Biology subjects

Choudhuri, S.

Publications and source records attributed to Choudhuri, S..

5 recordsLinked to original sources

bioRxiv2026

Insulin controls olfactory gain at the first central synapse by regulating periglomerular neuron excitability

Sensory processing is dynamically tuned by internal state, yet how metabolic signals reshape the earliest stages of sensory circuits remains poorly understood. Here we identify a circuit mechanism by which satiety suppresses olfactory sensitivity at the first central synapse in the mouse olfactory bulb. Using a within-animal paradigm modelling fasted and glucose-induced sated states, we show that satiety impairs food-finding behaviour and reduces olfactory receptor neuron input to the olfactory bulb. Periglomerular (PG) cells, which co-express insulin receptors and the potassium channel Kv1.3, mediate this effect: insulin inhibits the low-voltage-activated Kv1.3 current in PG cells, increasing their spontaneous and odour-evoked activity. This heightened PG cell activity drives enhanced presynaptic inhibition of olfactory receptor neuron terminals, dampening sensory input before it reaches mitral cells. These findings establish insulin-dependent presynaptic inhibition of PG cells as a key locus of state-dependent sensory gain control.

neuroscience↗

bioRxiv2026

From Prefix to Path: Learning Temporally Consistent Biomolecular Dynamics from Limited Initial Data

Molecular dynamics (MD) simulations provide detailed insights into biomolecular motion but are often limited by the prohibitive cost of sampling long-timescale behavior. Here, we present a Transformer-based framework that reconstructs temporally continuous dynamical trajectories from only a small fraction of the initial data, directly targeting time-ordered evolution rather than independent ensemble snapshots. Using three systems spanning distinct dynamical regimes (intrinsically disordered -Synuclein, Cytochrome P450 ligand-binding motion, and a synthetic three-well potential), we show that the model learns both local fluctuations and long-range temporal structure. At inference time, the model generates full trajectories autoregressively from an initial prefix as prompt, capturing metastable transitions, basin-to-basin movements, and system-specific dynamical signatures. Free-energy surfaces computed from generated trajectories closely match ground-truth landscapes and, in several cases, we observe enhanced sampling in generated trajectories relative to the trained trajectories--while preserving kinetically meaningful transition patterns. These results demon-strate that Transformer architectures can serve as efficient, system-agnostic tools for time-continuous molecular trajectory prediction, offering a data-driven complement to long MD simulations and enabling accelerated exploration of conformational space.

biophysics↗

bioRxiv2026

A Comprehensive Atlas and Machine-Learning Framework for Predicting IDR-Protein Binding Affinity

Intrinsically disordered regions (IDRs) enable pervasive, regulatable protein interactions, yet predicting their binding affinities remains challenging because disorder permits heterogeneous interfaces and context-dependent recognition. Here we introduce IBPC-Kd, a curated dataset of 1,785 IDR-ordered protein complexes with experimentally measured dissociation constants (Kd), spanning more than six orders of magnitude in affinity and covering diverse IDR lengths and partner classes. Across this resource, we find that global interfacial shape complementarity is a consistent correlate of affinity, followed by the structural order of the partner surface and a systematic electrostatic asymmetry between IDRs and their binders. Local physicochemical patterning modulates binding in a partner-dependent manner. Guided by these observations, we develop IDRBindNet, a graph-transformer model that integrates protein language model embeddings with interface geometry and residue-level chemical context to predict dissociation constants from IDR-partner complexes. IDRBindNet achieves state-of-the-art accuracy on held-out complexes (R2 up to 0.911) while implicitly learning biologically relevant attention patterns corresponding to partner disorder and geometric fit. Notably, the framework generalizes through external validation on a set of recently reported de novo designed binders targeting the human surfaceome, showing encouraging concordance across nanomolar-to-micromolar affinities. Together, IBPC-Kdand the accompanying predictor IDRBindNet provide a quantitative framework for interrogating determinants of IDR recognition and for prioritizing candidate interactions for mechanistic and design studies.

biophysics↗

bioRxiv2024

A Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation

The convergence of biotechnology and artificial intelligence has the potential to transform drug development, especially in the field of therapeutic peptide design. Peptides are short chains of amino acids with diverse therapeutic applications that offer several advantages over small molecular drugs, such as targeted therapy and minimal side effects. However, limited oral bioavailability and enzymatic degradation have limited their effectiveness. With advances in deep learning techniques, innovative approaches to peptide design have become possible. In this work, we demonstrate HYDRA: a hybrid deep learning approach that leverages the distribution modeling capabilities of a diffusion model and combines it with a binding affinity maximization algorithm that can be used for de novo design of peptide binders given target receptors. As an application, we have used our approach to design therapeutic peptides targeting proteins expressed by Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) genes. The ability of our model to generate peptides conditioned on the target receptors binding sites makes it a promising approach for developing effective therapies for malaria and other diseases.

bioinformatics↗

bioRxiv2022

Machine learning approaches for classificationof Plasmodium falciparum life cycle stagesusing single-cell transcriptomes

Malaria, spread by the female Anopheles mosquito, is a highly fatal disease widespread in many parts of the world, causing 0.4 million deaths globally. Vital gene expressions form the basis in the detection of malaria infection levels. Quantification of malaria parasite infected RBCs and classification of its life cycle stages are done at macroscopic level by experts, for making informed decisions. Off late multiple computational approaches have been proposed to circumvent the problem of dimensionality leading to accurate predicted results. In this work a dimensionality reduction technique based on Genetic Algorithm (GA) is applied on P. falciparum single-cell transcriptomics to arrive at an optimized subset of features from the larger dataset. Features are chosen based on their class variants considering increased efficiency and accuracy, to separately transform the selected elements into a lower dimension. For the classification of the life cycle of malaria parasite based on single cell transcriptome data, a three-pronged approach employing the multiclass Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) techniques is used. Distribution of cells was visualised and mapped using the R-based Seurat package. Further, we constructed protein interaction networks of the genes identified by the feature selection method and elucidated the role of the proteins in progression of the parasite through its life cycle. Our approach presents a novel protocol to implement ML techniques on scRNA seq datasets and subsequently harnessing the extracted information for biomarker/drug target detection.

bioinformatics↗