Machine Learning in Protein Science

Aimed at researchers in the molecular life sciences, this unique reference summarizes current approaches for harnessing the power of machine learning for more efficient full quantum mechanical (FQM) calculations in protein systems. Application examples range from property calculations (energy, force field, stability, protein-protein interaction, thermostability, molecular dynamics) to protein structure prediction to protein design and the optimization of enzymatic activity. From a methodological point of view, the practical reference covers the most important machine learning models and algorithms, from deep neural network (DNN) and transfer learning (TL) to hybrid unsupervised and supervised learning.

Introduction

Fundamentals of Theoretical Calculations on Protein Systems

Protein Structure Prediction by Artificial Intelligence

Methods and Tools for Predicting Protein Folding from Free Energy Change upon Mutation

Deep Neural Network-assisted Full-System Quantum Mechanical (FQM) Calculations of Proteins

Transfer Learning-assisted Full-System Quantum Mechanical (FQM) Calculations of Proteins

Protein Interaction Prediction with Artificial Intelligence

Protein Function Annotation with Machine Learning

Machine Learning-driven ab initio Protein Design

Large Language Models of Protein Systems

Outlook

Jinjin Li is a professor at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University in Shanghai, China. Having obtained her Ph.D. degrees from Shanghai University, she performed postdoctoral work at the University of Illinois, USA and was a Senior Research Fellow at the University of California, USA. Professor Li has authored over 200 publications and four monographs. She is also a long-standing editorial board member and reviewer for several international academic journals.

Yanqiang Han is an assistant professor at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University in Shanghai, China. He obtained his Ph.D. degrees from Shanghai University. He has authored over 30 publications in the field of computational biology and machine learning and is a reviewer for several international academic journals.