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Automated multiple structure alignment and detection of a common substructural motif.
N Leibowitz1, Z Y Fligelman, R Nussinov
1School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel.
Proteins
|April 5, 2001
Summary
This study introduces a novel multiple structure alignment algorithm that simultaneously detects recurring substructural motifs across protein ensembles. The method identifies common C(alpha) atom cores in an order-independent manner, applicable to various molecules.
Area of Science:
- Structural Bioinformatics
- Computational Biology
- Biophysics
Background:
- Existing methods for multiple sequence alignment are abundant, but approaches for multiple structure alignment and substructural motif detection are limited.
- Current multiple structure alignment techniques often rely on pairwise comparisons and do not simultaneously perform motif detection.
- There is a need for algorithms that can analyze entire ensembles of structures at once to identify common structural patterns.
Purpose of the Study:
- To develop a novel algorithm for simultaneous multiple structure alignment and recurring substructural motif detection.
- To identify the largest common substructure (core) of C(alpha) atoms present in all molecules within a given ensemble.
- To provide a method applicable to protein surfaces, interfaces, and cores, as well as other molecules like RNA and drugs.
Main Methods:
- A new multiple structural alignment algorithm based on the geometric hashing paradigm.
- Simultaneous detection of common substructural cores and structural alignment of protein ensembles.
- Amino acid sequence order-independent comparison, making the alignment robust to insertions, deletions, and chain directionality.
Main Results:
- The algorithm successfully identifies the largest common C(alpha) atom substructure (core) present in all input structures.
- It generates multiple structural alignments ranked by the size of recurring substructural motifs found across the ensemble.
- Demonstrated efficiency and applicability across diverse protein folds and families, and potential for use with RNA and drug molecules.
Conclusions:
- The presented algorithm offers a simultaneous approach to multiple structure alignment and motif detection, overcoming limitations of previous methods.
- Its order-independent nature and ability to analyze entire ensembles make it a versatile tool for structural analysis.
- The method is effective for identifying spatially recurring substructural motifs without prior definition and has broad applicability.