PAR-3D: a server to predict protein active site residues

Developing Lipase Inhibitor as a Novel Approach to Address the Rice Bran Rancidity Issue─A Critical Review

Rice bran is a valuable byproduct from the food processing industry, which contains abundant protein, essential unsaturated fatty acids, and numerous bioactive compounds. However, its susceptibility to rancidity greatly restricts its wide utilization. Many strategies have been proposed to delay the rancidity of rice bran, but most of them have their respective limitations. Here, we proposed that developing rice ban lipase peptide inhibitors represents an alternative and promising prescription for impeding the rancidity of rice bran, in contrast to the conventional stabilization approaches for rice bran. For this reason, the rancidity mechanisms of rice bran and the research progress of rice bran lipases were discussed. In addition, the feasibility of utilizing in silico screening and phage display, two state-of-the-art technologies, in the design of the related peptide inhibitors was also highlighted. This knowledge is expected to provide a theoretical basis for opening a new avenue for stabilizing rice bran.

Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

A comparative analysis of the DNA recombination repair pathway in mycobacterial genomes

In prokaryotes, repair by homologous recombination provides a major means to reinstate the genetic information lost in DNA damage. Recombination repair pathway in mycobacteria has multiple differences as compared to that in Escherichia coli. Of about 20 proteins known to be involved in the pathway, a set of 9 proteins, namely, RecF, RecO, RecR, RecA, SSBa, RuvA, RuvB and RuvC was found to be indispensable among the 43 mycobacterial strains. A domain level analysis indicated that most domains involved in recombination repair are unique to these proteins and are present as single copies in the genomes. Synteny analysis reveals that the gene order of proteins involved in the pathway is not conserved, suggesting that they may be regulated differently in different species. Sequence conservation among the same protein from different strains suggests the importance of RecO-RecA and RecFOR-RecA presynaptic pathways in the repair of double strand-breaks and single strand-breaks respectively. New annotations obtained from the analysis, include identification of a protein with a probable Holliday junction binding role present in 41 mycobacterial genomes and that of a RecB-like nuclease, containing a cas4 domain, present in 42 genomes. New insights into the binding of small molecules to the relevant proteins are provided by binding pocket analysis using three dimensional structural models. Analysis of the various features of the recombination repair pathway, presented here, is likely to provide a framework for further exploring stress response and emergence of drug resistance in mycobacteria.

Prediction and experimental validation of enzyme substrate specificity in protein structures

Structural Genomics aims to elucidate protein structures to identify their functions. Unfortunately, the variation of just a few residues can be enough to alter activity or binding specificity and limit the functional resolution of annotations based on sequence and structure; in enzymes, substrates are especially difficult to predict. Here, large-scale controls and direct experiments show that the local similarity of five or six residues selected because they are evolutionarily important and on the protein surface can suffice to identify an enzyme activity and substrate. A motif of five residues predicted that a previously uncharacterized Silicibacter sp. protein was a carboxylesterase for short fatty acyl chains, similar to hormone-sensitive-lipase-like proteins that share less than 20% sequence identity. Assays and directed mutations confirmed this activity and showed that the motif was essential for catalysis and substrate specificity. We conclude that evolutionary and structural information may be combined on a Structural Genomics scale to create motifs of mixed catalytic and noncatalytic residues that identify enzyme activity and substrate specificity.

CLIPS-4D: a classifier that distinguishes structurally and functionally important residue-positions based on sequence and 3D data

MOTIVATION

The precise identification of functionally and structurally important residues of a protein is still an open problem, and state-of-the-art classifiers predict only one or at most two different categories.

RESULT

We have implemented the classifier CLIPS-4D, which predicts in a mutually exclusively manner a role in catalysis, ligand-binding or protein stability for each residue-position of a protein. Each prediction is assigned a P-value, which enables the statistical assessment and the selection of predictions with similar quality. CLIPS-4D requires as input a multiple sequence alignment and a 3D structure of one protein in PDB format. A comparison with existing methods confirmed state-of-the-art prediction quality, even though CLIPS-4D classifies more specifically than other methods. CLIPS-4D was implemented as a multiclass support vector machine, which exploits seven sequence-based and two structure-based features, each of which was shown to contribute to classification quality. The classification of ligand-binding sites profited most from the 3D features, which were the assessment of the solvent accessible surface area and the identification of surface pockets. In contrast, five additionally tested 3D features did not increase the classification performance achieved with evolutionary signals deduced from the multiple sequence alignment.

Protein Homology Analysis for Function Prediction with Parallel Sub-Graph Isomorphism

To annotate the biological function of a protein molecule, it is essential to have information on its 3D structure. Many successful methods for function prediction are based on determining structurally conserved regions because the functional residues are proved to be more conservative than others in protein evolution. Since the 3D conformation of a protein can be represented by a contact map graph, graph matching, algorithms are often employed to identify the conserved residues in weakly homologous protein pairs. However, the general graph matching algorithm is computationally expensive because graph similarity searching is essentially a NP-hard problem. Parallel implementations of the graph matching are often exploited to speed up the process. In this chapter,the authors review theoretical and computational approaches of graph theory and the recently developed graph matching algorithms for protein function prediction.

Identification and expression analyses of cytosolic glutamine synthetase genes in barley (Hordeum vulgare L.)

Glutamine synthetase (GS) is a key enzyme in nitrogen (N) assimilation, particularly during seed development. Three cytosolic GS isoforms (HvGS1) were identified in barley (Hordeum vulgare L. cv Golden Promise). Quantitation of gene expression, localization and response to N supply revealed that each gene plays a non-redundant role in different tissues and during development. Localization of HvGS1_1 in vascular cells of different tissues, combined with its abundance in the stem and its response to changes in N supply, indicate that it is important in N transport and remobilization. HvGS1_1 is located on chromosome 6H at 72.54 cM, close to the marker HVM074 which is associated with a major quantitative trait locus (QTL) for grain protein content (GPC). HvGS1_1 may be a potential candidate gene to manipulate barley GPC. HvGS1_2 mRNA was localized to the leaf mesophyll cells, in the cortex and pericycle of roots, and was the dominant HvGS1 isoform in these tissues. HvGS1_2 expression increased in leaves with an increasing supply of N, suggesting its role in the primary assimilation of N. HvGS1_3 was specifically and predominantly localized in the grain, being highly expressed throughout grain development. HvGS1_3 expression increased specifically in the roots of plants grown on high NH(+)4, suggesting that it has a primary role in grain N assimilation and also in the protection against ammonium toxicity in roots. The expression of HvGS1 genes is directly correlated with protein and enzymatic activity, indicating that transcriptional regulation is of prime importance in the control of GS activity in barley.

The use of evolutionary patterns in protein annotation

With genomic data skyrocketing, their biological interpretation remains a serious challenge. Diverse computational methods address this problem by pointing to the existence of recurrent patterns among sequence, structure, and function. These patterns emerge naturally from evolutionary variation, natural selection, and divergence--the defining features of biological systems--and they identify molecular events and shapes that underlie specificity of function and allosteric communication. Here we review these methods, and the patterns they identify in case studies and in proteome-wide applications, to infer and rationally redesign function.

ProBiS-2012: web server and web services for detection of structurally similar binding sites in proteins

The ProBiS web server is a web server for detection of structurally similar binding sites in the PDB and for local pairwise alignment of protein structures. In this article, we present a new version of the ProBiS web server that is 10 times faster than earlier versions, due to the efficient parallelization of the ProBiS algorithm, which now allows significantly faster comparison of a protein query against the PDB and reduces the calculation time for scanning the entire PDB from hours to minutes. It also features new web services, and an improved user interface. In addition, the new web server is united with the ProBiS-Database and thus provides instant access to pre-calculated protein similarity profiles for over 29 000 non-redundant protein structures. The ProBiS web server is particularly adept at detection of secondary binding sites in proteins. It is freely available at http://probis.cmm.ki.si/old-version, and the new ProBiS web server is at http://probis.cmm.ki.si.

Computational tools for the synthetic design of biochemical pathways

As the field of synthetic biology is developing, the prospects for de novo design of biosynthetic pathways are becoming more and more realistic. Hence, there is an increasing need for computational tools that can support these efforts. A range of algorithms has been developed that can be used to identify all possible metabolic pathways and their corresponding enzymatic parts. These can then be ranked according to various properties and modelled in an organism-specific context. Finally, design software can aid the biologist in the integration of a selected pathway into smartly regulated transcriptional units. Here, we review key existing tools and offer suggestions for how informatics can help to shape the future of synthetic microbiology.

Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns

Background

The unforgiving pace of growth of available biological data has increased the demand for efficient and scalable paradigms, models and methodologies for automatic annotation. In this paper, we present a novel structure-based protein function prediction and structural classification method: Cutoff Scanning Matrix (CSM). CSM generates feature vectors that represent distance patterns between protein residues. These feature vectors are then used as evidence for classification. Singular value decomposition is used as a preprocessing step to reduce dimensionality and noise. The aspect of protein function considered in the present work is enzyme activity. A series of experiments was performed on datasets based on Enzyme Commission (EC) numbers and mechanistically different enzyme superfamilies as well as other datasets derived from SCOP release 1.75.

Results

CSM was able to achieve a precision of up to 99% after SVD preprocessing for a database derived from manually curated protein superfamilies and up to 95% for a dataset of the 950 most-populated EC numbers. Moreover, we conducted experiments to verify our ability to assign SCOP class, superfamily, family and fold to protein domains. An experiment using the whole set of domains found in last SCOP version yielded high levels of precision and recall (up to 95%). Finally, we compared our structural classification results with those in the literature to place this work into context. Our method was capable of significantly improving the recall of a previous study while preserving a compatible precision level.

Conclusions

We showed that the patterns derived from CSMs could effectively be used to predict protein function and thus help with automatic function annotation. We also demonstrated that our method is effective in structural classification tasks. These facts reinforce the idea that the pattern of inter-residue distances is an important component of family structural signatures. Furthermore, singular value decomposition provided a consistent increase in precision and recall, which makes it an important preprocessing step when dealing with noisy data.

Active site detection by spatial conformity and electrostatic analysis--unravelling a proteolytic function in shrimp alkaline phosphatase

Computational methods are increasingly gaining importance as an aid in identifying active sites. Mostly these methods tend to have structural information that supplement sequence conservation based analyses. Development of tools that compute electrostatic potentials has further improved our ability to better characterize the active site residues in proteins. We have described a computational methodology for detecting active sites based on structural and electrostatic conformity - CataLytic Active Site Prediction (CLASP). In our pipelined model, physical 3D signature of any particular enzymatic function as defined by its active sites is used to obtain spatially congruent matches. While previous work has revealed that catalytic residues have large pKa deviations from standard values, we show that for a given enzymatic activity, electrostatic potential difference (PD) between analogous residue pairs in an active site taken from different proteins of the same family are similar. False positives in spatially congruent matches are further pruned by PD analysis where cognate pairs with large deviations are rejected. We first present the results of active site prediction by CLASP for two enzymatic activities - β-lactamases and serine proteases, two of the most extensively investigated enzymes. The results of CLASP analysis on motifs extracted from Catalytic Site Atlas (CSA) are also presented in order to demonstrate its ability to accurately classify any protein, putative or otherwise, with known structure. The source code and database is made available at www.sanchak.com/clasp/. Subsequently, we probed alkaline phosphatases (AP), one of the well known promiscuous enzymes, for additional activities. Such a search has led us to predict a hitherto unknown function of shrimp alkaline phosphatase (SAP), where the protein acts as a protease. Finally, we present experimental evidence of the prediction by CLASP by showing that SAP indeed has protease activity in vitro.

Novel feature for catalytic protein residues reflecting interactions with other residues

Owing to their potential for systematic analysis, complex networks have been widely used in proteomics. Representing a protein structure as a topology network provides novel insight into understanding protein folding mechanisms, stability and function. Here, we develop a new feature to reveal correlations between residues using a protein structure network. In an original attempt to quantify the effects of several key residues on catalytic residues, a power function was used to model interactions between residues. The results indicate that focusing on a few residues is a feasible approach to identifying catalytic residues. The spatial environment surrounding a catalytic residue was analyzed in a layered manner. We present evidence that correlation between residues is related to their distance apart most environmental parameters of the outer layer make a smaller contribution to prediction and ii catalytic residues tend to be located near key positions in enzyme folds. Feature analysis revealed satisfactory performance for our features, which were combined with several conventional features in a prediction model for catalytic residues using a comprehensive data set from the Catalytic Site Atlas. Values of 88.6 for sensitivity and 88.4 for specificity were obtained by 10fold crossvalidation. These results suggest that these features reveal the mutual dependence of residues and are promising for further study of structurefunction relationship.

A structural-alphabet-based strategy for finding structural motifs across protein families

Proteins with insignificant sequence and overall structure similarity may still share locally conserved contiguous structural segments; i.e. structural/3D motifs. Most methods for finding 3D motifs require a known motif to search for other similar structures or functionally/structurally crucial residues. Here, without requiring a query motif or essential residues, a fully automated method for discovering 3D motifs of various sizes across protein families with different folds based on a 16-letter structural alphabet is presented. It was applied to structurally non-redundant proteins bound to DNA, RNA, obligate/non-obligate proteins as well as free DNA-binding proteins (DBPs) and proteins with known structures but unknown function. Its usefulness was illustrated by analyzing the 3D motifs found in DBPs. A non-specific motif was found with a ‘corner’ architecture that confers a stable scaffold and enables diverse interactions, making it suitable for binding not only DNA but also RNA and proteins. Furthermore, DNA-specific motifs present ‘only’ in DBPs were discovered. The motifs found can provide useful guidelines in detecting binding sites and computational protein redesign.

ProBiS: a web server for detection of structurally similar protein binding sites

A web server, ProBiS, freely available at http://probis.cmm.ki.si, is presented. This provides access to the program ProBiS (Protein Binding Sites), which detects protein binding sites based on local structural alignments. Detailed instructions and user guidelines for use of ProBiS are available at the server under 'HELP' and selected examples are provided under 'EXAMPLES'.

SitesIdentify: a protein functional site prediction tool

Background

The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application.

Results

Here we present a functional site prediction tool (SitesIdentify), based on combining sequence conservation information with geometry-based cleft identification, that is freely available via a web-server. We have shown that SitesIdentify compares favourably to other functional site prediction tools in a comparison of seven methods on a non-redundant set of 237 enzymes with annotated active sites.

Conclusion

SitesIdentify is able to produce comparable accuracy in predicting functional sites to its closest available counterpart, but in addition achieves improved accuracy for proteins with few characterised homologues. SitesIdentify is available via a webserver at http://www.manchester.ac.uk/bioinformatics/sitesidentify/

RASMOT-3D PRO: a 3D motif search webserver

Detection of structural motif of residues in protein structures allows identification of structural or functional similarity between proteins. In the field of protein engineering, structural motif identification is essential to select protein scaffolds on which a motif of residues can be transferred to design a new protein with a given function. We describe here the RASMOT-3D PRO webserver (http://biodev.extra.cea.fr/rasmot3d/) that performs a systematic search in 3D structures of protein for a set of residues exhibiting a particular topology. Comparison is based on Cα and Cβ atoms in two steps: inter-atomic distances and RMSD. RASMOT-3D PRO takes in input a PDB file containing the 3D coordinates of the searched motif and provides an interactive list of identified protein structures exhibiting residues of similar topology as the motif searched. Each solution can be graphically examined on the website. The topological search can be conducted in structures described in PDB files uploaded by the user or in those deposited in the PDB. This characteristic as well as the possibility to reject scaffolds sterically incompatible with the target, makes RASMOT-3D PRO a unique webtool in the field of protein engineering.

UPF201 archaeal specific family members reveal structural similarity to RNA-binding proteins but low likelihood for RNA-binding function

We have determined X-ray crystal structures of four members of an archaeal specific family of proteins of unknown function (UPF0201; Pfam classification: DUF54) to advance our understanding of the genetic repertoire of archaea. Despite low pairwise amino acid sequence identities (10-40%) and the absence of conserved sequence motifs, the three-dimensional structures of these proteins are remarkably similar to one another. Their common polypeptide chain fold, encompassing a five-stranded antiparallel beta-sheet and five alpha-helices, proved to be quite unexpectedly similar to that of the RRM-type RNA-binding domain of the ribosomal L5 protein, which is responsible for binding the 5S- rRNA. Structure-based sequence alignments enabled construction of a phylogenetic tree relating UPF0201 family members to L5 ribosomal proteins and other structurally similar RNA binding proteins, thereby expanding our understanding of the evolutionary purview of the RRM superfamily. Analyses of the surfaces of these newly determined UPF0201 structures suggest that they probably do not function as RNA binding proteins, and that this domain specific family of proteins has acquired a novel function in archaebacteria, which awaits experimental elucidation.

Sequence and structural features of enzymes and their active sites by EC class

We have analysed a non-redundant set of 294 enzymes for differences in sequence and structural features between the six main Enzyme Commission (EC) classification groups. This systematic study of enzymes, and their active sites in particular, aims to increase understanding of how the structure of an enzyme relates to its functional role. Many features showed significant differences between the EC classes, including active-site polarity, enzyme size and active-site amino acid propensities. Many attributes correlate with each other to form clusters of related features from which we chose representative features for further analysis. Oxidoreductases have more non-polar active sites, which can be attributed to cofactor binding and a preference for Glu over Asp in active sites in comparison to the other classes. Lyases form a significantly higher proportion of oligomers than any other class, whilst the hydrolases form the largest proportion of monomers. These features were then used in a prediction model that classified each enzyme into its top EC class with an accuracy of 33.1%, which is an increase of 16.4% over random classification. Understanding the link between structure and function is critical to improving enzyme design and the prediction of protein function from structure without transfer of annotation from alignments.

Structural descriptor database: a new tool for sequence-based functional site prediction

Background

The Structural Descriptor Database (SDDB) is a web-based tool that predicts the function of proteins and functional site positions based on the structural properties of related protein families. Structural alignments and functional residues of a known protein set (defined as the training set) are used to build special Hidden Markov Models (HMM) called HMM descriptors. SDDB uses previously calculated and stored HMM descriptors for predicting active sites, binding residues, and protein function. The database integrates biologically relevant data filtered from several databases such as PDB, PDBSUM, CSA and SCOP. It accepts queries in fasta format and predicts functional residue positions, protein-ligand interactions, and protein function, based on the SCOP database.

Results

To assess the SDDB performance, we used different data sets. The Trypsion-like Serine protease data set assessed how well SDDB predicts functional sites when curated data is available. The SCOP family data set was used to analyze SDDB performance by using training data extracted from PDBSUM (binding sites) and from CSA (active sites). The ATP-binding experiment was used to compare our approach with the most current method. For all evaluations, significant improvements were obtained with SDDB.

Conclusion

SDDB performed better when trusty training data was available. SDDB worked better in predicting active sites rather than binding sites because the former are more conserved than the latter. Nevertheless, by using our prediction method we obtained results with precision above 70%.