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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Residue Adjacency Matrix Based Feature Engineering for Predicting Cysteine Reactivity in Proteins.

Norman John Mapes¹, Christopher Rodriguez¹, Pradeep Chowriappa¹

¹Program of Computer Science, College of Engineering and Science, Louisiana Tech University, 305 Wisteria St., Ruston, LA 71272, United States.

Computational and Structural Biotechnology Journal

|January 24, 2019

Summary

This summary is machine-generated.

Machine learning accurately identifies oxidized cysteine residues in proteins, crucial for understanding diseases like cancer and diabetes. This fast, cost-effective method aids research into oxidative stress and protein damage.

Keywords:

Cysteine reactivity Free radicals Oxidative stress PSSM Position specific scoring matrix RAM residue adjacency matrix Response pathways

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Targeting Cysteine Thiols for in Vitro Site-specific Glycosylation of Recombinant Proteins

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Published on: October 4, 2017

Area of Science:

Biochemistry
Computational Biology
Bioinformatics

Background:

Reactive oxygen and nitrogen species cause oxidative damage to cellular components.
This damage is implicated in chronic diseases such as diabetes, cancer, Parkinson's, and heart disease.
Cysteine residues in proteins are particularly susceptible to oxidation.

Purpose of the Study:

To develop a fast and inexpensive computational method for identifying oxidizable cysteine residues in proteins.
To improve upon existing methods for predicting cysteine oxidation.
To aid in understanding the role of cysteine oxidation in disease pathogenesis.

Main Methods:

Utilized machine learning algorithms for classification.
Developed novel features: RAMmod and RAMseq.
Incorporated established features: PROPKA, SASA, PSS, and PSSM.
Employed template matching with MODELLER for 3D coordinate acquisition for feature extraction.

Main Results:

The developed algorithm, RAM, demonstrated a significant mean improvement of 22.04% in Matthews Correlation Coefficient (MCC) over N6C (p=0.015).
RAM showed a significant increase in MCC over PSSM (p=0.040), with an average improvement of 70.09%.
The algorithm requires only the protein sequence as input.

Conclusions:

Machine learning offers a rapid and cost-effective alternative to traditional wet lab methods for identifying oxidizable cysteines.
The RAM algorithm shows significant potential for advancing research in oxidative stress and related chronic diseases.
Accurate identification of oxidized cysteines can enhance our understanding of disease mechanisms.