Biology subjects

Zoete, V.

Publications and source records attributed to Zoete, V..

17 recordsLinked to original sources

bioRxiv2026

Attracting Cavities 3.0: Faster and More Versatile Molecular Docking for the SwissDock Webserver

MotivationMolecular docking is a pillar of structure-based drug design and shows advantages in structure prediction of small-molecule ligand-protein complexes over co-folding methods for novel ligands and novel binding pockets. Here, we describe substantial improvements of our physics-based docking algorithm Attracting Cavities, which is widely used through the SwissDock webserver. ResultsAC 3.0 includes enhanced sampling features, new functionalities, and technical improvements. These lead to better sampling at lower execution times and higher versatility. Comparison with AutoDock Vina demonstrates better docking results on multiple test sets. AvailabilityAC 3.0 will be made available free of charge through the SwissDock webserver (www.swissdock.ch).

bioinformatics↗

bioRxiv2025

Knowledge-guided machine-learning and reverse screening combined method to predict cancer cell line responses to cytotoxic molecules

Estimating the cell line targets of cytotoxic small molecules is important for drug discovery and central for targeted therapy in oncology. Accurate prediction of sensitive cell lines enables early identification of efficacy and toxicity, optimization of drug selectivity, and can foster drug repurposing. While most bioactive compounds interact with multiple macromolecular targets, the cytotoxicity encompasses diverse complex biological and chemical mechanisms that could even not all be related to binding to macromolecules, making the prediction of cytotoxicity specificity particularly challenging. To address early-phase prediction of cancer cell line targets of cytotoxic compounds, we developed a method combining a machine-learning classification model with a ligand-based reverse screening procedure able to rank cell-line from the most probable to the least probable target of any cytotoxic molecule. The development focused on addressing the challenges related to the scarcity of available experimental data on non-cytotoxic compounds. A knowledge-guided generation of realistic alleged inactives allowed to train several binary logistic regression models. The most robust classification model was trained on 164,134 cytotoxic compounds extracted from ChEMBL to generate a score of predicted sensitivity of cell lines for any new cytotoxic molecule. The method demonstrated strong predictive ability, recovering at least one experimental target within the 15 most probable cell-lines for 71% of nearly 11,000 external cytotoxic compounds tested across 1018 cancer cell lines.

bioinformatics↗

bioRxiv2025

Rapid and Reliable Structural Modeling of Adaptive Immune Receptors Using an Optimized AlphaFold3 workflow

AlphaFold (AF), a deep-learning based protein modelling approach, has revolutionized structural biology by generally achieving near-experimental accuracy in protein structure prediction. An impactful application of AF is the modelling of antibodies (Abs) and T-cell receptors (TCRs), key mediators of cellular immunity, whose structural specificity underlies responses in cancer, infection, and autoimmune diseases. In this work, we analyse AF3 performance by systematically examining how MSA composition, number of inference phases and inference parameters affect prediction accuracy and computational efficiency. We present an acceleration of [~]45-fold of the AF3 MSA phase using reduced UniRef90 subsets, combined with up to a 3.6-fold increase in AF3 inference speed through optimized parameters. We provide a highly accurate variant of the AF3 workflow specifically optimized for the modelling of the Abs and TCRs receptor domains, enabling rapid, reliable structural predictions at a scale suitable for high-throughput immunological studies. Our findings provide a foundation for faster therapeutic discovery and deeper molecular mechanism understanding of immune recognition. TeaserBy improving key steps in the process, we made AlphaFold3 about 40 times faster at modeling specific immune proteins

immunology↗

bioRxiv2025

Comparative Assessment of the Utility of Co-Folding and Docking for Small-Molecule Drug Design

Deep-learning based co-folding methods predict the structures of proteins interacting with metal ions, small molecules, nucleic acids, peptides, and other proteins. One of their main objectives is their application for drug design, predicting the structure of small-molecule ligand/protein complexes. It has been shown that at present these models memorize ligand poses from the training data but do not generalize effectively to novel complexes and lack in the adherence to physical and chemical principles. Here, we use the recently introduced Runs N Poses benchmark set of 2,600 protein-ligand systems annotated by their similarity to the training data, to show that the physics-based docking algorithms Attracting Cavities and AutoDock Vina outperform co-folding methods for novel ligands and novel binding pockets. In addition to predicting ligand poses and at variance with co-folding methods, they provide a physical rationale on why a ligand binds (or does not bind) and insight into experimental structural model deficiencies.

bioinformatics↗

bioRxiv2025

Rethinking Molecular Beauty in the Deep Learning Era

As (deep) generative chemistry rapidly enters the landscape of drug discovery, evaluating the models and their structural output relevance, tractability, and innovation potential becomes both a scientific and practical challenge. Existing metrics such as QED and SA Score, though widely adopted, are rooted in biased historical datasets and often fail to capture what medicinal chemists truly seek: compounds with meaningful pharmacological potential and realistic tractability. In this work, we introduce the OiiSTER-map--a simple, intuitive, and interpretable two-dimensional heatmap that classifies molecules based on bioactivity-informed usuality and structural elaboration derived from molecular fingerprints. Unlike traditional filters, the OiiSTER-map helps identify not only the drug discovery chemical "sweet spot". (Regular/Balanced), but also underappreciated territories such as Minimal/Unusual or Over-elaborated/Trivial regions, offering actionable insights into compound quality, relevance, and diversity. We hope that a bioactivity-informed, structurally aware, and easily interpretable tool like the OiiSTER-map, employed in combination with other well implemented metrics, can be decisive to go beyond current limitations of assessing (deep) generative models and to ensure a more mechanistically relevant, nuanced and useful evaluation of the thus designed virtual molecules.

bioinformatics↗

bioRxiv2025

Key determinants of T cell epitope recognition revealed by TCR specificity profiles

Interactions between T-Cell Receptors (TCRs) and antigenic peptides presented on Major Histocompatibility Complex (MHC) molecules are central to the immune recognition of infected and malignant cells. The complexity of the TCR sequence space, the flexibility of the TCR-epitope interface and the lack of a standardized framework to visualize TCR specificity have hindered a comprehensive understanding of the principles governing TCR-epitope recognition across a broad range of epitopes. Here, we introduce a fully interpretable probabilistic framework, termed TCR specificity profiles (TSPs), which captures fundamental properties distinguishing epitope-specific from baseline TCR repertoires. We demonstrate that TSPs unravel key determinants of TCR-epitope recognition specificity. By identifying and analyzing TCRs recognizing dozens of epitope variants, we show that TSPs accurately predict cross-reactivity and reveal how TCR specificity evolves with epitope sequence, binding mode and MHC restriction. TSPs further enable interpretation of machine learning tools and reveal how AlphaFold3 can be used to decipher key determinants of TCR-epitope recognition specificity.

immunology↗

bioRxiv2025

Novel universal domain-centric method for protein classification

Human protein kinases constitute a large superfamily of about 500 genes, historically classified into subfamilies based on phylogenetic relationship. However, many kinases remain unclassified. Phylogeny is typically based on multiple sequence alignments, and neglects the physico-chemical properties of residues at each position of the sequence. By incorporating these properties, we can gain deeper insights beyond basic alignments. Here we use, for the first time, a detailed physico-chemical description of kinases to identify class-specific structural regions, supporting an unsupervised classification method capable of classifying previously unlabeled kinases. This novel approach aligns with existing phylogeny-based classifications while offering refinements and enhanced accuracy. Ultimately, we use machine learning techniques to classify unlabeled kinases, validated by analyzing class-specific structural regions. This new classification approach goes beyond current rankings and can be applied to any type of protein, such as immunoglobulins and G protein-coupled receptors.

bioinformatics↗

bioRxiv2025

Redirecting TCR specificity in regulatory T cells toward class I HLA antigens mediates tissue-specific homing

Type 1 diabetes (T1D) is marked by the overexpression of class I major histocompatibility complex (MHC) antigens in pancreatic islets, which are targeted by islet-specific CD8+ T cells. Here, we aimed to improve regulatory T cell (Treg) infiltration into pancreatic islets by redirecting their specificity toward class I-restricted islet antigens. We functionally validated two public islet specific HLA-A2 (*02:01) restricted TCRs, one specific for ZnT8186-194 (clone D222D), the second for IGRP265-273 (clone 32) by dual locus (TRAC/CD4) homology-directed editing. Clone D222D was peptide-specific and CD8{beta} dependent while clone 32 exhibited antigen promiscuity and showed CD8 dependency. Engineered CD4to8 TCR Tregs maintained stable phenotypes, suppressed significantly better than their polyclonal counterpart, and showed co-receptor-dependent migration in vivo. This approach demonstrates that TCR specificity, reflected by its functional activity, is crucial for tissue-specific trafficking, paving the way to improve the efficacy of Treg therapies for T1D.

immunology↗

bioRxiv2024

Phage display profiling of CDR3β loops enables machine learning predictions of NY-ESO-1 specific TCRs

T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. T-cell epitope recognition is mediated by the binding of the T-Cell Receptor (TCR) and TCRs recognizing clinically relevant epitopes are promising for T-cell based therapies. Starting from one of the few known TCRs targeting the cancer-testis antigen NY-ESO-1157-165 epitope, we built large phage display libraries of TCRs with randomized Complementary Determining Region 3 of the {beta} chain. The TCR libraries were panned against the NY-ESO-1 epitope, which enabled us to collect thousands of epitope-specific TCR sequences. We then trained a machine learning TCR-epitope interaction predictor with this data and could identify several epitope-specific TCRs directly from TCR repertoires. Cellular binding and functional assays revealed that the predicted TCRs displayed activity towards the NY-ESO-1 epitope and no detectable cross-reactivity with self-peptides. Overall, our work demonstrates how display technologies combined with machine learning models of TCR-epitope recognition can effectively leverage large TCR repertoires for TCR discovery.

bioinformatics↗

bioRxiv2023

TCRfp: a new fingerprint-based approach for TCR repertoire analysis

The development of cancer immunotherapy has accelerated in recent years. Understanding the specificity of T cell receptors (TCR) for peptides presented by the major histocompatibility complex (pMHC) is a major step towards improving immunotherapy approaches, such as adoptive cell transfer and peptide vaccination. Despite recent computational advances, the unambiguous pairing of TCR with pMHC, from pools of thousands of candidates, remains out of reach. To tackle this challenge, we have developed a new tool that converts the 3D structure of TCR into individual one-dimensional structural fingerprints (TCRfp). We have modelled over 10000 3D structures of paired TCR alpha and beta chains with known sequences and pMHC specificity and encoded them into 1D TCRfp. For future clinical needs, we have translated the TCR modelling process into a fast pipeline. Similarity measures between TCR FPs correlate with their ability to recognise similar or identical epitopes in the training set and in the external validation sets. TCRfp constitutes the first rapid approach for high-throughput TCR comparison and repertoire analysis based on molecular 3D structures, which is efficient enough to complement sequence-based approaches.

cancer biology↗

bioRxiv2023

A direct experimental test of Ohno's hypothesis

Gene duplication drives evolution by providing raw material for proteins with novel functions. The oldest and historically most influential hypothesis about the evolutionary fate and potential of duplicated genes has been proposed by Susumu Ohno in 1970. This hypothesis essentially posits that gene duplication can help genes tolerate new mutations and thus facilitates the evolution of new phenotypes. Competing hypotheses argue that deleterious mutations will usually inactivate gene duplicates too rapidly for Ohnos hypothesis to work. Here, we provide a first direct experimental test of Ohnos hypothesis. Specifically, we evolved one or exactly two copies of a gene encoding a fluorescent protein in Escherichia coli through multiple rounds of mutagenesis and selection. We then analyzed the genotypic and phenotypic evolutionary dynamics of the evolving populations through high-throughput DNA sequencing, biochemical assays, and engineering of selected variants. In support of Ohnos hypothesis, populations carrying two gene copies displayed higher mutational robustness than those carrying a single gene copy. As a consequence, the double-copy populations experienced relaxed purifying selection, evolved higher phenotypic and genetic diversity, carried more mutations and accumulated combinations of key beneficial mutations earlier. However, their phenotypic evolution was not accelerated, possibly because one gene copy rapidly became inactivated by deleterious mutations. Our work provides an experimental platform to test models of evolution by gene duplication, and it supports alternatives to Ohnos hypothesis that point to the importance of gene dosage.

evolutionary biology↗

bioRxiv2023

Combination of NY-ESO-1-TCR-T-cells coengineered to secrete SiRPalpha decoys with anti-tumor antibodies to augment macrophage phagocytosis

The adoptive transfer of T cell receptor (TCR)-engineered T cells (ACT) targeting the HLA-A2 restricted cancer-testis epitope NY-ESO-1157-165 (A2/NY) has yielded favorable clinical responses against a variety of cancers. Two promising approaches to improve ACT efficacy are TCR affinity-optimization and combinatorial treatment strategies to reprogram the tumor microenvironment (TME). By computational design, we previously developed a panel of affinity-enhanced A2/NY-TCRs. Here, we have demonstrated improved tumor control and engraftment by T cells gene-modified to express one such TCR comprising a single amino acid replacement in CDR3{beta} (A97L). To harness macrophages in the TME, we coengineered TCR-T cells to constitutively or inducibly secrete a high-affinity signal regulatory protein alpha (SiRP) decoy (CV1) to block the CD47 dont eat me signal. We demonstrated better control of tumor outgrowth by CV1-Fc coengineered TCR-T cells but in subcutaneous xenograft tumor models we observed depletion of both CV1-Fc and CV1 coengineered T cells. Importantly, CV1 coengineered T cells were not depleted by human macrophages in vitro. Moreover, Avelumab and Cetuximab enhanced macrophage-mediated phagocytosis in vitro in the presence of CV1, and augmented tumor control upon ACT. Taken together, our study indicates important clinical promise for harnessing macrophages by combining CV1 coengineered TCR-T cells with tumor-targeting monoclonal antibodies.

bioengineering↗

bioRxiv2023

TCRpcDist: Estimating TCR physico-chemical similarity to analyze repertoires and predict specificities

Approaches to analyse and cluster TCR repertoires to reflect antigen specificity are critical for the diagnosis and prognosis of immune-related diseases and the development of personalized therapies. Sequence-based approaches showed success but remain restrictive, especially when the amount of experimental data used for the training is scarce. Structure-based approaches which represent powerful alternatives, notably to optimize TCRs affinity towards specific epitopes, show limitations for large scale predictions. To handle these challenges, we present TCRpcDist, a 3D-based approach that calculates similarities between TCRs using a metric related to the physico-chemical properties of the loop residues predicted to interact with the epitope. By exploiting private and public datasets and comparing TCRpcDist with competing approaches, we demonstrate that TCRpcDist can accurately identify groups of TCRs that are likely to bind the same or similar epitopes. Additionally, we experimentally validated the ability of TCRpcDist to predict antigen-specificities of tumor-infiltrating lymphocytes orphan TCRs obtained from four cancer patients. TCRpcDist is a promising approach to support TCR repertoire analysis and cancer immunotherapies. One Sentence SummaryWe present a new approach for TCR clustering which allows TCR deorphanization for the first time.

immunology↗

bioRxiv2022

Rapid BCMA downmodulation on myeloma cells upon CAR T cell contact is mediated by trogocytosis and BCMA internalization

BackgroundChimeric antigen receptor (CAR) T cell therapy targeting B cell maturation antigen (BCMA) on multiple myeloma (MM) produces fast but not long-lasting responses. Reasons for treatment failure are poorly understood. CARs simultaneously targeting two antigens may represent an alternative. Here, we (1) designed and characterized novel A proliferation inducing ligand (APRIL) based dual-antigen targeting CARs, and (2) investigated mechanisms of resistance to CAR T cells with three different BCMA-binding moieties (APRIL, single-chain-variable-fragment, heavy-chain-only). MethodsThree new APRIL-CARs were designed and characterized. Human APRIL-CAR T cells were evaluated for their cytotoxic function in vitro and in vivo, for their polyfunctionality, immune synapse formation, memory, exhaustion phenotype and tonic signaling activity. To investigate resistance mechanisms, we analyzed BCMA levels and cellular localization and quantified CAR T cell - target cell interactions by live microscopy. Impact on pathway activation and tumor cell proliferation was assessed in vitro and in vivo. ResultsAPRIL-BB{zeta} CAR T cells in a trimeric ligand binding conformation conferred the best polyfunctionality, immune synapse formation and fast anti-tumor function in vivo in two different mouse xenograft models. Upon CAR T cell - myeloma cell contact, we found rapid BCMA downmodulation on target cells with all three evaluated binding moieties. CAR T cells acquired BCMA on their cell surface by trogocytosis, and BCMA on MM cells was rapidly internalized. Since trogocytosis can lead to CAR T cell exhaustion and presence of CAR T cells does not protect patients from relapse, we investigated whether non-functional CAR T cells play a role in tumor progression. While CAR T cell - MM cell interactions activated BCMA pathway, we did not find enhanced tumor growth in vitro or in vivo. ConclusionWe designed and characterized distinct APRIL-CAR T cells for dual-antigen targeting of MM. Rapid BCMA downmodulation occurred upon CAR T cell - tumor cell encounter independently of whether an APRIL or antibody-based binding moiety was used. BCMA internalization mostly contributed to this effect, but trogocytosis by CAR T cells was also observed. Our study sheds light on the mechanisms underlying CAR T cell failure in MM and can inform the development of improved treatment strategies.

immunology↗

bioRxiv2022

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on MHC-II molecules. The high polymorphism of MHC-II genes represents an important hurdle towards accurate prediction and identification of CD4+ T-cell epitopes in different individuals and different species. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across human, mouse, cattle and chicken. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse binding mode in MHC-II ligands. We then developed a machine learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T-cell epitopes, and enabled us to discover and characterize several viral and bacterial epitopes following the aforementioned reverse binding mode.

immunology↗

bioRxiv2022

The actin assembly requirements of the formin Fus1 to build the fusion focus

Formins form the largest family of actin filament nucleators and elongators, involved in the assembly of diverse actin structures. Actin filament nucleation and elongation activities reside in the formin homology 1 (FH1) and FH2 domains, common to all formins. However, the rate of these reactions varies between formins by at least 20-fold. Typically, each cell expresses several distinct formins, each contributing to the assembly of one or several actin structures, raising the question of what confers each formin its specificity. Here, using the formin Fus1 in the fission yeast Schizosaccharomyces pombe, we systematically probed the importance of formin nucleation and elongation rates for function in vivo. Fus1 assembles the actin fusion focus, an aster-like structure of actin filaments at the contact site between gametes, necessary for the process of cell fusion to form the zygote during sexual reproduction. By constructing chimeric formins with combinations of FH1 and FH2 domains previously characterized in vitro, we establish that changes in formin nucleation and elongation rates have direct consequences on the architecture of the fusion focus, and that Fus1 native high nucleation and low elongation rates are optimal for fusion focus assembly. We further describe a point mutant in the Fus1 FH2 domain that preserves native nucleation and elongation rates in vitro but alters function in vivo, indicating an additional property of the FH2 domain. Thus, rates of actin assembly are tailored for assembly of specific actin structures.

cell biology↗

bioRxiv2020

Dominant monoallelic variant in the PAK2 gene causes Knobloch syndrome type 2

Knobloch syndrome is an autosomal recessive phenotype mainly characterized by retinal detachment and encephalocele caused by biallelic pathogenic variants in the COL18A1 gene. However, there are patients clinically diagnosed as Knobloch syndrome with unknown molecular etiology not linked to COL18A1. We studied an historical pedigree (published in 1998) designated as KNO2 (Knobloch type 2 syndrome with intellectual disability, autistic behavior, retinal degeneration, encephalocele). Whole exome sequencing of the two affected siblings and the normal parents resulted in the identification of a PAK2 non-synonymous substitution p.(Glu435Lys) as a causative variant. The variant was monoallelic and apparently de novo in both siblings indicating a likely germline mosaicism in one of the parents; the mosaicism however could not be observed after deep sequencing of blood parental DNA. PAK2 encodes a member of a small group of serine/threonine kinases; these P21-activating kinases (PAKs) are essential in signal transduction and cellular regulation (cytoskeletal dynamics, cell motility, death and survival signaling, and cell cycle progression). Structural analysis of the PAK2 p.(Glu435Lys) variant which is located in the kinase domain of the protein predicts a possible compromise in the kinase activity. Functional analysis of the p.(Glu435Lys) PAK2 variant in transfected HEK293T cells results in a partial loss of the kinase activity. PAK2 has been previously suggested as an autism related gene. Our results show that PAK2 induced phenotypic spectrum is broad and not fully understood. We conclude that the KNO2 syndrome in the studied family is dominant and caused by a deleterious variant in the PAK2 gene.

genetics↗