This volume describes computational approaches to predict multitudes of PTM sites. Chapters describe in depth approaches on algorithms, state-of-the-art Deep Learning based approaches, hand-crafted features, physico-chemical based features, issues related to obtaining negative training, sequence-based features, and structure-based features. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols.

 Authoritative and cutting-edge, Authoritative and cutting-edge,  Computational Methods for Predicting Post-Translational Modification Sites aims to be a useful guide for researchers who are interested in the field of PTM site prediction. 

This book presents a comprehensive collection of methods, resources, and studies that use large language models (LLMs) in the field of protein bioinformatics. Reflecting the swift pace of LLM development today, the volume delves into numerous LLM-based tools to investigate proteins science, from protein language models to the prediction of protein-ligand binding sites. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice to ensure success in future research.

Authoritative and practical, Large Language Models (LLMs) in Protein Bioinformatics serves as an ideal guide for scientists seeking to tap into the potential of artificial intelligence in this vital area of biological study.

This volume describes computational approaches to predict multitudes of PTM sites. Chapters describe in depth approaches on algorithms, state-of-the-art Deep Learning based approaches, hand-crafted features, physico-chemical based features, issues related to obtaining negative training, sequence-based features, and structure-based features. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols.

 Authoritative and cutting-edge, Authoritative and cutting-edge,  Computational Methods for Predicting Post-Translational Modification Sites aims to be a useful guide for researchers who are interested in the field of PTM site prediction. 

This book presents a comprehensive collection of methods, resources, and studies that use large language models (LLMs) in the field of protein bioinformatics. Reflecting the swift pace of LLM development today, the volume delves into numerous LLM-based tools to investigate proteins science, from protein language models to the prediction of protein-ligand binding sites. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice to ensure success in future research.

Authoritative and practical, Large Language Models (LLMs) in Protein Bioinformatics serves as an ideal guide for scientists seeking to tap into the potential of artificial intelligence in this vital area of biological study.