M. Michael Gromiha
Petras Kundrotas
Marcelo Adrian Marti
Česlovas Venclovas
Minghui Li- 1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- 2Center for Computational Biology, The University of Kansas, Lawrence, KS, United States
- 3Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (FCEyN-UBA) e Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Buenos Aires, Argentina
- 4Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
- 5Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
Editorial on the Research Topic
Protein recognition and associated diseases
Protein-protein interactions are essential for many biological functions in all living organisms including cell signaling, molecular switching, transporters, receptors, and immunity. For the past few decades, tremendous advancements have been made in order to understand the recognition mechanism of protein-protein complex formation, reconstruct protein-protein interaction networks of an entire organism, and/or complete biochemical pathways. These efforts are mainly focussed on the identification of interacting proteins, prediction of binding site residues at their interface, evolutionary conservation of protein-protein complexes, prediction of protein-protein complex structures by docking, predicting the binding affinity of protein-protein complexes, and assessing the mutational effects on strength of binding and diseases (Gromiha, 2020). Recently, AlphaFold (Jumper et al., 2021) and its descendants (e.g., AlphaFoldMultimer, Evans et al., 2021), have demonstrated spectacular success in predicting structures of individual proteins and their complexes. Nevertheless, a significant number of cases and questions are still evading solutions and answers. This Research Topic on “Protein Recognition and Associated Diseases” addresses the recent advances in computational methodologies for the analysis and identification of important residues for binding, scoring, and ranking of structural models of protein-protein complexes, protein-protein interaction networks, and their applications in life sciences and human health.
The opening article by Brysbaert and Lensink analyzes the performance of several centrality measures for identifying major interacting residues involved in protein-protein binding using binding affinity data of interface mutations. Johansson-Åkhe et al. propose a machine learning-based method for scoring and ranking peptide-protein complexes. It encodes the structure of the complex as a graph with evolutionary and sequence features as nodes and physical pairwise interactions as edges. Su et al. integrate protein-protein interaction networks and gene expression profiles for detecting pancreatic adenocarcinoma candidate genes. Karan et al. report the development of four genomic information-based prediction methods, namely, 1) interolog, 2) domain, 3) gene ontology, and 4) phylogenetic for identifying the interaction between Oryza sativa and Magnaporthe grisea in a whole-genome scale.
In essence, this Research Topic covers the exciting developments in the area of protein-protein interactions both at fundamental and application levels. It will be a valuable resource for computational biologists, biochemists, biophysicists, bioinformaticians, and researchers working in the field of protein-protein interactions, and for those working on the biological role of protein-protein interaction networks and their relation to disease.
We would like to thank all the authors for their outstanding contributions. The guest editors thank the Editorial Assistant Dr. Sara Gomez Cabellos, Commissioning Specialist, and Dr. Rahila Esposito, Commissioning Manager for their help and support in successfully completing the Research Topic.
Author contributions
MMG drafted the manuscript. PK, MM, CV, and ML edited it. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. All authors contributed to the article and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Evans, R. (2021). Protein complex prediction with AlphaFold-Multimer. bioRviv. doi:10.1101/2021.10.04.463034
Gromiha, M. M. (2020). Protein interactions: Computational methods, analysis and applications. Singapore: World Scientific.
Keywords: protein-protein interactions, binding affinity, machine learning, protein-protein interaction networks, phylogenetic profiles
Citation: Gromiha MM, Kundrotas P, Marti MA, Venclovas Č and Li M (2023) Editorial: Protein recognition and associated diseases. Front. Bioinform. 3:1215141. doi: 10.3389/fbinf.2023.1215141
Received: 01 May 2023; Accepted: 09 May 2023;
Published: 22 May 2023.
Edited and reviewed by:
Domenico Cozzetto, Imperial College London, United KingdomCopyright © 2023 Gromiha, Kundrotas, Marti, Venclovas and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: M. Michael Gromiha, gromiha@iitm.ac.in