Protein Surface Shape Research Articles

Capturing surface complementarity in proteins using unsupervised learning and robust curvature measure.

The structure of a protein plays a pivotal role in determining its function. Often, the protein surface's shape and curvature dictate its nature of interaction with other proteins and biomolecules. However, marked by corrugations and roughness, a protein's surface representation poses significant challenges for its curvature-based characterization. In the present study, we employ unsupervised machine learning to segment the protein surface into patches. To measure the surface curvature of a patch, we present an algebraic sphere fitting method that is fast, accurate, and robust. Moreover, we use local curvatures to show the existence of "shape complementarity" in protein-protein, antigen-antibody, and protein-ligand interfaces. We believe that the current approach could help understand the relationship between protein structure and its biological function and can be used to find binding partners of a given protein.

Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics.

MotivationThe investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes.ResultsShape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm.Availabilityand implementationAll data are available online at http://datasetmachat.drugdesign.fr.Supplementary information Supplementary data are available at Bioinformatics online.

Capturing Surface Complementarity in Proteins Using Unsupervised Learning and Robust Curvature Measure

Structure of a protein plays a pivotal role in determining its function. Often, protein surface's shape and curvature dictate its nature of interaction with other proteins and biomolecules. However, marked by corrugations and roughness, a protein's surface poses significant challenges for its curvature-based characterization. In the present study, we employ unsupervised machine learning to segment the protein surface into patches. To measure surface curvature of a patch, we present an algebraic sphere fitting method that is fast, accurate, and robust. Moreover, we use local curvatures to show the existence of “shape complementarity” in protein-protein, antigen-antibody, and protein-ligand interfaces. We believe that the present approach could help understand the relationship between protein structure and its biological function and help finding binding partners of a given protein.

Protein 3D Structure and Electron Microscopy Map Retrieval Using 3D-SURFER2.0 and EM-SURFER.

With the rapid growth in the number of solved protein structures stored in the Protein Data Bank (PDB) and the Electron Microscopy Data Bank (EMDB), it is essential to develop tools to perform real-time structure similarity searches against the entire structure database. Since conventional structure alignment methods need to sample different orientations of proteins in the three-dimensional space, they are time consuming and unsuitable for rapid, real-time database searches. To this end, we have developed 3D-SURFER and EM-SURFER, which utilize 3D Zernike descriptors (3DZD) to conduct high-throughput protein structure comparison, visualization, and analysis. Taking an atomic structure or an electron microscopy map of a protein or a protein complex as input, the 3DZD of a query protein is computed and compared with the 3DZD of all other proteins in PDB or EMDB. In addition, local geometrical characteristics of a query protein can be analyzed using VisGrid and LIGSITECSC in 3D-SURFER. This article describes how to use 3D-SURFER and EM-SURFER to carry out protein surface shape similarity searches, local geometric feature analysis, and interpretation of the search results. © 2017 by John Wiley & Sons, Inc.

Atom depth analysis delineates mechanisms of protein intermolecular interactions

The systematic analysis of amino acid distribution, performed inside a large set of resolved protein structures, sheds light on possible mechanisms driving non random protein–protein approaches. Protein Data Bank entries have been selected using as filters a series of restrictions ensuring that the shape of protein surface is not modified by interactions with large or small ligands. 3D atom depth has been evaluated for all the atoms of the 2,410 selected structures. The amino acid relative population in each of the structural layers formed by grouping atoms on the basis of their calculated depths, has been evaluated. We have identified seven structural layers, the inner ones reproducing the core of proteins and the outer one incorporating their most protruding moieties. Quantitative analysis of amino acid contents of structural layers identified, as expected, different behaviors. Atoms of Q, R, K, N, D residues are increasingly more abundant in going from core to surfaces. An opposite trend is observed for V, I, L, A, C, and G. An intermediate behavior is exhibited by P, S, T, M, W, H, F and Y. The outer structural layer hosts predominantly E and K residues whose charged moieties, protruding from outer regions of the protein surface, reorient free from steric hindrances, determining specific electrodynamics maps. This feature may represent a protein signature for long distance effects, driving the formation of encounter complexes and the eventual short distance approaches that are required for protein–protein functional interactions.

Extended graph-based models for enhanced similarity retrieval in Cavbase

The problem of estimating the similarity between molecular structures is often tackled by means of graph-based approaches, using graphs for structure representation and measures based on the maximum common subgraph as similarity metrics. In the case of protein binding sites as molecular structures, however, where the graphs can be very large, the computation of these measures may easily become infeasible or at least unacceptably slow. To this end, Cavbase [1,2] was developed, a database for the automatic detection and storage of putative binding sites on the protein surfaces. Cavbase assigns so-called pseudocenters to the cavity-flanking amino acids, which characterize their physicochemical properties with respect to molecular recognition. On the one side, this representation leads to smaller and more generic representation of a binding site. On the other side, it comes with a loss of information, which is usually compensated by performing further calculations based on additional data. These steps, however, are most often computationally quite demanding, making the whole approach again very slow. The main drawback of a graph-based model solely based on pseudocenters is the loss of information about the shape of protein surface. In this study, we propose an extended modeling formalism that leads to graphs of the same size, but containing considerably more information. More specifically, additional descriptors of the surface characteristics are extracted from the surface points stored in Cavbase. These properties are included as attributes of the nodes of the graph, which leads to a gain of information and allows for more accurate comparisons between different structures.

Dissecting the protein–RNA interface: the role of protein surface shapes and RNA secondary structures in protein–RNA recognition

Protein–RNA interactions are essential for many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the protein surface shape (dented, intermediate or protruded) and the RNA base pairing properties (paired or unpaired nucleotides) at the interfaces of 91 protein–RNA complexes derived from the Protein Data Bank. Dented protein surfaces prefer unpaired nucleotides to paired ones at the interface, and hydrogen bonds frequently occur between the protein backbone and RNA bases. In contrast, protruded protein surfaces do not show such a preference, rather, electrostatic interactions initiate the formation of hydrogen bonds between positively charged amino acids and RNA phosphate groups. Interestingly, in many protein–RNA complexes that interact via an RNA loop, an aspartic acid is favored at the interface. Moreover, in most of these complexes, nucleotide bases in the RNA loop are flipped out and form hydrogen bonds with the protein, which suggests that aspartic acid is important for RNA loop recognition through a base-flipping process. This study provides fundamental insights into the role of the shape of the protein surface and RNA secondary structures in mediating protein–RNA interactions.

Novel Methods for Rapid Comparison and Multimeric Protein Complex Fitting for Low-Resolution Electron Microscopy Data

Recent advancements of experimental techniques for determining protein tertiary structures raise significant challenges for protein bioinformatics. Previously, we have introduced a method for protein surface shape representation using the 3D Zernike descriptors (3DZDs). The 3DZD enables fast structure database searches, taking advantage of its rotation invariance and compact representation. The 3DZD has been successfully applied for global protein surface shape comparison, local pocket shape comparison, protein docking prediction, and rapid small ligand molecule search. Here, we apply the 3DZD for comparing low-resolution structure data from the electron microscopy (EM). Two applications are presented. First, we use the 3DZD for rapid comparison for an EM density map of a protein structure to a database of EM data. We examined EM maps of varying resolutions and found that the method has good performance in identifying the structures of the same fold even for EM maps at a resolution of 15 Angstroms (Sael, Kihara , BMC Bioinformatics, in press). Next, we applied the 3DZD for fitting multiple component proteins into an EM map. The method integrates a multiple protein docking procedure and the 3DZD, which compares surface shape of docking conformation to the EM map. The multiple docking is performed by the Multi-LZerD algorithm, which starts by computing pairwise docking prediction of component chains by using the LZerD docking program, which our group have developed recently (Venkatraman et al., BMC Bioinformatics, 2010). Multi-LZerD combines the pairwise docking results generating a couple of hundreds solutions. Then the fitness of the multiple docking decoys and the EM map is quantified by using the 3DZD. Overall, we show that the 3DZD is powerful in comparing low-resolution structure data for comparison and multiple-docking guided by the low-resolution data.

Improved protein surface comparison and application to low-resolution protein structure data

BackgroundRecent advancements of experimental techniques for determining protein tertiary structures raise significant challenges for protein bioinformatics. With the number of known structures of unknown function expanding at a rapid pace, an urgent task is to provide reliable clues to their biological function on a large scale. Conventional approaches for structure comparison are not suitable for a real-time database search due to their slow speed. Moreover, a new challenge has arisen from recent techniques such as electron microscopy (EM), which provide low-resolution structure data. Previously, we have introduced a method for protein surface shape representation using the 3D Zernike descriptors (3DZDs). The 3DZD enables fast structure database searches, taking advantage of its rotation invariance and compact representation. The search results of protein surface represented with the 3DZD has showngood agreement with the existing structure classifications, but some discrepancies were also observed.ResultsThe three new surface representations of backbone atoms, originally devised all-atom-surface representation, and the combination of all-atom surface with the backbone representation are examined. All representations are encoded with the 3DZD. Also, we have investigated the applicability of the 3DZD for searching protein EM density maps of varying resolutions. The surface representations are evaluated on structure retrieval using two existing classifications, SCOP and the CE-based classification.ConclusionsOverall, the 3DZDs representing backbone atoms show better retrieval performance than the original all-atom surface representation. The performance further improved when the two representations are combined. Moreover, we observed that the 3DZD is also powerful in comparing low-resolution structures obtained by electron microscopy.

A Unified Protein Docking Procedure with a Shape Complementarity Screening using 3D Zernike Descriptors

Protein-protein interactions are a pivotal component of many biological processes. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. Experimental techniques to solve the structure of the complex are, however, often difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. We present a novel protein docking algorithm, VDOCK, based on the use of 3D Zernike descriptors (3DZD) as regional features of molecular shape. The key motivation of using these descriptors is their invariance to transformation, in addition to a compact representation of local surface shape characteristics. In our previous works we have shown that 3DZD are suitable for comparing global/local protein surface shape and surface physicochemical properties to quantify their similarity. Here we apply 3DZD for quantifying surface complementarity. Docking decoys are generated using geometric hashing, which are then initially screened by a shape-based scoring function that incorporates buried surface area and 3DZD. The benchmark studies show that 3DZD are not only efficient in identifying shape complementarity for bound docking cases but superior to other existing methods in accommodating a certain level of flexibility of the protein surface in unbound docking cases, taking advantage of 3DZD's controlable resolution of the surface description. In the next stage, generated docking decoys are evaluated using a physics-based scoring function. The weighting factors to combine these terms are trained using several different target metrics on a large dataset of docking decoys. Additional information and steps for selecting models are also employed, which include protein-protein interaction site predictions and optimization of global and side-chain conformations.

Local surface shape-based protein function prediction using Zernike descriptors

Structural genomics projects have been solving an increasing number of protein structures with unknown function. As the number of uncharacterized protein structures continues to grow, there is increasing need for effective computational strategies for structure-based protein function prediction. As of now, there are more than 2400 protein structures in the PDB database, which are categorized as unknown function and thus are awaiting functional characterization. Previously, we have reported a fast global protein surface shape comparison method based on 3D Zernike descriptors.

Fast protein tertiary structure retrieval based on global surface shape similarity

Characterization and identification of similar tertiary structure of proteins provides rich information for investigating function and evolution. The importance of structure similarity searches is increasing as structure databases continue to expand, partly due to the structural genomics projects. A crucial drawback of conventional protein structure comparison methods, which compare structures by their main-chain orientation or the spatial arrangement of secondary structure, is that a database search is too slow to be done in real-time. Here we introduce a global surface shape representation by three-dimensional (3D) Zernike descriptors, which represent a protein structure compactly as a series expansion of 3D functions. With this simplified representation, the search speed against a few thousand structures takes less than a minute. To investigate the agreement between surface representation defined by 3D Zernike descriptor and conventional main-chain based representation, a benchmark was performed against a protein classification generated by the combinatorial extension algorithm. Despite the different representation, 3D Zernike descriptor retrieved proteins of the same conformation defined by combinatorial extension in 89.6% of the cases within the top five closest structures. The real-time protein structure search by 3D Zernike descriptor will open up new possibility of large-scale global and local protein surface shape comparison.

The interdependence of protein surface topography and bound water molecules revealed by surface accessibility and fractal density measures

To characterize water binding to proteins, which is fundamental to protein folding, stability and activity, the relationships of 10,837 bound water positions to protein surface shape and residue type were analyzed in 56 high-resolution crystallographic structures. Fractal atomic density and accessibility algorithms provided an objective characterization of deep grooves in solvent-accessible protein surfaces. These deep grooves consistently had approximately the diameter of one water molecule, suggesting that deep grooves are formed by the interactions between protein atoms and bound water molecules. Protein surface topography dominates the chemistry and extent of water binding. Protein surface area within grooves bound three times as many water molecules as non-groove surface; grooves accounted for one-quarter of the total surface area yet bound half the water molecules. Moreover, only within grooves did bound water molecules discriminate between different side-chains. In grooves, main-chain surface was as hydrated as that of the most hydrophilic side-chains, Asp and Glu, whereas outside grooves all main and side-chains bound water to a similar, and much decreased, extent. This identification of the interdependence of protein surface shape and hydration has general implications for modelling and prediction of protein surface shape, recognition, local folding and solvent binding.

Shape distributions of protein topography.

A method is presented for measuring protein surface shape. It is an improvement of an earlier method that intersects a sphere with the solvent-excluded volume of a protein molecule. The new method, called a shape distribution, produces a more sophisticated description of the region of the sphere inside the protein than is provided by simply measuring the region's area or solid angle. This method is applied to the prediction of molecular complexes in three systems: the hemoglobin nonallosteric interface, trypsin and trypsin inhibitor, and heme and apomyoglobin. It does not uniquely predict the correct structure, even though the individual structures are taken from the experimentally determined complex structure. However, it does provide a list of several hundred predicted complexes, one of which is correct, and from which the correct complex might be extracted by a subsequent chemical filter.

Measurement of protein surface shape by solid angles

A method for measuring the gross shape of local regions of protein surface is presented and applied to an examination of three proteins. Any point on the protein surface can be assigned a number that measures the degree of convexity or concavity of the surface in the vicinity of the point. This number is computed by centring a sphere at that point and measuring how much of the sphere lies inside the protein. The sphere radius is a parameter chosen according to the scale of the features that are being analysed. The amount of sphere intersecting the protein is interpreted as a solid angle, denoted omega. Three proteins are analysed by this method: lysozyme, Superoxide dismutase and chymotrypsin. The resulting omega values are used to colour code the protein surfaces displayed on a colour raster graphics terminal. The method can be seen to reliably identify protrusions and depressions. The difficulty of developing a generally useful method for measuring protein surface shape is discussed. Possible applications of the solid-angle method include the analysis of shape complementarity at protein interfaces, the development of computer algorithms for predicting complexes between proteins, or between proteins and ligands, the identification of homologous epitopes on different proteins that might be immunologically crossreactive, and the determination of correlations between surface geometry and chemical properties.