Literature DB >> 9538521

Rational combinatorial library design. 2. Rational design of targeted combinatorial peptide libraries using chemical similarity probe and the inverse QSAR approaches.

S J Cho1, W Zheng, A Tropsha.   

Abstract

We have developed a novel strategy for rational design of targeted peptide libraries. The goal of this method is to select a subset of natural amino acids that are most likely to be present in active peptides for the synthesis of library. Two different protocols are employed where chemical structures of peptides are described either by topological indices or by a combination of physicochemical descriptors for individual amino acids. The selection of a peptide as a candidate for the targeted library is based either on its chemical similarity to a biologically active probe or on its biological activity predicted from a preconstructed quantitative structure-activity (QSAR) equation. The optimization of the library is achieved by means of genetic algorithms (GA). This method was tested by rational design of the library with bradykinin-potentiating activity. Twenty-eight bradykinin-potentiating pentapeptides were used as a training set for the development of a QSAR equation, and, alternatively, two active pentapeptides, VEWAK and VKWAP, were used as probe molecules. In each case, the frequency distribution of amino acids in the top 100 peptides suggested by the method resembles the frequency distribution of amino acids found in the active peptides. The results obtained after GA optimization also compared favorably with those obtained by the exhaustive analysis of all possible 3.2 million pentapeptides.

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Year:  1998        PMID: 9538521     DOI: 10.1021/ci9700945

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  7 in total

1.  Reactant- and product-based approaches to the design of combinatorial libraries.

Authors:  Valerie J Gillet
Journal:  J Comput Aided Mol Des       Date:  2002 May-Jun       Impact factor: 3.686

Review 2.  Reactant- and product-based approaches to the design of combinatorial libraries.

Authors:  Valerie J Gillet
Journal:  Mol Divers       Date:  2002       Impact factor: 2.943

3.  Rational selection of training and test sets for the development of validated QSAR models.

Authors:  Alexander Golbraikh; Min Shen; Zhiyan Xiao; Yun-De Xiao; Kuo-Hsiung Lee; Alexander Tropsha
Journal:  J Comput Aided Mol Des       Date:  2003 Feb-Apr       Impact factor: 3.686

Review 4.  Computational systems chemical biology.

Authors:  Tudor I Oprea; Elebeoba E May; Andrei Leitão; Alexander Tropsha
Journal:  Methods Mol Biol       Date:  2011

5.  Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method.

Authors:  Emili Besalú
Journal:  Int J Mol Sci       Date:  2016-05-26       Impact factor: 5.923

6.  Deep reinforcement learning for de novo drug design.

Authors:  Mariya Popova; Olexandr Isayev; Alexander Tropsha
Journal:  Sci Adv       Date:  2018-07-25       Impact factor: 14.136

Review 7.  Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design.

Authors:  Beatriz Suay-García; Jose I Bueso-Bordils; Antonio Falcó; Gerardo M Antón-Fos; Pedro A Alemán-López
Journal:  Int J Mol Sci       Date:  2022-01-30       Impact factor: 5.923

  7 in total

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