Biology subjects

Anteghini, M.

Publications and source records attributed to Anteghini, M..

6 recordsLinked to original sources

bioRxiv2024

Integrative Omics reveals changes in the cellular landscape of yeast without peroxisomes

Peroxisomes are organelles that are crucial for cellular metabolism. However, these organelles play also important roles in non-metabolic processes, such as signalling. To uncover the consequences of peroxisome deficiency, we compared two extremes, namely Saccharomyces cerevisiae wild-type and pex3 cells, which lack functional peroxisomes, employing transcriptomics and quantitative proteomics technology. Cells were grown on acetate, a carbon source that involves peroxisomal enzymes of the glyoxylate cycle and does not repress peroxisomal proteins. Transcripts of peroxisomal {beta}-oxidation genes and the corresponding proteins were enhanced in pex3 cells. Peroxisome-deficiency also caused reduced levels of membrane bound peroxins, while the soluble receptors Pex5 and Pex7 were enhanced at the protein level. In addition, we observed alterations in non-peroxisomal transcripts and proteins, especially mitochondrial proteins involved in respiration or import processes. Our results not only reveal the impact of the absence of peroxisomes in yeast, but also represent a rich resource of candidate genes/proteins that are relevant in peroxisome biology. SummaryOmics comparison of wild-type and peroxisome-deficient (pex3) yeast cells uncovered processes that are affected by loss of peroxisomes. {beta}-oxidation enzymes were upregulated, whereas most peroxins were decreased. Also, several non-peroxisomal transcripts/proteins were significantly altered. Our data represent a rich source of candidate genes connected to peroxisome biology.

cell biology↗

bioRxiv2023

P-PPI: accurate prediction of peroxisomal protein-protein interactions (P-PPI) using deep learning-based protein sequence embeddings

Protein-protein interactions (PPIs) are crucial for various biological processes, and their prediction is typically accomplished through experimental methods, which can be time-consuming and costly. Computational methods provide a faster and more cost-effective approach, leveraging protein sequences and other data sources to infer PPIs. Deep learning (DL) approaches have shown promising results in various protein-related tasks, including PPI prediction. However, DL-based embeddings are often not thoroughly compared or evaluated against state-of-the-art tools. Additionally, existing PPI predictors incorporate different types of information beyond protein sequence representation, making it important to assess the effectiveness of DL-based embeddings solely relying on protein sequences. In this work, we benchmark and compare commonly used DL-based embeddings for PPI prediction based solely on protein sequence information. We utilize high-quality training data, including experimentally validated negative interactions from the Negatome database. The best model, obtained through double cross-validation and hyperparameter optimization, is selected and evaluated to predict peroxisomal PPIs. The resulting tool, P-PPI, is further enhanced by combining AlphaFold2-Multimer predictions with the P-PPI model, leveraging DL-based embeddings and protein structure predictions for a comprehensive analysis of peroxisomal PPIs. This integrated approach holds significant potential to advance our understanding of complex protein networks and their functions.

bioinformatics↗

bioRxiv2023

PortPred: exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates

The physiology of every living cell is regulated at some level by transporter proteins which constitute a relevant portion of membrane-bound proteins and are involved in the movement of ions, small and macromolecules across bio-membranes. The importance of transporter proteins is unquestionable. The prediction and study of previously unknown transporters can lead to the discovery of new biological pathways, drugs and treatments. Here we present PortPred, a tool to accurately identify transporter proteins and their substrate starting from the protein amino acid sequence. PortPred successfully combines pre-trained deep learning-based protein embeddings and machine learning classification approaches and outperforms other state-of-the-art methods. In addition, we present a comparison of the most promising protein sequence embeddings (Unirep, SeqVec, ProteinBERT, ESM-1b) and their performances for this specific task.

bioinformatics↗

bioRxiv2022

OrganelX Web Server for Sub-Peroxisomal and Sub-Mitochondrial protein localisation

Computational approaches for sub-organelle protein localisation and identification are often neglected while general methods, not suitable for specific use cases, are promoted instead. In particular, organelle-specific research lacks user-friendly and easily accessible computational tools that allow researchers to perform computational analysis before starting time-consuming and expensive wet-lab experiments. We present the Organelx e-Science Web Server which hosts three sequence localisation predictive algorithms: In-Pero and In-Mito for classifying sub-peroxisomal and sub-mitochondrial protein localisations given their FASTA sequences, as well as the Is-PTS1 algorithm for detecting and validating potential peroxisomal proteins carrying a PTS1 signal. These tools can be used for a fast and accurate screening while looking for new peroxisomal and mitochondrial proteins. To our knowledge, this is the only service that provides these functionalities and can fasten the daily research of the peroxisomal science community.

bioinformatics↗

bioRxiv2021

In-Pero: Exploiting deep learning embeddings of protein sequences to predict the localisation of peroxisomal proteins

Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments, but there are no specific tools tailored for the sub-localisation (matrix vs membrane) of peroxisome proteins. We present here In-Pero, a new method for predicting protein sub-peroxisomal cellular localisation. In-Pero combines standard machine learning approaches with recently proposed multi-dimensional deep-learning representations of the protein amino-acid sequence. It showed a classification accuracy above 0.9 in predicting peroxisomal matrix and membrane proteins. The method is trained and tested using a double cross-validation approach on a curated data set comprising 160 peroxisomal proteins with experimental evidence for sub-peroxisomal localisation. We further show that the proposed approach can be easily adapted (In-Mito) to the prediction of mitochondrial protein localisation obtaining performances for certain classes of proteins (matrix and inner-membrane) superior to existing tools. All data sets and codes are available at https://github.com/MarcoAnteghini and at www.systemsbiology.nl

bioinformatics↗

bioRxiv2020

Thermodynamics and kinetics of the amyloid-β peptide revealed by Markov state models based on MD data in agreement with experiment

The amlyoid-{beta} peptide (A{beta}) is closely linked to the development of Alzheimers disease. Molecular dynamics (MD) simulations have become an indispensable tool for studying the behavior of this peptide at the (sub)molecular level, thereby providing insight into the molecular basis of Alzheimers disease. General key aspects of MD simulations are the force field used for modeling the peptide or protein and its environment, which is important for accurate modeling of the system of interest, and the length of the simulations, which determines whether or not equilibrium is reached. In this study we address these points by analyzing 30-{micro}s MD simulations acquired for A{beta}40 using seven different force fields. We assess the convergence of these simulations based on the convergence of various structural properties and of NMR and fluorescence spectroscopic observables. Moreover, we calculate Markov state models for each of the seven MD simulations, which provide an unprecedented view of the thermodynamics and kinetics of the amyloid-{beta} peptide. This further allows us to provide answers for pertinent questions, like: Which force fields are suitable for modeling A{beta}? (a99SB-UCB and a99SB-ILDN/TIP4P-D); What does A{beta} peptide really look like? (mostly extended and disordered) and; How long does it take MD simulations of A{beta} to attain equilibrium? (20-30 {micro}s). We believe the analyses presented in this study will provide a useful reference guide for important questions relating to the structure and dynamics of A{beta}in particular, and by extension other similar disordered peptides.

biophysics↗