Biology subjects

Morcos, F.

Publications and source records attributed to Morcos, F..

13 recordsLinked to original sources

bioRxiv2026

The DUF998 family protein DrmA modulates cationic glycolipid levels and the emergence of high-level daptomycin resistance in Enterococcus faecalis

Daptomycin resistance (DAP-R) in enterococci is associated with alterations in the membrane lipid composition. The membrane-bound protein MprF is responsible for the synthesis of amino acid-modified lipids in bacteria, and these modified lipids contribute to DAP-R in some Gram-positive pathogens. In enterococci, MprF synthesizes three lysine-modified lipids: the phospholipid lysyl-phosphatidylglycerol (Lys-PG), and the newly identified cationic glycolipids lysyl-diglucosyl-diacylglycerol (Lys-Glc2-DAG) and lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG). Given the recent discovery of cationic glycolipids in enterococci, we re-examined a collection of laboratory-evolved DAP-R E. faecalis to investigate whether these lipids contribute to DAP-R. We found that levels of Lys-Glc2-DAG were strikingly reduced in DAP-R variants with high-level resistance. The dramatic alterations in Lys-Glc2-DAG levels were temporally coupled with the emergence of loss-of-function mutations in the gene drmA, which encodes a DUF998 family protein of unknown function. DrmA is a membrane protein with six predicted transmembrane helices and is widely distributed among Gram-positive and Gram-negative bacteria, including plant and animal pathogens. Complementation of the DAP-R strains with wild-type E. faecalis drmA significantly lowered their DAP MIC, reversing their trajectory to high-level DAP-R. Using genetic and lipidomic approaches in the natively DAP-sensitive strain OG1RF, we conclusively linked drmA loss-of-function with significantly reduced Lys-Glc2-DAG levels as well as a small but significant increase in Lys-PG levels. Yet, drmA inactivation in OG1RF did not alter its DAP MIC. We conclude that drmA loss-of-function confers elevated DAP MIC on the background of preceding mutations in the DAP-R evolutionary trajectory, most likely mutations in cls1. The recurrence of drmA mutations in multiple studies underscores its importance in DAP-R evolution. Overall, our work identifies a role for the DUF998 family in cellular lipid homeostasis and confirms its significant role in the evolution of DAP-R.

microbiology↗

bioRxiv2026

Small Structural Variations, Large Functional Consequences: Comparative Analysis Reveals Structural Control of Ubiquitylation Site Selection by BRCA1/BARD1

BRCA1/BARD1 is a chromatin-associated E3 ubiquitin ligase that ubiquitylates histone H2A to coordinate DNA damage repair, transcriptional repression, and genome stability. In Caenorhabditis elegans (C. elegans), the orthologous BRC-1/BRD-1 com-plex performs analogous functions but exhibits structural variation, most notably through an additional 11-residue loop in BRD-1 that is absent from human BARD1. Prior exper-iments indicate this worm-specific insertion promotes nucleosome engagement and may alter the preferred lysine target for ubiquitylation. Here, we provide a cross-species comparison by integrating computational and experimental investigation to clarify how a discrete structural variation can tune BRCA1-family ligase behavior and, consequently, chromatin regulation. In vitro ubiquitylation assays and mass spectrometry reveal BRC-1/BRD-1 ubiquitylate the C-terminal tail of histone H2A with less specificity than the human homologs. All-atom molecular dynamics simulations of both the C. elegans BRC-1/BRD-1-LET-70-Ubiquitin assembly and the human BRCA1/BARD1-UbcH5c-Ubiquitin complex in the presence of the nucleosome core particle uncover that the BRD-1 loop makes transient contacts with nucleosomal DNA and histone tails, thereby modulating the positioning and conformational flexibility of the bound E2 (ubiquitin-conjugating enzyme). Together, our results suggest that the BRD-1 loop alters the E3-E2 geometry, thereby altering ubiquitylation-site specificity.

biophysics↗

bioRxiv2025

A Structure-Aware Generative Framework for Exploring Protein Sequence and Function Space

The rapid expansion of protein sequence databases has far outpaced experimental structure determination, leaving many unannotated sequences, particularly the more remote homologs with low sequence identity. Because protein folds are more conserved and functionally informative than sequences alone, structural information offers a powerful lens for analysis. Here, we introduce a generative, structure-aware framework that integrates geometric encoding and coevolutionary constraints to map, cluster, and design protein sequences. Our approach employs the 3D interaction (3Di) alphabet to convert local residue geometries into compact, 20-state discrete representations. Using ProstT5, we enable bidirectional translation between amino acid sequences and 3Di representations, facilitating sensitive homology detection and structure-guided sequence generation. We then augment the latent generative landscape methodology by combining 3Di-based alignments with direct coupling analysis (DCA) and variational autoencoders (VAE), imbuing tasks such as clustering, annotation, and design with structural information. This integrative framework enhances the detection of coevolutionary signals and enables rational sampling of structural variants, even without functional labels. We demonstrate the utility of our method across diverse protein families, including globins, kinases, and malate dehydrogenases, achieving improved contact prediction, homology inference, and sequence generation. Together, our approach offers a quantitative, generative view of protein structure space, advancing protein evolution and design studies. Significance StatementProtein sequence databases are growing far faster than our ability to experimentally determine structures, leaving much of protein space poorly annotated, especially for distant homologs. Because protein structure is more conserved and informative than sequence alone, new approaches are needed to exploit structural signals at scale. We present a generative framework that integrates compact structural representations with evolutionary constraints to map, cluster, and design protein sequences. By combining geometric encoding with coevolutionary modeling, our approach enables sensitive homology detection, improved inference of structural contacts, and rational exploration of sequence space without requiring functional labels. This work provides a quantitative bridge between protein sequence and structure, advancing our ability to interpret protein evolution and guide protein design.

biophysics↗

bioRxiv2025

Higher-order epistasis drives evolutionary unpredictability toward novel antibiotic resistance

The rapid evolution of extended-spectrum {beta}-lactamases (ESBLs) represents a global health threat, undermining the efficacy of {beta}-lactams, the most extensively used antibiotic class. To elucidate the evolutionary dynamics underlying {beta}-lactam resistance, we constructed a comprehensive combinatorial mutant library comprising all 55,296 possible TEM-1 {beta}-lactamase variants integrating 18 clinically observed mutations across 13 key residues. Over eight million empirical fitness measurements were obtained under selection pressure with both a native antibiotic substrate (ampicillin) and a novel antibiotic (aztreonam), establishing the largest experimentally determined fitness landscape for antibiotic resistance to date. Through graph-theoretic and epistatic analyses, we discovered that selection with ampicillin resulted in weak epistasis, with mutants rarely surpassing the fitness of the wild-type enzyme. Conversely, aztreonam selection elicited extensive higher-order epistasis, generating a rugged fitness landscape characterized by increased phenotypic unpredictability. Interpretable machine-learning analyses identified context-dependent epistatic interactions necessary for achieving high-level aztreonam resistance. Further evolutionary statistical analyses, including direct coupling analysis and latent generative landscapes, showed that top-performing TEM-1 variants consistently adhered to conserved epistatic patterns found in naturally occurring {beta}-lactamases. Our findings demonstrate that higher-order epistasis critically shapes fitness landscape ruggedness when enzymes adapt to novel substrates, whereas adaptations to native substrates exhibit predictably smoother landscapes. This integrated experimental and computational framework provides a foundation for predictive evolutionary pharmacology, enabling assessments of newly developed {beta}-lactams or emerging {beta}-lactamase variants for their potential contribution to ESBL evolution. Importantly, incorporating graph-theoretically informed evolutionary constraints can strategically disrupt evolutionary pathways, presenting a viable approach to mitigate the rise of antibiotic resistance.

evolutionary biology↗

bioRxiv2025

Generative Landscapes and Dynamics to Design Multidomain Artificial Transmembrane Transporters

Protein design is challenging as it requires simultaneous consideration of interconnected factors, such as fold, dynamics, and function. These evolutionary constraints are encoded in protein sequences and can be learned through the latent generative landscape (LGL) framework to predict functional sequences by leveraging evolutionary patterns, enabling exploration of uncharted sequence space. By simulating designed proteins through molecular dynamics (MD), we gain deeper insights into the interdependencies governing structure and dynamics. We present a synergized workflow combining LGL with MD and biochemical characterization, allowing us to explore the sequence space effectively. This approach has been applied to design and characterize two artificial multidomain ATP-driven transmembrane copper transporters, with native-like functionality. This integrative approach proved effective in unraveling the intricate relationships between sequence, structure, and function.

biophysics↗

bioRxiv2024

Thermal Adaptation of Cytosolic Malate Dehydrogenase Revealed by Deep Learning and Coevolutionary Analysis

Protein evolution has produced enzymes that maintain stability and function across various thermal environments. While sequence variation, structural dynamics, and intermolecular interactions are known to influence an enzymes thermal adaptation, how these factors collectively govern stability and function across diverse temperatures remains unresolved. Cytosolic malate dehydrogenase (cMDH), a citric acid cycle enzyme, is an ideal model for studying these mechanisms due to its temperature-sensitive flexibility and broad presence in species from diverse thermal environments. In this study, we employ techniques inspired by deep learning and statistical mechanics to uncover how sequence variation and structural dynamics shape patterns of cMDHs thermal adaptation. By integrating coevolutionary models with variational autoencoders (VAE), we generate a latent generative landscape (LGL) of cMDH sequence space, enabling us to explore evolutionary pathways and predict fitness using direct coupling analysis (DCA). Structural predictions via AlphaFold and molecular dynamics simulations further illuminate how variations in hydrophobic interactions and conformational flexibility contribute to the thermal stability of warm- and cold-adapted cMDH orthologs. The integrative computational framework employed in this study provides powerful insights into protein adaptation at both sequence and structural levels, offering new perspectives on the evolution of thermal stability and creating avenues for the rational design of proteins with optimized thermal properties for biotechnological applications.

biophysics↗

bioRxiv2023

Lipid discovery enabled by sequence statistics and machine learning

Bacterial membranes are complex and dynamic, arising from an array of evolutionary pressures. One enzyme that alters membrane compositions through covalent lipid modification is MprF. We recently identified that Streptococcus agalactiae MprF synthesizes lysyl-phosphatidylglycerol (Lys-PG) from anionic PG, and a novel cationic lipid, lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), from neutral glycolipid Glc-DAG. This unexpected result prompted us to investigate whether Lys-Glc-DAG occurs in other MprF-containing bacteria, and whether other novel MprF products exist. Here, we studied protein sequence features determining MprF substrate specificity. First, pairwise analyses identified several streptococ-cal MprFs synthesizing Lys-Glc-DAG. Second, a restricted Boltzmann machine-guided approach led us to discover an entirely new substrate for MprF in Enterococcus, diglucosyl-diacylglycerol (Glc2-DAG), and an expanded set of organisms that modify glycolipid substrates using MprF. Overall, we combined the wealth of available sequence data with machine learning to model evolutionary constraints on MprF sequences across the bacterial domain, thereby identifying a novel cationic lipid.

microbiology↗

bioRxiv2023

In vivo functional phenotypes from a computational epistatic model of evolution

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called Sequence Evolution with Epistatic Contributions. Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo {beta}-lactamase activity in E. coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their WT predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes and facilitate vaccine development.

evolutionary biology↗

bioRxiv2022

VanRS and CroRS cross-talk revealed by coevolutionary modeling regulates antibiotic resistance in VanA-type vancomycin-resistant Enterococcus faecalis

Enterococcus faecalis is an opportunistic pathogen that can cause bacteremia and endocarditis. Previous studies have shown that concurrent treatment with cephalosporin and vancomycin antibiotics exhibit synergy in vancomycin-resistant E. faecalis to render the bacterium susceptible to antibiotic treatment whereas treatment with each antibiotic separately was not successful. Proteins responsible for mediating vancomycin and cephalosporin resistance are classified as two-component systems (TCS). TCS consist of a histidine kinase that phosphorylates a response regulator after environmental activation. These signaling networks have been shown to exhibit cross-talk interactions, and through direct coupling analysis, we identify encoded specificity between vancomycin resistance TCS, which are horizontally acquired, and cephalosporin resistance TCS, which are endogenous to E. faecalis. To verify cross-talk between these pathways is responsible for vancomycin and cephalosporin synergy, we use RNA-Seq to identify differentially expressed genes in VanA- and VanB-type vancomycin resistant enterococci after treatment with the cephalosporin antibiotic, ceftriaxone, and also with vancomycin. We find that cross-talk between VanSA and CroR in strain HIP11704 may be responsible for synergy, demonstrating that horizontally acquired TCS can have large impacts on pre-existing signaling networks. The presence of encoded specificity between exogenous TCS and endogenous TCS show that the systems co-evolve, and cross-talk between these systems may be exploited to engineer genetic elements that disrupt antibiotic resistance TCS pathways. Author SummaryBacteria may transmit genetic elements to other bacteria through the process known as horizontal gene transfer. In some enterococci, vancomycin resistance genes are acquired this way. Proteins encoded within the bacterial genome can interact with proteins acquired through horizontal gene transfer. The interaction that occurs between proteins VanSA and VanRA is known to mediate vancomycin antibiotic resistance in VanA-type vancomycin resistant enterococci (VRE), and the interaction between proteins CroS and CroR is an important pathway in cephalosporin antibiotic resistance. We show that the VanSA, which is obtained through horizontal gene transfer, inhibits CroR under treatment with antibiotics vancomycin and ceftriaxone. This interaction is responsible for the observed synergy between vancomycin and ceftriaxone in VanA-type VREs. These findings demonstrate how horizontally acquired genes may produce proteins that interrupt known protein interactions, including antibiotic resistance signaling pathways in bacteria. Furthermore, the specific mechanism found for VanA-type VREs provides a basis for engineering of horizontally acquired proteins that disrupt antibiotic resistance pathways.

microbiology↗

bioRxiv2022

Coupling a Live Cell Directed Evolution Assay with Coevolutionary Landscapes to Engineer an Improved Fluorescent Rhodopsin Chloride Sensor

Our understanding of chloride in biology can be accelerated through the application of fluorescent protein-based sensors in living cells. Laboratory-guided evolution can be used to diversify and identify sensors with specific properties. Recently, we established that the fluorescent proton-pumping rhodopsin wtGR from Gloeobacter violaceus can be converted into a fluorescent sensor for chloride. To unlock this non-natural function, a single point mutation at the Schiff counterion position from D121V was introduced into wtGR fused with cyan fluorescent protein (CFP) resulting in GR1-CFP. Here, we have integrated coevolutionary analysis with directed evolution to understand how the rhodopsin sequence space can be explored and engineered to improve this starting point. We first show how evolutionary couplings are predictive of functional sites in the rhodopsin family and how a fitness metric based on sequence can be used to quantify known proton-pumping activities of GR-CFP variants. Then, we couple this ability to predict potential functional outcomes with a screening and selection assay in live Escherichia coli to reduce the mutational search space of five residues along the proton-pumping pathway in GR1-CFP. This iterative selection process results in GR2-CFP with four additional mutations, E132K, A84K, T125C, and V245I. Finally, bulk, and single fluorescence measurements in live E. coli reveal that GR2-CFP is a reversible, ratiometric fluorescent sensor for extracellular chloride with an improved dynamic range. We anticipate that our framework will be applicable to other systems, providing a more efficient methodology to engineer fluorescent-protein based sensors with desired properties. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=47 SRC="FIGDIR/small/474067v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1ddb801org.highwire.dtl.DTLVardef@15b4a9corg.highwire.dtl.DTLVardef@7f497corg.highwire.dtl.DTLVardef@b19c5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2021

Computational Compensatory Mutation Discovery Approach: Predicting a PARP1 Variant Rescue Mutation

The prediction of protein mutations that affect function may be exploited for multiple uses. In the context of disease variants, the prediction of compensatory mutations that reestablish functional phenotypes could aid in the development of genetic therapies. In this work, we present an integrated approach that combines coevolutionary analysis and molecular dynamics (MD) simulations to discover functional compensatory mutations. This approach is employed to investigate possible rescue mutations of a poly(ADP-ribose) polymerase 1 (PARP1) variant, PARP1 V762A, associated with lung cancer and follicular lymphoma. MD simulations show PARP1 V762A exhibits noticeable changes in structural and dynamical behavior compared with wild type PARP1. Our integrated approach predicts A755E as a possible compensatory mutation based on coevolutionary information, and molecular simulations indicate that the PARP1 A755E/V762A double mutant exhibits similar structural and dynamical behavior to WT PARP1. Our methodology can be broadly applied to a large number of systems where single nucleotide polymorphisms (SNPs) have been identified as connected to disease and can shed light on the biophysical effects of such changes as well as provide a way to discover potential mutants that could restore wild type-like functionality. This can in turn be further utilized in the design of molecular therapeutics that aim to mimic such compensatory effect. Significance StatementDiscovering protein mutations with desired phenotypes can be challenging due to its combinatorial nature. Herein we employ a methodology combining gene SNP association to disease, direct coupling analysis and molecular dynamics simulations to systematically predict rescue mutations. Our workflow identifies A755E as a potential rescue for the PARP1 V762A mutation, which has been associated with cancer. This methodology is general and can be applied broadly.

biophysics↗

bioRxiv2021

Divergence in dimerization and activity of primate APOBEC3C

The APOBEC3 (A3) family of single-stranded DNA cytidine deaminases are host restriction factors that inhibit lentiviruses, such as HIV-1, in the absence of the Vif protein that causes their degradation. Deamination of cytidine in HIV-1 (-)DNA forms uracil that causes inactivating mutations when uracil is used as a template for (+)DNA synthesis. For APOBEC3C (A3C), the chimpanzee and gorilla orthologues are more active than human A3C, and the Old World Monkey A3C from rhesus macaque (rh) is not active against HIV-1. Multiple integrated analyses determined why rhA3C was not active against HIV-1 and how to increase this activity. Biochemical, virological, and coevolutionary analyses combined with molecular dynamics simulations showed that the key amino acids needed to promote rhA3C antiviral activity also promoted dimerization. Although rhA3C shares a similar dimer interface with hominid A3C, the key amino acid contacts were different. Overall, our results determine the basis for why rhA3C is less active than human A3C, establish the amino acid network for dimerization and increased activity, and track the loss and gain of A3C antiviral activity in primates. The coevolutionary analysis of the A3C dimerization interface provides a basis from which to analyze dimerization interfaces of other A3 family members.

biochemistry↗

bioRxiv2017

Guiding the design of bacterial signaling interactions using a coevolutionary landscape

The selection of amino acid identities that encode new interactions between two-component signaling (TCS) proteins remains a significant challenge. Recent work constructed a co-evolutionary landscape that can be used to select mutations to maintain signal transfer interactions between partner TCS proteins without introducing signal transfer between non-partners (crosstalk). A bigger challenge is to introduce mutations between non-natural partner TCS proteins using the landscape to enhance, suppress, or have a neutral effect on their basal signal transfer rates. This study focuses on the selection of mutations to a response regulator (RR) from Bacilus subtilis and its effect on phosphotransfer with a histidine kinase (HK) from Escherichia Coli. Twelve single-point mutations of the RR protein are selected from the landscape and experimentally expressed to directly test the theoretical predictions on the effect of signal transfer. Differential Scanning Calorimetry is used to monitor any protein stability effects caused by the mutations, which could be detrimental to proper protein function. Of these proteins, seven mutants successfully perturb phosphoryl transfer activity in the computationally predicted manner between the TCS proteins. Furthermore, brute-force exhaustive mutagenesis approaches indicate that only 1% of mutations result in enhanced activity. In comparison, of the six mutations predicted to enhance phosphotransfer, two mutations exhibit a significant enhancement while two mutations are comparable to the wild-type. Thus co-evolutionary landscape theory offers significant improvement over traditional large-scale mutational studies in the efficiency of selecting mutations for protein engineering and design.

bioinformatics↗