Protein Functional Sites Research Articles

PROSITE, a protein domain database for functional characterization and annotation

PROSITE consists of documentation entries describing protein domains, families and functional sites, as well as associated patterns and profiles to identify them. It is complemented by ProRule, a collection of rules based on profiles and patterns, which increases the discriminatory power of these profiles and patterns by providing additional information about functionally and/or structurally critical amino acids. PROSITE is largely used for the annotation of domain features of UniProtKB/Swiss-Prot entries. Among the 983 (DNA-binding) domains, repeats and zinc fingers present in Swiss-Prot (release 57.8 of 22 September 2009), 696 (∼70%) are annotated with PROSITE descriptors using information from ProRule. In order to allow better functional characterization of domains, PROSITE developments focus on subfamily specific profiles and a new profile building method giving more weight to functionally important residues. Here, we describe AMSA, an annotated multiple sequence alignment format used to build a new generation of generalized profiles, the migration of ScanProsite to Vital-IT, a cluster of 633 CPUs, and the adoption of the Distributed Annotation System (DAS) to facilitate PROSITE data integration and interchange with other sources. The latest version of PROSITE (release 20.54, of 22 September 2009) contains 1308 patterns, 863 profiles and 869 ProRules. PROSITE is accessible at: http://www.expasy.org/prosite/.

Evaluation of protein surface roughness index using its heat denatured aggregates

AbstractRecent research works on potential of different protein surface describing parameters to predict protein surface properties gained significance for its possible implication in extracting clues on protein's functional site. In this direction, Surface Roughness Index, a surface topological parameter, showed its potential to predict SCOP-family of protein. The present work stands on the foundation of these works where a semi-empirical method for evaluation of Surface Roughness Index directly from its heat denatured protein aggregates (HDPA) was designed and demonstrated successfully. The steps followed consist, the extraction of a feature, Intensity Level Multifractal Dimension (ILMFD) from the microscopic images of HDPA, followed by the mapping of ILMFD into Surface Roughness Index (SRI) through recurrent backpropagation network (RBPN). Finally SRI for a particular protein was predicted by clustering of decisions obtained through feeding of multiple data into RBPN, to obtain general tendency of decision, as well as to discard the noisy dataset. The cluster centre of the largest cluster was found to be the best match for mapping of Surface Roughness Index of each protein in our study. The semi-empirical approach adopted in this paper, shows a way to evaluate protein's surface property without depending on its already evaluated structure.

A Collaborative Multiagent System for Mining Transcriptional Regulatory Elements

Identification of transcriptional regulatory elements offers a key means of insight into regulation mechanisms. However, the number of known regulatory elements is inadequate and state-of-the-art identification methods are inaccurate. Moreover, it is difficult for a biologist to select interdependent tools, and existing systems ignore overall performance issues. Agent technology can provide solutions through its information integration and coordination capabilities. TREMAgent is the first multiagent-based system for mining transcriptional regulatory elements. It uses novel algorithms combined with biological domain knowledge (for example, protein functional site information) to achieve superior accuracy and collaborate with existing tools using agent technology. The autonomous problem-solving capability of agents enables the system to provide the appropriate workflow rather than having users select interdependent tools. Experiments on the real data sets show that TREMAgent can provide superior accuracy and flexible services, promising excellent potential for bioinformatics.

Prediction of Functional Sites in 50,000 Protein Domains Using Dynamics Perturbation Analysis

Dynamics perturbation analysis (DPA) [1-3] finds regions in a protein structure where proteins are “ticklish,” i.e., where interactions cause a large change in protein dynamics. Such regions corresponded to native binding sites in a test set of hundreds of protein-ligand complexes [3]. We have further tested DPA for prediction of protein functional sites by applying an accelerated algorithm, Fast DPA [4], to over 50,000 structures of protein domains from the SCOP database [5]. We compared the predicted sites with known functional sites obtained from two sources: catalytic residues in the Catalytic Site Atlas; and putative binding sites identified by finding protein residues near small molecules in crystal structures. Combining predictions with information from sequence conservation and multiple sequence alignments reduced false positive rates. Overall, prediction of functional sites using DPA recapitulates much of the known information about functional sites in SCOP domains, and validates the use of DPA to predict functional sites in proteins. These results further suggest that functional sites in proteins tend to evolve at control points where interactions cause a large change in protein dynamics [1, 3].1. Ming D. and Wall M.E. Quantifying allosteric effects in proteins. 2005. Proteins 59:697-707.2. Ming D. and Wall M.E. Allostery in a coarse-grained model of protein dynamics. 2005. Phys Rev Lett 95:198301.3. Ming D. and Wall M.E. Interactions in native binding sites cause a large change in protein dynamics. 2006. J Mol Biol 358:213-223.4. Ming D., Cohn J.D. and Wall M.E. Fast dynamics perturbation analysis for prediction of functional sites. 2008. BMC Struct Biol 8:5.5. Murzin A.G., Brenner S.E., Hubbard T. and Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. 1995. J Mol Biol 247:536-540.

Prediction of Allosteric Linkages in Proteins

Cells often control protein activity using allostery, in which a functional site is controlled by interactions at a remote allosteric site. Although much is known about functional sites in proteins, less is known about the allosteric sites that control them. We are developing a method for high-throughput prediction of linkages between functional sites and allosteric sites. Our approach is distinct from previous computational methods to study allosteric linkages [1,2,3]: it works by seeking relatively strong thermodynamic linkages between interactions at a functional site and interactions at all possible allosteric sites on the protein surface. To implement the method, we use a coarse-grained model that enables fast predictions using a highly scalable algorithm: this approach enables predictions to be made in a few minutes per processor for a typical protein. We illustrate the method and characterize its performance using results from several test cases. The results are consistent with known allosteric linkages in proteins, and yield new insights into allosteric mechanisms.1. D. Ming and M.E. Wall. Allostery in a coarse-grained model of protein dynamics. 2005. Phys Rev Lett 95:198301.2. C. Chennubhotla, I. Bahar. Markov propagation of allosteric effects in biomolecular systems: application to GroEL-GroES. 2006. Mol Syst Biol 2:36.3. V.J. Hilser, E.B. Thompson. Intrinsic disorder as a mechanism to optimize allosteric coupling in proteins. 2007. Proc Natl Acad Sci U S A 104:8311-8315.

A tool for calculating binding-site residues on proteins from PDB structures

BackgroundIn the research on protein functional sites, researchers often need to identify binding-site residues on a protein. A commonly used strategy is to find a complex structure from the Protein Data Bank (PDB) that consists of the protein of interest and its interacting partner(s) and calculate binding-site residues based on the complex structure. However, since a protein may participate in multiple interactions, the binding-site residues calculated based on one complex structure usually do not reveal all binding sites on a protein. Thus, this requires researchers to find all PDB complexes that contain the protein of interest and combine the binding-site information gleaned from them. This process is very time-consuming. Especially, combing binding-site information obtained from different PDB structures requires tedious work to align protein sequences. The process becomes overwhelmingly difficult when researchers have a large set of proteins to analyze, which is usually the case in practice.ResultsIn this study, we have developed a tool for calculating binding-site residues on proteins, TCBRP . For an input protein, TCBRP can quickly find all binding-site residues on the protein by automatically combining the information obtained from all PDB structures that consist of the protein of interest. Additionally, TCBRP presents the binding-site residues in different categories according to the interaction type. TCBRP also allows researchers to set the definition of binding-site residues.ConclusionThe developed tool is very useful for the research on protein binding site analysis and prediction.

Compo: composite motif discovery using discrete models

BackgroundComputational discovery of motifs in biomolecular sequences is an established field, with applications both in the discovery of functional sites in proteins and regulatory sites in DNA. In recent years there has been increased attention towards the discovery of composite motifs, typically occurring in cis-regulatory regions of genes.ResultsThis paper describes Compo: a discrete approach to composite motif discovery that supports richer modeling of composite motifs and a more realistic background model compared to previous methods. Furthermore, multiple parameter and threshold settings are tested automatically, and the most interesting motifs across settings are selected. This avoids reliance on single hard thresholds, which has been a weakness of previous discrete methods. Comparison of motifs across parameter settings is made possible by the use of p-values as a general significance measure. Compo can either return an ordered list of motifs, ranked according to the general significance measure, or a Pareto front corresponding to a multi-objective evaluation on sensitivity, specificity and spatial clustering.ConclusionCompo performs very competitively compared to several existing methods on a collection of benchmark data sets. These benchmarks include a recently published, large benchmark suite where the use of support across sequences allows Compo to correctly identify binding sites even when the relevant PWMs are mixed with a large number of noise PWMs. Furthermore, the possibility of parameter-free running offers high usability, the support for multi-objective evaluation allows a rich view of potential regulators, and the discrete model allows flexibility in modeling and interpretation of motifs.

Dinucleotides docking to scorpion polypeptide toxins: A molecular modeling method for protein functional site recognition

To understand the principles underlying protein folding, many molecular modeling methods are being developed for predicting functional positions. In this work, fully flexible dinucleotides d(pApA), d(pApC), d(pApG), d(pApT), d(pCpA), d(pCpC), d(pCpG), d(pCpT), d(pGpA), d(pGpC), d(pGpG), d(pGpT), d(pTpA), d(pTpC), d(pTpG), and d(pTpT) were first docked onto the surface of scorpion polypeptide toxins (LqhIT2, PDB ID:2I61) and homology modeled ANEPIII. Automated docking was able to identify sites on scorpion polypeptide toxins where favorable nucleotide interactions can occur, and those sites were in agreement with the mutation data of this protein published recently [I. Karbat, R. Kahn, L. Cohen, N. Ilan, N. Gilles, G. Corzo, O. Froy, M. Gur, G. Albrecht, S.H. Heinemann, D. Gordon, M. Gurevitz, The unique pharmacology of the scorpion alpha-like toxin Lqh3 is associated with its flexible C-tail, Febs J 274 (2007) 1918–1931]. Simulation results suggested that dinucleotides docking is a suitable molecular modeling method that could be developed for protein functional site recognition.

Simulating Virtual Protein Cα Traces with Applications

We propose a simple procedure for generating virtual protein C(alpha) traces. One of the key ingredients of our method, to build a three-dimensional structure from a random sequence of amino acids, is to work directly on torsional angles of the chain which we sample from a von Mises distribution. With simple modeling of the hydrophobic effect in protein folding, the procedure produces compact and globular structures. Some characteristics of real proteins (i.e., compactness and globularity) are well mimicked by this procedure. These virtual traces are used to assess algorithms for matching protein structures or functional sites.

InterPro: the integrative protein signature database

The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or ‘signatures’ representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. Integration is performed manually and approximately half of the total ∼58 000 signatures available in the source databases belong to an InterPro entry. Recently, we have started to also display the remaining un-integrated signatures via our web interface. Other developments include the provision of non-signature data, such as structural data, in new XML files on our FTP site, as well as the inclusion of matchless UniProtKB proteins in the existing match XML files. The web interface has been extended and now links out to the ADAN predicted protein–protein interaction database and the SPICE and Dasty viewers. The latest public release (v18.0) covers 79.8% of UniProtKB (v14.1) and consists of 16 549 entries. InterPro data may be accessed either via the web address above, via web services, by downloading files by anonymous FTP or by using the InterProScan search software (http://www.ebi.ac.uk/Tools/InterProScan/).

Protein Meta-Functional Signatures from Combining Sequence, Structure, Evolution, and Amino Acid Property Information

Protein function is mediated by different amino acid residues, both their positions and types, in a protein sequence. Some amino acids are responsible for the stability or overall shape of the protein, playing an indirect role in protein function. Others play a functionally important role as part of active or binding sites of the protein. For a given protein sequence, the residues and their degree of functional importance can be thought of as a signature representing the function of the protein. We have developed a combination of knowledge- and biophysics-based function prediction approaches to elucidate the relationships between the structural and the functional roles of individual residues and positions. Such a meta-functional signature (MFS), which is a collection of continuous values representing the functional significance of each residue in a protein, may be used to study proteins of known function in greater detail and to aid in experimental characterization of proteins of unknown function. We demonstrate the superior performance of MFS in predicting protein functional sites and also present four real-world examples to apply MFS in a wide range of settings to elucidate protein sequence–structure–function relationships. Our results indicate that the MFS approach, which can combine multiple sources of information and also give biological interpretation to each component, greatly facilitates the understanding and characterization of protein function.

INTREPID—INformation-theoretic TREe traversal for Protein functional site IDentification

Motivation: Identification of functionally important residues in proteins plays a significant role in biological discovery. Here, we present INTREPID—an information–theoretic approach for functional site identification that exploits the information in large diverse multiple sequence alignments (MSAs). INTREPID uses a traversal of the phylogeny in combination with a positional conservation score, based on Jensen–Shannon divergence, to rank positions in an MSA. While knowledge of protein 3D structure can significantly improve the accuracy of functional site identification, since structural information is not available for a majority of proteins, INTREPID relies solely on sequence information. We evaluated INTREPID on two tasks: predicting catalytic residues and predicting specificity determinants.Results: In catalytic residue prediction, INTREPID provides significant improvements over Evolutionary Trace, ConSurf as well as over a baseline global conservation method on a set of 100 manually curated enzymes from the Catalytic Site Atlas. In particular, INTREPID is able to better predict catalytic positions that are not globally conserved and hence, attains improved sensitivity at high values of specificity. We also investigated the performance of INTREPID as a function of the evolutionary divergence of the protein family. We found that INTREPID is better able to exploit the diversity in such families and that accuracy improves when homologs with very low sequence identity are included in an alignment. In specificity determinant prediction, when subtype information is known, INTREPID-SPEC, a variant of INTREPID, attains accuracies that are competitive with other approaches for this task.Availability: INTREPID is available for 16919 families in the PhyloFacts resource (http://phylogenomics.berkeley.edu/phylofacts).Contact: sriram_s@cs.berkeley.eduSupplementary information: Relevant online supplementary material is available at http://phylogenomics.berkeley.edu/INTREPID.

Cis-acting sequences and trans-acting factors in the localization of mRNA for mitochondrial ribosomal proteins

mRNA localization is a conserved post-transcriptional process crucial for a variety of systems. Although several mechanisms have been identified, emerging evidence suggests that most transcripts reach the protein functional site by moving along cytoskeleton elements. We demonstrated previously that mRNA for mitochondrial ribosomal proteins are asymmetrically distributed in the cytoplasm, and that localization in the proximity of mitochondria is mediated by the 3'-UTR. Here we show by biochemical analysis that these mRNA transcripts are associated with the cytoskeleton through the microtubule network. Cytoskeleton association is functional for their intracellular localization near the mitochondrion, and the 3'-UTR is involved in this cytoskeleton-dependent localization. To identify the minimal elements required for localization, we generated DNA constructs containing, downstream from the GFP gene, deletion mutants of mitochondrial ribosomal protein S12 3'-UTR, and expressed them in HeLa cells. RT-PCR analysis showed that the localization signals responsible for mRNA localization are located in the first 154 nucleotides. RNA pull-down assays, mass spectrometry, and RNP immunoprecipitation assay experiments, demonstrated that mitochondrial ribosomal protein S12 3'-UTR interacts specifically with TRAP1 (tumor necrosis factor receptor-associated protein1), hnRNPM4 (heterogeneous nuclear ribonucleoprotein M4), Hsp70 and Hsp60 (heat shock proteins 70 and 60), and alpha-tubulin in vitro and in vivo.

Prediction of Functional Sites in SCOP Domains using Dynamics Perturbation Analysis

AbstractDynamics perturbation analysis (DPA) finds regions in a protein structure where proteins are "ticklish", i.e., where interactions cause a large change in protein dynamics. Previously, such regions were shown to predict the location of native binding sites in a docking test set, but the more general applicability of DPA to the prediction of functional sites in proteins was not shown. Here we describe the results of applying an accelerated algorithm, called Fast DPA, to predict functional sites in over 50,000 SCOP domains.

Improving position-specific predictions of protein functional sites using phylogenetic motifs

Accurate computational prediction of protein functional sites is critical to maximizing the utility of recent high-throughput sequencing efforts. Among the available approaches, position-specific conservation scores remain among the most popular due to their accuracy and ease of computation. Unfortunately, high false positive rates remain a limiting factor. Using phylogenetic motifs (PMs), we have developed two combined (conservation + PMs) prediction schemes that significantly improve prediction accuracy. Our first approach, called position-specific MINER (psMINER), rank orders alignment columns by conservation. Subsequently, positions that are also not identified as PMs are excluded from the prediction set. This approach improves prediction accuracy, in a statistically significant way, compared to the underlying conservation scores. Increased accuracy is a general result, meaning improvement is observed over several different conservation scores that span a continuum of complexity. In addition, a hybrid MINER (hMINER) that quantitatively considers both scoring regimes provides further improvement. More importantly, it provides critical insight into the relative importance of phylogeny versus alignment conservation. Both methods outperform other common prediction algorithms that also utilize phylogenetic concepts. Finally, we demonstrate that the presented results are critically sensitive to functional site definition, thus highlighting the need for more complete benchmarks within the prediction community.

High-Field EPR and ESEEM Investigation of the Nitrogen Quadrupole Interaction of Nitroxide Spin Labels in Disordered Solids: Toward Differentiation between Polarity and Proticity Matrix Effects on Protein Function

The combination of high-field electron paramagnetic resonance (EPR) with site-directed spin labeling (SDSL) techniques employing nitroxide radicals has turned out to be particularly powerful in revealing subtle changes of the polarity and proticity profiles in proteins enbedded in membranes. This information can be obtained by orientation-selective high-field EPR resolving principal components of the nitroxide Zeeman (g) and hyperfine ( A) tensors of the spin labels attached to specific molecular sites. In contrast to the g- and A-tensors, the (14)N ( I = 1) quadrupole interaction tensor of the nitroxide spin label has not been exploited in EPR for probing effects of the microenvironment of functional protein sites. In this work it is shown that the W-band (95 GHz) high-field electron spin echo envelope modulation (ESEEM) method is well suited for determining with high accuracy the (14)N quadrupole tensor principal components of a nitroxide spin label in disordered frozen solution. By W-band ESEEM the quadrupole components of a five-ring pyrroline-type nitroxide radical in glassy ortho-terphenyl and glycerol solutions have been determined. This radical is the headgroup of the MTS spin label widely used in SDSL protein studies. By DFT calulations and W-band ESEEM experiments it is demonstrated that the Q(yy) value is especially sensitive to the proticity and polarity of the nitroxide environment in H-bonding and nonbonding situations. The quadrupole tensor is shown to be rather insensitive to structural variations of the nitroxide label itself. When using Q(yy) as a testing probe of the environment, its ruggedness toward temperature changes represents an important advantage over the g xx and A(zz) parameters which are usually employed for probing matrix effects on the spin labeled molecular site. Thus, beyond measurenments of g xx and A(zz) of spin labeled protein sites in disordered solids, W-band high-field ESEEM studies of (14)N quadrupole interactions open a new avenue to reliably probe subtle environmental effects on the electronic structure. This is a significant step forward on the way to differentiate between effects from matrix polarity and hydrogen-bond formation.

The combination approach of SVM and ECOC for powerful identification and classification of transcription factor.

BackgroundTranscription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some extent, low accuracy is still a common problem. With the fast growing number of new proteins, more precise algorithms for identifying TFs from new proteins and classifying the consequent TFs are in a high demand.ResultsThe support vector machine (SVM) algorithm was utilized to construct an automatic detector for TF identification, where protein domains and functional sites were employed as feature vectors. Error-correcting output coding (ECOC) algorithm, which was originated from information and communication engineering fields, was introduced to combine with support vector machine (SVM) methodology for TF classification. The overall success rates of identification and classification achieved 88.22% and 97.83% respectively. Finally, a web site was constructed to let users access our tools (see Availability and requirements section for URL).ConclusionThe SVM method was a valid and stable means for TFs identification with protein domains and functional sites as feature vectors. Error-correcting output coding (ECOC) algorithm is a powerful method for multi-class classification problem. When combined with SVM method, it can remarkably increase the accuracy of TF classification using protein domains and functional sites as feature vectors. In addition, our work implied that ECOC algorithm may succeed in a broad range of applications in biological data mining.

Detecting evolutionary relationships across existing fold space, using sequence order-independent profile-profile alignments.

Here, a scalable, accurate, reliable, and robust protein functional site comparison algorithm is presented. The key components of the algorithm consist of a reduced representation of the protein structure and a sequence order-independent profile-profile alignment (SOIPPA). We show that SOIPPA is able to detect distant evolutionary relationships in cases where both a global sequence and structure relationship remains obscure. Results suggest evolutionary relationships across several previously evolutionary distinct protein structure superfamilies. SOIPPA, along with an increased coverage of protein fold space afforded by the structural genomics initiative, can be used to further test the notion that fold space is continuous rather than discrete.

An improved prediction of catalytic residues in enzyme structures

The protein databases contain a huge number of function unknown proteins, including many proteins with newly determined 3D structures resulted from the Structural Genomics Projects. To accelerate experiment-based assignment of function, de novo prediction of protein functional sites, like active sites in enzymes, becomes increasingly important. Here, we attempted to improve the prediction of catalytic residues in enzyme structures by seeking and refining different encodings (i.e. residue properties) as well as employing new machine learning algorithms. In particular, considering that catalytic residues can often reveal specific network centrality when representing enzyme structure as a residue contact network, the corresponding measurement (i.e. closeness centrality) was used as one of the most important encodings in our new predictor. Meanwhile, a genetic algorithm integrated neural network (GANN) was also employed. Thanks to the above strategies, our GANN predictor demonstrated a high accuracy of 91.2% in the prediction of catalytic residues based on balanced datasets (i.e. the 1:1 ratio of catalytic to non-catalytic residues). When the GANN method was optimally applied to real enzyme structures, 73.9% of the tested structures had the active site correctly located. Compared with two existing methods, the proposed GANN method also demonstrated a better performance.

Functional site profiling and electrostatic analysis of cysteines modifiable to cysteine sulfenic acid.

Cysteine sulfenic acid (Cys-SOH), a reversible modification, is a catalytic intermediate at enzyme active sites, a sensor for oxidative stress, a regulator of some transcription factors, and a redox-signaling intermediate. This post-translational modification is not random: specific features near the cysteine control its reactivity. To identify features responsible for the propensity of cysteines to be modified to sulfenic acid, a list of 47 proteins (containing 49 known Cys-SOH sites) was compiled. Modifiable cysteines are found in proteins from most structural classes and many functional classes, but have no propensity for any one type of protein secondary structure. To identify features affecting cysteine reactivity, these sites were analyzed using both functional site profiling and electrostatic analysis. Overall, the solvent exposure of modifiable cysteines is not different from the average cysteine. The combined sequence, structure, and electrostatic approaches reveal mechanistic determinants not obvious from overall sequence comparison, including: (1) pKaS of some modifiable cysteines are affected by backbone features only; (2) charged residues are underrepresented in the structure near modifiable sites; (3) threonine and other polar residues can exert a large influence on the cysteine pKa; and (4) hydrogen bonding patterns are suggested to be important. This compilation of Cys-SOH modification sites and their features provides a quantitative assessment of previous observations and a basis for further analysis and prediction of these sites. Agreement with known experimental data indicates the utility of this combined approach for identifying mechanistic determinants at protein functional sites.