Journal of Computational Biophysics and Chemistry

Mitotic arrest deficient 2 like 1 (MAD2L1) is a component of the mitotic spindle assembly checkpoint that prevents the onset of anaphase until all chromosomes are properly aligned at the metaphase plate, a process that confirms genomic stability. As several human cancers experience abnormal expression of such components, MAD2L1 has been studied in different human cancers owing to its oncogenic behavior. Here, we employed several omics-related tools to investigate the characteristics of MAD2L1 in a pan-cancer model of analysis, with a focus on its impact on the infiltration of multiple immune components in the tumor microenvironment as a mechanism allowing for tumor progression and poor patient survival. Our analysis revealed that MAD2L1 was significantly upregulated in several human tumors, and this upregulation was further confirmed at the protein level. In addition, a significant hypomethylation status of MAD2L1 was detected in multiple cancers compared with the corresponding controls. The consequences of this upregulation on tumor progression and clinical outcome were further assessed, where tumor stage, grade and metastasis were positively correlated with MAD2L1 overexpression in a panel of tumors. Moving to the immune interaction, our analysis demonstrated that MAD2L1 significantly increased the infiltration of the myeloid-derived suppressor cells (MDSCs) and reduced the infiltration of tumor-attacking natural killer (NK) cells. Furthermore, MAD2L1 expression positively correlated with the expression of exhaustion markers and immunosuppressive chemokines. Owing to its role in tumor progression, we detected MAD2L1 as a target for anticancer therapy, where high-throughput screening (HTS) was performed to identify possible inhibitors that could act as antitumor drugs. Our screening process identified nine FDA-approved drugs with favorable binding energies (>−7 kcal/mol) among the 3180 compounds examined. Meropenem, Glipizide and Dolutegravir were particularly promising candidates, showing distinctive interactions within the active pocket of MAD2L1. Here, we describe the stages and output of our multi-omics analysis with a drug screening assessment.

Bacterial infection and resistance continue to pose a threat to global health. Antibacterial peptides have been proposed as next-generation therapeutics to address this threat owing to their reduced susceptibility to resistance. The interprotofilament interface of the bacterial actin MreB is a prospective target for peptide-based antibiotics to impede MreB polymerization into the double protofilaments required for function. Unfortunately, there have been no reports of inhibitors of MreB targeting the interprotofilament interface. In this study, molecular modeling and simulation strategies were used to assess the MreB-binding prospects of four proposed antiMreB peptides to identify leads for the design and development of peptide-based antibiotics. The PEP-FOLD server was employed to predict the structures of the peptides, followed by structural refinement using the ModRifner server. Subsequently, the HADDOCK server was used to predict the mode of binding of each peptide to the interprotofilament interface of MreB. Furthermore, the predicted peptide-MreB complexes were subjected to molecular dynamics simulations, identifying Pep4 and Pep7 as hits. The results showed that Pep4 and Pep7 remained stably bound to the interprotofilament interface of MreB for up to 100 ns of simulation time. In addition to demonstrating the possibility of binding to the interprotofilament interface of MreB to disrupt MreB–MreB interactions, Pep4 and Pep7 also showed the ability to impede the ATP-dependent conformational change and dynamics of MreB required for polymerization into double protofilaments. Collectively, the results suggest that Pep4 and Pep7 are potential inhibitors or leads for the design, discovery and development of peptide-based MreB-targeting antibiotics.

Chronic bronchitis (CB) is a challenging condition that worsens over time. Patients continue to experience an abundance of symptoms despite their best attempts to manage them. The etiology of CB is goblet cell overproduction and mucus hypersecretion, which aggravates airflow obstruction by luminal blockage of small airways, epithelial remodeling and modification of airway surface tension that predisposes to collapse. Larger randomized controlled trials are required to validate the pilot data that are currently accessible and assess the treatment’s durability as therapies are still in the early stages of clinical development. To overcome the prevailing increase in CB with the rise in air pollution globally, this study proposes a de novo drug discovery candidate for the effective treatment of CB. With the aid of cutting-edge technology of high-throughput screening, a phytochemical, apigenin, has been administered to target the Beta-2 adrenergic receptor protein and utilized as an exemplary ligand to target the discovery of a suitable drug candidate for Beta-2 adrenergic receptor protein. The drug likeness scores, no evident toxicity and binding affinity −9.092 kcal/mol the “CHEMBL294878” proves to be a potential drug candidate discovered for CB. A therapeutic drug specifically meant to treat CB can be developed with the help of the computational data provided by the research presented in this study.

Schiff bases possess a range of unique physical, chemical and medicinal properties, which contribute to their potency as biological agents. A Schiff base was synthesized, which consisted of 4-chloro aniline and vanillin. The successful formation of the compound was confirmed by their comparable FTIR, UV–Vis, ¹HNMR and ${}^{13}$CNMR spectra. The compound was analyzed using X-ray crystallography, which revealed that it crystallizes in the monoclinic system with the space group P21/c. The Hirshfeld surface analysis revealed valuable information about the intermolecular interactions present in the crystal lattice. The most common contacts observed were between hydrogen and hydrogen, as well as carbon and hydrogen. The obtained fluorescence values of 375 nm and 412 nm at excitation wavelength 322 nm strongly indicated the luminescent nature of the compound. The thermal stability of the compound was assessed through thermogravimetric analysis (TGA). DFT was used to optimize the geometry using the B3LYP/cc-pVDZ basis set. The compound exhibited an energy gap of 2.35 electron volts (eV). According to the analysis of molecular electrostatic potential, C and O atoms were found to be electrophilic. According to Mulliken charge analysis, C atoms possess both positive and negative charges, hydrogen atoms exclusively carry positive charges, and Cl, O and N atoms exclusively carry negative charges. Upon analysis of the molecule, the electron localization function discovered that the atoms of carbon, nitrogen and chlorine exhibited delocalized electron density. Also, the localized orbital locator identified the localized orbital positions in the hydrogen atoms. The reduced density gradient (RDG) maps exhibit a wide range of noncovalent interactions. In comparison to the standard amoxicillin, the synthesized compound demonstrated moderate antimicrobial efficacy against the gram-positive bacteria Klebsiella pneumoniae and the fungi Candida albicans. The compound’s concentration-dependent antioxidant and anti-inflammatory properties were demonstrated through in vitro biological evaluation using 2,2-diphenyl-1-picrylhydrazyl and human red blood cell (HRBC) methods. The compound was docked using the proteins 7BYE and 4LEB were chosen from both organisms. The lowest binding attractions obtained were −3.40 kcal/mol for 7BYE and −4.51 kcal/mol for 4LEB. To investigate the stability of the protein–ligand complex, molecular dynamic simulation was employed. The ligands in silico toxicity potentialities were analyzed using seven toxicity models and the results indicated that the ligand is biocompatible.

Autism Spectrum Disorder (ASD) and Huntington’s Disease (HD) are distinct neurodevelopmental and neurodegenerative disorders, respectively, characterized by significant genetic and molecular alterations. ASD primarily affects early childhood and is associated with genetic mutations impacting brain development, while HD, an autosomal dominant disorder, leads to progressive neurodegeneration due to mutations in the HTT gene. Despite their differences, both disorders share common genetic pathways and molecular mechanisms. This study aims to explore the genetic and molecular connections between ASD and HD through a comprehensive analysis of differentially expressed genes (DEGs) and protein–protein interaction (PPI) networks to uncover shared pathways and potential overlapping mechanisms. Transcriptomic data were acquired from the NCBI-GEO database, specifically GSE180185 for ASD and GSE1751 for HD. DEGs were identified using thresholds of log2 fold change (FC)>1 and an adjusted $p$-value <0.05. Common DEGs between the two disorders were determined and analyzed using Cytoscape’s STRING app to construct a PPI network with a confidence level of 0.7. Functional enrichment was conducted through KEGG and Gene Ontology (GO) analyses. Key regulatory modules and hubs were identified using CytoNCA and MCODE plugins. The ASD dataset revealed 565 DEGs, with 206 upregulated and 347 downregulated, while the HD dataset had 1091 DEGs, with 743 upregulated and 202 downregulated. Twelve genes were common to both conditions, including 4 upregulated and 8 downregulated. The PPI network comprised 62 nodes and 215 edges, with significant pathways including ascorbate metabolism and steroid hormone biosynthesis. Notably, Module 3, containing 12 nodes, was linked to EGFR tyrosine kinase resistance and apoptosis. This study identifies shared genetic and molecular pathways between ASD and HD, highlighting common regulatory mechanisms and potential targets for further research. The use of transcriptomic data and PPI network analysis reveals significant overlaps in the molecular mechanisms underlying these disorders. Further experimental validation and expanded dataset analyses could elucidate specific interactions and enhance our understanding of the shared pathways. Investigating these common mechanisms may also provide insights into potential therapeutic approaches for both ASD and HD.

Ab initio MD studies have been performed on stacked adenine–thymine (A–T) and guanine–cytosine (G–C) nucleobase pairs solvated in water using Born–Oppenheimer molecular dynamics. These two systems have been analyzed using RDF, SDF, angle-distance CDF (combined distribution functions), plane projection analysis, hydrogen bond analysis, and non-covalent interaction (NCI) plots. The stacking property studies have shown that the molecular orientation of the A–T pair is completely opposite to that of the G–C pair though the center of mass distance between the two molecules is similar. The G–C pair shows significantly more stability in water than the A–T system does. RDF analyses show that the –NH– group and carbonyl groups show predominant interaction with water molecules. Water molecules are found to be most in numbers around the cytosine molecule and least around the adenine molecule. Hydrogen bonding studies show that the guanine molecule forms the highest number of hydrogen bonds with water molecules. On the other hand, the hydrogen atom attached to the –NH– group of adenine molecule shows the longest mean H-bond lifetime with water oxygen atoms. NCI interactions calculated through the ab initio method help us to visualize the $\pi$-electron stacking and H-bonding between aromatic nucleobases and water molecules. A great shift in bond polarity and interaction intensity in different functional groups is observed for the nucleobases going from individually solvated molecules to paired states.

Focal adhesion kinase (FAK) is an important non-receptor tyrosine kinase (nRTK) with crucial role in primary cell functions. Fundamental cellular processes, such as migration, adhesion, proliferation, and survival, are regulated by FAK. The tumorigenesis of FAK is very important since it is overexpressed in advanced cancers, causes malignant features and induces cancer metastasis. The available evidence proposes FAK as a promising target for cancer therapy. At the moment, there are no FAK inhibitors (FAKIs) in the market but several small molecule FAKIs have been developed and a few of them entered into clinical studies as anticancer agents. In spite of these achievements, development of selective and potent FAKIs may be partly hampered by the lack of crystallographic or structural data. This limitation could be resolved through proposing robust ligand–protein binding models to design new FAKIs. GSK2256098 is a small molecule ATP-competitive FAKI that undergoes clinical trials as a monotherapy or in combination with other medications against advanced solid tumors. GSK2256098 have been proven to be a selective and potent agent (IC${}_{50}$ 0.4 nM) with respect to other clinical candidates. With regard to appropriate characteristics, this molecule is particularly amenable for performing computational modeling studies. In this study, we try to propose an applicable binding model of GSK2256098 inside the FAK kinase domain. For this purpose, molecular docking, all-atom 200-ns molecular dynamics simulations and free-energy-calculation methods were applied as consecutive modeling techniques. Atomic insight and dynamic evolution of the system were used to attain a stable binding mode and infer chemical interactions of GSK2256098 inside the kinase domain of FAK. Although complementary investigations need to be conducted, obtained results may offer a structural basis for the development of potent and selective FAKIs.

Artificial intelligence-assisted drug design is revolutionizing the pharmaceutical industry. Effective molecular features are crucial for accurate machine learning predictions, and advanced mathematics plays a key role in designing these features. Persistent homology theory, which equips topological invariants with persistence, provides valuable insights into molecular structures. The standard homology theory is based on a differential rule for the boundary operator that satisfies $d^2$ = 0. Our recent work has extended this rule by employing Mayer homology with generalized differentials that satisfy $d^N$ = 0 for $N\ge$ 2, leading to the development of persistent Mayer homology (PMH) theory and richer topological information across various scales. In this study, we utilize PMH to create a novel multiscale topological vectorization for molecular representation, offering valuable tools for descriptive and predictive analyses in molecular data and machine learning prediction. Specifically, benchmark tests on established protein-ligand datasets, including PDBbind-v2007, PDBbind-v2013, and PDBbind-v2016, demonstrate the superior performance of our Mayer homology models in predicting protein-ligand binding affinities.