2026/01/04 by Madeleine F Clore, Joseph F. Thole, Suchetan Dontha +9 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · #Machine Learning in Bioinformatics #Protein Structure and Dynamics #Evolutionary Algorithms and Applications
paper · doi:10.64898/2025.12.30.697132
openalex publication_date 2026/01/04 · openalex created_date 2026/01/05 · openalex updated_date 2026/07/30
Transformer models, neural networks that learn context by identifying relationships in sequential data, underpin many recent advances in artificial intelligence. Nevertheless, their inner workings are difficult to explain. Here, we find that a transformer model within the AlphaFold architecture uses simple, sparse patterns of amino acids to select protein conformations. To identify these patterns, we developed a straightforward algorithm called Conformational Attention Analysis Tool (CAAT). CAAT identifies amino acid positions that affect AlphaFold's predictions substantially when modified. These effects are corroborated by experiments in several cases. By contrast, modifying amino acids ignored by CAAT affects AlphaFold predictions less, regardless of experimental ground truth. Our results demonstrate that CAAT successfully identifies the positions of some amino acids important for protein structure prediction, narrowing the search space required to predict effective mutations and suggesting a framework that can be applied to other transformer-based neural networks.