Biology subjects

Sacquin-Mora, S.

Publications and source records attributed to Sacquin-Mora, S..

3 recordsLinked to original sources

bioRxiv2018

Protein interaction energy landscapes are shaped by functional and also non-functional partners

In the crowded cell, a strong selective pressure operates on the proteome to limit the competition between functional and non-functional protein-protein interactions. We developed an original theoretical framework in order to interrogate how this competition constrains the behavior of proteins with respect to their partners or random encounters. Our theoretical framework relies on a two-dimensional (2D) representation of interaction energy landscapes with 2D energy maps that reflect in a synthetic way the propensity of a protein to interact with another protein. We investigated the propensity of protein surfaces to interact with functional and arbitrary partners and asked whether their interaction propensity is conserved during the evolution. Therefore, we performed several thousands of cross-docking simulations to systematically characterize the whole energy landscapes of 74 proteins interacting with different sets of homologs, corresponding to their functional partners family or arbitrary protein families. Then, we systematically compared the energy maps resulting from the docking of a given protein with the different protein families of the dataset. Strikingly, we show that the interaction propensity not only of the binding site but also of the rest of the protein surface is conserved for docking partners belonging to the same protein family. Interestingly, this observation holds for docked proteins corresponding to true but also to arbitrary partners. Our theoretical framework enables the characterization of the energy behavior of a protein in interaction with hundreds of selected partners and opens the way for further developments to study the behavior of proteins in a specific environment.

bioinformatics↗

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.

bioinformatics↗

bioRxiv2017

Mechanical changes in proteins with large-scale motions highlight the formation of structural locks

Protein function depends just as much on flexibility as on structure, and in numerous cases, a proteins biological activity involves transitions that will impact both its conformation and its mechanical properties. Here, we use a coarse-grain approach to investigate the impact of structural changes on protein flexibility. More particularly, we focus our study on proteins presenting large-scale motions. We show how calculating directional force constants within residue pairs, and investigating their variation upon protein closure, can lead to the detection of a limited set of residues that form a structural lock in the proteins closed conformation. This lock, which is composed of residues whose side-chains are tightly interacting, highlights a new class of residues that are important for protein function by stabilizing the closed structure, and that cannot be detected using earlier tools like local rigidity profiles or distance variations maps, or alternative bioinformatics approaches, such as coevolution scores.

biophysics↗