Huntingtin: A protein with a peculiar solvent accessible surface

Taking advantage of the last cryogenic electron microscopy structure of human hunt-ingtin, we explored with computational methods its physicochemical properties, focusing on the solvent accessible surface of the protein and highlighting a quite interesting mix of hydrophobic and hydrophilic patterns, with the prevalence of the latter ones. We then evaluated the probability of exposed residues to be in contact with other proteins, discovering that they tend to cluster in specific regions of the protein. We then found that the remaining portions of the protein surface can contain calcium-binding sites that we propose here as putative mediators for the protein to interact with membranes. Our findings are justified in relation to the present knowledge of huntingtin functional annotation

Finding functional motifs in protein sequences with deep learning and natural language models

Recently, prediction of structural/functional motifs in protein sequences takes advantage of powerful machine learning based approaches. Protein encoding adopts protein language models overpassing standard procedures. Different combinations of machine learning and encoding schemas are available for predicting different structural/functional motifs. Particularly interesting is the adoption of protein language models to encode proteins in addition to evolution information and physicochemical parameters. A thorough analysis of recent predictors developed for annotating transmembrane regions, sorting signals, lipidation and phosphorylation sites allows to investigate the state-of-the-art focusing on the relevance of protein language models for the different tasks. This highlights that more experimental data are necessary to exploit available powerful machine learning methods

Large-scale prediction and analysis of protein sub-mitochondrial localization with DeepMito

Background: The prediction of protein subcellular localization is a key step of the big effort towards protein functional annotation. Many computational methods exist to identify high-level protein subcellular compartments such as nucleus, cytoplasm or organelles. However, many organelles, like mitochondria, have their own internal compartmentalization. Knowing the precise location of a protein inside mitochondria is crucial for its accurate functional characterization. We recently developed DeepMito, a new method based on a 1-Dimensional Convolutional Neural Network (1D-CNN) architecture outperforming other similar approaches available in literature. Results: Here, we explore the adoption of DeepMito for the large-scale annotation of four sub-mitochondrial localizations on mitochondrial proteomes of five different species, including human, mouse, fly, yeast and Arabidopsis thaliana. A significant fraction of the proteins from these organisms lacked experimental information about sub-mitochondrial localization. We adopted DeepMito to fill the gap, providing complete characterization of protein localization at sub-mitochondrial level for each protein of the five proteomes. Moreover, we identified novel mitochondrial proteins fishing on the set of proteins lacking any subcellular localization annotation using available state-of-the-art subcellular localization predictors. We finally performed additional functional characterization of proteins predicted by DeepMito as localized into the four different sub-mitochondrial compartments using both available experimental and predicted GO terms. All data generated in this study were collected into a database called DeepMitoDB (available at http://busca.biocomp.unibo.it/deepmitodb), providing complete functional characterization of 4307 mitochondrial proteins from the five species. Conclusions: DeepMitoDB offers a comprehensive view of mitochondrial proteins, including experimental and predicted fine-grain sub-cellular localization and annotated and predicted functional annotations. The database complements other similar resources providing characterization of new proteins. Furthermore, it is also unique in including localization information at the sub-mitochondrial level. For this reason, we believe that DeepMitoDB can be a valuable resource for mitochondrial research

DeepREx-WS: A web server for characterising protein–solvent interaction starting from sequence

Protein–solvent interaction provides important features for protein surface engineering when the structure is absent or partially solved. Presently, we can integrate the notion of solvent exposed/buried residues with that of their flexibility and intrinsic disorder to highlight regions where mutations may increase or decrease protein stability in order to modify proteins for biotechnological reasons, while preserving their functional integrity. Here we describe a web server, which provides the unique possibility of integrating knowledge of solvent and non-solvent exposure with that of residue conservation, flexibility and disorder of a protein sequence, for a better understanding of which regions are relevant for protein integrity. The core of the webserver is DeepREx, a novel deep learning-based tool that classifies each residue in the sequence as buried or exposed. DeepREx is trained on a high-quality, non-redundant dataset derived from the Protein Data Bank comprising 2332 monomeric protein chains and benchmarked on a blind test set including 200 protein sequences unrelated with the training set. Results show that DeepREx performs at the state-of-the-art in the field. In turn, the Web Server, DeepREx-WS, supplements the predictions of DeepREx with features that allow a better characterisation of exposed and buried regions: i) residue conservation derived from multiple sequence alignment; ii) local sequence hydrophobicity; iii) residue flexibility computed with MEDUSA; iv) a predictor of secondary structure; v) the presence of disordered regions as derived from MobiDB-Lite3.0. The web server allows browsing, selecting and intersecting the different features. We demonstrate a possible application of the DeepREx-WS for assisting the identification of residues to be variated in protein surface engineering processes

Large-scale annotation of proteins with labelling methods

We revise a major important problem in bioinformatics: how to annotate protein sequences in the genomic era and all the solutions that have been described by implementing tools based on labelling methods. In this paper we mainly focus on our own work and the theoretical methods that are popular in the field of biosequence analysis in modern molecular biology. We will also review a recent application from our group that largely improves on the topology prediction of disulfide bonds in proteins from Eukaryotic organisms

CoCoNat: a novel method based on deep learning for coiled-coil prediction

MOTIVATION: Coiled-coil domains (CCD) are widespread in all organisms and perform several crucial functions. Given their relevance, the computational detection of CCD is very important for protein functional annotation. State-of-the-art prediction methods include the precise identification of CCD boundaries, the annotation of the typical heptad repeat pattern along the coiled-coil helices as well as the prediction of the oligomerization state. RESULTS: In this article, we describe CoCoNat, a novel method for predicting coiled-coil helix boundaries, residue-level register annotation, and oligomerization state. Our method encodes sequences with the combination of two state-of-the-art protein language models and implements a three-step deep learning procedure concatenated with a Grammatical-Restrained Hidden Conditional Random Field for CCD identification and refinement. A final neural network predicts the oligomerization state. When tested on a blind test set routinely adopted, CoCoNat obtains a performance superior to the current state-of-the-art both for residue-level and segment-level CCD. CoCoNat significantly outperforms the most recent state-of-the-art methods on register annotation and prediction of oligomerization states. AVAILABILITY AND IMPLEMENTATION: CoCoNat web server is available at https://coconat.biocomp.unibo.it. Standalone version is available on GitHub at https://github.com/BolognaBiocomp/coconat

A glance into mthfr deficiency at a molecular level

MTHFR deficiency still deserves an investigation to associate the phenotype to protein structure variations. To this aim, considering the MTHFR wild type protein structure, with a catalytic and a regulatory domain and taking advantage of state‐of‐the‐art computational tools, we explore the properties of 72 missense variations known to be disease associated. By computing the thermodynamic ΔΔG change according to a consensus method that we recently introduced, we find that 61% of the disease‐related variations destabilize the protein, are present both in the catalytic and regulatory domain and correspond to known biochemical deficiencies. The propensity of solvent accessible residues to be involved in protein‐protein interaction sites indicates that most of the interacting residues are located in the regulatory domain, and that only three of them, located at the interface of the functional protein homodimer, are both disease‐related and destabilizing. Finally, we compute the protein architecture with Hidden Markov Models, one from Pfam for the catalytic domain and the second computed in house for the regulatory domain. We show that patterns of disease‐associated, physicochemical variation types, both in the catalytic and regulatory domains, are unique for the MTHFR deficiency when mapped into the protein architecture

Comparative genomics of Hox and ParaHox genes among major lineages of Branchiopoda with emphasis on tadpole shrimps

Hox and ParaHox genes (HPHGs) are key developmental genes that pattern regional identity along the anterior–posterior body axis of most animals. Here, we identified HPHGs in tadpole shrimps (Pancrustacea, Branchiopoda, Notostraca), an iconic example of the so-called “living fossils” and performed a comparative genomics analysis of HPHGs and the Hox cluster among major branchiopod lineages. Notostraca possess the entire Hox complement, and the Hox cluster seems to be split into two different subclusters, although we were not able to support this finding with chromosome-level assemblies. However, the genomic structure of Hox genes in Notostraca appears more derived than that of Daphnia spp., which instead retains the plesiomorphic condition of a single compact cluster. Spinicaudata and Artemia franciscana show instead a Hox cluster subdivided across two or more genomic scaffolds with some orthologs either duplicated or missing. Yet, branchiopod HPHGs are similar among the various clades in terms of both intron length and number, as well as in their pattern of molecular evolution. Sequence substitution rates are in fact generally similar for most of the branchiopod Hox genes and the few differences we found cannot be traced back to natural selection, as they are not associated with any signals of diversifying selection or substantial switches in selective modes. Altogether, these findings do not support a significant stasis in the Notostraca Hox cluster and further confirm how morphological evolution is not tightly associated with genome dynamics

Comparative genomics of tadpole shrimps (Crustacea, Branchiopoda, Notostraca): Dynamic genome evolution against the backdrop of morphological stasis

This analysis presents five genome assemblies of four Notostraca taxa. Notostraca origin dates to the Permian/Upper Devonian and the extant forms show a striking morphological similarity to fossil taxa. The comparison of sequenced genomes with other Branchiopoda genomes shows that, despite the morphological stasis, Notostraca share a dynamic genome evolution with high turnover for gene families' expansion/contraction and a transposable elements content comparable to other branchiopods. While Notostraca substitutions rate appears similar or lower in comparison to other branchiopods, a subset of genes shows a faster evolutionary pace, highlighting the difficulty of generalizing about genomic stasis versus dynamism. Moreover, we found that the variation of Triops cancriformis transposable elements content appeared linked to reproductive strategies, in line with theoretical expectations. Overall, besides providing new genomic resources for the study of these organisms, which appear relevant for their ecology and evolution, we also confirmed the decoupling of morphological and molecular evolution