Protein Function Prediction: Methods and Protocols

by Lukasz Kurgan (Editor), Daisuke Kihara (Editor)

Overview

This fully updated volume explores a wide array of new and state-of-the-art tools and resources for protein function prediction. Beginning with in-depth overviews of essential underlying computational techniques, such as machine learning, multi-task learning, protein language models, and deep learning, the book continues by covering specific tools for protein function prediction, ranging from gene ontology-term predictions to the predictions of binding sites, protein localization and solubility, signal peptides, intrinsic disorder, and intrinsically disordered binding regions, as well as presenting databases that address protein moonlighting and protein binding. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, step-by-step instructions on how to use software and web resources, use cases, and tips on troubleshooting and avoiding known pitfalls.

Authoritative and up-to-date, Protein Function Prediction: Methods and Prools, Second Edition helps readers to understand and appreciate this vibrant and growing research area and guides in the quest to identify and use the best computational methods and resources for their projects.

Product Details

ISBN-13:	9781071646618
Publisher:	Springer US
Publication date:	08/28/2025
Series:	Methods in Molecular Biology , #2947
Edition description:	Second Edition 2025
Pages:	340
Product dimensions:	7.01(w) x 10.00(h) x (d)

Table of Contents

Computational Prediction of Protein Functional Annotations.- Machine Learning for Protein Function Prediction.- Graph Neural Network-Based Approaches for Protein Function Prediction.- Multi-Task Learning-Based Approaches for Protein Function Prediction.- A Survey of Deep Learning Methods and Tools for Protein Binding Site Prediction.- A Survey of Current Status in AI-Based Topology Prediction of Transmembrane Proteins.- NetGO 3.0: A Recent Protein Function Prediction Tool Based on Protein Language Model.- Predicting Protein Functions with Function-Aware Domain Embeddings Using Domain-PFP.- Integrating Gene Ontology Relationships for Protein Function Prediction Using PFresGO.- Annotating Genomes with DeepGO Protein Function Prediction Tools.- An Online Server for Geometry-Aware Protein Function Annotations through Predicted Structure.- Exploring Binding Sites on Proteins for Function Prediction Using the PoSSuM Databases.- Comprehensive Prediction of Protein Localization and Signal Peptides Using MULocDeep.- A Benchmarking Platform for Assessing Protein Language Models on Function-Related Prediction Tasks.- Prediction of Intrinsic Disorder Functions with DEPICTER2.- Prediction of Disordered Linear Interacting Peptides with CLIP.- Prediction of Intrinsically Disordered Lipid Binding Residues with DisoLipPred.- NaviGO: An Interactive Tool for Gene Ontology Functional Analysis with Free Text GO Summaries.- Using the MoonProt Database for Understanding Protein Functions.- Illustrative Features and Utilities of MPAD: Thermodynamic Database for Membrane Protein-Protein Complexes.