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.
A Survey of Pre-Trained Protein Language Models.- Enhancing
Structure-Aware Protein Language Models with Efficient Fine-Tuning for
Various Protein Prediction Tasks.- Exploring ProtFlash: An Efficient Approach
to Protein Data Analysis.- Ranking Protein-Protein Models with Large Language
Models and Graph Neural Networks.- Translating a GO Term List to Human
Readable Function Description Using GO2Sum.- TransFun: A Tool of Integrating
Large Language Models, Transformers, and Equivariant Graph Neural Networks to
Predict Protein Function.- Using InterLabelGO+ for Accurate Protein Language
Model-Based Function Prediction.- Functional Annotation of Proteomes Using
Protein Language Models: A High-Throughput Implementation of the ProtTrans
Model.- Advances in Language-Model-Informed Protein-Nucleic Acid Binding Site
Prediction.- Practical Applications of Language Models in Protein Sorting
Prediction: SignalP 6.0, DeepLoc 2.1, and DeepLocPro 1.0.- CNN-Meth: A Tool
to Accurately Predict Lysine Methylation Sites Using Evolutionary
Information-Based Protein Modeling.- Predicting the Pathogenicity of Human
Protein Variants: Not Only a Matter of Residue Labeling.- A Survey of
Biological Function Prediction Methods with Focus on Natural Language
Processing (NLP) and Large Language Models (LLM).- PLMSearch and PLMAlign:
Protein Language Model-Based Homologous Sequence Search and Alignment.- Large
Context, Deeper Insights: Harnessing Large Language Models for Advancing
Protein-Protein Interaction Analysis.- Prediction of Protein-Peptide Binding
Sites Using PepBCL.- Predicting Peptide Bioactivity Using the Unified Model
Architecture UniDL4BioPep.- CLAPE: Protein-Ligand Binding Site Prediction via
Protein Language Models.- Large Language Model-Based Advances in Prediction
of Post-Translational Modification Sites in Proteins.
