Dockomatic - automated ligand creation and docking

Abstract

BackgroundThe application of computational modeling to rationally design drugs and characterize macro biomolecular receptors has proven increasingly useful due to the accessibility of computing clusters and clouds. AutoDock is a well-known and powerful software program used to model ligand to receptor binding interactions. In its current version, AutoDock requires significant amounts of user time to setup and run jobs, and collect results. This paper presents DockoMatic, a user friendly Graphical User Interface (GUI) application that eases and automates the creation and management of AutoDock jobs for high throughput screening of ligand to receptor interactions.ResultsDockoMatic allows the user to invoke and manage AutoDock jobs on a single computer or cluster, including jobs for evaluating secondary ligand interactions. It also automates the process of collecting, summarizing, and viewing results. In addition, DockoMatic automates creation of peptide ligand .pdb files from strings of single-letter amino acid abbreviations.ConclusionsDockoMatic significantly reduces the complexity of managing multiple AutoDock jobs by facilitating ligand and AutoDock job creation and management.

Biocloud: A Systemic Review and Classification

“…We categorize this layer into general overview, biocloud framework, biocloud algorithm and biocloud development guidelines. With the widespread popularity of genomic applications, the "bioinformatics bottleneck" issue appears due to the uncertainty about the cost and infrastructure needed to meet increasing demands for large amount sequence analysis [39]. By comparing with traditional scientific data centers, biocloud could offer economies of scale and the ability to adjust to workload variations [3], [39].…”

“…With the widespread popularity of genomic applications, the "bioinformatics bottleneck" issue appears due to the uncertainty about the cost and infrastructure needed to meet increasing demands for large amount sequence analysis [39]. By comparing with traditional scientific data centers, biocloud could offer economies of scale and the ability to adjust to workload variations [3], [39]. Especially, with MapReduce or DryadLINQ based frameworks, cloud infrastructure services could provide users with scalable, highly available alternatives without the burden of managing their traditional counterparts [23].…”

“…Computational biologists could rent these virtual machines, instead of purchasing server, storage and networking hardware. The network of biocloud requires automated analysis pipelines to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2) [39]. These pipelines could be implemented in virtual machine [39].…”

“…The network of biocloud requires automated analysis pipelines to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2) [39]. These pipelines could be implemented in virtual machine [39]. In the field of genome informatics, pipelines or workflows could chain a number of discrete compute and data intensive analysis tasks into sophisticated analysis procedures [42].…”

Antiplatelet pyrazolopyridines derivatives: pharmacological, biochemical and toxicological characterization

“…Õ software 27 and molecular docking was performed using AutoDock 4 Õ software 27,28 . Lamarckian genetic algorithms were used as search parameter to find the best conformation assumed by each docking ligand in a total of 50 energy assessment (generations) 27 .…”