Biology subjects

Hossan, M. S.

Publications and source records attributed to Hossan, M. S..

4 recordsLinked to original sources

bioRxiv2026

Targeting PRMT5 Across Cancers and AI-Driven Discovery of Candidate Compounds

Protein arginine methyltransferase 5 (PRMT5) is a promising anticancer target, yet existing inhibitors shown limited clinical efficacy. Here, we paired multi-omics pan-cancer profiling with a machine learning virtual screening pipeline. Bioinformatics analyses with TIMER3, GEPIA2, UALCAN/CPTAC, the Human Protein Atlas, cBioPortal, STRING and Enrichr revealed widespread of PRMT5 mRNA expression, protein abundance, survival, genetic alteration, immune infiltration and co-expression networks with tumor-specific prognostic impact in GBM, LGG and KIRC. A deep neural network screened about 1.5 million PubChem compounds and deep neural network reached a ROC-AUC = 0.9857 and prioritized three candidates, CID 44345182, CID 2794188 and CID 44345334. Subsequent molecular docking against PRMT5 (PDB ID: 4X61), ADMET filtering and toxicity prediction, and 100 ns molecular dynamics (MD) simulations identified CID 44345182 as the potential hit, similar the docking scores of -9.8 compared with -9.2 kcal/mol for the reference ligand in binding affinity and trajectory stability. These findings clarify the context-dependent roles of PRMT5 and further experiment validations are needed to establish CID 44345182 as a promising candidate.

bioinformatics↗

bioRxiv2026

Recombinant SIRT1 expression and SIRT1 METTL3 interaction analysis with computational identification of phytochemical SIRT1 activators and inhibitors

Sirtuin 1 (SIRT1), a NAD{+/-}dependent histone deacetylase, regulates diverse biological processes including aging, stress response, and cancer progression. Owing to its dual roles in tumor suppression and promotion, selective modulators of SIRT1 have attracted considerable therapeutic interest. This study explored the interaction of SIRT1 with METTL3 and screened phytochemicals from Indian medicinal plants for potential SIRT1 activation or inhibition. Human SIRT1 (residues 183-665) was cloned, expressed in E. coli, purified, and validated by isothermal titration calorimetry (ITC) to confirm binding with METTL3. Computational approaches, including molecular docking, ADMET profiling, molecular dynamics (MD) simulations, MM-PBSA free energy estimation, and frontier molecular orbital (FMO) analysis, were employed to characterize ligand interactions. Docking revealed Catechin, Epicatechin, Kaempferol, and Bisdemethoxycurcumin as potent inhibitors, showing stronger binding than the reference inhibitor Selisistat by stabilizing the catalytic pocket via key hydrogen bonds and {pi}-{pi} stacking interactions. Among activators, Ginsenosides and Carnosol demonstrated higher affinities than resveratrol, binding the allosteric site through hydrophobic and electrostatic interactions. MD simulations highlighted Catechin and Ginsenosides as the most stable modulators, supported by reduced RMSD, compact Rg values, and favorable hydrogen bond occupancy. MM-PBSA confirmed their strong binding free energies, while ADMET analysis predicted favorable pharmacokinetic properties and low toxicity, particularly for Catechin and Ginsenosides. Overall, this study integrates expression and purification of the protein and computational modeling to identify phytochemicals as potential SIRT1 modulators and provides novel insights into the SIRT1-METTL3 regulatory axis. These findings highlight Catechin and Ginsenosides as promising scaffolds for therapeutic strategies targeting cancer and metabolic disorders.

molecular biology↗

bioRxiv2026

Long-read, whole-genome sequencing and chemotherapy response of two patient-derived organoids from a TP53- and KRAS-mutant ovarian carcinoma

Patient-derived organoids (PDOs) have transformed translational cancer research, allowing tractable models that better represent clinical features than traditional immortalized cell lines. Here we describe two PDOs with differential responses to carboplatin derived from sequential ascites fluid collections from a patient with high-grade mullerian carcinoma, that could not be further subclassified on the omental biopsy. Uterine origin was clinically excluded by pelvic imaging/CT scan of the uterus and absence of vaginal bleeding. Successful derivation from independent collections enabled comparison of intra-patient heterogeneity across sequential ascites samples and demonstrates that PDO efficiency rate is at least partly patient-specific or tumor-dependent. We performed long-read whole genome sequencing on the two PDOs, OC104 and OC109, to better characterize the structural variant landscape while also obtaining information on single nucleotide variants and DNA methylation. In addition to confirming single nucleotide variants noted in clinical sequencing (TP53, KRAS, SPOP, PPP2R1A, KMT2D), we identified additional variants in TSC2, NCOR2, and CTNNA2 that are predicted to be likely pathogenic. The spectrum of mutations, particularly the coincident KRAS and TP53, highlighted unexpected overlap with ovarian mucinous carcinoma. We also identified larger insertions and deletions that result in non-synonymous variants in MUC5AC, TPRX1, and BMX, as well as four translocation events, including two that could not have been resolved with short-read sequencing. Differentially methylated promoters between the two PDOs include 201 oncogenes and tumor suppressor genes, with HNF1A, MSI2, and SETBP1 having methylation directions consistent with these genes roles in platinum response differences observed between the PDOs. Notably, the clonal nature of PDOs produced from two samples taken one week apart is important for the field to appreciate, particularly since they have clonal differences in platinum response. The temporal differences in clonality may indicate a limitation of low volume sampling, however may provide opportunity to longitudinally predict clinical outcomes. We also demonstrate the ability of long-read sequencing to add detail into the genomics and epigenetics of ovarian cancer.

cancer biology↗

bioRxiv2026

Computational Structure Modeling, Functional Characterization, and Identification of Potential Inhibitors for the cyclic-di-GMP Mediated Biofilm Forming Membrane Protein in Vibrio cholerae

Cholera, caused by Vibrio cholerae, continues to pose a serious global public health challenge, with its impact worsened by rising antibiotic resistance associated with bacterial biofilm formation. This study reveals the role of the hypothetical protein (HP) TYC33605.1 in cyclic-di-GMP (c-di-GMP)-mediated biofilm regulation and identifies natural inhibitors that disrupt this mechanism. Functional annotation revealed TYC33605.1 as a membrane-associated diguanylate cyclase (DGC) with GGDEF and sensory domains, critical for c-di-GMP synthesis and biofilm persistence. Homology modelling and molecular dynamics (MD) simulations validated its stable 3D structure (C-score: -1.22, Ramachandran favoured regions: 91.1%) and dynamic behaviour (average RMSD: 8.55 [A]). Virtual molecular docking screening of 1,092 natural compounds identified Luteolin (CID 5280445) and Sativanone (CID 13886678) as top candidates, exhibiting strong binding affinities (-9.1 and -9.0 kcal/mol, respectively) and forming hydrogen bonds, {pi}-cation, and hydrophobic interactions with key residues (Glu293, Arg364, Ala176). MD simulations (100 ns) confirmed complex stability, with Luteolin and Sativanone showing lower RMSD fluctuations (7.78 [A] and 8.13 [A]) compared to the control and apoprotein. The ADME/Tox profiles highlighted favourable pharmacodynamics (PD), pharmacokinetics (PK), high gastrointestinal absorption, no hepatotoxicity, and drug-likeness (Lipinski compliance). Principal component, probability density function, and binding free energy analyses underscore ligand-induced conformational stability. This study proposes the molecular characterisation of the HP and the bioactive compounds Luteolin and Sativanone as promising inhibitors targeting TYC33605.1, offering a novel strategy to combat biofilm-mediated antibiotic resistance and a framework for analogous antimicrobial discovery in Vibrio cholerae.

bioinformatics↗