Biology subjects

Monzon, A. M.

Publications and source records attributed to Monzon, A. M..

11 recordsLinked to original sources

bioRxiv2025

AlphaFlex: Ensembles of the human proteome representing disordered regions

More than two thirds of proteins in the human proteome are predicted to contain intrinsically disordered regions (IDRs), which lack stable folded structure. IDRs are critical for biological regulation and organization, as targets for post-translational modifications, and as mediators of biomolecular condensates. To address the pressing need for better structural models enabling functional insight, we developed AlphaFlex to model fully atomistic conformer ensembles for proteins predicted to have IDRs, modeled in the context of AlphaFold folded domains and an implicit bilayer for transmembrane proteins. The AlphaFlex resource provides conformational ensembles of human proteins from the AlphaFold database with identified IDRs in the Protein Ensemble Database that is mirrored in UniProt. This transformative resource of AlphaFlex ensembles provides physically and biologically relevant full-length models for IDR proteins, including scaffold proteins, those with IDR:folded-domain interactions, regulatory and condensate proteins requiring exposed binding elements, conditionally folding IDRs, and transmembrane proteins containing IDRs.

biochemistry↗

bioRxiv2025

GeomeTRe: accurate calculation of geometrical descriptors of tandem repeat proteins

Structured tandem repeat proteins (STRPs) are characterized by preserved structural motifs arranged in a modular way. The structural and functional diversity of STRPs makes them particularly important for studying evolution, novel structure and function relationships, and ultimately for designing new synthetic proteins with specific functions. One crucial aspect of their classification is the estimation of geometrical parameters, which can provide better insight into their properties and the relationship between the spatial arrangement of repeated units and protein function. Calculating geometric descriptors for STRPs is challenging because naturally occurring repeats are not perfect and often contain insertions and deletions. Existing tools for predicting structural symmetry work well on simple cases but often fail for most natural proteins. Here we present GeomeTRe, an algorithm that calculates geometrical descriptors such as curvature (yaw), twist (roll), and pitch for a protein structure with known repeat unit positions. The algorithm simulates the movement of consecutive units, identifies rotational axes, and calculates the corresponding Tait-Bryan angles. The parameters of GeomeTRe can enhance STRP annotation and classification by identifying variations in geometric arrangements among different functional groups. The package is fast and suitable for processing large protein structure datasets when repeat region information (e.g., from RepeatsDB) is available. GeomeTRe is available as a Python package; source code and documentation can be found at https://github.com/BioComputingUP/GeomeTRe

bioinformatics↗

bioRxiv2024

STRPsearch: fast detection of structured tandem repeat proteins

MotivationState-of-the-art prediction methods are generating millions of publicly available protein structures. Structured Tandem Repeats Proteins (STRPs) constitute a subclass of tandem repeats characterized by repetitive structural motifs. STRPs exhibit distinct propensities for secondary structure and form regular tertiary structures, often comprising large molecular assemblies. They can perform important and diverse biological functions due to their highly degenerated sequences, which maintain a similar structure while displaying a variable number of repeat units. This suggests a disconnection between structural size and protein function. However, automatic detection of STRPs remains challenging with current state-of-the-art tools due to their lack of accuracy and long execution times, hindering their application on large datasets. In most cases, manual curation is the most accurate method for detecting and classifying them, making it impossible to inspect millions of structures. ResultsWe present STRPsearch, a novel computational tool for rapid identification, classification, and mapping of STRPs. Leveraging the manually curated entries in RepeatsDB as the known conformational space of the STRPs, STRPsearch utilizes the latest advancements in structural alignment techniques for a fast and accurate detection of repeated structural motifs in protein structures, followed by an innovative approach to map units and insertions through the generation of TM-score graphs. STRPsearch can serve researchers in structural bioinformatics and protein science as an efficient and practical tool for analysis and detection of STRPs. Availability and implementationSTRPsearch is coded in Python, all the scripts and the associated documentation are available at https://github.com/BioComputingUP/STRPsearch. Contactalexander.monzon@unipd.it, silvio.tosatto@unipd.it Supplementary informationSupplementary data are available..

bioinformatics↗

bioRxiv2022

Intrinsic Protein Disorder, Conditional Folding and AlphaFold2

Intrinsically disordered regions (IDRs) defying the traditional protein structure-function paradigm have been difficult to analyze. AlphaFolds recent breakthrough in predicting protein structures accurately offers a fresh perspective on IDR prediction as assessed on the CAID dataset. Surprisingly, AlphaFold is highly competitive for predicting both IDRs and conditionally folded regions, demonstrating the plasticity of the disorder to structure continuum.

bioinformatics↗

bioRxiv2022

CoDNaS-Q: a database of conformational diversity of the native state of proteins with quaternary structure

SummaryA collection of conformers that exist in a dynamical equilibrium defines the native state of a protein. The structural differences between them describe their conformational diversity, a defining characteristic of the protein with an essential role in multiple cellular processes. Since most proteins carry out their functions by assembling into complexes, we have developed CoDNaS-Q, the first online resource to explore conformational diversity in homooligomeric proteins. It features a curated collection of redundant protein structures with known quaternary structure. CoDNaS-Q integrates relevant annotations that allow researchers to identify and explore the extent and possible reasons of conformational diversity in homooligomeric protein complexes. Availability and implementationCoDNaS-Q is freely accessible at http://ufq.unq.edu.ar/codnasq/. The data can be retrieved from the website. The source code of the database can be downloaded from https://github.com/SfrRonaldo/codnas-q. Contactnpalopoli@unq.edu.ar

bioinformatics↗

bioRxiv2021

A novel splice variant of human TGF-β type II receptor encodes a soluble protein and its Fc-tagged version prevents liver fibrosis in vivo.

We describe, for the first time, a new splice variant of the human TGF-{beta} type II receptor (T{beta}RII). The new transcript lacks 149 nucleotides, causing a frameshift with the appearance of an early stop codon, rendering a truncated mature protein of 57 amino acids. The predicted protein, lacking the transmembrane domain and with a distinctive 13 amino acid stretch in the C-terminus, was named T{beta}RII-Soluble Endogenous (T{beta}RII-SE). Binding predictions indicated that the novel 13 amino acid stretch interacts with all three TGF-{beta} cognate ligands and generate a more extensive protein-protein interface than T{beta}RII. T{beta}RII-SE and human IgG1 Fc-domain, were fused in frame in a lentiviral vector (Lv) for further characterization. With this vector, we transduced 293T cells and purified T{beta}RII-SE/Fc by A/G protein chromatography from conditioned medium. Immunoblotting revealed homogeneous bands of approximately 37 kDa (reduced) and 75 kD (non-reduced), indicating that T{beta}RII-SE/Fc is secreted as a disulphide-linked homodimer. Moreover, high affinity binding of T{beta}RII-SE to the three TGF-{beta} isoforms was confirmed by Surface Plasmon Resonance (SPR) analysis. Also, intrahepatic delivery of Lv.T{beta}RII-SE/Fc in a carbon tetrachloride-induced liver fibrosis model revealed amelioration of liver injury and fibrosis. Our results indicate that T{beta}RII-SE is a novel member of the TGF-{beta} signaling pathway with distinctive characteristics. This novel protein offers an alternative for the prevention and treatment of pathologies caused by the overproduction of TGF-{beta} ligands.

cell biology↗

bioRxiv2020

Intrinsically disordered protein ensembles shape evolutionary rates revealing conformational patterns

Intrinsically disordered proteins (IDPs) lack stable tertiary structure under physiological conditions. The unique composition and complex dynamical behaviour of IDPs make them a challenge for structural biology and molecular evolution studies. Using NMR ensembles, we found that IDPs evolve under a strong site-specific evolutionary rate heterogeneity, mainly originated by different constraints derived from their inter-residue contacts. Evolutionary rate profiles correlate with the experimentally observed conformational diversity of the protein, allowing the description of different conformational patterns possibly related to their structure-function relationships. The correlation between evolutionary rates and contact information improves when structural information is taken not from any individual conformer or the whole ensemble, but from combining a limited number of conformers. Our results suggest that residue contacts in disordered regions constrain evolutionary rates to conserve the dynamic behaviour of the ensemble and that evolutionary rates can be used as a proxy for the conformational diversity of IDPs. Significance StatementIntrinsically disordered proteins (IDPs) challenge the structure-function relationship paradigm. In this work we found that individual sites of IDPs evolve under a strong rate heterogeneity, mainly due to the structural constraints imposed by contacts between their residues. This can be better explained if the contacts are taken from selected subsets of their alternative native conformations, rather than from individual conformations or the whole native ensemble. From an evolutionary point of view, this result indicates that experimentally-based ensembles are redundant. We also observed that the evolutionary rates follow the structural variability between conformers, unveiling conformational preferences. Our results set the stage for establishing novel evolutionary-based methods to study IDP ensembles.

bioinformatics↗

bioRxiv2020

Experimentally determined long intrinsically disordered protein regions are now abundant in the Protein Data Bank

Intrinsically disordered protein regions are commonly defined from missing electron density in X-ray structures. Experimental evidence for long disorder regions (LDRs) of at least 30 residues was so far limited to a less than a thousand manually curated proteins. Here, we describe a comprehensive and large-scale analysis of experimental LDRs for 3,133 unique proteins, demonstrating an increasing coverage of intrinsic disorder in the Protein Data Bank (PDB) in the last decade. The results suggest that long missing residue regions are a good quality source to annotate intrinsically disordered regions and perform functional analysis in large data sets. The consensus approach used to define LDRs allows to evaluate context dependent disorder and provide a common definition at the protein level.

bioinformatics↗

bioRxiv2020

Assessing predictors for new post translational modification sites: a case study on hydroxylation

Post-translational modification (PTM) sites have become popular for predictor development. However, with the exception of phosphorylation and a handful of other examples, PTMs suffer from a limited number of available training examples and their sparsity in protein sequences. Here, proline hydroxylation is taken as an example to compare different methods and evaluate their performance on new experimentally determined sites. As a proxy for an effective experimental design, predictors require both high specificity and sensitivity. However, the self-reported performance is often not indicative of prediction quality and detection of new sites is not guaranteed. We have benchmarked seven published hydroxylation site predictors on two newly constructed independent datasets. The self-reported performance widely overestimates the real accuracy measured on independent datasets. No predictor performs better than random on new examples, indicating the refined models are not sufficiently general to detect new sites. The number of false positives is high and precision low, in particular for non-collagen proteins whose motifs are not conserved. In short, existing predictors for hydroxylation sites do not appear to generalize to new data. Caution is advised when dealing with PTM predictors in the absence of independent evaluations, in particular for unique specific sites such as those involved in signalling. Author SummaryMachine learning methods are extensively used by biologists to design and interpret experiments. Predictors which take the only sequence as input are of particular interest due to the large amount of sequence data available and self-reported performance is often very high. In this work, we evaluated post-translational modification (PTM) predictors for hydroxylation sites and found that they perform no better than random, in strong contrast to performances reported in the original publications. PTMs are chemical amino acids alterations providing the cell with conditional mechanisms to fine tune protein function, thereby regulating complex biological processes such as signalling and cell cycle. Hydroxylation sites are a good PTM test case due to the availability of a range of predictors and an abundance of newly experimentally detected modification sites. Poor performances in our results highlight the overlooked problem of predicting PTMs when best practices are not followed and training data are likely incomplete. Experimentalists should be careful when using PTM predictors blindly and more independent assessments are needed to separate the wheat from the chaff in the field.

bioinformatics↗

bioRxiv2017

How is structural divergence related to evolutionary information?

Conservation and covariation measures, as other evolutionary analysis, require a high number of distant homologous sequences, therefore a lot of structural divergence can be expected in such divergent alignments. However, most works linking evolutionary and structural information use a single structure ignoring the structural variability inside a protein family. That common practice seems unrealistic to the light of this work.\n\nIn this work we studied how structural divergence affects conservation and covariation estimations. We uncover that, within a protein family, ~51% of multiple sequence alignment columns change their exposed/buried status between structures. Also, ~53% of residue pairs that are in contact in one structure are not in contact in another structure from the same family. We found out that residue conservation is not directly related to the relative solvent accessible surface area of a single protein structure. Using information from all the available structures rather than from a single representative structure gives more confidence in the structural interpretation of the evolutionary signals. That is particularly important for diverse multiple sequence alignments, where structures can drastically differ. High covariation scores tend to indicate residue contacts that are conserved in the family, therefore, are not suitable to find protein/conformer specific contacts.\n\nOur results suggest that structural divergence should be considered for a better understanding of protein function, to transfer annotation by homology and to model protein evolution.

bioinformatics↗

bioRxiv2017

Homology Modeling In A Dynamical World

A key concept in template-based modeling is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity.\n\nIn this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair Template-based modelling (TMB) results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.\n\nAuthor summaryTemplate-based modelling (TBM) is the most reliable and fastest approach to obtain protein structural models. TBM relies in the high correlation between sequence and structural divergence, with the practical consequence that proteins that are similar at the sequence level will also be similar at the structural level, allowing in this way the selection of the better template to obtain the 3D model of the target sequence. However, protein native state could be described by a collection of conformers in equilibrium where their structural differences are called conformational diversity.\n\nIn this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We firstly found that the extent of conformational diversity can be as high as the maximum structural differences reached by families differing in their sequences. In these proteins with higher conformational diversity levels, the well-established correlation between sequence and structural divergence is nosier than previously suggested due to the presence of structural change without sequence variation. This lack of correlation could impair TBM results due to the uncertainty in the correct template selection. Finally, we also found that the presence of order/disorder can provide useful beforehand information for better TBM performance.

bioinformatics↗