Evidence of colorectal cancer-associated mutation in MCAK: a computational report

Computational studies on the functional and structural impact of pathogenic mutations in enzymes

Enzymes are critical biological catalysts involved in maintaining the intricate balance of metabolic processes within living organisms. Mutations in enzymes can result in disruptions to their functionality that may lead to a range of diseases. This review focuses on computational studies that investigate the effects of disease-associated mutations in various enzymes. Through molecular dynamics simulations, multiscale calculations, and machine learning approaches, computational studies provide detailed insights into how mutations impact enzyme structure, dynamics, and catalytic activity. This review emphasizes the increasing impact of computational simulations in understanding molecular mechanisms behind enzyme (dis)function by highlighting the application of key computational methodologies to selected enzyme examples, aiding in the prediction of mutation effects and the development of therapeutic strategies.

Exploring Chemical Space to Identify Partial Binders Against hMPV Nucleocapsid Protein

Human metapneumovirus (hMPV) has gained prominence in recent times as the predominant etiological agent of acute respiratory tract infections. This virus targets children, the elderly, and individuals with compromised immune systems. Given the protracted duration of hMPV transmission, it is probable that the majority of children will have acquired the virus by the age of 5. In individuals with compromised immune systems, recurrence of hMPV infection is possible. As hMPV matures, it remains latent from the time of acquisition. The genome of hMPV encompasses a pivotal protein referred to as the nucleocapsid protein (N). This protein assumes the form of a left-handed helical nucleocapsid, enveloping the viral RNA genome. The primary function of this structure is to protect nucleases, rendering it a potentially promising target for therapeutic advancements. The present study employs a methodology that involves structure-based virtual screening, followed by molecular dynamics simulation at a 250-ns time scale, to identify potential natural molecules or their derivatives from the ZINC Database. These molecules are investigated for their binding properties against the hMPV nucleoprotein. Based on an evaluation of the docking score, binding site interaction, and molecular dynamics studies, it has been found that two naturally occurring molecules, namely M1 (ZINC85629735) and M3 (ZINC85569125), have shown notable docking scores of -9.6 and -10.7 kcal/mol, acceptable RMSD, RMSF, Rg, and so on calculated from molecular dynamics trajectory associated with MMGBSA binding energy of -81.94 and -99.63 kcal/mol, respectively. These molecules have shown the highest binding affinity toward nucleocapsid protein and demonstrated promising attributes as potential binders against hMPV.

Mechanism to disrupt ESCRT-mediated intracellular trafficking through Vps28-small molecules interaction: an in silico approach

The ESCRT (Endosomal Sorting Complex Required for Transport) machinery comprising protein complexes ESCRT-0 to ESCRT-III and Vps4 plays a pivotal role in intracellular trafficking, a process of endocytosing cell surface proteins into the cell for various biological activities. The ESCRT protein complexes are sequentially assembled which interact amongst each other to form a functional ESCRT machinery. Deregulation of these events are shown to be involved in various disease development including tumor formation and viral infections. Recently upregulation of a crucial ESCRT protein, Vps28 has been shown to be implicated in tumor formation. However, Vps28 in ESCRT-I interacts with Vps36 in ESCRT-II to function as a connecting protein during ESCRT machinery formation. Until now biomolecular approaches to inhibit the formation/assembly of ESCRT machinery have not been developed. Hence, we hypothesized that disrupting Vps28/Vps36 interaction would prevent assembly of ESCRT machinery and offer therapeutic potential to restrict disease development and progression. To address this, we utilized a virtual screening approach using a flavonoid-based library to identify potential small molecule inhibitors that can bind to Vps28 active site. Based on the binding affinity, top-hit compounds were identified. Molecular dynamics simulations set over a 500 ns timescale demonstrated the stability of the Vps28-small molecule complexes. Per-residue decomposition analysis using Molecular Mechanics/Poisson-Boltzmann surface area highlighted the significant contributions of active site residues Asn189, Arg190, Arg193 and Asn210 in Vps28 for interaction with small molecules. Absorption, Distribution, Metabolism, Excretion, and Toxicity analysis for toxicity evaluation indicates that molecules Z0131, H0194, Z0199 and DQ00112 exhibited physicochemical properties suitable for drug development.

Molecular Regulator Driving Endometriosis Towards Endometrial Cancer: A Multi-Scale Computational Investigation to Repurpose Anti-Cancer drugs

Endometriosis is a gynecological disorder among reproductive-aged women. Recent epidemiological investigations suggest endometriosis increases the risk of endometrial cancer. However, the molecular entity leading to endometriosis-to-endometrial cancer is largely unknown. This study aimed to combine a variety of computational approaches to identify the key therapeutic target promoting endometriosis-to-endometrial cancer and screen potential inhibitors against target to prevent cancer development. Our systematic investigations, includes transcriptomic profiling, protein network, pharmacophore modeling, docking, binding free energy calculation, dynamics simulation, and quantum mechanics. The gene expression analysis on endometriosis and endometrial cancer was performed and showed 108 shared upregulated genes in both conditions. Further construction of interaction network with 108 genes showed intercellular adhesion molecule 1 (ICAM1) to be a crucial molecule with a high degree of connectivity that influences vital mechanisms related to cancer pathways. We then generated ligand-based pharmacophore models using established ICAM1 inhibitors. Among the models, the ADRRR_8 pharmacophore exhibited a robust area under curve (AUC = 0.83), was employed to screen 1739 anti-cancer drugs. On screening, 421 anti-cancer drugs displayed ICAM1-inhibiting pharmacophore features. Further, the docking of 421 drugs with ICAM1 showed lanreotide (-7.80 kcal/mol) with better affinity than the reference ICAM1 inhibitor (-3.59 kcal/mol). Further validation though binding free energy and dynamics simulation of the lanreotide-ICAM1 complex showed a high binding affinity of -55.90 kcal/mol and contributed stable confirmation. According to quantum chemical calculations, lanreotide's electronic properties favour ICAM1 binding with highest occupied molecular orbital was -6.91 eV and lowest unoccupied molecular orbital was -3.93 eV. Our study supports using lanreotide to treat endometriosis, which could delay or prevent endometrial cancer. These predictions need to be confirmed and examined to determine the use of lanreotide in endometriosis treatment.

Siling decoction ameliorates adenine-induced renal fibrosis in rats by the AKT/IKKβ/NFκB signaling pathway

OBJECTIVE

Investigating how Siling decoction (SLD) mitigates fibrosis in rats with chronic kidney disease CKD (chronic kidney disease) through network pharmacology analysis and experimental verification.

METHODS

Initially, the primary active components and their target actions of SLD (Fuling, Zhuling, Zexie, and Baizhu) were identified by the TCMSP database and liquid chromatography mass spectrometry (LC-MS). Treatment targets for renal fibrosis were screened through databases such as GeneCard, OMIM, PharmGkb, and GEO. Subsequently, a drug-disease-target network was constructed and subjected to PPI analysis. Intersecting targets underwent GO and KEGG pathway enrichment analyses. Renal fibrosis model was induced by adenine gavage, then treat with SLD. Masson, Sirius red, immunohistochemistry, and Western blot were used to detect renal function and fibrosis-related indicators. The mechanism was further validated in vitro experiments.

RESULTS

Network pharmacology analysis identified 100 common targets associated with the active components of SLD, including core genes such as AKT1 and CCND1. GO enrichment analysis revealed that the top three biological processes impacted include response to xenobiotic stimulus, response to nutrient levels and response to oxidative stress. These processes involved cellular components such as membrane raft, membrane microdomain and synaptic membrane, with molecular functions predominantly associated with ubiquitin-like protein ligase binding, ubiquitin protein ligase binding, DNA-binding transcription factor binding, and RNA polymerase II-specific DNA binding transcription factor binding. KEGG pathway enrichment analysis indicated potential involvement of pathways like Lipid and atherosclerosis, PI3K-AKT signaling pathway, and prostate cancer are likely involved in the anti-fibrotic effect of SLD. Notably, the highlighted was the AKT/IKKβ/NFκB signaling pathway as a key mechanism. These findings were further confirmed in vivo and in vitro.

CONCLUSION

The SLD effectively ameliorates adenine-induced chronic kidney disease fibrosis in rats, potentially by inhibiting the AKT/IKKβ/NFκB signaling pathway.

Stack-HDAC3i: A high-precision identification of HDAC3 inhibitors by exploiting a stacked ensemble-learning framework

Epigenetics involves reversible modifications in gene expression without altering the genetic code itself. Among these modifications, histone deacetylases (HDACs) play a key role by removing acetyl groups from lysine residues on histones. Overexpression of HDACs is linked to the proliferation and survival of tumor cells. To combat this, HDAC inhibitors (HDACi) are commonly used in cancer treatments. However, pan-HDAC inhibition can lead to numerous side effects. Therefore, isoform-selective HDAC inhibitors, such as HDAC3i, could be advantageous for treating various medical conditions while minimizing off-target effects. To date, computational approaches that use only the SMILES notation without any experimental evidence have become increasingly popular and necessary for the initial discovery of novel potential therapeutic drugs. In this study, we develop an innovative and high-precision stacked-ensemble framework, called Stack-HDAC3i, which can directly identify HDAC3i using only the SMILES notation. Using an up-to-date benchmark dataset, we first employed both molecular descriptors and Mol2Vec embeddings to generate feature representations that cover multi-view information embedded in HDAC3i, such as structural and contextual information. Subsequently, these feature representations were used to train baseline models using nine popular ML algorithms. Finally, the probabilistic features derived from the selected baseline models were fused to construct the final stacked model. Both cross-validation and independent tests showed that Stack-HDAC3i is a high-accuracy prediction model with great generalization ability for identifying HDAC3i. Furthermore, in the independent test, Stack-HDAC3i achieved an accuracy of 0.926 and Matthew's correlation coefficient of 0.850, which are 0.44-6.11% and 0.83-11.90% higher than its constituent baseline models, respectively.

Discovering novel targets of abscisic acid using computational approaches

Abscisic acid (ABA) is a crucial plant hormone that is naturally produced in various mammalian tissues and holds significant potential as a therapeutic molecule in humans. ABA is selected for this study due to its known roles in essential human metabolic processes, such as glucose homeostasis, immune responses, cardiovascular system, and inflammation regulation. Despite its known importance, the molecular mechanism underlying ABA's action remain largely unexplored. This study employed computational techniques to identify potential human ABA receptors. We screened 64 candidate molecules using online servers and performed molecular docking to assess binding affinity and interaction types with ABA. The stability and dynamics of the best complexes were investigated using molecular dynamics simulation over a 100 ns time period. Root mean square fluctuations (RMSF), root mean square deviation (RMSD), solvent-accessible surface area (SASA), radius of gyration (Rg), free energy landscape (FEL), and principal component analysis (PCA) were analyzed. Next, the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method was employed to calculate the binding energies of the complexes based on the simulated data. Our study successfully pinpointed four key receptors responsible for ABA signaling (androgen receptor, glucocorticoid receptor, mineralocorticoid receptor, and retinoic acid receptor beta) that have a strong affinity for binding with ABA and remained structurally stable throughout the simulations. The simulations with Hydralazine as an unrelated ligand were conducted to validate the specificity of the identified receptors for ABA. The findings of this study can contribute to further experimental validation and a better understanding of how ABA functions in humans.

KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker

KIF2C/MCAK (KIF2C) is the most well-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule (MT) dynamics during mitosis, as well as interphase. This systematic review briefly describes the important structural elements of KIF2C, its regulation by multiple molecular mechanisms, and its broad cellular functions. Furthermore, it systematically summarizes its oncogenic potential in malignant progression and performs a meta-analysis of its prognostic value in cancer patients. KIF2C was shown to be involved in multiple crucial cellular processes including cell migration and invasion, DNA repair, senescence induction and immune modulation, which are all known to be critical during the development of malignant tumors. Indeed, an increasing number of publications indicate that KIF2C is aberrantly expressed in multiple cancer entities. Consequently, we have highlighted its involvement in at least five hallmarks of cancer, namely: genome instability, resisting cell death, activating invasion and metastasis, avoiding immune destruction and cellular senescence. This was followed by a systematic search of KIF2C/MCAK's expression in various malignant tumor entities and its correlation with clinicopathologic features. Available data were pooled into multiple weighted meta-analyses for the correlation between KIF2Chigh protein or gene expression and the overall survival in breast cancer, non-small cell lung cancer and hepatocellular carcinoma patients. Furthermore, high expression of KIF2C was correlated to disease-free survival of hepatocellular carcinoma. All meta-analyses showed poor prognosis for cancer patients with KIF2Chigh expression, associated with a decreased overall survival and reduced disease-free survival, indicating KIF2C's oncogenic potential in malignant progression and as a prognostic marker. This work delineated the promising research perspective of KIF2C with modern in vivo and in vitro technologies to further decipher the function of KIF2C in malignant tumor development and progression. This might help to establish KIF2C as a biomarker for the diagnosis or evaluation of at least three cancer entities.

Deciphering the pH-dependent oligomerization of aspartate semialdehyde dehydrogenase from Wolbachia endosymbiont of Brugia malayi: An in vitro and in silico approaches

The enzyme aspartate semialdehyde dehydrogenase (ASDH) plays a pivotal role in the amino acid biosynthesis pathway, making it an attractive target for the development of new antimicrobial drugs due to its absence in humans. This study aims to investigate the presence of ASDH in the filarial parasite Wolbachia endosymbiont of Brugia malayi (WBm) using both in vitro and in silico approaches. The size exclusion chromatography (SEC) and Native-PAGE analysis demonstrate that WBm-ASDH undergoes pH-dependent oligomerization and dimerization. To gain a deeper understanding of this phenomenon, the modelled monomer and dimer structures were subjected to pH-dependent dynamics simulations in various conditions. The results reveal that residues Val240, Gln161, Thr159, Tyr160, and Trp316 form strong hydrogen bond contacts in the intersurface area to maintain the structure in the dimeric form. Furthermore, the binding of NADP+ induces conformational changes, leading to an open or closed conformation in the structure. Importantly, the binding of NADP+ does not disturb either the dimerization or oligomerization of the protein, a finding confirmed through both in vitro and in silico analysis. These findings shed light on the structural characteristics of WBm-ASDH and offer valuable insights for the development of new inhibitors specific to WBm, thereby contributing to the development of potential therapies for filarial parasitic infections.

Bioinformatics and computational studies of chabamide F and chabamide G for breast cancer and their probable mechanisms of action

Globally, the prevalence of breast cancer (BC) is increasing at an alarming level, despite early detection and technological improvements. Alkaloids are diverse chemical groups, and many within this class have been reported as potential anticancer compounds. Chabamide F (F) and chabamide G (G) are two dimeric amide alkaloids found in a traditional medicinal plant, Piper chaba, and possess significant cytotoxic effects. However, their scientific rationalization in BC remains unknown. Here, we aimed to investigate their potential and molecular mechanisms for BC through in silico approaches. From network pharmacology, we identified 64 BC-related genes as targets. GO and KEGG studies showed that they were involved in various biological processes and mostly expressed in BC-related pathways such as RAS, PI3K-AKT, estrogen, MAPK, and FoxO pathways. However, PPI analysis revealed SRC and AKT1 as hub genes, which play key roles in BC tumorigenesis and metastasis. Molecular docking revealed the strong binding affinity of F (- 10.7 kcal/mol) and G (- 9.4 and - 11.7 kcal/mol) for SRC and AKT1, respectively, as well as the acquisition of vital residues to inhibit them. Their long-term stability was evaluated using 200 ns molecular dynamics simulation. The RMSD, RMSF, Rg, and SASA analyses showed that the G-SRC and G-AKT1 complexes were excellently stable compared to the control, dasatinib, and capivasertib, respectively. Additionally, the PCA and DCCM analyses revealed a significant reduction in the residual correlation and motions. By contrast, the stability of the F-SRC complex was greater than that of the control, whereas it was moderately stable in complex with AKT1. The MMPBSA analysis demonstrated higher binding energies for both compounds than the controls. In particular, the binding energy of G for SRC and AKT1 was - 120.671 ± 16.997 and - 130.437 ± 19.111 kJ/mol, respectively, which was approximately twice as high as the control molecules. Van der Waal and polar solvation energies significantly contributed to this energy. Furthermore, both of them exhibited significant interactions with the binding site residues of both proteins. In summary, this study indicates that these two molecules could be a potential ATP-competitive inhibitor of SRC and an allosteric inhibitor of AKT1.

Synthesis of new two 1,2-disubstituted benzimidazole compounds: their in vitro anticancer and in silico molecular docking studies

In this study, two new molecules were synthesized from the reaction of 2-methyl-1H-benzo[d]imidazole with aryl halides in the presence of a strong base. The structures newly of synthesized 1,2-disubstituted benzimidazole compounds were characterized using spectroscopic techniques (FT-IR, 1HNMR, 13CNMR) and chromatographic technique (LC/MS). For discovering an effective anticancer drug, the developed heterocyclic compounds were screened against three different human cancer cell lines (A549, DLD-1, and L929). The results demonstrated that of IC50 values of compound 2a were higher as compared to cisplatin for the A549 and DLD-1 cell lines. The frontier molecular orbital (FMO), and molecular electrostatic potential map (MEP) analyses were studied by using DFT (density functional theory) calculations at B3LYP/6-31G** level of theory. The molecular docking studies of the synthesized compound with lung cancer protein, PDB ID: 1M17, and colon cancer antigen proteins, PDB ID: 2HQ6 were performed to compare with experimental and theoretical data. Compound 2a had shown the best binding affinity with -6.6 kcal/mol. It was observed that the theoretical and experimental studies carried out supported each other.

Integrated metabolomics and serum-feces pharmacochemistry-based network pharmacology to reveal the mechanisms of an herbal prescription against ulcerative colitis

BACKGROUND

CDD-2103 is an herbal prescription used to treat ulcerative colitis (UC). This study aimed to uncover its mechanism by integrating metabolomics and serum-feces pharmacochemistry-based network pharmacology.

METHODS

A DSS-induced chronic colitis mice model was used to evaluate the anti-colitis effect of CDD-2103. Serum and feces metabolomics were conducted to identify differential metabolites and pathways. In the serum-feces pharmacochemistry study, biological samples were collected from rats treated with CDD-2103. Then, network pharmacology was utilized to predict the targets of the identified compounds. Critical genes were extracted through the above-integrated analysis. The interactions between targets, CDD-2103, and its compounds were validated through molecular docking, immunoblotting, and enzyme activity assays.

RESULTS

CDD-2103 alleviated ulcerous symptoms and colonic injuries in colitis mice. Metabolomics study identified differential metabolites associated with tryptophan, glycerophospholipid, and linoleic acid metabolisms. The serum-feces pharmacochemistry study revealed twenty-three compounds, which were subjected to network pharmacology analysis. Integration of these results identified three key targets (AHR, PLA2, and PTGS2). Molecular docking showed strong affinities between the compounds and targets. PTGS2 was identified as a hub gene targeted by most CDD-2103 compounds. Immunoblotting and enzyme activity assays provided further evidence that CDD-2103 alleviates UC, potentially through its inhibitory effect on cyclooxygenase-2 (COX-2, encoded by PTGS2), with alkaloids and curcuminoids speculated as crucial anti-inflammatory compounds.

CONCLUSION

This integrated strategy reveals the mechanism of CDD-2103 and provides insights for developing herbal medicine-based therapies for UC.

Marine fungal diversity unlocks potent antivirals against monkeypox through methyltransferase inhibition revealed by molecular dynamics and free energy landscape

The escalating threat posed by the Monkeypox virus (MPXV) to global health necessitates the urgent discovery of effective antiviral agents, as there are currently no specific drugs available for its treatment, and existing inhibitors are hindered by toxicity and poor pharmacokinetic profiles. This study aimed to identify potent MPXV inhibitors by screening a diverse library of small molecule compounds derived from marine fungi, focusing on the viral protein VP39, a key methyltransferase involved in viral replication. An extensive virtual screening process identified four promising compounds-CMNPD15724, CMNPD28811, CMNPD30883, and CMNPD18569-alongside a control molecule. Rigorous evaluations, including re-docking, molecular dynamics (MD) simulations, and hydrogen bond analysis, were conducted to assess their inhibitory potential against MPXV VP39. CMNPD15724 and CMNPD30883, in particular, demonstrated a superior binding affinity and stable interactions within the target protein's active site throughout the MD simulations, suggesting a capacity to overcome the limitations associated with sinefungin. The stability of these VP39-compound complexes, corroborated by MD simulations, provided crucial insights into the dynamic behavior of these interactions. Furthermore, Principal Component Analysis (PCA) based free energy landscape assessments offered a detailed understanding of the dynamic conformational changes and energetic profiles underlying these compounds' functional disruption of VP39. These findings establish CMNPD15724, CMNPD28811, CMNPD30883, and CMNPD18569 as promising MPXV inhibitors and highlight marine fungi as a valuable source of novel antiviral agents. These compounds represent potential candidates for further experimental validation, advancing the development of safer and more effective therapeutic options to combat this emerging viral infection.

Molecular insights into the interactions between PEG carriers and drug molecules from Celastrus hindsii: a multi-scale simulation study

Efficient drug delivery is crucial for the creation of effective pharmaceutical treatments, and polyethylene glycol (PEG) carriers have been emerged as promising candidates for this purpose due to their bio-compatibility, enhancement of drug solubility, and stability. In this study, we utilized molecular simulations to examine the interactions between PEG carriers and selected drug molecules extracted from Celastrus hindsii: Hindsiilactone A, Hindsiiquinoflavan B, Maytenfolone A, and Celasdin B. The simulations provided detailed insights into the binding affinity, stability, and structural properties of these drug molecules when complexed with PEG carriers. A multi-scale approach combining density functional theory (DFT), extended tight-binding (xTB), and molecular dynamics (MD) simulations was conducted to investigate both unbound and bound states of PEG/drug systems. The results from DFT and xTB calculations revealed that the unbound complex has an unfavorable binding free energy, primarily due to negative contributions of delta solvation free energy and entropy. The MD simulations provided more detailed insights into the interactions between PEG and drug molecules in water solutions. By integrating the findings from the multi-scale simulations, a comprehensive picture of the unbound and bound states of PEG and drug systems were obtained. This information is valuable for understanding the molecular mechanisms governing the binding of drugs in PEG-based delivery platforms, and it contributes to the rational design and optimization of these systems.

Transcriptomic and proteomic investigations identify PI3K-akt pathway targets for hyperthyroidism management in rats via polar iridoids from radix Scrophularia

High-polarity iridoids from Radix Scrophulariae (R. Scrophulariae) offer a range of benefits, including anti-inflammatory, antioxidant, antitumour, antibacterial, antiviral, and antiallergic effects. Although previous studies have indicated the potential of R. Scrophulariae for hyperthyroidism prevention and treatment, the specific active compounds involved and their mechanisms of action are not fully understood. This study explored the effects of high-polarity iridoid glycosides from R. Scrophulariae on hyperthyroidism induced in rats by levothyroxine sodium. The experimental design included a control group, a hyperthyroidism model group, and a group treated with iridoid glycosides. Serum triiodothyronine (T3) and thyroxine (T4) levels were quantified using an enzyme-linked immunosorbent assay (ELISA). Transcriptomic and proteomic analyses were applied to liver samples to identify differentially expressed genes and proteins. These analyses were complemented by trend analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The effectiveness of key factors was further examined through molecular biology techniques. ELISA results indicated a notable increase in T3 and T4 in the hyperthyroid rats, which was significantly mitigated by treatment with iridoid glycosides. Transcriptomic analysis revealed 6 upregulated and 6 downregulated genes in the model group, showing marked improvement following treatment. Proteomic analysis revealed changes in 30 upregulated and 50 downregulated proteins, with improvements observed upon treatment. The PI3K-Akt signalling pathway was investigated through KEGG enrichment analysis. Molecular biology methods verified the upregulation of Spp1, Thbs1, PI3K, and Akt in the model group, which was reversed in the treatment group. This study revealed that highly polar iridoids from R. Scrophulariae can modulate the Spp1 gene and Thbs1 protein via the PI3K-Akt signalling pathway, suggesting a therapeutic benefit for hyperthyroidism and providing a basis for drug development targeting this condition.

Molecular Mechanism of Yangshen Maidong Decoction in the Treatment of Chronic Heart Failure based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations

Chronic heart failure (CHF) is a complex multifactorial clinical syndrome leading to abnormal cardiac structure and function. The severe form of this ailment is characterized by high disability, high mortality, and morbidity. Worldwide, 2-17% of patients die at first admission, of which 17-45% die within 1 year of admission and >50% within 5 years. Yangshen Maidong Decoction (YSMDD) is frequently used to treat the deficiency and pain of the heart. The specific mechanism of action of YSMDD in treating CHF, however, remains unclear. Therefore, a network pharmacology-based strategy combined with molecular docking and molecular dynamics simulations was employed to investigate the potential molecular mechanism of YSMDD against CHF. The effective components and their targets of YSMDD and related targets of CHF were predicted and screened based on the public database. The network pharmacology was used to explore the potential targets and possible pathways that involved in YSMDD treated CHF. Molecular docking and molecular dynamics simulations were performed to elucidate the binding affinity between the YSMDD and CHF targets. Screen results, 10 main active ingredients, and 6 key targets were acquired through network pharmacology analysis. Pathway enrichment analysis showed that intersectional targets associated pathways were enriched in the Prostate cancer pathway, Hepatitis B pathway, and C-type lectin receptor signaling pathways. Molecular docking and molecular dynamics simulations analysis suggested 5 critical active ingredients have high binding affinity to the 5 key targets. This research shows the multiple active components and molecular mechanisms of YSMDD in the treatment of CHF and offers resources and suggestions for future studies.

Role of CSF1R 550th-tryptophan in kusunokinin and CSF1R inhibitor binding and ligand-induced structural effect

Binding affinity is an important factor in drug design to improve drug-target selectivity and specificity. In this study, in silico techniques based on molecular docking followed by molecular dynamics (MD) simulations were utilized to identify the key residue(s) for CSF1R binding affinity among 14 pan-tyrosine kinase inhibitors and 15 CSF1R-specific inhibitors. We found tryptophan at position 550 (W550) on the CSF1R binding site interacted with the inhibitors' aromatic ring in a π-π way that made the ligands better at binding. Upon W550-Alanine substitution (W550A), the binding affinity of trans-(-)-kusunokinin and imatinib to CSF1R was significantly decreased. However, in terms of structural features, W550 did not significantly affect overall CSF1R structure, but provided destabilizing effect upon mutation. The W550A also did not either cause ligand to change its binding site or conformational changes due to ligand binding. As a result of our findings, the π-π interaction with W550's aromatic ring could be still the choice for increasing binding affinity to CSF1R. Nevertheless, our study showed that the increasing binding to W550 of the design ligand may not ensure CSF1R specificity and inhibition since W550-ligand bound state did not induce significantly conformational change into inactive state.

Ligand-based drug design against Herpes Simplex Virus-1 capsid protein by modification of limonene through in silico approaches

The pharmacological effects of limonene, especially their derivatives, are currently at the forefront of research for drug development and discovery as well and structure-based drug design using huge chemical libraries are already widespread in the early stages of therapeutic and drug development. Here, various limonene derivatives are studied computationally for their potential utilization against the capsid protein of Herpes Simplex Virus-1. Firstly, limonene derivatives were designed by structural modification followed by conducting a molecular docking experiment against the capsid protein of Herpes Simplex Virus-1. In this research, the obtained molecular docking score exhibited better efficiency against the capsid protein of Herpes Simplex Virus-1 and hence we conducted further in silico investigation including molecular dynamic simulation, quantum calculation, and ADMET analysis. Molecular docking experiment has documented that Ligands 02 and 03 had much better binding affinities (- 7.4 kcal/mol and - 7.1 kcal/mol) to capsid protein of Herpes Simplex Virus-1 than Standard Acyclovir (- 6.5 kcal/mol). Upon further investigation, the binding affinities of primary limonene were observed to be slightly poor. But including the various functional groups also increases the affinities and capacity to prevent viral infection of the capsid protein of Herpes Simplex Virus-1. Then, the molecular dynamic simulation confirmed that the mentioned ligands might be stable during the formation of drug-protein complexes. Finally, the analysis of ADMET was essential in establishing them as safe and human-useable prospective chemicals. According to the present findings, limonene derivatives might be a promising candidate against the capsid protein of Herpes Simplex Virus-1 which ultimately inhibits Herpes Simplex Virus-induced encephalitis that causes interventions in brain inflammation. Our findings suggested further experimental screening to determine their practical value and utility.

The Role of PNPLA3_rs738409 Gene Variant, Lifestyle Factors, and Bioactive Compounds in Nonalcoholic Fatty Liver Disease: A Population-Based and Molecular Approach towards Healthy Nutrition

This study aimed to investigate the impact of a common non-synonymous gene variant (C>G, rs738409) in patatin-like phospholipase domain-containing 3 (PNPLA3), leading to the substitution of isoleucine with methionine at position 148 (PNPLA3-I148M), on susceptibility to nonalcoholic fatty liver disease (NAFLD) and explore potential therapeutic nutritional strategies targeting PNPLA3. It contributed to understanding sustainable dietary practices for managing NAFLD, recently referred to as metabolic-dysfunction-associated fatty liver. NAFLD had been diagnosed by ultrasound in a metropolitan hospital-based cohort comprising 58,701 middle-aged and older Korean individuals, identifying 2089 NAFLD patients. The interaction between PNPLA3 and lifestyle factors was investigated. In silico analyses, including virtual screening, molecular docking, and molecular dynamics simulations, were conducted to identify bioactive compounds from foods targeting PNPLA3(I148M). Subsequent cellular experiments involved treating oleic acid (OA)-exposed HepG2 cells with selected bioactive compounds, both in the absence and presence of compound C (AMPK inhibitor), targeting PNPLA3 expression. Carriers of the risk allele PNPLA3_rs738409G showed an increased association with NAFLD risk, particularly with adherence to a plant-based diet, avoidance of a Western-style diet, and smoking. Delphinidin 3-caffeoyl-glucoside, pyranocyanin A, delta-viniferin, kaempferol-7-glucoside, and petunidin 3-rutinoside emerged as potential binders to the active site residues of PNPLA3, exhibiting a reduction in binding energy. These compounds demonstrated a dose-dependent reduction in intracellular triglyceride and lipid peroxide levels in HepG2 cells, while pretreatment with compound C showed the opposite trend. Kaempferol-7-glucoside and petunidin-3-rutinoside showed potential as inhibitors of PNPLA3 expression by enhancing AMPK activity, ultimately reducing intrahepatic lipogenesis. In conclusion, there is potential for plant-based diets and specific bioactive compounds to promote sustainable dietary practices to mitigate NAFLD risk, especially in individuals with genetic predispositions.

Computational Tools to Assist in Analyzing Effects of the SERPINA1 Gene Variation on Alpha-1 Antitrypsin (AAT)

In the rapidly advancing field of bioinformatics, the development and application of computational tools to predict the effects of single nucleotide variants (SNVs) are shedding light on the molecular mechanisms underlying disorders. Also, they hold promise for guiding therapeutic interventions and personalized medicine strategies in the future. A comprehensive understanding of the impact of SNVs in the SERPINA1 gene on alpha-1 antitrypsin (AAT) protein structure and function requires integrating bioinformatic approaches. Here, we provide a guide for clinicians to navigate through the field of computational analyses which can be applied to describe a novel genetic variant. Predicting the clinical significance of SERPINA1 variation allows clinicians to tailor treatment options for individuals with alpha-1 antitrypsin deficiency (AATD) and related conditions, ultimately improving the patient's outcome and quality of life. This paper explores the various bioinformatic methodologies and cutting-edge approaches dedicated to the assessment of molecular variants of genes and their product proteins using SERPINA1 and AAT as an example.

Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes

Introduction

Colorectal cancers are the world's third most commonly diagnosed type of cancer. Currently, there are several diagnostic and treatment options to combat it. However, a delay in detection of the disease is life-threatening. Additionally, a thorough analysis of the exomes of cancers reveals potential variation data that can be used for early disease prognosis.

Methods

By utilizing a comprehensive computational investigation, the present study aimed to reveal mutations that could potentially predispose to colorectal cancer. Ten colorectal cancer exomes were retrieved. Quality control assessments were performed using FastQC and MultiQC, gapped alignment to the human reference genome (hg19) using Bowtie2 and calling the germline variants using Haplotype caller in the GATK pipeline. The variants were filtered and annotated using SIFT and PolyPhen2 successfully categorized the mutations into synonymous, non-synonymous, start loss and stop gain mutations as well as marked them as possibly damaging, probably damaging and benign. This mutational profile helped in shortlisting frequently occurring mutations and associated genes, for which the downstream multi-dimensional expression analyses were carried out.

Results

Our work involved prioritizing the non-synonymous, deleterious SNPs since these polymorphisms bring about a functional alteration to the phenotype. The top variations associated with their genes with the highest frequency of occurrence included LGALS8, CTSB, RAD17, CPNE1, OPRM1, SEMA4D, MUC4, PDE4DIP, ELN and ADRA1A. An in-depth multi-dimensional downstream analysis of all these genes in terms of gene expression profiling and analysis and differential gene expression with regard to various cancer types revealed CTSB and CPNE1 as highly expressed and overregulated genes in colorectal cancer.

Conclusion

Our work provides insights into the various alterations that might possibly lead to colorectal cancer and suggests the possibility of utilizing the most important genes identified for wet-lab experimentation.

KIF2C promotes clear cell renal cell carcinoma progression via activating JAK2/STAT3 signaling pathway

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant tumors that can be highly aggressive. Despite advances in the exploration of its underlying molecular biology, the clinical outcome for advanced ccRCC is still unsatisfied. Recently, more attention was paid to the functions of Kinesin family member 2C (KIF2C) in cancer progression, while the specific function of KIF2C in ccRCC has not been sufficiently elucidated. The present study aims to investigate the role of KIF2C in the progression of ccRCC and reveal potential mechanisms.

METHODS

Expression of KIF2C in ccRCC tissues and adjacent normal tissue was compared and the association of KIF2C expression level with tumor grade, stage, and metastasis were analyzed using online web tool. Kaplan-Meier survival was performed to detect the association of KIF2C expression and patient' prognosis. Stably cell lines with KIF2C knockdown or overexpression were constructed by lentivirus infection. CCK-8, colony formation, scratch healing, and transwell invasion assays were carried out to explore the effect of KIF2C knockdown or overexpression on the proliferation, migration, and invasion of ccRCC cells. Gene set enrichment analysis (GSEA) was conducted to reveal signaling pathways associated with KIF2C expression. The effect of KIF2C on JAK2/STAT3 signaling pathway were explored by western blot assay.

RESULTS

KIF2C expression was significantly upregulated in ccRCC tissues and was higher with the increase of tumor grade, stage, and metastasis. Higher expression of KIF2C was correlated with worse overall survival and diseases free survival in ccRCC patients. Silence of KIF2C inhibited proliferation, migration, and invasion in ccRCC cells. Conversely, overexpression of KIF2C had the opposite effect. GSEA results showed that JAK/STAT signaling pathway was markedly enriched in KIF2Chigh group. Pearson' correlation revealed that KIF2C expression was significantly associated with genes in JAK2/STAT3 signaling. Western blot results showed that KIF2C knockdown decreased protein expression of p-JAK2 and p-STAT3, and KIF2C overexpression increased the phosphorylation of JAK2 and STAT3. AG490, a JAK2/STAT3 signaling inhibitor, could partly impair the tumor-promoting effects of KIF2C in ccRCC.

CONCLUSION

KIF2C expression was significantly upregulated in ccRCC and correlated with tumor grade, stage, metastasis, and patients' prognosis. KIF2C promoted ccRCC progression via activating JAK2/STAT3 signaling pathway, and KIF2C might be a novel target in ccRCC therapy.

In silico comprehensive analysis of coding and non-coding SNPs in human mTOR protein

The mammalian/mechanistic target of rapamycin (mTOR) protein is an important growth regulator and has been linked with multiple diseases including cancer and diabetes. Non-synonymous mutations of this gene have already been found in patients with renal clear cell carcinoma, melanoma, and acute lymphoid leukemia among many others. Such mutations can potentially affect a protein’s structure and hence its functions. In this study, therefore, the most deleterious SNPs of mTOR protein have been determined to identify potential biomarkers for various disease treatments. The aim is to generate a structured dataset of the mTOR gene’s SNPs that may prove to be an asset for the identification and treatment of multiple diseases associated with the target gene. Both sequence and structure-based approaches were adopted and a wide variety of bioinformatics tools were applied to analyze the SNPs of mTOR protein. In total 11 nsSNPs have been filtered out of 2178 nsSNPs along with two non-coding variations. All of the nsSNPs were found to destabilize the protein structure and disrupt its function. While R619C, A1513D, and T1977R mutations were shown to alter C alpha distances and bond angles of the mTOR protein, L509Q, R619C and N2043S were predicted to disrupt the mTOR protein’s interaction with NBS1 protein and FKBP1A/rapamycin complex. In addition, one of the non-coding SNPs was shown to alter miRNA binding sites. Characterizing nsSNPs and non-coding SNPs and their harmful effects on a protein’s structure and functions will enable researchers to understand the critical impact of mutations on the molecular mechanisms of various diseases. This will ultimately lead to the identification of potential targets for disease diagnosis and therapeutic interventions.

High-Risk Polymorphisms Associated with the Molecular Function of Human HMGCR Gene Infer the Inhibition of Cholesterol Biosynthesis

HMG-CoA reductase or HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase) is a rate-limiting enzyme involved in cholesterol biosynthesis. HMGCR plays an important role in the possible occurrence of hypercholesterolemia leading to atherosclerosis and coronary heart disease. This enzyme is a major target for cholesterol-lowering drugs such as "statin" which blocks the synthesis of mevalonate, a precursor for cholesterol biosynthesis. This study is aimed at characterizing deleterious mutations and classifying functional single nucleotide polymorphisms (SNPs) of the HMGCR gene through analysis of functional and structural evaluation, domain association, solvent accessibility, and energy minimization studies. The functional and characterization tools such as SIFT, PolyPhen, SNPs and GO, Panther, I-Mutant, and Pfam along with programming were employed to explore all the available SNPs in the HMGCR gene in the database. Among 6815 SNP entries from different databases, approximately 388 SNPs were found to be missense. Analysis showed that seven missense SNPs are more likely to have deleterious effects. A tertiary model of the mutant protein was constructed to determine the functional and structural effects of the HMGCR mutation. In addition, the location of the mutations suggests that they may have deleterious effects because most of the mutations are residing in the functional domain of the protein. The findings from the analysis predicted that rs147043821 and rs193026499 missense SNPs could cause significant structural and functional instability in the mutated proteins of the HMGCR gene. The findings of the current study will likely be useful in future efforts to uncover the mechanism and cause of hypercholesterolemia. In addition, the identified SNPs of HMGCR gene could set up a strong foundation for further therapeutic discovery.

Isolation and structure elucidation of a steroidal moiety from Withania somnifera and in silico evaluation of antimalarial efficacy against artemisinin resistance Plasmodium falciparum kelch 13 protein

According to the 2021 Malaria report, 241 million clinical episodes with 627000 deaths penalty was estimated across the worldwide. However, mutation in the propeller domain of Plasmodium falciparum kelch 13 protein resulted in longer parasite clearance time following an artemisinin-based treatment and had a greater survival rate of ring-stage parasites even after a brief exposure to a high dose of artesunate. Clinical manifestations become more complex and worse with the emerging trend of drug resistance against artemisinin derivatives and the poor effectiveness of malaria vaccination drive. Steroidal lactone (withanolide) moiety (C-28) isolated from methanolic leaf extract Withania somnifera show a greater affinity towards Pfkelch 13 protein in comparison to the artemisinin derivatives (artesunate, artemether). The isolated compound was characterized to be withaferin A with a percentage yield of 29.01% w/w in chloroform fraction, 1.75% w/w in methanolic extract, and 0.29% w/w in raw leaf powder. Structure-based analysis shows that withaferin A (docking score -8.253, -9.802) has a higher affinity for two distinct binding pockets I and II of the Plasmodium falciparum kelch 13 protein than artesunate (docking score -4.470, -3.656). Further, Gibbs binding free energy signifies thermodynamic stability of the docked complex of withaferin A (-43.25, -43.76 Kcal/mol) in comparison to artesunate docked complex (-8.49, -5.75 Kcal/mol). The pharmacokinetic profile of withaferin A shows more drug-likeness characteristics without violating Jorgensen's rule of three, and Lipinski's rule of five. Hence above experimental findings suggest withaferin A could be a suitable therapeutic adjunct for preclinical evaluation of antimalarial potentiality in artemisinin-resistant malaria.

Molecular Dynamics Simulation and Essential Dynamics of Deleterious Proline 12 Alanine Single-Nucleotide Polymorphism in PPARγ2 Associated with Type 2 Diabetes, Cardiovascular Disease, and Nonalcoholic Fatty Liver Disease

Background. Peroxisome proliferator-activated receptor-γ (PPARγ) gene is located at 3p25 position. PPARγ functions as the master regulator of glucose homeostasis and lipoprotein metabolism, and recent studies have reported that it is involved in various metabolic diseases such as diabetes mellitus, hyperlipidemia, coronary artery disease (CAD), and nonalcoholic fatty liver disease (NAFLD). PPARγ1 and PPARγ2 are necessary for the development of adipose tissue and insulin sensitivity regulation. But PPARγ2 is the isoform that was controlled in response to nutrient intake and obesity. Methodology. In this study, we used computational techniques to show the impact of Pro12Ala polymorphism on PPARγ2. The 3-D structure of PPARγ2 was modeled using I-TASSER server. The modeled structure was validated with the ZLab server, and the mutation was created with SPDB viewer. Stability prediction tools were used. Molecular dynamics simulation (MDS) was used to understand the structural and functional behavior of the wild type and mutant. Essential dynamics was also applied. Results and Conclusions. Stability prediction tools were showed that this mutation has a destabilizing effect on the PPARγ2 structure. The RMSD, RMSF, Rg, SASA, and DSSP were in line with H-bond results that showed less flexibility in the mutant structure. Essential dynamics was used to verify MDS results. Pro12Ala polymorphism leads to rigidity of the PPARγ2 protein and might disturb the conformational changes and interactions of PPARγ2 and results in type 2 diabetes mellitus (T2DM), CAD, and NAFLD. This study can help scientists to develop a drug therapy against these diseases.

Identification, analysis of deleterious SNPs of the human GSR gene and their effects on the structure and functions of associated proteins and other diseases

Hereditary glutathione reductase deficiency, caused by mutations of the GSR gene, is an autosomal recessive disorder characterized by decreased glutathione disulfide (GSSG) reduction activity and increased thermal instability. This study implemented computational analysis to screen the most likely mutation that might be associated with hereditary glutathione reductase deficiency and other diseases. Using ten online computational tools, the study revealed four nsSNPs among the 17 nsSNPs identified as most deleterious and disease associated. Structural analyses and evolutionary confirmation study of native and mutant GSR proteins using the HOPE project and ConSruf. HOPE revealed more flexibility in the native GSR structure than in the mutant structure. The mutation in GSR might be responsible for changes in the structural conformation and function of the GSR protein and might also play a significant role in inducing hereditary glutathione reductase deficiency. LD and haplotype studies of the gene revealed that the identified variations rs2978663 and rs8190955 may be responsible for obstructive heart defects (OHDs) and hereditary anemia, respectively. These interethnic differences in the frequencies of SNPs and haplotypes might help explain the unpredictability that has been reported in association studies and can contribute to predicting the pharmacokinetics and pharmacodynamics of drugs that make use of GSR.

In-silico phenotype prediction by normal mode variant analysis in TUBB4A-related disease

TUBB4A-associated disorder is a rare condition affecting the central nervous system. It displays a wide phenotypic spectrum, ranging from isolated late-onset torsion dystonia to a severe early-onset disease with developmental delay, neurological deficits, and atrophy of the basal ganglia and cerebellum, therefore complicating variant interpretation and phenotype prediction in patients carrying TUBB4A variants. We applied entropy-based normal mode analysis (NMA) to investigate genotype–phenotype correlations in TUBB4A-releated disease and to develop an in-silico approach to assist in variant interpretation and phenotype prediction in this disorder. Variants included in our analysis were those reported prior to the conclusion of data collection for this study in October 2019. All TUBB4A pathogenic missense variants reported in ClinVar and Pubmed, for which associated clinical information was available, and all benign/likely benign TUBB4A missense variants reported in ClinVar, were included in the analysis. Pathogenic variants were divided into five phenotypic subgroups. In-silico point mutagenesis in the wild-type modeled protein structure was performed for each variant. Wild-type and mutated structures were analyzed by coarse-grained NMA to quantify protein stability as entropy difference value (ΔG) for each variant. Pairwise ΔG differences between all variant pairs in each structural cluster were calculated and clustered into dendrograms. Our search yielded 41 TUBB4A pathogenic variants in 126 patients, divided into 11 partially overlapping structural clusters across the TUBB4A protein. ΔG-based cluster analysis of the NMA results revealed a continuum of genotype–phenotype correlation across each structural cluster, as well as in transition areas of partially overlapping structural clusters. Benign/likely benign variants were integrated into the genotype–phenotype continuum as expected and were clearly separated from pathogenic variants. We conclude that our results support the incorporation of the NMA-based approach used in this study in the interpretation of variant pathogenicity and phenotype prediction in TUBB4A-related disease. Moreover, our results suggest that NMA may be of value in variant interpretation in additional monogenic conditions.

Rational Design of Anti-Angiogenic Peptides to Inhibit VEGF/VEGFR2 Interactions for Cancer Therapeutics

BACKGROUND

Angiogenesis is a critical physiological process that plays a key role in tumor progression, metastatic dissemination, and invasion. In the last two decades, the vascular endothelial growth factor (VEGF) signaling pathway has been the area of extensive researches. VEGF executes its special effects by binding to vascular endothelial growth factor receptors (VEGFRs), particularly VEGFR-2.

OBJECTIVE

The inhibition of VEGF/VEGFR2 interaction is known as an effective cancer therapy strategy. The current study pointed to design and model an anti-VEGF peptide based on VEGFR2 binding regions.

METHOD

The large-scale peptide mutation screening was used to achieve a potent peptide with high binding affinity to VEGF for possible application in inhibition of VEGF/VEGFR2 interaction. The AntiCP and Peptide Ranker servers were used to generate the possible peptides library with anticancer activities and prediction of peptides bioactivity. Then, the interaction of VEGF and all library peptides were analyzed using Hex 8.0.0 and ClusPro tools. A number of six peptides with favorable docking scores were achieved. All of the best docking scores of peptides in complexes with VEGF were evaluated to confirm their stability, using molecular dynamics simulation (MD) with the help of the GROMACS software package.

RESULTS

As a result, two antiangiogenic peptides with 13 residues of PepA (NGIDFNRDFFLGL) and PepC (NGIDFNRDKFLFL) were achieved and introduced to inhibit VEGF/VEGFR2 interactions.

CONCLUSIONS

In summary, this study provided new insights into peptide-based therapeutics development for targeting VEGF signaling pathway in tumor cells. PepA and PepC are recommended as potentially promising anticancer agents for further experimental evaluations.

Computational Analysis of Gly482Ser Single-Nucleotide Polymorphism in PPARGC1A Gene Associated with CAD, NAFLD, T2DM, Obesity, Hypertension, and Metabolic Diseases

Peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PPARGC1A) regulates the expression of energy metabolism's genes and mitochondrial biogenesis. The essential roles of PPARGC1A encouraged the researchers to assess the relation between metabolism-related diseases and its variants. To study Gly482Ser (+1564G/A) single-nucleotide polymorphism (SNP) after PPARGC1A modeling, we substitute Gly482 for Ser482. Stability prediction tools showed that this substitution decreases the stability of PPARGC1A or has a destabilizing effect on this protein. We then utilized molecular dynamics simulation of both the Gly482Ser variant and wild type of the PPARGC1A protein to analyze the structural changes and to reveal the conformational flexibility of the PPARGC1A protein. We observed loss flexibility in the RMSD plot of the Gly482Ser variant, which was further supported by a decrease in the SASA value in the Gly482Ser variant structure of PPARGC1A and an increase of H-bond with the increase of β-sheet and coil and decrease of turn in the DSSP plot of the Gly482Ser variant. Such alterations may significantly impact the structural conformation of the PPARGC1A protein, and it might also affect its function. It showed that the Gly482Ser variant affects the PPARGC1A structure and makes the backbone less flexible to move. In general, molecular dynamics simulation (MDS) showed more flexibility in the native PPARGC1A structure. Essential dynamics (ED) also revealed that the range of eigenvectors in the conformational space has lower extension of motion in the Gly482Ser variant compared with WT. The Gly482Ser variant also disrupts PPARGC1A interaction. Due to this single-nucleotide polymorphism in PPARGC1A, it became more rigid and might disarray the structural conformation and catalytic function of the protein and might also induce type 2 diabetes mellitus (T2DM), coronary artery disease (CAD), and nonalcoholic fatty liver disease (NAFLD). The results obtained from this study will assist wet lab research in expanding potent treatment on T2DM.

Polymorphisms at site 469 of B-RAF protein associated with skin melanoma may be correlated with dabrafenib resistance: An in silico study

Melanoma is a type of skin cancer. Numerous genes and their proteins are strongly associated with melanoma susceptibility. This study aims to use an in silico method to identify genetic variants in the melanoma susceptibility gene. The COSMIC database was queried for genes and cross-referenced with three environment-gene interaction databases (EGP, SeattleSNPs and CTD) to identify shared genes. The majority of approved skin melanoma drugs were found to act on the protein serine/threonine-protein kinase (B-RAF) encoded by the BRAF gene, which was also present in all three referenced databases. Comprehensive computational analysis was performed to predict deleterious genetic variants associated with skin melanoma, and the nsSNPs G469V and G469E were prioritized based on their predicted deleterious effects. Molecular dynamic simulation analysis of the B-RAF protein mutants G469V and G469E reveals that variations in the amino acid conformation at the drug binding site result in inconsistency in drug interaction. Additionally, this analysis showed that the G469V and G469E mutants have lower binding energy for dabrafenib than the wild type. The population with the highest frequency of each deleterious and pathogenic variant has been determined. The study's findings would support the development of more effective treatment strategies for skin melanoma. Communicated by Ramaswamy H. Sarma.

Structural effects driven by rare point mutations in amylin hormone, the type II diabetes-associated peptide

BACKGROUND

Amylin is a 37-amino-acid peptide hormone co-secreted with insulin, which participates in glucose homeostasis. This hormone is able to aggregate in a β-sheet conformation and deposit in islet amyloids, a hallmark in type II diabetes. Since amylin is a gene-encoded hormone, this peptide has variants caused by point mutations that can impact its functions.

METHODS

Here, we analyzed the structural effects caused by S20G and G33R point mutations which, according to the 1000 Genomes Project, have frequency in East Asian and European populations, respectively. The analyses were performed by means of aggrescan server, SNP functional effect predictors, and molecular dynamics.

RESULTS

We found that both mutations have aggregation potential and cause changes in the monomeric forms when compared with wild-type amylin. Furthermore, comparative analyses with pramlintide, an amylin drug analogue, allowed us to infer that second α-helix maintenance may be related to the aggregation potential.

CONCLUSIONS

The S20G mutation has been described as pathologically related, which is in agreement with our findings. In addition, our data suggest that the G33R mutation might have a deleterious effect. The data presented here also provide new therapy opportunities, whether for creating more effective drugs for diabetes or implementing specific treatment for patients with these mutations.

GENERAL SIGNIFICANCE

Our data could help to better understand the impact of mutations on the wild-type amylin sequence, as a starting point for the evaluation and characterization of other variations. Moreover, these findings could improve the health of patients with type II diabetes.

In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review

Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.

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

Serine-threonine kinase11 (STK11) is a tumor suppressor gene which plays a key role in regulating cell growth and apoptosis. It is widely known as a multitasking kinase and engaged in cell polarity, cell cycle arrest, chromatin remodeling, energy metabolism, and Wnt signaling. The substitutions of single amino acids in highly conserved regions of the STK11 protein are associated with Peutz-Jeghers syndrome (PJS), which is an autosomal dominant inherited disorder. The abnormal function of the STK11 protein is still not well understood. In this study, we classified disease susceptible single nucleotide polymorphisms (SNPs) in STK11 by using different computational algorithms. We identified the deleterious nsSNPs, constructed mutant protein structures, and evaluated the impact of mutation by employing molecular docking and molecular dynamics analysis. Our results show that W239R and W308C variants are likely to be highly deleterious mutations found in the catalytic kinase domain, which may destabilize structure and disrupt the activation of the STK11 protein as well as reduce its catalytic efficiency. The W239R mutant is likely to have a greater impact on destabilizing the protein structure compared to the W308C mutant. In conclusion, these mutants can help to further realize the large pool of disease susceptibilities linked with catalytic kinase domain activation of STK11 and assist to develop an effective drug for associated diseases.

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.

FAM172A expression in circulating tumor cells for prediction of high-risk subgroups of colorectal cancer

Objectives

Previous studies used enumerated circulating tumor cells (CTCs) to predict prognosis and therapeutic effect in several types of cancers. However, increasing evidence showed that only enumerated CTCs were not enough to reflect the heterogeneity of tumors. Therefore, we classified different metastasis potentials of CTCs from colorectal cancer (CRC) patients to improve the accuracy of prognosis by CTCs.

Methods

Blood samples were collected from 45 primary CRC patients. CTCs were enriched by blood filtration, and the RNA in situ hybridization method was used to identify and discriminate subgroups of CTCs. Later, FAM172A expression in individual CTCs was measured.

Results

Three CTC subgroups (epithelial/biophenotypic/mesenchymal CTCs) were identified using epithelial–mesenchymal transition markers. In our research, mesenchymal CTCs significantly increased along with tumor progression, including developing distant metastasis and vascular invasion. Furthermore, FAM172A expression rate in mesenchymal CTCs was significantly higher than that in epithelial CTCs, which suggested that FAM172A may correlate with tumor malignancy. This hypothesis was further verified by FAM172A expression in mesenchymal CTCs strictly related to tumor aggressiveness factors. Finally, we revealed that mesenchymal CTCs and FAM172A expression may predict high-risk subgroups in stage II CRC.

Conclusion

Our research proved that CTCs could serve as feasible surrogate samples to detect gene expression as a predictive biomarker for tumor evaluation.

Identification of colorectal cancer-restricted microRNAs and their target genes based on high-throughput sequencing data

To identify potential key microRNAs (miRNAs) and their target genes for colorectal cancer (CRC). High-throughput sequencing data of miRNA expression and gene expression (ID: GSE46622) were downloaded from Gene Expression Omnibus, including matched colon tumor, normal colon epithelium, and liver metastasis tissues from eight CRC patients. Paired t-test and NOISeq separately were utilized to identify differentially expressed miRNAs (DE-miRNAs) and genes. Then, target genes with differential expression and opposite expression trends were identified for DE-miRNAs. Combined with tumor suppressor gene, tumor-associated gene, and TRANSFAC databases, CRC-restricted miRNAs were screened out based on miRNA-target pairs. Compared with normal tissues, there were 56 up- and 37 downregulated miRNAs in metastasis tissues, as well as eight up- and 30 downregulated miRNAs in tumor tissues. miRNA-1 was downregulated in tumor and metastasis tissues, while its target oncogenes TWIST1 and GATA4 were upregulated. Besides, miRNA-let-7f-1-3p was downregulated in tumor tissues, which also targeted TWIST1. In addition, miRNA-133b and miRNA-4458 were downregulated in tumor tissues, while their common target gene DUSP9 was upregulated. Conversely, miRNA-450-b-3p was upregulated in metastasis tissues, while its target tumor suppressor gene CEACAM7 showed downregulation. The identified CRC-restricted miRNAs might be implicated in cancer progression via their target genes, suggesting their potential usage in CRC treatment.

Identification of Dual Natural Inhibitors for Chronic Myeloid Leukemia by Virtual Screening, Molecular Dynamics Simulation and ADMET Analysis

Chronic myeloid leukemia (CML) is a disease of bone marrow stem cells caused by excessive growth and accumulation of granulocytes in the blood. Aberrant expression of the BCR-ABL proteins in bone marrow stem cells have found out in 95 % cases of CML. Tyrosine Kinase domains (SH2 and SH3) of BCR-ABL proteins are the potent targets to inhibit the process. Initially, imatinib is preferred as an efficient inhibitor to control functional activity of disease. Recently, it has been reported that the advanced stage of CML developed resistance against imatinib. In continuation, dasatinib is the first drug to combat against this disease by targeting multiple receptors and proven better as compared to imatinib. Here, an attempt has been made to identify similar analogs of dasatinib. Virtual screening was performed against various natural compound databases to get some potent natural compounds which are able to inhibit more than one receptor. Binding affinity of screened natural compounds was compared with some of the well-known inhibitors like imatinib, dasatinib, nilotinib etc., by analyzing their docking score and binding efficiency with the receptor. Stability of the best ligand-receptor complex was checked by performing 10 ns molecular dynamics simulation. ADMET properties of the obtained screened compounds were analyzed to check drug like property. Based on the aforementioned analysis, it has been suggested that these screened potent compounds are capable to inhibit multiple receptor proteins like ABL and SRC and consequently combat against the deadly disease CML.

The expression and significance of Gal-3 and MUC1 in colorectal cancer and colon cancer

Objective

The objective of the present investigation was to explore the expression and significance of Gal-3 and MUC1 in colorectal cancer tissue and tissue adjacent to carcinoma.

Methods

In this study we collected colorectal cancer tissues and the tissues adjacent to carcinoma from 45 cases from the Colorectal Cancer Surgery Department of Zhengzhou People’s Hospital from December of 2009 to June of 2010. At the same time, this study also collected nontumor tissues adjacent to carcinoma from 20 cases as the control group. The expression of Gal-3 and MUC1 of these tissues was detected by using immunohistochemistry streptavidin-peroxidase method, and the correlation between colorectal cancer and expression of Gal-3 and MUC1 was analyzed.

Results

The positive expression rates of Gal-3 in the tissues adjacent to carcinoma and colorectal cancer were 15.0% and 73.3%, respectively. The positive expression rate of Gal-3 in colorectal cancer was significantly higher than that in the tissue adjacent to carcinoma. The positive expression rate of Gal-3 of the patients without lymph node metastasis was 61.5% (16/26). The positive expression rate of Gal-3 in the patients with lymph node metastasis was 89.5% (17/19), and the difference was statistically significant (P=0.0363). The positive expression rates of MUC1 in the tissues adjacent to carcinoma and in colorectal cancer tissues were 0.0% and 54.5%, respectively. The positive expression rate of MUC1 in colorectal cancer tissues was significantly higher than that in the normal tissues adjacent to carcinoma (P<0.05); the positive expression rate of MUC1 in the patients without lymph node metastasis was 34.6% (9/26). The positive expression rate of MUC1 in the patients with lymph node metastasis was 84.2% (16/19), and the expression difference was statistically significant (P=0.0009).

Conclusion

The expression of Gal-3 and MUC1 was significantly higher than that in the nontumor tissue adjacent to carcinoma. There was a correlation between Gal-3 and MUC1 expression and lymphatic metastasis.

Functional and Structural Consequences of Damaging Single Nucleotide Polymorphisms in Human Prostate Cancer Predisposition Gene RNASEL

A commonly diagnosed cancer, prostate cancer (PrCa), is being regulated by the gene RNASEL previously known as PRCA1 codes for ribonuclease L which is an integral part of interferon regulated system that mediates antiviral and antiproliferative role of the interferons. Both somatic and germline mutations have been implicated to cause prostate cancer. With an array of available Single Nucleotide Polymorphism data on dbSNP this study is designed to sort out functional SNPs in RNASEL by implementing different authentic computational tools such as SIFT, PolyPhen, SNPs&GO, Fathmm, ConSurf, UTRScan, PDBsum, Tm-Align, I-Mutant, and Project HOPE for functional and structural assessment, solvent accessibility, molecular dynamics, and energy minimization study. Among 794 RNASEL SNP entries 124 SNPs were found nonsynonymous from which SIFT predicted 13 nsSNPs as nontolerable whereas PolyPhen-2 predicted 28. SNPs found on the 3' and 5' UTR were also assessed. By analyzing six tools having different perspectives an aggregate result was produced where nine nsSNPs were found to be most likely to exert deleterious effect. 3D models of mutated proteins were generated to determine the functional and structural effect of the mutations on ribonuclease L. The initial findings were reinforced by the results from I-Mutant and Project HOPE as these tools predicted significant structural and functional instability of the mutated proteins. Expasy-ProSit tool defined the mutations to be situated in the functional domains of the protein. Considering previous analysis this study revealed a conclusive result deducing the available SNP data on the database by identifying the most damaging three nsSNP rs151296858 (G59S), rs145415894 (A276V), and rs35896902 (R592H). As such studies involving polymorphisms of RNASEL were none to be found, the results of the current study would certainly be helpful in future prospects concerning prostate cancer in males.

Gene expression profile analysis of colorectal cancer to investigate potential mechanisms using bioinformatics

This study aimed to explore the underlying molecular mechanisms of colorectal cancer (CRC) using bioinformatics analysis. Using GSE4107 datasets downloaded from the Gene Expression Omnibus, the differentially expressed genes (DEGs) were screened by comparing the RNA expression from the colonic mucosa between 12 CRC patients and ten healthy controls using a paired t-test. The Gene Ontology (GO) functional and pathway enrichment analyses of DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) software followed by the construction of a protein–protein interaction (PPI) network. In addition, hub gene identification and GO functional and pathway enrichment analyses of the modules were performed. A total of 612 up- and 639 downregulated genes were identified. The upregulated DEGs were mainly involved in the regulation of cell growth, migration, and the MAPK signaling pathway. The downregulated DEGs were significantly associated with oxidative phosphorylation, Alzheimer’s disease, and Parkinson’s disease. Moreover, FOS, FN1, PPP1CC, and CYP2B6 were selected as hub genes in the PPI networks. Two modules (up-A and up-B) in the upregulated PPI network and three modules (d-A, d-B, and d-C) in the downregulated PPI were identified with the threshold of Molecular Complex Detection (MCODE) Molecular Complex Detection (MCODE) score ≥4 and nodes ≥6. The genes in module up-A were significantly enriched in neuroactive ligand–receptor interactions and the calcium signaling pathway. The genes in module d-A were enriched in four pathways, including oxidative phosphorylation and Parkinson’s disease. DEGs, such as FOS, FN1, PPP1CC, and CYP2B6, may be used as potential targets for CRC diagnosis and treatment.

Meta-analysis of the association between APC promoter methylation and colorectal cancer

Previous studies investigating the association between adenomatous polyposis coli (APC) gene promoter methylation and colorectal cancer (CRC) have yielded conflicting results. The aim of this study was to comprehensively evaluate the potential application of the detection of APC promoter methylation to the prevention and treatment of CRC. PubMed, Embase, and MEDLINE (results updated to October 2014) were searched for relevant studies. The effect size was defined as the weighted odds ratio (OR), which was calculated using either the fixed-effects or random-effects model. Prespecified subgroup and sensitivity analyses were conducted to evaluate potential heterogeneity among the included studies. Nineteen studies comprising 2,426 participants were selected for our meta-analysis. The pooled results of nine studies comprising a total of 740 subjects indicated that APC promoter methylation was significantly associated with CRC risk (pooled OR 5.53; 95% confidence interval [CI] 3.50–8.76; P<0.01). Eleven studies with a total of 1,219 patients evaluated the association between APC promoter methylation and the presence of CRC metastasis, and the pooled OR was 0.80 (95% CI 0.44–1.46; P=0.47). A meta-analysis conducted with four studies with a total of 467 patients found no significant correlation between APC promoter methylation and the presence of colorectal adenoma (pooled OR 1.85; 95% CI 0.67–5.10; P=0.23). No significant correlation between APC promoter methylation and patients’ Dukes’ stage, TNM stage, differentiation grade, age, or sex was identified. In conclusion, APC promoter methylation was found to be significantly associated with a higher risk of developing CRC. The findings indicate that APC promoter methylation may be a potential biomarker for the carcinogenesis of CRC.

Use of long term molecular dynamics simulation in predicting cancer associated SNPs

Computational prediction of cancer associated SNPs from the large pool of SNP dataset is now being used as a tool for detecting the probable oncogenes, which are further examined in the wet lab experiments. The lack in prediction accuracy has been a major hurdle in relying on the computational results obtained by implementing multiple tools, platforms and algorithms for cancer associated SNP prediction. Our result obtained from the initial computational compilations suggests the strong chance of Aurora-A G325W mutation (rs11539196) to cause hepatocellular carcinoma. The implementation of molecular dynamics simulation (MDS) approaches has significantly aided in raising the prediction accuracy of these results, but measuring the difference in the convergence time of mutant protein structures has been a challenging task while setting the simulation timescale. The convergence time of most of the protein structures may vary from 10 ns to 100 ns or more, depending upon its size. Thus, in this work we have implemented 200 ns of MDS to aid the final results obtained from computational SNP prediction technique. The MDS results have significantly explained the atomic alteration related with the mutant protein and are useful in elaborating the change in structural conformations coupled with the computationally predicted cancer associated mutation. With further advancements in the computational techniques, it will become much easier to predict such mutations with higher accuracy level.

Inter-individual variation in nucleotide excision repair pathway is modulated by non-synonymous polymorphisms in ERCC4 and MBD4 genes

Inter-individual differences in DNA repair capacity (DRC) may lead to genome instability and, consequently, modulate individual cancer risk. Among the different DNA repair pathways, nucleotide excision repair (NER) is one of the most versatile, as it can eliminate a wide range of helix-distorting DNA lesions caused by ultraviolet light irradiation and chemical mutagens. We performed a genotype-phenotype correlation study in 122 healthy subjects in order to assess if any associations exist between phenotypic profiles of NER and DNA repair gene single nucleotide polymorphisms (SNPs). Individuals were genotyped for 768 SNPs with a custom Illumina Golden Gate Assay, and peripheral blood mononuclear cells (PBMCs) of the same subjects were tested for a NER comet assay to measure DRC after challenging cells by benzo(a)pyrene diolepoxide (BPDE). We observed a large inter-individual variability of NER capacity, with women showing a statistically significant lower DRC (mean ± SD: 6.68 ± 4.76; p = 0.004) than men (mean ± SD: 8.89 ± 5.20). Moreover, DRC was significantly lower in individuals carrying a variant allele for the ERCC4 rs1800124 non-synonymous SNP (nsSNP) (p = 0.006) and significantly higher in subjects with the variant allele of MBD4 rs2005618 SNP (p = 0.008), in linkage disequilibrium (r(2) = 0.908) with rs10342 nsSNP. Traditional in silico docking approaches on protein-DNA and protein-protein interaction showed that Gly875 variant in ERCC4 (rs1800124) decreases the DNA-protein interaction and that Ser273 and Thr273 variants in MBD4 (rs10342) indicate complete loss of protein-DNA interactions. Our results showed that NER inter-individual capacity can be modulated by cross-talk activity involving nsSNPs in ERCC4 and MBD4 genes, and they suggested to better investigate SNP effect on cancer risk and response to chemo- and radiotherapies.

Molecular mechanisms of disease-causing missense mutations

Genetic variations resulting in a change of amino acid sequence can have a dramatic effect on stability, hydrogen bond network, conformational dynamics, activity and many other physiologically important properties of proteins. The substitutions of only one residue in a protein sequence, so-called missense mutations, can be related to many pathological conditions and may influence susceptibility to disease and drug treatment. The plausible effects of missense mutations range from affecting the macromolecular stability to perturbing macromolecular interactions and cellular localization. Here we review the individual cases and genome-wide studies that illustrate the association between missense mutations and diseases. In addition, we emphasize that the molecular mechanisms of effects of mutations should be revealed in order to understand the disease origin. Finally, we report the current state-of-the-art methodologies that predict the effects of mutations on protein stability, the hydrogen bond network, pH dependence, conformational dynamics and protein function.