Improved mutant function prediction via PACT: Protein Analysis and Classifier Toolkit

Justin R. Klesmith, Benjamin J. Hackel

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

MOTIVATION: Deep mutational scanning experiments have enabled the measurement of the sequence-function relationship for thousands of mutations in a single experiment. The Protein Analysis and Classifier Toolkit (PACT) is a Python software package that marries the fitness metric of a given mutation within these experiments to sequence and structural features enabling downstream analyses. PACT enables the easy development of user sharable protocols for custom deep mutational scanning experiments as all code is modular and reusable between protocols. Protocols for mutational libraries with single or multiple mutations are included. To exemplify its utility, PACT assessed two deep mutational scanning datasets that measured the tradeoff of enzyme activity and enzyme stability.

RESULTS: PACT efficiently evaluated classifiers that predict protein mutant function tested on deep mutational scanning screens. We found that the classifiers with the lowest false positive and highest true positive rate assesses sequence homology, contact number and if mutation involves proline.

AVAILABILITY AND IMPLEMENTATION: PACT and the processed datasets are distributed freely under the terms of the GPL-3 license. The source code is available at GitHub (https://github.com/JKlesmith/PACT).

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)2707-2712
Number of pages6
JournalBioinformatics
Volume35
Issue number16
DOIs
StatePublished - Aug 15 2019

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health [GM121777 to B.J.H.].

Publisher Copyright:
© 2018 The Author(s) 2018. Published by Oxford University Press. All rights reserved.

PubMed: MeSH publication types

  • Journal Article

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