Biology subjects

Bonet, J.

Publications and source records attributed to Bonet, J..

15 recordsLinked to original sources

bioRxiv2026

ModCRElib: A standalone package to model cis-regulatory elements.

SummaryThe ModCRElib package provides various tools for the analysis and modelling of transcription factor(TF)-DNA and regulatory complex inter-protein interactions. It takes structural information on these interactions to predict TF binding motifs, generate binding profiles along DNA sequences that score the binding affinity, predict TF binding sites and model the structure of higher order regulatory complexes. It is capable of working with a variety of input data formats and sources. The user may follow the analysis pipeline as outlined in the documentation or the user can make use of any of the multiple functionalities in an isolated manner. The package takes the service offered by the ModCRE server and enables users to apply its tools in an unrestricted and customizable manner. In this paper we provide 5 example uses of ModCRElib. This includes (i) TF binding affinity prediction, (ii) TF binding aggregation, (iii) characterization of specificity in TF binding sites along target DNA sequences,(iv) modelling TF bound to predicted binding sites, and (v) the generation of statistical potential derived scoring profiles of TF interacting with DNA. Availabilityhttps://github.com/structuralbioinformatics/ModCRElib Contactbaldo.oliva@upf.edu Supplementary informationAvailable at https://github.com/structuralbioinformatics/ModCRElib. doi:10.5281/zenodo.17484081

bioinformatics↗

bioRxiv2022

A deep learning approach for improved detection of homologous recombination deficiency from shallow genomic profiles

Homologous Recombination Deficiency (HRD) is a predictive biomarker of poly-ADP ribose polymerase 1 inhibitors (PARPi) response. Most HRD detection methods are based on genome wide enumeration of scarring events and require deep genome sequence profiles (> 30x). The cost and workflow-specific biases introduced by these genome profiling methods currently limits clinical adoption of HRD testing. We introduce the Genomic Integrity Index (GII), a Convolutional Neuronal Network, that leverages features from low pass (1x) Whole Genome Sequencing data to distinguish HRD positive and negative samples. In a cohort of 230 ovarian and breast cancer, we found GII supports accurate stratification of samples yielding results that are highly concordant with state-of-the-art HRD detection methods (0.865<AUC<0.996) which require 50x deeper coverage. We conclude that the deep learning framework supporting GII allows accurate detection of HRD from shallow genome profiles, reducing biases and data generation costs making it uniquely suited for clinical applications.

cancer biology↗

bioRxiv2022

A generic framework for hierarchical de novo protein design

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test to our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold. Here, we built on the TopoBuilder de novo design method to automatically assemble structural templates with native-like features starting from string descriptors that capture the overall topology of proteins. Our framework eliminates the dependency of hand-crafted and fold-specific rules through an iterative, data-driven approach that extracts geometrical parameters from structural tertiary motifs. We evaluated the TopoBuilder framework by designing sequences for a set of five protein folds and experimental characterization revealed that several sequences were folded and stable in solution. The TopoBuilder de novo design framework will be broadly useful to guide the generation of artificial proteins with customized geometries, enabling the exploration of the protein universe.

bioinformatics↗

bioRxiv2021

DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data

MotivationDNA Methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods employ either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination. ResultsHere we present DeepMP, a convolutional neural network (CNN)-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on E. coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets. AvailabilityDeepMP is implemented and freely available under MIT license at github.com/pepebonet/DeepMP Contactjose.bonet@irbbarcelona.org -- mandiche@kth.se

bioinformatics↗

bioRxiv2021

RosettaSurf - a surface-centric computational design approach

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins. Author SummaryFinely orchestrated protein-protein interactions are at the heart of virtually all fundamental cellular processes. Altering these processes or encoding new functions in proteins has been a long-standing goal in computational protein design. Protein design methods commonly rely on scoring functions that seek to identify amino acid sequences that optimize structural configurations of atoms while minimizing a variety of physics-based and statistical terms. The objectives of the large majority of computational design protocols have been focused on obtaining a predefined structural conformation. However, routinely introducing a functional aspect on designer proteins has been more challenging. Our results suggest that the molecular surface features can be a useful optimization parameter to guide the design process towards functional surfaces that mimic known protein binding sites and interact with their intended targets. Specifically, we demonstrate that our design method can optimize experimental libraries through computational screening, creating a basis for highly specific protein binders, as well as design a potent immunogen that engages with site-specific antibodies. The ability to create proteins with novel functions will be transformative for biomedical applications, providing many opportunities for the design of novel immunogens, protein components for synthetic biology, and other protein-based biotechnologies.

bioinformatics↗

bioRxiv2021

Mutational hotspot in the SARS-CoV-2 Spike protein N-terminal domain conferring immune escape potential

Global efforts are being taken to monitor the evolution of SARS-CoV-2, aiming at early identification of mutations with the potential of increasing viral infectivity or virulence. We report a striking increase in the frequency of recruitment of diverse substitutions at a critical residue (W152), positioned in the N-terminal domain (NTD) of the Spike protein, observed repeatedly across independent phylogenetic and geographical contexts. We investigate the impact these mutations might have on the evasion of neutralizing antibodies. Finally, we uncover that NTD is a region exhibiting particularly high frequency of mutation recruitments, suggesting an evolutionary path on which the virus maintains optimal efficiency of ACE2 binding combined with the flexibility facilitating the immune escape.

genomics↗

bioRxiv2020

A bottom-up approach for the de novo design of functional proteins

De novo protein design has enabled the creation of novel protein structures. To design novel functional proteins, state-of-the-art approaches use natural proteins or first design protein scaffolds that subsequently serve as templates for the transplantation of functional motifs. In these approaches, the templates are function-agnostic and motifs have been limited to those with regular secondary structure. Here, we present a bottom-up approach to build de novo proteins tailored to structurally complex functional motifs. We applied a bottom-up strategy to design scaffolds for four different binding motifs, including one bi-functionalized protein with two motifs. The de novo proteins were functional as biosensors to quantify epitope-specific antibody responses and as orthogonal ligands to activate a signaling pathway in engineered mammalian cells. Altogether, we present a versatile strategy for the bottom-up design of functional proteins, applicable to a wide range of functional protein design challenges.

bioengineering↗

bioRxiv2020

Bayesian non-parametric clustering of single-cell mutation profiles

The high resolution of single-cell DNA sequencing (scDNA-seq) offers great potential to resolve intra-tumor heterogeneity by distinguishing clonal populations based on their mutation profiles. However, the increasing size of scDNA-seq data sets and technical limitations, such as high error rates and a large proportion of missing values, complicate this task and limit the applicability of existing methods. Here we introduce BnpC, a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles. BnpC employs a Dirichlet process mixture model coupled with a Markov chain Monte Carlo sampling scheme, including a modified split-merge move and a novel posterior estimator to predict clones and genotypes. We benchmarked our method comprehensively against state-of-the-art methods on simulated data using various data sizes, and applied it to three cancer scDNA-seq data sets. On simulated data, BnpC compared favorably against current methods in terms of accuracy, runtime, and scalability. Its inferred genotypes were the most accurate, and it was the only method able to run and produce results on data sets with 10,000 cells. On tumor scDNA-seq data, BnpC was able to identify clonal populations missed by the original cluster analysis but supported by supplementary experimental data. With ever growing scDNA-seq data sets, scalable and accurate methods such as BnpC will become increasingly relevant, not only to resolve intra-tumor heterogeneity but also as a pre-processing step to reduce data size. BnpC is freely available under MIT license at https://github.com/cbg-ethz/BnpC.

bioinformatics↗

bioRxiv2019

On the prediction of DNA-binding preferences of C2H2-ZF domains using structural models: application on human CTCF.

Cis2-His2 zinc finger (C2H2-ZF) proteins are the largest family of transcription factors in human and higher metazoans. However, the DNA-binding preferences of many members of this family remain unknown. We have developed a computational method to predict these DNA-binding preferences. We combine information from crystal structures composed by C2H2-ZF domains and from bacterial one-hybrid experiments to compute scores for protein-DNA binding based on statistical potentials. We apply the scores to compute theoretical position weight matrices (PWMs) of proteins with a DNA-binding domain composed by C2H2-ZF domains, with the only requirement of an input structure (experimentally determined or modelled). We have tested the capacity to predict PWMs of zinc finger domains, successfully predicting 3-2 nucleotides of a trinucleotide binding site for about 70% variants of single zinc-finger domains of Zif268. We have also tested the capacity to predict the PWMs of proteins composed by three C2H2-ZF domains, successfully matching between 60% and 90% of the binding-site motif according to the JASPAR database. The tests are used as a proof of the capacity to scan a DNA fragment and find the potential binding sites of transcription-factors formed by C2H2-ZF domains. As an example, we have tested the approach to predict the DNA-binding preferences of the human chromatin binding factor CTCF.

bioinformatics↗

bioRxiv2019

Trivalent cocktail of de novo designed immunogens enables the robust induction and focusing of functional antibodies in vivo

De novo protein design has been successful in expanding the natural protein repertoire. However, most de novo proteins lack biological function, presenting a major methodological challenge. In vaccinology, the induction of precise antibody responses remains a cornerstone for next-generation vaccines. Here, we present a novel protein design algorithm, termed TopoBuilder, with which we engineered epitope-focused immunogens displaying complex structural motifs. Both in mice and non-human primates, cocktails of three de novo designed immunogens induced robust neutralizing responses against the respiratory syncytial virus. Furthermore, the immunogens refocused pre-existing antibody responses towards defined neutralization epitopes. Overall, our de novo design approach opens the possibility of targeting specific epitopes for vaccine and therapeutic antibody development, and more generally will be applicable to design de novo proteins displaying complex functional motifs.

bioengineering↗

bioRxiv2018

Alternative interaction sites in the influenza A virus nucleoprotein mediate viral escape from the importin-α7 mediated nuclear import pathway

Influenza A viruses are able to adapt to restrictive conditions due to their high mutation rates. Here, we addressed the question by which mechanisms influenza A viruses may escape restriction by the cellular importin-7 protein, a component of the nuclear import machinery required for avian-mammalian adaptation and replicative fitness in human cells. Therefore, we assessed viral evolution in mice lacking the importin-7 gene. Here, we show that particularly three mutations occur with high frequency in the viral NP protein (G102R, M105K and D375N) in a specific structural area upon in vivo adaptation. Moreover, our findings suggest that the adaptive NP mutations mediate viral escape from importin-7 requirement likely due to the utilization of alternative interaction sites in NP beyond the classical nuclear localization signal and importin- isoforms. However, viral escape from importin-7 is, at least in part, associated with reduced replicative fitness in human cells.

molecular biology↗

bioRxiv2018

Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen

Throughout the last decades, vaccination has been key to prevent and eradicate infectious diseases. However, many pathogens (e.g. respiratory syncytial virus (RSV), influenza, dengue and others) have resisted vaccine development efforts, largely due to the failure to induce potent antibody responses targeting conserved epitopes. Deep profiling of human B-cells often reveals potent neutralizing antibodies that emerge from natural infection, but these specificities are generally subdominant (i.e., are present in low titers). A major challenge for next-generation vaccines is to overcome established immunodominance hierarchies and focus antibody responses on crucial neutralization epitopes. Here, we show that a computationally designed epitope-focused immunogen presenting a single RSV neutralization epitope elicits superior epitope-specific responses compared to the viral fusion protein. In addition, the epitope-focused immunogen efficiently boosts antibodies targeting the Palivizumab epitope, resulting in enhanced neutralization. Overall, we show that epitope-focused immunogens can boost subdominant neutralizing antibody responses in vivo and reshape established antibody hierarchies.

immunology↗

bioRxiv2018

rstoolbox: management and analysis of computationally designed structural ensembles.

MotivationComputational protein design (CPD) calculations rely on the generation of large amounts of data on the search for the best sequences. As such, CPD workflows generally include the batch generation of designed decoys (sampling) followed by ranking and filtering stages to select those with optimal metrics (scoring). Due to these factors, the proper analysis of the decoy population is a key element for the effective selection of designs for experimental validation.\n\nResultsHere, we present a set of tools for the analysis of protein design ensembles. The tool is oriented towards protein designers with basic coding training aiming to process efficiently their decoy sets as well as for protocol developers interested in benchmarking their new approaches. Although initially devised to process Rosetta design outputs, the library is extendable to other design tools.\n\nAvailability and Implementationrstoolbox is implemented for python2.7 and 3.5+. Code is freely available at https://github.com/lpdi-epfl/rstoolbox under the MIT license. Full documentation and examples can be found at https://lpdi-epfl.github.io/rstoolbox.

bioinformatics↗

bioRxiv2018

RADI (Reduced Alphabet Direct Information): Improving execution time for direct-coupling analysis

MotivationDirect-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. Current algorithms for DCA, although efficient, have a high computational cost of determining Direct Information (DI) values for large proteins or domains. In this paper, we present RADI (Reduced Alphabet Direct Information), a variation of the original DCA algorithm that simplifies the computation of DI values by grouping physicochemically equivalent residues.\n\nResultsWe have compared the first top ranking 40 pairs of DI values and their closest paired contact in 3D. The ranking is also compared with results obtained using a similar but faster approach based on Mutual Information (MI). When we simplify the number of symbols used to describe a protein sequence to 9, RADI achieves similar results as the original DCA (i.e. with the classical alphabet of 21 symbols), while reducing the computation time around 30-fold on large proteins (with length around 1000 residues) and with higher accuracy than predictions based on MI. Interestingly, the simplification produced by grouping amino acids into only two groups (polar and non-polar) is still representative of the physicochemical nature that characterizes the protein structure, having a relevant and useful predictive value, while the computation time is reduced between 100 and 2500-fold.\n\nAvailabilityRADI is available at https://github.com/structuralbioinformatics/RADI\n\nContactbaldo.oliva@upf.edu\n\nSupplementary informationSupplementary data is available in the git repository.

bioinformatics↗

bioRxiv2018

Rosetta FunFolDes - a general framework for the computational design of functional proteins

The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function, nevertheless, it remains a challenge for state-of-the-art methodologies. Here, we present a computational design approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins. We performed extensive benchmarks, where the most unexpected finding was that the design of function into proteins may not necessarily reside in the global minimum of the energetic landscape, which could have important implications in the field. We have computationally designed and experimentally characterized a distant structural template and a de novo \"functionless\" fold, two prototypical design challenges, to present important viral epitopes. Overall, we present an accessible strategy to repurpose old protein folds for new functions, which may lead to important improvements on the computational design of functional proteins.

bioinformatics↗