Prediction of Deleterious Non-synonymous SNPs of Human STK11 Gene by Combining Algorithms, Molecular Docking, and Molecular Dynamics Simulation

Prediction of deleterious non-synonymous SNPs of human MDC1 gene: an in silico approach

MDC1 (Mediator of DNA damage Checkpoint protein 1) functions to facilitate the localization of numerous DNA damage response (DDR) components to DNA double-strand break sites. MDC1 is an integral component in preserving genomic stability and appropriate DDR regulation. There haven't been systematic investigations of MDC1 mutations that induce cancer and genomic instability. Variations in nsSNPs have the potential to modify the protein chemistry and their function. Describing functional SNPs in disease-associated genes presents a significant conundrum for investigators, it is possible to assess potential functional SNPs before conducting larger population examinations. Multiple sequences and structure-based bioinformatics strategies were implemented in the current in-silico investigation to discern potential nsSNPs of the MDC1 genes. The nsSNPs were identified with SIFT, SNAP2, Align GVGD, PolyPhen-2, and PANTHER, and their stability was determined with MUpro. The conservation, solvent accessibility, and structural effects of the mutations were identified with ConSurf, NetSurfP-2.0, and SAAFEC-SEQ respectively. Cancer-related analysis of the nsSNPs was conducted using cBioPortal and TCGA web servers. The present study appraised five nsSNPs (P1426T, P69S, P194R, P203L, and H131Y) as probably mutilating due to their existence in highly conserved regions and propensity to deplete protein stability. The nsSNPs P194R, P203L, and H131Y were concluded as deleterious and possibly damaging from the 5 prediction tools. The functional nsSNP P194R mutation is associated with skin cutaneous melanoma while no significant records were found for other nsSNPs. The present study concludes that the highly deleterious P194R mutations can potentially induce genomic instability and contribute to various cancers' pathogenesis. Developing drugs targeting these mutations can undoubtedly be advantageous in large population-based studies, particularly in the development of precision medicine.

Structure and dynamics of whole-sequence homology model of ORF3a protein of SARS-CoV-2: An insight from microsecond molecular dynamics simulations

The ORF3a is a large accessory protein in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which plays an important role in virulence and viral replication; especially in inflammasome activation and apoptosis. However,, the existing cryo-EM structure of SARS-CoV-2 ORF3a is incomplete, . making it challenging to understand its structural and functional features. The aim of this study is to investigate the dynamic behaviors of the full-sequence homology model of ORF3a and compare it with the cryo-EM structure using microsecond molecular dynamics simulations. The previous studies indicated that the unresolved residues of the cryo-EM structure are not only involved in the pathogenesis of the SARS-CoV-2 but also exhibit a significant antigenicity. The dynamics scenario of homology model revealed higher RMSD, Rg, and SASA values with stable pattern when compared to the cryo-EM structure. Moreover, the RMSF analysis demonstrated higher fluctuations at specific positions (1-43, 97-110, 172-180, 219-243) in the model structure, whereas the cryo-EM structure displayed lower overall drift (except 1-43) in comparison to the model structure.Secondary structural features indicated that a significant unfolding in the transmembrane domains and β-strand at positions 166 to 172, affecting the stability and compactness of the cryo-EM structure , whereas the model exhibited noticeable unfolding in transmembrane domains and small-coiled regions in the N-terminal. , The results from molecular docking and steered molecular dynamics investigations showed the model structure had a greater number of non-bonding interactions, leading to enhanced stability when compared to the cryo-EM structure. Consequently, higher forces were necessary for unbinding of the baricitinib and ruxolitinib inhibitors from the model structure.. Our findings can help better understanding of the significance of unresolved residues at the molecular level. Additionally, this information can guide researchers for experimental endeavors aimed at completing the full-sequence structure of the ORF3a.Communicated by Ramaswamy H. Sarma.

Impact of protein and small molecule interactions on kinase conformations

Protein kinases act as central molecular switches in the control of cellular functions. Alterations in the regulation and function of protein kinases may provoke diseases including cancer. In this study we investigate the conformational states of such disease-associated kinases using the high sensitivity of the kinase conformation (KinCon) reporter system. We first track BRAF kinase activity conformational changes upon melanoma drug binding. Second, we also use the KinCon reporter technology to examine the impact of regulatory protein interactions on LKB1 kinase tumor suppressor functions. Third, we explore the conformational dynamics of RIP kinases in response to TNF pathway activation and small molecule interactions. Finally, we show that CDK4/6 interactions with regulatory proteins alter conformations which remain unaffected in the presence of clinically applied inhibitors. Apart from its predictive value, the KinCon technology helps to identify cellular factors that impact drug efficacies. The understanding of the structural dynamics of full-length protein kinases when interacting with small molecule inhibitors or regulatory proteins is crucial for designing more effective therapeutic strategies.

Regulation of NS5B Polymerase Activity of Hepatitis C Virus by Target Specific Phytotherapeutics: An In-Silico Molecular Dynamics Approach

Chronic hepatitis caused by the hepatitis C virus (HCV) is closely linked with the advancement of liver disease. The research hypothesis suggests that the NS5B enzyme (non-structural 5B protein) of HCV plays a pivotal role in facilitating viral replication within host cells. Hence, the objective of the present investigation is to identify the binding interactions between the structurally diverse phytotherapeutics and those of the catalytic residue of the target NS5B polymerase protein. Results of our docking simulations reveal that compounds such as arjunolic acid, sesamin, arjungenin, astragalin, piperic acid, piperidine, piperine, acalyphin, adhatodine, amyrin, anisotine, apigenin, cuminaldehyde, and curcumin exhibit a maximum of three interactions with the catalytic residues (Asp 220, Asp 318, and Asp 319) present on the Hepatitis C virus NS5B polymerase of HCV. Molecular dynamic simulation, particularly focusing on the best binding lead compound, arjunolic acid (-8.78 kcal/mol), was further extensively analyzed using RMSD, RMSF, Rg, and SASA techniques. The results of the MD simulation confirm that the NS5B-arjunolic acid complex becomes increasingly stable from 20 to 100 ns. The orientation of both arjunolic acid and sofosbuvir triphosphate (standard) within the active site was investigated through DCCM, PCA, and FEL analysis, indicating highly stable interactions of the lead arjunolic acid with the catalytic region of the NS5B enzyme. The findings of our current investigation suggest that bioactive therapeutics like arjunolic acid could serve as promising candidates for limiting the NS5B polymerase activity of the hepatitis C virus, offering hope for the future of HCV treatment.

Structural and functional effects of the L84S mutant in the SARS-COV-2 ORF8 dimer based on microsecond molecular dynamics study

The L84S mutation has been observed frequently in the ORF8 protein of SARS-CoV-2, which is an accessory protein involved in various important functions such as virus propagation, pathogenesis, and evading the immune response. However, the specific effects of this mutation on the dimeric structure of ORF8 and its impacts on interactions with host components and immune responses are not well understood. In this study, we performed one microsecond molecular dynamics (MD) simulation and analyzed the dimeric behavior of the L84S and L84A mutants in comparison to the native protein. The MD simulations revealed that both mutations caused changes in the conformation of the ORF8 dimer, influenced protein folding mechanisms, and affected the overall structural stability. In particular, the 73YIDI76 motif has found to be significantly affected by the L84S mutation, leading to structural flexibility in the region connecting the C-terminal β4 and β5 strands. This flexibility might be responsible for virus immune modulation.  The free energy landscape (FEL) and principle component analysis (PCA) have also supported our investigation. Overall, the L84S and L84A mutations affect the ORF8 dimeric interfaces by reducing the frequency of protein-protein interacting residues (Arg52, Lys53, Arg98, Ile104, Arg115, Val117, Asp119, Phe120, and Ile121) in the ORF8 dimer.  Our findings provide detail insights for further research in designing structure-based therapeutics against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

LKB1 biology: assessing the therapeutic relevancy of LKB1 inhibitors

Liver Kinase B1 (LKB1), encoded by Serine-Threonine Kinase 11 (STK11), is a master kinase that regulates cell migration, polarity, proliferation, and metabolism through downstream adenosine monophosphate-activated protein kinase (AMPK) and AMPK-related kinase signalling. Since genetic screens identified STK11 mutations in Peutz-Jeghers Syndrome, STK11 mutants have been implicated in tumourigenesis labelling it as a tumour suppressor. In support of this, several compounds reduce tumour burden through upregulating LKB1 signalling, and LKB1-AMPK agonists are cytotoxic to tumour cells. However, in certain contexts, its role in cancer is paradoxical as LKB1 promotes tumour cell survival by mediating resistance against metabolic and oxidative stressors. LKB1 deficiency has also enhanced the selectivity and cytotoxicity of several cancer therapies. Taken together, there is a need to develop LKB1-specific pharmacological compounds, but prior to developing LKB1 inhibitors, further work is needed to understand LKB1 activity and regulation. However, investigating LKB1 activity is strenuous as cell/tissue type, mutations to the LKB1 signalling pathway, STE-20-related kinase adaptor protein (STRAD) binding, Mouse protein 25-STRAD binding, splicing variants, nucleocytoplasmic shuttling, post-translational modifications, and kinase conformation impact the functional status of LKB1. For these reasons, guidelines to standardize experimental strategies to study LKB1 activity, associate proteins, spliced isoforms, post-translational modifications, and regulation are of upmost importance to the development of LKB1-specific therapies. Therefore, to assess the therapeutic relevancy of LKB1 inhibitors, this review summarizes the importance of LKB1 in cell physiology, highlights contributors to LKB1 activation, and outlines the benefits and risks associated with targeting LKB1.

De novo structure prediction of meteorin and meteorin-like protein for identification of domains, functional receptor binding regions, and their high-risk missense variants

Meteorin (Metrn) and Meteorin-like (Metrnl) are homologous secreted proteins involved in neural development and metabolic regulation. In this study, we have performed de novo structure prediction and analysis of both Metrn and Metrnl using Alphafold2 (AF2) and RoseTTAfold (RF). Based on the domain and structural homology analysis of the predicted structures, we have identified that these proteins are composed of two functional domains, a CUB domain and an NTR domain, connected by a hinge/loop region. We have identified the receptor binding regions of Metrn and Metrnl using the machine-learning tools ScanNet and Masif. These were further validated by docking Metrnl with its reported KIT receptor, thus establishing the role of each domain in the receptor interaction. Also, we have studied the effect of non-synonymous SNPs on the structure and function of these proteins using an array of bioinformatics tools and selected 16 missense variants in Metrn and 10 in Metrnl that can affect the protein stability. This is the first study to comprehensively characterize the functional domains of Metrn and Metrnl at their structural level and identify the functional domains, and protein binding regions. This study also highlights the interaction mechanism of the KIT receptor and Metrnl. The predicted deleterious SNPs will allow further understanding of the role of these variants in modulating the plasma levels of these proteins in disease conditions such as diabetes.Communicated by Ramaswamy H. Sarma.

A computational approach for structural and functional analyses of disease-associated mutations in the human CYLD gene

Tumor suppressor cylindromatosis protein (CYLD) regulates NF-κB and JNK signaling pathways by cleaving K63-linked poly-ubiquitin chain from its substrate molecules and thus preventing the progression of tumorigenesis and metastasis of the cancer cells. Mutations in CYLD can cause aberrant structure and abnormal functionality leading to tumor formation. In this study, we utilized several computational tools such as PANTHER, PROVEAN, PredictSNP, PolyPhen-2, PhD-SNP, PON-P2, and SIFT to find out deleterious nsSNPs. We also highlighted the damaging impact of those deleterious nsSNPs on the structure and function of the CYLD utilizing ConSurf, I-Mutant, SDM, Phyre2, HOPE, Swiss-PdbViewer, and Mutation 3D. We shortlisted 18 high-risk nsSNPs from a total of 446 nsSNPs recorded in the NCBI database. Based on the conservation profile, stability status, and structural impact analysis, we finalized 13 nsSNPs. Molecular docking analysis and molecular dynamic simulation concluded the study with the findings of two significant nsSNPs (R830K, H827R) which have a remarkable impact on binding affinity, RMSD, RMSF, radius of gyration, and hydrogen bond formation during CYLD-ubiquitin interaction. The principal component analysis compared native and two mutants R830K and H827R of CYLD that signify structural and energy profile fluctuations during molecular dynamic (MD) simulation. Finally, the protein-protein interaction network showed CYLD interacts with 20 proteins involved in several biological pathways that mutations can impair. Considering all these in silico analyses, our study recommended conducting large-scale association studies of nsSNPs of CYLD with cancer as well as designing precise medications against diseases associated with these polymorphisms.

Two missense STK11 gene variations impaired LKB1/adenosine monophosphate-activated protein kinase signaling in Peutz-Jeghers syndrome

BACKGROUND

Peutz-Jeghers syndrome (PJS) is a rare hereditary neoplastic disorder mainly associated with serine/threonine kinase 11 (STK11/LKB1) gene mutations. Preimplantation genetic testing can protect a patient's offspring from mutated genes; however, some variations in this gene have been interpreted as variants of uncertain significance (VUS), which complicate reproductive decision-making in genetic counseling.

AIM

To identify the pathogenicity of two missense variants and provide clinical guidance.

METHODS

Whole exome gene sequencing and Sanger sequencing were performed on the peripheral blood of patients with PJS treated at the Reproductive and Genetic Hospital of Citic-Xiangya. Software was employed to predict the protein structure, conservation, and pathogenicity of the two missense variation sites in patients with PJS. Additionally, plasmids were constructed and transfected into HeLa cells to observe cell growth. The differences in signal pathway expression between the variant group and the wild-type group were compared using western blot and immunohistochemistry. Statistical analysis was performed using one-way analysis of variance. P < 0.05 was considered statistically significant.

RESULTS

We identified two missense STK11 gene VUS [c.889A>G (p.Arg297Gly) and c.733C>T (p.Leu245Phe)] in 9 unrelated PJS families who were seeking reproductive assistance. The two missense VUS were located in the catalytic domain of serine/threonine kinase, which is a key structure of the liver kinase B1 (LKB1) protein. In vitro experiments showed that the phosphorylation levels of adenosine monophosphate-activated protein kinase (AMPK) at Thr172 and LKB1 at Ser428 were significantly higher in transfected variation-type cells than in wild-type cells. In addition, the two missense STK11 variants promoted the proliferation of HeLa cells. Subsequent immunohistochemical analysis showed that phosphorylated-AMPK (Thr172) expression was significantly lower in gastric, colonic, and uterine polyps from PJS patients with missense variations than in non-PJS patients. Our findings indicate that these two missense STK11 variants are likely pathogenic and inactivate the STK11 gene, causing it to lose its function of regulating downstream phosphorylated-AMPK (Thr172), which may lead to the development of PJS. The identification of the pathogenic mutations in these two clinically characterized PJS patients has been helpful in guiding them toward the most appropriate mode of pregnancy assistance.

CONCLUSION

These two missense variants can be interpreted as likely pathogenic variants that mediated the onset of PJS in the two patients. These findings not only offer insights for clinical decision-making, but also serve as a foundation for further research and reanalysis of missense VUS in rare diseases.

Identification and In-Silico study of non-synonymous functional SNPs in the human SCN9A gene

Single nucleotide polymorphisms are the most common form of DNA alterations at the level of a single nucleotide in the genomic sequence. Genome-wide association studies (GWAS) were carried to identify potential risk genes or genomic regions by screening for SNPs associated with disease. Recent studies have shown that SCN9A comprises the NaV1.7 subunit, Na+ channels have a gene encoding of 1988 amino acids arranged into 4 domains, all with 6 transmembrane regions, and are mainly found in dorsal root ganglion (DRG) neurons and sympathetic ganglion neurons. Multiple forms of acute hypersensitivity conditions, such as primary erythermalgia, congenital analgesia, and paroxysmal pain syndrome have been linked to polymorphisms in the SCN9A gene. Under this study, we utilized a variety of computational tools to explore out nsSNPs that are potentially damaging to heath by modifying the structure or activity of the SCN9A protein. Over 14 potentially damaging and disease-causing nsSNPs (E1889D, L1802P, F1782V, D1778N, C1370Y, V1311M, Y1248H, F1237L, M936V, I929T, V877E, D743Y, C710W, D623H) were identified by a variety of algorithms, including SNPnexus, SNAP-2, PANTHER, PhD-SNP, SNP & GO, I-Mutant, and ConSurf. Homology modeling, structure validation, and protein-ligand interactions also were performed to confirm 5 notable substitutions (L1802P, F1782V, D1778N, V1311M, and M936V). Such nsSNPs may become the center of further studies into a variety of disorders brought by SCN9A dysfunction. Using in-silico strategies for assessing SCN9A genetic variations will aid in organizing large-scale investigations and developing targeted therapeutics for disorders linked to these variations.

A computational analysis to evaluate deleterious SNPs of GSK3β, a multifunctional and regulatory protein, for metabolism, wound healing, and migratory processes

This study focused on GSK-3β, a critical serine/threonine kinase with diverse cellular functions. However, there is limited understanding of the impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on its structure and function. Through an exhaustive in-silico investigation 12 harmful nsSNPs were predicted from a pool of 172 acquired from the NCBI dbSNP database using 12 established tools that detects deleterious SNPs. Consistently, these nsSNPs were discovered in locations with high levels of conservation. Notably, the three harmful nsSNPs F67C, A83T, and T138I were situated in the active/binding site of GSK-3β, which may affect the protein's capacity to bind to substrates and other proteins. Molecular dynamics simulations revealed that the F67C and T138I mutants had stable structures, indicating rigidness, whereas the A83T mutant was unstable. Analysis of secondary structures revealed different modifications in all mutant forms, which may affect the stability, functioning, and interactions of the protein. These mutations appear to alter the structural dynamics of GSK-3β, which may have functional ramifications, such as the formation of novel secondary structures and variations in coil-to-helix transitions. In conclusion, this study illuminates the possible structural and functional ramifications of these GSK-3 nsSNPs, revealing how protein compactness, stiffness, and interactions may affect biological activities.

Revealing anti-fungal potential of plant-derived bioactive therapeutics in targeting secreted aspartyl proteinase (SAP) of Candida albicans: a molecular dynamics approach

Candida species have established themselves as a major source of nosocomial infections. Increased expression of secreted aspartyl proteinases (SAP5) plays a crucial role in the pathogenesis of Candida species. Phytotherapeutics continue to serve as a viable resource for discovering novel antifungal agents. Hence the main aim of the present investigation is to explore the possible inhibitory role of the selected bioactive molecules against the SAP5 enzyme of C. albicans using in silico approach. Molecular docking and dynamic simulations were utilized to predict the binding affinity of the lead molecules using the AutoDock and Gromacs in-silico screening tools. Results of preliminary docking simulations show that the compounds hesperidin, vitexin, berberine, adhatodine, piperine, and chlorogenic acid exhibit significant interactions with the core catalytic residues of the target protein. The best binding ligands (hesperidin, vitexin, fluconazole) were subjected to molecular dynamics (MD) and essential dynamics of the trajectories. Results of the MD simulation confirm that the ligand-protein complexes became more stable from 20 ns until 100 ns. The calculated residue-level contributions to the interaction energy along a steady simulation trajectory of all three hits (hesperidin (-132.720 kJ/mol), vitexin (-83.963 kJ/mol) and fluconazole (-98.864 kJ/mol)) ensure greater stability of the leads near the catalytic region. Essential dynamics of PCA and DCCM analysis signifies that the binding of hesperidin and vitexin created a more structurally stable environment in the protein target. The overall outcomes of this study clearly emphasize that the bioactive therapeutics found in medicinal herbs may have remarkable scope in managing Candida infection.

In silico screening of non-synonymous SNPs in human TUFT1 gene

BACKGROUND

Tuftelin 1 (TUFT1) gene is important in the development and mineralization of dental enamel. The study aimed to identify potential functionally deleterious non-synonymous SNPs (nsSNPs) in the TUFT1 gene by using different in silico tools. The deleterious missense SNPs were identified from SIFT, PolyPhen-2, PROVEAN, SNPs & GO, PANTHER, and SNAP2. The stabilization, conservation, and three-dimensional modeling of mutant proteins were analyzed by I-Mutant 3.0, Consurf, and Project HOPE, respectively. The protein-protein interaction using STRING, GeneMANIA for gene-gene interaction, and DynaMut for evaluating the impact of the mutation on protein stability, conformation, and flexibility.

RESULTS

Eight deleterious nsSNPs (E242A, R303W, K182N, K123N, R117W, H289Q, R203W, and Q107R) out of 304 were found to have high-risk damaging effects using six in silico tools. Among them, K182N and K123N alone had increased stability, whereas E242A, R303W, R117W, H289Q, Q107R, and R203W exhibited a decrease in protein stability, based on DDG values. Meanwhile, all the eight deleterious nsSNPs altered the size, charge, hydrophobicity, and spatial organization of the amino acids and predominantly had alpha helix domains. These deleterious variants were located in highly conserved regions except R203W. Protein-protein interaction predicted that TUFT1 interacted with ten proteins that are involved in enamel mineralization and odontogenesis. Gene-gene interaction network showed that TUFT1 is involved in physical interactions, gene co-localization, and pathway interactions. DynaMut ΔΔG values predicted that five nsSNPs were destabilizing the protein, ΔΔG ENCoM values showed a destabilizing effect for all mutants, and seven nsSNPs increased the molecular flexibility of TUFT1.

CONCLUSION

Our study predicted eight functional SNPs that had detrimental effects on the structure and function of the TUFT1 gene. This will aid in the development of candidate deleterious markers as a potential target for disease diagnosis and therapeutic interventions.

Therapeutic Potential of Antiviral Peptides against the NS2B/NS3 Protease of Zika Virus

The NS2B/NS3 protease is highly conserved among various proteases of the Zika virus, making it an important therapeutic target for developing broad-spectrum antiviral drugs. The NS2B/NS3 protease is a crucial enzyme in the replication cycle of Zika virus and plays a significant role in viral maturation and assembly. Inhibiting the activity of this protease can potentially prevent viral replication, making it an attractive target for developing therapies against Zika virus infection. This work screens 429 antiviral peptides in comparison with substrate peptide against the NS2B/NS3 of Zika virus using molecular docking and molecular dynamics (MD) simulation. Based on the docking screening, MD simulation conducted for the best four peptides including AVP0239, AVP0642, AVP0660, and AVP2044, could be effective against NS2B/NS3. These results were compared with the control substrate peptide. Further analysis indicates that AVP0642 and AVP2044 are the most promising candidates. The interaction analysis showed that the catalytic site residues including His51, Asp75, Ser135 and other non-catalytic residues such as Asp129, Asp83, and Asp79 contribute substantial interactions. Hydrogen bonds (41%) and hydrophobic interactions (33%) are observed as the prominent non-covalent interaction prompting the peptide-protein complex formation. Furthermore, the structure-activity relationship (SAR) illustrates that positively charged (Lys, Arg) residues in the peptides dominate the interactions. This study provides the basis for developing novel peptide-based protease inhibitors for Zika virus.

Unraveling the impact of ORF3a Q57H mutation on SARS-CoV-2: insights from molecular dynamics

ORF3a is a conserved accessory protein of SARS-CoV-2, linked to viral infection and pathogenesis, with acquired mutations at various locations. Previous studies have shown that the occurrence of the Q57H mutation is higher in comparison to other positions in ORF3a. This mutation is known to induce conformational changes, yet the extent of structural alteration and its role in the viral adaptation process remain unknown. Here we performed molecular dynamics (MD) simulations of wt-ORF3a, Q57H, and Q57A mutants to analyze structural changes caused by mutations compared to the native protein. The MD analysis revealed that Q57H and Q57A mutants show significant structural changes in the dimer conformation than the wt-ORF3a. This dimer conformer narrows down the ion channel cavity, which reduces Na + or K + permeability leading to decrease the antigenic response that can help the virus to escape the host immune system. Non-bonding interaction analysis shows the Q57H mutant has more interacting residues, resulting in more stability within dimer conformation than the wt-ORF3a and Q57A. Moreover, both mutant dimers (Q57H and Q57A) form a novel salt-bridge interaction at the same position between A:Asp142 and B:Lys61, whereas such an interaction is absent in the wt-ORF3a dimer. We have also noticed that the TM3 domain's flexibility in Q57H is increased because of strong inter-domain interactions of TM1 and TM2 within the dimer conformation. These unusual interactions and flexibility of Q57H mutant can have significant impacts on the SARS-CoV-2 adaptations, virulence, transmission, and immune system evasion. Our findings are consistent with the previous experimental data and provided details information on the structural perturbation in ORF3a caused by mutations, which can help better understand the structural change at the molecular level as well as the reason for the high virulence properties of this variant.Communicated by Ramaswamy H. Sarma.

In silico prediction of deleterious non-synonymous SNPs in STAT3

Background

STAT3, a pleiotropic transcription factor, plays a critical role in the pathogenesis of autoimmunity, cancer, and many aspects of the immune system, as well as having a link with inflammatory bowel disease. Changes caused by non-synonymous single nucleotide polymorphisms (nsSNPs) have the potential to damage the protein's structure and function.

Objective

We identified disease susceptible single nucleotide polymorphisms (SNPs) in STAT3 and predicted structural changes associated with mutants that disrupt normal protein-protein interactions using different computational algorithms.

Methods

Several in silico tools, such as SIFT, PolyPhen v2, PROVEAN, PhD-SNP, and SNPs&GO, were used to determine nsSNPs of the STAT3. Further, the potentially deleterious SNPs were evaluated using I-Mutant, ConSurf, and other computational tools like DynaMut for structural prediction.

Result

417 nsSNPs of STAT3 were identified, 6 of which are considered deleterious by in silico SNP prediction algorithms. Amino acid changes in V507F, R335W, E415K, K591M, F561Y, and Q32K were identified as the most deleterious nsSNPs based on the conservation profile, structural conformation, relative solvent accessibility, secondary structure prediction, and protein-protein interaction tools.

Conclusion

The in silico prediction analysis could be beneficial as a diagnostic tool for both genetic counseling and mutation confirmation. The 6 deleterious nsSNPs of STAT3 may serve as potential targets for different proteomic studies, large population-based studies, diagnoses, and therapeutic interventions.

Unlocking SGK1 inhibitor potential of bis-[1-N,7-N, pyrazolo tetraethoxyphthalimido{-4-(3,5-Dimethyl-4-(spiro-3-methylpyazolo)-1,7-dihydro-1H-dipyrazolo[3,4-b;4',3'-e]pyridin-8-yl)}]p-disubstituted phenyl compounds: a computational study

SGK1 (Serum and Glucocorticoid Regulated Kinase 1), a serine/threonine kinase that is activated by various stimuli, including serum and glucocorticoids. It controls inflammation, apoptosis, hormone release, neuro-excitability and cell proliferation, all of which play an important role in cancer progression and metastasis. SGK1 was recently proposed as a potential drug target for cancer, diabetes, and neurodegenerative diseases. In this study, molecular docking, physiochemical, toxicological properties and molecular dynamic simulation of the Bis-[1-N,7-N, Pyrazolo tetraethoxyphthalimido{-4-(3,5-Dimethyl-4-(spiro-3-methylpyazolo)-1,7-dihydro-1H-dipyrazolo[3,4-b;4',3'-e]pyridin-8-yl)}]p-disubstituted phenyl compoundsand reference EMD638683 against new SGK1 target protein. Compared to the reference inhibitor EMD638683, we choose the best compounds (series 2-6) based on the binding energy (in the range from -11.0 to -10.6 kcal/mol). With the exception of compounds 2 and 6, none of the compounds posed a risk for AMES toxicity or carcinogenicity due to their toxicological properties. 100 ns MD simulation accompanied by MM/PBSA energy calculations and PCA. According to MD simulation results, the binding of compounds 3, 4 and 5 stabilizes the SGK1 structure and causes febrile conformational changes compared to EMD638683. As a result of this research, the final selected compounds 3, 4 and 5 can be used as scaffolds to develop promising SGK1 inhibitors for the treatment of related diseases such as cancer.Communicated by Ramaswamy H. Sarma.

Deleterious single nucleotide polymorphisms (SNPs) of human IFNAR2 gene facilitate COVID-19 severity in patients: a comprehensive in silico approach

In humans, the dimeric receptor complex IFNAR2-IFNAR1 accelerates cellular response triggered by type I interferon (IFN) family proteins in response to viral infection including Coronavirus infection. Studies have revealed the association of the IFNAR2 gene with severe illness in Coronavirus infection and indicated the association of genomic variants, i.e. single nucleotide polymorphisms (SNPs). However, comprehensive analysis of SNPs of the IFNAR2 gene has not been performed in both coding and non-coding region to find the causes of loss of function of IFNAR2 in COVID-19 patients. In this study, we have characterized coding SNPs (nsSNPs) of IFNAR2 gene using different bioinformatics tools and identified deleterious SNPs. We found 9 nsSNPs as pathogenic and disease-causing along with a decrease in protein stability. We employed molecular docking analysis that showed 5 nsSNPs to decrease binding affinity to IFN. Later, MD simulations showed that P136R mutant may destabilize crucial binding with the IFN molecule in response to COVID-19. Thus, P136R is likely to have a high impact on disrupting the structure of the IFNAR2 protein. GTEx portal analysis predicted 14 sQTLs and 5 eQTLs SNPs in lung tissues hampering the post-transcriptional modification (splicing) and altering the expression of the IFNAR2 gene. sQTLs and eQTLs SNPs potentially explain the reduced IFNAR2 production leading to severe diseases. These mutants in the coding and non-coding region of the IFNAR2 gene can help to recognize severe illness due to COVID 19 and consequently assist to develop an effective drug against the infection.Communicated by Ramaswamy H. Sarma.

Large scale peptide screening against main protease of SARS CoV-2

The COVID-19 pandemic has been a public health emergency, with deadly forms constantly emerging around the world, highlighting the dire need for highly effective antiviral therapeutics. Peptide therapeutics show significant potential for this viral disease due to their efficiency, safety, and specificity. Here, two thousand seven hundred eight antibacterial peptides were screened computationally targeting the Main protease (Mpro) of SARS CoV-2. Six top-ranked peptides according to their binding scores, binding pose were investigated by molecular dynamics to explore the interaction and binding behavior of peptide-Mpro complexes. The structural and energetic characteristics of Mpro-DRAMP01760 and Mpro-DRAMP01808 complexes fluctuated less during a 250 ns MD simulation. In addition, three peptides (DRAMP01760, DRAMP01808, and DRAMP01342) bind strongly to Mpro protein, according to the free energy landscape and principal component analysis. Peptide helicity and secondary structure analysis are in agreement with our findings. Interaction analysis of protein-peptide complexes demonstrated that Mpro's residue CYS145, HIS41, PRO168, GLU166, GLN189, ASN142, MET49, and THR26 play significant contributions in peptide-protein attachment. Binding free energy analysis (MM-PBSA) demonstrated the energy profile of interacting residues of Mpro in peptide-Mpro complexes. To summarize, the peptides DRAMP01808 and DRAMP01760 may be highly Mpro specific, resulting disruption in a viral replication and transcription. The results of this research are expected to assist future research toward the development of antiviral peptide-based therapeutics for Covid-19 treatment.

Analyzing PKC Gamma (+ 19,506 A/G) polymorphism as a promising genetic marker for HCV-induced hepatocellular carcinoma

BACKGROUND

HCC is a major health concern worldwide. PKC gamma, a member of the conventional PKC subclass, is involved in many cancer types, but the protein has received little attention in the context of single nucleotide polymorphisms and HCC. Therefore, the study aims to investigate the association of PKC gamma missense SNP with HCV-induced hepatocellular carcinoma.

METHODS

The PKC gamma nsSNPs were retrieved from the ENSEMBL genome browser and the deleterious nsSNPs were filtered out through involvingPredictSNP2, CADD, DANN, FATHMM, FunSeq2 and GWAVA. Among the filtered nsSNPs, nsSNP rs1331262028 was identified to be the most pathogenic one. Through involving I-TASSER, ProjectHOPE, I-Mutant, MUpro, mCSM, SDM, DynaMut and MutPred, the influence of SNP rs1331262028 on protein structure, function and stability was estimated. A molecular Dynamic simulation was run to determine the conformational changes in mutant protein structure compared to wild. The blood samples were collected for genotyping analysis and for assessing ALT levels in the blood.

RESULTS

The study identified for the first time an SNP (rs1331262028) of PRKCG to strongly decrease protein stability and induce HCC. The RMSD, RMSF, and Rg values of mutant and wild types found were significantly different. Based on OR and RR values of 5.194 and 2.287, respectively, genotype analysis revealed a higher correlation between the SNP homozygous wild Typeform, AA, and the disease while patients with genotype AG have higher viral load.

CONCLUSION

Outcomes of the current study delineated PKC gamma SNP rs1331262028 as a genetic marker for HCV-induced HCC that could facilitate disease management after further validation.

Genotyping and In Silico Analysis of Delmarva (DMV/1639) Infectious Bronchitis Virus (IBV) Spike 1 (S1) Glycoprotein

Genetic diversity and evolution of infectious bronchitis virus (IBV) are mainly impacted by mutations in the spike 1 (S1) gene. This study focused on whole genome sequencing of an IBV isolate (IBV/Ck/Can/2558004), which represents strains highly prevalent in Canadian commercial poultry, especially concerning features related to its S1 gene and protein sequences. Based on the phylogeny of the S1 gene, IBV/Ck/Can/2558004 belongs to the GI-17 lineage. According to S1 gene and protein pairwise alignment, IBV/Ck/Can/2558004 had 99.44–99.63% and 98.88–99.25% nucleotide (nt) and deduced amino acid (aa) identities, respectively, with five Canadian Delmarva (DMV/1639) IBVs isolated in 2019, and it also shared 96.63–97.69% and 94.78–97.20% nt and aa similarities with US DMV/1639 IBVs isolated in 2011 and 2019, respectively. Further homology analysis of aa sequences showed the existence of some aa substitutions in the hypervariable regions (HVRs) of the S1 protein of IBV/Ck/Can/2558004 compared to US DMV/1639 isolates; most of these variant aa residues have been subjected to positive selection pressure. Predictive analysis of potential N-glycosylation and phosphorylation motifs showed either loss or acquisition in the S1 glycoprotein of IBV/Ck/Can/2558004 compared to S1 of US DMV/1639 IBV. Furthermore, bioinformatic analysis showed some of the aa changes within the S1 protein of IBV/Ck/Can/2558004 have been predicted to impact the function and structure of the S1 protein, potentially leading to a lower binding affinity of the S1 protein to its relevant ligand (sialic acid). In conclusion, these findings revealed that the DMV/1639 IBV isolates are under continuous evolution among Canadian poultry.

A comprehensive in silico analysis of the deleterious nonsynonymous SNPs of human FOXP2 protein

FOXP2 encodes the forkhead transcription factor that plays a significant role in language development. Single nucleotide polymorphisms in FOXP2 have been linked to speech- language disorder, autism, cancer and schizophrenia. So, scrutinizing the functional SNPs to better understand their association in disease is an uphill task. The purpose of the current study was to identify the missense SNPs which have detrimental structural and functional effects on the FOXP2 protein. Multiple computational tools were employed to investigate the deleterious role of non-synonymous SNPs. Five variants as Y531H, L558P, R536G and R553C were found to be associated with diseases and located at the forkhead domain of the FOXP2 protein. Molecular docking analysis of FOXP2 DNA binding domain with its most common target sequence 5’-CAAATT-3’ predicted that R553C and L558P mutant variants destabilize protein structure by changing protein-DNA interface interactions and disruption of hydrogen bonds that may reduce the specificity and affinity of the binding. Further experimental investigations may need to verify whether this kind of structural and functional variations dysregulate protein activities and induce formation of disease.

Structure and dynamics of membrane protein in SARS-CoV-2

SARS-CoV-2 membrane (M) protein performs a variety of critical functions in virus infection cycle. However, the expression and purification of membrane protein structure is difficult despite tremendous progress. In this study, the 3 D structure is modeled followed by intensive validation and molecular dynamics simulation. The lack of suitable homologous templates (>30% sequence identities) leads us to construct the membrane protein models using template-free modeling (de novo or ab initio) approach with Robetta and trRosetta servers. Comparing with other model structures, it is evident that trRosetta (TM-score: 0.64; TM region RMSD: 2 Å) can provide the best model than Robetta (TM-score: 0.61; TM region RMSD: 3.3 Å) and I-TASSER (TM-score: 0.45; TM region RMSD: 6.5 Å). 100 ns molecular dynamics simulations are performed on the model structures by incorporating membrane environment. Moreover, secondary structure elements and principal component analysis (PCA) have also been performed on MD simulation data. Finally, trRosetta model is utilized for interpretation and visualization of interacting residues during protein-protein interactions. The common interacting residues including Phe103, Arg107, Met109, Trp110, Arg131, and Glu135 in the C-terminal domain of M protein are identified in membrane-spike and membrane-nucleocapsid protein complexes. The active site residues are also predicted for potential drug and peptide binding. Overall, this study might be helpful to design drugs and peptides against the modeled membrane protein of SARS-CoV-2 to accelerate further investigation. Communicated by Ramaswamy H. Sarma.

Identification of the most damaging nsSNPs in the human CFL1 gene and their functional and structural impacts on cofilin-1 protein

The cofilin-1 protein, encoded by CFL1, is an actin-binding protein that regulates F-actin depolymerization and nucleation activity through phosphorylation and dephosphorylation. CFL1 has been implicated in the development of neurodegenerative diseases (Alzheimer's disease and Huntington's disease), neuronal migration disorders (lissencephaly, epilepsy, and schizophrenia), and neural tube closure defects. Mutations in CFL1 have been associated with impaired neural crest cell migration and neural tube closure defects. In our study, various computational approaches were utilized to explore single-nucleotide polymorphisms (SNPs) in CFL1. The Variation Viewer and gnomAD databases were used to retrieve CFL1 SNPs, including 46 nonsynonymous SNPs (nsSNPs). The functional and structural annotation of SNPs was performed using 12 sequence-based web applications, which identified 20 nsSNPs as being the most likely to be deleterious or disease-causing. The conservation of cofilin-1 protein structures was illustrated using the ConSurf and PROSITE web servers, which projected the 12 most deleterious nsSNPs onto conserved domains, with the potential to disrupt the protein's functionality. These 12 nsSNPs were selected for protein structure construction, and the DynaMut/DUET servers predicted that the protein variants V7G, L84P, and L99A were the most likely to be damaging to the cofilin-1 protein structure or function. The evaluation of molecular docking studies demonstrated that the L99A and L84P cofilin-1 variants reduce the binding affinity for actin compared with the native cofilin-1 structure, and molecular dynamic simulation studies confirmed that these variants might destabilize the protein structure. The consequences of putative mutations on protein-protein interactions and post-translational modification sites in the cofilin-1 protein structure were analyzed. This study represents the first complete approach to understanding the effects of nsSNPs within the actin-depolymerizing factor/cofilin family, which suggested that SNPs resulting in L84P (rs199716082) and L99A (rs267603119) variants represent significant CFL1 mutations associated with disease development.

Impact of deleterious missense PRKCI variants on structural and functional dynamics of protein

Protein kinase C iota (PKCɩ) is a novel protein containing 596 amino acids and is also a member of atypical kinase family. The role of PKCɩ has been explored in neurodegenerative diseases, neuroblastoma, ovarian and pancreatic cancers. Single nucleotide polymorphisms (SNPs) have not been studied in PKCɩ till date. The purpose of the current study is to scrutinize the deleterious missense variants in PKCɩ and determine the effect of these variants on stability and dynamics of the protein. The structure of protein PKCɩ was predicted for the first time and post translational modifications were determined. Genetic variants of PKCɩ were retrieved from ENSEMBL and only missense variants were further analyzed because of its linkage with diseases. The pathogenicity of missense variants, effect on structure and function of protein, association with cancer and conservancy of the protein residues were determined through computational approaches. It is observed that C1 and the pseudo substrate region has the highest number of pathogenic SNPs. Variations in the kinase domain of the protein are predicted to alter overall phosphorylation of the protein. Molecular dynamic simulations predicted noteworthy change in structural and functional dynamics of the protein because of these variants. The study revealed that nine deleterious variants can possibly contribute to malfunctioning of the protein and can be associated with diseases. This can be useful in diagnostics and developing therapeutics for diseases related to these polymorphisms.

Repurposing fusion inhibitor peptide against SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving. Although several vaccines were approved, this pandemic is still a major threat to public life. Till date, no established therapies are available against SARS-CoV-2. Peptide inhibitors hold great promise for this viral pathogen due to their efficacy, safety, and specificity. In this study, seventeen antiviral peptides which were known to inhibit SARS-CoV-1 are collected and computationally screened against heptad repeat 1 (HR1) of the SARS-CoV-2 spike protein (S2). Out of 17 peptides, Fp13 and Fp14 showed better binding affinity toward HR1 compared to a control peptide EK1 (a modified pan-coronavirus fusion inhibitor) in molecular docking. To explore the time-dependent interactions of the fusion peptide with HR1, molecular dynamics simulation was performed incorporating lipid membrane. During 100 ns MD simulation, structural and energy parameters of Fp13-HR1 and Fp14-HR1 complexes demonstrated lower fluctuations compared to the control EK1-HR1 complex. Furthermore, principal component analysis and free energy landscape study revealed that these two peptides (Fp13 and Fp14) strongly bind to the HR1 with higher affinity than that of control EK1. Tyr917, Asn919, Gln926, lys933, and Gln949 residues in HR1 protein were found to be crucial residues for peptide interaction. Notably, Fp13, Fp14 showed reasonably better binding free energy and hydrogen bond contribution than that of EK1. Taken together, Fp13 and Fp14 peptides may be highly specific for HR1 which can potentially prevent the formation of the fusion core and could be further developed as therapeutics for treatment or prophylaxis of SARS-CoV-2 infection.

Comprehensive Characterization of the Coding and Non-Coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools

Single nucleotide polymorphisms (SNPs) help to understand the phenotypic variations in humans. Genome-wide association studies (GWAS) have identified SNPs located in the tumor protein 63 (TP63) locus to be associated with the genetic susceptibility of cancers. However, there is a lack of in-depth characterization of the structural and functional impacts of the SNPs located at the TP63 gene. The current study was designed for the comprehensive characterization of the coding and non-coding SNPs in the human TP63 gene for their functional and structural significance. The functional and structural effects of the SNPs were investigated using a wide variety of computational tools and approaches, including molecular dynamics (MD) simulation. The deleterious impact of eight nonsynonymous SNPs (nsSNPs) affecting protein stability, structure, and functions was measured by using 13 bioinformatics tools. These eight nsSNPs are in highly conserved positions in protein and were predicted to decrease protein stability and have a deleterious impact on the TP63 protein function. Molecular docking analysis showed five nsSNPs to reduce the binding affinity of TP63 protein to DNA with significant results for three SNPs (R319H, G349E, and C347F). Further, MD simulations revealed the possible disruption of TP63 and DNA binding, hampering the essential protein function. PolymiRTS study found five non-coding SNPs in miRNA binding sites, and the GTEx portal recognized five eQTLs SNPs in single tissue of the lung, heart (LV), and cerebral hemisphere (brain). Characterized nsSNPs and non-coding SNPs will help researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

Functional assessment of somatic STK11 variants identified in primary human non-small cell lung cancers

Serine/Threonine Kinase 11 (STK11) encodes an important tumor suppressor that is frequently mutated in lung adenocarcinoma. Clinical studies have shown that mutations in STK11 resulting in loss of function correlate with resistance to anti-PD-1 monoclonal antibody therapy in KRAS-driven non-small cell lung cancer (NSCLC), but the molecular mechanisms responsible remain unclear. Despite this uncertainty, STK11 functional status is emerging as a reliable biomarker for predicting non-response to anti-PD-1 therapy in NSCLC patients. The clinical utility of this biomarker ultimately depends upon accurate classification of STK11 variants. For nonsense variants occurring early in the STK11 coding region, this assessment is straightforward. However, rigorously demonstrating the functional impact of missense variants remains an unmet challenge. Here we present data characterizing four STK11 splice-site variants by analyzing tumor mRNA, and 28 STK11 missense variants using an in vitro kinase assay combined with a cell-based p53-dependent luciferase reporter assay. The variants we report were identified in primary human NSCLC biopsies in collaboration with the University of Vermont Genomic Medicine group. Additionally, we compare our experimental results with data from 22 in silico predictive algorithms. Our work highlights the power, utility and necessity of functional variant assessment and will aid STK11 variant curation, provide a platform to assess novel STK11 variants and help guide anti-PD-1 therapy utilization in KRAS-driven NSCLCs.

Investigating the binding affinity, interaction, and structure-activity-relationship of 76 prescription antiviral drugs targeting RdRp and Mpro of SARS-CoV-2

SARS-CoV-2 virus outbreak poses a major threat to humans worldwide due to its highly contagious nature. In this study, molecular docking, molecular dynamics, and structure-activity relationship are employed to assess the binding affinity and interaction of 76 prescription drugs against RNA dependent RNA polymerase (RdRp) and Main Protease (Mpro) of SARS-CoV-2. The RNA-dependent RNA polymerase is a vital enzyme of coronavirus replication/transcription complex whereas the main protease acts on the proteolysis of replicase polyproteins. Among 76 prescription antiviral drugs, four drugs (Raltegravir, Simeprevir, Cobicistat, and Daclatasvir) that are previously used for human immunodeficiency virus (HIV), hepatitis C virus (HCV), Ebola, and Marburg virus show higher binding energy and strong interaction with active sites of the receptor proteins. To explore the dynamic nature of the interaction, 100 ns molecular dynamics (MD) simulation is performed on the selected protein-drug complexes and apo-protein. Binding free energy of the selected drugs is performed by MM/PBSA. Besides docking and dynamics, partial least square (PLS) regression method is applied for the quantitative structure activity relationship to generate and predict the binding energy for drugs. PLS regression satisfactorily predicts the binding energy of the effective antiviral drugs compared to binding energy achieved from molecular docking with a precision of 85%. This study highly recommends researchers to screen these potential drugs in vitro and in vivo against SARS-CoV-2 for further validation of utility.

Identification of potent inhibitors against transmembrane serine protease 2 for developing therapeutics against SARS-CoV-2

In viral binding and entry, the Spike(S) protein of SARS-CoV-2 uses transmembrane serine protease 2 (TMPRSS2) for priming to cleavage themselves. In this study, we have screened 'drug-like' 7476 ligands and found that over thirty ligands can effectively inhibit the TMPRSS-2 better than the control ligand. Finally, the three best drug agents L1, L2, and L6 were selected according to their average binding affinities and fitting score. These ligands interact with Asp435, Cys437, Ser436, Trp461, and Cys465 amino acid residues. The three best candidates and a reported drug Nafamostat mesylate (NAM) were selected to run 250 ns molecular dynamics (MD) simulations. Various properties of ligand-protein interactions obtained from MD simulation such as bonds, angle, dihedral, planarity, coulomb, and van der Waals (VdW) were used for principal component analysis (PCA) calculation. PCA discloses the evidence of the structural similarities to the corresponding complexes of L1, L2, and L6 with the complex of TMPRSS2(TM) and Nafamostat mesylate (TM-NAM). Moreover, Quantitative structure-activity relationship (QSAR) pattern recognition was generated using PCA for the investigation of structural similarities among the selected ligands. Multiple Linear Regression (MLR) model was built to predict the binding energy compared to the binding energy obtained from molecular docking. The MLR regression model reveals an accuracy of 80% for the prediction of the binding energy of ligands. ADMET analysis demonstrates that these drug agents are appeared to be safer inhibitors. These three ligands can be used as potential inhibitors against the TMPRSS2.Communicated by Ramaswamy H. Sarma.

Resistance to durvalumab and durvalumab plus tremelimumab is associated with functional STK11 mutations in non-small-cell lung cancer patients and is reversed by STAT3 knockdown

Mutations in the STK11 (LKB1) gene regulate resistance to PD-1/PD-L1 blockade. This study evaluated this association in patients with nonsquamous non-small-cell lung cancer enrolled in three Phase 1/2 trials. STK11 mutations were associated with resistance to the anti-PD-L1 antibody durvalumab (alone/with the anti-CTLA-4 antibody tremelimumab) independently of KRAS mutational status, highlighting STK11 as a potential driver of resistance to checkpoint blockade. Retrospective assessments of tumor tissue, whole blood and serum revealed a unique immune phenotype in patients with STK11 mutations, with increased expression of markers associated with neutrophils (i.e. CXCL2, IL6), Th17 contexture (i.e. IL17A) and immune checkpoints. Associated changes were observed in the periphery. Reduction of STAT3 in the tumor microenvironment using an antisense oligonucleotide reversed immunotherapy resistance in preclinical STK11 knockout models. These results suggest that STK11 mutations may hinder response to checkpoint blockade through mechanisms including suppressive myeloid cell biology, which could be reversed by STAT3-targeted therapy.

Designing potent inhibitors against the multidrug resistance P-glycoprotein

The multidrug transporter P-glycoprotein is an ATP binding cassette (ABC) exporter responsible for resistance to tumor cells during chemotherapy. This study was designed with computational approaches aimed at identifying the best potent inhibitors of P-glycoprotein. Although many compounds have been suggested to inhibit P-glycoprotein, however, their information on bioavailability, selectivity, ADMET properties, and molecular interactions has not been revealed. Molecular docking, ADMET analysis, molecular dynamics, Principal component analysis (PCA), and binding free energy calculations were performed. Two compounds D1 and D2 showed the best docking score against P-glycoprotein and both compounds have 4-thiazolidinone derivatives containing indolin-3 one moiety are novel anti-tumor compounds. ADMET calculation analysis predicted D1 and D2 to have acceptable pharmacokinetic properties. The MD simulation discloses that D1-P-glycoprotein and D2-P-glycoprotein complexes are in stable conformation as apo-form. Hydrophobic amino acid such as phenylalanine plays significant on the interactions of inhibitors. Principal component analysis shows that both complexes are relatively similar variables as apo-form except planarity and Columbo energy profile. In addition, Quantitative Structural Activity Relationship (QSAR) of the ligand candidates were subjected to the principal component analysis (PCA) for pattern recognition. Partial-least-square (PLS) regression analysis was further utilized to model drug candidates' QSAR for subsequent prediction of the binding energy of validated drug candidates. PCA revealed groupings of the drug candidates based on the similarity or differences in drug candidates QSAR. Moreover, the developed PLS regression accurately predicted the values of the binding energy of drug candidates, with low residual error of prediction.Communicated by Ramaswamy H. Sarma.

A molecular modeling approach to identify effective antiviral phytochemicals against the main protease of SARS-CoV-2

The main protease of SARS-CoV-2 is one of the important targets to design and develop antiviral drugs. In this study, we have selected 40 antiviral phytochemicals to find out the best candidates which can act as potent inhibitors against the main protease. Molecular docking is performed using AutoDock Vina and GOLD suite to determine the binding affinities and interactions between the phytochemicals and the main protease. The selected candidates strongly interact with the key Cys145 and His41 residues. To validate the docking interactions, 100 ns molecular dynamics (MD) simulations on the five top-ranked inhibitors including hypericin, cyanidin 3-glucoside, baicalin, glabridin, and α-ketoamide-11r are performed. Principal component analysis (PCA) on the MD simulation discloses that baicalin, cyanidin 3-glucoside, and α-ketoamide-11r have structural similarity with the apo-form of the main protease. These findings are also strongly supported by root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA) investigations. PCA is also used to find out the quantitative structure-activity relationship (QSAR) for pattern recognition of the best ligands. Multiple linear regression (MLR) of QSAR reveals the R2 value of 0.842 for the training set and 0.753 for the test set. Our proposed MLR model can predict the favorable binding energy compared with the binding energy detected from molecular docking. ADMET analysis demonstrates that these candidates appear to be safer inhibitors. Our comprehensive computational and statistical analysis show that these selected phytochemicals can be used as potential inhibitors against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Implementation of computational approaches to explore the deleterious effects of non-synonymous SNPs on pRB protein

Retinoblastoma 1 (RB1) is the first discovered tumor suppressor gene and recognized as the simple model system whose encoded defective protein can cause a pediatric cancer retinoblastoma. It functions as a negative regulator of the cell cycle through the interactions with members of the E2F transcription factors family. The protein of the RB1 gene (pRB) is engaged in various cell cycle processes including apoptosis, cell cycle arrest and chromatin remodeling. Recent studies on Retinoblastoma also exhibited multiple sets of point mutation in the associated protein due to its large polymorphic information in the local database. In this study, we identified the list of disease associated non-synonymous single nucleotide polymorphisms (nsSNPs) in RB1 by incorporating different computational algorithms, web servers, modeling of the mutants and finally superimposing it. Out of 826 nsSNPs, W516G and W563G were predicted to be highly deleterious variants in the conserved regions and found to have an impact on protein structure and protein-protein interaction. Moreover, our study concludes the effect of W516G variant was more detrimental in destabilizing protein's nature as compared to W563G variant. We also found defective binding of pRB having W516G mutation with E2F2 protein. Findings of this study will aid in shortening of the expensive experimental cost of identifying disease associated SNPs in retinoblastoma for which specialized personalized treatment or therapy can be formulated.Communicated by Ramaswamy H. Sarma.

Whole proteome screening and identification of potential epitopes of SARS-CoV-2 for vaccine design-an immunoinformatic, molecular docking and molecular dynamics simulation accelerated robust strategy

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the most cryptic pandemic outbreak of the 21st century, has gripped more than 1.8 million people to death and infected almost eighty six million. As it is a new variant of SARS, there is no approved drug or vaccine available against this virus. This study aims to predict some promising cytotoxic T lymphocyte epitopes in the SARS-CoV-2 proteome utilizing immunoinformatic approaches. Firstly, we identified 21 epitopes from 7 different proteins of SARS-CoV-2 inducing immune response and checked for allergenicity and conservancy. Based on these factors, we selected the top three epitopes, namely KAYNVTQAF, ATSRTLSYY, and LTALRLCAY showing functional interactions with the maximum number of MHC alleles and no allergenicity. Secondly, the 3D model of selected epitopes and HLA-A*29:02 were built and Molecular Docking simulation was performed. Most interestingly, the best two epitopes predicted by docking are part of two different structural proteins of SARS-CoV-2, namely Membrane Glycoprotein (ATSRTLSYY) and Nucleocapsid Phosphoprotein (KAYNVTQAF), which are generally target of choice for vaccine designing. Upon Molecular Docking, interactions between selected epitopes and HLA-A*29:02 were further validated by 50 ns Molecular Dynamics (MD) simulation. Analysis of RMSD, Rg, SASA, number of hydrogen bonds, RMSF, MM-PBSA, PCA, and DCCM from MD suggested that ATSRTLSYY is the most stable and promising epitope than KAYNVTQAF epitope. Moreover, we also identified B-cell epitopes for each of the antigenic proteins of SARS CoV-2. Findings of our work will be a good resource for wet lab experiments and will lessen the timeline for vaccine construction.Communicated by Ramaswamy H. Sarma.

Nature's therapy for COVID-19: Targeting the vital non-structural proteins (NSP) from SARS-CoV-2 with phytochemicals from Indian medicinal plants

BACKGROUND

Containing COVID-19 is still a global challenge. It has affected the "normal" world by targeting its economy and health sector. The effect is shifting of focus of research from life threatening diseases like cancer. Thus, we need to develop a medical solution at the earliest. The purpose of this present work was to understand the efficacy of 22 rationally screened phytochemicals from Indian medicinal plants obtained from our previous work, following drug-likeness properties, against 6 non-structural-proteins (NSP) from SARS-CoV-2.

METHODS

100 ns molecular dynamics simulations were performed, and relative binding free energies were computed by MM/PBSA. Further, principal component analysis, dynamic cross correlation and hydrogen bond occupancy were analyzed to characterize protein-ligand interactions. Biological pathway enrichment analysis was also carried out to elucidate the therapeutic targets of the phytochemicals in comparison to SARS-CoV-2.

RESULTS

The potential binding modes and favourable molecular interaction profile of 9 phytochemicals, majorly from Withania somnifera with lowest free binding energies, against the SARS-CoV-2 NSP targets were identified. It was understood that phytochemicals and 2 repurposed drugs with steroidal moieties in their chemical structures formed stable interactions with the NSPs. Additionally, human target pathway analysis for SARS-CoV-2 and phytochemicals showed that cytokine mediated pathway and phosphorylation pathways were with the most significant p-value.

CONCLUSIONS

To summarize this work, we suggest a global approach of targeting multiple proteins of SARS-CoV-2 with phytochemicals as a natural alternative therapy for COVID-19. We also suggest that these phytochemicals need to be tested experimentally to confirm their efficacy.

STK11 p.F354L Germline Mutation in a Case of Multiple Gastrointestinal Tumors

Serine/threonine kinase 11 (STK11) is known as a critical tumor-suppressor gene that is frequently mutated in a broad spectrum of human cancers. Among these, the p.F354L mutation of STK11 has been identified in sporadic colon or lung cancer cases. Here, we report the case of a 75-year-old male patient who underwent surgical treatment for multiple tumors of the gastrointestinal system. Genetic mutations were screened in all resected samples, including duodenal high-grade adenoma, gastric high-grade adenoma, rectal adenocarcinoma, and liver metastasis of rectal adenocarcinoma, by next-generation sequencing for mutational hotspots involving 50 oncogenes and tumor suppressor genes. The characteristic hamartomatous polyp of Peutz-Jeghers syndrome was not detected in any tumor specimen. However, all samples as well as the normal rectal mucosa harbored the genetic mutation p.F354L in STK11. In addition, somatic mutations coexisted in the tumor samples, including KRAS p.A146T, TP53 p.G238X, and APC p.T1556fs in the duodenal adenoma; TP53 p.G238Y and APC p.T1556fs in the gastric adenoma; and TP53 p.R282W in the rectal adenocarcinoma and metastatic liver cancer. No somatic mutation was detected in the normal rectal mucosa as a control sample. To our knowledge, this is the first report of an STK11 germline mutation in a patient with multiple tumors of the gastrointestinal tract.

Antiviral Peptides as Promising Therapeutics against SARS-CoV-2

Over 50 peptides, which were known to inhibit SARS-CoV-1, were computationally screened against the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2. Based on the binding affinity and interaction, 15 peptides were selected, which showed higher affinity compared to the α-helix of the human ACE2 receptor. Molecular dynamics simulation demonstrated that two peptides, S2P25 and S2P26, were the most promising candidates, which could potentially block the entry of SARS-CoV-2. Tyr489 and Tyr505 residues present in the "finger-like" projections of the RBD were found to be critical for peptide interaction. Hydrogen bonding and hydrophobic interactions played important roles in prompting peptide-protein binding and interaction. Structure-activity relationship indicated that peptides containing aromatic (Tyr and Phe), nonpolar (Pro, Gly, Leu, and Ala), and polar (Asn, Gln, and Cys) residues were the most significant contributors. These findings can facilitate the rational design of selective peptide inhibitors targeting the spike protein of SARS-CoV-2.

Cysteine focused covalent inhibitors against the main protease of SARS-CoV-2

In viral replication and transcription, the main protease (Mpro) of SARS-CoV-2 plays an important role and appears to be a vital target for drug design. In Mpro, there is a Cys-His catalytic dyad, and ligands that interact with the Cys145 assumed to be an effective approach to inhibit the Mpro. In this study, approximately 1400 cysteine-focused ligands were screened to identify the best candidates that can act as potent inhibitors against Mpro. Our results show that the selected ligands strongly interact with the key Cys145 and His41 residues. Covalent docking was performed for the selected candidates containing the acrylonitrile group, which can form a covalent bond with Cys145. All atoms molecular dynamics (MD) simulation was performed on the selected four inhibitors including L1, L2, L3 and L4 to validate the docking interactions. Our results were also compared with a control ligand, α-ketoamide (11r). Principal component analysis on structural and energy data obtained from the MD trajectories shows that L1, L3, L4 and α-ketoamide (11r) have structural similarity with the apo-form of the Mpro. Quantitative structure-activity relationship method was employed for pattern recognition of the best ligands, which discloses that ligands containing acrylonitrile and amide warheads can show better performance. ADMET analysis displays that our selected candidates appear to be safer inhibitors. Our combined studies suggest that the best cysteine focused ligands can help to design an effective lead drug for COVID-19 treatment. Communicated by Ramaswamy H. Sarma.

The natural way forward: Molecular dynamics simulation analysis of phytochemicals from Indian medicinal plants as potential inhibitors of SARS-CoV-2 targets

The pandemic COVID‐19 has become a global panic‐forcing life towards a compromised “new normal.” Antiviral therapy against SARS‐CoV‐2 is still lacking. Thus, development of natural inhibitors as a prophylactic measure is an attractive strategy. In this context, this work explored phytochemicals as potential inhibitors for SARS‐CoV‐2 by performing all atom molecular dynamics simulations using high performance computing for 8 rationally screened phytochemicals from Withania somnifera and Azadirachta indica and two repurposed drugs docked with the spike glycoprotein and the main protease of SARS‐CoV‐2. These phytochemicals were rationally screened from 55 Indian medicinal plants in our previous work. MM/PBSA, principal component analysis (PCA), dynamic cross correlation matrix (DCCM) plots and biological pathway enrichment analysis were performed to reveal the therapeutic efficacy of these phytochemicals. The results revealed that Withanolide R (−141.96 KJ/mol) and 2,3‐Dihydrowithaferin A (−87.60 KJ/mol) were with the lowest relative free energy of binding for main protease and the spike proteins respectively. It was also observed that the phytochemicals exhibit a remarkable multipotency with the ability to modulate various human biological pathways especially pathways in cancer. Conclusively we suggest that these compounds need further detailed in vivo experimental evaluation and clinical validation to implement them as potent therapeutic agents for combating SARS‐CoV‐2.

Virtual screening, molecular dynamics and structure-activity relationship studies to identify potent approved drugs for Covid-19 treatment

Computer-aided drug screening by molecular docking, molecular dynamics (MD) and structural–activity relationship (SAR) can offer an efficient approach to identify promising drug repurposing candidates for COVID-19 treatment. In this study, computational screening is performed by molecular docking of 1615 Food and Drug Administration (FDA) approved drugs against the main protease (Mpro) of SARS-CoV-2. Several promising approved drugs, including Simeprevir, Ergotamine, Bromocriptine and Tadalafil, stand out as the best candidates based on their binding energy, fitting score and noncovalent interactions at the binding sites of the receptor. All selected drugs interact with the key active site residues, including His41 and Cys145. Various noncovalent interactions including hydrogen bonding, hydrophobic interactions, pi–sulfur and pi–pi interactions appear to be dominant in drug–Mpro complexes. MD simulations are applied for the most promising drugs. Structural stability and compactness are observed for the drug–Mpro complexes. The protein shows low flexibility in both apo and holo form during MD simulations. The MM/PBSA binding free energies are also measured for the selected drugs. For pattern recognition, structural similarity and binding energy prediction, multiple linear regression (MLR) models are used for the quantitative structural–activity relationship. The binding energy predicted by MLR model shows an 82% accuracy with the binding energy determined by molecular docking. Our details results can facilitate rational drug design targeting the SARS-CoV-2 main protease.

Communicated by Ramaswamy H. Sarma

Novel ssDNA Ligand Against Ovarian Cancer Biomarker CA125 With Promising Diagnostic Potential

The in-vitro diagnostic industry is always striving to explore specific and high-affinity recognition entities, sensitive probes, and newer technology or platforms to develop disease detection methods with lower production and time costs and with minimum interference or variability. Aptamers suffice as a reliable recognition element by addressing the issues mentioned earlier. Hence, this work focuses on screening high-affinity ssDNA ligands to capture an exemplary biomarker CA125 using membrane-SELEX technology coupled with aptainformatics (translational bioinformatics using aptamers). The ssDNA ligands or aptamers have been screened and characterized extensively through an array of assays to ensure a valuable diagnostic potential with K_D (dissociation constant) of 166 nM. The robustness of the selected aptamer ligand 2.26 and its complex with target CA125 is investigated in the presence of serum and extreme salt concentrations. Its diagnostic potential is convincingly demonstrated by running a competitive nucleic acid lateral flow assay at various sample concentrations. The ssDNA ligand reported in this manuscript holds immense potential in the detection and specific targeting of CA125 biomarker.