Application of fourier transform and proteochemometrics principles to protein engineering

• Published in: BMC bioinformatics Vol. 19; no. 1; p. 382
• Main Authors: Cadet, Frédéric, Fontaine, Nicolas, Vetrivel, Iyanar, Ng Fuk Chong, Matthieu, Savriama, Olivier, Cadet, Xavier, Charton, Philippe
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 16-10-2018; BioMed Central; BMC
• Subjects: Amino acid sequence; Amino acids; Biological activity; Computer applications; Cytochrome; Cytochrome P450; Cytochromes P450; Data processing; Datasets; Digital signal processing; Digital signal processors; Directed evolution; Engineering; Enterotoxins; Fast Fourier transformations; Fitness; Fourier Analysis; Fourier transforms; Gene mutation; Humans; Least squares method; Methods; Mutants; Mutation; Peptides; Physicochemical properties; Protein engineering; Protein Engineering - methods; Protein sequence activity relationship; Protein spectrum; Proteins; Proteins - chemistry; Rational screening; Regression; Reproductive fitness; Signal processing; Spectrum analysis; Statistical modelling; Statistical models; Strategic management; Studies; Thermal stability; TNF inhibitors; Training; Tumor necrosis factor-TNF; Tumor necrosis factor-α; Rational screening; Directed evolution; Protein sequence activity relationship; Protein spectrum; Statistical modelling
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2407-8

Connecting the dots between the protein sequence and its function is of fundamental interest for protein engineers. In-silico methods are useful in this quest especially when structural information is not available. In this study we propose a mutant library screening tool called iSAR (innovative Sequence Activity Relationship) that relies on the physicochemical properties of the amino acids, digital signal processing and partial least squares regression to uncover these sequence-function correlations.
We show that the digitalized representation of the protein sequence in the form of a Fourier spectrum can be used as an efficient descriptor to model the sequence-activity relationship of proteins. The iSAR methodology that we have developed identifies high fitness mutants from mutant libraries relying on physicochemical properties of the amino acids, digital signal processing and regression techniques. iSAR correlates variations caused by mutations in spectra with biological activity/fitness. It takes into account the impact of mutations on the whole spectrum and does not focus on local fitness alone. The utility of the method is illustrated on 4 datasets: cytochrome P450 for thermostability, TNF-alpha for binding affinity, GLP-2 for potency and enterotoxins for thermostability. The choice of the datasets has been made such as to illustrate the ability of the method to perform when limited training data is available and also when novel mutations appear in the test set, that have not been featured in the training set.
The combination of Fast Fourier Transform and Partial Least Squares regression is efficient in capturing the effects of mutations on the function of the protein. iSAR is a fast algorithm which can be implemented with limited computational resources and can make effective predictions even if the training set is limited in size.