Comparison of protein surfaces using a genetic algorithm

FTIP: an accurate and efficient method for global protein surface comparison

MOTIVATION

Global protein surface comparison (GPSC) studies have been limited compared to other research works on protein structure alignment/comparison due to lack of real applications associated with GPSC. However, the technology advances in cryo-electron tomography (CET) have made methods to identify proteins from their surface shapes extremely useful.

RESULTS

In this study, we developed a new method called Farthest point sampling (FPS)-enhanced Triangulation-based Iterative-closest-Point (ICP) (FTIP) for GPSC. We applied it to protein classification using only surface shape information. Our method first extracts a set of feature points from protein surfaces using FPS and then uses a triangulation-based efficient ICP algorithm to align the feature points of the two proteins to be compared. Tested on a benchmark dataset with 2329 proteins using nearest-neighbor classification, FTIP outperformed the state-of-the-art method for GPSC based on 3D Zernike descriptors. Using real and simulated cryo-EM data, we show that FTIP could be applied in the future to address problems in protein identification in CET experiments.

AVAILABILITY AND IMPLEMENTATION

Programs/scripts we developed/used in the study are available at http://ani.stat.fsu.edu/∼yuan/index.fld/FTIP.tar.bz2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

VASP-E: specificity annotation with a volumetric analysis of electrostatic isopotentials

Algorithms for comparing protein structure are frequently used for function annotation. By searching for subtle similarities among very different proteins, these algorithms can identify remote homologs with similar biological functions. In contrast, few comparison algorithms focus on specificity annotation, where the identification of subtle differences among very similar proteins can assist in finding small structural variations that create differences in binding specificity. Few specificity annotation methods consider electrostatic fields, which play a critical role in molecular recognition. To fill this gap, this paper describes VASP-E (Volumetric Analysis of Surface Properties with Electrostatics), a novel volumetric comparison tool based on the electrostatic comparison of protein-ligand and protein-protein binding sites. VASP-E exploits the central observation that three dimensional solids can be used to fully represent and compare both electrostatic isopotentials and molecular surfaces. With this integrated representation, VASP-E is able to dissect the electrostatic environments of protein-ligand and protein-protein binding interfaces, identifying individual amino acids that have an electrostatic influence on binding specificity. VASP-E was used to examine a nonredundant subset of the serine and cysteine proteases as well as the barnase-barstar and Rap1a-raf complexes. Based on amino acids established by various experimental studies to have an electrostatic influence on binding specificity, VASP-E identified electrostatically influential amino acids with 100% precision and 83.3% recall. We also show that VASP-E can accurately classify closely related ligand binding cavities into groups with different binding preferences. These results suggest that VASP-E should prove a useful tool for the characterization of specific binding and the engineering of binding preferences in proteins.

Proteins, the ubiquitous worker molecules of the cell, are a diverse class of molecules that perform very specific tasks. Understanding how proteins achieve specificity is a critical step towards understanding biological systems and a key prerequisite for rationally engineering new proteins. To examine electrostatic influences on specificity in proteins, this paper presents VASP-E, a software tool that generates solid representations of the electrostatic potential fields that surround proteins. VASP-E compares solids with constructive solid geometry, a class of techniques developed first for modeling complex machine parts. We observed that solid representations could quantify the degree of charge complementarity in protein-protein interactions and identify key residues that strengthen or weaken them. VASP-E correctly identified amino acids with established experimental influences on protein-protein binding specificity. We also observed that solid representations of electrostatic fields could identify electrostatic conservations and variations that relate to similarities and differences in binding specificity between proteins and small molecules.

Surface characterization of oxidized myofibrils using X-ray photoelectron spectroscopy and scanning electron microscopy

The functional properties of myofibrils depend largely on their surface characteristics. Changes in surface characteristics of myofibrils after chemical oxidation were elucidated using X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy. Myofibrils were oxidized by a hydroxyl radical generating system. Lipid oxidation and phospholipid distribution were altered during the oxidative processing. Results from particle size analysis, sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and salt solubility indicated that protein cross-linking and fragmentation occurred during the oxidation of myofibrils. XPS analysis of C 1s, N 1s, and O 1s spectra suggested that surface chemical function concentrations changed significantly because of the modification of amino acid side chains that rendered protein cross-links and fragmentation and phospholipid alteration. Analysis of the correlation between the surface chemical composition and parameters of particle size distributions confirmed that protein carbonylation and phospholipid alteration were involved in protein surface modification. Results of the microstructure analysis were in agreement with those of particle size and XPS analysis.

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.

Cofactor-binding sites in proteins of deviating sequence: comparative analysis and clustering in torsion angle, cavity, and fold space

Small molecules are recognized in protein-binding pockets through surface-exposed physicochemical properties. To optimize binding, they have to adopt a conformation corresponding to a local energy minimum within the formed protein-ligand complex. However, their conformational flexibility makes them competent to bind not only to homologous proteins of the same family but also to proteins of remote similarity with respect to the shape of the binding pockets and folding pattern. Considering drug action, such observations can give rise to unexpected and undesired cross reactivity. In this study, datasets of six different cofactors (ADP, ATP, NAD(P)(H), FAD, and acetyl CoA, sharing an adenosine diphosphate moiety as common substructure), observed in multiple crystal structures of protein-cofactor complexes exhibiting sequence identity below 25%, have been analyzed for the conformational properties of the bound ligands, the distribution of physicochemical properties in the accommodating protein-binding pockets, and the local folding patterns next to the cofactor-binding site. State-of-the-art clustering techniques have been applied to group the different protein-cofactor complexes in the different spaces. Interestingly, clustering in cavity (Cavbase) and fold space (DALI) reveals virtually the same data structuring. Remarkable relationships can be found among the different spaces. They provide information on how conformations are conserved across the host proteins and which distinct local cavity and fold motifs recognize the different portions of the cofactors. In those cases, where different cofactors are found to be accommodated in a similar fashion to the same fold motifs, only a commonly shared substructure of the cofactors is used for the recognition process.

Mapping of ligand-binding cavities in proteins

The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of "orphan structures", selection of protein structures for docking studies in structure-based design, and identification of proteins for selectivity screens in drug design programs.

Functional classification of protein kinase binding sites using Cavbase

Increasingly, drug-discovery processes focus on complete gene families. Tools for analyzing similarities and differences across protein families are important for the understanding of key functional features of proteins. Herein we present a method for classifying protein families on the basis of the properties of their active sites. We have developed Cavbase, a method for describing and comparing protein binding pockets, and show its application to the functional classification of the binding pockets of the protein family of protein kinases. A diverse set of kinase cavities is mutually compared and analyzed in terms of recurring functional recognition patterns in the active sites. We are able to propose a relevant classification based on the binding motifs in the active sites. The obtained classification provides a novel perspective on functional properties across protein space. The classification of the MAP and the c-Abl kinases is analyzed in detail, showing a clear separation of the respective kinase subfamilies. Remarkable cross-relations among protein kinases are detected, in contrast to sequence-based classifications, which are not able to detect these relations. Furthermore, our classification is able to highlight features important in the optimization of protein kinase inhibitors. Using small-molecule inhibition data we could rationalize cross-reactivities between unrelated kinases which become apparent in the structural comparison of their binding sites. This procedure helps in the identification of other possible kinase targets that behave similarly in "binding pocket space" to the kinase under consideration.

From the similarity analysis of protein cavities to the functional classification of protein families using cavbase

In this contribution, the classification of protein binding sites using the physicochemical properties exposed to their pockets is presented. We recently introduced Cavbase, a method for describing and comparing protein binding pockets on the basis of the geometrical and physicochemical properties of their active sites. Here, we present algorithmic and methodological enhancements in the Cavbase property description and in the cavity comparison step. We give examples of the Cavbase similarity analysis detecting pronounced similarities in the binding sites of proteins unrelated in sequence. A similarity search using SARS M(pro) protease subpockets as queries retrieved ligands and ligand fragments accommodated in a physicochemical environment similar to that of the query. This allowed the characterization of the protease recognition pockets and the identification of molecular building blocks that can be incorporated into novel antiviral compounds. A cluster analysis procedure for the functional classification of binding pockets was implemented and calibrated using a diverse set of enzyme binding sites. Two relevant protein families, the alpha-carbonic anhydrases and the protein kinases, are used to demonstrate the scope of our cluster approach. We propose a relevant classification of both protein families, on the basis of the binding motifs in their active sites. The classification provides a new perspective on functional properties across a protein family and is able to highlight features important for potency and selectivity. Furthermore, this information can be used to identify possible cross-reactivities among proteins due to similarities in their binding sites.

A fourier fingerprint-based method for protein surface representation

A crucial enabling technology for structural genomics is the development of algorithms that can predict the putative function of novel protein structures: the proposed functions can subsequently be experimentally tested by functional studies. Testable assignments of function can be made if it is possible to attribute a putative, or indeed probable, function on the basis of the shapes of the binding sites on the surface of a protein structure. However the comparison of the surfaces of 3D protein structures is a computationally demanding task. Here we present four surface representations that can be used locally to describe the global shape of specifically bounded local region models. The most successful of these representations is obtained by a Fourier analysis of the distribution of surface curvature on concentric spheres around a surface point and summarizes a 24 A diameter spherically clipped region of protein surface by a fingerprint of 18 Fourier amplitude values. Searching experiments using these fingerprints on a set of 366 proteins demonstrate that this provides an effective and an efficient technique for the matching of protein surfaces.

Comparison of substructural epitopes in enzyme active sites using self-organizing maps

This paper presents a new algorithm to compare substructural epitopes in protein binding cavities. Through the comparison of binding cavities accommodating well characterized ligands with cavities whose actual guests are yet unknown, it is possible to draw some conclusions on the required shape of a putative ligand likely to bind to the latter cavities. To detect functional relationships among proteins, their binding-site exposed physicochemical characteristics are described by assigning generic pseudocenters to the functional groups of the amino acids flanking the particular active site. The cavities are divided into small local regions of four pseudocenters having the shape of a pyramid with triangular basis. To find similar local regions, an emergent self-organizing map is used for clustering. Two local regions within the same cluster are similar and form the basis for the superpositioning of the corresponding cavities to score this match. First results show that the similarities between enzymes with the same EC number can be found correctly. Enzymes with different EC numbers are detected to have no common substructures. These results indicate the benefit of this method and motivate further studies.

Matching of trastuzumab (Herceptin) epitope mimics onto the surface of Her-2/neu--a new method of epitope definition

As seen with the proto-oncogene Her-2/neu, antibodies targeting different parts of a receptor can have opposing effects. Depending on epitope specificity, in this case, tumor growth can be inhibited--but also enhanced. Therefore, the definition of molecular binding sites is of increasing importance in modern medicine. We here introduce a novel approach for binding site localization, utilizing information obtained by the phage display technique. This is a high throughput screening method for identification of peptide mimics, so called mimotopes, of any binding structure of interest. All target molecules whose structure is available in the RCSB Protein Data Bank can be scanned for mimotope matches on their surface. In this study, we present the matching results of five mimotopes defined for the epitope recognized by trastuzumab (Herceptin), a humanized monoclonal antibody inhibiting tumor growth, on Her-2/neu. The localization thus obtained corresponds to the known trastuzumab epitope. We therefore suggest the algorithm as a novel way of binding site definition, circumventing co-crystallization experiments.

SURFCOMP: A Novel Graph-Based Approach to Molecular Surface Comparison

Analysis of the distributions of physicochemical properties mapped onto molecular surfaces can highlight important similarities or differences between compound classes, contributing to rational drug design efforts. Here we present an approach that uses maximal common subgraph comparison and harmonic shape image matching to detect locally similar regions between two molecular surfaces augmented with properties such as the electrostatic potential or lipophilicity. The complexity of the problem is reduced by a set of filters that implement various geometric and physicochemical heuristics. The approach was tested on dihydrofolate reductase and thermolysin inhibitors and was shown to recover the correct alignments of the compounds bound in the active sites.

New fuzzy logic strategies for bio-molecular recognition

The concepts of molecular similarity and molecular complementarity, playing important roles in the broad field of molecular recognition, are chemical problems, in which the eyeball technique used by a human observer is very successful but which are very hard to code into a computer algorithm. Based on the model of molecular surfaces, our new approach defines overlapping surface patches with similar molecular properties. These patches are used to represent local features of the molecule in a way, which is beyond the atomistic resolution but can nevertheless be applied in partial similarity as well as complementarity analyses in a very general sense. It is shown that this molecular description can be used as the first step in a docking algorithm for complexes, where the structures of both molecules are known, as well as for the identification of possible active sites without the knowledge of specific molecules binding to this site.

Protein docking using a genetic algorithm

A genetic algorithm (GA) for protein-protein docking is described, in which the proteins are represented by dot surfaces calculated using the Connolly program. The GA is used to move the surface of one protein relative to the other to locate the area of greatest surface complementarity between the two. Surface dots are deemed complementary if their normals are opposed, their Connolly shape type is complementary, and their hydrogen bonding or hydrophobic potential is fulfilled. Overlap of the protein interiors is penalized. The GA is tested on 34 large protein-protein complexes where one or both proteins has been crystallized separately. Parameters are established for which 30 of the complexes have at least one near-native solution ranked in the top 100. We have also successfully reassembled a 1,400-residue heptamer based on the top-ranking GA solution obtained when docking two bound subunits.

A novel method of aligning molecules by local surface shape similarity

A novel shape-based method has been developed for overlaying a series of molecule surfaces into a common reference frame. The surfaces are represented by a set of circular patches of approximately constant curvature. Two molecules are overlaid using a clique-detection algorithm to find a set of patches in the two surfaces that correspond, and overlaying the molecules so that the similar patches on the two surfaces are coincident. The method is thus able to detect areas of local, rather than global, similarity. A consensus overlay for a group of molecules is performed by examining the scores of all pairwise overlays and performing a set of overlays with the highest scores. The utility of the method has been examined by comparing the overlaid and experimental configurations of 4 sets of molecules for which there are X-ray crystal structures of the molecules bound to a protein active site. Results for the overlays are generally encouraging. Of particular note is the correct prediction of the 'reverse orientation' for ligands binding to human rhinovirus coat protein HRV14.

Partial molecular alignment via local structure analysis

Molecular alignment remains as one of the most problematic aspects of molecular design. A technique is introduced that facilitates the alignment of a range of structures that could not be handled easily using existing alignment procedures. The flexibility of the method is illustrated with a series of test sets. First, an alignment is performed on a series of molecules from a typical 3D-quantitative structure-activity relationship data set. The results of this test show the technique to outperform many existing alignment methodologies based upon the optimization of molecular similarity of molecular overlaps. This test set is then extended to consider the alignment of more structurally diverse inhibitors of HIV-1 reverse transcriptase and HIV-1 protease. Finally, in the most challenging test, a large protein-based inhibitor is matched with a small-molecule mimic. It is believed that the existence of such a versatile alignment technique will prove invaluable in the fields of molecular design and chemical information handling.

Graph-theoretic techniques for macromolecular docking

We propose a solution to the problem of docking two macromolecules. We represent each of two proteins as a set of potential hydrogen bond donors and acceptors and use a clique-detection algorithm to find maximally complementary sets of donor/acceptor pairs. Preliminary results are presented which demonstrate the feasibility of the method.