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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Computational prediction of disordered binding regions.

Sushmita Basu¹, Daisuke Kihara^2,3, Lukasz Kurgan¹

¹Department of Computer Science, Virginia Commonwealth University, USA.

Computational and Structural Biotechnology Journal

|February 28, 2023

Summary

This summary is machine-generated.

Intrinsically disordered regions (IDRs) interact with many molecules. This study reviews 38 predictors for these binding IDRs, highlighting deep learning advancements and the impact of accessible tools on research use.

Keywords:

CAID, Critical Assessment of Intrinsic Disorder CASP, Critical Assessment of techniques for protein Structure Prediction DL, deep learning Disordered binding regions IDP, intrinsically disordered protein IDR, intrinsically disordered region Intrinsic disorder ML, machine learning MoRF, molecular recognition fragment Molecular recognition features NN, neural network Protein-lipid interactions Protein-nucleic acids interactions Protein-protein interactions SLiM, short linear sequence motif Short linear motifs

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Area of Science:

Biochemistry and Molecular Biology
Computational Biology
Bioinformatics

Background:

Intrinsically disordered regions (IDRs) are crucial for molecular interactions, encompassing diverse binding types like molecular recognition fragments (MoRFs) and short linear sequence motifs (SLiMs).
Predicting these binding IDRs in protein sequences is an active area of research, with significant progress in recent years.
The ability of IDRs to bind proteins, nucleic acids (RNA, DNA), and lipids underscores their functional versatility.

Purpose of the Study:

To survey and analyze existing computational tools for predicting binding intrinsically disordered regions (IDRs).
To provide a historical perspective on the development of IDR prediction methods.
To identify trends and recommend future directions in the field of binding IDR prediction.

Main Methods:

A comprehensive review of 38 distinct predictors for binding IDRs was conducted.
The study analyzed the diverse range of predictive architectures employed, including scoring functions, regular expressions, machine learning (traditional and deep), and meta-models.
The availability and accessibility (implementations, webservers) of these prediction tools were assessed.

Main Results:

The survey identified 38 predictors targeting interactions with peptides, proteins, RNA, DNA, and lipids.
Deep neural network-based architectures represent a recent focus, with increasing coverage for RNA, DNA, and lipid-binding IDRs.
Tools with available implementations and webservers demonstrated significantly higher citation and usage rates.

Conclusions:

The development of binding IDR predictors has evolved significantly, with deep learning showing great promise.
Accessible tools (webservers, code) are critical for widespread adoption and impact in the research community.
Future efforts should focus on advanced deep learning architectures, integrated prediction of multiple binding types, and modeling of complex structures.