AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold.

The ability of proteins to adopt multiple conformations is fundamental to their biological function. With the advent of AlphaFold, machine learning (ML)-based methods have extended their capabilities to more broadly sample this intrinsic conformational diversity. However, the extent to which ML approaches can independently generate ensembles of diverse and biologically relevant conformations remains an open question. We sought to tackle this challenge by developing AlphaFold-RandomWalk (AF-RW) and AlphaFold-Ensemble (AF-Ensemble), novel ML-based methods to generate diverse protein conformations. As opposed to traditional approaches which rely on modifying the input multiple sequence alignment, AF-RW systematically adds noise to the weights of the model on a per-target basis, significantly increasing the conformational diversity of predicted models compared to conventional methods. AF-Ensemble takes the complementary approach fine-tuning an ensemble of models to produce diversity from a set of two-state systems. Additionally, both methods were incorporated into an automated, multistage computational pipeline that seeds unbiased molecular dynamics simulations from ML-generated conformations to efficiently sample alternative conformations. When evaluated on a diverse set of ten proteins, our pipeline provided useful, MD-guided hypotheses for determining biologically meaningful alternative conformations. Moreover, simulations seeded from diverse ML-generated conformations provided a reasonable approximation to the free energy landscape of two challenging protein targets, K-Ras and ribose-binding protein. Overall, our work highlights the potential of combining diverse conformations generated by perturbing the weights of AF with molecular dynamics simulations to efficiently probe protein conformational heterogeneity.