Biology subjects

Coyote-Maestas, W.

Publications and source records attributed to Coyote-Maestas, W..

18 recordsLinked to original sources

bioRxiv2026

Distinct activation mechanisms underlie ligand efficacy at a GPCR

Ligands that bind to the same G protein-coupled receptor (GPCR) can produce widely different levels of signaling. These differences in efficacy are central to pharmacology, yet how a receptor sequence determines signaling level remains incompletely understood. Here we develop a deep mutational pharmacology approach to measure how each residue in the -opioid receptor (OR) shapes responses to 15 opioids, yielding ~400,000 measurements of variant effects on signaling and expression, including full concentration-response series for three agonists. Mutational effects on potency are strongest near the orthosteric pocket, whereas effects on efficacy extend through the transmembrane core and intracellular face. Ligands share many functional determinants, but sensitivity to mutation differs with efficacy. As efficacy decreases, the residues required for signaling shift from the binding pocket towards the intracellular face; partial agonists therefore depend on residues that are dispensable for full agonists. Mutations can broadly increase receptor responses, allowing antagonists such as naloxone to produce signaling comparable to a full agonist and increasing the efficacy of partial agonist ligands. Combined mutations can also reorder the relative efficacies of ligands, showing that sequence changes reshape pharmacological differences rather than simply increasing or decreasing all responses together. These findings support a model in which ligands of different efficacy activate the receptor through distinct allosteric mechanisms which are determined by residues distributed across the receptor. Mapping these contributions across diverse ligands with deep mutational pharmacology provides a way to understand how one receptor converts molecular recognition into a range of signaling outputs.

biophysics↗

bioRxiv2026

Cardiomyocyte vulnerability to lamin polymer disruption revealed by saturation mutagenesis

Hundreds of mutations to the broadly expressed LMNA gene cause disease primarily within cardiac, muscular, and adipose tissues (1). Tissue-specific pathogenesis arises when mutant protein dysfunction collides with the unique demands of a specific cell type. Here, we decipher the cell-type-specific consequences of ~15,000 LMNA mutations by completing the first saturation mutagenesis screens in human induced pluripotent cells (hiPSCs) and hiPSC-derived cardiomyocytes using our newly developed single large serine integrase cassette exchange (SLICE) platform. We find that destabilization is a predominant consequence of pathogenic LMNA mutations, is selected against in human populations, and is associated with cardiomyopathy. Mutation sensitivity maps reveal Lamin A quality control at both the subunit and multimer level, resolve lateral and head-to-tail polymerization interfaces, and uncover a convergence between disruption of lamin polymer assembly and pathogenesis. Uniquely in cardiomyocytes, lamin A polymer assembly defects drive profound protein loss, nuclear abnormalities, and cellular toxicity, explaining the origins of cardiac specificity in laminopathy syndromes.

cell biology↗

bioRxiv2025

Deep Receptor Scanning Reveals General Sequence Constraints on GPCR Biosynthesis

G protein-coupled receptors (GPCRs) mediate a variety of signaling pathways and are the most common pharmacological targets. While advances in structural biochemistry have provided deep functional insights into key receptors, many of the 800+ human GPCRs remain understudied. We introduce a versatile "deep receptor scanning" platform that can be used to experimentally characterize 766 human GPCRs and 174 known GPCR splice variants in parallel. We use this platform to quantitatively characterize the relative abundance of canonical and alternative receptor transcripts, their translational efficiency, and the plasma membrane expression of each receptor in the context of a recombinant pool of HEK293T cells expressing individual GPCRs. We then employ machine learning to identify specific structural features that modulate GPCR expression. This experimental platform and informatic approach are compatible with a variety of assays and can be used to efficiently explore the biochemical and pharmacological properties of the GPCRome.

biochemistry↗

bioRxiv2025

Cosmos: A Position-Resolution Causal Model for Direct and Indirect Effects in Protein Functions

Multi-phenotype deep mutational scanning (DMS) experiments provide a powerful means to dissect how protein variants affect different layers of molecular function, such as abundance, surface expression, and ligand binding. When these phenotypes are connected through a molecular pathway, interpreting variant effects becomes challenging because downstream phenotypes often reflect both direct and indirect consequences of mutation. We introduce Cosmos, a Bayesian framework for residue-level causal inference in multi-phenotype DMS data. Cosmos addresses three key questions: (1) whether a causal relationship exists between two phenotypes; (2) the strength of that relationship; and (3) the expected downstream phenotype if the upstream phenotype were normalized, enabling counterfactual interpretation. The framework uses position-level aggregation and Bayesian model selection to infer interpretable causal structures, without requiring phenotype-specific biophysical assumptions. We apply Cosmos to three datasets--Kir2.1 (abundance and surface expression), PSD95-PDZ3 (abundance and CRIPT binding), and KRAS (abundance and RAF1-RBD binding) and show that it effectively distinguishes direct from indirect functional effects. Across these applications, Cosmos provides a generalizable and interpretable approach to disentangle causal relationships in high-throughput protein functional screens.

bioinformatics↗

bioRxiv2025

Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace

Deep mutational scanning (DMS) experiments interrogate the effect of genetic variants on protein function, often using fluorescence-activated cell sorting (FACS) to quantitatively measure molecular phenotypes, such as abundance or activity. Analysis of DMS experiments with a FACS readout is challenging due to measurement variance and the unique multidimensional nature of the phenotype. However, no statistical method has yet been developed to address the challenges of FACS-based DMS. Here we present Lilace, a Bayesian statistical model to estimate variant effects with uncertainty quantification from FACS-based DMS experiments. We validate Lilaces performance and robustness using simulated data and apply it to OCT1 and Kir2.1 DMS experiments, demonstrating an improved false discovery rate (FDR) while largely maintaining sensitivity.

bioinformatics↗

bioRxiv2025

Rosace-AA: Enhancing Interpretation of Deep Mutational Scanning Data with Amino Acid Substitution and Position-Specific Insights

Proteins are dynamic systems whose function and behavior are sensitive to environmental conditions and often involve multiple cellular roles. Deep mutational scanning (DMS) experiments generate extensive datasets to capture the functional consequences of mutations. However, the sheer volume of data presents challenges in visualization and interpretation. Current approaches often rely on heatmaps, but these methods fail to capture the nuanced effects of amino acid (AA) substitutions, which are essential for understanding mutational impact. To address this, we extend the Rosace framework with Rosace-AA, a model that incorporates both position-specific information and AA substitution trends. Using substitution matrices like BLOSUM90, Rosace-AA offers a flexible and interpretable approach to summarize DMS data oil both protein-level and position-level. We demonstrate its utility across datasets, including OCTI and MET kinase, showing that Rosace-AA highlights key positions where mutations deviate from expected substitution patterns and captures functionally relevant variation in protein behavior across multiple DMS screens. These results suggest that Rosace-AA enables more robust and interpretable analysis of complex DMS datasets.

bioinformatics↗

bioRxiv2024

A Suite of Foundation Models Captures the Contextual Interplay Between Codons

In the canonical genetic code, many amino acids are assigned more than one codon. Work by us and others has shown that the choice of these synonymous codon is not random, and carries regulatory and functional consequences. Existing protein foundation models ignore this context-dependent role of coding sequence in shaping the protein landscape of the cell. To address this gap, we introduce cdsFM, a suite of codon-resolution large language models, including both EnCodon and DeCodon models, with up to 1B parameters. Pre-trained on 60 million protein-coding sequences from more than 5,000 species, our models effectively learn the relationship between codons and amino acids, recapitualing the overall structure of the genetic code. In addition to outperforming state-of-the-art genomic foundation models in a variety of zero-shot and few-shot learning tasks, the larger pre-trained models were superior in predicting the choice of synonymous codons. To systematically assess the impact of synonymous codon choices on protein expression and our models ability to capture these effects, we generated a large dataset measuring overall and surface expression levels of three proteins as a function of changes in their synonymous codons. We showed that our EnCodon models could be readily fine-tuned to predict the contextual consequences of synonymous codon choices. Armed with this knowledge, we applied EnCodon to existing clinical datasets of synonymous variants, and we identified a large number of synonymous codons that are likely pathogenic, several of which we experimentally confirmed in a cellbased model. Together, our findings establish the cdsFM suite as a powerful tool for decoding the complex functional grammar underlying the choice of synonymous codons.

genomics↗

bioRxiv2024

Deep mutational scanning of EccD3 reveals the molecular basis of its essentiality in the mycobacterium ESX secretion system

Tuberculosis remains the deadliest infectious disease in the world and requires novel therapeutic targets. The ESX-3 secretion system, which is essential for iron and zinc homeostasis and thus M. tuberculosis survival, is a promising target. In this study, we perform a deep mutational scan on the ESX-3 core protein EccD3 in the model organism M. smegmatis. We systematically investigated the functional roles of 145 residues across the soluble ubiquitin-like domain, the conformationally distinct flexible linker, and selected transmembrane helices of EccD3. Our data combined with structural comparisons to ESX-5 complexes support a model where EccD3 stabilizes the complex, with the hinge motif within the linker being particularly sensitive to disruption. Our study is the first deep mutational scan in mycobacteria, which could help guide drug development toward novel treatment of tuberculosis. This study underscores the importance of context-specific mutational analyses for discovering essential protein interactions within mycobacterial systems.

microbiology↗

bioRxiv2024

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of [~]5,764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I [1/2] inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.

cancer biology↗

bioRxiv2024

Molecular basis of proton-sensing by G protein-coupled receptors

Three proton-sensing G protein-coupled receptors (GPCRs), GPR4, GPR65, and GPR68, respond to changes in extracellular pH to regulate diverse physiology and are implicated in a wide range of diseases. A central challenge in determining how protons activate these receptors is identifying the set of residues that bind protons. Here, we determine structures of each receptor to understand the spatial arrangement of putative proton sensing residues in the active state. With a newly developed deep mutational scanning approach, we determined the functional importance of every residue in proton activation for GPR68 by generating [~]9,500 mutants and measuring effects on signaling and surface expression. This unbiased screen revealed that, unlike other proton-sensitive cell surface channels and receptors, no single site is critical for proton recognition in GPR68. Instead, a network of titratable residues extend from the extracellular surface to the transmembrane region and converge on canonical class A GPCR activation motifs to activate proton-sensing GPCRs. More broadly, our approach integrating structure and unbiased functional interrogation defines a new framework for understanding the rich complexity of GPCR signaling. One-sentence summaryThe protonation networks governing activation of human pH-sensing GPCRs are uncovered by integrative cryo-EM and deep mutational scanning.

biochemistry↗

bioRxiv2023

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

Deep mutational scanning (DMS) enables functional insight into protein mutations with multiplexed measurements of thousands of genetic variants in a protein simultaneously. The small sample size of DMS renders classical statistical methods ineffective, for example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based deep mutational scanning data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. To benchmark Rosace against existing methods, we developed Rosette, a simulation framework that simulates the distributional properties of DMS. Further, we show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools under Rosette simulation and real data.

bioinformatics↗

bioRxiv2023

Conserved regulatory motifs in the juxtamembrane domain and kinase N-lobe revealed through deep mutational scanning of the MET receptor tyrosine kinase domain.

MET is a receptor tyrosine kinase (RTK) responsible for initiating signaling pathways involved in development and wound repair. MET activation relies on ligand binding to the extracellular receptor, which prompts dimerization, intracellular phosphorylation, and recruitment of associated signaling proteins. Mutations, which are predominantly observed clinically in the intracellular juxtamembrane and kinase domains, can disrupt typical MET regulatory mechanisms. Understanding how juxtamembrane variants, such as exon 14 skipping (MET{Delta}Ex14), and rare kinase domain mutations can increase signaling, often leading to cancer, remains a challenge. Here, we perform a parallel deep mutational scan (DMS) of the MET intracellular kinase domain in two fusion protein backgrounds: wild type and MET{Delta}Ex14. Our comparative approach has revealed a critical hydrophobic interaction between a juxtamembrane segment and the kinase C-helix, pointing to potential differences in regulatory mechanisms between MET and other RTKs. Additionally, we have uncovered a {beta}5 motif that acts as a structural pivot for the kinase domain in MET and other TAM family of kinases. We also describe a number of previously unknown activating mutations, aiding the effort to annotate driver, passenger, and drug resistance mutations in the MET kinase domain.

molecular biology↗

bioRxiv2023

The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics

Membrane transporters play a fundamental role in the tissue distribution of endogenous compounds and xenobiotics and are major determinants of efficacy and side effects profiles. Polymorphisms within these drug transporters result in inter-individual variation in drug response, with some patients not responding to the recommended dosage of drug whereas others experience catastrophic side effects. For example, variants within the major hepatic Human organic cation transporter OCT1 (SLC22A1) can change endogenous organic cations and many prescription drug levels. To understand how variants mechanistically impact drug uptake, we systematically study how all known and possible single missense and single amino acid deletion variants impact expression and substrate uptake of OCT1. We find that human variants primarily disrupt function via folding rather than substrate uptake. Our study revealed that the major determinants of folding reside in the first 300 amino acids, including the first 6 transmembrane domains and the extracellular domain (ECD) with a stabilizing and highly conserved stabilizing helical motif making key interactions between the ECD and transmembrane domains. Using the functional data combined with computational approaches, we determine and validate a structure-function model of OCT1s conformational ensemble without experimental structures. Using this model and molecular dynamic simulations of key mutants, we determine biophysical mechanisms for how specific human variants alter transport phenotypes. We identify differences in frequencies of reduced function alleles across populations with East Asians vs European populations having the lowest and highest frequency of reduced function variants, respectively. Mining human population databases reveals that reduced function alleles of OCT1 identified in this study associate significantly with high LDL cholesterol levels. Our general approach broadly applied could transform the landscape of precision medicine by producing a mechanistic basis for understanding the effects of human mutations on disease and drug response.

biophysics↗

bioRxiv2022

Deep Insertion, Deletion, and Missense Mutation Libraries for Exploring Protein Variation in Evolution, Disease, and Biology

Insertions and deletions (indels) are a major source of genetic variation in evolution and the cause of nearly 30% of Mendelian disease. Despite their importance, indels are left out of nearly every systematic mutational scan to date due to technical challenges associated with making indel-containing libraries, limiting our understanding of indels in disease, biology, and evolution. Here we present a library generation method, DIMPLE, that generates deletions, insertions, and missense at similar frequencies within any gene. To benchmark DIMPLE, we generated libraries within four genes (Kir2.1, VatD, TRPV1, and OPRM1) of varying length and evolutionary origin. DIMPLE produces libraries that are near complete, low cost, and low bias. We measured how missense mutations and indels of varying length impact the potassium channel Kir2.1 surface expression. Across all Kir2.1s secondary structure, deletions are more disruptive than insertions, beta sheets are extremely sensitive to large deletions, and flexible loops allow insertions far more frequently than deletions. DIMPLEs low bias, ease of use, and low cost will enable high throughput probing of the importance of indels in disease and evolution.

genomics↗

bioRxiv2022

Determinants of trafficking, conduction, and disease within a K+ channel revealed through multiparametric deep mutational scanning

A longstanding goal in protein science and clinical genetics is to develop quantitative models of sequence, structure, and function relationships and delineate the mechanisms by which mutations cause disease. Deep Mutational Scanning (DMS) is a promising strategy to map how amino acids contribute to protein structure and function and to advance clinical variant interpretation. Here, we introduce 7,429 single residue missense mutation into the Inward Rectifier K+ channel Kir2.1 and determine how this affects folding, assembly, and trafficking, as well as regulation by allosteric ligands and ion conduction. Our data provides high-resolution information on a cotranslationally{-}folded biogenic unit, trafficking and quality control signals, and segregated roles of different structural elements in fold-stability and function. We show that Kir2.1 trafficking mutants are underrepresented in variant effect databases, which has implications for clinical practice. By comparing fitness scores with expert-reviewed variant effects, we can predict the pathogenicity of variants of unknown significance and disease mechanisms of know pathogenic mutations. Our study in Kir2.1 provides a blueprint for how multiparametric DMS can help us understand the mechanistic basis of genetic disorders and the structure-function relationships of proteins.

biochemistry↗

bioRxiv2020

The biophysical basis of protein domain compatibility in ion channels

Understanding the biophysical mechanisms that govern the combination of protein domains into viable proteins is essential for advancing synthetic biology and biomedical engineering. Here, we use massively parallel genotype/phenotype assays to determine cell surface expression of over 300,000 variants of the inward rectifier K+ channel Kir2.1 recombined with hundreds of protein motifs. We use machine learning to derive a quantitative biophysical model and practical rules for domain recombination. Insertional fitness depends on nonlinear interactions between the biophysical properties of inserted motifs and the recipient protein, which adds a new dimension to the rational design of fusion proteins. Insertion maps reveal a generalizable hierarchical organization of Kir2.1 and several other ion channels that balances stability needed for folding and dynamics required for function. SummaryMassively parallel assays reveal interactions between donor domains and recipient proteins govern domain compatibility

biophysics↗

bioRxiv2020

A survey of pairwise epistasis supports an outside-in hierarchy of clade-specifying and function-defining residues in PSD95 PDZ3

Deep mutational scanning enables data-driven models of protein structure and function. Here, we adapted Saturated Programmable Insertion Engineering as an economical and programmable deep mutational scanning technique. We validate this approach with an existing single mutant dataset in the PSD95 PDZ3 domain, and further characterize most pairwise double mutants to study how a mutation’s phenotype depends on mutations at other sites, a phenomenon called epistasis. We observe wide-spread proximal negative epistasis, which we attribute to mutations affecting thermodynamic stability, and strong long-range positive epistasis, which is enriched in an evolutionarily conserved and function-defining network of ‘sector’ and clade-specifying residues. Conditional neutrality of mutations in clade-specifying residues compensates for deleterious mutations in sector positions. This suggests an outside-in hierarchy of interactions through which positive epistasis between clade-specifying residues and the PDZ sector facilitated the evolutionary expansion and specialization of PDZ domains.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗

bioRxiv2018

Domain Insertion Permissibility is a Measure of Engineerable Allostery in Ion Channels

Allostery is a fundamental principle of protein regulation that remains poorly understood and hard to engineer, in particular in ion channels. Here we use human Inward Rectifier K+ Channel Kir2.1 to establish domain insertion permissibility as a new experimental paradigm to identify engineerable allosteric sites. We find that permissibility is best explained by dynamic protein properties, such as conformational flexibility. Many allosterically regulated sites in Kir2.1 or sites equivalent to those regulated in homologs, such as G-protein-gated inward rectifier K+ channels (GIRK), have differential permissibility; that is, for these sites permissibility depends on the structural properties of the inserted domain. Our data and the well-established link between protein dynamics and allostery led us to propose that differential permissibility is a metric of both existing and latent allostery in Kir2.1. In support of this notion, inserting light-switchable domains into either existing or latent allosteric sites, but not elsewhere, renders Kir2.1 activity sensitive to light.

biochemistry↗