Computational approaches for the design of modulators targeting protein-protein interactions

ABSTRACT

Background

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

Areas covered

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

Expert opinion

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

KEYWORDS:

• Protein-protein interactions
• Computer-aided drug design (CADD)
• computational approaches
• machine-based learning
• molecular dynamics simulations
• docking
• screening

Article highlights

• PPIs were considered undruggable due to their flat surfaces, irregular conformations, and lack of grooves.

• Rapid advances in computational biology have made PPIs a major research field in drug discovery.

• Reviewed different in-silico approaches to find PPI interfaces and methods that are successfully applied to analyze PPI interfaces.

• Reviewed examples that use computational methods to analyze PPIs and their impacts in diverse physiological processes.

This box summarizes key points contained in the article.

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewers disclosure

Peer reviewers in this manuscript have no relevant financial relationships or otherwise to disclose.

Additional information

Funding

This paper was supported by Data Center of Management Science, National Natural Science Foundation of China - Peking University (81925034); Innovation Program of Shanghai Municipal Education Commission (2019-01-07-00-01-E00036); National Institute of Health/NIGMS (GM130367); National Key R&D Program of China (2021YFF0704000); National Natural Science Foundation of China (21977068-CHF & 32171242-CHF); The innovative research team of high-level local universities in Shanghai (SHSMU-ZDCX20212700); The Key Research and Development Program of Ningxia Hui Autonomous Region (022CMG01002 and 2022CMG01002).