AFsample2: Predicting multiple conformations and ensembles with AlphaFold2

Understanding protein dynamics and conformational states carries profound scientific and practical implications for several areas of research, ranging from a general understanding of biological processes at the molecular level to a detailed understanding of disease mechanisms, which in turn can open up new avenues in drug development. Multiple solutions have been recently developed to widen the conformational landscape of predictions made by Alphafold2 (AF2). Here, we introduce AFsample2, a method employing random MSA column masking to reduce the influence of co-evolutionary signals to enhance the structural diversity of models generated by the AF2 neural network. AFsample2 improves the prediction of alternative states for a broad range of proteins, yielding high-quality end states and diverse conformational ensembles. In the data set of open-closed conformations (OC23), alternate state models improved in 17 out of 23 cases without compromising the generation of the preferred state. Consistent results were observed in 16 membrane protein transporters, with improvements in 12 out of 16 targets. TM-score improvements to experimental end states were substantial, sometimes exceeding 50%, elevating mediocre scores from 0.58 to nearly perfect 0.98. Furthermore, AFsample2 increased the diversity of intermediate conformations by 70% compared to the standard AF2 system, producing highly confident models, that could potentially be on-path between the two states. In addition, we also propose a way of selecting the end-states in generated model ensembles. These solutions could potentially enhance the generation and identification of alternative protein conformations, thereby providing a more comprehensive understanding of protein function and dynamics. Future work will focus on validating the accuracy of these intermediate conformations and exploring their relevance to functional transitions in proteins.