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. 2024 Mar 14:2024:gigabyte114.
doi: 10.46471/gigabyte.114. eCollection 2024.

Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal

Lipsa Priyadarsinee  1   2 , Esther Jamir  1 , Selvaraman Nagamani  1   2 , Hridoy Jyoti Mahanta  1   2 , Nandan Kumar  1 , Lijo John  1 , Himakshi Sarma  1 , Asheesh Kumar  1 , Anamika Singh Gaur  3 , Rosaleen Sahoo  1   2 , S Vaikundamani  1 , N Arul Murugan  4 , U Deva Priyakumar  5 , G P S Raghava  4 , Prasad V Bharatam  6 , Ramakrishnan Parthasarathi  2   3 , V Subramanian  7 , G Madhavi Sastry  8 , G Narahari Sastry  1   2   9
Affiliations

Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal

Lipsa Priyadarsinee et al. GigaByte. .

Abstract

Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development.

Availability & implementation: The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1.
Figure 1.
The home page of MPDSCOVID-19. The portal can be accessed at https://mpds.neist.res.in:8085. The MPDSCOVID-19 has been structured in Data library, Data processing, Data analysis, and Advanced modules.
Figure 2.
Figure 2.
The architecture of Galaxy core components and tool configuration.
Figure 3.
Figure 3.
The Galaxy architecture of MPDSCOVID-19. The users can upload their data (i.e., macro and small molecules) or select from the MPDS data library. The uploaded or selected information will be available in the Galaxy history panel. The users can submit the calculation in the main window, and the results will appear in the history panel.
Figure 4.
Figure 4.
The diagrammatic representation depicting various disease-specific information incorporated in the MPDSCOVID-19 disease library.
Figure 5.
Figure 5.
Evolution of the MPDSCOVID-19 portal, in terms of time and features, with the integration of new disease-dependent modules (shown in green headers and boxes) and independent modules (shown in red headers and boxes). The MPDSTB and MPDSDM were developed in Galaxy 16.01 (released in 2016), while MPDSCOVID-19 was developed with Galaxy 19.05 (released in 2019). There were 35 tools from the Galaxy Toolshed that were integrated into MPDSTB and MPDSDM, whereas 59 tools from the Galaxy Toolshed were integrated into MPDSCOVID-19. Compound Library (https://mpds.neist.res.in:8086/), Antiviral Prediction Tool (http://acds.neist.res.in:8501/), and Fragment Library are tools/libraries developed by the group and have been integrated into MPDSCOVID-19. All modules shown in MPDSCOVID-19 are additions to the earlier versions from 2017 and 2018.
Figure 6.
Figure 6.
The scheme of the architecture of data integration and manual curation, along with the use of an indigenously developed compound library, fragment library, and antiviral prediction machine learning model.
Figure 7.
Figure 7.
The diagram enlists different modules available in the MPDSCOVID-19 portal. Galaxy allows the development of workflows by integrating modules to address the problems posed in the area, such as drug discovery. Workflows to address such problems are depicted in some representative case studies (six of them), described in the supporting information hosted in GitHub (Figure S1a–S1f in [15]).
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