Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization
Protein fitness optimization is challenged by a vast combinatorial landscape where high-fitness variants are extremely sparse. Many current methods either underperform or require computationally expensive gradient-based sampling. We present CHASE, a framework that repurposes the evolutionary knowledge of pretrained protein language models by compressing their embeddings into a compact latent space. By training a conditional flow-matching model with classifier-free guidance, we enable the direct generation of high-fitness variants without predictor-based guidance during the ODE sampling steps. CHASE achieves state-of-the-art performance on AAV and GFP protein design benchmarks. Finally, we show that bootstrapping with synthetic data can further enhance performance in data-constrained settings.
Code (0)
등록된 구현이 없습니다.
Tasks
Protein DesignSimilar Papers 제목 키워드 기반
A Variational Perspective on Generative Protein Fitness Optimization
The goal of protein fitness optimization is to discover new protein variants with enhanced fitness for a given use. The vast search space and the sparsely populated fitness landscape, along with the discrete nature of pr…
Protein DesignRobust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industries. However, protein optimization using …
DecoderLanguage ModelingLanguage ModellingProtein Language ModelBinary Latent Protein Fitness Landscapes for Quantum Annealing Optimization
We propose Q-BIOLAT, a framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Starting from protein sequences, we leverage pretrained protein language models to obtain continuous embedd…
Representation LearningReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins
The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mutagenesis, directed evolution, and next-…
Q-BIOLAT: Binary Latent Protein Fitness Landscapes for QUBO-Based Optimization
Protein fitness optimization is inherently a discrete combinatorial problem, yet most learning-based approaches rely on continuous representations and are primarily evaluated through predictive accuracy. We introduce Q-B…
Protein Language Model