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Updated: Jan 25, 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

LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for

Wei Zheng¹, Chengxin Zhang¹, Qiqige Wuyun²

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.

Nucleic Acids Research

|May 14, 2019

Summary

The LOMETS2 server enhances protein structure prediction using advanced threading programs and deep sequence profiles. This update significantly improves template detection for difficult targets, aiding biological research.

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Area of Science:

Computational biology
Structural bioinformatics
Bioinformatics tools

Background:

The LOMETS server, a widely used online system for template-based protein structure prediction, has become outdated.
Aging algorithms and poor performance in identifying distant-homology templates necessitate an updated system.

Purpose of the Study:

To report advancements in the LOMETS2 server, integrating state-of-the-art methods for improved protein structure prediction.
To enhance template identification and introduce structure-based function annotations.

Main Methods:

Implemented new state-of-the-art threading programs, including contact-map-based approaches.
Utilized deep sequence search for improved sequence profile construction.
Redesigned the web interface with structure-based function annotations.

Main Results:

LOMETS2 demonstrated significantly improved structure modeling quality and template detection compared to the previous LOMETS server.
Detected 176% more templates (TM-scores >0.5) for difficult targets lacking homologous templates.
Incorporated structure-based function prediction to broaden the server's utility.

Conclusions:

The updated LOMETS2 server represents a significant advancement in template-based protein structure prediction.
Enhanced template detection and added functional annotation capabilities increase the server's value for the biological community.