Towards predicting Ca2+‐binding sites with different coordination numbers in proteins with atomic resolution

Ca²⁺‐binding sites in proteins exhibit a wide range of polygonal geometries that directly relate to an equally‐diverse set of biological functions. Although the highly‐conserved EF‐Hand motif has been studied extensively, non‐EF‐Hand sites exhibit much more structural diversity which has inhibited efforts to determine the precise location of Ca²⁺‐binding sites, especially for sites with few coordinating ligands. Previously, we established an algorithm capable of predicting Ca²⁺‐binding sites using graph theory to identify oxygen clusters comprised of four atoms lying on a sphere of specified radius, the center of which was the predicted calcium position. Here we describe a new algorithm, MUG (MUltiple Geometries), which predicts Ca²⁺‐binding sites in proteins with atomic resolution. After first identifying all the possible oxygen clusters by finding maximal cliques, a calcium center (CC) for each cluster, corresponding to the potential Ca²⁺ position, is located to maximally regularize the structure of the (cluster, CC) pair. The structure is then inspected by geometric filters. An unqualified (cluster, CC) pair is further handled by recursively removing oxygen atoms and relocating the CC until its structure is either qualified or contains fewer than four ligand atoms. Ligand coordination is then determined for qualified structures. This algorithm, which predicts both Ca²⁺ positions and ligand groups, has been shown to successfully predict over 90% of the documented Ca²⁺‐binding sites in three datasets of highly‐diversified protein structures with 0.22 to 0.49 Å accuracy. All multiple‐binding sites (i.e. sites with a single ligand atom associated with multiple calcium ions) were predicted, as were half of the low‐coordination sites (i.e. sites with less than four protein ligand atoms) and 14/16 cofactor‐coordinating sites. Additionally, this algorithm has the flexibility to incorporate surface water molecules and protein cofactors to further improve the prediction for low‐coordination and cofactor‐coordinating Ca²⁺‐binding sites. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Analysis and prediction of calcium-binding pockets from apo-protein structures exhibiting calcium-induced localized conformational changes

Calcium binding in proteins exhibits a wide range of polygonal geometries that relate directly to an equally diverse set of biological functions. The binding process stabilizes protein structures and typically results in local conformational change and/or global restructuring of the backbone. Previously, we established the MUG program, which utilized multiple geometries in the Ca²⁺‐binding pockets of holoproteins to identify such pockets, ignoring possible Ca²⁺‐induced conformational change. In this article, we first report our progress in the analysis of Ca²⁺‐induced conformational changes followed by improved prediction of Ca²⁺‐binding sites in the large group of Ca²⁺‐binding proteins that exhibit only localized conformational changes. The MUG^SR algorithm was devised to incorporate side chain torsional rotation as a predictor. The output from MUG^SR presents groups of residues where each group, typically containing two to five residues, is a potential binding pocket. MUG^SR was applied to both X‐ray apo structures and NMR holo structures, which did not use calcium distance constraints in structure calculations. Predicted pockets were validated by comparison with homologous holo structures. Defining a “correct hit” as a group of residues containing at least two true ligand residues, the sensitivity was at least 90%; whereas for a “correct hit” defined as a group of residues containing at least three true ligand residues, the sensitivity was at least 78%. These data suggest that Ca²⁺‐binding pockets are at least partially prepositioned to chelate the ion in the apo form of the protein.     

Network properties of protein-decoy structures

Convergence of the vast sequence space of proteins into a highly restricted fold/conformational space suggests a simple yet unique underlying mechanism of protein folding that has been the subject of much debate in the last several decades. One of the major challenges related to the understanding of protein folding or in silico protein structure prediction is the discrimination of non-native structures/decoys from the native structure. Applications of knowledge-based potentials to attain this goal have been extensively reported in the literature. Also, scoring functions based on accessible surface area and amino acid neighbourhood considerations were used in discriminating the decoys from native structures. In this article, we have explored the potential of protein structure network (PSN) parameters to validate the native proteins against a large number of decoy structures generated by diverse methods. We are guided by two principles: (a) the PSNs capture the local properties from a global perspective and (b) inclusion of non-covalent interactions, at all-atom level, including the side-chain atoms, in the network construction accommodates the sequence dependent features. Several network parameters such as the size of the largest cluster, community size, clustering coefficient are evaluated and scored on the basis of the rank of the native structures and the Z-scores. The network analysis of decoy structures highlights the importance of the global properties contributing to the uniqueness of native structures. The analysis also exhibits that the network parameters can be used as metrics to identify the native structures and filter out non-native structures/decoys in a large number of data-sets; thus also has a potential to be used in the protein ‘structure prediction’ problem.     

Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.     

Method for identification of rigid domains and hinge residues in proteins based on exhaustive enumeration

Many proteins undergo large‐scale motions where relatively rigid domains move against each other. The identification of rigid domains, as well as the hinge residues important for their relative movements, is important for various applications including flexible docking simulations. In this work, we develop a method for protein rigid domain identification based on an exhaustive enumeration of maximal rigid domains, the rigid domains not fully contained within other domains. The computation is performed by mapping the problem to that of finding maximal cliques in a graph. A minimal set of rigid domains are then selected, which cover most of the protein with minimal overlap. In contrast to the results of existing methods that partition a protein into non‐overlapping domains using approximate algorithms, the rigid domains obtained from exact enumeration naturally contain overlapping regions, which correspond to the hinges of the inter‐domain bending motion. The performance of the algorithm is demonstrated on several proteins. Proteins 2015; 83:1054–1067. © 2015 Wiley Periodicals, Inc.     

Identification of protein biochemical functions by similarity search using the molecular surface database eF-site

The identification of protein biochemical functions based on their three-dimensional structures is strongly required in the post-genome-sequencing era. We have developed a new method to identify and predict protein biochemical functions using the similarity information of molecular surface geometries and electrostatic potentials on the surfaces. Our prediction system consists of a similarity search method based on a clique search algorithm and the molecular surface database eF-site (electrostatic surface of functional-site in proteins). Using this system, functional sites similar to those of phosphoenoylpyruvate carboxy kinase were detected in several mononucleotide-binding proteins, which have different folds. We also applied our method to a hypothetical protein, MJ0226 from Methanococcus jannaschii, and detected the mononucleotide binding site from the similarity to other proteins having different folds.     

FAST: a novel protein structure alignment algorithm

We present a novel algorithm named FAST for aligning protein three-dimensional structures. FAST uses a directionality-based scoring scheme to compare the intra-molecular residue-residue relationships in two structures. It employs an elimination heuristic to promote sparseness in the residue-pair graph and facilitate the detection of the global optimum. In order to test the overall accuracy of FAST, we determined its sensitivity and specificity with the SCOP classification (version 1.61) as the gold standard. FAST achieved higher sensitivities than several existing methods (DaliLite, CE, and K2) at all specificity levels. We also tested FAST against 1033 manually curated alignments in the HOMSTRAD database. The overall agreement was 96%. Close inspection of examples from broad structural classes indicated the high quality of FAST alignments. Moreover, FAST is an order of magnitude faster than other algorithms that attempt to establish residue-residue correspondence. Typical pairwise alignments take FAST less than a second with a Pentium III 1.2GHz CPU. FAST software and a web server are available at http://biowulf.bu.edu/FAST/.     

Clique formation of super-sister honeybee workers (<Emphasis Type="Italic">Apis mellifera</Emphasis>) in experimental groups

Summary One day old honeybee workers (Apis mellifera) were observed in small experimental groups (10 workers per group). These groups were either composed of offspring workers of singly inseminated queens (super-sister groups) or multiply inseminated queens (mixed groups). The groups thus consisted of either super-sisters or a mix of super- and halfsisters. The positions of the individually labelled workers were observed with infrared sensitive video equipment over a 24 h period and analysed with digital image analysis. The spatial distribution of workers in super-sister and mixed groups differed significantly. The distance between super-sister workers was significantly less than in mixed groups (n = 339; p < 0.01). Also the distance of the workers from the group centre was significantly less in super-sister groups as compared to mixed groups (n = 3440, p<0.05). The super-sisters thus formed tighter groups than the groups including half-sisters. A genotypic analysis of the mixed groups with microsatellite DNA markers revealed that workers were significantly more frequently observed next to a super-sister rather than a half-sister (p < 0.001). Irrespective whether this results from kin recognition or not, we expect clique formation to be an important factor for the development of task specialisation among worker bees.Received 26 August 2002; revised 22 April 2003; accepted 25 July 2003.     

Graph theory, tionary trees and classification

Many methods have been used for analysing information about organisms in order to understand tionary relationships and/or to determine classifications. The reationship between some of these methods is illustrated for the character state matrix, incompatibility and similarity matrices, minimal unrooted and rooted trees, and tionary classifications. Existing methods of determining the shortest possible tree are described. In addition a new method of building a minimal tree is introduced which starts with the largest possible subset (clique) of characters that is compatible for all pairs of characters. The remaining characters are ranked in order of their increasing number of incompatibilities. These characters are added singly, a tree constructed and then tested for minimality by previously described methods for partitioning characters into subsets. The procedure is repeated at least until the tree can no longer be proved minimal. The relationship between trees and tionary and phylogenetic classifications has been neglected but three methods are metioned and a new criterion suggested. It is suggested that graph theory, rather than statistics, is better suited for the primary analysis of comparative data.