A two-step process for in silico screening to assess the performance of qRTPCR kits against variant strains of SARS-CoV-2.

BACKGROUND: Since inception of the COVID-19 pandemic, early detection and isolation of positive cases is one of the key strategies to restrict disease transmission. Real time reverse transcription polymerase chain reaction (qRTPCR) has been the mainstay of diagnosis. Most of the qRTPCR kits were designed against the target genes of original strain of SARS-CoV-2. However, with the emergence of variant strains of SARS-CoV-2, sensitivity of the qRTPCR assays has reportedly reduced. In view of this, it is critical to continuously monitor the performance of the qRTPCR kits in the backdrop of variant strains of SARS-CoV-2. Real world monitoring of assay performance is challenging. Therefore, we developed a two-step in-silico screening process for evaluating the performance of various qRTPCR kits used in India. RESULTS: We analysed 73 qRT-PCR kits marketed in India, against the two SARS-CoV-2 VoCs. Sequences of both Delta (B.1.617.2) and Omicron (B.1.1.529) VoCs submitted to GISAID within a specific timeframe were downloaded, clustered to identify unique sequences and aligned with primer and probe sequences. Results were analysed following a two-step screening process. Out of 73 kits analysed, seven were unsatisfactory for detection of both Delta and Omicron VoCs, 10 were unsatisfactory for Delta VoC whereas 2 were unsatisfactory for only Omicron VoC. CONCLUSION: Overall, we have developed a useful screening process for evaluating the performance of qRTPCR assays against Delta and Omicron VoCs of SARS-CoV-2 which can be used for detecting SARS-CoV-2 VoCs that may emerge in future and can also be redeployed for other evolving pathogens of public health importance.

Development of the first DNA database and identification portal for identification of Unidentified bodies in India - UMID.

Identifying missing persons and unidentified dead bodies is a well-documented global problem in recent years. To curb this issue, countries such as the USA, UK, and Australia already have well-established DNA databases. Considering the alarming number of unidentified/unclaimed dead bodies reported in India every year, it is evident that the current practices are not sufficient to establish their identities. Forensic medicine professionals are ethically, morally, and dutybound to collect information about missing and unidentified persons and work with the government agencies to determine their identity. Concerning the social and public interest, we have developed the first-ever identification portal and DNA database of unidentified dead bodies autopsied at the Department of Forensic Medicine and Toxicology, AIIMS, New Delhi, India. After the investigation officer's informed consent, biological samples from unidentified dead bodies and a detailed phenotypic description, anthropological data and other visual characteristics of the deceased are recorded at the time of autopsy. This information is uploaded on our database which is available for public access, and the genotypic information generated through STR analysis is only available for internal usage.Claimants (biological relatives) may browse through the URL (https://umid-aiims.icmr.org.in/), and if they wish to claim an unidentified dead body, they may approach as per the given guidelines. The DNA profiles generated include a total of 16 STRs (15 autosomal tetranucleotide microsatellite STRs and 1 Sex Chromosome Specific STR). The claimant's STR profile is run through the questioned database to look for a potential match. If positive, the investigating officer of that particular case is informed for further necessary action. Until December 2020, our database consisted the information of 255 individuals and two unidentified cadavers were identified. This project's success can also lead to a pioneering National DNA database of unidentified and missing persons in India.

GCAC: galaxy workflow system for predictive model building for virtual screening.

BACKGROUND: Traditional drug discovery approaches are time-consuming, tedious and expensive. Identifying a potential drug-like molecule using high throughput screening (HTS) with high confidence is always a challenging task in drug discovery and cheminformatics. A small percentage of molecules that pass the clinical trial phases receives FDA approval. This whole process takes 10-12 years and millions of dollar of investment. The inconsistency in HTS is also a challenge for reproducible results. Reproducible research in computational research is highly desirable as a measure to evaluate scientific claims and published findings. This paper describes the development and availability of a knowledge based predictive model building system using the R Statistical Computing Environment and its ensured reproducibility using Galaxy workflow system. RESULTS: We describe a web-enabled data mining analysis pipeline which employs reproducible research approaches to confront the issue of availability of tools in high throughput virtual screening. The pipeline, named as "Galaxy for Compound Activity Classification (GCAC)" includes descriptor calculation, feature selection, model building, and screening to extract potent candidates, by leveraging the combined capabilities of R statistical packages and literate programming tools contained within a workflow system environment with automated configuration. CONCLUSION: GCAC can serve as a standard for screening drug candidates using predictive model building under galaxy environment, allowing for easy installation and reproducibility. A demo site of the tool is available at http://ccbb.jnu.ac.in/gcac.

Solvent sensitivity of protein aggregation in Cu, Zn superoxide dismutase: a molecular dynamics simulation study.

Misfolding and aggregation of Cu, Zn Superoxide Dismutase (SOD1) is often found in amyotrophic lateral sclerosis (ALS) patients. The central apo SOD1 barrel was involved in protein maturation and pathological aggregation in ALS. In this work, we employed atomistic molecular dynamics (MD) simulations to study the conformational dynamics of SOD1barrel monomer in different concentrations of trifluoroethanol (TFE). We find concentration dependence unusual structural and dynamical features, characterized by the local unfolding of SOD1barrel. This partially unfolded structure is characterized by the exposure of hydrophobic core, is highly dynamic in nature, and is the precursor of aggregation seen in SOD1barrel. Our computational studies supports the hypothesis of the formation of aggregation 'building blocks' by means of local unfolding of apo monomer as the mechanism of SOD1 fibrillar aggregation. The non-monotonic TFE concentration dependence of protein conformational changes was explored through simulation studies. Our results suggest that altered protein conformation and dynamics within its structure may underlie the aggregation of SOD1 in ALS.

QSAR based predictive modeling for anti-malarial molecules.

Malaria is a predominant infectious disease, with a global footprint, but especially severe in developing countries in the African subcontinent. In recent years, drug-resistant malaria has become an alarming factor, and hence the requirement of new and improved drugs is more crucial than ever before. One of the promising locations for antimalarial drug target is the apicoplast, as this organelle does not occur in humans. The apicoplast is associated with many unique and essential pathways in many Apicomplexan pathogens, including Plasmodium. The use of machine learning methods is now commonly available through open source programs. In the present work, we describe a standard protocol to develop molecular descriptor based predictive models (QSAR models), which can be further utilized for the screening of large chemical libraries. This protocol is used to build models using training data sourced from apicoplast specific bioassays. Multiple model building methods are used including Generalized Linear Models (GLM), Random Forest (RF), C5.0 implementation of a decision tree, Support Vector Machines (SVM), K-Nearest Neighbour and Naive Bayes. Methods to evaluate the accuracy of the model building method are included in the protocol. For the given dataset, the C5.0, SVM and RF perform better than other methods, with comparable accuracy over the test data.