Structures and diseases

Predicting protein flexibility through the prediction of local structures

Protein structures are valuable tools for understanding protein function. However, protein dynamics is also considered a key element in protein function. Therefore, in addition to structural analysis, fully understanding protein function at the molecular level now requires accounting for flexibility. However, experimental techniques that produce both types of information simultaneously are still limited. Prediction approaches are useful alternative tools for obtaining otherwise unavailable data. It has been shown that protein structure can be described by a limited set of recurring local structures. In this context, we previously established a library composed of 120 overlapping long structural prototypes (LSPs) representing fragments of 11 residues in length and covering all known local protein structures. On the basis of the close sequence-structure relationship observed in LSPs, we developed a novel prediction method that proposes structural candidates in terms of LSPs along a given sequence. The prediction accuracy rate was high given the number of structural classes. In this study, we use this methodology to predict protein flexibility. We first examine flexibility according to two different descriptors, the B-factor and root mean square fluctuations from molecular dynamics simulations. We then show the relevance of using both descriptors together. We define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. The prediction rate reaches 49.6%. This method competes rather efficiently with the most recent, cutting-edge methods based on true flexibility data learning with sophisticated algorithms. Accordingly, flexibility information should be taken into account in structural prediction assessments.

Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins

Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.