Computational structure prediction protocols

The trRosetta server, a web-based platform for fast and accurate protein structure prediction, is powered by deep learning and Rosetta. This protocol includes procedures for using the web-based server as well as the standalone package.

A protocol is described for predicting the structures and functions of multi-domain proteins using the freely available deep-learning-based web platform I-TASSER-MTD.

We describe the use of ColabFold to perform structure prediction of monomers, complexes and alternative conformations, either on the web or locally, and provide guidance on interpreting the results through confidence metrics and visualizations.

Weitzner et al. describe a computational protocol that uses RosettaAntibody to predict antibody structures from sequence data. SnugDock is then used for docking of these structures to protein antigens.

StarMap is a software package that increases the accuracy of macromolecular structures by refining models using state-of-the-art Rosetta algorithms. StarMap’s graphical user interface is fully integrated into UCSF ChimeraX.

Phyre2 is a web-based tool for predicting and analyzing protein structure and function. Phyre2 uses advanced remote homology detection methods to build 3D models, predict ligand binding sites, and analyze amino acid variants in a protein sequence.

ClusPro is a web server that performs rigid-body docking of two proteins by sampling billions of conformations. Low-energy docked structures are clustered, and centers of the largest clusters are used as likely models of the complex.

This protocol describes the use of the AutoDock suite for computational docking in the study of protein–ligand interactions. A number of methods are described ranging from basic docking of drug molecules to virtual screening using a large ligand library of chemical compounds.

Structure-based virtual screening via docking can find molecules strongly binding to a target. This protocol describes how to use machine learning to improve this by building a target-specific scoring function and evaluating it on that target.

Screening chemical databases by computational docking is prohibitively time consuming when the databases are very large. Deep docking is a deep-learning approach aimed at reducing the number of compounds that need to be docked.

This protocol describes how to use distance restraints obtained from cross-linking mass spectrometry (XL-MS) experiments to guide the structural prediction of proteins and protein complexes.

The HADDOCK2.4 web server is a modeling platform that can integrate experimental and theoretical data for guiding 3D prediction of biomolecular complexes.

Cryogenic electron microscopy is well suited to uncovering structural heterogeneity in protein complexes, but analyses of such heterogeneous datasets are challenging. CryoDRGN is a machine learning approach to reconstructing heterogeneous ensembles of cryogenic electron microscopy density maps.

Many biological complexes are flexible or heterogeneous. Integrative modeling using Assembline enables structure determination of these macromolecular complexes by combining data from multiple experimental sources, including electron microscopy maps.

AbEMap generates large ensembles of docked antigen–antibody structures based on the structure of an antigen and either the structure or the sequence of an antibody. For each antigen residue, a likelihood score for being part of the epitope is obtained.

Off-target effects of programmable nucleases remain a critical issue for therapeutic applications of genome editing. This review compares experimental and computational tools for off-target analysis and provides recommendations for better assessments of off-target effects.

This Tutorial provides guide for how to evaluate, select and use publicly available computational tools for predicting intrinsic disorder in proteins, with a focus on performance and ease of use results, exemplified using results from the Critical Assessment of protein Intrinsic Disorder prediction experiment.