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ppdx: Automated modeling of protein–protein interaction descriptors for use with machine learning.

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Title: ppdx: Automated modeling of protein–protein interaction descriptors for use with machine learning.
Authors: Conti, Simone1 (AUTHOR) simonecnt@gmail.com, Ovchinnikov, Victor1 (AUTHOR), Karplus, Martin1,2 (AUTHOR) marci@tammy.harvard.edu
Source: Journal of Computational Chemistry. 9/30/2022, Vol. 43 Issue 25, p1747-1757. 11p.
Subjects: Protein-protein interactions, Machine learning, Amino acid sequence, Simple machines, Python programming language, Sequence alignment, Supercomputers
Abstract: This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein–protein interactions. The descriptors can be used to predict various properties of interest, such as protein–protein binding affinities, or inhibitory concentrations (IC50), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC50 of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computational Chemistry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 158677620
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: ppdx: Automated modeling of protein–protein interaction descriptors for use with machine learning.
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  Data: <searchLink fieldCode="AR" term="%22Conti%2C+Simone%22">Conti, Simone</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> simonecnt@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ovchinnikov%2C+Victor%22">Ovchinnikov, Victor</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Karplus%2C+Martin%22">Karplus, Martin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> marci@tammy.harvard.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Computational+Chemistry%22">Journal of Computational Chemistry</searchLink>. 9/30/2022, Vol. 43 Issue 25, p1747-1757. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Protein-protein+interactions%22">Protein-protein interactions</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Amino+acid+sequence%22">Amino acid sequence</searchLink><br /><searchLink fieldCode="DE" term="%22Simple+machines%22">Simple machines</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Sequence+alignment%22">Sequence alignment</searchLink><br /><searchLink fieldCode="DE" term="%22Supercomputers%22">Supercomputers</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein–protein interactions. The descriptors can be used to predict various properties of interest, such as protein–protein binding affinities, or inhibitory concentrations (IC50), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC50 of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Computational Chemistry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1002/jcc.26974
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 1747
    Subjects:
      – SubjectFull: Protein-protein interactions
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Amino acid sequence
        Type: general
      – SubjectFull: Simple machines
        Type: general
      – SubjectFull: Python programming language
        Type: general
      – SubjectFull: Sequence alignment
        Type: general
      – SubjectFull: Supercomputers
        Type: general
    Titles:
      – TitleFull: ppdx: Automated modeling of protein–protein interaction descriptors for use with machine learning.
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            NameFull: Conti, Simone
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            NameFull: Ovchinnikov, Victor
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            NameFull: Karplus, Martin
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          Dates:
            – D: 30
              M: 09
              Text: 9/30/2022
              Type: published
              Y: 2022
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              Value: 43
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            – TitleFull: Journal of Computational Chemistry
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