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HBcompare: Classifying Ligand Binding Preferences with Hydrogen Bond Topology.
Tam, Justin Z; Kong, Zhaoming; Ahmed, Omar; He, Lifang; Chen, Brian Y.
Afiliação
  • Tam JZ; Department Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA 19004, USA.
  • Kong Z; Department Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA 19004, USA.
  • Ahmed O; Department Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA 19004, USA.
  • He L; Department Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA 19004, USA.
  • Chen BY; Department Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA 19004, USA.
Biomolecules ; 12(11)2022 10 28.
Article em En | MEDLINE | ID: mdl-36358939
ABSTRACT
This paper presents HBcompare, a method that classifies protein structures according to ligand binding preference categories by analyzing hydrogen bond topology. HBcompare excludes other characteristics of protein structure so that, in the event of accurate classification, it can implicate the involvement of hydrogen bonds in selective binding. This approach contrasts from methods that represent many aspects of protein structure because holistic representations cannot associate classification with just one characteristic. To our knowledge, HBcompare is the first technique with this capability. On five datasets of proteins that catalyze similar reactions with different preferred ligands, HBcompare correctly categorized proteins with similar ligand binding preferences 89.5% of the time. Using only hydrogen bond topology, classification accuracy with HBcompare surpassed standard structure-based comparison algorithms that use atomic coordinates. As a tool for implicating the role of hydrogen bonds in protein function categories, HBcompare represents a first step towards the automatic explanation of biochemical mechanisms.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Proteínas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Proteínas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article