Biology subjects

Gupta, M.

Publications and source records attributed to Gupta, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Chemical Dice Integrator (CDI): A Scalable Framework for Multimodal Molecular Representation Learning

The machine learning landscape for molecular property prediction is fragmented, with numerous Featurizers each capturing a narrow, specialized view of chemical structure. This heterogeneity forces a suboptimal choice of representation a priori, limiting model generalizability. We introduce the Chemical Dice Integrator (CDI), a hierarchical framework that unifies six orthogonal molecular representations, physicochemical (Mordred), topological (GROVER), visual (ImageMol), biological (Signaturizer), quantum-mechanical (MOPAC), and linguistic (ChemBERTa), into a single, coherent embedding. The framework consists of CDI-Basic, a two-tiered autoencoder that fuses these modalities, and CDI-Generalised, a Mamba State-Space Model (SSM) that learns a direct, efficient map from SMILES strings to the unified embedding space. Extensive benchmarking across 23 classification (171 tasks) and 10 regression datasets demonstrates that CDI embeddings consistently achieve superior predictive performance compared to individual Featurizers and standard feature aggregation methods. The CDI-Generalised model achieves this performance with exceptional computational efficiency, outperforming deep learning Featurizers in terms of speed and resource overhead. Furthermore, we demonstrate that the CDI embedding is chemically intuitive, allowing for the sensitive distinction of nuanced structural variants, such as chiral enantiomers and kekulized SMILES forms. By bridging multimodal chemical intelligence with scalable, sequence-based inference, CDI offers a strong foundation for molecular machine learning.

bioinformatics↗

bioRxiv2025

Scalable flow synthesis of ultrasmall inorganic nanoparticles for biomedical applications via a confined impinging jet mixer

Ultrasmall inorganic nanoparticles (sub-5 nm) have unique biomedical advantages due to rapid clearance, enhanced imaging contrast, and potent therapeutic properties. However, current synthesis methods are limited by low throughput, polydispersity, and reliance on harsh conditions such as organic solvents or high temperatures. We report a scalable, single-step aqueous synthesis using a confined impinging jet mixer (CIJM) that produces size-controlled, clinically relevant nanoparticles, including silver sulfide, silver telluride, cerium oxide, and iron oxide, under ambient conditions. The resulting nanoparticles are homogeneous, stable, and preserve their functional biological properties. We demonstrate consistent performance across scales, establishing the CIJM as a versatile and reproducible method for producing ultrasmall inorganic nanoparticles suitable for clinical translation and high-throughput biomedical applications.

bioengineering↗

bioRxiv2025

Donor Sex and Platelet Storage Influence the Therapeutic Effects of Platelet-Derived Extracellular Vesicles on Endothelial Barrier Function

Platelet-derived extracellular vesicles (PEVs) play an active role in vascular protection and repair and are being explored as a viable alternative to platelet therapy. Because platelet function and stability are shaped by donor sex and storage conditions, these same factors are likely to influence the PEVs they release. Understanding these influences is key to developing PEVs into a safe and dependable therapeutic option. In this study, we investigated how donor sex and platelet storage affect the therapeutic properties of PEVs. To address this, PEVs were isolated from platelets of healthy male and female donors. Platelets were either processed immediately after blood collection to represent a resting state or stored overnight at room temperature on a rocker to mimic platelet storage conditions. PEVs isolated from these preparations displayed similar size, morphology, and cellular uptake across groups, but their biological effects diverged. Female PEVs, particularly from resting platelets, provided the strongest protection against thrombin-induced endothelial barrier disruption, stabilized junctional proteins, and reduced oxidative stress. Male PEVs showed weaker barrier protection compared to female-derived PEVs but more pronounced modulation of certain inflammatory mediators. In addition, PEVs derived from resting platelets (RP-PEVs) consistently showed stronger protective effects than those from stored platelets (SP-PEVs), regardless of donor sex. These results highlight that donor sex and platelet storage influence PEVs function and underscore the need to account for both when developing PEV-based therapies. Key PointThe endothelial-protective effects of platelet-derived extracellular vesicles are modulated by platelet storage conditions and donor sex.

molecular biology↗

bioRxiv2025

Generating ecological baselines on short-grass formations or grazing lawns in Central Terai landscape

Short-grass formations or the "grazing lawns" as they are popularly called support large congregations of mammalian herbivores globally. These systems are known to be maintained by cyclic process of grazing and nutrient deposition in the soil by foraging animals. The basic ecological characteristics of these short-grass systems have extensively been studied in the African context with little understanding particularly from the Himalayan foothills in Terai. In this study, we focused on understanding the ecological baselines such as grass species associations and associated nutrient content in these grazing lawns and tall grass formations across two protected areas of the Uttar Pradesh Terai in India. Our results show that, the protected areas had relatively similar grass species composition and associated nutrient content in the short-grass formations. However, when compared with tall grass formations, the grazing lawns differed markedly in both their grass associations and thus forage quality. Therefore, the results from this study indicate the need to maintain such grazing lawn habitats within the characteristic tall-wet grasslands of the Terai. This also indicates the importance of short-grass formations in providing palatable forage to the mammalian herbivores particularly in peak dry seasons of autumn and winters.

ecology↗

bioRxiv2025

Non-Invasive Diagnostic Evaluation of Urinary Exosomal Let-7c Cluster Expression in Bladder Cancer Using Machine Learning Approaches

BackgroundBladder cancer (BCa) diagnosis typically relies on invasive cystoscopy, which is effective but costly and uncomfortable. Urinary microRNAs (miRNAs), especially exosomal ones, are promising non-invasive biomarkers due to their stability in biological fluids and disease specificity. However, challenges such as population variability, methodological inconsistencies and normalization issues hinder their clinical translation, emphasizing the need for innovative approaches to enhance diagnostic performance. ObjectiveTo evaluate the diagnostic potential of urinary exosomal let-7c cluster (let-7c-5p, miR-99a-5p and miR-125b-5p) in BCa patients by integrating miRNA expression data with Machine Learning (ML) models. MethodsUrine samples were collected from 66 participants, including 50 BCa patients and 16 healthy controls (HC). Exosomal miRNAs were isolated and quantified using Quantitative Real-Time-Polymerase-Chain-Reaction (qRT-PCR). Statistical analysis and hypothesis tests were conducted to explore the nature and diagnostic relevance of individual biomarkers. A logistic regression classifier was applied to evaluate both the combined and differential diagnostic capabilities of the selected biomarkers. Accuracy, precision, recall and AU-ROC scores were used to assess model performance. Bioinformatics analysis was performed to identify pathways associated with the features prioritized by the ML models, ensuring their relevance to BCa. ResultsThe result revealed significant differentiation between BCa patients and HC, with miR-99a-5p (p=0.013, AU-ROC=0.71) and miR-125b-5p (p=0.047, AU-ROC=0.64) demonstrating reliable diagnostic performance (let-7c-5p showed weaker discrimination, AU-ROC=0.65, p>0.1). The logistic regression ML model achieved an accuracy of 80.0% (AU-ROC=0.86, recall=100%) in distinguishing cancer from HC and 53.3% (AU-ROC=0.63) when applied to miRNA-only grade classification. When clinical variables were integrated with miRNA expression, performance improved to 73.3% accuracy (AU-ROC=0.61) for high-versus low-grade differentiation. Across Ta-T2, miR-99a-5p displayed relatively better separation, whereas let-7c-5p and miR-125b-5p showed weak stage-related differences. The integration of bioinformatics analysis confirmed the biological relevance of these miRNAs in BCa-related pathways, including PI3K-Akt, p53, NF-{kappa}B and RAS/MAPK signaling, with hub genes such as TP53, MYC, EGFR, and CCND1 identified, further validating the diagnostic utility of the selected biomarkers. ConclusionUrinary let-7c cluster miRNAs demonstrate promising diagnostic potential when analyzed with ML models, offering a non-invasive alternative to conventional methods. These findings highlight the promise of ML-based approaches alongside molecular markers for advancing clinical diagnostics in BCa.

molecular biology↗

bioRxiv2025

Parallel but distinct adaptive routes in the budding and fission yeasts after 10,000 generations of experimental evolution

Quantitative genetics approaches designed to understand the evolution of traits have helped improve our understanding of the genetic basis of adaptation. However, they often overlook crucial aspects of adaptation, including the long-term temporal evolutionary dynamics, the predictability of evolutionary outcomes, the influence of past evolution on future evolutionary trajectories (contingency), and the diversity of molecular mechanisms underlying adaptation. Experimental evolution has been a useful tool for answering these questions, but extracting fundamental principles and predictive features of evolutionary outcomes from these datasets remains challenging due to the large number of covariates and confounding effects, such as differences in experimental setup, species lifestyle, gene content, and evolution rate. Here, we sought to circumvent these challenges by comparing distant yeast species that share several evolutionary features but differ mainly in evolutionary history and genome architecture, i.e. Saccharomyces cerevisiae and Schizosaccharomyces pombe. Thus, we evolved 10 populations of the fission yeast for 10,000 generations in the same conditions as a pre-existing budding yeast dataset (i.e. high-sugar media and hypoxic conditions), allowing us to observe repeatable evolutionary outcomes within species but diverse molecular mechanisms and targets of adaptation across species. The most frequent adapting route in these conditions involved upregulating fermentation genes and downregulating the glycolysis gene pyk1, which has not previously been observed in S. cerevisiae evolved populations or in wild Kluyveromyces lactis, but similar evolutionary paths have been observed in Schizosaccharomyces japonicus and in clinically relevant populations, such as some cancer cells. This suggests that parallelism is pervasive in the tree of life and that mechanisms of adaptation can be shared among closely related or distant species. Despite similar gene content and identical environments, recurrent adaptation across S. pombe populations involved different genes than in S. cerevisiae and was mostly detectable at the transcriptomic level. This suggests that trans-regulatory effects may play an important role in adaptation on short evolutionary timescales and that differences in evolutionary outcomes between these species may be attributed to contingency.

evolutionary biology↗

bioRxiv2025

Mechanistic Insights into Lenacapavir-Induced Off-Pathway HIV-1 Capsid Assembly

The HIV-1 capsid is a fullerene-like shell composed of hexamer and pentamer arrangements of the capsid (CA) proteins. The cone shape of the capsid is particularly important for packaging the viral genome and coordinating nuclear entry. Lenacapavir (LEN), a potent long-acting inhibitor, has been shown to disrupt capsid morphogenesis by binding at the FG-binding pocket located between neighboring CA subunits. Interestingly, inositol hexakisphosphate (IP6), a cellular polyanion, binds within the central pore of capsid pentamers and some hexamers while playing a key role in regulating the hexamer/pentamer switch. As LEN and IP6 interact with overlapping structural elements, they can compete to influence the capsid assembly pathway and outcomes. Using coarse-grained molecular simulations, we examined capsid assembly across varying IP6 and LEN conditions. Our results reveal a concentration-dependent shift in assembly outcomes: LEN accelerates hexamer assembly and reduces pentamer incorporation, leading to malformed, multilayered, or incomplete capsids. Simulations including a model for the viral ribonucleoprotein (RNP) complex further show that LEN-treated capsids frequently fail to encapsidate the RNA genome, indicating impaired maturation. Our calculations confirm that LEN impairs the formation of high-curvature CA lattice regions necessary for closure, supporting a model of off-pathway assembly as a mechanism of viral inhibition.

biophysics↗

bioRxiv2025

Reprogrammed E. coli for secretion of thermophilic cellulase cocktail for seawater-compatible lignocellulosic bioprocessing

The development of efficient cellulase systems is crucial for sustainable lignocellulosic biorefining. In this study, we engineered Escherichia coli to co-express and secrete a thermophilic cellulase cocktail consisting of a Bacillus licheniformis processive endoglucanase (H1AD14), a Chaetomium thermophilum cellobiohydrolase (G0SAE6), and a glucose-tolerant engineered {beta}-glucosidase B8CYA8tm (V169C/E173L/I246A B8CYA8) from Halothermothrix orenii. Fusion with the AnsB secretion tag enabled extracellular yields of 0.2-0.4 g/L for each enzyme, bypassing costly purification while retaining activity. The secretion was confirmed via immunoblotting using anti-His antibody, and catalytic activity was validated through enzymatic assays. The absence of E. coli native periplasmic maltose-binding protein (detected with anti-MBP antibody) in the extracellular medium ruled out cell lysis, confirming protein secretion. Using the Golden Gate assembly, we constructed and systematically evaluated synergistic enzyme combinations for hydrolytic performance. By optimizing secretion conditions, including media, inducer concentration, and incubation temperature, the total yield of secreted enzyme cocktail increased from 0.31 g/L to 0.74 g/L. The secreted cocktail effectively hydrolysed diverse lignocellulosic substrates and exhibited 100 % activity in seawater, demonstrating potential for freshwater-independent biorefining. The work addresses key economic and environmental challenges in biomass conversion by integrating secretion engineering, thermostability, and seawater tolerance into a single scalable system.

bioengineering↗

bioRxiv2025

Brain-Informed Fine-Tuning for Improved Multilingual Understanding in Language Models

Recent studies have demonstrated that fine-tuning language models with brain data can improve their semantic understanding, although these findings have so far been limited to English. Interestingly, similar to the shared multilingual embedding space of pretrained multilingual language models, human studies provide strong evidence for a shared semantic system in bilingual individuals. Here, we investigate whether fine-tuning language models with bilingual brain data changes model representations in a way that improves them across multiple languages. To test this, we fine-tune monolingual and multilingual language models using brain activity recorded while bilingual participants read stories in English and Chinese. We then evaluate how well these representations generalize to the bilingual participants first language, their second language, and several other languages that the participants are not fluent in. We assess the fine-tuned language models on brain encoding performance and downstream NLP tasks. Our results show that bilingual brain-informed fine-tuned language models outperform their vanilla (pretrained) counterparts in both brain encoding performance and most downstream NLP tasks across multiple languages. These findings suggest that brain-informed fine-tuning improves multilingual understanding in language models, offering a bridge between cognitive neuroscience and NLP research. We make our code publicly available. 2

neuroscience↗

bioRxiv2025

Brain Stroke Prediction: A PSO Optimized Stacked Ensemble Machine Learning Approach with SMOTEEN Data Balancing

Prediction of stroke is a critical challenge in healthcare, where early intervention can significantly reduce risks and improve patient outcomes. Traditional methods often struggle with imbalanced datasets and low prediction accuracy. This paper proposes a novel approach to address these issues by combining PSO-optimized Stacked Ensemble ML Classifiers with SMOTEEN data balancing to predict strokes effectively. The technique optimizes 3 Baseline ML Classifiers(RF, SVM, XGB) using Particle Swarm Optimization (PSO) and Stacked Ensemble approach is applied that significantly improving prediction performance. The proposed methodology achieved an impressive 95.57% accuracy, outperforming conventional models such as SVM, LR, RF, KNN, XGB. Overall, the PSO-optimized stacked ensemble approach outperforms traditional machine learning techniques in stroke prediction, providing a more accurate and robust model for early detection and intervention.

bioengineering↗

bioRxiv2025

Molecular mechanism of substrate transport by human peroxisomal ABCD3

Peroxisomes are eukaryotic oxidative organelles involved in numerous metabolic functions that include fatty acid oxidation, bile acid synthesis, and detoxification of reactive oxygen species. ATP-binding cassette transporters of the D subfamily (ABCD1-3) mediate the import of CoA thioesters of fatty acids into the peroxisome. ABCD3, the most abundant of these transporters in the peroxisomal membrane, facilitates the transport of a broad spectrum of substrates including branched-chain fatty acids, very long-chain fatty acids, bile salt intermediates, and dicarboxylic acids. Mutations in ABCD3 are associated with defects in congenital bile acid synthesis and variants of Zellweger syndrome. The structural and functional details of the human ABCD3 transporter remain unclear, despite its significance. In this study, we report the cryogenic sample electron microscopy (cryo-EM) structures of full-length human ABCD3 in its apo state and bound to one of the physiological substrates (phytanoyl-CoA) at resolutions of 3.33 [A] and 3.13 [A], respectively. Our biochemical assays reveal that substrate binding induces ATPase activity in ABCD3, suggesting a substrate-dependent conformational change. Structural comparison of the apo and substrate bound states demonstrate that the substrate interaction brings nucleotide-binding domains closer, providing a mechanistic basis of substrate induced ATPase activity. These findings offer critical insights into the transport mechanism of ABCD3 and lay a structural foundation for understanding its role in peroxisomal metabolite import and related diseases. Significance StatementPeroxisomes are involved in essential cellular metabolic processes that include fatty acid oxidation, bile acid synthesis, and detoxification. The ABCD subfamily of membrane transporter proteins transport fatty acyl-CoA molecules from the cytosol into peroxisomes. Among the ABCD subfamily members, ABCD3 transports branched chain fatty acids and a range of other important metabolites. Dysfunction in ABCD3 has been associated with several inherited and acquired diseases. Using cryo-EM, we determined high-resolution structures of full-length human ABCD3 in both unbound and phytanoyl-CoA-bound forms. We show that substrate binding stimulates ATP hydrolysis activity by apposition of the two nucleotide binding domains of ABCD3, adding structural insight into the mechanism of action of ABCD3 and its role in peroxisomal function and disease.

biochemistry↗

bioRxiv2025

Loss of the PPE71-esxX-esxY-PPE38 locus drives adaptive transcriptional responses and hypervirulence of Mycobacterium tuberculosis Lineage 2

Mycobacterium tuberculosis (M.tb) is remarkable for its immense global disease burden and low mutation rate. Despite strong selective pressure, M.tb shows frequent deletions at the PPE71-38 locus, most notably in hypervirulent L2 Beijing strains. Here, we show that loss of the PPE71- 38 locus causes increased stress response gene expression and increased triglyceride levels. In addition, we demonstrate that re-introduction of PPE71 into the L2 strain HN878 suppresses the baseline elevation of these transcripts, while overexpression of PPE71 increases the localization of PE_PGRS proteins and lipoproteins to the M.tb outer mycomembrane. Mouse infection confirmed the hypervirulence of the PPE71-38 deletion strain and conversely showed that PPE71 overexpression attenuates M.tb. Our results indicate that loss of PPE71-38 is sufficient to drive an adaptive transcriptional response seen in M.tb L2 strains that likely contributes to the hypervirulence of this lineage.

microbiology↗

bioRxiv2025

Loss of the ESX-5 secretion locus in Mycobacterium tuberculosis reshapes the mycomembrane and enhances ESX-1 substrate secretion

The ESX-5 secretion system, uniquely found in slow-growing mycobacteria, is predicted to secrete over 150 proteins across the inner membrane of Mycobacterium tuberculosis (M.tb). Although many of these substrates are believed to promote M.tb virulence, most remain poorly characterized. Here, we use a complete locus deletion strain of ESX-5 in M.tb to examine the molecular changes caused by a broad loss in ESX-5 secretory substrates. We confirmed the selective loss of PE/PPE proteins secreted by ESX-5 into both the culture filtrate (CF) and outer mycomembrane (OMM) fractions of the M.tb {Delta}esx5 mutant. In examining other ESX systems, we found that ESX-1 substrate levels were increased in both the CF and OMM fractions of the {Delta}esx5 mutant. Conversely, the ESX-3 locus was transcriptionally repressed upon ESX-5 deletion. We noted that the {Delta}esx5 mutant had altered morphology in the form of wrinkled distortions of the bacterial surface. Likewise, we identified increased susceptibility of the {Delta}esx5 mutant to a variety of large (molecular weight >550 g/mol) antimicrobial compounds, suggesting that an intact ESX-5 system is required for M.tb to exclude such molecules. Our findings suggest that removing the ESX-5 system from M.tb fundamentally alters the properties of the mycobacterial OMM and impacts the expression and secretion activity of other ESX systems. Significance StatementMycobacterium tuberculosis (M.tb) uses the ESX-5 secretion system to export numerous proteins that shape host-pathogen interactions. Here, we found that deleting ESX-5 from M.tb not only prevented the secretion of many ESX-5 substrates but also impacted other ESX systems. The M.tb {Delta}esx5 mutant had increased ESX-1 substrate secretion but reduced ESX-3 expression. In addition, the M.tb {Delta}esx5 mutant displayed altered cell surface morphology and increased vulnerability to large antibiotic drugs, suggesting a critical role for ESX-5 for maintaining outer membrane integrity. These findings highlight ESX-5 as a central modulator of secretion and cell envelope composition with implications for drug targeting and vaccine development.

microbiology↗

bioRxiv2025

Modeling tumor dynamics and predicting response to chemo-, targeted-, and immune-therapies in a murine model of pancreatic cancer

We seek to establish a parsimonious mathematical framework for understanding the interaction and dynamics of the response of pancreatic cancer to the NGC triple chemotherapy regimen (mNab-paclitaxel, gemcitabine, and cisplatin), stromal-targeting drugs (calcipotriol and losartan), and an immune checkpoint inhibitor (anti-PD-L1). We developed a set of ordinary differential equations describing changes in tumor size (growth and regression) under the influence of five cocktails of treatments. Model calibration relies on three tumor volume measurements obtained over a 14-day period in a genetically engineered pancreatic cancer model (KrasLSLG12D-Trp53LSLR172H-Pdx1-Cre). Our model reproduces tumor growth in the control and treatment scenarios with an average concordance correlation coefficient (CCC) of 0.99{+/-}0.01. We conduct leave-one-out predictions (average CCC=0.74{+/-}0.06), mouse-specific predictions (average CCC=0.75{+/-}0.02), and hybrid, group-informed, mouse-specific predictions (average CCC=0.85{+/-}0.04). The developed mathematical model demonstrates high accuracy in fitting the experimental tumor data and a robust ability to predict tumor response to treatment. This approach has important implications for optimizing combination NGC treatment strategies.

bioinformatics↗

bioRxiv2024

Structure-Based Design of Small-Molecule Inhibitors of Human Interleukin-6

Human Interleukin-6 (hIL-6) is a pro inflammatory cytokine that binds to its receptor, IL-6R followed by binding to gp130 and subsequent dimerization to form a hexamer signaling complex. A critical inflammation mediator, hIL-6 is associated with a diverse range of diseases and monoclonal antibodies are in clinical use that either target IL-6R or hIL-6 to inhibit signaling. Here, we perform high throughput structure-based computational screening using ensemble docking for small molecule antagonists for which the target conformations were taken from 600 ns long molecular dynamics simulations of the apo protein. Prior knowledge of the contact sites from binary complex studies and experimental work was incorporated into the docking studies. The top 20 scored ligands from the in silico studies after post analysis were subjected to in vitro functional assays. Among these compounds, the ligand with second-highest calculated binding affinity showed experimentally [~]84% inhibitory effect on IL6-induced STAT3 reporter activity at 10-5 molar concentration. This finding may pave the way for designing small molecule inhibitors of hIL-6 of therapeutic significance.

bioinformatics↗

bioRxiv2024

Effect of Stroma-directed Drugs in Combination with Chemotherapy Against Pancreatic Cancer- a Preclinical Study

Cytotoxic chemotherapy plays an important role for extending the survival of patients with pancreatic ductal adenocarcinoma (PDAC). To enhance the efficacy of chemotherapy for eradicating the cancer cells, we have compared the standard care chemotherapy (combination of nab-paclitaxel, gemcitabine and cisplatin, NGC) versus NGC plus stroma-directed agents (calcipotriol and losartan, respectively) in a genetically engineered mouse model of PDAC. Over a 2-week study period, MRI was conducted to measure the tumor size and to test the sensitivity of imaging markers derived from diffusion-weighted imaging (DWI), dynamic contrast enhanced MRI (DCE) and magnetization transfer ratio (MTR) for assessing the tumor cellularity and stromal changes. Detailed immunohistochemistry and preliminary single cell RNA sequencing (scRNAseq) study were applied to tumor tissues collected upon euthanasia on day-14. Our major findings are: 1. Compared the untreated controls, NGC chemotherapy induced significant tumor growth inhibition and stromal changes including pronounced reduction of fibroblast associated protein (FAP) level accompanied by increased matrix collagen content, significantly reduced microvascular permeability revealed by DCE corroborated with reduced microvascular density. 2. Losartan+NGC significantly enhanced inhibition of tumor growth beyond NGC and increased lymphocytes infiltration in the tumor which may contribute to enhanced cancer cells eradication. 3. NGC treatment enriched the fraction of mesenchymal (M) subtype while reducing the epithelial (E) subtype of cancer cells compared to the controls, and this trend was reversed by calcipotriol+NGC. In conclusion, our study captured changes in cancer cell and tumor microenvironment in response to chemo stromal therapy versus chemotherapy alone with mechanistic insights.

cancer biology↗

bioRxiv2024

Recognizing amino acid sidechains in a medium resolution cryo-electron density map

Building an accurate atomic structure model of a protein into a cryo-electron microscopy (cryo-EM) map at worse than 3 [A] resolution is difficult. To facilitate this task, we devised a method for assigning the amino acid residue sequence to the backbone fragments traced in an input cryo-EM map (EMSequenceFinder). EMSequenceFinder relies on a Bayesian scoring function for ranking 20 standard amino acid residue types at a given backbone position, based on the fit to a density map, map resolution, and secondary structure propensity. The fit to a density is quantified by a convolutional neural network that was trained on [~]5.56 million amino acid residue densities extracted from cryo-EM maps at 3-10 [A] resolution and corresponding atomic structure models deposited in the Electron Microscopy Data Bank (EMDB). We benchmarked EMSequenceFinder by predicting the sequences of 58,044 distinct L-helix and {beta}-strand fragments, given the fragment backbone coordinates fitted in their density maps. EMSequenceFinder identifies the correct sequence as the best-scoring sequence in 77.8% of these cases. We also assessed EMSequenceFinder on separate datasets of cryo-EM maps at resolutions from 4 to 6 L. The accuracy of EMSequenceFinder (63.5%) was better than that of three tested state-of-the-art methods, including findMysequence (45%), ModelAngelo (27%), and sequence_from_map in Phenix (12.9%). We further illustrate EMSequenceFinder by threading the SARS-CoV-2 NSP2 sequence into eight cryo-EM maps at resolutions from 3.7 to 7.0 [A]. EMSequenceFinder is implemented in our open-source Integrative Modeling Platform (IMP) program. Thus, it is expected to be helpful for integrative structure modeling based on a cryo-EM map and other information, such as models of protein complex components and chemical crosslinks between them. EMSequenceFinder is available as part of our open source IMP distribution at https://integrativemodeling.org/.

biophysics↗

bioRxiv2024

Kinetic Implications of IP6 Anion Binding on the Molecular Switch of the HIV-1 Capsid Assembly

HIV-1 capsid proteins (CA) self-assemble into a fullerene-shaped capsid, enabling cellular transport and nuclear entry of the viral genome. A structural switch comprising the Thr-Val-Gly-Gly (TVGG) motif either assumes a disordered coil or a 310 helix conformation to regulate hexamer or pentamer assembly, respectively. The cellular polyanion inositol hexakisphosphate (IP6) binds to a positively charged pore of CA capsomers rich in arginine and lysine residues mediated by electrostatic interactions. Both IP6 binding and TVGG coil-to-helix transition are essential for pentamer formation. However, the connection between IP6 binding and TVGG conformational switch remains unclear. Using extensive atomistic simulations, we show that IP6 imparts structural order at the central ring, which results in multiple kinetically controlled events leading to the coil- to-helix conformational change of the TVGG motif. IP6 facilitates the helix-to-coil transition by allowing the formation of intermediate conformations. Our results identify the key kinetic role of IP6 in HIV-1 pentamer formation.

biophysics↗