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MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison
Angel R Ortiz1, Charlie E M Strauss, Osvaldo Olmea
1Department of Physiology and Biophysics, Mount Sinai School of Medicine, New York University, New York, New York 10029, USA. ortiz@inka.mssm.edu
Protein Science : a Publication of the Protein Society
|October 17, 2002
Summary
A new method, MAMMOTH, offers fast and accurate sequence-independent protein structure comparison for structural genomics. It derives a similarity score from alignment likelihood, outperforming existing tools and correlating well with human evaluation.
Area of Science:
- Structural biology
- Bioinformatics
- Computational biology
Background:
- Structural genomics and protein structure prediction demand efficient methods for comparing low-resolution 3D structures.
- Existing methods for structural alignment can be slow or lack objectivity.
Purpose of the Study:
- To develop a novel, fast, and objective method for sequence-independent protein structural alignment.
- To create a reliable structural similarity score for comparing protein structures and conformations.
Main Methods:
- A heuristic algorithm for sequence-independent structural alignment was developed.
- A structural similarity score was derived based on the statistical likelihood of random structural alignments.
- The new method (MAMMOTH) was benchmarked against established scores and human evaluations.
Main Results:
- The MAMMOTH algorithm accurately describes random structural alignments using extreme value distribution.
- The derived similarity score shows better correlation with human evaluation than other tools.
- The method is computationally efficient, suitable for large-scale database comparisons.
Conclusions:
- MAMMOTH provides a fast, objective, and accurate tool for protein structure comparison.
- This method is well-suited for applications in structural genomics and protein modeling.
- The program is publicly available for research use.