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Updated: Jan 2, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Molecular replacement using structure predictions from databases.

Adam J Simpkin¹, Jens M H Thomas¹, Felix Simkovic¹

¹Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England.

Acta Crystallographica. Section D, Structural Biology

|December 4, 2019

Summary

This summary is machine-generated.

This study demonstrates that ab initio protein structure predictions from databases like GREMLIN can successfully solve macromolecular crystallography structures, even with low sequence identity. The AMPLE pipeline, particularly its cluster-and-truncate method, is key to these successes.

Keywords:

ab initio modelling ab initio structure predictions databases molecular replacement

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Area of Science:

Macromolecular crystallography
Structural bioinformatics
Computational biology

Background:

Molecular replacement (MR) is the primary method for solving the phase problem in macromolecular crystallography.
When homologous structures are unavailable, ab initio (or de novo) modelling using bioinformatics is an alternative.
Recent advances in evolutionary covariance analysis have significantly improved the accuracy of ab initio models.

Purpose of the Study:

To evaluate the utility of large-scale ab initio protein structure predictions from GREMLIN and PconsFam databases as search models for molecular replacement.
To assess the effectiveness of the AMPLE pipeline in utilizing these predicted structures to solve experimental structures.

Main Methods:

Utilized the AMPLE pipeline to process structure predictions from GREMLIN and PconsFam databases.
Tested the ability of processed predictions to solve PDB entries subsequently deposited.
Employed the cluster-and-truncate approach within AMPLE.
Applied Rosetta remodelling for PconsFam predictions.
Assessed model quality using the SIMBAD pipeline.

Main Results:

Successfully solved 9 out of 27 GREMLIN cases, with target lengths from 109-355 residues and resolutions of 1.4-2.9 Å.
Achieved structure solution with target-model sequence identity as low as 20%.
The cluster-and-truncate method in AMPLE was crucial for most successful solutions.
Remodelling with Rosetta within AMPLE improved results for lower-quality PconsFam predictions.
AMPLE-derived models from GREMLIN were suitable for the sequence-independent MR pipeline SIMBAD.

Conclusions:

Ab initio structure predictions from databases like GREMLIN are valuable resources for solving macromolecular structures via MR.
The AMPLE pipeline, with specific strategies like cluster-and-truncate and Rosetta remodelling, effectively leverages these predictions.
These findings pave the way for optimizing the use of expanding ab initio structure prediction databases in structural biology.