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Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints.

ABSTRACT: The self-assembly of low molecular weight gelators to form gels has enormous potential for cell culturing, optoelectronics, sensing, and for the preparation of structured materials. There is an enormous "chemical space" of gelators. Even within one class, functionalised dipeptides, there are many structures based on both natural and unnatural amino acids that can be proposed and there is a need for methods that can successfully predict the gelation propensity of such molecules. We have successfully developed computational models, based on experimental data, which are robust and are able to identify in silico dipeptide structures that can form gels. A virtual computational screen of 2025 dipeptide candidates identified 9 dipeptides that were synthesised and tested. Every one of the 9 dipeptides synthesised and tested were correctly predicted for their gelation properties. This approach and set of tools enables the "dipeptide space" to be searched effectively and efficiently in order to deliver novel gelator molecules.

SUBMITTER: Gupta JK

PROVIDER: S-EPMC6016447 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints.

Gupta Jyoti K JK Adams Dave J DJ Berry Neil G NG

Chemical science 20160413 7

The self-assembly of low molecular weight gelators to form gels has enormous potential for cell culturing, optoelectronics, sensing, and for the preparation of structured materials. There is an enormous "chemical space" of gelators. Even within one class, functionalised dipeptides, there are many structures based on both natural and unnatural amino acids that can be proposed and there is a need for methods that can successfully predict the gelation propensity of such molecules. We have successfu ...[more]

PMID: 30155120

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Project description:In this work we report on gelation properties, self-assembly motifs, chirality effects and morphological characteristics of gels formed by chiral retro-dipeptidic gelators in the form of terminal diacids (1a-5a) and their dimethyl ester (1b-5b) and dicarboxamide (1c-5c) derivatives. Terminal free acid retro-dipeptides (S,S)-bis(LeuLeu) 1a, (S,S)-bis(PhgPhg) 3a and (S,S)-bis(PhePhe) 5a showed moderate to excellent gelation of highly polar water/DMSO and water/DMF solvent mixtures. Retro-peptides incorporating different amino acids (S,S)-(LeuPhg) 2a and (S,S)-(PhgLeu) 4a showed no or very weak gelation. Different gelation effectiveness was found for racemic and single enantiomer gelators. The heterochiral (S,R)-1c diastereoisomer is capable of immobilizing up to 10 and 4 times larger volumes of dichloromethane/DMSO and toluene/DMSO solvent mixtures compared to homochiral (S,S)-1c. Based on the results of (1)H NMR, FTIR, CD investigations, molecular modeling and XRPD studies of diasteroisomeric diesters (S,S)-1b/(S,R)-1b and diacids (S,S)-1b/(S,R)-1a, a basic packing model in their gel aggregates is proposed. The intermolecular hydrogen bonding between extended gelator molecules utilizing both, the oxalamide and peptidic units and layered organization were identified as the most likely motifs appearing in the gel aggregates. Molecular modeling studies of (S,S)-1a/(S,R)-1a and (S,S)-1b/(S,R)-1b diasteroisomeric pairs revealed a decisive stereochemical influence yielding distinctly different low energy conformations: those of (S,R)-diastereoisomers with lipophilic i-Bu groups and polar carboxylic acid or ester groups located on the opposite sides of the oxalamide plane resembling bola amphiphilic structures and those of (S,S)-diasteroisomers possessing the same groups located at both sides of the oxalamide plane. Such conformational characteristics were found to strongly influence both, gelator effectiveness and morphological characteristics of gel aggregates.

Dataset Information

Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints.

Publications

Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints.

Similar Datasets

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