MolLoc: a web tool for the local structural alignment of molecular surfaces

MolLoc stands for Molecular Local surface comparison, and is a web server for the structural comparison of molecular surfaces. Given two structures in PDB format, the user can compare their binding sites, cavities or any arbitrary residue selection. Moreover, the web server allows the comparison of a query structure with a list of structures. Each comparison produces a structural alignment that maximizes the extension of the superimposition of the surfaces, and returns the pairs of atoms with similar physicochemical properties that are close in space after the superimposition. Based on this subset of atoms sharing similar physicochemical properties a new rototranslation is derived that best superimposes them. MolLoc approach is both local and surface-oriented, and therefore it can be particularly useful when testing if molecules with different sequences and folds share any local surface similarity. The MolLoc web server is available at http://bcb.dei.unipd.it/MolLoc

MultiBind and MAPPIS: webservers for multiple alignment of protein 3D-binding sites and their interactions

Analysis of protein–ligand complexes and recognition of spatially conserved physico-chemical properties is important for the prediction of binding and function. Here, we present two webservers for multiple alignment and recognition of binding patterns shared by a set of protein structures. The first webserver, MultiBind (http://bioinfo3d.cs.tau.ac.il/MultiBind), performs multiple alignment of protein binding sites. It recognizes the common spatial chemical binding patterns even in the absence of similarity of the sequences or the folds of the compared proteins. The input to the MultiBind server is a set of protein-binding sites defined by interactions with small molecules. The output is a detailed list of the shared physico-chemical binding site properties. The second webserver, MAPPIS (http://bioinfo3d.cs.tau.ac.il/MAPPIS), aims to analyze protein–protein interactions. It performs multiple alignment of protein–protein interfaces (PPIs), which are regions of interaction between two protein molecules. MAPPIS recognizes the spatially conserved physico-chemical interactions, which often involve energetically important hot-spot residues that are crucial for protein–protein associations. The input to the MAPPIS server is a set of protein-protein complexes. The output is a detailed list of the shared interaction properties of the interfaces

A global optimization algorithm for protein surface alignment

Background A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. Several matching strategies have been designed for the recognition of protein-ligand binding sites and of protein-protein interfaces but the problem cannot be considered solved. Results In this paper we propose a new method for local structural alignment of protein surfaces based on continuous global optimization techniques. Given the three-dimensional structures of two proteins, the method finds the isometric transformation (rotation plus translation) that best superimposes active regions of two structures. We draw our inspiration from the well-known Iterative Closest Point (ICP) method for three-dimensional (3D) shapes registration. Our main contribution is in the adoption of a controlled random search as a more efficient global optimization approach along with a new dissimilarity measure. The reported computational experience and comparison show viability of the proposed approach. Conclusions Our method performs well to detect similarity in binding sites when this in fact exists. In the future we plan to do a more comprehensive evaluation of the method by considering large datasets of non-redundant proteins and applying a clustering technique to the results of all comparisons to classify binding sites

Structure-based prediction of protein-protein interaction sites

Protein-protein interactions play a central role in the formation of protein complexes and the biological pathways that orchestrate virtually all cellular processes. Reliable identification of the specific amino acid residues that form the interface of a protein with one or more other proteins is critical to understanding the structural and physico-chemical basis of protein interactions and their role in key cellular processes, predicting protein complexes, validating protein interactions predicted by high throughput methods, and identifying and prioritizing drug targets in computational drug design. Because of the difficulty and the high cost of experimental characterization of interface residues, there is an urgent need for computational methods for reliable predicting protein-protein interface residues from the sequence, and when available, the structure of a query protein, and when known, its putative interacting partner. Against this background, this thesis develops improved methods for predicting protein-protein interface residues and protein-protein interfaces from the three dimensional structure of an unbound query protein without considering information of its binding protein partner. Towards this end, we develop (i) ProtInDb (http://protindb.cs.iastate.edu), a database of protein-protein interface residues to facilitate (a) the generation of datasets of protein-protein interface residues that can be used to perform analysis of interaction sites and to train and evaluate predictors of interface residues, and (b) the visualization of interaction sites between proteins in both the amino acid sequences and the 3D protein structures, among other applications; (ii) PoInterS (http://pointers.cs.iastate.edu/), a method for predicting protein-protein interaction sites formed by spatially contiguous clusters of interface residues based on the predictions generated by a protein interface residue predictor. PoInterS divides a protein surface into a series of patches composed of several surface residues, and uses the outputs of the interface residue predictors to rank and select a small set of patches that are the most likely to constitute the interaction sites; and (iii) PrISE (http://prise.cs.iastate.edu/), a method for predicting protein-protein interface residues based on the similarity of the structural element formed by the query residue and its neighboring residues and the structural elements extracted from the interface and non-interface regions of proteins that are members of experimentally determined protein complexes. A structural element captures the atomic composition and solvent accessibility of a central residue and its closest neighbors in the protein structure. PrISE decomposes a query protein into a set of structural elements and searches for similar elements in a large set of proteins that belong to one or more experimentally determined complexes. The structural elements that are most similar to each structural element extracted from the query protein are then used to infer whether its central residue is or is not an interface residue. The results of our experiments using a variety of benchmark datasets show that PoInterS and PrISE generally outperform the state-of-the-art structure-based methods for predicting interaction patches and interface residues, respectively

PROTEIN SURFACE SIMILARITIES EVALUATION FOR FUNCTIONAL ANNOTATION STUDIES

One of the main targets of bioinformatics is to assign functions to proteins whose function is unknown relying on homologies identifications with proteins with known functions. Several approaches are currently available: the best choice depends on the evolutionary distance that separates the protein of interest from its homologous. Recently attention has been focused on molecular surfaces since they do not depend on the three-dimensional structure and allow similarities to be identified which other methods can\u2019t identify. Furthermore, molecular surfaces are the interface of interaction between molecules, and their geometrical and physical descriptions will lead to the comprehension of the molecular recognition process, since the geometrical component has a fundamental role in the early stage of complex formation. This particular aspect would have a major impact in the field of drug design and in the understanding of the side effects due to interactions between proteins. During this thesis a protocol for similarities identification on molecular surfaces has been developed and optimized. In this process, molecular surfaces are calculated according to Lee Richard\u2019s model, and then are represented through triangular meshes. Successively surfaces are transformed into a set of object oriented images using a computer vision approach. This type of representation has the advantage of being independent from the position of the objects represented, and thus similar surfaces can be described by similar images. The search for similarities is then performed by indentifying correspondences between pairs of similar images, by filtering matches relying on geometrical criteria and then by clustering correspondences in high similarity groups. These groups are then used to align surfaces in order to evaluate results both by visual inspection and through appropriate indexes. This process can be applied in the field of functional annotation, through the identification of similarities between surfaces of homologous proteins, and in study of interaction between proteins, through the identification of complementary areas between interacting proteins. The whole process of similarities detection depends on the configuration of 15 parameters that balance the time needed to perform calculation with the quality of results found. The problem of parameters estimation has been addressed using an implementation of genetic algorithm, which allowed representing different configuration parameters as a population in which individuals that are able to align surfaces satisfactory are rewarded with an high fitness score. The effectiveness of the algorithm was then improved by the introduction of neighbor heuristic which reduced the computational time required for correspondence clustering on surfaces. Particular interest was placed in results displaying and in the construction of indices that can quantify the quality of results. Regarding the visualization problem, a display system was implemented based on the Visualization ToolKit libraries in order to represent surfaces aligned as objects in three-dimensional space, enabling the user to interact with the scene represented by changing the point of view or enlarging details of the scene represented. Regarding the definition of useful indexes for results evaluation, two indexes had a fundamental role. The first one, called overlap index, measures the percentage of vertices of two surfaces that are closer than 1 A\ub0 after the alignment. This index in particular is useful for evaluating the surface similarity since similar aligned surfaces will have a large number of vertices closer than this distance. The second index, called RMSD, is important because it evaluates the Root Mean Square Deviation of alpha carbons of two aligned proteins in the case of a complementary search. This index allows evaluating how the aligned protein is distant from the correct position in the crystal complex. Concerning results evaluation, we have noticed that the consideration of electrostatic potential allows assigning good scores in case of strong geometrical similarity in context of functional annotations, thus facilitating the identification of homologous surfaces. This method has been validated both in the search of similarities and in the search of complementarities. Regarding the search of similarities, we tried to analyze a sample of 13 known proteins with a prosite domain in order to identify the presence of such domains on molecular surfaces. For doing this, we first reduced the number of structures present in the Protein Data Bank to a group of representative structures. Then we calculated the molecular surfaces for each representative protein and we created a dataset of patches corresponding to the prosite functional domain. The test was then performed trying to align the surface of the 13 known proteins to the patches dataset of functional domains. The results showed that in most cases we are able to properly align a functional domain to a protein surface with the same functional domain, and that these evidence was easily identifiable both by the parameters used for results evaluations, both by visually inspecting the results of the alignments. The method was then tested for complementary research, trying to reconstruct the protein-protein complex present in a well known dataset used to validate docking methods. In the case of searching for similarities it is important to describe surfaces in details in order to increase the accuracy, but high precision when searching for complementarity is counterproductive, since the interaction between proteins is not only determined by geometrical features but also involves the formation of favorable electrostatic interactions and rearrangements of side chains. Thus molecular surfaces were calculated using smoothed surfaces, where most details are lost but allowing to detect more easily interacting surfaces. Results showed that the algorithm is able to align complexes with comparable scores than the programs currently available; Considering this experimental design and that the method does not take into account the electrostatic potential, we can assume that the results obtained are particularly interesting since the proposed method provides a wider set of conformations than other algorithms, upon which we can extend the analysis in order to identify a better prediction. In conclusions the proposed system is able to identify similarities on molecular surfaces through the analysis of images of local description. The results show that the system implemented is effective in identifying similar surface areas in the context of functional annotation. In regards to the search for complementarities, the algorithm seems to have an interesting perspective, even though the best complex proposed is not always biologically correct. From this point of view, we have to do more analysis in order to improve the methods in protein interaction studies

Discovery of Similar Regions on Protein Surfaces

none3Discovery of a similar region on two protein surfaces can lead to important inference about the functional role or molecular interaction of this region for one of the proteins if such information is available for the other. We propose a new characterization of protein surfaces based on a spin image representation of the surfaces that facilitates the simultaneous search of the entire surface of each of two proteins for a matching region. For a surface point, we introduce spin image profiles which are related to the degree of exposure of the point to identify structurally equivalent surface regions in two proteins. Unlike some related methods, we do not assume that a known fixed region of one of the proteins surfaces is to be matched on the other protein surface. Rather, we search for the largest similar regions on each of the two surfaces. In spite of the fact that this approach is entirely geometric and no use is made of physicochemical properties of the protein surfaces or fold information, it is effective in identifying similar regions on both surfaces even when the region corresponds to a binding site on one of the proteins. The discovery of similar regions on two or more proteins also has implications for drug design and pharmacophore identification. We present experimental results from datasets of more than 50 protein surfaces.mixedBOCK M.E; GARUTTI C; GUERRA C.BOCK M., E; Garutti, C; Guerra, Concettin