their different features and the related issues of ligand binding site prediction. Some cautionary notes about the use of these tools are also provided.
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Affiliation(s)
- Zhong-Ru Xie
- Institute of Biomedical Sciences, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan
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