Literature DB >> 29338232

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

Mark H Barley1, Nicholas J Turner1, Royston Goodacre1.   

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.

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Year:  2018        PMID: 29338232     DOI: 10.1021/acs.jcim.7b00488

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  7 in total

1.  Machine learning-assisted directed protein evolution with combinatorial libraries.

Authors:  Zachary Wu; S B Jennifer Kan; Russell D Lewis; Bruce J Wittmann; Frances H Arnold
Journal:  Proc Natl Acad Sci U S A       Date:  2019-04-12       Impact factor: 11.205

2.  Towards generalizable predictions for G protein-coupled receptor variant expression.

Authors:  Charles P Kuntz; Hope Woods; Andrew G McKee; Nathan B Zelt; Jeffrey L Mendenhall; Jens Meiler; Jonathan P Schlebach
Journal:  Biophys J       Date:  2022-06-17       Impact factor: 3.699

3.  A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.

Authors:  Frédéric Cadet; Nicolas Fontaine; Guangyue Li; Joaquin Sanchis; Matthieu Ng Fuk Chong; Rudy Pandjaitan; Iyanar Vetrivel; Bernard Offmann; Manfred T Reetz
Journal:  Sci Rep       Date:  2018-11-13       Impact factor: 4.379

4.  Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction.

Authors:  Magdalena Wiercioch
Journal:  Int J Mol Sci       Date:  2019-05-02       Impact factor: 5.923

5.  Novel Descriptors and Digital Signal Processing- Based Method for Protein Sequence Activity Relationship Study.

Authors:  Nicolas T Fontaine; Xavier F Cadet; Iyanar Vetrivel
Journal:  Int J Mol Sci       Date:  2019-11-11       Impact factor: 5.923

6.  Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.

Authors:  Guangyue Li; Youcai Qin; Nicolas T Fontaine; Matthieu Ng Fuk Chong; Miguel A Maria-Solano; Ferran Feixas; Xavier F Cadet; Rudy Pandjaitan; Marc Garcia-Borràs; Frederic Cadet; Manfred T Reetz
Journal:  Chembiochem       Date:  2020-11-17       Impact factor: 3.164

7.  A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry.

Authors:  Yulia V Samukhina; Dmitriy D Matyushin; Oksana I Grinevich; Aleksey K Buryak
Journal:  Biomolecules       Date:  2021-12-19
  7 in total

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