Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Recently, due to their capacity and representation ability, pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without experimental data. However, their predictions are limited in accuracy as well as interpretability. Furthermore, such deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity. By combining the techniques of meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. The experiments across 87 deep mutational scanning datasets underscore its superiority over both unsupervised and supervised approaches, revealing its potential in facilitating AI-guided protein design.
Toward the Explainability of Protein Language Models
Centre for Genomic Regulation, the Barcelona Institute of Science and Technology（基因组调控中心，巴塞罗那科学技术研究所）；Universitat de Barcelona, Facultat de Farmàcia I Ciències de l’Alimentació（巴塞罗那大学，药学与食品科学学院）；Universitat Pompeu Fabra（庞培法布拉大学）
ProteinGuide: On-the-fly property guidance for protein sequence generative models
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（电气工程与计算机科学系，加州大学伯克利分校）；Center for Computational Biology, University of California, Berkeley（计算生物学中心，加州大学伯克利分校）；Department of Molecular and Cell Biology, University of California, Berkeley（分子与细胞生物学系，加州大学伯克利分校）；Innovative Genomics Institute, University of California, Berkeley（创新基因组研究所，加州大学伯克利分校）；Howard Hughes Medical Institute, University of California, Berkeley（霍华德·休斯医学研究所，加州大学伯克利分校）
Multi-Scale Representation Learning for Protein Fitness Prediction
Mila - Québec AI Institute（米拉 - 魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Harvard Medical School（哈佛医学院）；IBM Research（IBM研究院）；Broad Institute（布罗德研究所）；HEC Montréal（蒙特利尔高等商学院）；CIFAR（加拿大高级研究所）
TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering
Toursun Synbio（拓荪合成生物）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Cambridge（剑桥大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；City University of Hong Kong（香港城市大学）
Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
Harvard Medical School（哈佛医学院）；University of Copenhagen（哥本哈根大学）；Novo Nordisk A/S（诺和诺德公司）；Microsoft Research（微软研究院）；Technical University of Denmark（丹麦技术大学）
Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context Information
Computational Science Initiative（计算科学计划）；Brookhaven National Laboratory（布鲁克海文国家实验室）；Joint Genome Institute（联合基因组研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
Seoul National University（首尔国立大学）；LG AI Research（LG人工智能研究院）；NAVER Corp.（NAVER公司）；Kangwon National University（江原国立大学）；Seoul National University of Science and Technology（首尔科技大学）
