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MacMatch: a tool for pattern-based protein secondary structure prediction

S R Presnell¹, B I Cohen, F E Cohen

¹Department of Pharmaceutical Chemistry, University of California-San Francisco 94143-0446.

Computer Applications in the Biosciences : CABIOS

|June 1, 1993

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This study introduces MacMatch, a program for predicting globular protein secondary structure using pattern matching and neural networks. It improves accuracy by incorporating protein structural class information.

Area of Science:

Computational biology
Protein structure prediction

Background:

Secondary structure prediction is crucial for understanding protein function.
Accurate prediction of protein secondary structure remains a challenge.

Purpose of the Study:

To present MacMatch, a user-friendly program for predicting globular protein secondary structure on Apple Macintosh computers.
To enhance prediction accuracy by leveraging protein structural class information.

Main Methods:

Utilizes augmented regular expression pattern matching for turn predictions.
Employs a neural network for helix and strand predictions.
Integrates protein structural class (alpha/alpha, beta/beta, alpha/beta) to refine predictions.

Main Results:

Related Experiment Videos

MacMatch provides accurate secondary structure predictions.
The program demonstrates improved accuracy when considering protein structural class.
Includes pre-tested patterns and trained neural networks for ease of use.

Conclusions:

MacMatch offers a simple and effective tool for secondary structure prediction.
Incorporating protein structural class information enhances prediction accuracy.
The program is a valuable resource for researchers in protein bioinformatics.