AFToolkit: a framework for molecular modeling of proteins with AlphaFold-derived representations

A key challenge in protein engineering is understanding how mutations affect protein fitness and stability. Most of current state-of-the-art models fine-tune protein structure prediction or protein language models or even pretrain their own. Despite its widespread use within computational workflows, AlphaFold2 exhibits limited sensitivity in assessing the effects of amino acid point mutations on protein structure, thereby constraining its utility in sequence design and protein engineering. In this work, we propose a simple modification of AlphaFold2 inference that improves the model's capacity to capture the structural impacts of amino acid mutations. We achieve this by discarding the multiple sequence alignment and masking the template in recycling stages. Moreover, we introduce AFToolkit, a framework that leverages the embeddings of the modified AlphaFold2 model and simple adapter models to solve multiple protein engineering tasks. In contrast to other methods, our approach does not require fine-tuning the AlphaFold2 model or pretraining a new model from scratch on large datasets. It also supports handling multiple mutations, insertions, and deletions by directly modifying the input protein sequence. The proposed approach achieves strong performance across established benchmarks in terms of Spearman correlation: $0.68$ on PTMul, $0.60$ on cDNA-indel, and $0.57$ on C380.