Identification Of Functional Sites Research Articles

Making Functional Predictions Using Local Spatial Arrangements in the Haloacid Dehalogenase Superfamily

Beginning with the sequencing of the human genome in the 1990s, major efforts have been underway to expand significantly upon our current working knowledge of proteins. Between the years 2000–2015, the Protein Structure Initiative (PSI) solved 15,000+ protein structures, but most of them have unknown or uncertain biochemical function or have an incorrect assigned putative function. With all these structures, methodology must be developed to functionally annotate these proteins in order to identify potential applications, such as bioremediation and understanding cellular processes. Enzymes in the Haloacid Dehalogenase Superfamily (HADSF) possess a wide range of functions, including phosphatases important in cell membrane biosynthesis and dehalogenases that possesses the ability to detoxify and degrade halogenated compounds. The Ondrechen Research Group (ORG) at Northeastern University has successfully developed methodology that is being used to predict biochemical function for Structural Genomics (SG) proteins in the HADSF. These methods are Partial Order Optimum Likelihood (POOL) and Structurally Aligned Local Sites of Activity (SALSA). POOL is a machine learning method that uses the electrostatic features and metrics from THEoreticalMicroscope Anomalous TItration Curve Shapes (THEMATICS), ligand binding pocket and geometric features from ConCavity, and the evolutionary scores and phylogenetic trees from INformation‐theoretic TREe traversal for Protein functional site IDentification (INTREPID) to make the predictions about which residues are catalytically active or otherwise important for protein function. SALSA uses the functional residue predictions for SG proteins obtained from POOL and aligns them with consensus signatures from known enzyme subfamilies according to the local spatial arrangement of predicted residues at the active site. So far for the HAD superfamily, using SALSA we have made predictions for 20 SG proteins: one dehalogenase, eight sugar phosphatases, three NagD‐like phosphatases, four P‐Type ATPases, and four soluble epoxide hydrolases. These predictions will be experimentally validated by biochemical assays to establish the function of each protein and to verify our computational approach to protein function prediction. The ability to predict computationally the biochemical function of protein structures of unknown or uncertain function adds tremendous value to genomics data.Support or Funding InformationThis project is funded by NSF CHE‐1305655, CHE‐1905214, and CHE‐1757078.

Simulated Epidemics in 3D Protein Structures to Detect Functional Properties.

The outcome of an epidemic is closely related to the network of interactions between individuals. Likewise, protein functions depend on the 3D arrangement of their residues and the underlying energetic interaction network. Borrowing ideas from the theoretical framework that has been developed to address the spreading of real diseases, we study for the first time the diffusion of a fictitious epidemic inside the protein nonbonded interaction network, aiming to study network features and properties. Our approach allows us to probe the overall stability and the capability of propagating information in complex 3D structures, proving to be very efficient in addressing different problems, from the assessment of thermal stability to the identification of functional sites.

Orphan Three-Finger Toxins Bind at Tissue Factor-Factor VIIa Interface to Inhibit Factor X Activation: Identification of Functional Site by Docking.

Three-finger toxins (3FTxs) contribute to toxicity of venomous snakes belonging to the family Elapidae. Currently, functions of a considerable proportion of 3FTxs are still unknown. Here, we describe the function of orphan group I 3FTxs consisting of four members. We also identified a new member of this group by sequencing a transcript isolated from Naja naja venom. This transcript, named najalexin, is identical to that previously described 3FTx from Naja atra venom gland, and shared high sequence identity with ringhalexin from Hemachatus haemachatus and a hypothetical protein from Ophiophagus hannah (here named as ophiolexin). The three-dimensional structure, as predicted by molecular modeling, showed that najalexin and ophiolexin share the same conserved structural organization as ringhalexin and other 3FTxs. Since ringhalexin inhibits the activation of factor X by the tissue factor–factor VIIa complex (TF-FVIIa), we evaluated the interaction of this group of 3FTxs with all components using in silico protein–protein docking studies. The binding of orphan group I 3FTxs to TF-FVIIa complex appears to be driven by their interaction with TF. They bind to fibronectin domain closer to the 170-loop of the FVIIa heavy chain to inhibit factor X activation. The docking studies reveal that functional site residues Tyr7, Lys9, Glu12, Lys26, Arg34, Leu35, Arg40, Val55, Asp56, Cys57, Cys58, and Arg65 on these 3FTxs are crucial for interaction. In silico replacement of these residues by Ala resulted in significant effects in the binding energies. Furthermore, these functional residues are not found in other groups of 3FTxs, which exhibit distinct pharmacological properties.

Applying Knowledge of Enzyme Biochemistry to the Prediction of Functional Sites for Aiding Drug Discovery.

Enzymes are biological catalysts that play an important role in determining the patterns of chemical transformations pertaining to life. Many milestones have been achieved in unraveling the mechanisms in which the enzymes orchestrate various cellular processes using experimental and computational approaches. Experimental studies generating nearly all possible mutations of target enzymes have been aided by rapid computational approaches aiming at enzyme functional classification, understanding domain organization, functional site identification. The functional architecture, essentially, is involved in binding or interaction with ligands including substrates, products, cofactors, inhibitors, providing for their function, such as in catalysis, ligand mediated cell signaling, allosteric regulation and post-translational modifications. With the increasing availability of enzyme information and advances in algorithm development, computational approaches have now become more capable of providing precise inputs for enzyme engineering, and in the process also making it more efficient. This has led to interesting findings, especially in aberrant enzyme interactions, such as hostpathogen interactions in infection, neurodegenerative diseases, cancer and diabetes. This review aims to summarize in retrospection - the mined knowledge, vivid perspectives and challenging strides in using available experimentally validated enzyme information for characterization. An analytical outlook is presented on the scope of exploring future directions.

Logic minimization and rule extraction for identification of functional sites in molecular sequences

BackgroundLogic minimization is the application of algebraic axioms to a binary dataset with the purpose of reducing the number of digital variables and/or rules needed to express it. Although logic minimization techniques have been applied to bioinformatics datasets before, they have not been used in classification and rule discovery problems. In this paper, we propose a method based on logic minimization to extract predictive rules for two bioinformatics problems involving the identification of functional sites in molecular sequences: transcription factor binding sites (TFBS) in DNA and O-glycosylation sites in proteins. TFBS are important in various developmental processes and glycosylation is a posttranslational modification critical to protein functions.MethodsIn the present study, we first transformed the original biological dataset into a suitable binary form. Logic minimization was then applied to generate sets of simple rules to describe the transformed dataset. These rules were used to predict TFBS and O-glycosylation sites. The TFBS dataset is obtained from the TRANSFAC database, while the glycosylation dataset was compiled using information from OGLYCBASE and the Swiss-Prot Database.We performed the same predictions using two standard classification techniques, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), and used their sensitivities and positive predictive values as benchmarks for the performance of our proposed algorithm. SVM were also used to reduce the number of variables included in the logic minimization approach.ResultsFor both TFBS and O-glycosylation sites, the prediction performance of the proposed logic minimization method was generally comparable and, in some cases, superior to the standard ANN and SVM classification methods with the advantage of providing intelligible rules to describe the datasets. In TFBS prediction, logic minimization produced a very small set of simple rules. In glycosylation site prediction, the rules produced were also interpretable and the most popular rules generated appeared to correlate well with recently reported hydrophilic/hydrophobic enhancement values of amino acids around possible O-glycosylation sites. Experiments with Self-Organizing Neural Networks corroborate the practical worth of the logic minimization method for these case studies.ConclusionsThe proposed logic minimization algorithm provides sets of rules that can be used to predict TFBS and O-glycosylation sites with sensitivity and positive predictive value comparable to those from ANN and SVM. Moreover, the logic minimization method has the additional capability of generating interpretable rules that allow biological scientists to correlate the predictions with other experimental results and to form new hypotheses for further investigation. Additional experiments with alternative rule-extraction techniques demonstrate that the logic minimization method is able to produce accurate rules from datasets with large numbers of variables and limited numbers of positive examples.

Relevant and Non-Redundant Amino Acid Sequence Selection for Protein Functional Site Identification

In order to apply a powerful pattern recognition algorithm to predict functional sites in proteins, amino acids cannot be used directly as inputs since they are non-numerical variables. Therefore, they need encoding prior to input. In this regard, the bio-basis function maps a non-numerical sequence space to a numerical feature space. One of the important issues for the bio-basis function is how to select a minimum set of bio-basis strings with maximum information. In this paper, an efficient method to select bio-basis strings for the bio-basis function is described integrating the concepts of the Fisher ratio and “degree of resemblance”. The integration enables the method to select a minimum set of most informative bio-basis strings. The “degree of resemblance” enables efficient selection of a set of distinct bio-basis strings. In effect, it reduces the redundant features in numerical feature space. Quantitative indices are proposed for evaluating the quality of selected bio-basis strings. The effectiveness of the proposed bio-basis string selection method, along with a comparison with existing methods, is demonstrated on different data sets.

A functional perspective on map generalisation

In the context of map generalisation, the ambition is to store once and then maintain a very detailed geographic database. Using a mix of modelling and cartographic generalisation techniques, the intention is to derive map products at varying levels of detail – from the fine scale to the highly synoptic. We argue that in modelling this process, it is highly advantageous to take a ‘functional perspective’ on map generalisation – rather than a geometric one. In other words to model the function as it manifests itself in the shapes and patterns of distribution of the phenomena being mapped – whether it be hospitals, airports, or cities. By modelling the functional composition of such features we can create relationships (partonomic, taxonomic and topological) that lend themselves directly to modelling, to analysis and most importantly to the process of generalisation. Borrowing from ideas in robotic vision this paper presents an approach for the automatic identification of functional sites (a collection of topographic features that perform a collective function) and demonstrates their utility in multi-scale representation and generalisation.

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.

Rank information: A structure‐independent measure of evolutionary trace quality that improves identification of protein functional sites

Protein functional sites are key targets for drug design and protein engineering, but their large-scale experimental characterization remains difficult. The evolutionary trace (ET) is a computational approach to this problem that has been useful in a variety of case studies, but its proteomic scale application is partially hindered because automated retrieval of input sequences from databases often includes some with errors that degrade functional site identification. To recognize and purge these sequences, this study introduces a novel and structure-free measure of ET quality called rank information (RI). It is shown that RI decreases in response to errors in sequences, alignments, or functional classifications. Conversely, an automated procedure to increase RI by selectively removing sequences improves functional site identification so as to nearly match manually curated traces in kinases and in a test set of 79 diverse proteins. Thus we conclude that RI partially reflects the evolutionary consistency of sequence, structure, and function. In practice, as the size of the proteome continues to grow exponentially, it provides a novel and structure-free measure of ET quality that increases its accuracy for large-scale automated annotation of protein functional sites.

Tramontano , Anna : The Ten Most Wanted Solutions in Protein Bioinformatics.

Tramontano , Anna : The Ten Most Wanted Solutions in Protein Bioinformatics.

Computational methods for functional site identification suggest a substrate access channel in transaldolase.

The homozygous deletion of Serine 171 results in the catalytic inactivation of the human transaldolase. Since Ser171 is in an outside loop, whereas the catalytic site is inside of the alpha/beta-barrel of the protein at least 15 A away, the loss of activity is difficult to explain. Two distinct computational methods are used to elucidate the potential origin of inactivation. Computational solvent mapping, which moves small organic molecules as probes around a protein surface and finds favorable binding positions, identifies the region around Ser171 as an important binding site. Three-dimensional cluster analysis, based both on a reference structure and multiple sequence alignment, shows that a patch of functionally important residues extends from Ser171 toward the catalytic site. Based on the findings of these two methods, we propose a novel ligand access path connecting these specific sites to the enzyme's active site. We also suggest that this mechanism may be aided by a significant conformational change involving the separation of two helices, alphaD and alphaG, in order to create an easy-access channel between the Ser171-related site and the active site. Further experimental procedures will be necessary to examine the biological feasibility of this proposed ligand shuttling path.

Genome-wide characterization of tetrahymena thermophila chromosome breakage sites. I. Cloning and identification of functional sites.

The chromosomes of the macronuclear (expressed) genome of Tetrahymena thermophila are generated by developmental fragmentation of the five micronuclear (germline) chromosomes. This fragmentation is site specific and directed by a conserved 15-bp chromosome breakage sequence (Cbs element). This article reports the construction of a library enriched for chromosome breakage junctions and the development of a successful scheme for the genome-wide isolation and characterization of functional Cbs junctions. Twenty-three new Cbs junctions were characterized and each was assigned to a specific micronuclear chromosome or chromosome arm. Two distinct previously unreported variant chromosome breakage sequences were found, each in two or more functional Cbs elements. Analysis of natural Cbs junctions confirmed that microheterogeneity in the macronuclear telomere addition site is associated with chromosome fragmentation. The physical and genetic characterization of these functional chromosome breakage junctions is reported in the accompanying article in this issue. The whole-genome shotgun sequencing and auto-assembly phase of the Tetrahymena Genome Initiative has recently been completed at The Institute for Genome Research (TIGR). By providing unique sequence from the natural ends of macronuclear chromosomes, Cbs junctions characterized in the work reported here will serve as useful sequence tags for relating macro- and micronuclear genetic, physical, and sequence maps.

Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes

We developed a new computational algorithm for the accurate identification of ligand binding envelopes rather than surface binding sites. We performed a large scale classification of the identified envelopes according to their shape and physicochemical properties. The predicting algorithm, called PocketFinder, uses a transformation of the Lennard-Jones potential calculated from a three-dimensional protein structure and does not require any knowledge about a potential ligand molecule. We validated this algorithm using two systematically collected data sets of ligand binding pockets from complexed (bound) and uncomplexed (apo) structures from the Protein Data Bank, 5616 and 11,510, respectively. As many as 96.8% of experimental binding sites were predicted at better than 50% overlap level. Furthermore 95.0% of the asserted sites from the apo receptors were predicted at the same level. We demonstrate that conformational differences between the apo and bound pockets do not dramatically affect the prediction results. The algorithm can be used to predict ligand binding pockets of uncharacterized protein structures, suggest new allosteric pockets, evaluate feasibility of protein-protein interaction inhibition, and prioritize molecular targets. Finally the data base of the known and predicted binding pockets for the human proteome structures, the human pocketome, was collected and classified. The pocketome can be used for rapid evaluation of possible binding partners of a given chemical compound.

Accurate and scalable identification of functional sites by evolutionary tracing.

A common difficulty in post genomics biology is that large-scale techniques of data collection often strip away information on the biological context of these data. The result is a massive number of disconnected observations on sequence, structure, and function from which underlying patterns and biological meaning are obscured. One solution is to build computational filters that pick out sufficiently few facts, relevant to a query, that their relationship is immediately apparent and experimentally testable. Typically, these filters rely on mathematics and statistics, and on first principles from physics and chemistry. We show here that evolution itself can be used to filter sequence and structure data in order to identify evolutionarily important amino acids. A general property of these residues is that they form clusters in native protein structures and point to regions where mutations have the greatest biological impact. The result is an accurate method of functional site annotation that is scalable for structural proteomics.

NUBIScan, an in Silico Approach for Prediction of Nuclear Receptor Response Elements

Nuclear receptors (NRs) are transcription factors activated by a multitude of hormones, other endogenous substances, and exogenous molecules. These proteins modulate the regulation of target genes by contacting their promoter or enhancer sequences at specific recognition sites. The identification of these response elements is the first step toward detailed insight into the regulatory mechanisms affecting a gene. We have developed NUBIScan, a computer algorithm to predict DNA recognition sites for NRs in the regulatory regions of genes. The algorithm is based on weighted nucleotide distribution matrices and combines scores from both half-sites necessary for NR dimer binding. It provides more specific identification of functional sites than previous in silico approaches, as evidenced by scanning published regulatory regions of drug-inducible genes and comparing the obtained predictions with experimental results. In prospective analyses, NUBIScan consistently identified new functional NR binding sites in sets of large sequences, which had eluded previous analyses. This is exemplified by the detailed functional analysis of the flanking region of two genes. This approach therefore facilitates the selection of likely sites of gene regulation for subsequent experimental analysis.

NUBIScan, an in silico approach for prediction of nuclear receptor response elements.

Nuclear receptors (NRs) are transcription factors activated by a multitude of hormones, other endogenous substances, and exogenous molecules. These proteins modulate the regulation of target genes by contacting their promoter or enhancer sequences at specific recognition sites. The identification of these response elements is the first step toward detailed insight into the regulatory mechanisms affecting a gene. We have developed NUBIScan, a computer algorithm to predict DNA recognition sites for NRs in the regulatory regions of genes. The algorithm is based on weighted nucleotide distribution matrices and combines scores from both half-sites necessary for NR dimer binding. It provides more specific identification of functional sites than previous in silico approaches, as evidenced by scanning published regulatory regions of drug-inducible genes and comparing the obtained predictions with experimental results. In prospective analyses, NUBIScan consistently identified new functional NR binding sites in sets of large sequences, which had eluded previous analyses. This is exemplified by the detailed functional analysis of the flanking region of two genes. This approach therefore facilitates the selection of likely sites of gene regulation for subsequent experimental analysis.

JEvTrace: refinement and variations of the evolutionary trace in JAVA.

Details of functional speciation within gene families can be difficult to identify using standard multiple sequence alignment (MSA) methods. The evolutionary trace (ET) was developed as a visualization tool to combine MSA, phylogenetic and structural data for identification of functional sites in proteins. The method has been successful in extracting evolutionary details of functional surfaces in a number of biological systems and modifications of the method are useful in creating hypotheses about the function of previously unannotated genes. We wish to facilitate the graphical interpretation of disparate data types through the creation of flexible software implementations. We have implemented the ET method in a JAVA graphical interface, JEvTrace. Users can analyze and visualize ET input and output with respect to protein phylogeny, sequence and structure. Function discovery with JEvTrace is demonstrated on two proteins with recently determined crystal structures: YlxR from Streptococcus pneumoniae with a predicted RNA-binding function, and a Haemophilus influenzae protein of unknown function, YbaK. To facilitate analysis and storage of results we propose a MSA coloring data structure. The sequence coloring format readily captures evolutionary, biological, functional and structural features of MSAs. Protein families and phylogeny represent complex data with statistical outliers and special cases. The JEvTrace implementation of the ET method allows detailed mining and graphical visualization of evolutionary sequence relationships.

Structure-based functional motif identifies a potential disulfide oxidoreductase active site in the serine/threonine protein phosphatase-1 subfamily.

In previous work, 3-dimensional descriptors of protein function ('fuzzy functional forms') were used to identify disulfide oxidoreductase active sites in high-resolution protein structures. During this analysis, a potential disulfide oxidoreductase active site in the serine/threonine protein phosphatase-1 (PP1) crystal structure was discovered. In PP1, the potential redox active site is located in close proximity to the phosphatase active site. This result is interesting in view of literature suggesting that serine/threonine phosphatases could be subject to redox control mechanisms within the cell; however, the actual source of this control is unknown. Additional analysis presented here shows that the putative oxidoreductase active site is highly conserved in the serine/threonine phosphatase-1 subfamily, but not in the serine/threonine phosphatase-2A or -2B subfamilies. These results demonstrate the significant advantages of using structure-based motifs for protein functional site identification. First, a putative disulfide oxidoreductase active site has been identified in serine-threonine phosphatases using a descriptor built from the glutaredoxin/thioredoxin family, proteins that have no apparent evolutionary relationship whatsoever to the PP1 proteins. Second, the proximity of the putative disulfide oxidoreductase active site to the phosphatase active site provides evidence toward a regulatory control mechanism. No sequence-based method could provide either piece of information.

Computational Methods for Protein Sequence Analysis

This unit is presented as a guide to addressing the issue of what to do with a protein sequence once it is obtained. A theoretical background for protein sequence analysis is provided first, followed by a discussion of matrix methods for sequence comparison (Matrix Methods for Sequence Comparison: Dot Plots). Sequence similarity searching is then presented, including the BLAST and FASTA databases. Other aspects of protein sequence analysis covered here are alignment methods, scoring matrices, multiple alignments, cluster methods and trees, and identification of functional sites.

Identification of functional sites on bovine leukemia virus envelope glycoproteins using structural and immunological data

Sequence analysis using the sensitive hydrophobic cluster analysis method shows that the bovine leukemia virus envelope glycoproteins conserve the general organization of the influenza hemagglutinin into a 'stem', containing the external part of the transmembrane glycoprotein and the N-terminal and C-terminal parts of the external glycoprotein, and a 'head', containing only external glycoprotein residues. However, our analysis suggests, for the first time, that the bovine leukemia virus envelope head will not adopt the typical 'jelly-roll' fold of the influenza A hemagglutinin head, but most likely folds into another type of 'Greek-key' structure corresponding to the overall topology of constant immunoglobulin domains. We constructed a three-dimensional model for the bovine leukemia virus envelope head by homology modeling using the crystal structure of the human histocompatibility antigen HLA-A2 alpha 3 domain. Furthermore, we propose a general model for the oligomeric organization of this head, based on the hemagglutinin trimer. The proposed structural organization of bovine leukemia virus external glycoprotein is further supported by antipeptide and monoclonal antibody reactivities. Our modeling study suggests that the loops of the two neutralizing peptides located in the head are adjacent at the top of the domain and define a potential interaction site of the external glycoprotein with its cellular receptor. This site is topologically similar to the binding site of hemagglutinin with its cellular receptor, sialic acid. The other neutralizing peptides are located within a small domain linking the head to the stem. These data are of interest for defining other oncoviral glycoproteins heads and receptor-binding sites.