Democratizing Protein Language Model Training, Sharing and Collaboration

2024/05/28 by Jin Su, Zhikai Li, Tianli Tao +17 · 1 voice · 2 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · #Bioinformatics and Genomic Networks #Genomics and Phylogenetic Studies #Advanced Proteomics Techniques and Applications

paper · pdf · doi:10.1101/2024.05.24.595648

openalex publication_date 2024/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/14

Abstract

Training and deploying large-scale protein language models typically requires deep machine learning expertise — a barrier for researchers outside this field. SaprotHub overcomes this challenge by offering an intuitive platform that facilitates training and prediction as well as storage and sharing of models. We provide the ColabSaprot framework built on Google Colab which potentially powers hundreds of protein training and prediction applications, enabling researchers to collaboratively build and share customized models.

Citations

• Genetic regulatory mechanisms in the synthesis of proteins
• Parameter-Efficient Transfer Learning for NLP
• Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
• Compacter: Efficient Low-Rank Hypercomplex Adapter Layers
• ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
• Highly accurate protein structure prediction with AlphaFold
• КОМПЬЮТЕРНАЯ ТОМОГРАФИЯ – ОСНОВА ОПТИМИЗАЦИИ МИНИТОРАКОТОМНОГО ДОСТУПА
• Learning inverse folding from millions of predicted structures
• Contrastive Representation Learning for 3D Protein Structures
• Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
• PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
• ColabFold: making protein folding accessible to all
• Robust deep learning–based protein sequence design using ProteinMPNN
• Evolutionary-scale prediction of atomic-level protein structure with a language model
• ProGen2: Exploring the boundaries of protein language models
• Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning
• Biophysics-based protein language models for protein engineering
• Accurate structure prediction of biomolecular interactions with AlphaFold 3
• One protein is all you need

Cited by

• Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
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Discussions

• #SaprotHub: Making #protein #modeling accessible to all biologists: -create & train own #DeepLearning models without the need for advanced #MachineLearning & coding expertise doi.org/10.1101/2024... [bsky, 0 points, 0 comments]

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