State‐of‐the‐art computational methods to predict protein–protein interactions with high accuracy and coverage
Date
2023-11
Authors
Kewalramani, Neal
Emili, Andrew
Crovella, Mark
Version
Published version
OA Version
Citation
N. Kewalramani, A. Emili, M. Crovella. 2023. "State‐of‐the‐art computational methods to predict protein–protein interactions with high accuracy and coverage" Proteomics, Volume 23, Issue 21-22. https://doi.org/10.1002/pmic.202200292
Abstract
Prediction of protein–protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state‐of‐the‐art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co‐abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.
Description
License
This is an open access article under the terms of the Creative Commons Attribution License, which permitsuse, distribution and reproductionin any medium, provided the original work is properlycited. ©2023 The Authors. Proteomics published by Wiley-VCHGmbH.