Gramm: A webserver for free and template-based protein docking.

Abstract

Most biological functions depend on protein interactions. Structural modeling of proteins and protein assemblies is important for understanding the fundamental properties of biomolecular mechanisms and for our ability to manipulate them. Recently released artificial intelligence (AI) based programs, AlphaFold and RoseTTAFold, can accurately predict three-dimensional structures of proteins, even in the absence of the structures of homologous proteins. With the success of these AI-based approaches to protein structure prediction, the research community is anticipating methods/algorithms that will enable us to reliably predict the structures of protein complexes, including those in transient interactions, in the crowded environment inside a cell, accounting for often significant conformational flexibility of the interacting proteins. Prediction of the structure of protein complexes by docking methods is a well-established research field. The intermolecular energy landscape mapped by systematic docking approaches, like the FFT-based docking algorithms, can be used to refine docking predictions, to detect macro characteristics such as the binding funnel, and to generate ensembles of transient interactions for investigation of the protein interaction patterns. We present a user-friendly web interface to our popular GRAMM docking software for structural characterization of protein-protein interactions. Users can choose between the free and the template-based docking methodology, and select a number of advanced features, including mapping of the intermolecular energy landscape by distributions of docking poses, and the dynamic visualization of the docked models.