SDM: a server for predicting effects of mutations on protein stability.

Area of Science:

• Biochemistry
• Structural Biology
• Bioinformatics

Background:

• Predicting the impact of mutations on protein stability is crucial in molecular biology.
• The original SDM (Structure-based protein Stability prediction) method is a pioneering knowledge-based approach for this task.
• Existing methods often require extensive computational resources or lack accuracy.

Purpose of the Study:

• To report an improved webserver for predicting mutation effects on protein stability.
• To enhance the accuracy and applicability of the SDM method.
• To provide a user-friendly tool for researchers studying protein stability.

Main Methods:

• Updated environment-specific amino-acid substitution tables using an expanded Protein Data Bank (PDB).
• Introduced new residue-conformation and interaction parameters, including packing density and residue depth.
• Developed a webserver that takes a PDB file and a point mutation as input to calculate stability differences.

Main Results:

• The updated SDM server was tested on 2690 point mutations from 132 protein structures.
• The revised method showed strong correlation against reverse mutations, outperforming machine-learning approaches.
• The knowledge-based approach demonstrated its strength in identifying stabilizing mutations.

Conclusions:

• The improved SDM webserver provides a robust and accurate tool for predicting mutation-induced changes in protein stability.
• The updated knowledge-based approach offers a reliable alternative to machine-learning methods for stability prediction.
• The webserver is publicly available for researchers at http://structure.bioc.cam.ac.uk/sdm2.