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Updated: Dec 3, 2025

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

Protein Structure Modeling with MODELLER.

Benjamin Webb^1,2,3, Andrej Sali^4,5,6

¹Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|October 30, 2020

Summary

This summary is machine-generated.

Computational protein structure modeling can bridge the sequence-structure gap, providing useful models for over half of known protein sequences. This approach aids in understanding protein function when experimental data is limited.

Keywords:

Comparative modeling Fold assignment Model assessment Multiple templates Sequence-structure alignment

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Area of Science:

Structural biology
Bioinformatics
Computational chemistry

Background:

Genome sequencing has rapidly increased protein sequence data.
Experimental protein structure determination is time-consuming and costly.
A significant gap exists between known protein sequences and experimentally determined structures.

Purpose of the Study:

To demonstrate the utility of computational protein structure modeling.
To illustrate the use of MODELLER for comparative protein modeling.
To assess the accuracy and applicability of automated modeling protocols.

Main Methods:

Utilized MODELLER software for comparative protein structure modeling.
Developed and automated a protocol for constructing protein models.
Evaluated the accuracy of generated models.

Main Results:

Successfully constructed a comparative model for a protein with an unknown structure.
Automated protocols yielded models of useful accuracy.
Models were generated for domains in over 50% of known protein sequences.

Conclusions:

Computational protein structure modeling is a powerful tool to address the sequence-structure gap.
Automated modeling protocols offer a scalable solution for structure prediction.
This approach significantly expands the structural information available for biological research.