Raquel C. de Melo-Minardi

Revealing the hidden functional diversity of an enzyme family

Millions of protein database entries are not assigned reliable functions, preventing the full understanding of chemical diversity in living organisms. Here, we describe an integrated strategy for the discovery of various enzymatic activities catalyzed within protein families of unknown or little known function. This approach relies on the definition of a generic reaction conserved within the family, high-throughput enzymatic screening on representatives, structural and modeling investigations and analysis of genomic and metabolic context. As a proof of principle, we investigated the DUF849 Pfam family and unearthed 14 potential new enzymatic activities, leading to the designation of these proteins as β-keto acid cleavage enzymes. We propose an in vivo role for four enzymatic activities and suggest key residues for guiding further functional annotation. Our results show that the functional diversity within a family may be largely underestimated. The extension of this strategy to other families will improve our knowledge of the enzymatic landscape.

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

BackgroundThe 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.ResultsCSM 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.ConclusionsWe 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.

aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction

MOTIVATION Receptor-ligand interactions are a central phenomenon in most biological systems. They are characterized by molecular recognition, a complex process mainly driven by physicochemical and structural properties of both receptor and ligand. Understanding and predicting these interactions are major steps towards protein ligand prediction, target identification, lead discovery and drug design. RESULTS We propose a novel graph-based-binding pocket signature called aCSM, which proved to be efficient and effective in handling large-scale protein ligand prediction tasks. We compare our results with those described in the literature and demonstrate that our algorithm overcomes the competitors techniques. Finally, we predict novel ligands for proteins from Trypanosoma cruzi, the parasite responsible for Chagas disease, and validate them in silico via a docking protocol, showing the applicability of the method in suggesting ligands for pockets in a real-world scenario. AVAILABILITY AND IMPLEMENTATION Datasets and the source code are available at http://www.dcc.ufmg.br/∼dpires/acsm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Identification of subfamily-specific sites based on active sites modeling and clustering

MOTIVATION Current computational approaches to function prediction are mostly based on protein sequence classification and transfer of annotation from known proteins to their closest homologous sequences relying on the orthology concept of function conservation. This approach suffers a major weakness: annotation reliability depends on global sequence similarity to known proteins and is poorly efficient for enzyme superfamilies that catalyze different reactions. Structural biology offers a different strategy to overcome the problem of annotation by adding information about protein 3D structures. This information can be used to identify amino acids located in active sites, focusing on detection of functional polymorphisms residues in an enzyme superfamily. Structural genomics programs are providing more and more novel protein structures at a high-throughput rate. However, there is still a huge gap between the number of sequences and available structures. Computational methods, such as homology modeling provides reliable approaches to bridge this gap and could be a new precise tool to annotate protein functions. RESULTS Here, we present Active Sites Modeling and Clustering (ASMC) method, a novel unsupervised method to classify sequences using structural information of protein pockets. ASMC combines homology modeling of family members, structural alignment of modeled active sites and a subsequent hierarchical conceptual classification. Comparison of profiles obtained from computed clusters allows the identification of residues correlated to subfamily function divergence, called specificity determining positions. ASMC method has been validated on a benchmark of 42 Pfam families for which previous resolved holo-structures were available. ASMC was also applied to several families containing known protein structures and comprehensive functional annotations. We will discuss how ASMC improves annotation and understanding of protein families functions by giving some specific illustrative examples on nucleotidyl cyclases, protein kinases and serine proteases. AVAILABILITY http://www.genoscope.fr/ASMC/.

3-Keto-5-aminohexanoate Cleavage Enzyme A COMMON FOLD FOR AN UNCOMMON CLAISEN-TYPE CONDENSATION

The exponential increase in genome sequencing output has led to the accumulation of thousands of predicted genes lacking a proper functional annotation. Among this mass of hypothetical proteins, enzymes catalyzing new reactions or using novel ways to catalyze already known reactions might still wait to be identified. Here, we provide a structural and biochemical characterization of the 3-keto-5-aminohexanoate cleavage enzyme (Kce), an enzymatic activity long known as being involved in the anaerobic fermentation of lysine but whose catalytic mechanism has remained elusive so far. Although the enzyme shows the ubiquitous triose phosphate isomerase (TIM) barrel fold and a Zn2+ cation reminiscent of metal-dependent class II aldolases, our results based on a combination of x-ray snapshots and molecular modeling point to an unprecedented mechanism that proceeds through deprotonation of the 3-keto-5-aminohexanoate substrate, nucleophilic addition onto an incoming acetyl-CoA, intramolecular transfer of the CoA moiety, and final retro-Claisen reaction leading to acetoacetate and 3-aminobutyryl-CoA. This model also accounts for earlier observations showing the origin of carbon atoms in the products, as well as the absence of detection of any covalent acyl-enzyme intermediate. Kce is the first representative of a large family of prokaryotic hypothetical proteins, currently annotated as the “domain of unknown function” DUF849.

GASS: Identifying Enzyme Active Sites with Genetic Algorithms

MOTIVATION Currently, 25% of proteins annotated in Pfam have their function unknown. One way of predicting proteins function is by looking at their active site, which has two main parts: the catalytic site and the substrate binding site. The active site is more conserved than the other residues of the protein and can be a rich source of information for protein function prediction. This article presents a new heuristic method, named genetic active site search (GASS), which searches for given active site 3D templates in unknown proteins. The method can perform non-exact amino acid matches (conservative mutations), is able to find amino acids in different chains and does not impose any restrictions on the active site size. RESULTS GASS results were compared with those catalogued in the catalytic site atlas (CSA) in four different datasets and compared with two other methods: amino acid pattern search for substructures and motif and catalytic site identification. The results show GASS can correctly identify >90% of the templates searched. Experiments were also run using data from the substrate binding sites prediction competition CASP 10, and GASS is ranked fourth among the 18 methods considered.

Using linear algebra for protein structural comparison and classification.

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering

As increasingly more genomes are sequenced, the vast majority of proteins may only be annotated computationally, given experimental investigation is extremely costly. This highlights the need for computational methods to determine protein functions quickly and reliably. We believe dividing a protein family into subtypes which share specific functions uncommon to the whole family reduces the function annotation problem’s complexity. Hence, this work’s purpose is to detect isofunctional subfamilies inside a family of unknown function, while identifying differentiating residues. Similarity between protein pairs according to various properties is interpreted as functional similarity evidence. Data are integrated using genetic programming and provided to a spectral clustering algorithm, which creates clusters of similar proteins. The proposed framework was applied to well-known protein families and to a family of unknown function, then compared to ASMC. Results showed our fully automated technique obtained better clusters than ASMC for two families, besides equivalent results for other two, including one whose clusters were manually defined. Clusters produced by our framework showed great correspondence with the known subfamilies, besides being more contrasting than those produced by ASMC. Additionally, for the families whose specificity determining positions are known, such residues were among those our technique considered most important to differentiate a given group. When run with the crotonase and enolase SFLD superfamilies, the results showed great agreement with this gold-standard. Best results consistently involved multiple data types, thus confirming our hypothesis that similarities according to different knowledge domains may be used as functional similarity evidence. Our main contributions are the proposed strategy for selecting and integrating data types, along with the ability to work with noisy and incomplete data; domain knowledge usage for detecting subfamilies in a family with different specificities, thus reducing the complexity of the experimental function characterization problem; and the identification of residues responsible for specificity.

ENZYMAP: Exploiting Protein Annotation for Modeling and Predicting EC Number Changes in UniProt/Swiss-Prot

The volume and diversity of biological data are increasing at very high rates. Vast amounts of protein sequences and structures, protein and genetic interactions and phenotype studies have been produced. The majority of data generated by high-throughput devices is automatically annotated because manually annotating them is not possible. Thus, efficient and precise automatic annotation methods are required to ensure the quality and reliability of both the biological data and associated annotations. We proposed ENZYMatic Annotation Predictor (ENZYMAP), a technique to characterize and predict EC number changes based on annotations from UniProt/Swiss-Prot using a supervised learning approach. We evaluated ENZYMAP experimentally, using test data sets from both UniProt/Swiss-Prot and UniProt/TrEMBL, and showed that predicting EC changes using selected types of annotation is possible. Finally, we compared ENZYMAP and DETECT with respect to their predictions and checked both against the UniProt/Swiss-Prot annotations. ENZYMAP was shown to be more accurate than DETECT, coming closer to the actual changes in UniProt/Swiss-Prot. Our proposal is intended to be an automatic complementary method (that can be used together with other techniques like the ones based on protein sequence and structure) that helps to improve the quality and reliability of enzyme annotations over time, suggesting possible corrections, anticipating annotation changes and propagating the implicit knowledge for the whole dataset.

VERMONT: Visualizing mutations and their effects on protein physicochemical and topological property conservation

In this paper, we propose an interactive visualization called VERMONT which tackles the problem of visualizing mutations and infers their possible effects on the conservation of physicochemical and topological properties in protein families. More specifically, we visualize a set of structure-based sequence alignments and integrate several structural parameters that should aid biologists in gaining insight into possible consequences of mutations. VERMONT allowed us to identify patterns of position-specific properties as well as exceptions that may help predict whether specific mutations could damage protein function.