Planning combinatorial disulfide cross-links for protein fold determination

Area of Science:

• * Computational biology
• * Structural biology
• * Bioinformatics

Background:

• * Fold recognition techniques predict protein structure from sequence but struggle with low sequence identity.
• * Current methods may not rank the correct model highest due to scoring function sensitivity.
• * Experimental methods are needed to resolve ambiguities in computational model rankings.

Purpose of the Study:

• * To present an integrated computational-experimental method for accurate protein fold determination.
• * To overcome limitations of purely computational fold recognition.
• * To reduce experimental effort compared to traditional structure elucidation.

Main Methods:

• * Utilizes predicted structural models from fold recognition.
• * Employs an information-theoretic algorithm to plan disulfide cross-links targeting secondary structure element pairs.
• * Uses a Bayes error framework to assess decision-making probability and minimize experimental complexity.

Main Results:

• * The method effectively differentiates between protein fold models based on topological differences.
• * Planned disulfide cross-links accurately probe selected secondary structure element pairs.
• * Case studies on CASP targets demonstrate low error risk and high accuracy in fold selection.
• * Simulation studies confirm the method's ability to identify correct folds using simulated cross-links.

Conclusions:

• * This integrated approach enhances fold determination beyond computational scoring limitations.
• * It requires less experimental effort than conventional protein structure determination methods.