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Justin R. Klesmith

dblp:247/2194 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein function prediction
0.412019
Improved mutant function prediction via PACT: Protein Analysis and Classifier Toolkit · Bioinform. 2019
Bioinformatics and computational biology › functional genomics
deep mutational scanning
0.112019
Improved mutant function prediction via PACT: Protein Analysis and Classifier Toolkit · Bioinform. 2019

Methods — techniques the papers use, named apart from their topics

structural features · 0.4sequence homology · 0.4machine learning classifier · 0.4
YearPublicationVenuePosition
2019 Improved mutant function prediction via PACT: Protein Analysis and Classifier Toolkit
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.
Justin R. Klesmith, Benjamin J. Hackel
Bioinform.1