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Updated: Apr 19, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Refinement by shifting secondary structure elements improves sequence alignments.

Jing Tong¹, Jimin Pei, Zbyszek Otwinowski

¹Department of Biophysics, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 75390; Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 75390.

|December 30, 2014

Summary

This summary is machine-generated.

This study introduces SFESA, a novel method to refine protein sequence alignments by locally shifting secondary structures. This approach improves alignment accuracy, crucial for protein structure prediction when using homologous templates.

Keywords:

alignment improvement alignment refinement contact energy local secondary structure shifting pairwise alignment

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Area of Science:

Computational biology
Structural bioinformatics
Protein structure prediction

Background:

Homology modeling is a key protein structure prediction method.
Alignment errors, particularly in distantly related proteins, limit homology modeling accuracy.
Misalignments often involve shifts in secondary structural elements.

Purpose of the Study:

To develop a refinement method for improving pairwise sequence alignments in homology modeling.
To address the bottleneck of alignment errors caused by misaligned secondary structures.
To enhance the accuracy of protein structure prediction through better sequence alignments.

Main Methods:

A refinement method, SFESA (Sequence and Structure Alignment), was developed.
SFESA evaluates alignment variants generated by local shifts of template-defined secondary structures.
A novel scoring function combines profile-based sequence scores and structure-based residue contact scores.

Main Results:

The combined scoring function effectively selects superior alignment variants.
SFESA significantly increases the overall accuracy of pairwise sequence alignments.
Evaluations demonstrate substantial improvements over existing automatic alignment methods like PROMALS, HHpred, and CNFpred.

Conclusions:

The SFESA refinement method enhances alignment accuracy for homology modeling.
Improved sequence alignments contribute to more reliable protein structure prediction.
The developed method offers a valuable tool for computational structural biology.