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Biology subjects

Amin, M.

Publications and source records attributed to Amin, M..

13 recordsLinked to original sources

Biomolecular Identification and Analyte Charge Measurement Using Drain-Current Readings from a Single Transistor: An Asymmetric Dual-Gate a-IGZO FET Biosensor

Permittivity-based biomolecular identification becomes ambiguous when analyte charge also changes the measured electrical response: different combinations of charge and permittivity can produce similar currents, so a current change cannot be uniquely attributed to permittivity. Hence recovering permittivity when the analyte charge is unknown is a central sensing challenge. Our fundamental idea is to extract both charge and permittivity from a single transistor, enabling permittivity-based biomolecular identification under varied and initially unknown analyte charge conditions. This work demonstrates that strategy through a TCAD-based proof-of-concept study of an asymmetric dual-gate, dual-nanogap a-IGZO transistor. Gate-insulator dielectric engineering and nanogap design establish complementary responses: the bottom gate predominantly senses charge, while the top gate predominantly senses permittivity. The bottom-gate charge estimate and a top-gate pilot current select a charge-conditioned top-gate voltage, allowing permittivity recovery through one neutral-charge reference. After calibration, three sequential drain-current readings provide both properties while charge and permittivity vary simultaneously in both sensing regions. Mean charge and permittivity extraction errors remain below 1.8\% and 0.6\%, respectively, across the calibration and independent test datasets. The central contribution is a single-transistor device-and-readout strategy combining complementary gate responses with adaptive bias selection to support permittivity-based biomolecular identification across initially unknown analyte charge conditions in positive, negative, and neutral ranges.

biophysics↗

XAI-ecDNA: A Proof-of-Concept Framework for Explainable Multi-Modal Extrachromosomal DNA Detection Using Synthetic FISH and Genomics

Extrachromosomal DNA (ecDNA) is a key contributor to aggressive tumor evolution and drug resistance across multiple cancer types. Yet detection methods suffer from limited performance and lack interpretability. Moreover, paired fluorescence in situ hybridization (FISH) and genomic datasets necessary for developing multi-modal artificial intelligence tools remain unavailable in public repositories. We developed an explainable multi-modal framework combining genomic copy number profiles from 500 samples (TCGA, CytoCellDB) with synthetic FISH images generated via StyleGAN2-ADA trained on 247 published images. The framework employs early fusion of ResNet-50 image features and multi-layer perceptron processed genomic data through gated attention. A Biological Rationale Score (BRS) provides SHAP-based explanations with stability quantification and uncertainty estimates. Expert cytogeneticists validated synthetic image biological plausibility. Twelve clinical experts evaluated predictions in a crossover study. On synthetic images, our framework achieved AUROC 0.87, significantly outperforming genomic-only (0.72), image-only (0.79), and late fusion (0.84) baselines (all p<0.001). BRS identified MYC amplification (76% of cases), nuclear morphology (68%), and spatial clustering (56%) as top predictive features, aligning with known biology. Explanation stability reached Kendalls {tau}=0.74. Expert diagnostic confidence improved by 0.9 points (Cohens d=1.26, p<0.001) with explanations. Model calibration was excellent (ECE=0.042). This proof-of-concept demonstrates multi-modal explainable ecDNA detection potential. However, all results are on synthetic images; validation with real clinical FISH data is essential before deployment. The methodology provides a transparent template for interpretable AI development in data-scarce medical domains.

genomics↗

Graph Neural Networks Model Based on Atomic Hybridization for Predicting Drug Targets

Accurate prediction of half-maximal inhibitory concentration (IC50) values is critical for accelerating drug discovery, yet traditional quantitative structure-activity relationship (QSAR) models often have limited ability to capture both local structural patterns and global physicochemical properties essential for bioactivity. We developed a hybrid deep learning framework that integrates graph neural networks with explicit molecular descriptors to address this limitation. The model learns from molecular graphs encoding atomic and bond features while incorporating interpretable physicochemical properties and structural fingerprints. Trained and validated on 14,316 compounds across nine diverse biological targets including kinases, nuclear receptors, and proteases, our approach achieved an overall test R2 of 0.87, consistently outperforming previously reported methods by 6-42% across evaluated targets. The model demonstrated robust generalization with near-identical training and test performance, while maintaining partial interpretability through transparent descriptor contributions and attention mechanisms. By synergistically combining data-driven learning with domain knowledge, this hybrid framework offers improved accuracy and interpretability for structure-activity modeling, facilitating more efficient compound prioritization and optimization in early-stage drug discovery programs.

bioinformatics↗

Machine Learning-Driven Drug Repurposing for KRAS G12C and KRAS G12D Inhibition

KRAS is a predominant oncogenic driver across multiple cancers, long considered "undruggable" due to its high nucleotide affinity and lack of classical binding pockets. Although recent advances have led to covalent inhibitors like Sotorasib and Adagrasib for the KRAS G12C mutation, effective therapies for other common variants--most notably KRAS G12D, which is highly prevalent in aggressive pancreatic cancers--remain limited. In this study, we employ machine learning to identify potential inhibitors for both KRAS G12D and G12C by screening FDA-approved compounds from the ChEMBL database. Random Forest and Neural Network models were trained on binding affinity data from three BindingDB datasets: wild-type KRAS GTPase, KRAS G12C, and KRAS G12D. Our models identified high-affinity candidates including anti-cancer kinase inhibitors (e.g., Cobimetinib, Gilteritinib) as well as drugs from other categories (e.g., Bromocriptine, Cefepime). By incorporating atomic hybridization as a feature, we aim to capture the effects of induced polarization, potentially improving prediction accuracy. Despite modest agreement across models, our approach highlights several promising candidates--such as Acalabrutinib, which has prior evidence of G12C activity--for further investigation. These results offer a data-driven foundation for experimental validation and the future development of targeted KRAS therapies.

cancer biology↗

The role of induced polarization in drug discovery applications

Induced polarization plays a pivotal role in ligand-protein binding by enhancing both the specificity and strength of molecular interactions. As a ligand approaches a protein, their respective electronic clouds redistribute in response to each others electrostatic fields--a phenomenon governed by induced polarization. The response of a molecules electron density to an external field is quantitatively described by its polarizability tensor. In this study, we calculated polarizability tensors for thousands of drug-like molecules from the CHEMBL database, focusing on compounds targeting the Thrombin, Estrogen Receptor alpha, and Phosphodiesterase 5A proteins using Density Functional Theory (DFT). We show that a machine learning model based on atomic hybridization accurately predict the polarizabilities eigenvalues, calculated with DFT. Then, we build a neural network and random forest models to predict the IC50s based on the same features. The success of these models, despite utilizing a limited number of features, underscores the critical role of induced polarizabilities in determining binding energies.

bioinformatics↗

Nuclear lamin A-associated proteins are required for centromere assembly

Many Lamin A-associated proteins (LAAPs) that are key constituents of the nuclear envelope (NE), assemble at the "core" domains of chromosomes during NE reformation and mitotic exit. However, the identity and function of the chromosomal core domains remain ill-defined. Here, we show that a distinct section of the core domain overlaps with the centromeres/kinetochores of chromosomes during mitotic telophase. The core domain can thus be demarcated into a kinetochore proximal core (KPC) on one side of the segregated chromosomes and the kinetochore distal core (KDC) on the opposite side, close to the central spindle. We next tested if centromere assembly is connected to NE re-formation. We find that centromere assembly is markedly perturbed after inhibiting the function of LMNA and the core-localized LAAPs, BANF1 and Emerin. We also find that the LAAPs exhibit multiple biochemical interactions with the centromere and inner kinetochore proteins. Consistent with this, normal mitotic progression and chromosome segregation was severely impeded after inhibiting LAAP function. Intriguingly, the inhibition of centromere function also interferes with the assembly of LAAP components at the core domain, suggesting a mutual dependence of LAAP and centromeres for their assembly at the core domains. Finally, we find that the localization of key proteins involved in the centromeric loading of CENP-A, including the Mis18 complex and HJURP were markedly affected in LAAP-inhibited cells. Our evidence points to a model where LAAP assembly at the core domain serves a key function in loading new copies of centromeric proteins during or immediately after mitotic exit.

cell biology↗

Cross-Protection Induced by Highly Conserved Human B, CD4+, and CD8+ T Cell Epitopes-Based Coronavirus Vaccine Against Severe Infection, Disease, and Death Caused by Multiple SARS-CoV-2 Variants of Concern

BackgroundThe Coronavirus disease 2019 (COVID-19) pandemic has created one of the largest global health crises in almost a century. Although the current rate of SARS-CoV-2 infections has decreased significantly; the long-term outlook of COVID-19 remains a serious cause of high death worldwide; with the mortality rate still surpassing even the worst mortality rates recorded for the influenza viruses. The continuous emergence of SARS-CoV-2 variants of concern (VOCs), including multiple heavily mutated Omicron sub-variants, have prolonged the COVID-19 pandemic and outlines the urgent need for a next-generation vaccine that will protect from multiple SARS-CoV-2 VOCs. MethodsIn the present study, we designed a multi-epitope-based Coronavirus vaccine that incorporated B, CD4+, and CD8+ T cell epitopes conserved among all known SARS-CoV-2 VOCs and selectively recognized by CD8+ and CD4+ T-cells from asymptomatic COVID-19 patients irrespective of VOC infection. The safety, immunogenicity, and cross-protective immunity of this pan-Coronavirus vaccine were studied against six VOCs using an innovative triple transgenic h-ACE-2-HLA-A2/DR mouse model. ResultsThe Pan-Coronavirus vaccine: (i) is safe; (ii) induces high frequencies of lung-resident functional CD8+ and CD4+ TEM and TRM cells; and (iii) provides robust protection against virus replication and COVID-19-related lung pathology and death caused by six SARS-CoV-2 VOCs: Alpha (B.1.1.7), Beta (B.1.351), Gamma or P1 (B.1.1.28.1), Delta (lineage B.1.617.2) and Omicron (B.1.1.529). Conclusions: A multi-epitope pan-Coronavirus vaccine bearing conserved human B and T cell epitopes from structural and non-structural SARS-CoV-2 antigens induced cross-protective immunity that cleared the virus, and reduced COVID-19-related lung pathology and death caused by multiple SARS-CoV-2 VOCs.

immunology↗

Safety and Immunogenicity Evaluation of Inactivated whole-virus-SARS-COV-2 In Mice As Emerging Vaccine Development In Egypt

The current worldwide pandemic COVID-19 is causing severe human health problems, with high numbers of mortality rates and huge economic burdens that require an urgent demand for safe, and effective and vaccine development. Our study was the first trail to development and evaluation of safety and immune response to inactivated whole SARS-COV-2 virus vaccine adjuvanted with aluminium hydroxide. We used characterized SARS-COV-2 strain, severe acute respiratory syndrome coronavirus 2 isolates (SARS-CoV-2/human/EGY/Egy-SERVAC/2020) with accession numbers; MT981440; MT981439; MT981441; MT974071; MT974069 and MW250352 at GenBank that isolated from Egyptian patients SARS-CoV-2-positive. Development of the vaccine was carried out in a BSL - 3 facilities and the immunogenicity was determined in mice at two doses (55{micro}g and 100{micro}g per dose). All vaccinated mice were received a booster dose 14 days post first immunization. Our results demonstrated distinct cytopathic effect on the vero cell monolayers induced through SARS-COV-2 propagation and the viral particles were identified as Coronaviridae by transmission electron microscopy. SARS-CoV-2 was identified by RT-PCR performed on the cell culture. Immunogenicity of the developed vaccine indicated the high antigen-binding and neutralizing antibody titers, regardless the dose concentration, with excellent safety profiles.However, no deaths or clinical symptoms in mice groups. The efficacy of the inactivated vaccine formulation was tested by wild virus challenge the vaccinated mice and detection of viral replication in lung tissues. Vaccinated mice recorded complete protection from challenge infection three weeks post second dose. SARS-COV-2 replication was not observed in the lungs of mice following SARS-CoV-2 challenge, regardless of the level of serum neutralizing antibodies. This finding will support the future trials for evaluation an applicable SARS-CoV-2 vaccine candidate.

immunology↗

ACE2 coding variants in different populations and their potential impact on SARS-CoV-2 binding affinity

The susceptibility of different populations to the SARS-CoV-2 infection is not yet understood. A deeper analysis of the genomes of individuals from different populations might explain their risk for infection. In this study, a combined analysis of ACE2 coding variants in different populations and computational chemistry calculations are conducted in order to probe the potential effects of ACE2 coding variants on SARS-CoV-2/ACE2 binding affinity. Our study reveals novel interaction data on the variants and SARS-CoV-2. We could show that ACE2-K26R; which is more frequent in the Ashkenazi Jewish population decrease the electrostatic attraction between SARS-CoV-2 and ACE2. On the contrary, ACE2-I468V, R219C, K341R, D206G, G211R were found to increase the electrostatic attraction and increase the binding to SARS-CoV-2; ordered by the strength of binding from weakest to strongest. I468V, R219C, K341R, D206G and G211R were more frequent in East Asian, South Asian, African and African American, European and European and South Asian populations, respectively. SARS-CoV-2/ACE2 interface in the WT protein and corresponding variants is showed to be a dominated by van der Waals (vdW) interactions. All the mutations except K341R induce an increase in the vdW attractions between the ACE2 and the SARS-CoV-2. The largest increase of is observed for the R219C mutant.

biophysics↗

Computational model demonstrates that Ndc80-associated proteins strengthen kinetochore-microtubule attachments in metaphase

Chromosome segregation is mediated by spindle microtubules that attach to kinetochore via dynamic protein complexes, such as Ndc80, Ska, Cdt1 and ch-TOG during mitotic metaphase. While experimental studies have previously shown that these proteins and protein complexes are all essential for maintaining a stable kinetochore-microtubule interface, their exact roles in this process remains elusive. In this study, we employed experimental and computational methods in order to characterize how these proteins can strengthen kMT attachments in both non load-bearing and load-bearing conditions, typical of prometaphase and metaphase, respectively. Immunofluorescence staining of Hela cells showed that the levels of Ska and Cdt1 significantly increase from prometaphase to metaphase. Our new computational model showed that, by incorporating binding and unbinding of each protein complex, coupled with a biased diffusion mechanism, the displacement of a possible complex formed by Ndc80-Ska-Cdt1 is significantly higher than that formed by Ndc80 alone or Ndc80-Ska. In addition, when we use Ndc80/ch-TOG in the model, rupture force and time of attachment of the kMT interface increases. These results support the hypothesis that Ndc80-associated proteins strengthen kMT attachments and that it is the interplay between kMT protein complexes in metaphase that ensures stable attachments.

cell biology↗

MALARIA and BURKITT′S LYMPHOMA: An IN SILICO ANALYSIS of GENE EXPRESSION LINKS BETWEEN MALARIA and BURKITT’S LYMPHOMA and POTENTIAL ANTICANCER ACTIVITY of ARTEMISININ DERIVATIVES

BackgroundBurkitts lymphoma (BL) is an aggressive form of B-cell non-Hodgkin lymphoma. Endemic subtype of the disease showed a remarkable statistical and epidemiological association with malaria infection. Despite the numerous studies performed to explain this association; molecular mechanisms underlie such coincidence still remain unclear. Dissecting molecular mechanisms which link Malaria infection and Burkitts lymphoma can provide insights about reported anticancer action of certain antimalarial drugs, namely artemisinin derivatives.\n\nMethodsHere we applied an integrative approach to investigate for potential links between malaria infection and endemic Burkitts lymphoma regarding their gene expression, and further explore common molecular mechanisms through which artemisinin compounds might act in endemic Burkitts lymphoma. Using gene expression data of malaria (Plasmodium falciparum infected erythroblasts) and endemic Burkitts lymphoma from Gene Expression Omnibus database, expression patterns in the two conditions were examined through clustering analysis using Self Organizing Maps, and then by significance testing of differentially expressed genes in each condition followed by Functional annotation using Gene Ontology clustering and Pathways analysis.\n\nResultsClustering analysis identified a significant overlap between the expression patterns in endemic Burkitts lymphoma and Plasmodium falciparum infected cells. Four out of the 12 identified clusters contained genes with similar expression patterns in both conditions. Differential expression analysis identified 1689 genes as significantly differentially expressed in endemic Burkitts lymphoma and 405 in malaria. Those genes were found to be related to important Gene Ontology terms and pathways. Interestingly 65% of the identified pathways in Malaria were overlapped with those identified in endemic Burkitts lymphoma. Several of these pathways reported to be related to actions of artemisinin derivatives.\n\nConclusionOur In-silico analysis showed a significant molecular convergence between endemic Burkitts lymphoma and malaria. A number of 43pathways which demonstrated enrichment in tumour were shared with Plasmodium falciparum infected erythrocytes. Such pathways represent potential targets for antimalarial drugs to exert therapeutic effects in such malignancy.

bioinformatics↗

Dynamics and spatial genomics of the nascent transcriptome in single mESCs by intron seqFISH

Recent single cell experiments have revealed significant heterogeneities at the levels of transcription, DNA methylation and chromosome organization in individual cells. However, existing method of profiling mRNAs effectively averages transcriptional dynamics over many hours due to hours-long life time of most mRNAs. To capture the instantaneous activity of the transcriptome that reflects the rapid regulatory changes in cells, we imaged up to 10,421 nascent transcription active sites (TAS) in single mouse embryonic stem cells using seqFISH followed by multiple rounds of single molecule FISH and immunofluorescence. We observed that nascent transcription active sites appear to be distributed on the surface of individual chromosome territories and are dispersed throughout the nucleus. In addition, there are significant variability in the number of active transcription sites in single cells, representing globally more active to quiescent states. These states interconverted on the time scale of 2 hours as determined by a single cell pulse-chase experiment. Thus, transcriptome level seqFISH experiments provide an unprecedented spatial and dynamic view of chromosome organization and global nascent transcription activity in single cells.

systems biology↗

A bioinformatic panel to interrogate thousands of ExAC variants with minor reference allele that are missed by conventional variant calling

In variation sites with minor reference alleles, overlooking the detection of homozygous reference genotypes results in inadequate identification of potential disease variants. Current variant calling practices miss these clinically relevant alleles warranting new approaches. More than 26,000 Eome Aggregation Consortium (ExAC) variants have a minor reference allele including 44 variants with known ClinVar disease alleles. We demonstrated how the current variant calling standards miss homozygous reference disease variants in these sites. We developed a bioinformatic panel that can be used to screen these variants using commonly available variant callers. We provide here a simple strategy to screen potential disease-causing variants when present in homozygous reference state.

genomics↗