Biology subjects

Paggi, J. M.

Publications and source records attributed to Paggi, J. M..

11 recordsLinked to original sources

bioRxiv2025

Euchromatin forms condensed domains with short active regions on the surface

Technological advances in chromatin structure characterization have continually refined our understanding of transcriptional regulation in eukaryotic systems. Despite these developments, achieving nucleosome-resolution structural characterization remains a significant challenge. As a result, it remains unclear what structural features distinguish active enhancers and promoters and how these elements are organized. To address this, we developed a simulation framework that leverages high-resolution Region-Capture Micro-C (RCMC) contact maps to infer conformational ensembles of megabase-scale chromatin segments at nucleosome resolution. A key component of this framework is a balancing strategy tailored for Micro-C data, which identifies per-nucleosome variation in contact density, in contrast to existing methods that assume uniform contact density. Our model accurately reproduces contact frequencies observed in RCMC data, pairwise spatial distances measured via chromatin tracing, and local structural motifs observed in imaging studies. The high spatial and genomic resolution of the inferred structures reveal a striking departure from the classical view of euchromatin as uniformly open. Instead, euchromatin generally folds into compact domains, consistent with the "packing domains" observed in imaging studies. These domains are further organized into disordered, insulated clusters of approximately 50 nucleosomes, resembling nucleosome clutches. Notably, kilobase-scale regions surrounding active promoters and enhancers often protrude from these condensed domains, becoming highly accessible. This spatial arrangement effectively compartmentalizes regulatory elements from the surrounding chromatin, facilitating protein binding and promoting enhancer-promoter communication. The distinct structural features of euchromatin revealed here offer new insights into enhancer regulation and may help explain their enigmatic behavior.

biophysics↗

bioRxiv2023

A non-canonical mechanism of GPCR activation

The goal of designing safer, more effective drugs has led to tremendous interest in molecular mechanisms through which ligands can precisely manipulate signaling of G-protein-coupled receptors (GPCRs), the largest class of drug targets. Decades of research have led to the widely accepted view that all agonists--ligands that trigger GPCR activation--function by causing rearrangement of the GPCRs transmembrane helices, opening an intracellular pocket for binding of transducer proteins. Here we demonstrate that certain agonists instead trigger activation of free fatty acid receptor 1 by directly rearranging an intracellular loop that interacts with transducers. We validate the predictions of our atomic-level simulations by targeted mutagenesis; specific mutations which disrupt interactions with the intracellular loop convert these agonists into inverse agonists. Further analysis suggests that allosteric ligands could regulate signaling of many other GPCRs via a similar mechanism, offering rich possibilities for precise control of pharmaceutically important targets.

molecular biology↗

bioRxiv2022

Structural basis for ion selectivity in potassium-selective channelrhodopsins

The KCR channelrhodopsins are recently-discovered light-gated ion channels with high K+ selectivity, a property that has attracted broad attention among biologists- due to intense interest in creating novel inhibitory tools for optogenetics leveraging this K+ selectivity, and due to the mystery of how this selectivity is achieved in the first place. Indeed, the molecular and structural mechanism for K+ selectivity in KCRs has remained especially puzzling since these 7-transmembrane retinal-binding proteins completely lack structural similarity with known K+ channels, which generally coordinate K+ in a precisely symmetric conduction pathway formed by a tight interface among multiple small monomeric channel subunits (presumably not an accessible mechanism for the large KCR rhodopsin proteins). Here we present the cryo-electron microscopy structures of two KCRs from Hyphochytrium catenoides with distinct spectral properties for light absorption and channel actuation, HcKCR1, and HcKCR2, at resolutions of 2.6 and 2.5 [A], respectively. Structural comparison revealed first an unusually-shaped retinal binding pocket which induces rotation of the retinal in HcKCR2, explaining the large spectral difference between HcKCR1 and 2. Next, our combined structural, electrophysiological, computational, and spectroscopic analyses revealed a new solution to the challenging problem of K+-selective transport. KCRs indeed do not exhibit the canonical tetrameric K+ selectivity filter that specifically coordinates dehydrated K+; instead, single KCR monomers form a size exclusion filter using aromatic residues at the extracellular side of the pore which inhibits passage of bulky hydrated ions. This unique feature allows KCRs to function as K+ channels under relevant physiological conditions, providing not only a novel mechanism for achieving high K+ permeability ratios in biological ion channels, but also a framework for designing the next generation of inhibitory optogenetic tools. In BriefThe first structures of K+-selective channelrhodopsins (HcKCR1 and 2) are determined, revealing a K+ selectivity mechanism distinctly different from canonical K+ channels. HighlightsO_LIThe cryo-EM structures of K+-selective channelrhodopsins, HcKCR1 and 2, in nanodisc C_LIO_LIConditions under which naturally-occurring microbial rhodopsins have a 6-s-cis retinal C_LIO_LIIdentification of key residues for high K+ permeability ratios C_LIO_LIThe unique K+ selectivity mechanism of KCRs C_LI

biophysics↗

bioRxiv2021

Structural insights into distinct signaling profiles of the μOR activated by diverse agonists

Drugs targeting the G protein-coupled -opioid receptor (OR) are the most effective analgesics available but are also associated with fatal respiratory depression. While some partial opioid agonists appear to be safer than full agonists, the signaling pathways responsible for respiratory depression have yet to be elucidated. Here we investigated the structural and mechanistic basis of action of lofentanil (LFT) and mitragynine pseudoindoxyl (MP), two OR agonists with different safety profiles. LFT, one of the most potent and lethal opioids, and MP, a derivative from the kratom plant with reduced respiratory depression in animal studies at equianalgesic doses, exhibited markedly different signaling efficacy profiles for G protein subtype activation and recruitment of {beta}-arrestins. Cryo-EM structures of the OR-Gi1 complex with MP (2.5[A]) and LFT (3.2[A]) revealed that the two ligands engage distinct sub-pockets, and molecular dynamics (MD) simulations showed additional differences in the binding site that propagate to the intracellular side of the receptor where G proteins and {beta}-arrestins bind. While MP favors the precise G protein-bound active state observed in the cryo-EM structures, LFT favors a distinct active state. These results highlight how drugs engaging different parts of the OR orthosteric pocket can lead to distinct signaling outcomes.

biophysics↗

bioRxiv2021

Structural basis for channel conduction in the pump-like channelrhodopsin ChRmine

ChRmine1, a recently-discovered bacteriorhodopsin-like cation-conducting channelrhodopsin1, 2, exhibits puzzling properties (unusually-large photocurrents, exceptional red-shift in action spectrum, and extreme light-sensitivity) that have opened up new opportunities in optogenetics1, 3-5. ChRmine and its homologs function as light-gated ion channels, but by primary sequence more closely resemble ion pump rhodopsins; the molecular mechanisms for passive channel conduction in this family of proteins, as well as the unusual properties of ChRmine itself, have remained mysterious. Here we present the cryo-electron microscopy structure of ChRmine at 2.0 [A] resolution. The structure reveals striking architectural features never seen before in channelrhodopsins including trimeric assembly, a short transmembrane-helix 3 unwound in the middle of the membrane, a prominently-twisting extracellular-loop 1, remarkably-large intracellular cavities and extracellular vestibule, and an unprecedented hydrophilic pore that extends through the center of the trimer, separate from the three individual monomer pores. Electrophysiological, spectroscopic, and computational analyses provide insight into conduction and gating of light-gated channels with these distinct design features, and point the way toward structure-guided creation of novel channelrhodopsins for optogenetic applications in biology.

biophysics↗

bioRxiv2020

Leveraging non-structural data to predict structures of protein-ligand complexes

Over the past fifty years, tremendous effort has been devoted to computational methods for predicting properties of ligands that bind macromolecular targets, a problem critical to rational drug design. Such methods generally fall into two categories: physics-based methods, which directly model ligand interactions with the target given the targets three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligands pose--the 3D structure of the ligand bound to its protein target--that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves upon state-of-the-art pose prediction accuracy across all major families of drug targets. As an illustrative application, we predict binding poses of antipsychotics and validate the results experimentally. Our statistical framework and results suggest broad opportunities to predict diverse ligand properties using machine learning methods that draw on physical modeling and ligand data simultaneously.

biophysics↗

bioRxiv2019

Medical relevance of common protein-altering variants in GPCR genes across 337,205 individuals in the UK Biobank study

G protein-coupled receptors (GPCRs) drive an array of critical physiological functions and are an important class of drug targets, though a map of which GPCR genetic variants are associated with phenotypic variation is lacking. We performed a phenome-wide association analysis for 269 common protein-altering variants in 156 GPCRs and 275 phenotypes, including disease outcomes and diverse quantitative measurements, using 337,205 UK Biobank participants and identified 138 associations. We discovered novel associations between GPCR variants and migraine risk, hypothyroidism, and dietary consumption. We also demonstrated experimentally that variants in the {beta}2 adrenergic receptor (ADRB2) associated with immune cell counts and pulmonary function and variants in the gastric inhibitory polypeptide receptor (GIPR) associated with food intake and body size affect downstream signaling pathways. Overall, this study provides a map of genetic associations for GPCR coding variants across a wide variety of phenotypes, which can inform future drug discovery efforts targeting GPCRs.

genetics↗

bioRxiv2019

Structural and Functional Characterization of G Protein-Coupled Receptors with Deep Mutational Scanning

In humans, the 813 G protein-coupled receptors (GPCRs) are responsible for transducing diverse chemical stimuli to alter cell state, and are the largest class of drug targets. Their myriad structural conformations and various modes of signaling make it challenging to understand their structure and function. Here we developed a platform to characterize large libraries of GPCR variants in human cell lines with a barcoded transcriptional reporter of G-protein signal transduction. We tested 7,800 of 7,828 possible single amino acid substitutions to the beta-2 adrenergic receptor ({beta}2AR) at four concentrations of the agonist isoproterenol. We identified residues specifically important for {beta}2AR signaling, mutations in the human population that are potentially loss of function, and residues that modulate basal activity. Using unsupervised learning, we resolve residues critical for signaling, including all major structural motifs and molecular interfaces. We also find a previously uncharacterized structural latch spanning the first two extracellular loops that is highly conserved across Class A GPCRs and is conformationally rigid in both the inactive and active states of the receptor. More broadly, by linking deep mutational scanning with engineered transcriptional reporters, we establish a generalizable method for exploring pharmacogenomics, structure and function across broad classes of drug receptors.

molecular biology↗

bioRxiv2018

S-CAP extends clinical-grade pathogenicity prediction to genetic variants that affect RNA splicing

There are over 15,000 known variants that cause human inherited disease by disrupting RNA splicing. While several in silico methods such as CADD, EIGEN and LINSIGHT are commonly used to predict the pathogenicity of noncoding variants, we introduce S-CAP, a tool developed specially for splicing which is better able to effectively distinguish pathogenic splicing-relevant variants from benign variants. S-CAP is a novel splicing pathogenicity predictor that reduces the number of splicing-relevant variants of uncertain significance in patient exomes by 41%, a nearly 3-fold improvement over existing noncoding pathogenicity measures while correctly classifying known pathogenic splicing-relevant variants with a clinical-grade 95% sensitivity.

genomics↗

bioRxiv2018

HISAT-genotype: Next Generation Genomic Analysis Platform on a Personal Computer

Rapid advances in next-generation sequencing technologies have dramatically changed our ability to perform genome-scale analyses of human genomes. The human reference genome used for most genomic analyses represents only a small number of individuals, limiting its usefulness for genotyping. We designed a novel method, HISAT-genotype, for representing and searching an expanded model of the human reference genome, in which a comprehensive catalogue of known genomic variants and haplotypes is incorporated into the data structure used for searching and alignment. This strategy for representing a population of genomes, along with a very fast and memory-efficient search algorithm, enables more detailed and accurate variant analyses than previous methods. We demonstrate HISAT-genotypes accuracy for HLA typing, a critical task in human organ transplantation, and for the DNA fingerprinting tests widely used in forensics. In both applications, HISAT-genotype not only improves upon earlier computational methods, but matches or exceeds the accuracy of laboratory-based assays.\n\nOne Sentence SummaryHISAT-genotype is a software platform that has the ability to genotype all the genes in an individuals genome within a few hours on a desktop computer.

bioinformatics↗

bioRxiv2017

A sequence-based, deep learning model accurately predicts RNA splicing branchpoints

Experimental detection of RNA splicing branchpoints, the nucleotide serving as the nucleophile in the first catalytic step of splicing, is difficult. To date, annotations exist for only 16-21% of 3 splice sites in the human genome and even these limited annotations have been shown to be plagued by noise. We develop a sequence-only, deep learning based branchpoint predictor, LaBranchoR, which we conclude predicts a correct branchpoint for over 90% of 3 splice sites genome-wide. Our predicted branchpoints show large agreement with trends observed in the raw data, but analysis of conservation signatures and overlap with pathogenic variants reveal that our predicted branchpoints are generally more reliable than the raw data itself. We use our predicted branchpoints to identify a sequence element upstream of branchpoints consistent with extended U2 snRNA base pairing, show an association between weak branchpoints and alternative splicing, and explore the effects of variants on branchpoints.

bioinformatics↗