ArticleBioinformatics (Oxford, England)2019
Improved mutant function prediction via PACT: Protein Analysis and Classifier Toolkit.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Enhanced T cell receptor specificity through framework engineering.Frontiers in immunology · 2024Article
- An orthogonalized PYR1-based CID module with reprogrammable ligand-binding specificity.Nature chemical biology · 2024Article
- Stabilization of the SARS-CoV-2 receptor binding domain by protein core redesign and deep mutational scanning.Protein engineering, design & selection : PEDS · 2022Article
- Identification of SARS-CoV-2 S RBD escape mutants using yeast screening and deep mutational scanning.STAR protocols · 2021Article
- Facilitating Machine Learning-Guided Protein Engineering with Smart Library Design and Massively Parallel Assays.Advanced genetics (Hoboken, N.J.) · 2021Article
- Stabilization of the SARS-CoV-2 Receptor Binding Domain by Protein Core Redesign and Deep Mutational Scanning.bioRxiv : the preprint server for biology · 2021Article
- Optimization of multi-site nicking mutagenesis for generation of large, user-defined combinatorial libraries.Protein engineering, design & selection : PEDS · 2021Article
- Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning.Cell systems · 2021Article
- Saturation Mutagenesis Genome Engineering of Infective ΦX174 BacteriophageACS synthetic biology · 2020Article
- User-defined single pot mutagenesis using unamplified oligo pools.Protein engineering, design & selection : PEDS · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
motivationDeep 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.
resultsPACT 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.