Biology subjects

Punta, M.

Publications and source records attributed to Punta, M..

8 recordsLinked to original sources

bioRxiv2024

Unsupervised domain classification of AlphaFold2-predicted protein structures

AO_SCPLOWBSTRACTC_SCPLOWThe release of the AlphaFold database, which contains 214 million predicted protein structures, represents a major leap forward for proteomics and its applications. However, lack of comprehensive protein annotation limits its accessibility and usability. Here, we present DPCstruct, an unsupervised clustering algorithm designed to provide domain-level classification of protein structures. Using structural predictions from AlphaFold2 and comprehensive all-against-all local alignments from Foldseek, DPCstruct identifies and groups recurrent structural motifs into domain clusters. When applied to the Foldseek Cluster database, a representative set of proteins from the AlphaFoldDB, DPCstruct successfully recovers the majority of protein folds catalogued in established databases such as SCOP and CATH. Out of the 28,246 clusters identified by DPCstruct, 24% have no structural or sequence similarity to known protein families. Supported by a modular and efficient implementation, classifying 15 million entries in less than 48 hours, DPCstruct is well suited for large-scale proteomics and metagenomics applications. It also facilitates the rapid incorporation of updates from the latest structural prediction tools, ensuring that the classification remains up-to-date. The DPCstruct pipeline and associated database are freely available in a dedicated repository, enhancing the navigation of the AlphaFoldDB through domain annotations and enabling rapid classification of other protein datasets.

bioinformatics↗

bioRxiv2023

Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering

Technological advances in massively parallel sequencing have led to an exponential growth in the number of known protein sequences. Much of this growth originates from metagenomic projects producing new sequences from environmental and clinical samples. The Unified Human Gastrointestinal Proteome (UHGP) catalogue is one of the most relevant metagenomic datasets with applications ranging from medicine to biology. However, the lack of sequence annotation impairs its usability. This work aims to produce a family classification of UHGP sequences to facilitate downstream structural and functional annotation. This is achieved through the release of the DPCfam-UHGP50 dataset containing 10,778 putative protein families generated using DPCfam clustering, an unsupervised pipeline grouping sequences into multi-domain architectures. DPCfam-UHGP50 considerably improves family coverage at protein and residue levels compared to the manually curated repository Pfam. It is our hope that DPCfam-UHGP50 will foster future discoveries in the field of metagenomics of the human gut by the release of a FAIR-compliant database easily accessible via a searchable web server and Zenodo repository.

bioinformatics↗

bioRxiv2022

Driver mutations in GNAQ and GNA11 genes as potential targets for precision immunotherapy in uveal melanoma patients

Uveal melanoma (UM) is the most common ocular malignancy in adults. Nearly 95% of UM patients carry the mutually exclusive mutations in the homologous genes GNAQ (aminoacid change Q209L/Q209P) and GNA11 (aminoacid change Q209L). UM is located in an immunosuppressed organ and do not suffer immunoediting. Therefore, we hypothesize that driver mutations in GNAQ/11 genes could be recognized by the immune system. Genomic and transcriptomic data for primary uveal tumors was collected from TCGA-UM dataset (n=80). The immunogenic potential for GNAQ/GNA11 Q209L/Q209P mutations was assessed using a variety of tools and HLA types information. The immune microenvironment was characterized using gene expression data. All prediction tools showed stronger GNAQ/11 Q209L binding to HLA. The immunogenicity analysis revealed that Q209L is likely to be presented by more than 73% of individuals in 1000G database whereas Q209P is only predicted to be presented in 24% of individuals. GNAQ/11 Q209L showed higher likelihood to be presented by HLA-I molecules than almost all driver mutations analyzed. Samples carrying Q209L had a higher immune-reactive phenotype: (i) expression of antigen presenting genes HLA-A (p=0.009) and B2M (p=0.043); (ii) immunophenoscore (p=0.008); (iii) infiltration of immune system cells NK (p=0.002) and CD8+ T lymphocytes (p=0.02). Results suggest a high potential immunogenicity of the GNAQ/11 Q209L variant that could allow the generation of novel therapeutic tools to treat UM like neoantigen vaccinations.

genomics↗

bioRxiv2020

DPCfam: a new method for unsupervised protein family classification

MotivationAs the UniProt database approaches the 200 million entries mark, the vast majority of proteins it contains lack any experimental validation of their functions. In this context, the identification of homologous relationships between proteins remains the single most widely applicable tool for generating functional and structural hypotheses in silico. Although many databases exist that classify proteins and protein domains into homologous families, large sections of the sequence space remain unassigned. ResultsWe introduce DPCfam, a new unsupervised procedure that uses sequence alignments and Density Peak Clustering to automatically classify homologous protein regions. Here, we present a proof-of-principle experiment based on the analysis of two clans from the Pfam protein family database. Our tests indicate that DPCfam automatically-generated clusters are generally evolutionary accurate corresponding to one or more Pfam families and that they cover a significant fraction of known homologs. Overall, DPCfam shows potential both for assisting manual annotation efforts (domain discovery, detection of classification inconsistencies, improvement of family coverage and boosting of clan membership) and as a stand-alone tool for unsupervised classification of sparsely annotated protein datasets such as those from environmental metagenomics studies (domain discovery, analysis of domain diversity). AvailabilityAlgorithm implementation used in this paper is available at https://gitlab.com/ETRu/dpcfam (Requires Python 3, C++ compiler and runs on Linux systems.); data are available at https://zenodo.org/record/3934399

bioinformatics↗

bioRxiv2019

The immunogenic potential of recurrent cancer drug resistance mutations: an in silico study

Cancer somatic mutations have been identified as a source of antigens that can be targeted by cancer immunotherapy. In this work, expanding on previous studies, we analyse the immunogenic properties of mutations that are known to drive resistance to cancer targeted-therapies. We survey a large dataset of mutations that confer resistance to different drugs and occur in numerous genes and tumour types. We show that a significant number of these mutations are predicted in silico to have immunogenic potential across a large proportion of the human population. Two of these mutations had previously been experimentally validated and it was confirmed that some of their associated neopeptides elicit T-cell responses in vitro. The identification of potent cancer-specific antigens can be instrumental for developing more effective immunotherapies. Resistance mutations, several of which are known to recur in different patients, could be of particular interest in the context of off-the-shelf precision immunotherapies such as therapeutic cancer vaccines.

bioinformatics↗

bioRxiv2019

Extreme intratumour heterogeneity and driver evolution in mismatch repair deficient gastro-oesophageal cancer

Mismatch repair deficient (dMMR) gastro-oesophageal adenocarcinomas (GOAs) show better outcomes than their MMR-proficient counterparts and high immunotherapy sensitivity. The hypermutator-phenotype of dMMR tumours theoretically enables high evolvability but how these cancers evolve has not been investigated in detail. We applied multi-region exome sequencing (MSeq) to four treatment-naive dMMR GOAs. This revealed extreme intratumour heterogeneity (ITH), exceeding ITH in other cancer types >20-fold, but also long phylogenetic trunks which may explain the exquisite immunotherapy sensitivity of dMMR tumours. Subclonal driver mutations were common and parallel evolution occurred in RAS, PIK3CA, SWI/SNF-complex genes and in immune evasion regulators. MSeq data and evolution analysis of single region-data from 64 MSI GOAs showed that chromosome 8 gains were among the earliest genetic events and that the hypermutator-phenotype remained active during progression. MSeq may be necessary for biomarker development in these heterogeneous cancers. Comparison with other MSeq-analysed tumour types revealed mutation rates and their timing as major determinants of phylogenetic tree morphologies.

cancer biology↗

bioRxiv2018

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

BackgroundTargeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).\n\nMethodsTo address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.\n\nResultsOur tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.\n\nConclusionsAmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.

genomics↗

bioRxiv2017

Structure-based analysis of CysZ-mediated cellular uptake of sulfate

Sulfur, most abundantly found in the environment as sulfate (SO42-), is an essential element in metabolites required by all living cells, including amino acids, co-factors and vitamins. Current understanding of the cellular delivery of SO42- at the molecular level is limited however. CysZ has been described as a SO42- permease, but its sequence family is without known structural precedent. Based on crystallographic structure information, SO42- binding and uptake experiments in cells and proteoliposomes, and single-channel conductance measurements, we provide insight into the molecular mechanism of CysZ-mediated translocation of SO42- across membranes. CysZ properties differ markedly from those of known transporters and ion channels. The structures display a hitherto unknown fold with dual topology, assembling in CysZ from Pseudomonas denitrificans as a trimer of antiparallel dimers in the membrane. CysZ structures from two other species recapitulate dimers from this assembly. Mutational studies highlight the functional relevance of conserved CysZ residues.

biophysics↗