Cytochrome P450 2C19 gene polymorphisms (CYP2C19*2 and CYP2C19*3) in chronic myeloid leukemia patients: in vitro and in silico studies

Whole exome sequencing identifies novel variants of PIK3CA and validation of hotspot mutation by droplet digital PCR in breast cancer among Indian population

BACKGROUND

Breast cancer (BC) is the most common malignancy with very high incidence and relatively high mortality in women. The PIK3CA gene plays a pivotal role in the pathogenicity of breast cancer. Despite this, the mutational status of all exons except exons 9 and 20 still remains unknown.

METHODS

This study uses the whole exome sequencing (WES) based approach to identify somatic PIK3CA mutations in Indian BC cohorts. The resultant hotspot mutations were validated by droplet digital PCR (ddPCR). Further, molecular dynamics (MD) simulation was applied to elucidate the conformational and functional effects of hotspot position on PIK3CA protein.

RESULTS

In our cohort, PIK3CA showed a 44.4% somatic mutation rate and was among the top mutated genes. The mutations of PIK3CA were confined in Exons 5, 9, 11, 18, and 20, whereas the maximum number of mutations lies within exons 9 and 20. A total of 9 variants were found in our study, of which 2 were novel mutations observed on exons 9 (p.H554L) and 11 (p.S629P). However, H1047R was the hotspot mutation at exon 20 (20%). In tumor tissues, there was a considerable difference between copy number of wild-type and H1047R mutant was detected by ddPCR. Significant structural and conformational changes were observed during MD simulation, induced due to point mutation at H1047R/L position.

CONCLUSIONS

The current study provides a comprehensive view of novel as well as reported single nucleotide variants (SNVs) in PIK3CA gene associated with Indian breast cancer cases. The mutation status of H1047R/L could serve as a prognostic value in terms of selecting targeted therapy in BC.

Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics

Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.

Structural analysis and conformational dynamics of SOCS1 gene mutations involved in diffuse large B-cell lymphoma

OBJECTIVES

The SOCS1 gene is frequently mutated in primary Diffuse Large B-Cell Lymphoma (DLBCL) patients and is associated with a reduced survival rate. Using various computational techniques, the current study aims to identify Single Nucleotide Polymorphisms (SNPs) in the SOCS1 gene that are associated with the mortality rate of DLBCL patients. This study also evaluates the effects of SNPs on the structural instability of the SOCS1 protein in DLBCL patient.

METHODS

The cBioPortal webserver was used for mutations and determining how the SNP mutations affect the SOCS1 protein with various algorithms (PolyPhen-2.0, Provean, PhD-SNPg, SNPs&GO, SIFT, FATHMM, Predict SNP and SNAP). Five webservers (I-Mutant 2.0, MUpro, mCSM, DUET and SDM) were used for protein instability and the conserved status and were also predicted through different tools (ConSurf, Expasy, SOMPA). Lastly, MD simulations were run on the two chosen mutations (S116N and V128G) using GROMACS 5.0.1 to study how the mutations change the structure of SOCS1.

RESULTS

Among the 93 SOCS1 mutations detected in DLBCL patients, nine mutations were found to have a detrimental effect (damaging/deleterious/pathogenic/altered) on the SOCS1 protein. All the nine selected mutations are in the conserved region and four are on the extended strand site, four on the random coil site and one on the alpha helix position of the secondary protein structure. After anticipating the structural effects of these nine mutations, two were chosen (S116N and V128G) based on mutational frequency, location within the protein, structural effect (primary, secondary and tertiary) on stability and conservation status within the SOCS1 protein. The simulation of a 50 ns time interval revealed that the Rg value of S116N (2.17 nm) is higher than that of WT (1.98 nm), indicating a loss of structural compactness. In the case of the RMSD value, this mutated type (V128G) shows more deviation (1.54 nm) in comparison to the wild-type (2.14 nm) and another mutant type (S116N) (2.12 nm). The average RMSF values of wild-type and mutant types (V128G and S116N) were 0.88 nm, 0.49 nm, and 0.93 nm, respectively. The RMSF result shows that the mutant V128G structure is more stable than the wild-type and mutant S116N structures.

CONCLUSION

Based on all these computational predictions, this study finds that certain mutations, particularly S116N, have a destabilising and robust effect on the SOCS1 protein. These results can be used to learn more about the importance of SOCS1 mutations in DLBCL patients and to develop new ways to treat DLBCL.

Computational investigation reveals that the mutant strains of SARS-CoV2 have differential structural and binding properties

BACKGROUND AND OBJECTIVES

Remarkable infectivity of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) is due to the rapid emergence of various strains which enable the virus to ruling the world. Over the course of SARS-CoV2 pandemic, the scientific communities worldwide are responding to newly emerging genetic variants. However, mechanism behind the persistent infection of these variants is still not known due to the paucity of study of these variants at molecular level. In this scenario, computational methods have immense utility in understanding the molecular and functional properties of different variants.

METHODS

The various mutants (MTs) of SpikeS1 receptor binding domain (RBD) of highly infectious SARS-CoV2 strains were manifested and elucidated the protein structure and binding strength using molecular dynamics (MD) simulation and protein-protein docking approaches.

RESULTS

MD simulation study showed that all MTs exhibited stable structures with altered functional properties. Furthermore, the binding strength of different MTs along with WT (wildtype) was revealed that MTs showed differential binding affinities to host protein with high binding strength exhibited by V367F and V483A MTs.

CONCLUSION

Hence, this study shed light on the molecular basis of infection caused by different variants of SARS-CoV2, which might play an important role in to cease the transmission and pathogenesis of virus and also implicate in rational designing of a specific drug.