Biology subjects

Pires, D. E. V.

Publications and source records attributed to Pires, D. E. V..

10 recordsLinked to original sources

bioRxiv2025

BENDER DB: a database of protein binding sites across neglected disease proteomes

Identifying binding sites is crucial for expanding our knowledge of various biological processes, supporting drug discovery, and repositioning strategies, particularly in the early research phases of target hopping. This is especially important for neglected diseases, which predominantly impact vulnerable populations in developing regions. To address this, we created BENDER DB, a database designed to map predicted protein binding sites within the proteomes of pathogens associated with neglected diseases. Utilizing AlphaFold-predicted structures, BENDER DB integrates results from five leading binding site prediction tools, resulting in over one million binding sites across over 100,000 proteins from 10 different proteomes. BENDER DB offers unique features, such as integrating multiple predictors, interactive visualization tools, and comprehensive graphical representations, allowing for a detailed visual comparative analysis of different prediction methods and the predicted binding sites. We combined the computational approaches to offer a consensus of binding site predictions, leveraging the strengths of multiple techniques. Additionally, we introduce BENDER AI, a meta-predictor that combines the outputs of the predictors to provide a unified binding site classification. BENDER AI outperformed all individual methods in 4 out of 5 evaluated metrics. Specifically, it achieved an MCC of 0.64 and an AUC of 0.89, surpassing the highest MCC of 0.63 and the highest AUC of 0.84 obtained by individual predictors, demonstrating strong performance and effectiveness in supporting binding site prediction. By consolidating these results, BENDER DB enables detailed comparative analyses of prediction methods and integrates interactive visualization tools to provide users with an intuitive platform for binding site exploration. The database aims to accelerate drug discovery efforts, particularly in underdeveloped regions, by providing a robust and detailed resource for studying protein binding sites. BENDER DB is available at https://benderdb.ufv.br.

bioinformatics↗

bioRxiv2024

Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations

Developing therapeutic antibodies is a challenging endeavour, often requiring large-scale screening to produce initial binders, that still often require optimisation for developability. We present a computational pipeline for the discovery and design of therapeutic antibody candidates, which incorporates physics- and AI-based methods for the generation, assessment, and validation of developable candidate antibodies against diverse epitopes, via efficient few-shot experimental screens. We demonstrate that these orthogonal methods can lead to promising designs. We evaluated our approach by experimentally testing a small number of candidates against multiple SARS-CoV-2 variants in three different tasks: (i) traversing sequence landscapes of binders, we identify highly sequence dissimilar antibodies that retain binding to the Wuhan strain, (ii) rescuing binding from escape mutations, we show up to 54% of designs gain binding affinity to a new subvariant and (iii) improving developability characteristics of antibodies while retaining binding properties. These results together demonstrate an end-to-end antibody design pipeline with applicability across a wide range of antibody design tasks. We experimentally characterised binding against different antigen targets, developability profiles, and cryo-EM structures of designed antibodies. Our work demonstrates how combined AI and physics computational methods improve productivity and viability of antibody designs.

bioinformatics↗

bioRxiv2024

PRIMITI: a computational approach for accurate prediction of miRNA-target mRNA interaction

Current medical research has been demonstrating the roles of miRNAs in a variety of cellular mechanisms, lending credence to the association between miRNA dysregulation and multiple diseases. Understanding the mechanisms of miRNA is critical for developing effective diagnostic and therapeutic strategies. miRNA-mRNA interactions emerge as the most important mechanism to be understood despite their experimental validation constraints. Accordingly, several computational models have been developed to predict miRNA-mRNA interactions, albeit presenting limited predictive capabilities, poor characterisation of miRNA-mRNA interactions and low usability. To address these drawbacks, we developed PRIMITI, a PRedictive model for the Identification of novel MIRNA-Target mRNA Interactions. PRIMITI is a novel machine learning model that utilises CLIP-seq and expression data to characterise functional target sites in 3-untranslated regions (3-UTRs) and predict miRNA-target mRNA repression activity. The model was trained using a reliable negative sample selection approach and the robust extreme gradient boosting (XGBoost) model, which was coupled with newly introduced features, including sequence and genetic variation information. PRIMITI achieved an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.96 for a prediction of functional miRNA-target site binding and 0.96 for a prediction of miRNA-target mRNA repression activity on cross-validation and an independent blind test. Additionally, the model outperformed state-of-the-art methods in recovering miRNA-target repressions in an unseen microarray dataset and in a collection of validated miRNA-mRNA interactions, highlighting its utility for preliminary screening. PRIMITI is available on a reliable, scalable and user-friendly web server at https://biosig.lab.uq.edu.au/primiti.

bioinformatics↗

bioRxiv2023

The Topological Properties of the Protein Universe

Deep learning methods have revolutionized our ability to predict protein structures, allowing us a glimpse into the entire protein universe. As a result, our understanding of how protein structure drives function is now lagging behind our ability to determine and predict protein structure. Here, we describe how topology, the branch of mathematics concerned with qualitative properties of spatial structures, provides a lens through which we can identify fundamental organizing features across the known protein universe. We identify topological determinants that capture global features of the protein universe, such as domain architecture and binding sites. Additionally, our analysis also identified highly specific properties, so-called topological generators, that can be used to provide deeper insights into protein structure-function and evolutionary relationships. We used our approach to determine structural, functional and disease consequences of mutations, explain differences in properties of proteins in mesophiles and thermophiles, and the likely structural and functional consequences of polymorphisms in a protein. Overall, we present a practical methodology for mapping the topology of the known protein universe at scale.

bioinformatics↗

bioRxiv2023

A broad-spectrum family GH13 α-glucosidase from Marinovum sp., a member of the Roseobacter clade

Glycoside hydrolases (GHs) are a diverse group of enzymes that catalyze the hydrolysis of glycosidic bonds. The Carbohydrate-Active enZymes (CAZy) classification organizes GHs into families based on sequence data and function, with fewer than 1% of the predicted proteins characterized biochemically. Consideration of genomic context can provide clues to infer possible enzyme activities for proteins of unknown function. We used the MultiGeneBLAST tool to discover a gene cluster in Marinovum sp., a member of the marine Roseobacter clade, that encodes homologues of enzymes belonging to the sulfoquinovose monooxygenase pathway for sulfosugar catabolism. This cluster lacks a gene encoding a classical family GH31 sulfoquinovosidase candidate, but which instead includes an uncharacterized family GH13 protein (MsGH13) that we hypothesized could be a non-classical sulfoquinovosidase. Surprisingly, recombinant MsGH13 lacks sulfoquinovosidase activity and is a broad spectrum -glucosidase that is active on a diverse array of -linked disaccharides, including: maltose, sucrose, nigerose, trehalose, isomaltose, and kojibiose. Using AlphaFold, a 3D model for the MsGH13 enzyme was constructed that predicted its active site shared close similarity with an -glucosidase from Halomonas sp. H11 of the same GH13 subfamily that shows narrower substrate specificity.

biochemistry↗

bioRxiv2022

epitope1D: Accurate Taxonomy-Aware B-Cell Linear Epitope Prediction

The ability to identify B-cell epitopes is an essential step in vaccine design, immunodiagnostic tests, and antibody production. Several computational approaches have been proposed to identify, from an antigen protein, which residues are likely to be part of an epitope, but have limited performance on relatively homogeneous data sets and lack interpretability, limiting biological insights that could be derived. To address these limitations, we have developed epitope1D, an explainable machine learning method capable of accurately identifying linear B-cell epitopes, leveraging two new descriptors: a graph-based signature representation of protein sequences, based on our well established CSM (Cutoff Scanning Matrix) algorithm and Organism Ontology information. Our model achieved Area Under the ROC curve of up to 0.935 on cross-validation and blind tests, demonstrating robust performance and outperforming state-of-the-art tools. epitope1D has been made available as a user-friendly web server interface and API at http://biosig.lab.uq.edu.au/epitope1d.

bioinformatics↗

bioRxiv2020

Designing safe and potent herbicides with the cropCSM online resource

Herbicides have revolutionised weed management, increased crop yields and improved profitability allowing for an increase in worldwide food security. Their widespread use, however, has also led to not only a rise in resistance but also concerns about their environmental impact. To help identify new, potent, non-toxic and environmentally safe herbicides we have employed interpretable predictive models to develop the online tool cropCSM (http://biosig.unimelb.edu.au/crop_csm).

bioinformatics↗

bioRxiv2020

COVID-3D: An online resource to explore the structural distribution of genetic variation in SARS-CoV-2 and its implication on therapeutic development

The emergence of the COVID-19 pandemic has spurred a global rush to uncover basic biological mechanisms, to inform effective vaccine and drug development. Despite viral novelty, global sequencing efforts have already identified genomic variation across isolates. To enable easy exploration and spatial visualization of the potential implications of SARS-CoV-2 mutations on infection, host immunity and drug development we have developed COVID-3D (http://biosig.unimelb.edu.au/covid3d/).

bioinformatics↗

bioRxiv2019

Widespread remodelling of proteome solubility in response to different protein homeostasis stresses

The accumulation of protein deposits in neurodegenerative diseases involves the presence of a metastable subproteome vulnerable to aggregation. To investigate this subproteome and the mechanisms that regulates it, we measured the proteome solubility of the Neuro2a cell line under protein homeostasis stresses induced by Huntington Disease proteotoxicity; Hsp70, Hsp90, proteasome and ER-mediated folding inhibition; and oxidative stress. We found one-quarter of the proteome extensively changed solubility. Remarkably, almost all the increases in insolubility were counteracted by increases in solubility of other proteins. Each stress directed a highly specific pattern of change, which reflected the remodelling of protein complexes involved in adaptation to perturbation, most notably stress granule proteins, which responded differently to different stresses. These results indicate that the robustness of protein homeostasis relies on the absence of proteins highly vulnerable to aggregation and on large changes in aggregation state of regulatory mechanisms that restore protein solubility upon specific perturbations.

biochemistry↗

bioRxiv2018

TAG.ME: Taxonomic Assignment of Genetic Markers for Ecology

1.BackgroundSequencing of amplified genetic markers, such as the 16S rRNA gene, have been extensively used to characterize microbial community composition. Recent studies suggested that Amplicon Sequences Variants (ASV) should replace the Operational Taxonomic Units (OTU), given the arbitrary definition of sequence identity thresholds used to define units. Alignment-free methods are an interesting alternative for the taxonomic classification of the ASVs, preventing the introduction of biases from sequence identity thresholds.\n\nResultsHere we present TAG.ME, a novel alignment-independent and amplicon-specific method for taxonomic assignment based on genetic markers. TAG.ME uses a multilevel supervised learning approach to create predictive models based on user-defined genetic marker genes. The predictive method can assign taxonomy to sequenced amplicons efficiently and effectively. We applied our method to assess gut and soil sample classification, and it outperformed alternative approaches, identifying a substantially larger proportion of species. Benchmark tests performed using the RDP database, and Mock communities reinforced the precise classification into deep taxonomic levels.\n\nConclusionTAG.ME presents a new approach to assign taxonomy to amplicon sequences accurately. Our classification model, trained with amplicon specific sequences, can address resolution issues not solved by other methods and approaches that use the whole 16S rRNA gene sequence. TAG.ME is implemented as an R package and is freely available at http://gabrielrfernandes.github.io/tagme/

bioinformatics↗