Biology subjects

Carbone, A.

Publications and source records attributed to Carbone, A..

27 records · Page 2Linked to original sources

bioRxiv2021

From complete cross-docking to partners identification and binding sites predictions

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. We applied it to a few hundred of proteins, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.

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bioRxiv2020

Predicting substitutions to modulate disorder and stability in coiled-coils

Coiled-coils are described as stable structural motifs, where two or more helices wind around each other. However, coiled-coils are associated with local mobility and intrinsic disorder. Intrinsically disordered regions (IDRs) in proteins are characterized by lack of stable secondary and tertiary structure under physiological conditions in vitro. They are increasingly recognized as important for protein function. However, characterizing their behaviour in solution and determining precisely the extent of disorder of a protein region remains challenging, both experimentally and computationally. In this work, we propose a computational framework to quantify the extent of disorder within a coiled-coil in solution and to help design substitutions modulating such disorder. Our method relies on the analysis of conformational ensembles generated by relatively short all-atom Molecular Dynamics (MD) simulations. We apply it to the phosphoprotein multimerisation domains (PMD) of Measles virus (MeV) and Nipah virus (NiV), both forming tetrameric left-handed coiled-coils. We show that our method can help quantify the extent of disorder of the C-terminus region of MeV and NiV PMDs, without requiring the input MD trajectory to actually sample the unfolded states of these regions. Moreover, this study provided a conceptual framework for the rational design of substitutions aimed at modulating the stability of the coiled-coils. By assessing the impact of four substitutions known to destabilize coiled-coils, we derive a set of rules to control MeV PMD structural stability and cohesiveness. We therefore design two contrasting substitutions, one increasing the stability of the tetramer and the other increasing its flexibility. Consequently, our method can be considered as a platform to reason about how to design substitutions aimed at regulating flexibility and stability.

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bioRxiv2019

Disorder and interfaces in proteins are two sides of the same coin

The importance of unstructured biology has quickly grown during the last decades accompanying the explosion of the number of experimentally resolved structures. The idea that structural disorder might be a novel mechanism of protein interaction is widespread in the literature, although the number of statistically significant structural studies supporting this idea is surprisingly low. In this work, through a large-scale-analysis of all the crystallographic structures of the Protein Data Bank averaged over clusters of homologous sequences, we show clear evidences that both the (experimentally verified) interaction interfaces and the disordered regions are involving roughly the same amino-acids of the protein. And beyond, disordered regions appear to carry information about the location of alternative interfaces when the protein lies within complexes, thus playing an important role in determining the order of assembly of protein complexes.

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bioRxiv2019

Phylogenetic reconstruction based on synteny block and gene adjacencies

Gene order can be used as an informative character to reconstruct phylogenetic relationships-between species independently from the local information present in gene/protein sequences. PhyChro is a reconstruction method based on chromosomal rearrangements, applicable to a wide range of eukaryotic genomes with different gene contents and levels of synteny conservation. For each synteny breakpoint issued from pairwise genome comparisons, the algorithm defines two disjoint sets of genomes, named partial splits, respectively supporting the two block adjacencies defining the breakpoint. Considering all partial splits issued from all pairwise comparisons, a distance between two genomes is computed from the number of partial splits separating them. Tree reconstruction is achieved through a bottom-up approach by iteratively grouping sister genomes minimizing genome distances. PhyChro estimates branch lengths based on the number of synteny breakpoints and provides confidence scores for the branches. PhyChro performance isevaluatedon two datasets of 13 vertebrates and 21 yeast genomes by using up to 130 000 and 179 000 breakpoints respectively, a scale of genomic markers that has been out of reach until now. PhyChro reconstructs very accurate tree topologies even at known problematic branching positions. Its robustness has been benchmarked for different synteny block reconstruction methods. On simulated data PhyChro reconstructs phylogenies perfectly in almost all cases, and shows the highest accuracy compared to other existing tools. PhyChro is very fast, reconstructing the vertebrate and yeast phylogenies in less than 15 min. AvailabilityPhyChro will be freely available under the BSD license after publication Contactalessandra.carbone@lip6.fr

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bioRxiv2019

Multiple protein-DNA interfaces unravelled by evolutionary information, physico-chemical and geometrical properties

The usage made of protein surfaces by nucleic acids still remains largely unknown, due to the lack of available structural data and the inherent complexity associated to protein surface deformability and evolution. In this work, we present a method that contributes to decipher such complexity by predicting protein-DNA interfaces and characterizing their properties. It relies on three biologically and physically meaningful descriptors, namely evolutionary conservation, physico-chemical properties and surface geometry. We carefully assessed its performance on several hundreds of protein structures. We achieve a higher sensitivity compared to state-of-the-art methods, and similar precision. Importantly, we show that our method is able to unravel hidden binding sites by applying it to unbound protein structures and to proteins binding to DNA via multiple sites and in different conformations. It is implemented as a fully automated tool, [Formula], freely accessible at: http://www.lcqb.upmc.fr/JET2DNA. We also provide a new reference dataset of 187 protein-DNA complex structures, representative of all types of protein-DNA interactions, along with a subset of associated unbound structures: http://www.lcqb.upmc.fr/PDNAbenchmarks.

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bioRxiv2019

ProfileView: multiple probabilistic models resolve protein families functional diversity

Sequence functional classification has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple probabilistic models whose construction explores evolutionary information in available databases, and a new definition of a representation space where to look at sequences from the point of view of probabilistic models combined together. ProfileView classifies families of proteins for which functions should be discovered or characterised within known groups. We validate ProfileView on seven classes of widespread proteins, involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. ProfileView agrees with the large set of functional data collected for these proteins from the literature regarding the organisation into functional subgroups and residues that characterize the functions. Furthermore, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of new biological functions. ProfileView proves to outperform three functional classification approaches, CUPP, PANTHER, and a recently developed neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter.

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bioRxiv2019

GEMME: a simple and fast global epistatic model predicting mutational effects

AbstractsThe systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modeling inter-site dependencies within biological sequences. However, state-of-the-art methods require numerous highly variable sequences and remain time consuming. Here, we present GEMME (www.lcqb.upmc.fr/GEMME), a method that overcomes these limitations by explicitly modeling the evolutionary history of natural sequences. This allows accounting for all positions in a sequence when estimating the effect of a given mutation. Assessed against 41 experimental high-throughput mutational scans, GEMME overall performs similarly or better than existing methods and runs faster by several orders of magnitude. It greatly improves predictions for viral sequences and, more generally, for very conserved families. It uses only a few biologically meaningful and interpretable parameters, while existing methods work with hundreds of thousands of parameters.

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bioRxiv2018

Decrypting protein surfaces by combining evolution, geometry and molecular docking

The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a proteins surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physico-chemical and geometrical properties of the protein surface with information coming from complete cross-docking (CC-D) simulations. We show that our predictions match well interacting regions and that the different sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET2 algorithm and assessed on a new dataset of 262 protein on which we performed CC-D. The code and the data are available at: http://www.lcqb.upmc.fr/dynJET2/.\n\nAuthor summaryThe multiplicity and versatility of protein interactions make protein surfaces complex biological objects. For instance, a protein may interact with several partners at different moments via the same region at its surface, or it may use several regions, with different shapes and evolutionary origins, to establish different types of interactions. In addition, interfaces can re-adjust depending on environmental conditions. In this work, we introduce the notion of interaction region\", in contrast to interaction site\", which accounts for the interface variability coming from molecular flexibility and for the multiple usage of the protein surface by several partners. Moreover, we use four biologically meaningful descriptors to delineate protein surface patches. Three of them, namely evolutionary conservation, physico-chemical composition and local geometry, are computed for a given protein, without any knowledge on its potential partners. The fourth property is inferred from the behavior of the protein with respect to other proteins, partners or not, in docking simulations. We show that predicted patches match well with known interacting regions, that the four descriptors are complementary and that they enable capturing most of the signal relevant to protein interactions. We further exploit the way patches are grown to precisely localize interaction regions, and to estimate whether a protein have a few or many partners.

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bioRxiv2018

Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions

Protein-protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein-protein interactions. Cross-docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross-docking simulations of 358 proteins with two different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity-sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, i.e. partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.\n\nAbbreviationsANOVA: ANalysis Of Variance; AUC: Area Under the Curve; Best Interface: BI; CAPRI: Critical Assessment of Prediction of Interactions; CC-D: Complete Cross-Docking; DNA: DesoxyriboNucleic Acid; FDR: False Discovery Rate; FRIres(type): Fraction of each Residue type in the Interface; FP: False Positives; GI: Global Interface; HCMD: Help Cure Muscular Dystrophy; JET: Joint Evolutionary Tree; MAXDo: Molecular Association via Cross Docking; NAI: Nucleic Acid Interface; NPV: Negative Predicted Value; PDB: Protein Data Bank; PIP: Protein Interface Propensity; PiQSi: Protein Quaternary Structure investigation; PPIs: Protein-Protein Interactions; PPV: Positive Predicted Value; Prec.: Precision; PrimI: Primary Interface; RNA: RiboNucleic Acid; ROC: Receiver Operating Characteristic; SecI: Secondary Interface; Sen.: Sensitivity; Spe.: Specificity; TN: True Negatives; TP: True Positives; WCG: World Community Grid.

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