Evolutionary algorithms and de novo peptide design

Belda, Ignasi; Llorà, Xavier; Giralt, Ernest

doi:10.1007/s00500-005-0487-7

Cited by 13 publications

(8 citation statements)

References 41 publications

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“…The optimization algorithm runs in this case in a blackbox fashion. Belda et al [3] did a through study on the use of EAs in De Novo peptide design. After analyzing the problem structure, the authors claimed the superiority of EAs for searching chemical spaces to synthesize peptides.…”

Section: Boamentioning

confidence: 99%

“…Accordingly, a high performance parallel implementation for fragment-based design with EDAs is essential to achieve high quality structures. This paper capitalizes on the robustness of GPUs and strong potential of BOA (as shown by Belda et al [3]) to construct an end-to-end system for De Novo fragment-based drug design. In addition, as shown be-low in the results section, the overlapping volume between the target structure and the generated molecules implies the need for a larger the number of individuals which can exchange genetic material.…”

Section: Boamentioning

confidence: 99%

“…Several projects Utilized evolutionary algorithms in general to apply virtual screening. Belda et al [2] used BOA among other EDAs to tackle the docking problem, however the authors reported relatively poor performance for BOA as BOA needs a large population to construct a well-representing BN. This was the motive to utilize GPUs and thus being capable of using large population sizes to overcome this limitation in BOA.…”

Section: Introductionmentioning

confidence: 99%

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A GPU Accelerated Fragment-based De Novo Ligand Design by a Bayesian Optimization Algorithm

Wahib

Munawar

Munetomo

et al. 2012

IPSJ Transactions on Bioinformatics

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De Novo ligand design is an automatic fragment-based design of molecules within a protein binding site of a known structure. A Bayesian Optimization Algorithm (BOA), a meta-heuristic algorithm, is introduced to join predocked fragments with a user-supplied list of fragments. A novel feature proposed is the simultaneous optimization of force field energy and a term enforcing 3D-overlap to known binding mode(s). The performance of the algorithm is tested on Liver X receptors (LXRs) using a library of about 14,000 fragments and the binding mode of a known heterocyclic phenyl acetic acid to bias the design. We further introduce the use of GPU (Graphics Processing Unit) to overcome the excessive time required in evaluating each possible fragment combination. We show how the GPU utilization enables experimenting larger fragment sets and target receptors for more complex instances. The results show how the nVidia's Tesla C2050 GPU was utilized to enable the generation of complex agonists effectively. In fact, eight of the 1,809 molecules designed for LXRs are found in the ZINC database of commercially available compounds.

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Section: Boamentioning

confidence: 99%

Section: Boamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A GPU Accelerated Fragment-based De Novo Ligand Design by a Bayesian Optimization Algorithm

Wahib

Munawar

Munetomo

et al. 2012

IPSJ Transactions on Bioinformatics

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“…They are: Darwinist genetic algorithm [4], Lamarckian genetic algorithm [5] where for the local search we have used evolutionary strategies (1 + 1) [24], population-based incremental learning [6] and Bayesian Optimization Algorithm [7]. Details on how we have used these algorithms can be found in [25], as well as in the previous listed references.…”

Section: The Problemmentioning

confidence: 99%

Computer-Aided Peptide Evolution for Virtual Drug Design

Belda

Llorà

Martinell

et al. 2004

Genetic and Evolutionary Computation – GECCO 2004

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Abstract.One of the goals of computational chemistry is the automated de novo design of bioactive molecules. Despite significant progress in computational approaches to ligand design and efficient evaluation of binding energy, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design issue. A reliable framework for obtaining ligands via evolutionary algorithms has been implemented. It provides an automatic tool for peptide de novo design, based on protein surface patches defined by user. A special emphasis has been given to the evaluation of the proposed peptides. Hence, we have devised two different evaluation heuristics to carry out this task. Then, we have tested the proposed framework in the design of ligands for the protein Prolyl oligopetidase, p53, and DNA Gyrase.

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“…GA has been applied to a wide range of problems, including automated drug design, but mostly with regard to small molecule modifications [ 29 – 31 ]. In the case of a peptide molecule, the combination of GA with molecular docking software has been used to automate de novo peptide design to target specific proteins [ 32 – 34 ]. These methods create new peptides containing 4–6 residues.…”

Section: Introductionmentioning

confidence: 99%

Computational Design of Hypothetical New Peptides Based on a Cyclotide Scaffold as HIV gp120 Inhibitor

et al. 2015

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Cyclotides are a family of triple disulfide cyclic peptides with exceptional resistance to thermal/chemical denaturation and enzymatic degradation. Several cyclotides have been shown to possess anti-HIV activity, including kalata B1 (KB1). However, the use of cyclotides as anti-HIV therapies remains limited due to the high toxicity in normal cells. Therefore, grafting anti-HIV epitopes onto a cyclotide might be a promising approach for reducing toxicity and simultaneously improving anti-HIV activity. Viral envelope glycoprotein gp120 is required for entry of HIV into CD4+ T cells. However, due to a high degree of variability and physical shielding, the design of drugs targeting gp120 remains challenging. We created a computational protocol in which molecular modeling techniques were combined with a genetic algorithm (GA) to automate the design of new cyclotides with improved binding to HIV gp120. We found that the group of modified cyclotides has better binding scores (23.1%) compared to the KB1. By using molecular dynamic (MD) simulation as a post filter for the final candidates, we identified two novel cyclotides, GA763 and GA190, which exhibited better interaction energies (36.6% and 22.8%, respectively) when binding to gp120 compared to KB1. This computational design represents an alternative tool for modifying peptides, including cyclotides and other stable peptides, as therapeutic agents before the synthesis process.

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Evolutionary algorithms and de novo peptide design

Cited by 13 publications

References 41 publications

A GPU Accelerated Fragment-based De Novo Ligand Design by a Bayesian Optimization Algorithm

A GPU Accelerated Fragment-based De Novo Ligand Design by a Bayesian Optimization Algorithm

Computer-Aided Peptide Evolution for Virtual Drug Design

Computational Design of Hypothetical New Peptides Based on a Cyclotide Scaffold as HIV gp120 Inhibitor

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