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

Belda, Ignasi; Madurga, Sergio; Llorà, Xavier; Martinell, Marc; Tarragó, Teresa; Piqueras, Mireia; Nicolás, Ernesto; Giralt, Ernest

doi:10.1007/s10822-005-9015-1

Cited by 39 publications

(28 citation statements)

References 61 publications

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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%

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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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Section: Docking Algorithms and Virtual Screeningmentioning

confidence: 99%

“…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]. We start with a library of n randomly generated distinct peptides.…”

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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“…Although this approach can be used without three-dimensional (3D) structural information about the target protein, it requires laborious experimental procedures, including library constructions and the screening of bioactive peptide ligands. In this respect, if information on the structure and the active site of the target protein is known, an in silico approach based on the 3D structure of the target protein is a useful approach to designing the peptide ligand (Belda et al, 2005;Rubinstein and Niv, 2009). …”

Section: Introductionmentioning

confidence: 99%

Target validation and ligand development for a pathogenic fungal profilin, using a knock‐down strain of pathogenic yeast Candida glabrata and structure‐based ligand design

Ueno

Tsutsumi

Chibana

2010

Yeast

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The emergence of antifungal drug resistance is triggering vigorous searches for novel antifungal targets and lead compounds. In this study, we focused on fungal profilin, which is a small actin control protein sharing limited homology to human profilin. To validate its potentiality as a target, a profilin-conditional mutant of the pathogenic yeast Candida glabrata was constructed, using a regulatable Tet promoter, and its growth was monitored in vitro. Repression of profilin expression led to severe growth defect, demonstrating the potential of this protein as a novel antifungal target. Next, novel peptides binding to the active interface of profilin were designed by computer simulation. ELISA analysis showed that these peptides did bind to the wild-type profilin but bound less strongly to a profilin with amino acid substitutions at the active interface. Hence, we show here that profilin is a potential antifungal target and offer novel peptide ligands.

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“…[24][25][26][27][28][29][30][31] However, there are only a few methods, such as SYNOPSIS, ENPDA, ADAPT, and a multiobjective graph evolution method recently developed by Pattichis et al, [32][33][34][35][36] that use docking scores as fitness criteria in the selection and optimization of putative drug candidate molecules. EAISFD is a method that combines the EA-inventor de novo design engine with the molecular docking tool Surflex-Dock.…”

Section: Introductionmentioning

confidence: 99%

GARLig: A Fully Automated Tool for Subset Selection of Large Fragment Spaces via a Self-Adaptive Genetic Algorithm

Pfeffer

Fober

Hüllermeier

et al. 2010

J. Chem. Inf. Model.

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In combinatorial chemistry, molecules are assembled according to combinatorial principles by linking suitable reagents or decorating a given scaffold with appropriate substituents from a large chemical space of starting materials. Often the number of possible combinations greatly exceeds the number feasible to handle by an in-depth in silico approach or even more if it should be experimentally synthesized. Therefore, powerful tools to efficiently enumerate large chemical spaces are required. They can be provided by genetic algorithms, which mimic Darwinian evolution. GARLig (genetic algorithm using reagents to compose ligands) has been developed to perform subset selection in large chemical compound spaces subject to target-specific 3D-scoring criteria. GARLig uses different scoring schemes, such as AutoDock4 Score, GOLDScore, and DrugScore(CSD), as fitness functions. Its genetic parameters have been optimized to characterize combinatorial libraries with respect to the binding to various targets of pharmaceutical interest. A large tripeptide library of 20(3) members has been used to profile amino acid frequencies in putative substrates for trypsin, thrombin, factor Xa, and plasmin. A peptidomimetic scaffold assembled from a selection of a 25(3) building block was used to test the performance of the evolutionary algorithm in suggesting potent inhibitors of the enzyme cathepsin D. In a final case study, our program was used to characterize and rank a combinatorial drug-like library comprising 33,750 potential thrombin inhibitors. These case studies demonstrate that GARLig finds experimentally confirmed potent leads by processing a significantly smaller subset of the fully enumerated combinatorial library. Furthermore, the profiles of amino acids computed by the genetic algorithm match the observed amino acid frequencies found by screening peptide libraries in substrate cleavage assays.

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

Cited by 39 publications

References 61 publications

Old and New Strategies for the Discovery of Antibacterial Agents

Old and New Strategies for the Discovery of Antibacterial Agents

Target validation and ligand development for a pathogenic fungal profilin, using a knock‐down strain of pathogenic yeast Candida glabrata and structure‐based ligand design

GARLig: A Fully Automated Tool for Subset Selection of Large Fragment Spaces via a Self-Adaptive Genetic Algorithm

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