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One of the goals of computational chemists is to automate the de novo design of bioactive molecules. Despite significant advances in computational approaches to ligand design and binding energy evaluation, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design endeavor. We propose an evolutionary tool for de novo peptide design, based on the evaluation of energies for peptide binding to a user-defined protein surface patch. Special emphasis has been placed on the evaluation of the proposed peptides, leading to two different evaluation heuristics. The software developed was successfully tested on the design of ligands for the proteins prolyl oligopeptidase, p53, and DNA gyrase.

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“…ADAPT [31] is a program for the total design of small organic molecules. The underlying mechanism uses docking as part of the fitness measure.…”

Section: Related Workmentioning

confidence: 99%

ENPDA: an evolutionary structure-based de novo peptide design algorithm

Belda

Madurga

Llorà

et al. 2005

J Comput Aided Mol Des

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“…An even more promising approach for ligand "de novo" design is based on the use of the so-called "evolutionary algorithms" [95,96,97,98]. To illustrate this technique, we have chosen the method used in the laboratory of one of the authors for the design of peptides that bind specifically to predetermined protein-surface patches [95,99].…”

Section: Docking Algorithms and Virtual Screeningmentioning

confidence: 99%

Old and New Strategies for the Discovery of Antibacterial Agents

Vila¹,

Sánchez‐Céspedes²,

Giralt

2005

CMCAIA

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In this review we have tried to present a complete and integrated picture of the old and new ways to discover antibacterial agents. The development of new antibacterial agents can be made from derivatives of known antibacterial agents or by identification of novel agents active against previously unexploited targets. The genetic and biochemical basis of resistance to most classes of antibacterial agents is now known and this has been important in the design of a rational strategy that can be used to counteract resistance. This strategy can follow two approaches: i. Modification of the basic structure of the antibacterial agent, which circumvents antibacterial resistant mechanisms, and ii. Development of a compound inhibiting the mechanism of resistance for an antibacterial agent, hence the concomitant administration of the antibacterial agent plus the inhibitor, as a co-drug, will potentiate this activity. There are also two main approaches to find new protein targets: 1. Classical and, 2. Genomic. The first includes the study of secondary metabolites of bacteria and fungi with antibacterial activity, and it has now been expanded to include plant extracts and marine macro-and microorganisms, as well as non-cultivable soil bacteria. Recent tools such as comparative genomic, combinatorial chemistry, and computerized modelling have helped in the development of new antibacterial agents. Finally, other approaches, such as bacteriophages, antisense RNA and proteins involved in pathogenicity to find new antibacterial drugs are currently investigated.

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“…GROW, 27 BOMB, 28,29 FOG 30 ), ( iv ) genetic or evolutionary algorithms (e.g. TOPAS, 31 ADAPT, 32 LEA3D, 33,34 MEGA, 35 AutoGrow 36 ), ( v ) combinatorial library searches (e.g. CombiDOCK, 37,38 FORECASTER, 39 MAntA 40 ), and ( vi ) a combination of methods (e.g.…”

Section: Introductionmentioning

confidence: 99%

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets

et al. 2017

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De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK’s advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known x-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ~13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIV gp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets.

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Cited by 126 publications

References 26 publications

ENPDA: an evolutionary structure-based de novo peptide design algorithm

ENPDA: an evolutionary structure-based de novo peptide design algorithm

Old and New Strategies for the Discovery of Antibacterial Agents

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets

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