Protein structure prediction using chemical reaction optimization

Chatterjee, Siddhartha; Smrity, Resheta Ahmed; Islam, Md. Rafiqul

doi:10.1109/iccitechn.2016.7860217

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…This situation leads to PSP researches, that try to foretell the tertiary structure based on the primary one (the amino acids). The research in PSP has come up against significant obstacles in the evolution of technology—it has been an substantial challenge in the past decade …”

Section: Introductionmentioning

confidence: 99%

“…In PSP experiments, the amino acids work as input. There are a handful of chemical forces that act over the protein, like Van der Waals interactions, hydrogen bonds, and one of the most important, the hydrophobic interaction . During the execution, many tertiary structures are generated until achieving the one with the lowest energy, that is the most stable.…”

Section: Introductionmentioning

confidence: 99%

“…During the execution, many tertiary structures are generated until achieving the one with the lowest energy, that is the most stable. The formula in Equation expresses that, where c ∈ C ( s ), where c ( s ) represents the family of valid sequences for a given set, and E ( c ), the energy function of the desired structure

\min = {E (c) | c \in C (s)} .

…”

Section: Introductionmentioning

confidence: 99%

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Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

et al. 2020

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Science Gateways provide portals for experiments execution, regardless of the users' computational background. Nowadays its construction and performance need enhancement in terms of resource provision and task scheduling. We present the Modular Distributed Architecture to support the Protein Structure Prediction (MDAPSP), a Service-Oriented Architecture for management and construction of Science Gateways, with resource provisioning on a heterogeneous environment. The Decision Maker, central module of MDAPSP, defines the best computational environment according to experiment parameters. The proof of concept for MDAPSP is presented in WorkflowSim, with two novel schedulers. Our results demonstrate good Quality of Service (QoS), capable of correctly distributing the workload, fair response times, providing load balance, and overall system improvement. The study case relies on PSP algorithms and the Galaxy framework, with monitoring experiments to show the bottlenecks and critical aspects. K E Y W O R D Soptimization, protein structure prediction, science gateways, scientific workflow, service-oriented architecture, WorkflowSim 1 Softw: Pract Exper. 2020;50:899-924. wileyonlinelibrary.com/journal/spe © 2020 John Wiley & Sons, Ltd. 899 900 MARTINS DE OLIVEIRA et al.Another limitation of scientific gateways is the resource provision for its machines, that affect usability and performance. The problem with resource provisioning is a quest for the best configuration, requiring the smallest investment and minimal overheads. In general, inexperienced users cannot make good decisions on what machines to contract. Limited economic resources complicate the scenario.Cloud computing paradigm is among the principal resources available, as it has many advantages: scalability, pay-as-you-go features, on-demand provisioning, and management by third parties. Multiple providers infrastructure and decentralization of resources are expanding and are expected to create impact on diverse areas. 4 Resource providing and management are one of the challenging aspects of this expansion. 5 Despite its success, cloud is not the only option for provisioning. 6 Computer clusters are reliable infrastructures for conducting experiments, mainly High-Performance Computing (HPC) and concurrent algorithms. The advantages of cluster machines are local management, freedom of configuration, reliable storage, and fast network transfers. 3 For those reasons, we investigate the scientific gateway Galaxy, because it is an adaptable system, capable to operate over different infrastructures, including local workstations.Galaxy is a well-known framework for scientific experiments, with workflow management and reproducibility support. Its tools offer practical solutions and addition of new algorithms according to the evolution of the system. The focus is Biomedical science, protein prediction, gene sequencing, and other bioinformatics-related areas. Its advantages are simple interface, reliability, powerful tools, and an active community. 7 However, as any other...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

\min = {E (c) | c \in C (s)} .

…”

Section: Introductionmentioning

confidence: 99%

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Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

et al. 2020

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“…In the sequence of the execution, many structures are produced until the best one has been achieved, which represents the most stable one. The identification is defined by the structure of lowest energy and can be expressed by the following formula: if

where

represents the family of valid sequences for a given set and

is the energy function of the desired structure, the general formula is defined as

[25] .…”

Section: Introductionmentioning

confidence: 99%

“…Research on prediction of protein structures has faced obstacles, despite the evolution of technology [25] . The main objective of researchers is to improve the prediction quality, however, they rarely focus on computational efficiency or reduction of processing time [26] .…”

Section: Introductionmentioning

confidence: 99%

Selection of computational environments for PSP processing on scientific gateways

Oliveira

Estrella

Delbem

et al. 2018

Heliyon

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Science Gateways have been widely accepted as an important tool in academic research, due to their flexibility, simple use and extension. However, such systems may yield performance traps that delay work progress and cause waste of resources or generation of poor scientific results. This paper addresses an investigation on some of the failures in a Galaxy system and analyses of their impacts. The use case is based on protein structure prediction experiments performed. A novel science gateway component is proposed towards the definition of the relation between general parameters and capacity of machines. The machine-learning strategies used appoint the best machine setup in a heterogeneous environment and the results show a complete overview of Galaxy, a diverse platform organization, and the workload behavior. A Support Vector Regression (SVR) model generated and based on a historic data-set provided an excellent learning module and proved a varied platform configuration is valuable as infrastructure in a science gateway. The results revealed the advantages of investing in local cluster infrastructures as a base for scientific experiments.

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