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Updated: Jun 15, 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

TOPTMH: topology predictor for transmembrane alpha-helices.

Rezwan Ahmed¹, Huzefa Rangwala, George Karypis

¹Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA. ahmed@cs.umn.edu

Journal of Bioinformatics and Computational Biology

|February 26, 2010

Summary

We developed TOPTMH, a new computational method for predicting transmembrane helix topology in proteins. This approach accurately identifies protein structures, outperforming existing methods on benchmarks.

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Last Updated: Jun 15, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Determining Membrane Protein Topology Using Fluorescence Protease Protection (FPP)

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Published on: May 26, 2011

Area of Science:

Biochemistry and Molecular Biology
Bioinformatics and Computational Biology

Background:

Alpha-helical transmembrane proteins are crucial for cellular functions, constituting a significant portion of genes.
Experimental structure determination of these proteins is challenging, necessitating computational prediction methods.
Accurate prediction of transmembrane helix location and orientation is vital for understanding protein function.

Purpose of the Study:

To introduce TOPTMH, a novel computational method for predicting transmembrane helix topology.
To improve the accuracy of transmembrane protein structure prediction using sequence information.
To provide a robust tool for identifying transmembrane helix segments.

Main Methods:

TOPTMH integrates Support Vector Machines (SVM), Hidden Markov Models (HMM), and a rule-based scheme.
A binary SVM classifier is used to predict residues involved in transmembrane helices.
HMM models incorporate SVM predictions and hydropathy features for segment identification.

Main Results:

TOPTMH demonstrates superior performance compared to state-of-the-art prediction methods.
The method achieves top performance on an independent benchmark dataset.
The combined approach effectively captures the structural characteristics of transmembrane proteins.

Conclusions:

TOPTMH offers a significant advancement in computational prediction of transmembrane helix topology.
The method provides a reliable and accurate tool for analyzing transmembrane protein structures.
This work contributes to a better understanding of the structure-function relationships of these essential proteins.