Biology subjects

Gupta, C.

Publications and source records attributed to Gupta, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Pipette: Encoding scientific literature into an executable Skill Graph for multi-agent bioinformatics

The cost of genomic sequencing has fallen by several orders of magnitude, yet data analysis remains a bottleneck concentrated among researchers with specialized computational expertise. While Large Language Models can generate bioinformatics code, they frequently produce incoherent multi-step workflows due to the absence of domain-specific analytical constraints. Here, we present Pipette, a multi-agent AI framework that orchestrates end-to-end bioinformatics workflows through natural language interaction, guided by a literature-derived Skill Graph. This directed, edge-weighted knowledge graph, extracted from over 20,000 peer-reviewed publications, constrains workflow generation to biologically valid analytical transitions, preventing incomplete or incoherent workflows. We benchmarked Pipette across four biological domains using published datasets: single-cell RNA-seq analysis of peripheral blood mononuclear cells and a human pancreas atlas, bulk RNA-seq differential expression in rice under environmental stress, and two structure-based computational drug design workflows. In ablation against two LLMs operating without Skill Graph constraints, Pipette matched or exceeded both baselines across all quantitative metrics while uniquely completing multi-step cross-domain transitions. We further evaluated Pipette on a clinical genomics task, where it executed an ACMG/AMP-compliant variant classification on a reference human genome. In all cases, Pipette recapitulated established biological and clinical findings while generating a fully reproducible, machine-readable provenance record. By reducing the computational expertise required to execute standard genomic analyses, Pipette lowers the barrier between sequencing data and biological insight for bench scientists. Pipette is available at https://pipette.bio.

bioinformatics↗

bioRxiv2025

Machine Learning-Augmented Analysis of Nano-electrochemical Sensor Data for Predictive and Quantitative Assays of Complex Biological Samples

Analytical chemistry provides the content of nearly every scientific, technical and business decision relating to what atoms, molecules and devices are and how they interact in their environment. Various workflows come together in describing those interactions--separation, identification, quantitation, classification--to inform decisions across society. Each workflow has associated equipment, infrastructure and subject matter experts that integrate results for input into those decisions. However, those existing workflows are constrained by their evolution and often require integrated applications of the multiple analytical techniques integrated to form conclusions, thereby increasing cost, complexity and quality. This paper describes an alternative approach to addressing the many limitations of contemporary analytical chemistry. Here machine learning is leveraged to analyze high dimensional data derived from a nano-electrochemical sensor platform, to perform classification, identification and quantitation analyses in an integrative manner from a single-shot measurement. The approach described here for biological samples does not require sample-specific preparation or marker-specific probes and labels to identify and/or quantitate the analyte of interest, thereby liberating the physical measurement of the sample from chemicals and workflows that are tied to the underlying biochemical hypothesis. The nano-electrochemical sensor, as described in a prior publication, transduces the vibrational frequencies of multiple species in the sample into a collection of discrete electronic signatures that represent the sample in a high dimensional space, while using only 2-4{micro}l sample volume. The sample analysis relies on training data to identify features in the high dimensional input dataset that are unique to the molecule or phenotype under consideration and distinct from the sample background matrix and/or a set of defined sample controls. The molecule- or phenotype-associated features can be tracked with clinically identified disease burden in the patient or with biomolecule concentration in the biological sample. The identification of patients with dementia from a plasma test is outlined here to demonstrate the applicability of this approach to classifying phenotypes associated with complex neurological disorders in a marker-agnostic manner. This approach is applied to a specific and quantitative assay of insulin Humalog and insulin Toujeo in a batch that comprises of a mixture of the two peptides, where Toujeo differs from Humalog in three amino acids residues only. We also describe the development of an assay for IL-6 in spiked human plasma samples with this method, thereby demonstrating the applicability of this analysis paradigm to more complex samples and protein-like species.

bioengineering↗

bioRxiv2025

Dynamic landscapes of gene regulatory networks in early mammalian neurogenesis: Insights into brain evolution and disorder risk

Neurogenesis--the process of generating neurons--is governed by dynamic transcriptional programs that vary across time, brain regions, and cell types, forming regionally specialized neuronal circuits. To understand these dynamics, we constructed a comprehensive gene regulatory network (GRN) resource encompassing 22 neurogenic lineages from human, macaque, and mouse, enabling cross-species and cross-regional comparisons. Leveraging state-of-the-art trajectory analysis and GRN inference, we characterized temporal regulatory dynamics and introduced a "dynamic score" to identify key subnetworks with lineage-specific dynamics, including hundreds of regulons and co-regulatory modules. Our analysis uncovered both known and novel candidate regulators driving neuronal differentiation and regional identity, spanning the entire human brain, as well as evolutionary divergence in neurogenic GRNs distinguishing human brains. Mapping risk genes to the resource helped understand associated early gene regulatory dynamics with 35 neurodevelopmental disorders and traits including autism, schizophrenia, severe intellectual disability, and microcephaly. This resource is publicly available as an interactive online platform.

developmental biology↗

bioRxiv2024

Performance Evaluation of Ciprofloxacin loaded Micellar Bioconjugate Carrier for Eradication of Bacterial Biofilms

This study reports the utilization of a Curcumin-Exopolysaccharide (Cur-EPS) micellar bioconjugate comprising acid-labile ester bond as a carrier for ciprofloxacin (Cip) to improve its ability to eradicate mature bacterial biofilms. The most significant attributes of Cip-loaded Cur-EPS micelles, including hydrodynamic particle size, polydispersity index (PDI), and encapsulation efficiency (EE), were statistically optimized to be 305.43 {+/-} 1.47 nm, 0.38 {+/-} 1.23, and 84.50 {+/-} 2.01 %, respectively. At an acidic pH of 5.6, the pH-responsive release of Cip from Cur-EPS micelles was 82.51 {+/-} 2.95 % over a duration of 100 h, but it was reduced to 39.10 {+/-} 1.55 % at a pH of 7.4 over the same period. This pH-dependent Cip release may be attributed to the destabilisation of Cur-EPS micelles due to hydrolysis of the succinic acid linker in the acidic pH range. Thus, the acidic pH microenvironment of biofilms formed by pathogenic bacterial strains, such as methicillin resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa, enhances the pH-responsive release of Cip from Cur-EPS micelles, thereby leading to the eradication of mature biofilms. This enhanced antibiofilm effect of Cip-Cur-EPS, in contrast to the free form of Cip, was evident from the significant decrease in the biofilm biomass. Furthermore, mature biofilms that formed on the surface of the urinary catheter were almost eradicated when treated in vitro with Cip-Cur-EPS for 72 h in the presence of artificial urine media (AUM). Moreover, Cip-Cur-EPS could also eradicate the biofilms formed by P. aeruginosa and MRSA in a catheterized bladder model employing recirculated artificial urine. Overall, this study demonstrated the antibiofilm potential of Cip-Cur-EPS micelles, rendering them suitable therapeutic agents for addressing biofilm-associated infections in clinical settings. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/630303v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@135f68org.highwire.dtl.DTLVardef@1dcd61forg.highwire.dtl.DTLVardef@16d9976org.highwire.dtl.DTLVardef@762425_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Transient protein structure guides surface diffusion pathways for electron transport in membrane supercomplexes

The biological significance of protein supercomplexes have remained contentious, particularly how they tune the shuttling of charge-carrier redox proteins across cell membranes during biological energy conversion. We employ multiscale modeling and single particle cryo-electron microscopy (cryo-EM) to determine the mechanisms of diffusive electron transfer in mitochondrial supercomplexes, composed of respiratory complexes III and IV (CIII and CIV). Using a combination of bioinformatic and entropy maximization tools, we model an ensemble of structures representing the conformational space of CIIIs disordered QCR6 hinge within the yeast CIII2CIV2 supercomplex. Molecular and Brownian Dynamics simulations of the entire supercomplex reveal a mechanism for electrostatic coupling between these negatively charged hinge conformations, and binding and directional diffusion of the redox proteins on the mitochondrial membrane, which is simulated over the millisecond timescale. Anionic lipids reinforce this conformationally-coupled recognition of the supercomplex by retaining a pool of the redox proteins in the vicinity of the membrane when the hinges are of a critical length. Cryo-EM models reveal a large-scale rearrangement of the {Delta}QCR6 supercomplex, which retains a surprisingly robust electrostatic environment for recognition of the redox protein, despite compromise in the supercomplexs negative charge, still enabling a surface-mediated electron transfer in this CIII2CIV2 variant. Altogether, the evolutionary need of confining electron carriers on the surface of bioenergetic membranes is found to give rise to a refolding-guided diffusion model of the redox proteins, which improves the energy conversion efficiency within the supercomplex by nearly 30%.

biophysics↗

bioRxiv2024

Finding the E-Channel Proton Loading Sites by Calculating the Ensemble of Protonation Microstates

The aerobic electron transfer chain builds a proton gradient by proton coupled electron transfer reactions through multiple proteins. Complex I is the first enzyme in this chain. On transfer of two electrons from NADH to quinone four protons are pumped from the N- (negative, higher pH) to the P- (positive, lower) side. Protons move pthrough three linear antiporter paths, with a few amino acids and waters providing the route and the E-channel, which is a complex of competing paths, with clusters of interconnected protonatable residues. Proton loading sites (PLS) are residues that transiently bind protons as they are transported from N- to P-compartments. PLS can be individual residues or extended clusters. The program MCCE uses Monte Carlos sampling to analyze the E-channel proton binding in equilibrium with individual Molecular Dynamics snapshots from trajectories of Thrmus thermuphillus Complex I in the apo, quinone or quinol bound states. At pH 7, the five E-channel subunits (Nqo4, Nqo7, Nqo8, Nqo10, and Nqo11) have accepted >25,000 protonation microstates, each with different residues protonated. The microstate explosion is tamed by analyzing interconnected clusters of residues along the proton transfer paths. A proton is bound and released to five coupled residues in a cluster on the protein N-side and to six coupled residues in the protein center. Loaded microstates bind protons to sites closer to the P-side in the forward pumping direction. MCCE microstate analysis shows the strong coupling of proton binding amongst individual residues in the two PLS clusters.

biophysics↗

bioRxiv2024

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized [~]250 disease-risk genes and drug targets with associated cell types. Summary Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/585576v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@70b24org.highwire.dtl.DTLVardef@6c9eddorg.highwire.dtl.DTLVardef@131fae2org.highwire.dtl.DTLVardef@4e0f1e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Multilayer Label Free Non-Faradic Electrochemical Impedance Immunosensor for Cortisol Detection

Cortisol, a well-known psychological stress biomarker, produced by the hypothalamic-pituitary-adrenal system, tends to intensify with stressors. Prolonged overexpression of cortisol leads to chronic stress that causes disparities in the proper functioning of the human body. Thus, there is a huge demand for developing a rapid cortisol detection system. Several point-of-care diagnostic techniques are available for rapid cortisol detection, such as electrochemical sensing, which works on changes in the electrical properties due to the binding of an analyte with a biorecognition element. Researchers have used different electrochemical methodologies such as cyclic voltammetry (CV), chronoamperometry, and faradic electrochemical impedance spectroscopy (EIS) for the detection of cortisol, but usage of external redox active reagents, low sensitivity, limited dynamic range, and electrode fouling nature limits their use. Hence, we reported a label-free and non-invasive cortisol detection using non-faradic EIS. A novel multilayer immunosensor was fabricated on PEDOT: PSS coated ITO glass by functionalizing with cortisol antibodies. Specific and rapid detection of cortisol was measured by monitoring the change in impedance in a dynamic range from 50-200 ng/mL. We envision the developed immunosensor has the potential for new developments in stress monitoring, disease prognosis, and enable personalized care. HighlightsO_LINovel PEDOT: PSS based multilayer immunosensor for cortisol detection C_LIO_LIImpedance based label free detection of cortisol using non-faradic EIS C_LIO_LIPresentation of detailed multilayer immunosensor fabrication, experimental detection, and equivalent circuit model with working mechanism C_LIO_LICortisol detection in a dynamic range of 50-200 ng/mL C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/550009v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@16f486forg.highwire.dtl.DTLVardef@fc9b3aorg.highwire.dtl.DTLVardef@333cf1org.highwire.dtl.DTLVardef@1e608e2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Novel Peptide Inhibitor of Human Tumor Necrosis Factor-α has Antiarthritic Activity

The inhibition of tumor necrosis factor- (TNF) trimer formation renders it inactive for binding to its receptors thus mitigating the vicious cycle of inflammation. We designed a peptide (PIYLGGVFQ) that simulates a sequence strand of human TNF monomer using a series of in silico methods, such as active site finding (Acsite), protein-protein interaction (PPI), docking studies (GOLD and Modeller) followed by molecular dynamics (MD) simulation studies. The MD studies confirmed the intermolecular interaction of the peptide with the TNF. Fluorescence-activated cell sorting (FACS) and fluorescence microscopy revealed that the peptide effectively inhibited the binding of TNF to the cell surface receptors. The cell culture assays showed that the peptide significantly inhibited the TNF-mediated cell death. In addition, the nuclear translocation of the nuclear factor kappa B (NF{kappa}B) was significantly suppressed in the peptide-treated A549 cells as observed in immunofluorescence and gel mobility-shift assays. Furthermore, peptide protected against joint damage in collagen-induced arthritis (CIA) mouse model as revealed in the microfocal-CT scans. In conclusion, this TNF antagonist would be useful for the prevention and repair of inflammatory bone destruction and subsequent loss in the mouse model of CIA as well as human rheumatoid arthritis (RA) patients. This calls upon further clinical investigation to utilize its potential effect as an anti-arthritic drug.

immunology↗

bioRxiv2022

Single-cell network biology characterizes cell-type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimers disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell-type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular. Several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment). Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals mechanisms on cell-type gene dyregulation in AD.

bioinformatics↗

bioRxiv2021

Revealing a hidden intermediate of rotatory catalysis with X-ray crystallography and Molecular simulations

The mechanism of rotatory catalysis in ATP-hydrolyzing molecular motors remain an unresolved puzzle in biological energy transfer. Notwith standing the wealth of available biochemical and structural information inferred from years of experiments, knowledge on how the coupling between the chemical and mechanical steps within motors enforces directional rotatory movements remains fragmentary. Even more contentious is to pinpoint the rate-limiting step of a multi-step rotation process. Here, using Vacuolar or V1-type hexameric ATPase as an exemplary rotational motor, we present a model of the complete 4-step conformational cycle involved in rotatory catalysis. First, using X-ray crystallography a new intermediate or dwell is identified, which enables the release of an inorganic phosphate (or Pi) after ATP hydrolysis. Using molecular dynamics simulations, this new dwell is placed in a sequence with three other crystal structures to derive a putative cyclic rotation path. Free-energy simulations are employed to estimate the rate of the hexameric protein transformations, and delineate allosteric effects that allow new reactant ATP entry only after hydrolysis product exit. An analysis of transfer entropy brings to light how the sidechain-level interactions transcend into larger-scale reorganizations, highlighting the role of the ubiquitous arginine-finger residues in coupling chemical and mechanical information. Inspection of all known rates encompassing the 4-step rotation mechanism implicates overcoming of the ADP interactions with V1-ATPase to be the rate-limiting step of motor action.

biophysics↗

bioRxiv2021

Evaluating Sample Augmentation in Microarray Datasets with Generative Models: A Comparative Pipeline and Insights in Tuberculosis.

High throughput screening technologies have created a fundamental challenge for statistical and machine learning analyses, i.e., the curse of dimensionality. Gene expression data are a quintessential example, high dimensional in variables (Large P) and comparatively much smaller in samples (Small N). However, the large number of variables are not independent. This understanding is reflected in Systems Biology approaches to the transcriptome as a network of coordinated biological functioning or through principal Axes of variation underlying the gene expression. Recent advances in generative deep learning offers a new paradigm to tackle the curse of dimensionality by generating new data from the underlying latent space captured as a deep representation of the observed data. These have led to widespread applications of approaches such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), especially in domains where millions of data points exist, such as in computer vision and single cell data. Very few studies have focused on generative modeling of bulk transcriptomic data and microarrays, despite being one of the largest types of publicly available biomedical data. Here we review the potential of Generative models in recapitulating and extending biomedical knowledge from microarray data, which may thus limit the potential to yield hundreds of novel biomarkers. Here we review the potential of generative models and conduct a comparative analysis of VAE, GAN and gaussian mixture model (GMM) in a dataset focused on Tuberculosis. We further review whether previously known axes genes can be used as an effective strategy to employ domain knowledge while designing generative models as a means to further reduce biological noise and enhance signals that can be validated by standard enrichment approaches or functional experiments.

genomics↗

bioRxiv2021

Mucin-mimetic glycan arrays integrating machine learning for analyzing receptor pattern recognition by influenza A viruses

Influenza A viruses (IAVs) exploit host glycans in airway epithelial mucosa to gain entry and initiate infection. Rapid detection of changes in IAV specificity towards host glycan classes can provide early indication of virus transmissibility and infection potential. IAVs use hemagglutinins (HA) to bind sialic acids linked to larger glycan structures and a switch in HA specificity from 2,3-to 2,6-linked sialoglycans is considered a prerequisite for viral transmission from birds to humans. While the changes in HA structure associated with the evolution of binding phenotype have been mapped, the influence of glycan receptor presentation on IAV specificity remains obscured. Here, we describe a glycan array platform which uses synthetic mimetics of mucin glycoproteins to model how receptor presentation in the mucinous glycocalyx, including glycan type and valency of the glycoconjugates and their surface density, impact IAV binding. We found that H1N1 virus produced in embryonated chicken eggs, which recognizes both receptor types, exclusively engaged mucin-mimetics carrying 2,3-linked sialic acids in their soluble form. The virus was able utilize both receptor structures when the probes were immobilized on the array; however, increasing density in the mucin-mimetic brush diminished viral adhesion. Propagation in mammalian cells produced a change in receptor pattern recognition by the virus, without altering its HA affinity, toward improved binding of 2,6-sialylated mucin mimetics and reduced sensitivity to surface crowding of the probes. Application of a support vector machine (SVM) learning algorithm as part of the glycan array binding analysis efficiently characterized this shift in binding preference and may prove useful to study the evolution of viral responses to a new host.

microbiology↗

bioRxiv2020

Mind reading of the proteins: Deep-learning to forecast molecular dynamics

Molecular dynamics (MD) simulations have emerged to become the back-bone of todays computational biophysics. Simulation tools such as, NAMD, AMBER and GROMACS have accumulated more than 100,000 users. Despite this remarkable success, now also bolstered by compatibility with graphics processor units (GPUs) and exascale computers, even the most scalable simulations cannot access biologically relevant timescales - the number of numerical integration steps necessary for solving differential equations in a million-to-billion-dimensional space is computationally in-tractable. Recent advancements in Deep Learning has made it such that patterns can be found in high dimensional data. In addition, Deep Learning have also been used for simulating physical dynamics. Here, we utilize LSTMs in order to predict future molecular dynamics from current and previous timesteps, and examine how this physics-guided learning can benefit researchers in computational biophysics. In particular, we test fully connected Feed-forward Neural Networks, Recurrent Neural Networks with LSTM / GRU memory cells with TensorFlow and PyTorch frame-works trained on data from NAMD simulations to predict conformational transitions on two different biological systems. We find that non-equilibrium MD is easier to train and performance improves under the assumption that each atom is independent of all other atoms in the system. Our study represents a case study for high-dimensional data that switches stochastically between fast and slow regimes. Applications of resolving these sets will allow real-world applications in the interpretation of data from Atomic Force Microscopy experiments.

biophysics↗

bioRxiv2020

Prediction and characterization of transcription factors involved in drought stress response

Transcription factors (TFs) play a central role in regulating molecular level responses of plants to external stresses such as water limiting conditions, but identification of such TFs in the genome remains a challenge. Here, we describe a network-based supervised machine learning framework that accurately predicts and ranks all TFs in the genome according to their potential association with drought tolerance. We show that top ranked regulators fall mainly into two age groups; genes that appeared first in land plants and genes that emerged later in the Oryza clade. TFs predicted to be high in the ranking belong to specific gene families, have relatively simple intron/exon and protein structures, and functionally converge to regulate primary and secondary metabolism pathways. Repeated trials of nested cross-validation tests showed that models trained only on regulatory network patterns, inferred from large transcriptome datasets, outperform models trained on heterogenous genomic features in the prediction of known drought response regulators. A new R/Shiny based web application, called the DroughtApp, provides a primer for generation of new testable hypotheses related to regulation of drought stress response. Furthermore, to test the system we experimentally validated predictions on the functional role of the rice transcription factor OsbHLH148, using RNA sequencing of knockout mutants in response to drought stress and protein-DNA interaction assays. Our study exemplifies the integration of domain knowledge for prioritization of regulatory genes in biological pathways of well-studied agricultural traits. One Sentence SummaryNetwork-based supervised machine learning accurately predicts transcription factors involved in drought tolerance.

systems biology↗

bioRxiv2019

CryoFold: Ab-initio structure determination from electron density maps using molecular dynamics

Cryo-EM is a powerful method for determining protein structures. But it requires computational assistance. Physics-based computations have the power to give low-free-energy structures and ensembles of populations, but have been computationally limited to only small soluble proteins. Here, we introduce CryoFold. By integrating data of varying sparsity from electron density maps of 3-5 [A] resolution with coarse-grained physical knowledge of secondary and tertiary interactions, CryoFold determines ensembles of protein structures directly from sequence. We give six examples showing its broad capabilities, over proteins ranging from 72 to 2000 residues, including membrane and multi-domain proteins, and including results from two EMDB competitions. The ensembles CryoFold predicts starting from the density data of a single known protein conformation encompass multiple low-energy conformations, all of which are experimentally validated and biologically relevant.

biophysics↗

bioRxiv2018

Cooperative Non-bonded Forces Control Membrane Binding of the pH-Low Insertion Peptide pHLIP

Peptides with the ability to bind and insert into the cell membrane have immense potential in biomedical applications. pH (Low) Insertion Peptide (pHLIP), a water-soluble polypeptide derived from helix C of bacteriorhodopsin, can insert into a membrane at acidic pH to form a stable transmembrane -helix. The insertion process takes place in three stages: pHLIP is unstructured and soluble in water at neutral pH (state I), unstructured and bound to the surface of a membrane at neutral pH (state II), and inserted into the membrane as an -helix at low pH (state III). Using molecular dynamics (MD) simulations, we have modeled state II of pHLIP and a fast-folding variant of pHLIP, in which each peptide is bound to a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer surface. Our results provide strong support for recently published spectroscopic studies, namely that pHLIP preferentially binds to the bilayer surface as a function of location of anionic amino acids and that backbone dehydration occurs upon binding. Unexpectedly, we also observed several instances of segments of pHLIP folding into a stable helical turn. Our results provide a molecular level of detail that is essential to providing new insights into pHLIP function and to facilitate design of variants with improved cell-penetrating capabilities.

biophysics↗

bioRxiv2017

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

Transcriptional profiling is a prevalent and powerful approach for capturing the response of crop plants to environmental stresses, e.g. response of rice to drought. However, functionally interpreting the resulting genome-wide gene expression changes is severely hampered by the large gaps in our genomic knowledge about which genes work together in cellular pathways/processes in rice. Here, we present a new web resource - RECoN - that relies on a network-based approach to go beyond currently limited annotations in delineating functional and regulatory perturbations in new rice stress transcriptome datasets generated by a researcher. To build RECoN, we first enumerated 1,744 stress-specific gene modules covering 28,421 rice genes (>72% of the genes in the genome). Each module contains a group of genes tightly coexpressed across a large number of environmental conditions and, thus, is likely to be functionally coherent. When a user provides a new differential expression profile, RECoN identifies modules substantially perturbed in their experiment and further suggests deregulated functional and regulatory mechanisms based on the enrichment of current annotations within the predefined modules. We demonstrate the utility of this resource by analyzing new drought transcriptomes of rice in three developmental stages, which revealed large-scale insights into the cellular processes and regulatory mechanisms involved in common and stage-specific drought responses. RECoN enables biologists to functionally explore new data from all abiotic stresses on a genome-scale and to uncover gene candidates, including those that are currently functionally uncharacterized, for engineering stress tolerance.

systems biology↗