Structural comparison and classification of alpha‐helical transmembrane domains based on helix interaction patterns

Structural classification of membrane proteins is still in its infancy due to the relative paucity of available three‐dimensional structures compared with soluble proteins. However, recent technological advances in protein structure determination have led to a significant increase in experimentally known membrane protein folds, warranting exploration of the structural universe of membrane proteins. Here, a new and completely membrane protein specific structural classification system is introduced that classifies α‐helical membrane proteins according to common helix architectures. Each membrane protein is represented by a helix interaction graph depicting transmembrane helices with their pairwise interactions resulting from individual residue contacts. Subsequently, proteins are clustered according to similarities among these helix interaction graphs using a newly developed structural similarity score called HISS. As HISS scores explicitly disregard structural properties of loop regions, they are more suitable to capture conserved transmembrane helix bundle architectures than other structural similarity scores. Importantly, we are able to show that a classification approach based on helix interaction similarity closely resembles conventional structural classification databases such as SCOP and CATH implying that helix interactions are one of the major determinants of α‐helical membrane protein folds. Furthermore, the classification of all currently available membrane protein structures into 20 recurrent helix architectures and 15 singleton proteins demonstrates not only an impressive variability of membrane helix bundles but also the conservation of common helix interaction patterns among proteins with distinctly different sequences. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

A method to search for similar protein local structures at ligand-binding sites and its application to adenine recognition

We have developed a method of searching for similar spatial arrangements of atoms around a given chemical moiety in proteins that bind a common ligand. The first step in this method is to consider a set of atoms that closely surround a given chemical moiety. Then, to compare the spatial arrangements of such surrounding atoms in different proteins, they are translated and rotated so that the chemical moieties are superposed on each other. Spatial arrangements of surrounding atoms in a pair of proteins are judged to be similar, when there are many corresponding atoms occupying similar spatial positions. Because the method focuses on the arrangements of surrounding atoms, it can detect structural similarities of binding sites in proteins that are dissimilar in their amino acid sequences or in their chain folds. We have applied this method to identify modes of nucleotide base recognition by proteins. An all-against-all comparison of the arrangements of atoms surrounding adenine moieties revealed an unexpected structural similarity between protein kinases, cAMP-dependent protein kinase (cAPK), and casein kinase-1 (CK1), and D-Ala:D-Ala ligase (DD-ligase) at their adenine-binding sites, despite a lack of similarity in their chain folds. The similar local structure consists of a four-residue segment and three sequentially separated residues. In particular the four-residue segments of these enzymes were found to have nearly identical conformations in their backbone parts, which are involved in the recognition of adenine. This common local structure was also found in substrate-free three-dimensional structures of other proteins that are similar to DD-ligase in the chain fold and of other protein kinases. As the proteins with different folds were found to share a common local structure, these proteins seem to constitute a remarkable example of convergent evolution for the same recognition mechanism. Received: 9 December 1996 / Accepted: 7 February 1997     

Searching for three-dimensional secondary structural patterns in proteins with ProSMoS

MOTIVATION: Many evolutionarily distant, but functionally meaningful links between proteins come to light through comparison of spatial structures. Most programs that assess structural similarity compare two proteins to each other and find regions in common between them. Structural classification experts look for a particular structural motif instead. Programs base similarity scores on superposition or closeness of either Cartesian coordinates or inter-residue contacts. Experts pay more attention to the general orientation of the main chain and mutual spatial arrangement of secondary structural elements. There is a need for a computational tool to find proteins with the same secondary structures, topological connections and spatial architecture, regardless of subtle differences in 3D coordinates. RESULTS: We developed ProSMoS--a Protein Structure Motif Search program that emulates an expert. Starting from a spatial structure, the program uses previously delineated secondary structural elements. A meta-matrix of interactions between the elements (parallel or antiparallel) minding handedness of connections (left or right) and other features (e.g. element lengths and hydrogen bonds) is constructed prior to or during the searches. All structures are reduced to such meta-matrices that contain just enough information to define a protein fold, but this definition remains very general and deviations in 3D coordinates are tolerated. User supplies a meta-matrix for a structural motif of interest, and ProSMoS finds all proteins in the protein data bank (PDB) that match the meta-matrix. ProSMoS performance is compared to other programs and is illustrated on a beta-Grasp motif. A brief analysis of all beta-Grasp-containing proteins is presented. Program availability: ProSMoS is freely available for non-commercial use from ftp://iole.swmed.edu/pub/ProSMoS.     

Evaluation of features for catalytic residue prediction in novel folds

Structural genomics projects are determining the three-dimensional structure of proteins without full characterization of their function. A critical part of the annotation process involves appropriate knowledge representation and prediction of functionally important residue environments. We have developed a method to extract features from sequence, sequence alignments, three-dimensional structure, and structural environment conservation, and used support vector machines to annotate homologous and nonhomologous residue positions based on a specific training set of residue functions. In order to evaluate this pipeline for automated protein annotation, we applied it to the challenging problem of prediction of catalytic residues in enzymes. We also ranked the features based on their ability to discriminate catalytic from noncatalytic residues. When applying our method to a well-annotated set of protein structures, we found that top-ranked features were a measure of sequence conservation, a measure of structural conservation, a degree of uniqueness of a residue's structural environment, solvent accessibility, and residue hydrophobicity. We also found that features based on structural conservation were complementary to those based on sequence conservation and that they were capable of increasing predictor performance. Using a family nonredundant version of the ASTRAL 40 v1.65 data set, we estimated that the true catalytic residues were correctly predicted in 57.0% of the cases, with a precision of 18.5%. When testing on proteins containing novel folds not used in training, the best features were highly correlated with the training on families, thus validating the approach to nonhomologous catalytic residue prediction in general. We then applied the method to 2781 coordinate files from the structural genomics target pipeline and identified both highly ranked and highly clustered groups of predicted catalytic residues.     

A model for statistical significance of local similarities in structure

Structural biology can provide three-dimensional structures for proteins of unknown function. When sequence or structure comparisons fail to suggest a function, insights can come from discovery of functionally important local structural patterns. Existing methods to detect such patterns lack rigorous statistics needed for widespread application. Here, we derive a formula to calculate statistical significance of the root-mean-square deviation between atoms in such patterns. When combined with a database search method, our statistics permit true functional or structural patterns in different folds to be discerned from noise. The approach is highly complementary to fold comparison for providing functional clues for new structures, and is key for the detection of recurrences of any new pattern.     

The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation

Structural genomics efforts have led to increasing numbers of novel, uncharacterized protein structures with low sequence identity to known proteins, resulting in a growing need for structure-based function recognition tools. Our method, SeqFEATURE, robustly models protein functions described by sequence motifs using a structural representation. We built a library of models that shows good performance compared to other methods. In particular, SeqFEATURE demonstrates significant improvement over other methods when sequence and structural similarity are low.     

Structural alignment of proteins by a novel TOPOFIT method, as a superimposition of common volumes at a topomax point

Similarity of protein structures has been analyzed using three-dimensional Delaunay triangulation patterns derived from the backbone representation. It has been found that structurally related proteins have a common spatial invariant part, a set of tetrahedrons, mathematically described as a common spatial subgraph volume of the three-dimensional contact graph derived from Delaunay tessellation (DT). Based on this property of protein structures, we present a novel common volume superimposition (TOPOFIT) method to produce structural alignments. Structural alignments usually evaluated by a number of equivalent (aligned) positions (N(e)) with corresponding root mean square deviation (RMSD). The superimposition of the DT patterns allows one to uniquely identify a maximal common number of equivalent residues in the structural alignment. In other words, TOPOFIT identifies a feature point on the RMSD N(e) curve, a topomax point, until which the topologies of two structures correspond to each other, including backbone and interresidue contacts, whereas the growing number of mismatches between the DT patterns occurs at larger RMSD (N(e)) after the topomax point. It has been found that the topomax point is present in all alignments from different protein structural classes; therefore, the TOPOFIT method identifies common, invariant structural parts between proteins. The alignments produced by the TOPOFIT method have a good correlation with alignments produced by other current methods. This novel method opens new opportunities for the comparative analysis of protein structures and for more detailed studies on understanding the molecular principles of tertiary structure organization and functionality. The TOPOFIT method also helps to detect conformational changes, topological differences in variable parts, which are particularly important for studies of variations in active/ binding sites and protein classification.     

Predicting fold novelty based on ProtoNet hierarchical classification

MOTIVATION: Structural genomics projects aim to solve a large number of protein structures with the ultimate objective of representing the entire protein space. The computational challenge is to identify and prioritize a small set of proteins with new, currently unknown, superfamilies or folds. RESULTS: We develop a method that assigns each protein a likelihood of it belonging to a new, yet undetermined, structural superfamily. The method relies on a variant of ProtoNet, an automatic hierarchical classification scheme of all protein sequences from SwissProt. Our results show that proteins that are remote from solved structures in the ProtoNet hierarchy are more likely to belong to new superfamilies. The results are validated against SCOP releases from recent years that account for about half of the solved structures known to date. We show that our new method and the representation of ProtoNet are superior in detecting new targets, compared to our previous method using ProtoMap classification. Furthermore, our method outperforms PSI-BLAST search in detecting potential new superfamilies.     

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.

     

Novel CalphaNN structural motif for protein recognition of phosphate ions

Phosphate is one of the most frequently exploited chemical moieties in nature, present in a wide range of naturally occurring and critically important small molecules. Several phosphate group recognition motifs have been found for a few narrow groups of proteins, but for many protein families and folds the mode of phosphate recognition remains unclear. Here, we have analyzed the structures of all fold-representative protein-ligand complexes listed in the FSSP database, regardless of whether the bound ligand included a phosphate group. Based on a phosphate-binding motif that we identified in pyridoxal phosphate binding proteins, we have identified a new anion-binding structural motif, CalphaNN, common to 104 fold-representative protein structures that belong to 62 different folds, of which 86% of the fold-representative structures (51 folds) bind phosphate or lone sulfate ions. This motif leads to a precise mode for phosphate group recognition forming a structure where atoms of the phosphate group occupy the most favorable stabilizing positions. The anion-binding CalphaNN motif is based only on main-chain atoms from three adjacent residues, has a conservative betaalphaalpha or betaalphabeta geometry, and recognizes the free phosphate (sulfate) ion as well as one or more phosphate groups in nucleotides and in a variety of cofactors. Moreover, the CalphaNN motif is positioned in functionally important regions of protein structures and often residues of the motif directly participate in the function of the protein.     

Comparing graph representations of protein structure for mining family-specific residue-based packing motifs.

We find recurring amino-acid residue packing patterns, or spatial motifs, that are characteristic of protein structural families, by applying a novel frequent subgraph mining algorithm to graph representations of protein three-dimensional structure. Graph nodes represent amino acids, and edges are chosen in one of three ways: first, using a threshold for contact distance between residues; second, using Delaunay tessellation; and third, using the recently developed almost-Delaunay edges. For a set of graphs representing a protein family from the Structural Classification of Proteins (SCOP) database, subgraph mining typically identifies several hundred common subgraphs corresponding to spatial motifs that are frequently found in proteins in the family but rarely found outside of it. We find that some of the large motifs map onto known functional regions in two protein families explored in this study, i.e., serine proteases and kinases. We find that graphs based on almost-Delaunay edges significantly reduce the number of edges in the graph representation and hence present computational advantage, yet the patterns extracted from such graphs have a biological interpretation approximately equivalent to that of those extracted from distance based graphs.     

Searching for protein signatures using a multilevel alphabet

Short motifs are known to play diverse roles in proteins, such as in mediating the interactions with other molecules, binding to membranes, or conducting a specific biological function. Standard approaches currently employed to detect short motifs in proteins search for enrichment of amino acid motifs considering mostly the sequence information. Here, we presented a new approach to search for common motifs (protein signatures) which share both physicochemical and structural properties, looking simultaneously at different features. Our method takes as an input an amino acid sequence and translates it to a new alphabet that reflects its intrinsic structural and chemical properties. Using the MEME search algorithm, we identified the proteins signatures within subsets of protein which encompass common sequence and structural information. We demonstrated that we can detect enriched structural motifs, such as the amphipathic helix, from large datasets of linear sequences, as well as predicting common structural properties (such as disorder, surface accessibility, or secondary structures) of known functional‐motifs. Finally, we applied the method to the yeast protein interactome and identified novel putative interacting motifs. We propose that our approach can be applied for de novo protein function prediction given either sequence or structural information. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Prediction of functional sites in proteins using conserved functional group analysis

A detailed knowledge of a protein's functional site is an absolute prerequisite for understanding its mode of action at the molecular level. However, the rapid pace at which sequence and structural information is being accumulated for proteins greatly exceeds our ability to determine their biochemical roles experimentally. As a result, computational methods are required which allow for the efficient processing of the evolutionary information contained in this wealth of data, in particular that related to the nature and location of functionally important sites and residues. The method presented here, referred to as conserved functional group (CFG) analysis, relies on a simplified representation of the chemical groups found in amino acid side-chains to identify functional sites from a single protein structure and a number of its sequence homologues. We show that CFG analysis can fully or partially predict the location of functional sites in approximately 96% of the 470 cases tested and that, unlike other methods available, it is able to tolerate wide variations in sequence identity. In addition, we discuss its potential in a structural genomics context, where automation, scalability and efficiency are critical, and an increasing number of protein structures are determined with no prior knowledge of function. This is exemplified by our analysis of the hypothetical protein Ydde_Ecoli, whose structure was recently solved by members of the North East Structural Genomics consortium. Although the proposed active site for this protein needs to be validated experimentally, this example illustrates the scope of CFG analysis as a general tool for the identification of residues likely to play an important role in a protein's biochemical function. Thus, our method offers a convenient solution to rapidly and automatically process the vast amounts of data that are beginning to emerge from structural genomics projects.     

Multiple flexible structure alignment using partial order graphs

MOTIVATION: Existing comparisons of protein structures are not able to describe structural divergence and flexibility in the structures being compared because they focus on identifying a common invariant core and ignore parts of the structures outside this core. Understanding the structural divergence and flexibility is critical for studying the evolution of functions and specificities of proteins. RESULTS: A new method of multiple protein structure alignment, POSA (Partial Order Structure Alignment), was developed using a partial order graph representation of multiple alignments. POSA has two unique features: (1) identifies and classifies regions that are conserved only in a subset of input structures and (2) allows internal rearrangements in protein structures. POSA outperforms other programs in the cases where structural flexibilities exist and provides new insights by visualizing the mosaic nature of multiple structural alignments. POSA is an ideal tool for studying the variation of protein structures within diverse structural families. AVAILABILITY: POSA is freely available for academic users on a Web server at http://fatcat.burnham.org/POSA     

LFM-Pro: a tool for detecting significant local structural sites in proteins

MOTIVATION: The rapidly growing protein structure repositories have opened up new opportunities for discovery and analysis of functional and evolutionary relationships among proteins. Detecting conserved structural sites that are unique to a protein family is of great value in identification of functionally important atoms and residues. Currently available methods are computationally expensive and fail to detect biologically significant local features. RESULTS: We propose Local Feature Mining in Proteins (LFM-Pro) as a framework for automatically discovering family-specific local sites and the features associated with these sites. Our method uses the distance field to backbone atoms to detect geometrically significant structural centers of the protein. A feature vector is generated from the geometrical and biochemical environment around these centers. These features are then scored using a statistical measure, for their ability to distinguish a family of proteins from a background set of unrelated proteins, and successful features are combined into a representative set for the protein family. The utility and success of LFM-Pro are demonstrated on trypsin-like serine proteases family of proteins and on a challenging classification dataset via comparison with DALI. The results verify that our method is successful both in identifying the distinctive sites of a given family of proteins, and in classifying proteins using the extracted features. AVAILABILITY: The software and the datasets are freely available for academic research use at http://bioinfo.ceng.metu.edu.tr/Pub/LFMPro.     

Functional differentiation of proteins: implications for structural genomics

Structural genomics is a broad initiative of various centers aiming to provide complete coverage of protein structure space. Because it is not feasible to experimentally determine the structures of all proteins, it is generally agreed that the only viable strategy to achieve such coverage is to carefully select specific proteins (targets), determine their structure experimentally, and then use comparative modeling techniques to model the rest. Here we suggest that structural genomics centers refine the structure-driven approach in target selection by adopting function-based criteria. We suggest targeting functionally divergent superfamilies within a given structural fold so that each function receives a structural characterization. We have developed a method to do so, and an itemized survey of several functionally rich folds shows that they are only partially functionally characterized. We call upon structural genomics centers to consider this approach and upon computational biologists to further develop function-based targeting methods.     

Life in the fast lane for protein crystallization and X-ray crystallography

The common goal for structural genomic centers and consortiums is to decipher as quickly as possible the three-dimensional structures for a multitude of recombinant proteins derived from known genomic sequences. Since X-ray crystallography is the foremost method to acquire atomic resolution for macromolecules, the limiting step is obtaining protein crystals that can be useful of structure determination. High-throughput methods have been developed in recent years to clone, express, purify, crystallize and determine the three-dimensional structure of a protein gene product rapidly using automated devices, commercialized kits and consolidated protocols. However, the average number of protein structures obtained for most structural genomic groups has been very low compared to the total number of proteins purified. As more entire genomic sequences are obtained for different organisms from the three kingdoms of life, only the proteins that can be crystallized and whose structures can be obtained easily are studied. Consequently, an astonishing number of genomic proteins remain unexamined. In the era of high-throughput processes, traditional methods in molecular biology, protein chemistry and crystallization are eclipsed by automation and pipeline practices. The necessity for high-rate production of protein crystals and structures has prevented the usage of more intellectual strategies and creative approaches in experimental executions. Fundamental principles and personal experiences in protein chemistry and crystallization are minimally exploited only to obtain “low-hanging fruit” protein structures. We review the practical aspects of today's high-throughput manipulations and discuss the challenges in fast pace protein crystallization and tools for crystallography. Structural genomic pipelines can be improved with information gained from low-throughput tactics that may help us reach the higher-bearing fruits. Examples of recent developments in this area are reported from the efforts of the Southeast Collaboratory for Structural Genomics (SECSG).     

A new method for identification of protein (sub)families in a set of proteins based on hydropathy distribution in proteins

Structural similarity among proteins is reflected in the distribution of hydropathicity along the amino acids in the protein sequence. Similarities in the hydropathy distributions are obvious for homologous proteins within a protein family. They also were observed for proteins with related structures, even when sequence similarities were undetectable. Here we present a novel method that employs the hydropathy distribution in proteins for identification of (sub)families in a set of (homologous) proteins. We represent proteins as points in a generalized hydropathy space, represented by vectors of specifically defined features. The features are derived from hydropathy of the individual amino acids. Projection of this space onto principal axes reveals groups of proteins with related hydropathy distributions. The groups identified correspond well to families of structurally and functionally related proteins. We found that this method accurately identifies protein families in a set of proteins, or subfamilies in a set of homologous proteins. Our results show that protein families can be identified by the analysis of hydropathy distribution, without the need for sequence alignment.     

Structural proteomics: developments in structure-to-function predictions

The major challenge for post-genomic research is to functionally assign and validate a large number of novel target genes and their corresponding proteins. Functional genomics approaches have, therefore, gained considerable attention in the quest to convert this massive data set into useful information. One of the crucial components for the functional understanding of unassigned proteins is the analysis of their experimental or modeled 3D structures. Structural proteomics initiatives are generating protein structures at an unprecedented rate but our current knowledge of 3D-structural space is still limited. Estimates on the completeness of the 3D-structural coverage of proteins vary but it is generally accepted that only a minority of the structural proteome has a template structure from which reliable conclusions can be drawn. Thus, structural proteomics has set out to build a map of protein structures that will represent all protein folds included in the 'global proteome'.     

A new bioinformatic approach to detect common 3D sites in protein structures

An innovative bioinformatic method has been designed and implemented to detect similar three-dimensional (3D) sites in proteins. This approach allows the comparison of protein structures or substructures and detects local spatial similarities: this method is completely independent from the amino acid sequence and from the backbone structure. In contrast to already existing tools, the basis for this method is a representation of the protein structure by a set of stereochemical groups that are defined independently from the notion of amino acid. An efficient heuristic for finding similarities that uses graphs of triangles of chemical groups to represent the protein structures has been developed. The implementation of this heuristic constitutes a software named SuMo (Surfing the Molecules), which allows the dynamic definition of chemical groups, the selection of sites in the proteins, and the management and screening of databases. To show the relevance of this approach, we focused on two extreme examples illustrating convergent and divergent evolution. In two unrelated serine proteases, SuMo detects one common site, which corresponds to the catalytic triad. In the legume lectins family composed of >100 structures that share similar sequences and folds but may have lost their ability to bind a carbohydrate molecule, SuMo discriminates between functional and non-functional lectins with a selectivity of 96%. The time needed for searching a given site in a protein structure is typically 0.1 s on a PIII 800MHz/Linux computer; thus, in further studies, SuMo will be used to screen the PDB.