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Clustering molecular dynamics trajectories for optimizing docking experiments.

ABSTRACT: Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20?ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

SUBMITTER: De Paris R

PROVIDER: S-EPMC4385651 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Clustering molecular dynamics trajectories for optimizing docking experiments.

De Paris Renata R Quevedo Christian V CV Ruiz Duncan D DD Norberto de Souza Osmar O Barros Rodrigo C RC

Computational intelligence and neuroscience 20150322

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely ...[more]

PMID: 25873944

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Project description:BACKGROUND:In the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and improve scoring to enhance selectiveness. At the same time, methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. This study introduces a novel method to optimize ensemble docking-based experiments by reducing the size of an InhA FFR model at docking runtime and scaling docking workflow invocations on cloud virtual machines. RESULTS:First, in order to find the most affordable cost-benefit pool of virtual machines, we evaluated the performance of the docking workflow invocations in different configurations of Azure instances. Second, we validated the gains obtained by the proposed method based on the quality of the Reduced Fully-Flexible Receptor (RFFR) models produced using AutoDock4.2. The analyses show that the proposed method reduced the model size by approximately 50% while covering at least 86% of the best docking results from the 74 ligands tested. Third, we tested our novel method using AutoDock Vina, a different docking software, and showed the positive accuracy achieved in the resulting RFFR models. Finally, our results demonstrated that the method proposed optimized ensemble docking experiments and is applicable to different docking software. In addition, it detected new binding modes, which would be unreachable if employing only the rigid structure used to generate the InhA FFR model. CONCLUSIONS:Our results showed that the selective method is a valuable strategy for optimizing ensemble docking-based experiments using different docking software. The RFFR models produced by discarding non-promising snapshots from the original model are accurately shaped for a larger number of ligands, and the elapsed time spent in the ensemble docking experiments are considerably reduced.

Project description:The phytoconstituents of the aqueous extract from Syzygium jambos L. (Alston) leaves were defined using HPLC-PDA-MS/MS and the antioxidant, anti-aging, antibacterial, and anti-biofilm activities of the extract were in silico and in vitro investigated. The antioxidant activities were performed using in vitro DPPH and FRAP assays as well as H2-DCFDA assay in HaCaT cells in which oxidative stress was induced by UVA radiation. Anti-aging activity was tested in vitro, using aging-related enzymes. The antibacterial, anti-biofilm and inhibitory effects on bacterial mobilities (swarming and swimming) were assessed against Pseudomonas aeruginosa. Results showed that S. jambos aqueous extract contained 28 phytochemicals belonging to different metabolite classes, mainly phenolic acids, gallic acid derivatives, flavonoids, and ellagitannins. Mineral content analysis showed that S. jambos leaves contained moderate amounts of nitrogen, potassium, manganese, magnesium, and zinc, relatively low amounts of phosphorus and copper, and high concentration of calcium and iron. The extract displayed strong antioxidant activities in vitro and inhibited UVA-induced oxidative stress in HaCaT cells. Docking the major compounds identified in the extract into the four main protein targets involved in skin aging revealed an appreciable inhibitory potential of these compounds against tyrosinase, elastase, hyaluronidase, and collagenase enzymes. Moreover, molecular dynamic simulations were adopted to confirm the binding affinity of some selected compounds towards the target enzymes. The extract exhibited pronounced in vitro anti-aging effects, compared to kojic acid and quercetin (the reference compounds). It also inhibited the growth of P. aeruginosa, counteracted its ability to form biofilm, and impeded its swarming and swimming mobilities. Altogether, these findings strongly propose S. jambos leaves as a promising source of bioactive metabolites for the development of natural cosmeceutical and dermatological agents.

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Clustering molecular dynamics trajectories for optimizing docking experiments.

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Clustering molecular dynamics trajectories for optimizing docking experiments.

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