Context-Dependent Protein Structure Prediction Analysis and Stoichiometry Inference with MultimerMapper

Abstract

Advances in artificial intelligence (AI) have transformed the field of protein structure, notably with the accuracy level reached by AlphaFold in the prediction of monomeric and multimeric protein structures. However, while cloud implementations have broadened access to these methods, there is still a lack of non-AI tools to systematically interpret and analyze the resulting data, especially under varying modeling contexts. Here, we present MultimerMapper, a pipeline and software suite designed to extract, integrate, analyze, and visualize the behavior of multimeric systems in ensembles of overlapping protein structure predictions. It utilizes statistics and graph theory, combined with novel approaches, to interpret how protein-protein interfaces and protein conformation behave across different modeling contexts. Starting from a list of input sequences, MultimerMapper guides users through the generation and interpretation of structure prediction ensembles. It produces interactive 2D and 3D visualizations, highlights higher-order subassemblies, infers probable stoichiometries, and identifies alternative interaction modes and conformational changes associated with the presence or absence of specific proteins. MultimerMapper is cross-platform, freely available, and easily integrates with existing workflows. It offers a new perspective on the dynamic nature of predicted protein complexes, supporting researchers in the exploration of functional mechanisms and assembly paths.