Molecular parameter optimization gateway (ParamChem)

Ghosh, Jayeeta; Marru, Suresh; Singh, Nikhil Kumar; Vanomesslaeghe, Kenno; Fan, Yongchang; Pamidighantam, Sudhakar

doi:10.1145/2016741.2016779

Cited by 15 publications

(17 citation statements)

References 17 publications

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“…The ParamChem server (Ghosh et al, 2011) (version 0.9.7.1) was used to generate parameters for the ligands. The SHAKE algorithm was applied to fix the bonds involving hydrogen atoms.…”

Section: Simulationsmentioning

confidence: 99%

A QM protein–ligand investigation of antipsychotic drugs with the dopamine D2 Receptor (D2R)

Salmas

İş

Durdağı

et al. 2017

Journal of Biomolecular Structure and Dynamics

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The dopamine D2 Receptor (D2R) is a member of the G-Protein-Coupled Receptor family and plays a critical role in neurotransmission activities in the human brain. Dysfunction in dopamine receptor signaling may lead to mental health illnesses such as schizophrenia and Parkinson's disease. D2R is the target protein of the commonly used antipsychotic drugs such as risperidone, clozapine, aripiprazole, olanzapine, ziprasidone, and quetiapine. Due to their significant side effects and non-selective profiles, the discovery of novel drugs has become a challenge for researchers working in this field. Recently, our group has focused on the interactions of these drug molecules in the active site of the D2R using different in silico approaches. We here compare the performances of different approaches in estimating the drug binding affinities using quantum chemical approaches. Conformations of drug molecules (ligands) at the binding site of the D2R taken from the preliminary docking studies and molecular dynamics simulations were used to generate protein-ligand interaction models. In a first approach, the BSSE-corrected interaction energies of the ligands with the most critical amino acid Asp114 and with the other amino acids closest to ligands in the binding cavity were calculated separately by density functional theory method in implicit water environment at the M06-2X/6-31 g(d,p) level of the theory. In a second approach, ligand binding affinities were calculated by taking into consideration not only the interaction energies but also deformation and desolvation energies of ligands with surrounding amino acid residues, in a radius of 5 Å of the protein-bound ligand. The quantum mechanically obtained results were compared with the experimentally obtained binding affinity values. We concluded that although H-bond interactions of ligands with Asp114 are the most dominant interaction in the binding site, if van der Waals and steric interactions of ligands which have cumulative effect on the ligand binding are not included in the calculations, the interaction energies are overestimated.

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“…The ParamChem server (Ghosh et al, 2011) (version 0.9.7.1) was used to generate parameters for the ligands. The SHAKE algorithm was applied to fix the bonds involving hydrogen atoms.…”

Section: Simulationsmentioning

confidence: 99%

A QM protein–ligand investigation of antipsychotic drugs with the dopamine D2 Receptor (D2R)

Salmas

İş

Durdağı

et al. 2017

Journal of Biomolecular Structure and Dynamics

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“…The Interpreter can submit and manage pipelines and graphs, but we also have examined its use in managing jobs that require iterative loops, conditionals, and human-in-the-loop executions [4] [5].…”

Section: B System Componentsmentioning

confidence: 99%

“…The ParamChem Science Gateway [5] uses Apache Airavata to address the computational intensive needs of empirical force field parameter optimization for chemical systems. ParamChem use case of Apache Airavata workflow capabilities can be summarized as follows.…”

Section: B the Paramchem Science Gatewaymentioning

confidence: 99%

Apache Airavata: Design and Directions of a Science Gateway Framework

Pierce

Marru

Gunathilake

et al. 2014

2014 6th International Workshop on Science Gateways

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Abstract-This paper provides an overview of the Apache Airavata software system for science gateways. Gateways use Airavata to manage application and workflow executions on a range of backend resources (grids, computing clouds, and local clusters). Airavata's design goal is to provide component abstractions for major tasks required to provide gateway application management. Components are not directly accessed but are instead exposed through a client Application Programming Interface. This design allows gateway developers to take full advantage of Airavata's capabilities, and Airavata developers (including those interested in middleware research) to modify Airavata's implementations and behavior. This is particularly important as Airavata evolves to become a scalable, elastic "platform as a service" for science gateways. We illustrate the capabilities of Airavata through the discussion of usage vignettes. As an Apache Software Foundation project, Airavata's open community governance model is as important as its software base. We discuss how this works within Airavata and how it may be applicable to other distributed computing infrastructure and cyberinfrastructure efforts.

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“…Gateways using Airavata are transitioning to take advantage of the capabilities discussed in Section IV. In this section we discuss two complementary use cases in detail: 1) the integration of MyProxy with the ParamChem [3] gateway and 2) the use of the SSH Key-based approach in the Indiana University Cyber Gateway.…”

Section: Use Cases -Integration With Apache Airavata Science Gatementioning

confidence: 99%

“…Further, each of these tiers may be operated by a distinct group. Ultrascan [2], Paramchem [3], DARE [4], and iPlant [5] are examples that follow this trend. The user interface layer interacts with the middleware layer through an Application Programmer Interface (API).…”

Section: Introductionmentioning

confidence: 99%

A Credential Store for Multi-tenant Science Gateways

Kanewala

Marru

Basney

et al. 2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Abstract-Science Gateways bridge multiple computational grids and clouds, acting as overlay cyberinfrastructure. Gateways have three logical tiers: a user interfacing tier, a resource tier and a bridging middleware tier. Different groups may operate these tiers. This introduces three security challenges. First, the gateway middleware must manage multiple types of credentials associated with different resource providers. Second, the separation of the user interface and middleware layers means that security credentials must be securely delegated from the user interface to the middleware. Third, the same middleware may serve multiple gateways, so the middleware must correctly isolate user credentials associated with different gateways. We examine each of these three scenarios, concentrating on the requirements and implementation of the middleware layer. We propose and investigate the use of a Credential Store to solve the three security challenges.

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Molecular parameter optimization gateway (ParamChem)

Cited by 15 publications

References 17 publications

A QM protein–ligand investigation of antipsychotic drugs with the dopamine D2 Receptor (D2R)

A QM protein–ligand investigation of antipsychotic drugs with the dopamine D2 Receptor (D2R)

Apache Airavata: Design and Directions of a Science Gateway Framework

A Credential Store for Multi-tenant Science Gateways

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