Carlos H. da Silveira

Protein cutoff scanning: A comparative analysis of cutoff dependent and cutoff free methods for prospecting contacts in proteins

In this study, we carried out a comparative analysis between two classical methodologies to prospect residue contacts in proteins: the traditional cutoff dependent (CD) approach and cutoff free Delaunay tessellation (DT). In addition, two alternative coarse‐grained forms to represent residues were tested: using alpha carbon (CA) and side chain geometric center (GC). A database was built, comprising three top classes: all alpha, all beta, and alpha/beta. We found that the cutoff value at about 7.0 Å emerges as an important distance parameter. Up to 7.0 Å, CD and DT properties are unified, which implies that at this distance all contacts are complete and legitimate (not occluded). We also have shown that DT has an intrinsic missing edges problem when mapping the first layer of neighbors. In proteins, it may produce systematic errors affecting mainly the contact network in beta chains with CA. The almost‐Delaunay (AD) approach has been proposed to solve this DT problem. We found that even AD may not be an advantageous solution. As a consequence, in the strict range up to 7.0 Å, the CD approach revealed to be a simpler, more complete, and reliable technique than DT or AD. Finally, we have shown that coarse‐grained residue representations may introduce bias in the analysis of neighbors in cutoffs up to 6.8 Å, with CA favoring alpha proteins and GC favoring beta proteins. This provides an additional argument pointing to the value of 7.0 Å as an important lower bound cutoff to be used in contact analysis of proteins. Proteins 2009.

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.

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.

An image-matching approach to protein similarity analysis

In this work we model the problem of identifying how close structurally two proteins are as a problem of measuring the similarity between color images that represent their contact maps, where the chromatic information encodes the chemical nature of the contacts. We study two conceptually distinct methods to measure the similarity between such contact maps: a content-based image retrieval one and another based on image registration. In experiments with contact maps constructed from the Protein Data Bank (PDB), the image registration approach was able to identify with 100% precision 8 instances of a protein class mixed with 28 proteins of other classes. The content-based image retrieval approach had an accuracy only a little worse than that.

GReMLIN: A Graph Mining Strategy to Infer Protein-Ligand Interaction Patterns

Interactions between proteins and ligands are relevant in many biological processes. In the last years, such interactions have gained even more attention as the comprehension of protein-ligand molecular recognition is an important step to ligand prediction, target identificantion, and drug design, among others. This article presents GReMLIN (Graph Mining strategy to infer protein-Ligand INteraction patterns), a strategy to search for conserved protein-ligand interactions in a set of related proteins, based on frequent subgraph mining, that is able to perceive structural arrangements relevant for protein-ligand interaction. When compared to experimentally determined interactions, our in silico strategy was able to find many of relevant binding site residues/atoms for CDK2 and active site residues/atoms for Ricin.

PDBest: a user-friendly platform for manipulating and enhancing protein structures

UNLABELLED PDBest (PDB Enhanced Structures Toolkit) is a user-friendly, freely available platform for acquiring, manipulating and normalizing protein structures in a high-throughput and seamless fashion. With an intuitive graphical interface it allows users with no programming background to download and manipulate their files. The platform also exports protocols, enabling users to easily share PDB searching and filtering criteria, enhancing analysis reproducibility. AVAILABILITY AND IMPLEMENTATION PDBest installation packages are freely available for several platforms at http://www.pdbest.dcc.ufmg.br CONTACT wellisson@dcc.ufmg.br, dpires@dcc.ufmg.br, raquelcm@dcc.ufmg.br SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

An Interactive Strategy to Visualize Common Subgraphs in Protein-Ligand Interaction

Interactions between proteins and ligands play an important role in biological processes of living systems. For this reason, the development of computational methods to facilitate the understanding of the ligand-receptor recognition process is fundamental, since this comprehension is a major step towards ligand prediction, target identification, lead discovery, among others. This article presents a visual interactive interface to explore protein-ligand interactions and their conserved substructures for a set of similar proteins. The protein-ligand interface is modeled as bipartite graphs, where nodes represents protein and ligand atoms, and edges depicts interactions between them. Such graphs are the input to search for frequent subgraphs that are the conserved interaction patterns over the datasets. To illustrate the potential of our strategy, we used two test datasets, Ricin and human CDK2. Availability: http://dcc.ufmg.br/~alexandrefassio/gremlin/.

How to compute protein residue contacts more accurately

Computing contacts in proteins is important to several types of studies from Bioinformatics to Structural Biology. An accurate computation of contacts is essential to the correctness and reliability of applications involving folding prediction, protein structure prediction, quality assessment of protein structures, network contacts analysis, thermodynamic stability prediction, protein-protein and protein-ligand interactions, docking and so forth. In this work, we built an extensive database of contacts using about 45,000 structures from PDB to compare three paradigms for contact prospection at the atomic level: distance-based only, distance-geometry-based and distance-angulation-based. The main contribution of this paper is a critical evaluation of three different paradigms that can be used to compute contacts between protein atoms. We focused on protein-protein interfaces and analyzed four types of contacts, namely hydrogen bonds, aromatic stackings, hydrophobic and ionic (attractive) interactions. We scanned for possible contacts in the range from 0 to 7 Å. Our database with all computed contacts as well as the source code used to populate this database is freely available at bioinfo.dcc.ufmg.br/capri Our data showed the importance of a geometric approach to filter out spurious occluded contacts after about 3.5 Å for aromatic stackings, hydrophobic and ionic interactions. For hydrogen bonds, to filter out spurious contacts, we need to consider the angles involved in the interactions.