Large Language Models (LLMs) in Protein BioinformaticsКНИГИ » ПРОГРАММИНГ
Название: Large Language Models (LLMs) in Protein Bioinformatics Автор: Dukka B. KC Издательство: Humana Press/Springer Год: 2025 Страниц: 360 Язык: английский Формат: pdf (true), epub Размер: 51.1 MB
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.
Large Language Models (LLMs) are a class of foundation models that are (pre)trained on enormous amounts of data to provide the foundational capabilities needed to drive multiple use cases and applications. LLMs are typically based on a transformer architecture and involve training on a massive corpus of data (e.g., text). The transformer architecture of LLMs allows LLMs to effectively handle long context and sequential information. LLMs represent a significant breakthrough in natural language processing (NLP) and are designed to understand and generate texts/contents. LLMs have found application in text/content generation, content summarization, AI assistants, code generation, and language translation, among others.
LLMs have shown significant promise in various research fields including protein bioinformatics. Thanks to advances in LLMs, the field of protein bioinformatics has also witnessed a lot of advances in various areas including but not limited to protein structure prediction, protein function prediction, and others. Starting with training of Protein Language Models (PLMs, LLMs that are trained on protein sequence/structure) and the subsequent application of these PLMs, the field has seen a plethora of approaches for various protein bioinformatics tasks.
In this chapter, we will provide a comprehensive introduction to using the ProtFlash library. We will guide readers through its installation and use in Python environments, such as IPython notebooks or Python scripts. Through step-by-step examples, we will demonstrate how to apply ProtFlash for various tasks, including generating protein embeddings and fine-tuning the model using techniques like low-rank adaptation (LoRA), which helps improve model performance with fewer parameters. These examples will showcase how ProtFlash can be effectively integrated into research workflows, offering a practical tool for protein sequence analysis.
Our structural visualizations employ PyMOL v.2.4.0, accessible at Github.com. For data visualization related to PLMSearch, we utilize Python (version 3.8.16), Seaborn (version 0.12.2), and Matplotlib-base (version 3.6.2).
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 (AI) in this vital area of biological study.
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Large Language Models (LLMs) in Protein Bioinformatics 
