Deciphering cryptic similarities in protein binding sites

Chemometric Models of Differential Amino Acids at the Navα and Navβ Interface of Mammalian Sodium Channel Isoforms

(1) Background: voltage-gated sodium channels (Na_vs) are integral membrane proteins that allow the sodium ion flux into the excitable cells and initiate the action potential. They comprise an α (Na_vα) subunit that forms the channel pore and are coupled to one or more auxiliary β (Na_vβ) subunits that modulate the gating to a variable extent. (2) Methods: after performing homology in silico modeling for all nine isoforms (Na_v1.1α to Na_v1.9α), the Na_vα and Na_vβ protein-protein interaction (PPI) was analyzed chemometrically based on the primary and secondary structures as well as topological or spatial mapping. (3) Results: our findings reveal a unique isoform-specific correspondence between certain segments of the extracellular loops of the Na_vα subunits. Precisely, loop S5 in domain I forms part of the PPI and assists Na_vβ1 or Na_vβ3 on all nine mammalian isoforms. The implied molecular movements resemble macroscopic springs, all of which explains published voltage sensor effects on sodium channel fast inactivation in gating. (4) Conclusions: currently, the specific functions exerted by the Na_vβ1 or Na_vβ3 subunits on the modulation of Na_vα gating remain unknown. Our work determined functional interaction in the extracellular domains on theoretical grounds and we propose a schematic model of the gating mechanism of fast channel sodium current inactivation by educated guessing.

Protein surface representation and analysis by dimension reduction

Background

Protein structures are better conserved than protein sequences, and consequently more functional information is available in structures than in sequences. However, proteins generally interact with other proteins and molecules via their surface regions and a backbone-only analysis of protein structures may miss many of the functional and evolutionary features. Surface information can help better elucidate proteins' functions and their interactions with other proteins. Computational analysis and comparison of protein surfaces is an important challenge to overcome to enable efficient and accurate functional characterization of proteins.

Methods

In this study we present a new method for representation and comparison of protein surface features. Our method is based on mapping the 3-D protein surfaces onto 2-D maps using various dimension reduction methods. We have proposed area and neighbor based metrics in order to evaluate the accuracy of this surface representation. In order to capture functionally relevant information, we encode geometric and biochemical features of the protein, such as hydrophobicity, electrostatic potential, and curvature, into separate color channels in the 2-D map. The resulting images can then be compared using efficient 2-D image registration methods to identify surface regions and features shared by proteins.

Results

We demonstrate the utility of our method and characterize its performance using both synthetic and real data. Among the dimension reduction methods investigated, SNE, LandmarkIsomap, Isomap, and Sammon's mapping provide the best performance in preserving the area and neighborhood properties of the original 3-D surface. The enriched 2-D representation is shown to be useful in characterizing the functional site of chymotrypsin and able to detect structural similarities in heat shock proteins. A texture mapping using the 2-D representation is also proposed as an interesting application to structure visualization.

Functional annotation by identification of local surface similarities: a novel tool for structural genomics

Background

Protein function is often dependent on subsets of solvent-exposed residues that may exist in a similar three-dimensional configuration in non homologous proteins thus having different order and/or spacing in the sequence. Hence, functional annotation by means of sequence or fold similarity is not adequate for such cases.

Results

We describe a method for the function-related annotation of protein structures by means of the detection of local structural similarity with a library of annotated functional sites. An automatic procedure was used to annotate the function of local surface regions. Next, we employed a sequence-independent algorithm to compare exhaustively these functional patches with a larger collection of protein surface cavities. After tuning and validating the algorithm on a dataset of well annotated structures, we applied it to a list of protein structures that are classified as being of unknown function in the Protein Data Bank. By this strategy, we were able to provide functional clues to proteins that do not show any significant sequence or global structural similarity with proteins in the current databases.

Conclusion

This method is able to spot structural similarities associated to function-related similarities, independently on sequence or fold resemblance, therefore is a valuable tool for the functional analysis of uncharacterized proteins. Results are available at

Computational identification of proteins for selectivity assays

At the stage of optimization of a chemical series the compounds are normally assayed for binding or inhibition on the target protein as well as on several proteins from a selectivity panel. These proteins are normally identified on the basis of sequence homology to the target protein. Experimental selectivity data are also taken into account if available. Cases when a nonhomologous protein has a significant affinity to the compound series are going to be missed if the selectivity panel is identified by homology. Experimental data is usually either unavailable or limited to a small fraction of proteins that should be considered. We have developed a computational method of identification of selectivity panel proteins. It is based on the evaluation of binding site similarity to the target protein using docking scores of target-selected molecular probes. These probes are obtained by docking a large library of drug-like compounds to the target protein followed by selecting a diverse subset from the best virtual binders. Docking scores of these probes to other proteins measure binding site similarity to the target. Because the method does not require prior knowledge of either affinities or structures of inhibitors for the target, it can be applied to any protein with known 3D structure. Validation of the method includes rediscovery of nonhomologous proteins that bind common ligands: estradiol, tamoxifen, and riboflavin. Given 3D structures, the method can effectively discriminate proteins with similar binding sites from random proteins independent of sequence homology.

SH2 Binding Site Comparison: A New Application of the SURFCOMP Method

To avoid side effects, it is often desirable to increase the specificity of a drug candidate when targeting one member of a family of related proteins, whereby one exploits small differences between the structures of the binding sites. Identification of such differences can be carried out by analyzing the distributions of physicochemical properties mapped onto molecular surfaces. Here we demonstrate that SURFCOMP, our local surface similarity detection method, is able to detect differences between the binding sites of two closely related proteins. We analyzed the SH2 domains of Sap and Eat-2, two highly similar signal transduction molecules involved in inflammatory processes and found differences between their binding sites that can possibly lead to a better understanding of the different specificities of the two proteins.

Kinomics-structural biology and chemogenomics of kinase inhibitors and targets

Classifying kinases based entirely on small molecule selectivity data is a new approach to drug discovery that allows scientists to understand relationships between targets. This approach combines the understanding of small molecules and targets, and thereby assists the researcher in finding new targets for existing molecules or understanding selectivity and polypharmacology of molecules in related targets. Currently, structural information is available for relatively few of the protein kinases encoded in the human genome (7% of the estimated 518); however, even the current knowledge base, when paired with structure-based design techniques, can assist in the identification and optimization of novel kinase inhibitors across the entire protein class. Chemogenomics attempts to combine genomic data, structural biological data, classical dendrograms, and selectivity data to explore, define, and classify the medicinally relevant kinase space. Exploitation of this information in the discovery of kinase inhibitors defines practical kinase chemogenomics (kinomics). In this paper, we review the available information on kinase targets and their inhibitors, and present the relationships between the various classification schema for kinase space. In particular, we present the first dendrogram of kinases based entirely on small molecule selectivity data. We find that the selectivity dendrogram differs from sequence-based clustering mostly in the higher-level groupings of the smaller clusters, and remains very comparable for closely homologous targets. Highly homologous kinases are, on average, inhibited comparably by small molecules. This observation, although intuitive, is very important to the process of target selection, as one would expect difficulty in achieving inhibitor selectivity for kinases that share high sequence identity.

SURFACE: a database of protein surface regions for functional annotation

The SURFACE (SUrface Residues and Functions Annotated, Compared and Evaluated, URL http://cbm.bio.uniroma2.it/surface/) database is a repository of annotated and compared protein surface regions. SURFACE contains the results of a large-scale protein annotation and local structural comparison project. A non-redundant set of protein chains is used to build a database of protein surface patches, defined as putative surface functional sites. Each patch is annotated with sequence and structure-derived information about function or interaction abilities. A new procedure for structure comparison is used to perform an all-versus-all patches comparison. Selection of the results obtained with stringent parameters offers a similarity score that can be used to associate different patches and allows reliable annotation by similarity. Annotation exerted through the comparison of regions of protein surface allows the highlighting of similarities that cannot be recognized by other methods of sequence or structure comparison. A graphic representation of the surface patches, functional annotations and the structural superpositions is available through the web interface.

Predicting the kinetics of peptide-antibody interactions using a multivariate experimental design of sequence and chemical space

A multivariate approach involving modifications in peptide sequence and chemical buffer medium was used as an attempt to predict the kinetics of peptide-antibody interactions. Using a BIACORE system the kinetic parameters of the interaction of Fab 57P with 18 peptide analogues of an epitope of tobacco mosaic virus protein were characterized in 20 buffers of various pH values and containing different chemical additives (NaCl, urea, EDTA, KSCN and DMSO). For multivariate peptide design, three amino acid positions were selected because their modification was known to moderately affect binding, without abolishing it entirely. Predictive mathematical models were developed which related kinetic parameters (k(a) or k(d)) measured in standard buffer to the amino acid sequence of the antigen. ZZ-scales and a helix-forming-tendency (HFT) scale were used as descriptors of the physico-chemical properties of amino acids in the peptide antigen. These mathematical models had good predictive power (Q(2) = 0.49 for k(a), Q(2) = 0.73 for k(d)). For the non-essential residues under study, HFT and charge were found to be the most important factors that influenced the activity. Experiments in 19 buffers were performed to assess the sensitivity of the interactions to buffer composition. The presence of urea, DMSO and NaCl in the buffer influenced binding properties, while change in pH and the presence of EDTA and KSCN had no effect. The chemical sensitivity fingerprints were different for the various peptides. The results indicate that multivariate experimental design and mathematical modeling can be applied to the prediction of interaction kinetics.

Molecular design versus empirical discovery in peptide-based vaccines. Coming to terms with fuzzy recognition sites and ill-defined structure-function relationships in immunology

In view of our increased understanding of the molecular basis of immunological recognition, it is commonly believed that it should be possible to apply molecular design strategies to the development of peptide-based vaccines. The stated aim is to transform the development of a vaccine from a trial and error empirical operation into a so-called rational, structure-based process. In the present review, it is argued that it is misleading to oppose rational and empirical approaches in vaccine research since both are needed in the practice of experimental science. Many reasons are given for the view that the molecular design of synthetic vaccines is not a realistic scientific enterprise. The capacity of a peptide to induce a protective immune response depends on many extrinsic factors and regulatory mechanisms of the recipient host which are not amenable to molecular design of the peptide immunogen. It seems safe to predict that the development of peptide-based vaccines will continue to be driven by empirical discovery rather than by so-called rational design.