Bringing Structural Implications and Deep Learning-Based Drug Identification for KRAS Mutants

Multitargeted docking approach reveals droxidopa against DNA replication and repair-related protein of cervical cancer

Cervical cancer begins in the cells lining the cervix and is caused by persistent infection with certain types of human papillomavirus (HPV). Initially, it has no symptoms, and later it causes pelvic pain, abnormal vaginal bleeding, and pain during intercourse. It is the fourth-ranked cancer among women, and many women die from cervical cancer every year, particularly in low-income countries and the majority could be prevented with early detection and treatment. In this study, we have taken Cervical Cancer DNA Replication and Repair-Related Protein with the PDBID- 3H15, 5VBN, and 6NT9 and performed the multitargeted molecular docking with the FDA-approved drug library using HTVS, SP and XP docking. Then, the poses were filtered with MM\GBSA for proper computations of free energy, identified a multitargeted inhibitor Droxidopa with docking and MM\GBSA scores ranging from - 5.559 to - 6.835 Kcal/mol and - 26.04 to - 37.33 Kcal/mol, respectively. We also performed interaction fingerprints revealing 2VAL, 2LYS, 1ALA, 1ARG, 1ASN, 1CYS, 1GLN, 1GLU, 1ILE, 1MET, 1PHE, 1PRO, 1SER, and 1THR were most interacted residues and computed the ADMET properties with QikProp and DFT with Jaguar, which supported the study and compounds' suitability. Moreover, we performed the 100ns MD simulation in water, showing the controlled deviation and fluctuations of the residues with many interactions, and MM\GBSA was performed with the same trajectories, showing a better understanding of each frame's total complex and binding-free energy. The whole study favours droxidopa as an inhibitor of cervical cancer DNA Replication and Repair-Related Proteins-however, experimental studies are needed before use.

Molecular dynamics simulations for the structure-based drug design: targeting small-GTPases proteins

INTRODUCTION

Molecular Dynamics (MD) simulations can support mechanism-based drug design. Indeed, MD simulations by capturing biomolecule motions at finite temperatures can reveal hidden binding sites, accurately predict drug-binding poses, and estimate the thermodynamics and kinetics, crucial information for drug discovery campaigns. Small-Guanosine Triphosphate Phosphohydrolases (GTPases) regulate a cascade of signaling events, that affect most cellular processes. Their deregulation is linked to several diseases, making them appealing drug targets. The broad roles of small-GTPases in cellular processes and the recent approval of a covalent KRas inhibitor as an anticancer agent renewed the interest in targeting small-GTPase with small molecules.

AREA COVERED

This review emphasizes the role of MD simulations in elucidating small-GTPase mechanisms, assessing the impact of cancer-related variants, and discovering novel inhibitors.

EXPERT OPINION

The application of MD simulations to small-GTPases exemplifies the role of MD simulations in the structure-based drug design process for challenging biomolecular targets. Furthermore, AI and machine learning-enhanced MD simulations, coupled with the upcoming power of quantum computing, are promising instruments to target elusive small-GTPases mutations and splice variants. This powerful synergy will aid in developing innovative therapeutic strategies associated to small-GTPases deregulation, which could potentially be used for personalized therapies and in a tissue-agnostic manner to treat tumors with mutations in small-GTPases.

Exploring potential biomarkers and lead molecules in gastric cancer by network biology, drug repurposing and virtual screening strategies

Gastric cancer poses a significant global health challenge, necessitating innovative approaches for biomarker discovery and therapeutic intervention. This study employs a multifaceted strategy integrating network biology, drug repurposing, and virtual screening to elucidate and expand the molecular landscape of gastric cancer. We identified and prioritized key genes implicated in gastric cancer by utilizing data from diverse databases and text-mining techniques. Network analysis underscored intricate gene interactions, emphasizing potential therapeutic targets such as CTNNB1, BCL2, TP53, etc, and highlighted ACTB among the top hub genes crucial in disease progression. Drug repurposing on 626 FDA-approved drugs for digestive system-related cancers revealed Norgestimate and Nimesulide as likely top candidates for gastric cancer, validated by molecular docking and dynamics simulations. Further, combinatorial synthesis of scaffold libraries derived from known chemotypes generated 56,160 virtual compounds, of which 76 new compounds were prioritized based on promising binding affinities and interactions at critical residues. Hotspot residue analysis identified GLU 214 and others as essential for ligand binding stability, enhancing compound efficacy and specificity. These findings support the therapeutic potential of targeting beta-actin protein in gastric cancer treatment, suggesting a future for further experimental validation and clinical translation. In conclusion, this study highlights the potential of repurposable drugs and virtual screening which can be used in combination with existing anti-gastric cancer drugs for gastric cancer therapy, emphasizing the role of computational methodologies in drug discovery.

Supervised screening of Tecovirimat-like compounds as potential inhibitors for the monkeypox virus E8L protein

Monkeypox virus (MPXV) is a budding public health threat worldwide, and there lacks a personalized drug availability to treat MPXV infections. Tecovirimat, an antiviral drug against pox viruses, is recently confirmed to be effective against the MPXV in vitro using nanomolar concentrations. Therefore, the current study considers Tecovirimat as a reference compound for a machine learning-based guided screening to scan bioactive compounds from the DrugBank with similar chemical features or moieties as the Tecovirimat to inhibit the MPXV E8L surface binding protein. We used AlphaFold2 to model the E8L's 3D structure, followed by the conformational activity investigation of shortlisted drugs through computational structural biology approaches, including molecular docking and molecular dynamics simulations. As a result, we have shortlisted five drugs named ABX-1431, Alflutinib, Avacopan, Caspitant, and Darapalib that effectively engage the MPXV surface binding protein. Furthermore, the affinity of the proposed drugs is relatively higher than the Tecovirimat by having higher docking scores, establishing more hydrogen and hydrophobic bonds, engaging key residues in the target's structure, and exhibiting stable molecular dynamics.Communicated by Ramaswamy H. Sarma.

In-silico study unveils potential phytocompounds in Andrographis paniculata against E6 protein of the high-risk HPV-16 subtype for cervical cancer therapy

Despite therapeutic advancements, cervical cancer caused by high-risk subtypes of the human papillomavirus (HPV) remains a leading cause of cancer-related deaths among women worldwide. This study aimed to discover potential drug candidates from the Asian medicinal plant Andrographis paniculata, demonstrating efficacy against the E6 protein of high-risk HPV-16 subtype through an in-silico computational approach. The 3D structures of 32 compounds (selected from 42) derived from A. paniculata, exhibiting higher binding affinity, were obtained from the PubChem database. These structures underwent subsequent analysis and screening based on criteria including binding energy, molecular docking, drug likeness and toxicity prediction using computational techniques. Considering the spectrometry, pharmacokinetic properties, docking results, drug likeliness, and toxicological effects, five compounds-stigmasterol, 1H-Indole-3-carboxylic acid, 5-methoxy-, methyl ester (AP7), andrographolide, apigenin and wogonin-were selected as the potential inhibitors against the E6 protein of HPV-16. We also performed 200 ns molecular dynamics simulations of the compounds to analyze their stability and interactions as protein-ligand complexes using imiquimod (CID-57469) as a control. Screened compounds showed favorable characteristics, including stable root mean square deviation values, minimal root mean square fluctuations and consistent radius of gyration values. Intermolecular interactions, such as hydrogen bonds and hydrophobic contacts, were sustained throughout the simulations. The compounds displayed potential affinity, as indicated by negative binding free energy values. Overall, findings of this study suggest that the selected compounds have the potential to act as inhibitors against the E6 protein of HPV-16, offering promising prospects for the treatment and management of CC.

Structural and functional insights into a novel homozygous missense pathogenic variant in CUL7 identified in consanguineous Pakistani family

3M syndrome is a rare genetic familial disorder characterized by short stature, growth retardation, facial dysmorphism, skeletal abnormalities, fleshy protruding heels, and normal intelligence, caused by mutations in the CUL7, OBSL1 and CCDC8 genes. In the present study, a novel homozygous missense variant of CUL7 (NP_001161842.1, c.4493T > C, p.L1498P) has been identified in a consanguineous Pakistani family by whole exome sequencing. In silico structural evaluation, molecular docking and simulation studies of mutant CUL7 provides substantial evidence about its crucial role in the progression of discussed ailment. The newly discovered variant significantly altered the protein's three dimensional structure, leading to abnormal interaction with binding proteins. This computational and experimental investigation provides useful information to drug developers for the synthesis of novel therapeutics against the discussed ailment.Communicated by Ramaswamy H. Sarma.

Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer

Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.

Peptide based vaccine designing against endemic causing mammarenavirus using reverse vaccinology approach

The rodent-borne Arenavirus in humans has led to the emergence of regional endemic situations and has deeply emerged into pandemic-causing viruses. Arenavirus have a bisegmented ambisense RNA that produces four proteins: glycoprotein, nucleocapsid, RdRp and Z protein. The peptide-based vaccine targets the glycoprotein of the virus encountered by the immune system. Screening of B-Cell and T-Cell epitopes was done based on their immunological properties like antigenicity, allergenicity, toxicity and anti-inflammatory properties were performed. Selected epitopes were then clustered and epitopes were stitched using linker sequences. The immunological and physico-chemical properties of the vaccine construct was checked and modelled structure was validated by a 2-step MD simulation. The thermostability of the vaccine was checked followed by the immune simulation to test the immunogenicity of the vaccine upon introduction into the body over the course of the next 100 days and codon optimization was performed. Finally a 443 amino acid long peptide vaccine was designed which could provide protection against several members of the mammarenavirus family in a variety of population worldwide as denoted by the epitope conservancy and population coverage analysis. This study of designing a peptide vaccine targeting the glycoprotein of mammarenavirues may help develop novel therapeutics in near future.

Computational Deciphering of the Role of S100A8 and S100A9 Proteins and Their Changes in the Structure Assembly Influences Their Interaction with TLR4, RAGE, and CD36

S100A8 and S100A9 belong to the calcium-binding, damage associated molecular pattern (DAMP) proteins shown to aggravate the pathogenesis of rheumatoid arthritis (RA) through their interaction with the TLR4, RAGE and CD36 receptors. S100A8 and S100A9 proteins tend to exist in monomeric, homo and heterodimeric forms, which have been implicated in the pathogenesis of RA, via interacting with Pattern Recognition receptors (PRRs). The study aims to assess the influence of changes in the structure and biological assembly of S100A8 and S100A9 proteins as well as their interaction with significant receptors in RA through computational methods and surface plasmon resonance (SPR) analysis. Molecular docking analysis revealed that the S100A9 homodimer and S100A8/A9 heterodimer showed higher binding affinity towards the target receptors. Most S100 proteins showed good binding affinity towards TLR4 compared to other receptors. Based on the 50 ns MD simulations, TLR4, RAGE, and CD36 formed stable complexes with the monomeric and dimeric forms of S100A8 and S100A9 proteins. However, SPR analysis showed that the S100A8/A9 heterodimers formed stable complexes and exhibited high binding affinity towards the receptors. SPR data also indicated that TLR4 and its interactions with S100A8/A9 proteins may play a primary role in the pathogenesis of RA, with additional contributions from CD36 and RAGE interactions. Subsequent in vitro and in vivo investigations are warranted to corroborate the involvement of S100A8/A9 and the expression of TLR4, RAGE, and CD36 in the pathophysiology of RA.

Novel computational and drug design strategies for inhibition of human papillomavirus-associated cervical cancer and DNA polymerase theta receptor by Apigenin derivatives

The present study deals with the advanced in-silico analyses of several Apigenin derivatives to explore human papillomavirus-associated cervical cancer and DNA polymerase theta inhibitor properties by molecular docking, molecular dynamics, QSAR, drug-likeness, PCA, a dynamic cross-correlation matrix and quantum calculation properties. The initial literature study revealed the potent antimicrobial and anticancer properties of Apigenin, prompting the selection of its potential derivatives to investigate their abilities as inhibitors of human papillomavirus-associated cervical cancer and DNA polymerase theta. In silico molecular docking was employed to streamline the findings, revealing promising energy-binding interactions between all Apigenin derivatives and the targeted proteins. Notably, Apigenin 4'-O-Rhamnoside and Apigenin-4'-Alpha-L-Rhamnoside demonstrated higher potency against the HPV45 oncoprotein E7 (PDB ID 2EWL), while Apigenin and Apigenin 5-O-Beta-D-Glucopyranoside exhibited significant binding energy against the L1 protein in humans. Similarly, a binding affinity range of - 7.5 kcal/mol to - 8.8 kcal/mol was achieved against DNA polymerase theta, indicating the potential of Apigenin derivatives to inhibit this enzyme (PDB ID 8E23). This finding was further validated through molecular dynamic simulation for 100 ns, analyzing parameters such as RMSD, RMSF, SASA, H-bond, and RoG profiles. The results demonstrated the stability of the selected compounds during the simulation. After passing the stability testing, the compounds underwent screening for ADMET, pharmacokinetics, and drug-likeness properties, fulfilling all the necessary criteria. QSAR, PCA, dynamic cross-correlation matrix, and quantum calculations were conducted, yielding satisfactory outcomes. Since this study utilized in silico computational approaches and obtained outstanding results, further validation is crucial. Therefore, additional wet-lab experiments should be conducted under in vivo and in vitro conditions to confirm the findings.

Identification of histidine kinase inhibitors through screening of natural compounds to combat mastitis caused by Streptococcus agalactiae in dairy cattle

BACKGROUND

Mastitis poses a major threat to dairy farms globally; it results in reduced milk production, increased treatment costs, untimely compromised genetic potential, animal deaths, and economic losses. Streptococcus agalactiae is a highly virulent bacteria that cause mastitis. The administration of antibiotics for the treatment of this infection is not advised due to concerns about the emergence of antibiotic resistance and potential adverse effects on human health. Thus, there is a critical need to identify new therapeutic approaches to combat mastitis. One promising target for the development of antibacterial therapies is the transmembrane histidine kinase of bacteria, which plays a key role in signal transduction pathways, secretion systems, virulence, and antibiotic resistance.

RESULTS

In this study, we aimed to identify novel natural compounds that can inhibit transmembrane histidine kinase. To achieve this goal, we conducted a virtual screening of 224,205 natural compounds, selecting the top ten based on their lowest binding energy and favorable protein-ligand interactions. Furthermore, molecular docking of eight selected antibiotics and five histidine kinase inhibitors with transmembrane histidine kinase was performed to evaluate the binding energy with respect to top-screened natural compounds. We also analyzed the ADMET properties of these compounds to assess their drug-likeness. The top two compounds (ZINC000085569031 and ZINC000257435291) and top-screened antibiotics (Tetracycline) that demonstrated a strong binding affinity were subjected to molecular dynamics simulations (100 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method.

CONCLUSION

Our results suggest that the selected natural compounds have the potential to serve as effective inhibitors of transmembrane histidine kinase and can be utilized for the development of novel antibacterial veterinary medicine for mastitis after further validation through clinical studies.

The Pathogenicity of Fusobacterium nucleatum Modulated by Dietary Fibers-A Possible Missing Link between the Dietary Composition and the Risk of Colorectal Cancer

The dietary composition has been approved to be strongly associated with the risk of colorectal cancer (CRC), one of the most serious malignancies worldwide, through regulating the gut microbiota structure, thereby influencing the homeostasis of colonic epithelial cells by producing carcinogens, i.e., ammonia or antitumor metabolites, like butyrate. Though butyrate-producing Fusobacterium nucleatum has been considered a potential tumor driver associated with chemotherapy resistance and poor prognosis in CRC, it was more frequently identified in the gut microbiota of healthy individuals rather than CRC tumor tissues. First, within the concentration range tested, the fermentation broth of F. nucleatum exhibited no significant effects on Caco-2 and NCM460 cells viability except for a notable up-regulation of the expression of TLR4 (30.70%, p < 0.0001) and Myc (47.67%, p = 0.021) and genes encoding proinflammatory cytokines including IL1B (197.57%, p < 0.0001), IL6 (1704.51%, p < 0.0001), and IL8 (897.05%, p < 0.0001) in Caco-2 cells exclusively. Although no marked effects of polydextrose or fibersol-2 on the growth of F. nucleatum, Caco-2 and NCM460 cells were observed, once culture media supplemented with polydextrose or fibersol-2, the corresponding fermentation broths of F. nucleatum significantly inhibited the growth of Caco-2 cells up to 48.90% (p = 0.0003, 72 h, 10%) and 52.96% (p = 0.0002, 72 h, 10%), respectively in a dose-dependent manner. These two kinds of fibers considerably promoted butyrate production of F. nucleatum up to 205.67% (p < 0.0001, 6% polydextrose at 24 h) and 153.46% (p = 0.0002, 6% fibersol-2 at 12 h), which explained why and how the fermentation broths of F. nucleatum cultured with fibers suppressing the growth of Caco-2 cells. Above findings indicated that dietary fiber determined F. nucleatum to be a carcinogenic or antitumor bacterium, and F. nucleatum played an important role in the association between the dietary composition, primarily the content of dietary fibers, and the risk of CRC.

Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature

Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field.

In-silico assessment of high-risk non-synonymous SNPs in ADAMTS3 gene associated with Hennekam syndrome and their impact on protein stability and function

Hennekam Lymphangiectasia-Lymphedema Syndrome 3 (HKLLS3) is a rare genetical disorder caused by mutations in a few genes including ADAMTS3. It is characterized by lymphatic dysplasia, intestinal lymphangiectasia, severe lymphedema and distinctive facial appearance. Up till now, no extensive studies have been conducted to elucidate the mechanism of the disease caused by various mutations. As a preliminary investigation of HKLLS3, we sorted out the most deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) that might affect the structure and function of ADAMTS3 protein by using a variety of in silico tools. A total of 919 nsSNPs in the ADAMTS3 gene were identified. 50 nsSNPs were predicted to be deleterious by multiple computational tools. 5 nsSNPs (G298R, C567Y, A370T, C567R and G374S) were found to be the most dangerous and can be associated with the disease as predicted by different bioinformatics tools. Modelling of the protein shows it can be divided into segments 1, 2 and 3, which are connected by short loops. Segment 3 mainly consists of loops without substantial secondary structures. With prediction tools and molecular dynamics simulation, some SNPs were found to significantly destabilize the protein structure and disrupt the secondary structures, especially in segment 2. The deleterious effects of mutations in segment 1 are possibly not from destabilization but from other factors such as the change in phosphorylation as suggested by post-translational modification (PTM) studies. This is the first-ever study of ADAMTS3 gene polymorphism, and the predicted nsSNPs in ADAMST3, some of which have not been reported yet in patients, will serve for diagnostic purposes and further therapeutic implications in Hennekam syndrome, contributing to better diagnosis and treatment.

Combination of furosemide, gold, and dopamine as a potential therapy for breast cancer

Breast cancer is one of the leading causes of death in women worldwide. Initially, it develops in the epithelium of the ducts or lobules of the breast glandular tissues with limited growth and the potential to metastasize. It is a highly heterogeneous malignancy; however, the common molecular mechanisms could help identify new targeted drugs for treating its subtypes. This study uses computational drug repositioning approaches to explore fresh drug candidates for breast cancer treatment. We also implemented reversal gene expression and gene expression-based signatures to explore novel drug candidates computationally. The drug activity profiles and related gene expression changes were acquired from the DrugBank, PubChem, and LINCS databases, and then in silico drug screening, molecular dynamics (MD) simulation, replica exchange MD simulations, and simulated annealing molecular dynamics (SAMD) simulations were conducted to discover and verify the valid drug candidates. We have found that compounds like furosemide, gold, and dopamine showed significant outcomes. Furthermore, the expression of genes related to breast cancer was observed to be reversed by these shortlisted drugs. Therefore, we postulate that combining furosemide, gold, and dopamine would be a potential combination therapy measurement for breast cancer patients.

Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics

Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.

In silico formulation of a next-generation multiepitope vaccine for use as a prophylactic candidate against Crimean-Congo hemorrhagic fever

BACKGROUND

Crimean-Congo hemorrhagic fever (CCHF) is a widespread disease transmitted to humans and livestock animals through the bite of infected ticks or close contact with infected persons' blood, organs, or other bodily fluids. The virus is responsible for severe viral hemorrhagic fever outbreaks, with a case fatality rate of up to 40%. Despite having the highest fatality rate of the virus, a suitable treatment option or vaccination has not been developed yet. Therefore, this study aimed to formulate a multiepitope vaccine against CCHF through computational vaccine design approaches.

METHODS

The glycoprotein, nucleoprotein, and RNA-dependent RNA polymerase of CCHF were utilized to determine immunodominant T- and B-cell epitopes. Subsequently, an integrative computational vaccinology approach was used to formulate a multi-epitopes vaccine candidate against the virus.

RESULTS

After rigorous assessment, a multiepitope vaccine was constructed, which was antigenic, immunogenic, and non-allergenic with desired physicochemical properties. Molecular dynamics (MD) simulations of the vaccine-receptor complex show strong stability of the vaccine candidates to the targeted immune receptor. Additionally, the immune simulation of the vaccine candidates found that the vaccine could trigger real-life-like immune responses upon administration to humans.

CONCLUSIONS

Finally, we concluded that the formulated multiepitope vaccine candidates would provide excellent prophylactic properties against CCHF.

Mutational Impacts on the N and C Terminal Domains of the MUC5B Protein: A Transcriptomics and Structural Biology Study

Cholangiocarcinoma (CCA) involves various epithelial tumors historically linked with poor prognosis because of its aggressive sickness course, delayed diagnosis, and limited efficacy of typical chemotherapy in its advanced stages. In-depth molecular profiling has exposed a varied scenery of genomic alterations as CCA's oncogenic drivers. Previous studies have mainly focused on commonly occurring TP53 and KRAS alterations, but there is limited research conducted to explore other vital genes involved in CCA. We retrieved data from The Cancer Genome Atlas (TCGA) to hunt for additional CCA targets and plotted a mutational landscape, identifying key genes and their frequently expressed variants. Next, we performed a survival analysis for all of the top genes to shortlist the ones with better significance. Among those genes, we observed that MUC5B has the most significant p-value of 0.0061. Finally, we chose two missense mutations at different positions in the vicinity of MUC5B N and C terminal domains. These mutations were further subjected to molecular dynamics (MD) simulation, which revealed noticeable impacts on the protein structure. Our study not only reveals one of the highly mutated genes with enhanced significance in CCA but also gives insights into the influence of its variants. We believe these findings are a good asset for understanding CCA from genomics and structural biology perspectives.

Discovering potent inhibitors against the Mpro of the SARS-CoV-2. A medicinal chemistry approach

The global pandemic caused by a single-stranded RNA (ssRNA) virus known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still at its peak, with new cases being reported daily. Although the vaccines have been administered on a massive scale, the frequent mutations in the viral gene and resilience of the future strains could be more problematic. Therefore, new compounds are always needed to be available for therapeutic approaches. We carried out the present study to discover potential drug compounds against the SARS-CoV-2 main protease (Mpro). A total of 16,000 drug-like small molecules from the ChemBridge database were virtually screened to obtain the top hits. As a result, 1032 hits were selected based on their docking scores. Next, these structures were prepared for molecular docking, and each small molecule was docked into the active site of the Mpro. Only compounds with solid interactions with the active site residues and the highest docking score were subjected to molecular dynamics (MD) simulation. The post-simulation analyses were carried out using the in-built GROMACS tools to gauge the stability, flexibility, and compactness. Principal component analysis (PCA) and hydrogen bonding were also calculated to observe trends and affinity of the drugs towards the target. Among the five top compounds, C1, C3, and C6 revealed strong interaction with the target's active site and remained highly stable throughout the simulation. We believe the predicted compounds in this study could be potential inhibitors in the natural system and can be utilized in designing therapeutic strategies against the SARS-CoV-2.

Design of a Multi-Epitopes Vaccine against Hantaviruses: An Immunoinformatics and Molecular Modelling Approach

Hantaviruses are negative-sense, enveloped, single-stranded RNA viruses of the family Hantaviridae. In recent years, rodent-borne hantaviruses have emerged as novel zoonotic viruses posing a substantial health issue and socioeconomic burden. In the current research, a reverse vaccinology approach was applied to design a multi-epitope-based vaccine against hantavirus. A set of 340 experimentally reported epitopes were retrieved from Virus Pathogen Database and Analysis Resource (ViPR) and subjected to different analyses such as antigenicity, allergenicity, solubility, IFN gamma, toxicity, and virulent checks. Finally, 10 epitopes which cleared all the filters used were linked with each other through specific GPGPG linkers to construct a multi-antigenic epitope vaccine. The designed vaccine was then joined to three different adjuvants-TLR4-agonist adjuvant, β-defensin, and 50S ribosomal protein L7/L12-using an EAAAK linker to boost up immune-stimulating responses and check the potency of vaccine with each adjuvant. The designed vaccine structures were modelled and subjected to error refinement and disulphide engineering to enhance their stability. To understand the vaccine binding affinity with immune cell receptors, molecular docking was performed between the designed vaccines and TLR4; the docked complex with a low level of global energy was then subjected to molecular dynamics simulations to validate the docking results and dynamic behaviour. The docking binding energy of vaccines with TLR4 is -29.63 kcal/mol (TLR4-agonist), -3.41 kcal/mol (β-defensin), and -11.03 kcal/mol (50S ribosomal protein L7/L12). The systems dynamics revealed all three systems to be highly stable with a root-mean-square deviation (RMSD) value within 3 Å. To test docking predictions and determine dominant interaction energies, binding free energies of vaccine(s)-TLR4 complexes were calculated. The net binding energy of the systems was as follows: TLR4-agonist vaccine with TLR4 (MM-GBSA, -1628.47 kcal/mol and MM-PBSA, -37.75 kcal/mol); 50S ribosomal protein L7/L12 vaccine with TLR4 complex (MM-GBSA, -194.62 kcal/mol and MM-PBSA, -150.67 kcal/mol); β-defensin vaccine with TLR4 complex (MM-GBSA, -9.80 kcal/mol and MM-PBSA, -42.34 kcal/mol). Finally, these findings may aid experimental vaccinologists in developing a very potent hantavirus vaccine.

Spatial-Frequency dual-branch attention model for determining KRAS mutation status in colorectal cancer with T2-weighted MRI

BACKGROUND AND OBJECTIVE

Identifying the KRAS mutation status accurately in medical images is very important for the diagnosis and treatment of colorectal cancer. Despite the substantial progress achieved by existing methods, it remains challenging due to limited annotated dataset, large intra-class variances, and a high degree of inter-class similarities.

METHODS

To tackle these challenges, we propose a spatial-frequency dual-branch attention model (SF-DBAM) to determine the KRAS mutation status of colorectal cancer patients using a limited T2-weighted MRI dataset. The dataset contains 169 wild-type patients (2151 images) and 137 mutation-type patients (1666 images). The first branch utilizes part of the pre-trained Xception model to capture spatial-domain information and alleviate the small-scale dataset problem. The second branch builds frequency-domain information into cube columns using block-based discrete cosine transform and channel rearrangement. Then the cube columns are fed into convolutional long short-term memory (convLSTM) to explore the effective information between the reconstructed frequency-domain channels. Also, we design a channel enhanced attention module (CEAM) at the end of each branch to make them focus on the lesion areas. Finally, we concatenate the two branches and output the classified results through fully connected layers.

RESULTS

The proposed method achieves 88.03% overall accuracy with AUC of 94.27% and specificity of 90.75% in 10-fold cross-validation, which is better than the current non-invasive methods for determining KRAS mutation status.

CONCLUSIONS

We believe that the proposed method can assist physicians to diagnose the KRAS mutation status in patients with colorectal cancer, and other medical problems can benefit from the spatial and frequency domains information.

Conformational transformation of switch domains in GDP/K-Ras induced by G13 mutants: An investigation through Gaussian accelerated molecular dynamics simulations and principal component analysis

Mutations in K-Ras are involved in a large number of all human cancers, thus, K-Ras is regarded as a promising target for anticancer drug design. Understanding the target roles of K-Ras is important for providing insights on the molecular mechanism underlying the conformational transformation of the switch domains in K-Ras due to mutations. In this study, multiple replica Gaussian accelerated molecular (MR-GaMD) simulations and principal component analysis (PCA) were applied to probe the effect of G13A, G13D and G13I mutations on conformational transformations of the switch domains in GDP-associated K-Ras. The results suggest that G13A, G13D and G13I enhance the structural flexibility of the switch domains, change the correlated motion modes of the switch domains and strengthen the total motion strength of K-Ras compared with the wild-type (WT) K-Ras. Free energy landscape analyses not only show that the switch domains of the GDP-bound inactive K-Ras mainly exist as a closed state but also indicate that mutations evidently alter the free energy profile of K-Ras and affect the conformational transformation of the switch domains between the closed and open states. Analyses of hydrophobic interaction contacts and hydrogen bonding interactions show that the mutations scarcely change the interaction network of GDP with K-Ras and only disturb the interaction of GDP with the switch (SW1). In summary, two newly introduced mutations, G13A and G13I, play similar adjustment roles in the conformational transformations of two switch domains to G13D and are possibly utilized to tune the activity of K-Ras and the binding of guanine nucleotide exchange factors.