Biology subjects

Clementi, C.

Publications and source records attributed to Clementi, C..

10 recordsLinked to original sources

bioRxiv2026

Integrative modelling reveals the structure of the human Mic60-Mic19 subcomplex and its role as a diffusion barrier in mitochondria

Mitochondrial crista junctions (CJs) operate as regulated gateways into the cristae microenvironment, whose protein, metabolite, and ion compositions are finely tuned for mitochondrial function. The Mic60-Mic19 complex of the mitochondrial contact site and cristae organizing system (MICOS) complex was suggested to span across CJs and act as a diffusion barrier, but little is known of how its dynamic architecture facilitates this task. To address this open question, we determined the crystal structure of an amino-terminal dimeric helical bundle of human Mic60. These and previous structural and biochemical data were harnessed in molecular dynamic (MD) simulations to develop a dynamic model of the human tetrameric Mic60-Mic19 subcomplex in the CJ environment, to validate its architecture using in organello cross-linking data and to computationally characterize its function as a diffusion barrier. Our integrative structural biology approach enables the functional investigation of flexible, multidomain protein complexes which escape conventional structural biology methods.

biochemistry↗

bioRxiv2024

Scalable emulation of protein equilibrium ensembles with generative deep learning

Following the sequence and structure revolutions, predicting the dynamical mechanisms of proteins that implement biological function remains an outstanding scientific challenge. Several experimental techniques and molecular dynamics (MD) simulations can, in principle, determine conformational states, binding configurations and their probabilities, but suffer from low throughput. Here we develop a Biomolecular Emulator (BioEmu), a generative deep learning system that can generate thousands of statistically independent samples from the protein structure ensemble per hour on a single graphical processing unit. By leveraging novel training methods and vast data of protein structures, over 200 milliseconds of MD simulation, and experimental protein stabilities, BioEmus protein ensembles represent equilibrium in a range of challenging and practically relevant metrics. Qualitatively, BioEmu samples many functionally relevant conformational changes, ranging from formation of cryptic pockets, over unfolding of specific protein regions, to large-scale domain rearrangements. Quantitatively, BioEmu samples protein conformations with relative free energy errors around 1 kcal/mol, as validated against millisecond-timescale MD simulation and experimentally-measured protein stabilities. By simultaneously emulating structural ensembles and thermodynamic properties, BioEmu reveals mechanistic insights, such as the causes for fold destabilization of mutants, and can efficiently provide experimentally-testable hypotheses.

molecular biology↗

bioRxiv2024

T cell interaction partners of DHHC20

Reversible palmitoylation of proteins at cysteine residues represents a post-translational modification that can alter the cellular localization of proteins, change their distribution within lipid membranes or modulate their conformation and molecular interaction patterns. DHHC enzymes catalyze protein palmitoylation, while thioesterases or hydrolases can rapidly remove the acyl-chain. In human T cells, DHHC proteins have been shown to modify proteins that are involved in major signaling pathways such as Ca2+-signaling or kinase-dependent activation of transcription factors for cytokines. For DHHC20, a role in the palmitoylation of the Orai1 Ca2+-channel has been demonstrated, but otherwise its T cell interaction partners are largely unknown. Here, we show that recombinantly expressed DHHC20 robustly interacts with 28 proteins from Jurkat T cells, as shown by affinity enrichment combined with mass spectrometric analysis. We find a robust interaction between DHHC20 and the {beta}-subunit of the trimeric G protein Gs{beta}1{gamma}2, while the typically palmitoylated Ga subunit is not identified. Cross-linking mass spectrometry with purified DHHC20 and Gs{beta}1{gamma}2 then confirms a direct interaction between the {beta}1{gamma}2 domains and the enzyme, while rigid docking offers structural poses that are in agreement with the observed intermolecular cross-linking constraints. Thus, we suggest a model where the {beta}1{gamma}2 subunits of a trimeric G protein serve as a stable interaction partner of a DHHC enzyme, presumably acting as a landing platform for the G subunit that is subsequently palmitoylated by the enzyme.

biochemistry↗

bioRxiv2024

Effective assessment of CD4+ T cell Immunodominance patterns: impact of antigen processing and HLA restriction

CD4+ T cell responses to viral infections are driven by immunodominant determinants. Accessing the peptides defining these determinants in humans is essential for advancing our understanding and potentially tuning immune responses to pathogens. State of the art methods identifying CD4+ T cell immunodominant epitopes are constrained by limitations in throughput, performance or both. Here, we leverage on the combined use of a reconstituted antigen processing system and of in silico prediction tools to query and study CD4+ T cell immunodominance of two model SARS-CoV-2 antigens. We applied this combined platform over a DRB1* panel with broad population and functional coverage to gain mechanistic insights beyond single allotypes. This approach delineates a minimalistic peptide pool (59 candidates) featuring a high immunogenic profile (similar response to 10-fold larger pools) through a representative human sample. Analysis of antigen-encoded and processing-related features on IEDB curated immunodominant peptides reveal that distinct antigen processing mechanisms apply for the Nucleocapsid and Spike model antigens. Notably, the First Bind and then cut mechanism was favored for Nucleocapsid-derived epitopes (up to 55 %), whereas the First Cut and then bind predominated for the Spike (80 %). Together, our results highlight differential processing pathways underlying CD4 T cell immunodominance for distinct viral antigens providing a mechanistic foundation to improve epitope prediction algorithms. Significance StatementEfficient access to immunodominant CD4+ T cell epitopes will inform improved peptide pool design for studying or tuning immunity towards any pathogen. Despite advances in the field, in silico epitope prediction remains error-prone and experimental efforts are logistically challenging. Epitope prediction tools are mainly focused on the identification of binding motifs while neglecting key processing steps. Considering antigen processing factors and constraints is expected to improve their performance. We describe and apply a platform for the systematic investigation of antigen processing mechanisms and how they impinge on the selection of CD4+ T cell immunodominance. Our results validate distinct immunodominant epitope selection pathways for two model antigens at the human population level.

immunology↗

bioRxiv2023

Structure prediction of protein-ligand complexes from sequence information with Umol

Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly, given a multiple sequence alignment representation of the protein and a SMILES string representing the ligand. At a high accuracy threshold, unseen protein-ligand complexes can be predicted more accurately than for RoseTTAFold-AA, and at medium accuracy even classical docking methods that use known protein structures as input are surpassed. The high accuracy presented here suggests that the goal of AI-based drug discovery is one step closer, but there is still a way to go to fully grasp the complexity of protein-ligand interactions. Umol is available at: https://github.com/patrickbryant1/Umol

bioinformatics↗

bioRxiv2023

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conformational ensembles. In this work we: (1) provide an overview of existing methods and tools for protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples found in the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

bioinformatics↗

bioRxiv2022

Deep learning to decompose macromolecules into independent Markovian domains

The increasing interest in modeling the dynamics of ever larger proteins has revealed a fundamental problem with models that describe the molecular system as being in a global configuration state. This notion limits our ability to gather sufficient statistics of state probabilities or state-to-state transitions because for large molecular systems the number of metastable states grows exponentially with size. In this manuscript, we approach this challenge by introducing a method that combines our recent progress on independent Markov decomposition (IMD) with VAMPnets, a deep learning approach to Markov modeling. We establish a training objective that quantifies how well a given decomposition of the molecular system into independent subdomains with Markovian dynamics approximates the overall dynamics. By constructing an end-to-end learning framework, the decomposition into such subdomains and their individual Markov state models are simultaneously learned, providing a data-efficient and easily interpretable summary of the complex system dynamics. While learning the dynamical coupling between Markovian subdomains is still an open issue, the present results are a significant step towards learning "Ising models" of large molecular complexes from simulation data.

biophysics↗

bioRxiv2020

Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire

Accurate assessment of TCR-antigen specificity at the whole immune repertoire level lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR-peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR-p-MHC systems. Here, we introduce a pairwise energy model, RACER, for rapid assessment of TCR-peptide affinity at the immune repertoire level. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR-peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each specific TCR-p-MHC system. When applied to simulate thymic selection of an MHC-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the large computational challenge of reliably identifying the properties of tumor antigen-specific T-cells at the level of an individual patients immune repertoire.

biophysics↗

bioRxiv2018

Genome-wide association studies on endometriosis and endometriosis-related infertility

Endometriosis affects [~]10% of women of reproductive age. It is characterized by the growth of endometrial-like tissue outside the uterus and is frequently associated with severe pain and infertility. We performed the largest endometriosis genome-wide association study (GWAS) to date, with 37,183 cases and 251,258 controls. All women were of European ancestry. We also performed the first GWAS of endometriosis-related infertility, including 2,969 cases and 3,770 controls. Our endometriosis GWAS study replicated, at genome-wide significance, seven loci identified in previous endometriosis GWASs (CELA3A-CDC42, SYNE1, KDR, FSHB-ARL14EP, GREB1, ID4, and CEP112) and identified seven new candidate loci with genome-wide significance (NGF, ATP1B1-F5, CD109, HEY2, OSR2-VPS13B, WT1, and TEX11-SLC7A3). No loci demonstrated genome-wide significance for endometriosis-related infertility, however, the three most strongly associated loci (MCTP1, EPS8L3-CSF1, and LPIN1) were in or near genes associated with female fertility or embryonic lethality in model organisms. These results reveal new candidate genes with potential involvement in the pathophysiology of endometriosis and endometriosis-related infertility.

genetics↗

bioRxiv2018

Large-scale genome-wide association meta-analysis of endometriosis reveals 13 novel loci and genetically-associated comorbidity with other pain conditions

Endometriosis is a common complex inflammatory condition characterised by the presence of endometrium-like tissue outside the uterus, mainly in the pelvic area. It is associated with chronic pelvic pain and infertility, and its pathogenesis remains poorly understood. The disease is typically classified according to the revised American Fertility Society (rAFS) 4-stage surgical assessment system, although stage does not correlate well with symptomatology or prognosis. Previously identified genetic variants mainly are associated with stage III/IV disease, highlighting the need for further phenotype-stratified analysis that requires larger datasets. We conducted a meta-analysis of 15 genome-wide association studies (GWAS) and a replication analysis, including 58,115 cases and 733,480 controls in total, and sub-phenotype analyses of stage I/II, stage III/IV and infertility-associated endometriosis cases. This revealed 27 genetic loci associated with endometriosis at the genome-wide p-value threshold (P<5x10-8), 13 of which are novel and an additional 8 novel genes identified from gene-based association analyses. Of the 27 loci, 21 (78%) had greater effect sizes in stage III/IV disease compared to stage I/II, 1 (4%) had greater effect size in stage I/II compared to stage III/IV and 17 (63%) had greater effect sizes when restricted to infertility-associated endometriosis cases compared to overall endometriosis. These results suggest that specific variants may confer risk for different sub-types of endometriosis through distinct pathways. Analyses of genetic variants underlying different pain symptoms reported in the UK Biobank showed that 7/9 had positive significant (p<1.28x103) positive genetic correlations with endometriosis, suggesting a genetic basis for sensitivity to pain in general. Additional conditions with significant positive genetic correlations with endometriosis included uterine fibroids, excessive and irregular menstrual bleeding, osteoarthritis, diabetes as well as menstrual cycle length and age at menarche. These results provide a basis for fine-mapping of the causal variants at these 27 loci, and for functional follow-up to understand their contribution to endometriosis and its potential subtypes.

genomics↗