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

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A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery.

Ian E H Yen¹, Xin Lin¹, Jiong Zhang²

¹Department of Computer Science, University of Texas at Austin, TX 78712, USA.

JMLR Workshop and Conference Proceedings

|August 26, 2016

Summary

This study introduces a novel convex relaxation method for Multiple Sequence Alignment and Motif Discovery, yielding superior results compared to existing bioinformatics tools.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Area of Science:

Bioinformatics
Computational Biology
Optimization

Background:

Multiple Sequence Alignment (MSA) and Motif Discovery are critical NP-hard problems in bioinformatics.
Current methods rely on local search (e.g., Expectation Maximization, Gibbs Sampling) or greedy heuristics.
These existing approaches often face limitations in solution quality and scalability.

Purpose of the Study:

To develop a novel convex relaxation approach for MSA and Motif Discovery.
To introduce an efficient algorithm for solving the proposed convex relaxation.
To demonstrate the superiority of the new method over established bioinformatics tools.

Main Methods:

Formulated MSA and Motif Discovery as convex optimization problems using atomic norms.
Developed the Greedy Direction Method of Multiplier (GDMM) algorithm.
Applied GDMM to solve the convex relaxation with two convex atomic constraints.

Main Results:

The proposed convex relaxation approach achieved higher quality solutions for MSA and Motif Discovery.
Experimental results demonstrated significant improvements over standard bioinformatics tools.
The GDMM algorithm efficiently solved the complex convex optimization problems.

Conclusions:

Convex relaxation offers a powerful new paradigm for tackling NP-hard problems in bioinformatics.
The developed method provides a more accurate and effective approach to MSA and Motif Discovery.
This work advances computational methods in bioinformatics, offering practical benefits for biological data analysis.