Does structural and chemical divergence play a role in precluding undesirable protein interactions?

The intrinsically disordered regions of the Drosophila melanogaster Hox protein ultrabithorax select interacting proteins based on partner topology

Interactions between structured proteins require a complementary topology and surface chemistry to form sufficient contacts for stable binding. However, approximately one third of protein interactions are estimated to involve intrinsically disordered regions of proteins. The dynamic nature of disordered regions before and, in some cases, after binding calls into question the role of partner topology in forming protein interactions. To understand how intrinsically disordered proteins identify the correct interacting partner proteins, we evaluated interactions formed by the Drosophila melanogaster Hox transcription factor Ultrabithorax (Ubx), which contains both structured and disordered regions. Ubx binding proteins are enriched in specific folds: 23 of its 39 partners include one of 7 folds, out of the 1195 folds recognized by SCOP. For the proteins harboring the two most populated folds, DNA-RNA binding 3-helical bundles and α-α superhelices, the regions of the partner proteins that exhibit these preferred folds are sufficient for Ubx binding. Three disorder-containing regions in Ubx are required to bind these partners. These regions are either alternatively spliced or multiply phosphorylated, providing a mechanism for cellular processes to regulate Ubx-partner interactions. Indeed, partner topology correlates with the ability of individual partner proteins to bind Ubx spliceoforms. Partners bind different disordered regions within Ubx to varying extents, creating the potential for competition between partners and cooperative binding by partners. The ability of partners to bind regions of Ubx that activate transcription and regulate DNA binding provides a mechanism for partners to modulate transcription regulation by Ubx, and suggests that one role of disorder in Ubx is to coordinate multiple molecular functions in response to tissue-specific cues.

Quantitative expression of protein heterogeneity: Response of amino acid side chains to their local environment

A general method has been developed to characterize the hydrophobicity or hydrophilicity of the microenvironment (MENV), in which a given amino acid side chain is immersed, by calculating a quantitative property descriptor (QPD) based on the relative (to water) hydrophobicity of the MENV. Values of the QPD were calculated for a test set of 733 proteins to analyze the modulating effects on amino acid residue properties by the MENV in which they are imbedded. The QPD values and solvent accessibility were used to derive a partitioning of residues based on the MENV hydrophobicities. From this partitioning, a new hydrophobicity scale was developed, entirely in the context of protein structure, where amino acid residues are immersed in one or more "MENVpockets." Thus, the partitioning is based on the residues "sampling" a large number of "solvents" (MENVs) that represent a very large range of hydrophobicity values. It was found that the hydrophobicity of around 80% of amino acid side chains and their MENV are complementary to each other, but for about 20%, the MENV and their imbedded residue can be considered as mismatched. Many of these mismatches could be rationalized in terms of the structural stability of the protein and/or the involvement of the imbedded residue in function. The analysis also indicated a remarkable conservation of local environments around highly conserved active site residues that have similar functions across protein families, but where members have relatively low sequence homology. Thus, quantitative evaluation of this QPD is suggested, here, as a tool for structure-function prediction, analysis, and parameter development for the calculation of properties in proteins.

Coevolution at protein complex interfaces can be detected by the complementarity trace with important impact for predictive docking

Protein surfaces are under significant selection pressure to maintain interactions with their partners throughout evolution. Capturing how selection pressure acts at the interfaces of protein-protein complexes is a fundamental issue with high interest for the structural prediction of macromolecular assemblies. We tackled this issue under the assumption that, throughout evolution, mutations should minimally disrupt the physicochemical compatibility between specific clusters of interacting residues. This constraint drove the development of the so-called Surface COmplementarity Trace in Complex History score (SCOTCH), which was found to discriminate with high efficiency the structure of biological complexes. SCOTCH performances were assessed not only with respect to other evolution-based approaches, such as conservation and coevolution analyses, but also with respect to statistically based scoring methods. Validated on a set of 129 complexes of known structure exhibiting both permanent and transient intermolecular interactions, SCOTCH appears as a robust strategy to guide the prediction of protein-protein complex structures. Of particular interest, it also provides a basic framework to efficiently track how protein surfaces could evolve while keeping their partners in contact.

A systematic comparative and structural analysis of protein phosphorylation sites based on the mtcPTM database

mtcPTM is a new database of phosphorylated protein sequences and atomic models. Analysis of the phosphosites in mtcPTM showed that phosphorylation sites are found in a highly heterogeneous range of structural and sequence contexts.

mtcPTM is an online repository of human and mouse phosphosites in which data are hierarchically organized to preserve biologically relevant experimental information, thus allowing straightforward comparisons of phosphorylation patterns found under different conditions. The database also contains the largest available collection of atomic models of phosphorylatable proteins. Detailed analysis of this structural dataset reveals that phosphorylation sites are found in a heterogeneous range of structural and sequence contexts. mtcPTM is available on the web .

Beta-strand recombination in tricalbin evolution and the origin of synaptotagmin-like C2 domains

Two protein families involved in membrane traffic, tricalbins and synaptotagmins, contain several copies of C2 domains and are related based on their sequence and domain architecture. Paradoxically, tricalbin and synaptotagmin C2 domains belong to different structural types with apparent circular permutation of terminal beta-strands. To understand whether a topological switch took place, we analyzed tricalbin and synaptotagmin-like C2 domains using two-dimensional structural analysis. We found that yeast tricalbins contain five to six C2 domains. One of these C2 domains possesses many features of synaptotagmin-like C2 domains and also carries a conserved C-terminal strand that is similar to its structural equivalent in synaptotagmin-like C2 domains, suggesting a structural permutation event. Indeed, among higher eukaryotes, animal tricalbins have evolved a C2 domain with synaptotagmin-like topology indicating that the structural conversion has taken place. Investigation of plant synaptotagmins, however, proves that they are direct tricalbin orthologs. Our analysis shows that beta-strand recombination is a possible evolutionary mechanism to generate new structural topologies with altered functional properties.

In silico functional and structural characterisation of ferlin proteins by mapping disease-causing mutations and evolutionary information onto three-dimensional models of their C2 domains

Ferlins are C2 domain proteins involved in membrane fusion events, including membrane repair and synaptic exocytosis, and their deficiency can result in muscular dystrophy and deafness. We have undertaken a structural study of their C2 domains by sequence comparison and homology modelling to understand the function of these poorly characterised proteins and to predict the molecular impact of disease-causing mutations. We observe that non-conservative mutations affecting buried residues tend to result in detrimental phenotypes, likely because of decreased protein stability, whereas most variants with replacements in surface residues do not. The few cases of exposed residues altered in variants known to cause diseases are found in conserved areas of functional importance, including essential calcium-binding regions, as deduced by analogy to other characterised C2 domains. Furthermore, we report distinct features of some C2 domains in the two known ferlin subfamilies that correlates with the presence or absence of the DysF domains. Taken altogether, our results highlight potential targets for further experimental analyses to understand the function of ferlin proteins. We believe our modelling data will aid the diagnosis of diseases associated with ferlin mutations and the development of therapeutic strategies.

Relating destabilizing regions to known functional sites in proteins

BACKGROUND

Most methods for predicting functional sites in protein 3D structures, rely on information on related proteins and cannot be applied to proteins with no known relatives. Another limitation of these methods is the lack of a well annotated set of functional sites to use as benchmark for validating their predictions. Experimental findings and theoretical considerations suggest that residues involved in function often contribute unfavorably to the native state stability. We examine the possibility of systematically exploiting this intrinsic property to identify functional sites using an original procedure that detects destabilizing regions in protein structures. In addition, to relate destabilizing regions to known functional sites, a novel benchmark consisting of a diverse set of hand-curated protein functional sites is derived.

RESULTS

A procedure for detecting clusters of destabilizing residues in protein structures is presented. Individual residue contributions to protein stability are evaluated using detailed atomic models and a force-field successfully applied in computational protein design. The most destabilizing residues, and some of their closest neighbours, are clustered into destabilizing regions following a rigorous protocol. Our procedure is applied to high quality apo-structures of 63 unrelated proteins. The biologically relevant binding sites of these proteins were annotated using all available information, including structural data and literature curation, resulting in the largest hand-curated dataset of binding sites in proteins available to date. Comparing the destabilizing regions with the annotated binding sites in these proteins, we find that the overlap is on average limited, but significantly better than random. Results depend on the type of bound ligand. Significant overlap is obtained for most polysaccharide- and small ligand-binding sites, whereas no overlap is observed for most nucleic acid binding sites. These differences are rationalised in terms of the geometry and energetics of the binding site.

CONCLUSION

We find that although destabilizing regions as detected here can in general not be used to predict binding sites in protein structures, they can provide useful information, particularly on the location of functional sites that bind polysaccharides and small ligands. This information can be exploited in methods for predicting function in protein structures with no known relatives. Our publicly available benchmark of hand-curated functional sites in proteins should help other workers derive and validate new prediction methods.

Prediction of functional sites based on the fuzzy oil drop model

A description of many biological processes requires knowledge of the 3-D structure of proteins and, in particular, the defined active site responsible for biological function. Many proteins, the genes of which have been identified as the result of human genome sequencing, and which were synthesized experimentally, await identification of their biological activity. Currently used methods do not always yield satisfactory results, and new algorithms need to be developed to recognize the localization of active sites in proteins. This paper describes a computational model that can be used to identify potential areas that are able to interact with other molecules (ligands, substrates, inhibitors, etc.). The model for active site recognition is based on the analysis of hydrophobicity distribution in protein molecules. It is shown, based on the analyses of proteins with known biological activity and of proteins of unknown function, that the region of significantly irregular hydrophobicity distribution in proteins appears to be function related.

Localization of ligand binding site in proteins identified in silico

Knowledge-based models for protein folding assume that the early-stage structural form of a polypeptide is determined by the backbone conformation, followed by hydrophobic collapse. Side chain-side chain interactions, mostly of hydrophobic character, lead to the formation of the hydrophobic core, which seems to stabilize the structure of the protein in its natural environment. The fuzzy-oil-drop model is employed to represent the idealized hydrophobicity distribution in the protein molecule. Comparing it with the one empirically observed in the protein molecule reveals that they are not in agreement. It is shown in this study that the irregularity of hydrophobic distributions is aim-oriented. The character and strength of these irregularities in the organization of the hydrophobic core point to the specificity of a particular protein's structure/function. When the location of these irregularities is determined versus the idealized fuzzy-oil-drop, function-related areas in the protein molecule can be identified. The presented model can also be used to identify ways in which protein-protein complexes can possibly be created. Active sites can be predicted for any protein structure according to the presented model with the free prediction server at http://www.bioinformatics.cm-uj.krakow.pl/activesite. The implication based on the model presented in this work suggests the necessity of active presence of ligand during the protein folding process simulation.