Biology subjects

Chopra, G.

Publications and source records attributed to Chopra, G..

17 recordsLinked to original sources

bioRxiv2025

Sleep-dependent clearance of brain lipids by peripheral blood cells

Sleep is typically viewed through a brain-centric lens, with little known about the role of the periphery. Here, we identify a sleep function for peripheral macrophage-like cells (hemocytes) in the Drosophila circulation, showing that hemocytes track to the brain during sleep and take up lipids accumulated in cortex glia due to wake-associated oxidative damage. Through a screen of phagocytic receptors expressed in hemocytes, we discovered that knockdown of eater, a member of the Nimrod receptor family, reduces sleep. Loss of eater also disrupts hemocyte adhesion to the brain and lipid uptake, which results in increased brain levels of Acetyl CoA and acetylated proteins, including mitochondrial proteins PGC1 and DRP1. Dysregulation of mitochondria, reflected in high oxidation and reduced NAD+, is accompanied by impaired memory and lifespan. Thus, peripheral blood cells, which we suggest are precursors of mammalian microglia, perform a daily function of sleep to maintain brain function and fitness.

molecular biology↗

bioRxiv2025

Impact Induces Phagocytic Defect in Reactive Microglia

We have developed traumatic brain injury (TBI)-on-a-chip in vitro models using primary microglia and neuronal networks and recorded the molecular and cellular changes following impact to represent impact injury. Using a pH-responsive amyloid {beta} (A{beta}pH), we showed that microglial phagocytosis was reduced at 7 days post-impact on the chip. Simultaneously, neurons increased their uptake of A{beta}, and decreased neuronal firing frequency at 7 days post-impact based on electrophysiological recordings. Given the importance of lipid metabolism in brain trauma and neurodegeneration, the lipidome secreted by impacted cells was analyzed to understand changes in cellular processes. Interestingly, many lipid species from the sphingomyelin, glycerophospholipid, and phosphatidylserine classes were significantly affected by impact, which are known to play important roles in the resolution of neuroinflammation and the pathogenesis of neurodegeneration.

neuroscience↗

bioRxiv2025

Neutral lipid processing in glia is sexually dimorphic and promotes sleep through diacylglycerol catabolism

Sleep is thought to have a protective role in clearing toxic waste from the brain, which may include processing of damaged lipids. We recently showed that blocking endocytosis in glia increases sleep and report here that this block is associated with an increase in peroxidized lipids and glial lipid droplet accumulation, raising the possibility that accumulation of these lipids increases the need to sleep. Sleep gain induced by blocking glial transport is exaggerated by knockout of the lipid droplet coat protein, Lipid Storage Droplet 2 (Lsd2), suggesting that sleep-promoting lipids are not contained in lipid droplets. To identify lipids regulated by sleep state, we performed global, targeted lipidomics analysis on Drosophila neurons and glia, screening nearly 3,000 lipids across 11 major classes. This revealed that sex influences lipid composition in both cell types and lipid homeostasis following extended wakefulness. Female neurons and glia are enriched in ultra-long chain fatty acids, triacylglycerols, and diacylglycerols, with glial diacylglycerol enrichment correlating with elevated sleep need. Based on manipulations of neutral lipid metabolic pathways, we propose that monoacylglycerols, products of glial diacylglycerol catabolism, promote sleep.

neuroscience↗

bioRxiv2024

An aging-sensitive compensatory secretory phospholipase that confers neuroprotection and cognitive resilience

Breakdown of lipid homeostasis is thought to contribute to pathological aging, the largest risk factor for neurodegenerative disorders such as Alzheimers Disease (AD). Cognitive reserve theory posits a role for compensatory mechanisms in the aging brain in preserving neuronal circuit functions, staving off cognitive decline, and mitigating risk for AD. However, the identities of such mechanisms have remained elusive. A screen for hippocampal dentate granule cell (DGC) synapse loss-induced factors identified a secreted phospholipase, Pla2g2f, whose expression increases in DGCs during aging. Pla2g2f deletion in DGCs exacerbates aging-associated pathophysiological changes including synapse loss, inflammatory microglia, reactive astrogliosis, impaired neurogenesis, lipid dysregulation and hippocampal-dependent memory loss. Conversely, boosting Pla2g2f in DGCs during aging is sufficient to preserve synapses, reduce inflammatory microglia and reactive gliosis, prevent hippocampal-dependent memory impairment and modify trajectory of cognitive decline. Ex vivo, neuronal-PLA2G2F mediates intercellular signaling to decrease lipid droplet burden in microglia. Boosting Pla2g2f expression in DGCs of an aging-sensitive AD model reduces amyloid load and improves memory. Our findings implicate PLA2G2F as a compensatory neuroprotective factor that maintains lipid homeostasis to counteract aging-associated cognitive decline.

neuroscience↗

bioRxiv2024

Modeling Molecular Mechanisms of Pirfenidone Interaction with Kinases

Scar formation is a process that occurs due to increased collagen deposition and uncontrolled inflammation. Previous studies have demonstrated that Pirfenidone (Pf), an FDA approved anti-inflammatory and anti-fibrotic drug can reduce inflammation in vivo as well as regulate activation of LPS-stimulated neutrophils. However, the molecular level mechanism of Pfs action is not well understood. Here, we used neural networks to identify new targets and molecular modeling methods to investigate the Pfs action pathways at the molecular level that are related to its ability to reduce both the inflammatory and remodeling phases of the wound healing process. Out of all the potential targets identified, both molecular docking and molecular dynamics results suggest that Pf has a noteworthy binding preference towards the active conformation of the p38 mitogen activated protein kinase-14 (MAPK14) and it is potentially a type I inhibitor-like molecule. In addition to p38 MAPK (MAPK14), additional potential targets of Pf include AKT1, MAP3K4, MAP2K3, MAP2K6, MSK2, MAP2K2, ERK1, ERK2, and PDK1. We conclude that several proteins/kinases, rather than a single target, are involved in Pfs wound healing ability to regulate signaling, inflammation, and proliferation.

systems biology↗

bioRxiv2024

Lesion-remote astrocytes govern microglia-mediated white matter repair

Spared regions of the damaged central nervous system undergo dynamic remodeling and exhibit a remarkable potential for therapeutic exploitation. Here, lesion-remote astrocytes (LRAs), which interact with viable neurons, glia and neural circuitry, undergo reactive transformations whose molecular and functional properties are poorly understood. Using multiple transcriptional profiling methods, we interrogated LRAs from spared regions of mouse spinal cord following traumatic spinal cord injury (SCI). We show that LRAs acquire a spectrum of molecularly distinct, neuroanatomically restricted reactivity states that evolve after SCI. We identify transcriptionally unique reactive LRAs in degenerating white matter that direct the specification and function of local microglia that clear lipid-rich myelin debris to promote tissue repair. Fueling this LRA functional adaptation is Ccn1, which encodes for a secreted matricellular protein. Loss of astrocyte CCN1 leads to excessive, aberrant activation of local microglia with (i) abnormal molecular specification, (ii) dysfunctional myelin debris processing, and (iii) impaired lipid metabolism, culminating in blunted debris clearance and attenuated neurological recovery from SCI. Ccn1-expressing white matter astrocytes are specifically induced by local myelin damage and generated in diverse demyelinating disorders in mouse and human, pointing to their fundamental, evolutionarily conserved role in white matter repair. Our findings show that LRAs assume regionally divergent reactivity states with functional adaptations that are induced by local context-specific triggers and influence disorder outcome. Astrocytes tile the central nervous system (CNS) where they serve vital roles that uphold healthy nervous system function, including regulation of synapse development, buffering of neurotransmitters and ions, and provision of metabolic substrates1. In response to diverse CNS insults, astrocytes exhibit disorder-context specific transformations that are collectively referred to as reactivity2-5. The characteristics of regionally and molecularly distinct reactivity states are incompletely understood. The mechanisms through which distinct reactivity states arise, how they evolve or resolve over time, and their consequences for local cell function and CNS disorder progression remain enigmatic. Immediately adjacent to CNS lesions, border-forming astrocytes (BFAs) undergo transcriptional reprogramming and proliferation to form a neuroprotective barrier that restricts inflammation and supports axon regeneration6-9. Beyond the lesion, spared but dynamic regions of the injured CNS exhibit varying degrees of synaptic circuit remodeling and progressive cellular responses to secondary damage that have profound consequences for neural repair and recovery10,11. Throughout these cytoarchitecturally intact, but injury-reactive regions, lesion-remote astrocytes (LRAs) intermingle with neurons and glia, undergo little to no proliferation, and exhibit varying degrees of cellular hypertrophy7,12,13. The molecular and functional properties of LRAs remain grossly undefined. Therapeutically harnessing spared regions of the injured CNS will require a clearer understanding of the accompanying cellular and molecular landscape. Here, we leveraged integrative transcriptional profiling methodologies to identify multiple spatiotemporally resolved, molecularly distinct states of LRA reactivity within the injured spinal cord. Computational modeling of LRA-mediated heterotypic cell interactions, astrocyte-specific conditional gene deletion, and multiple mouse models of acute and chronic CNS white matter degeneration were used to interrogate a newly identified white matter degeneration-reactive astrocyte subtype. We define how this reactivity state is induced and its role in governing the molecular and functional specification of local microglia that clear myelin debris from the degenerating white matter to promote repair.

neuroscience↗

bioRxiv2023

Amyloid β Induces Lipid Droplet-Mediated Microglial Dysfunction in Alzheimer's Disease

Several microglia-expressed genes have emerged as top risk variants for Alzheimers disease (AD). Impaired microglial phagocytosis is one of the main proposed outcomes by which these AD-risk genes may contribute to neurodegeneration, but the mechanisms translating genetic association to cellular dysfunction remain unknown. Here we show that microglia form lipid droplets (LDs) upon exposure to amyloid-beta (A{beta}), and that their LD load increases with proximity to amyloid plaques in brains from human patients and the AD mouse model 5xFAD. LD formation is dependent on age and disease progression and is prominent in the hippocampus in mice and humans. Despite differences in microglial LD load between brain regions and sexes in mice, LD-laden microglia exhibited a deficit in A{beta} phagocytosis. Unbiased lipidomic analysis identified a decrease in free fatty acids (FFAs) and a parallel increase in triacylglycerols (TGs) as the key metabolic transition underlying LD formation. DGAT2, a key enzyme for converting FFAs to TGs, promotes microglial LD formation and is increased in 5xFAD and human AD brains. Inhibition or degradation of DGAT2 improved microglial uptake of A{beta} and drastically reduced plaque load in 5xFAD mice, respectively. These findings identify a new lipid-mediated mechanism underlying microglial dysfunction that could become a novel therapeutic target for AD.

neuroscience↗

bioRxiv2022

Variability of Phenylalanine side chain conformations facilitates promiscuity of Fatty acid binding in Cockroach milk proteins.

The pacific beetle cockroach, Diploptera punctata, is a viviparous cockroach that produces a milk-like substance to support the growing embryo with a brood sac. The structure of the in vivo grown crystals present in the gut of the embryo showed that the milk-derived crystals are heterogenous and are made of three proteins (called Lili-Mips). Multiple fatty acids could be modeled into the active site, and we hypothesized that each of the three isoforms of the protein bound to a different fatty acid. We previously reported that the recombinantly expressed Lili-Mip2 has a structure similar to the structure of the protein determined from in vivo crystals, and this single isoform also binds to several fatty acids. In this study, we aimed to probe the specificity and affinity of fatty acid binding and test the stability of different isoforms. We show that all the isoforms can bind to different fatty acids with very similar affinities, and the local abundance of a fatty acid determined bound fatty acid ratios. Lili-Mips thermostability is pH dependent, where stability is highest at acidic pH and declines as the pH increases to physiological levels near 7.0. The measurement of the pH in the gut lumen and the gut cells suggests that the pH in the gut is acidic and the pH inside the gut cells is closer to neutral pH. We propose that the protein has evolved to be highly stable in the acidic gut lumen and, when absorbed inside the gut cells, becomes less stable to enable the breakdown of the glycosylated lipo-protein complex to provide essential metabolites for survival and development of the embryo. The different orientations of Phe-98 and Phe-100 control the binding pocket volume and allow the binding of different chain-length fatty acids to bind with similar affinities.

biochemistry↗

bioRxiv2021

One Scaffold - Different Organelles Sensors: pH-Activable Fluorescent Probes for Targeting Live Primary Microglial Cell Organelles

Targeting live cell organelles is important for imaging and to understand and control specific biochemical processes. Typically, fluorescent probes with distinct structural scaffolds have been used for targeting specific cell organelle. Herein, we aimed to design modular one-step synthetic strategies using a common reaction intermediate to develop new lysosomal, mitochondrial and nucleus targeting pH-activable fluorescent probes that are all based on a single boron dipyrromethane analogs. The divergent cell organelle targeting was achieved by synthesizing pH-activable fluorescent probes with specific functional groups changes to the main scaffold resulting in differential fluorescence and pKa. Specifically, we show that the functional group transformation of the same scaffold influences cellular localization and specificity of pH-activable fluorescent probes in live primary microglial cells with pKas ranging from ~4.5-6.0. We introduce a structure-organelle-relationship (SOR) framework targeting the nucleus (NucShine), lysosomes (LysoShine) and mitochondria (MitoShine) in primary mouse microglial cells. This work will result in future applications of SOR beyond imaging to target and control organelle-specific biochemical processes in disease-specific models.

biochemistry↗

bioRxiv2020

Integration of Biodynamic Imaging and RNA-seq predicts chemotherapy response in canine diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a common, aggressive cancer of notorious genotypic and phenotypic heterogeneity. A major challenge is predicting response to drug treatment, which has typically been done using genomic tools alone with little success. A novel method that incorporates phenotypic profiling for predicting the effectiveness of therapy for individual patients is desperately needed. BioDynamic Imaging (BDI) is a technique for measuring time-dependent fluctuations in back-scattered light through living tumor tissues to identify critical changes in intracellular dynamics that are associated with phenotypic response to drugs. In this study, BDI and RNA sequencing (RNA-seq) data were collected on tumor samples from dogs with naturally occurring DLBCL, an animal model of increasingly recognized relevance to the human disease. BDI and RNA-seq data were combined to identify correlations between gene co-expression modules and linear combinations of biomarkers to provide biological mechanistic interpretations of BDI biomarkers. Using regularized multivariate logistic regression, we combined RNA-seq and BDI data to develop a novel model to accurately classify the clinical response of canine DLBCL to combination chemotherapy (i.e. CHOP). Our model incorporates data on the expression of 4 genes and 3 BDI-derived phenotypic biomarkers, capturing changes in transcription, microtubule related processes, and apoptosis. This pilot study suggests that the combination of multi-scale transcriptomic and phenotypic data can identify patients that respond to a given treatment a priori in a disease that has been difficult to treat. Our work provides an important framework for future development of strategies and treatments in precision cancer medicine.

cancer biology↗

bioRxiv2020

Monitoring phagocytic uptake of amyloid β into glial cell lysosomes in real time

Phagocytosis by glial cells is essential to regulate brain function during development and disease. Given recent interest in using amyloid {beta} (A{beta})-targeted antibodies as a therapy for patients with Alzheimers disease, removal of A{beta} by phagocytosis is likely protective early in Alzheimers disease, but remains poorly understood. Impaired phagocytic function of glial cells surrounding A{beta} plaques during later stages in Alzheimers disease likely contributes to worsened disease outcomes, but the underlying mechanisms of how this occurs remain unknown. We have developed a human A{beta}1-42 analogue (A{beta}pH) that exhibits green fluorescence upon internalization into the acidic phagosomes of cells but is non-fluorescent at physiological pH. This allowed us to image, for the first time, glial uptake of A{beta}pH in real time in live animals. Microglia phagocytose more A{beta}pH than astrocytes in culture, in brain slices and in vivo. A{beta}pH can be used to investigate the phagocytic mechanisms removing A{beta} from the extracellular space, and thus could become a useful tool to study A{beta} clearance at different stages of Alzheimers disease.

neuroscience↗

bioRxiv2020

DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening

Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking datasets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly created benchmarking sets using the protein databank. DUBS uses a simple input text based format along with the Lemon data mining framework to efficiently access and organize data to protein databank and output commonly used inputs for virtual screening software. The simple input format used by DUBS allows users to define their own benchmarking datasets and access the corresponding information directly from the software package. Currently, it only takes DUBS less than 2 minutes to create a benchmark using this format. Since DUBS uses a simple python script, users can easily modify to create more complex benchmarks. We hope that DUBS will be a useful community resource to provide a standardized representation for benchmarking datasets in virtual screening.

bioinformatics↗

bioRxiv2019

Breaking a Strong Amide Bond: Structure and Properties of Dimethylformamidase.

Dimethylformamidase (DMFase) breaks down the human-made synthetic solvent N,N-dimethyl formamide(DMF) used extensively in industry(1). DMF is not known to exist in nature and was first synthesized in 1893. In spite of the recent origin of DMF, certain bacterial species such as Paracoccus, Pseudomonas, and Alcaligenes have evolved pathways to breakdown DMF and use them as carbon and nitrogen source for growth(2, 3). The work presented here provides a molecular basis for the ability of DMFase from Paracoccus to function in exacting conditions of high solvent concentrations, temperature and ionic strength to catalyze the hydrolysis of a stable amide bond. The structure reveals a multimeric complex of the 2{beta}2 type or (2{beta}2)2 type. One of the three domains of the large subunit and the small subunit are hitherto undescribed folds and as yet of unknown evolutionary origin. The active site is made of a distinctive mononuclear iron that is coordinated by two tyrosine residues and a glutamic acid residue. The hydrolytic cleavage of the amide bond is catalyzed at the Fe3+ site with a proximal glutamate probably acting as the base. The change in the quaternary structure is salt dependent with high salt resulting in the larger oligomeric state. Kinetic characterization reveals an enzyme that shows cooperativity between subunits and the structure provides clues on the interconnection between the active sites. Significance StatementN,N-dimethyl formamide(DMF) is a commonly used industrial solvent that was first synthesized in 1893. The properties that make DMF a highly desired solvent also makes it a difficult compound to breakdown. Yet, certain bacteria have evolved to survive in environments polluted by DMF and have enzymes that breakdown DMF and use it as their carbon and nitrogen source. The molecular structure of the enzyme that breaks down the stable amide bond in these bacteria, reveals two new protein folds and a unique mononuclear iron active site. The work reported here provides the structural and biochemical framework to query the evolutionary origins of the protein, as well as in engineering this enzyme for use in bioremediation of a human made toxic solvent.

biochemistry↗

bioRxiv2019

cando.py: Open source software for analyzing large scale drug-protein-disease data

Elucidating drug-protein interactions is essential for understanding the beneficial effects of small molecule therapeutics in human disease states. Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets. Analyzing drug interactions with a library of proteins provides further insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform [1-7]. The CANDO package allows for rapid drug similarity assessment, most notably via the bioinformatic docking protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of bench-marking protocols to determine how well drugs are related to each other in terms of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.

bioinformatics↗

bioRxiv2018

Stable and Reversible Functionalization of the Native Phosphate Groups on Live Cells

Surface modification of live cells has important biomedical and therapeutic applications. Current methods to label cells require artificial cell surface engineering (via metabolic, docking or anchoring methods) before conjugative chemistries, which is not always trivial to accomplish and/or not appropriate for multiple cell types. A new method without the need of initial cell surface anchoring will greatly facilitate live cell surface labelling. Herein, we provide a general strategy for live cell functionalization that utilizes the native phosphate groups on every cell. We have designed a dual conjugation cargo molecule with a cationic side chain for non-covalent bonds with the negatively-charged cell surface and a phosphoric acid containing ligand for covalent bonding with the cell membrane phospholipid phosphate. Our dual conjugation strategy on live cell surfaces is non-toxic with enhanced stability to functionalize live cells. This provides a stable, reversible and reusable reagent with direct conjugation strategy to image live cell membranes.\n\nSignificanceThe ability to label live cell surfaces has many applications ranging from in vivo monitoring of cell populations to diagnostics and use of cells as drugs. Thus far, most reported strategies to label cell surfaces are not broadly applicable or easy to use for any cell type as it has relied on engineering cells with artificial moieties or conjugations that may affect cellular function. We provide a general solution to this long-standing problem by developing two-sided functionalization of the phosphate moieties that are ubiquitous on all cells. We show one application of our chemical strategy as a general-purpose live-cell membrane imaging reagent with long-time stability. Our strategy is broadly applicable to imaging, sensing, drug delivery, bioengineering, diagnostics and cell therapy.\n\nO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

bioengineering↗

bioRxiv2018

CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials

Small molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations, such as, improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g. cofactors, metal-ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions and cofactors interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind and Astex proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions, such that, the statistical score of best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best docked pose with biological activity.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=28 SRC=\"FIGDIR/small/442897v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (16K):\norg.highwire.dtl.DTLVardef@1c1b2b0org.highwire.dtl.DTLVardef@1ba66a4org.highwire.dtl.DTLVardef@a8bfd3org.highwire.dtl.DTLVardef@c9d3bb_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2018

Lemon: a modern C++ tool for the rapid development of structural benchmarking datasets

MotivationThe protein data bank (PDB) currently holds over 140,000 biomolecular structures and continues to release new structures on a weekly basis. The PDB is an essential resource to the structural bioinformatics community to develop software that mine, use, categorize, and analyze such data. New computational biology methods are evaluated using custom benchmarking sets derived as subsets of 3D experimentally determined structures and structural features from the PDB. Currently, such benchmarking features are manually curated with custom scripts in a non-standardized manner that results in slow distribution and updates with new experimental structures. Finally, there is a scarcity of standardized tools to rapidly query 3D descriptors of the entire PDB. ApproachOur solution is the Lemon framework, a C++11 library with Python bindings, which provides a consistent workflow methodology for selecting biomolecular interactions based on user criterion and computing desired 3D structural features. This framework can parse and characterize the entire PDB in less than ten minutes on modern, multithreaded hardware. The speed in parsing is obtained by using the recently developed MacroMolecule Transmission Format (MMTF) to reduce the computational cost of reading text-based PDB files. The use of C++ lambda functions and Python binds provide extensive flexibility for analysis and categorization of the PDB by allowing the user to write custom functions to suite their objective. We think Lemon will become a one-stop-shop to quickly mine the entire PDB to generate desired structural biology features. The Lemon software is available as a C++ header library along with example functions at https://github.com/chopralab/lemon.

bioinformatics↗