A review of mathematical representations of biomolecules

TL;DR

This review discusses recent mathematical methods like algebraic topology, differential geometry, and graph theory for representing biomolecules, aiming to improve machine learning predictions in computational biology.

Contribution

It provides a comprehensive overview of low-dimensional, scalable mathematical representations of biomolecules, highlighting their development and application in ML-based predictions.

Findings

Mathematical representations improve ML efficiency in biomolecular data analysis.

Algebraic topology, differential geometry, and graph theory are key approaches.

These methods enhance protein-ligand binding prediction accuracy.

Abstract

Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges. However, the intricate structural complexity and high ML dimensionality of biomolecular datasets obstruct the efficient application of ML algorithms in the field. In addition to data and algorithm, an efficient ML machinery for biomolecular predictions must include structural representation as an indispensable component. Mathematical representations that simplify the biomolecular structural complexity and reduce ML dimensionality have emerged as a prime winner in D3R Grand Challenges. This review is devoted to the recent advances in developing low-dimensional and scalable mathematical representations of biomolecules in our laboratory. We discuss three classes of mathematical approaches, including algebraic topology, differential geometry, and graph theory. We elucidate how the physical and biological challenges have guided the evolution and development of these mathematical apparatuses for massive and diverse biomolecular data. We focus the performance analysis on the protein-ligand binding predictions in this review although these methods have had tremendous success in many other applications, such as protein classification, virtual screening, and the predictions of solubility, solvation free energy, toxicity, partition coefficient, protein folding stability changes upon mutation, etc.