Multiple protein sequence alignment from tertiary structure comparison: assignment of global and residue confidence...

Area of Science:

• Computational biology
• Structural bioinformatics
• Bioinformatics algorithms

Background:

• Accurate multiple protein sequence alignment is crucial for understanding protein function and evolution.
• Traditional methods often struggle with proteins lacking significant sequence homology.
• Tertiary structure comparisons offer an alternative approach for aligning distantly related proteins.

Purpose of the Study:

• To develop and present an algorithm for rapid and accurate generation of multiple protein sequence alignments based on tertiary structure comparisons.
• To introduce quantitative measures for assessing the quality of structural alignments.

Main Methods:

• A preliminary sequence-based alignment is used for initial structure superposition.
• A structure comparison algorithm calculates a similarity tree from superimposed protein structures.
• Multiple sequence alignments are generated by traversing the similarity tree.
• Two similarity indices, Sc and Pij', are introduced to quantify global structural similarity and residue alignment confidence, respectively.

Main Results:

• The STAMP (Structural Alignment of Multiple Proteins) algorithm generates alignments that agree well with published structural data for dehydrogenase fold domains, globins, and serine proteinases.
• The Sc index quantifies global structural similarity, while Pij' measures residue-specific alignment confidence.
• These indices reduce the need for manual verification of alignment quality.

Conclusions:

• STAMP provides a reproducible method for generating high-quality, structure-based multiple protein sequence alignments.
• The introduced similarity indices enhance the reliability and interpretability of the alignments.
• This approach facilitates studies on residue conservation within structural motifs, particularly for proteins with low sequence identity.