Biology subjects

Neupane, P.

Publications and source records attributed to Neupane, P..

12 recordsLinked to original sources

bioRxiv2026

Nutritional Regulation of DN1a-Dh44 Signaling Modulates Sleep Across the Lifespan

Across species, sleep amount and timing are tightly linked to the nutritional environment. While early life sleep and sleep in mature organisms are both dramatically influenced by reductions in the dietary environment, the mechanisms linking nutritional cues to conserved sleep-regulatory circuitry are not well understood. Using both early 3rd instar (L3) Drosophila larvae and adults, we examined the plasticity of sleep responses under shifting nutrient environments across the lifespan. We find that L3 larvae and adults exhibit changes in sleep duration in low sugar environments with L3 showing a loss of sleep-wake rhythms that can be rescued with additional nutrients. We show that larval and adult sleep plasticity is regulated by CCHamide-1 signaling between DN1a and Dh44 neurons and glucose metabolic genes in Dh44 neurons. Additionally, our data indicate that sleep plasticity is not dependent on anatomical and functional connectivity between clock-arousal circuitry, suggesting that peptidergic signaling alone is sufficient for diet-dependent sleep regulation. Finally, we demonstrate that Dh44 neurons in both L3 larvae and adults adjust mRNA levels of CCHamide-1 receptor (CCHa1-R) in response to changes in dietary sugar. Together, our findings suggest that organisms utilize conserved molecular signaling pathways across the lifespan to dynamically regulate their sleep in a changing environment.

neuroscience↗

bioRxiv2026

Multiscale Free-Energy Methods for Protonation-Coupled Light-Responsive Binding of Ionizable Photoswitchable eDHFR Inhibitors

Photoswitchable ligands enable photocontrol of biomolecular activity by binding to targets in an isomer-dependent, light-responsive manner. Recent developments in ionizable photoswitchable ligands greatly expand their applications but introduce a major design challenge: light-responsive binding can depend on isomeric form, chemical substitution, and binding-induced shifts in protonation equilibria. These effects are tightly coupled, subtle in magnitude, and difficult to predict. Consequently, few computational methods have been developed and systematically benchmarked for quantitatively predicting them. Here, we establish a multiscale free-energy method and benchmark it against experimental data for a series of recently developed photoswitchable inhibitors of Escherichia coli dihydrofolate reductase (eDHFR), a crucial target in photopharmacology. Constant pH replica-exchange molecular dynamics and quantum mechanics/molecular mechanics umbrella sampling quantitatively characterize the ligands protonation-state change upon binding to the eDHFR active site. Thermodynamic integration simulations using alternative alchemical pathways, thermodynamic cycles, and protonation-state assignments were evaluated for predicting light-responsive affinity differentials and substituent effects. Direct cis-to-trans transformations with explicit treatment of environment-dependent protonation states best reproduce experimental trends. Compound-to-compound pathways are less reliable because force-field inaccuracies introduce large pK errors that are difficult to correct when protonation/deprotonation processes implicitly enter the thermodynamic cycle. TI simulations that ignore binding-induced protonation-state changes fail to consistently reproduce experimental trends. Protein-ligand and ligand-water interaction analyses further reveal the energetic and structural origins of isomer-dependent binding. This study establishes a systematic free-energy method for designing ionizable photoswitches in photopharmacology.

pharmacology and toxicology↗

bioRxiv2026

Improving AlphaFold3 by Engineering MSA and Template Inputs

AlphaFold3 introduces a unified framework for predicting the structures and interaction of several biomolecules including single-chain protein monomers, multi-chain protein multimers, and protein-ligand complexes. While it achieves the state-of-the-art performance in most predictions, its prediction accuracy depends on the quality of multiple sequence alignment (MSA) and structural template inputs. There are few works of using customized MSAs and templates to improve AlphaFold3. In this work, we systematically investigate how diverse and carefully engineered MSAs and templates can be leveraged to improve AlphaFold3 predictions. We evaluate our methods on protein monomers, multimers, and protein-ligand complexes, and observe consistent, sizable gains in structure prediction accuracy for monomers (TM-score 0.937 vs 0.882), for multimers (DockQ score 0.550 vs 0.525), and for protein-ligand complexes (ligand RMSD 3.258 [A] vs 4 [A]) compared to the default AlphaFold3. Moreover, for the first time, we demonstrate that AlphaFold3 performs significantly better than AlphaFold2 when both use the same customized MSA and template inputs. The results highlight the importance and effectiveness of using diverse MSAs and templates to improve AlphaFold3.

bioinformatics↗

bioRxiv2026

Pharmacokinetics and Physiologically Based Pharmacokinetic Modeling of Mycobacteriophages: Insights into Pulmonary Distribution and Clearance

Bacteriophage therapy is being explored as an alternate therapeutic approach for treating drug- resistant bacteria, including mycobacteria. However, rational phage dosing remains limited by scarce pharmacokinetic (PK) data and an incomplete understanding of tissue distribution. We performed dose-ranging studies in mice of three therapeutic mycobacteriophages (BPs{Delta}, ZoeJ{Delta}, Muddy) after intravenous (IV) and intratracheal (IT) administration. All phages behaved similarly. IV dosing produced biphasic kinetics with non-proportional exposure and declining tissue-to-plasma ratios, indicating saturable uptake and elimination. IT delivery yielded monophasic profiles with [~]390-fold higher lung exposure and [~]490-fold lower plasma exposure, supporting inhaled therapy for pulmonary mycobacterial infections. Using BPs{Delta} data, we developed a mechanistic PBPK model incorporating transcytosis, saturable host clearance, plasma elimination, and lymphatic transport. The model accurately predicted ZoeJ{Delta} and Muddy PK, enabled cross-species extrapolation, and showed that phage morphology influences disposition. This framework advances phage therapy toward model-informed, exposure-guided dose and route selection for multidrug-resistant bacterial infections.

pharmacology and toxicology↗

bioRxiv2025

Extraction of essential oil, phytochemical screening, and antimicrobial activity study of Eucalyptus globulus

Eucalyptus globulus also referred to as Blue Gum is a native plant to Australia where they are cultivated extensively for the extraction of its oil. This oil is of great value in medicinal, therapeutic, and industrial applications. The present study is primarily aimed to the identification of phenolic composition and antimicrobial activity of Eucalyptus globulus leaves collected from the Terai region of Nepal. The leaves were partially air-dried, crushed, and subsequently subjected to extraction processes for the preparation of plant extract and essential oil. The GC-MS analysis of essential oil, phytochemical screening, and antimicrobial activity of plant extract were performed. The GC-MS analysis identified a diverse range of compounds, including the major compounds like Eucalyptol(2.52%), Valerianol(5.57%), 10-epi-{gamma}-eudesmol(6.93%), Bulnesol(19.97%) and epi-{gamma}-eudesmol (17.51%) The antimicrobial efficacy of ethyl acetate (EtOAC), methanol (MeOH), and hexane extracts was evaluated against Escherichia coli (ATCC 8739), Staphylococcus aureus (ATCC 6538P), and Candida albicans (ATCC 2091) using the Mueller-Hinton agar well diffusion method. The hexane extract exhibited significant antibacterial activity, particularly against E. coli and C. albicans, with inhibition zones of 2.0 cm and 2.1 cm, respectively. In contrast, the MeOH extract showed the least antibacterial activity across all tested strains. The phenolic profiling revealed Bulnesol as the most abundant compound with a concentration of 19.97 units at a retention time of 40.128 minutes, followed by epi-{gamma}-Eudesmol with 17.51 units. The study underscores the rich phenolic diversity and potent antimicrobial properties of E. globulus leaves, highlighting its potential applications in pharmaceuticals and natural product industries.

plant biology↗

bioRxiv2025

Boosting AlphaFold Protein Tertiary Structure Prediction through MSA Engineering and Extensive Model Sampling and Ranking in CASP16

AlphaFold2 and AlphaFold3 have revolutionized protein structure prediction by enabling high-accuracy tertiary structure predictions for most single-chain proteins. However, obtaining high-quality predictions for some hard protein targets with shallow or noisy multiple sequence alignments (MSAs) and complicated multi-domain architectures remains challenging. Here, we present MULTICOM4, an integrative protein structure prediction system that uses diverse MSA generation, large-scale model sampling, and an ensemble model quality assessment (QA) strategy of combining individual QA methods to improve model generation and ranking of AlphaFold2 and AlphaFold3. In the 16th Critical Assessment of Techniques for Protein Structure Prediction (CASP16), our predictors built on MULTICOM4 ranked among the top performers out of 120 predictors in tertiary structure prediction and outperformed a standard AlphaFold3 predictor. The average TM-score of our best performing predictor MULTCOMs top-1 prediction for 84 CASP16 domain is 0.902. It achieved high accuracy (TM-score > 0.9) for 73.8% of the 84 domains and correct fold predictions (TM-score > 0.5) for 97.6% domains in terms of top-1 prediction. In terms of best-of-top-5 prediction, it predicted correct folds for all the domains. The results show that MSA engineering through the use of different protein sequence databases, alignment tools, and domain segmentation as well as extensive model sampling are the key to generate accurate and correct structural models. Additionally, using multiple complementary QA methods and model clustering can improve the robustness and reliability of model ranking.

bioinformatics↗

bioRxiv2025

Improving AlphaFold2 and 3-based protein complex structure prediction with MULTICOM4 in CASP16

With AlphaFold achieving high-accuracy tertiary structure prediction for most single-chain proteins (monomers), the next major challenge in protein structure prediction is accurately modeling multi-chain protein complexes (multimers). We developed MULTICOM4, the latest version of the MULTICOM system, to improve protein complex structure prediction by integrating transformer-based AlphaFold2, diffusion model-based AlphaFold3, and our in-house techniques. These include protein complex stoichiometry prediction, diverse multiple sequence alignment (MSA) generation leveraging both sequence and structure comparison, modeling exception handling, and deep learning-based model quality assessment. MULTICOM4was blindly evaluated in the 16th communitywide Critical Assessment of Techniques for Protein Structure Prediction (CASP16) in 2024. In Phase 0 of CASP16, where stoichiometry information was unavailable, MULTICOM predictors performed best, with MULTICOM_human achieving a TM-score of 0.752 and a DockQ score of 0.584 for top-ranked predictions on average. In Phase 1 of CASP16, with stoichiometry information provided, MULTICOM_human remained among the top predictors, attaining a TM-score of 0.797 and a DockQ score of 0.558 on average. The CASP16 results demonstrate that integrating complementary AlphaFold2 and 3 with enhanced MSA inputs, comprehensive model ranking, exception handling, and accurate stoichiometry prediction can effectively improve protein complex structure prediction.

bioinformatics↗

bioRxiv2025

Estimating Protein Complex Model Accuracy Using Graph Transformers and Pairwise Similarity Graphs

MotivationEstimation of protein complex structure accuracy is an essential step in protein complex structure prediction and is also important for users to select good structural models for various applications, such as protein function analysis and drug design. Despite the success of structure prediction methods such as AlphaFold2 and AlphaFold3, predicting the quality of predicted complex structures (structural models) and selecting top ones from large model pools remains challenging. ResultsWe present GATE, a novel method that uses graph transformers on pairwise model similarity graphs to predict the quality (accuracy) of complex structural models. By integrating single-model and multi-model quality features, GATE captures both the characteristics of individual models and the geometric similarity between them to make robust predictions. On the dataset of the 15th Critical Assessment of Protein Structure Prediction (CASP15), GATE achieved the highest Pearsons correlation (0.748) and the lowest ranking loss (0.1191) compared to existing methods. In the blind CASP16 experiment, GATE was ranked 4th according to the overall sum of z-scores of multiple metrics based on both TM-score and Oligo-GDTTS scores. In terms of per-target average metrics based on TM-score, GATE achieved a Pearsons correlation of 0.7076 (1st place among all methods), a Spearmans correlation of 0.4514 (3rd place), a ranking loss of 0.1221 (3rd place), and an Area Under the Curve (AUC) score of 0.6680 (3rd place), highlighting its strong, balanced ability of estimating complex model accuracy and selecting good models. AvailabilityThe source code of GATE is freely available at https://github.com/BioinfoMachineLearning/GATE.

bioinformatics↗

bioRxiv2025

Accurate Stoichiometry Prediction of Protein Complexes by Integrating AlphaFold3 and Template Information

Protein structure prediction methods require stoichiometry information (i.e., subunit counts) to predict the quaternary structure of protein complexes. However, this information is often unavailable, making stoichiometry prediction crucial for complexes with unknown stoichiometry. Despite its importance, few computational methods address this challenge. In this study, we present an approach that integrates AlphaFold3 structure predictions with homologous template data to predict stoichiometry. The method generates candidate stoichiometries, builds structural models for them using AlphaFold3, ranks them based on AlphaFold3 scores, and further refine predictions with template-based information when available. In the 16th community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP16), our method achieved 71.4% top-1 accuracy and 92.9% top-3 accuracy, outperforming other predictors in terms of the overall performance. This demonstrates the complementary strengths of AlphaFold3- and template-based predictions and highlights its applicability for uncharacterized protein complexes lacking stoichiometry data.

bioinformatics↗

bioRxiv2024

Pharmacological inhibition of macrophage triglyceride biosynthesis pathways does not improve Mycobacterium tuberculosis control in infected mice

Triglyceride rich macrophages (foam cells) are a hallmark of necrotic granulomas in tuberculosis, and multiple antimicrobial functions are down-regulated in these cells. In this study, we assessed the ability of two different compounds to reduce triglyceride content and intracellular burden in Mycobacterium tuberculosis (Mtb)-infected macrophages: A-922500 (DGATi), an inhibitor of diacylglycerol acyltransferase 1, an enzyme involved in triglyceride synthesis; and LY2584702 (p70S6Ki), an inhibitor of p70 S6 kinase, a serine/threonine kinase involved in mTORC-1dependent lipid biogenesis. Additionally, we evaluated the adjunctive activity of these inhibitors as host-directed therapies against chronic Mtb infection in C3HeB/FeJ mice. DGATi and p70S6Ki significantly reduced the lipid content and bacillary burden in Mtb-infected human monocyte-derived macrophages. In Mtb-infected mice, each inhibitor reduced the triglyceride content (P[≤] 0.0001) in cells from bronchoalveolar lavage samples. Adjunctive treatment of DGATi with isoniazid and p70S6Ki monotherapy reduced the lipid droplet content (P[≤] 0.05) within lung macrophages of Mtb-infected mice. However, neither inhibitor reduced the lung bacterial burden in Mtb-infected mice alone or in combination with isoniazid, and they did not alter lung inflammation. These findings provide further insights into the role of foam cells in tuberculosis pathogenesis and the utility of interventions targeting these cell populations as adjunctive host-directed therapies.

pharmacology and toxicology↗

bioRxiv2023

The heme oxygenase-1 metalloporphyrin inhibitor stannsoporfin enhances the bactericidal activity of a novel regimen for multidrug-resistant tuberculosis in a murine model

Multidrug-resistant (MDR) Mycobacterium tuberculosis (Mtb) poses significant challenges to global tuberculosis (TB) control efforts. Host-directed therapies (HDT) offer a novel approach for TB treatment by enhancing immune-mediated clearance of Mtb. Prior preclinical studies found that inhibition of heme oxygenase-1 (HO-1), an enzyme involved in heme metabolism, with tin-protoporphyrin IX (SnPP) significantly reduced mouse lung bacillary burden when co-administered with the first-line antitubercular regimen. Here we evaluated the adjunctive HDT activity of a novel HO-1 inhibitor, stannsoporfin (SnMP), in combination with a novel MDR-TB regimen comprising a next-generation diarylquinoline, TBAJ-876 (S), pretomanid (Pa), and a new oxazolidinone, TBI-223 (O) (collectively, SPaO) in Mtb-infected BALB/c mice. After 4 weeks of treatment, SPaO + SnMP 5 mg/kg reduced mean lung bacillary burden by an additional 0.69 log10 (P=0.01) relative to SPaO alone. As early as 2 weeks post-treatment initiation, SnMP adjunctive therapy differentially altered the expression of pro-inflammatory cytokine genes, and CD38, a marker of M1 macrophages. Next, we evaluated the sterilizing potential of SnMP adjunctive therapy in a mouse model of microbiological relapse. After 6 weeks of treatment, SPaO + SnMP 10 mg/kg reduced lung bacterial burdens to 0.71 {+/-} 0.23 log10 CFU, a 0.78 log-fold greater decrease in lung CFU compared to SpaO alone (P=0.005). However, adjunctive SnMP did not reduce microbiological relapse rates after 5 or 6 weeks of treatment. SnMP was well tolerated and did not significantly alter gross or histological lung pathology. SnMP is a promising HDT candidate requiring further study in combination with regimens for drug-resistant TB.

microbiology↗

bioRxiv2022

An intranasal stringent response vaccine targeting dendritic cells as a novel adjunctive therapy against tuberculosis

Lengthy tuberculosis (TB) treatment is required to address the ability of a subpopulation of persistent Mycobacterium tuberculosis (Mtb) to remain in a non-replicating, antibiotic-tolerant state characterized by metabolic remodeling, including induction of the RelMtb-mediated stringent response. We developed a novel therapeutic DNA vaccine construct involving fusion of the relMtb gene with the immature dendritic cell-targeting gene encoding chemokine MIP-3/CCL20. To augment mucosal immune responses, intranasal delivery was also evaluated. We found that the intramuscular MIP-3/relMtb (fusion) vaccine potentiates isoniazid activity more than a similar DNA vaccine expressing relMtb alone in a chronic TB mouse model (absolute reduction of Mtb burden: 0.63 log10 colony-forming units, P=0.0001), inducing pronounced Mtb-protective immune signatures. The intranasal fusion vaccine, an approach combining relMtb fusion to MIP-3 and intranasal delivery, demonstrated the greatest therapeutic effect compared to each approach alone, as evidenced by robust Th1 and Th17 responses systemically and locally and the greatest mycobactericidal activity when combined with isoniazid (absolute reduction of Mtb burden: 1.13 log10, P<0.0001, when compared to the intramuscular vaccine targeting relMtb alone). This DNA vaccination strategy may be a promising adjunctive approach combined with standard therapy to shorten curative TB treatment, and also serve as proof-of-concept for treating other chronic infections.

immunology↗