Protocol for deriving distance restraints from AlphaFold for use in solution NMR structure determination

TL;DR

This paper introduces a protocol to use AlphaFold predictions to generate distance restraints for NMR structure determination, improving accuracy and efficiency.

Contribution

A novel protocol for deriving high-confidence distance restraints from AlphaFold to aid automated NOE assignment in NMR.

Findings

The protocol reduces NOE assignment ambiguity and enhances NMR structure determination accuracy.

It enables the elucidation of previously elusive proteins using AlphaFold-derived restraints.

The method validates structural predictions and expedites the NMR workflow.

Abstract

Artificial intelligence (AI) has revolutionized structural biology but must be applied reliably. Here, we present an approach for derivation of distance restraints from AlphaFold structure predictions to aid in automated nuclear Overhauser effect (NOE) assignment during solution NMR structure determination. We describe steps for selecting reliable AlphaFold structure predictions, determination of atom distances, and generation of high-confidence distance restraints. This protocol can expedite solution NMR structure determination, reduce NOE assignment ambiguity, enhance accuracy, enable elucidation of elusive proteins, and validate structural predictions.

For complete details on the use and execution of this protocol, please refer to Lin et al.1

•Generation and evaluation of AlphaFold structure predictions•Creation and installation of PyMOL and ChimeraX restraint-generating plugins•Visualization of confidently predicted atom-atom distances•Automated creation of distance restraints for solution NMR structure determination

Generation and evaluation of AlphaFold structure predictions

Creation and installation of PyMOL and ChimeraX restraint-generating plugins

Visualization of confidently predicted atom-atom distances

Automated creation of distance restraints for solution NMR structure determination

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Artificial intelligence (AI) has revolutionized structural biology but must be applied reliably. Here, we present an approach for derivation of distance restraints from AlphaFold structure predictions to aid in automated nuclear Overhauser effect (NOE) assignment during solution NMR structure determination. We describe steps for selecting reliable AlphaFold structure predictions, determination of atom distances, and generation of high-confidence distance restraints. This protocol can expedite solution NMR structure determination, reduce NOE assignment ambiguity, enhance accuracy, enable elucidation of elusive proteins, and validate structural predictions.

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