Work

Improved Descriptors for the Quantitative Structure–Activity Relationship Modeling of Peptides and Proteins

Year: 2018

Type: article

Abstract: The ability to model the activity of a protein using quantitative structure-activity relationships (QSAR) requires descriptors for the 20 naturally coded amino acids. In this work we show that by modifying some established descriptors we were able to model the activity data of 140 mutants of the enzyme epoxide hydrolase with improved accuracy. These new descriptors (referred to as physical descriptors) also gave very good results when tested against a series of four dipeptide data sets. The physical descriptors encode the amino acids using only two orthogonal scales: the first is strongly linked to hydrophilicity/hydrophobicity, and the second, to the volume of the amino acid residue. The use of these new amino acid descriptors should result in simpler and more readily interpretable models for the enzyme activity (and potentially other functions of interest, e.g., secondary and tertiary structure) of peptides and proteins.

Source: Journal of Chemical Information and Modeling

Authors: Mark H. Barley, Nicholas J. Turner, Royston Goodacre

Institution: University of Manchester

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,157

Funders: GlaxoSmithKline, Biotechnology and Biological Sciences Research Council

Awards: CENTRE FOR SYNTHETIC BIOLOGY OF FINE AND SPECIALITY CHEMICALS, G6014895610, G6740108216, Rapid Evolution of Enzymes and Synthetic Micro-organisms for the Development of Industrial Biocatalysts, G995922834