Protein Functional Similarity Research Articles

An Evolutionary Algorithm with Gene Ontology-Aware Crossover Operator for Protein Complex Detection

Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust EA with more biological consistency. For this purpose, a new crossover operator is suggested where biological information in terms of both gene semantic similarity and protein functional similarity is fed into its design. To reflect the heuristic roles of both semantic and functional similarities, this paper introduces two gene ontology (GO) aware crossover operators. These are direct annotation-aware and inherited annotation-aware crossover operators. The first strategy is handled with the direct gene ontology annotation of the proteins, while the second strategy is handled with the directed acyclic graph (DAG) of each gene ontology term in the gene product. To conduct our experiments, the proposed EAs with GO-aware crossover operators are compared against the state-of-the-art heuristic, canonical EAs with the traditional crossover operator, and GO-based EAs. Simulation results are evaluated in terms of recall, precision, and F measure at both complex level and protein level. The results prove that the new EA design encourages a more reliable treatment of exploration and exploitation and, thus, improves the detection ability for more accurate protein complex structures.

Site2Vec: a reference frame invariant algorithm for vector embedding of protein–ligand binding sites

Protein–ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Binding sites would also determine ADMET properties of a drug molecule. Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. To this end, methods for computing similarities between binding sites are still evolving and is an active area of research even today. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites. In this regard, deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame. We report here a novel algorithm, Site2Vec, that derives reference frame invariant vector embedding of a protein–ligand binding site. The method is based on pairwise distances between representative points and chemical compositions in terms of constituent amino acids of a site. The vector embedding serves as a locality sensitive hash function for proximity queries and determining similar sites. The method has been the top performer with more than 95% quality scores in extensive benchmarking studies carried over 10 data sets and against 23 other site comparison methods in the field. The algorithm serves for high throughput processing and has been evaluated for stability with respect to reference frame shifts, coordinate perturbations and residue mutations. We also provide the method as a standalone executable and a web service hosted at (http://services.iittp.ac.in/bioinfo/home).

Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework

Protein functional similarity based on gene ontology (GO) annotations serves as a powerful tool when comparing proteins on a functional level in applications such as protein-protein interaction prediction, gene prioritization, and disease gene discovery. Functional similarity (FS) is usually quantified by combining the GO hierarchy with an annotation corpus that links genes and gene products to GO terms. One large group of algorithms involves calculation of GO term semantic similarity (SS) between all the terms annotating the two proteins, followed by a second step, described as “mixing strategy”, which involves combining the SS values to yield the final FS value. Due to the variability of protein annotation caused e.g. by annotation bias, this value cannot be reliably compared on an absolute scale. We therefore introduce a similarity z-score that takes into account the FS background distribution of each protein. For a selection of popular SS measures and mixing strategies we demonstrate moderate accuracy improvement when using z-scores in a benchmark that aims to separate orthologous cases from random gene pairs and discuss in this context the impact of annotation corpus choice. The approach has been implemented in Frela, a fast high-throughput public web server for protein FS calculation and interpretation.

SimDEF: definition-based semantic similarity measure of gene ontology terms for functional similarity analysis of genes

Measures of protein functional similarity are essential tools for function prediction, evaluation of protein-protein interactions (PPIs) and other applications. Several existing methods perform comparisons between proteins based on the semantic similarity of their GO terms; however, these measures are highly sensitive to modifications in the topological structure of GO, tend to be focused on specific analytical tasks and concentrate on the GO terms themselves rather than considering their textual definitions. We introduce simDEF, an efficient method for measuring semantic similarity of GO terms using their GO definitions, which is based on the Gloss Vector measure commonly used in natural language processing. The simDEF approach builds optimized definition vectors for all relevant GO terms, and expresses the similarity of a pair of proteins as the cosine of the angle between their definition vectors. Relative to existing similarity measures, when validated on a yeast reference database, simDEF improves correlation with sequence homology by up to 50%, shows a correlation improvement >4% with gene expression in the biological process hierarchy of GO and increases PPI predictability by > 2.5% in F1 score for molecular function hierarchy. Datasets, results and source code are available at http://kiwi.cs.dal.ca/Software/simDEF CONTACT: ahmad.pgh@dal.ca or beiko@cs.dal.ca Supplementary data are available at Bioinformatics online.

Fuse: multiple network alignment via data fusion.

Discovering patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. However, the complexity of the multiple network alignment problem grows exponentially with the number of networks being aligned and designing a multiple network aligner that is both scalable and that produces biologically relevant alignments is a challenging task that has not been fully addressed. The objective of multiple network alignment is to create clusters of nodes that are evolutionarily and functionally conserved across all networks. Unfortunately, the alignment methods proposed thus far do not meet this objective as they are guided by pairwise scores that do not utilize the entire functional and evolutionary information across all networks. To overcome this weakness, we propose Fuse, a new multiple network alignment algorithm that works in two steps. First, it computes our novel protein functional similarity scores by fusing information from wiring patterns of all aligned PPI networks and sequence similarities between their proteins. This is in contrast with the previous tools that are all based on protein similarities in pairs of networks being aligned. Our comprehensive new protein similarity scores are computed by Non-negative Matrix Tri-Factorization (NMTF) method that predicts associations between proteins whose homology (from sequences) and functioning similarity (from wiring patterns) are supported by all networks. Using the five largest and most complete PPI networks from BioGRID, we show that NMTF predicts a large number protein pairs that are biologically consistent. Second, to identify clusters of aligned proteins over all networks, Fuse uses our novel maximum weight k-partite matching approximation algorithm. We compare Fuse with the state of the art multiple network aligners and show that (i) by using only sequence alignment scores, Fuse already outperforms other aligners and produces a larger number of biologically consistent clusters that cover all aligned PPI networks and (ii) using both sequence alignments and topological NMTF-predicted scores leads to the best multiple network alignments thus far. Our dataset and software are freely available from the web site: http://bio-nets.doc.ic.ac.uk/Fuse/ natasha@imperial.ac.uk Supplementary data are available at Bioinformatics online.

A graph-based integrative method of detecting consistent protein functional modules from multiple data sources.

Many clustering methods have been developed to identify functional modules in Protein-Protein Interaction (PPI) networks but the results are far from satisfaction. To overcome the noise and incomplete problems of PPI networks and find more accurate and stable functional modules, we propose an integrative method, bipartite graph-based Non-negative Matrix Factorisation method (BiNMF), in which we adopt multiple biological data sources as different views that describe PPIs. Specifically, traditional clustering models are adopted as preliminary analysis of different views of protein functional similarity. Then the intermediate clustering results are represented by a bipartite graph which can comprehensively represent the relationships between proteins and intermediate clusters and finally overlapping clustering results are achieved. Through extensive experiments, we see that our method is superior to baseline methods and detailed analysis has demonstrated the benefits of integrating diverse clustering methods and multiple biological information sources.

Information content-based Gene Ontology functional similarity measures: which one to use for a given biological data type?

The current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration.

Improving protein function prediction using domain and protein complexes in PPI networks

BackgroundCharacterization of unknown proteins through computational approaches is one of the most challenging problems in silico biology, which has attracted world-wide interests and great efforts. There have been some computational methods proposed to address this problem, which are either based on homology mapping or in the context of protein interaction networks.ResultsIn this paper, two algorithms are proposed by integrating the protein-protein interaction (PPI) network, proteins’ domain information and protein complexes. The one is domain combination similarity (DCS), which combines the domain compositions of both proteins and their neighbors. The other is domain combination similarity in context of protein complexes (DSCP), which extends the protein functional similarity definition of DCS by combining the domain compositions of both proteins and the complexes including them. The new algorithms are tested on networks of the model species of Saccharomyces cerevisiae to predict functions of unknown proteins using cross validations. Comparing with other several existing algorithms, the results have demonstrated the effectiveness of our proposed methods in protein function prediction. Furthermore, the algorithm DSCP using experimental determined complex data is robust when a large percentage of the proteins in the network is unknown, and it outperforms DCS and other several existing algorithms.ConclusionsThe accuracy of predicting protein function can be improved by integrating the protein-protein interaction (PPI) network, proteins’ domain information and protein complexes.

Quality assessment of protein model-structures based on structural and functional similarities

BackgroundExperimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology.ResultsGOBA - Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests.ConclusionsThe validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and one of CASP9, compared to the contest participants. Consequently, GOBA offers a novel single model quality assessment program that addresses the practical needs of biologists. In conjunction with other Model Quality Assessment Programs (MQAPs), it would prove useful for the evaluation of single protein models.

Network-based Auto-probit Modeling for Protein Function Prediction

Predicting the functional roles of proteins based on various genome-wide data, such as protein-protein association networks, has become a canonical problem in computational biology. Approaching this task as a binary classification problem, we develop a network-based extension of the spatial auto-probit model. In particular, we develop a hierarchical Bayesian probit-based framework for modeling binary network-indexed processes, with a latent multivariate conditional autoregressive Gaussian process. The latter allows for the easy incorporation of protein-protein association network topologies-either binary or weighted-in modeling protein functional similarity. We use this framework to predict protein functions, for functions defined as terms in the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functionality. Furthermore, we show how a natural extension of this framework can be used to model and correct for the high percentage of false negative labels in training data derived from GO, a serious shortcoming endemic to biological databases of this type. Our method performance is evaluated and compared with standard algorithms on weighted yeast protein-protein association networks, extracted from a recently developed integrative database called Search Tool for the Retrieval of INteracting Genes/proteins (STRING). Results show that our basic method is competitive with these other methods, and that the extended method-incorporating the uncertainty in negative labels among the training data-can yield nontrivial improvements in predictive accuracy.

Inferring protein function by domain context similarities in protein-protein interaction networks

BackgroundGenome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions.ResultsThe domain context similarity can be a useful index to predict protein function similarity. The prediction accuracy of our method in yeast is between 63%-67%, which outperforms the other methods in terms of ROC curves.ConclusionThis paper presents a novel protein function prediction method that combines protein domain composition information and PPI networks. Performance evaluations show that this method outperforms existing methods.

A Statistical Model of Protein Sequence Similarity and Function Similarity Reveals Overly-Specific Function Predictions

BackgroundPredicting protein function from primary sequence is an important open problem in modern biology. Not only are there many thousands of proteins of unknown function, current approaches for predicting function must be improved upon. One problem in particular is overly-specific function predictions which we address here with a new statistical model of the relationship between protein sequence similarity and protein function similarity.MethodologyOur statistical model is based on sets of proteins with experimentally validated functions and numeric measures of function specificity and function similarity derived from the Gene Ontology. The model predicts the similarity of function between two proteins given their amino acid sequence similarity measured by statistics from the BLAST sequence alignment algorithm. A novel aspect of our model is that it predicts the degree of function similarity shared between two proteins over a continuous range of sequence similarity, facilitating prediction of function with an appropriate level of specificity.SignificanceOur model shows nearly exact function similarity for proteins with high sequence similarity (bit score >244.7, e-value >1e−62, non-redundant NCBI protein database (NRDB)) and only small likelihood of specific function match for proteins with low sequence similarity (bit score <54.6, e-value <1e−05, NRDB). For sequence similarity ranges in between our annotation model shows an increasing relationship between function similarity and sequence similarity, but with considerable variability. We applied the model to a large set of proteins of unknown function, and predicted functions for thousands of these proteins ranging from general to very specific. We also applied the model to a data set of proteins with previously assigned, specific functions that were electronically based. We show that, on average, these prior function predictions are more specific (quite possibly overly-specific) compared to predictions from our model that is based on proteins with experimentally determined function.

Functionally guided alignment of protein interaction networks for module detection.

Motivation: Functional module detection within protein interaction networks is a challenging problem due to the sparsity of data and presence of errors. Computational techniques for this task range from purely graph theoretical approaches involving single networks to alignment of multiple networks from several species. Current network alignment methods all rely on protein sequence similarity to map proteins across species.Results: Here we carry out network alignment using a protein functional similarity measure. We show that using functional similarity to map proteins across species improves network alignment in terms of functional coherence and overlap with experimentally verified protein complexes. Moreover, the results from functional similarity-based network alignment display little overlap (<15%) with sequence similarity-based alignment. Our combined approach integrating sequence and function-based network alignment alongside graph clustering properties offers a 200% increase in coverage of experimental datasets and comparable accuracy to current network alignment methods.Availability: Program binaries and source code is freely available at http://www.stats.ox.ac.uk/research/bioinfo/resourcesContact: ali@stats.ox.ac.ukSupplementary Information: Supplementary data are available at Bioinformatics online.

In Silico Elucidation of the Molecular Mechanism Defining the Adverse Effect of Selective Estrogen Receptor Modulators

Early identification of adverse effect of preclinical and commercial drugs is crucial in developing highly efficient therapeutics, since unexpected adverse drug effects account for one-third of all drug failures in drug development. To correlate protein–drug interactions at the molecule level with their clinical outcomes at the organism level, we have developed an integrated approach to studying protein–ligand interactions on a structural proteome-wide scale by combining protein functional site similarity search, small molecule screening, and protein–ligand binding affinity profile analysis. By applying this methodology, we have elucidated a possible molecular mechanism for the previously observed, but molecularly uncharacterized, side effect of selective estrogen receptor modulators (SERMs). The side effect involves the inhibition of the Sacroplasmic Reticulum Ca2+ ion channel ATPase protein (SERCA) transmembrane domain. The prediction provides molecular insight into reducing the adverse effect of SERMs and is supported by clinical and in vitro observations. The strategy used in this case study is being applied to discover off-targets for other commercially available pharmaceuticals. The process can be included in a drug discovery pipeline in an effort to optimize drug leads and reduce unwanted side effects.

Identification of expression profiles of tapping panel dryness (TPD) associated genes from the latex of rubber tree (Hevea brasiliensis Muell. Arg.)

Tapping panel dryness (TPD) occurrence in high latex yielding rubber tree (Hevea brasiliensis) is characterized by the partial or complete cessation of latex flow upon tapping leading to severe loss in natural rubber production around the world. The goal of this study was to identify genes whose mRNA transcript levels are differentially regulated in rubber tree during the onset of TPD. To isolate TPD responsive genes, two cDNA libraries (forward and reverse) from total RNA isolated from latex of healthy and TPD trees were constructed using suppression subtractive hybridization (SSH) method. In total, 1,079 EST clones were obtained from two cDNA libraries and screened by reverse Northern blot analysis. Screening results revealed that about 352 clones were differentially regulated and they were selected for sequencing. Based on the nucleotide sequence data, the putative functions of cDNA clones were predicted by BLASTX/BLASTN analysis. Among these, 64 were genes whose function had been previously identified while the remaining clones were genes with either unknown protein function or insignificant similarity to other protein/DNA/EST sequences in existing databases. RT-PCR analysis was carried out to validate the up-regulated genes from both the libraries. Among them, two genes were strongly down-regulated in TPD trees. The level of mRNA transcripts of these two genes was further examined by conventional Northern and RT-PCR analysis. Results indicated that the expression level of two genes was significantly lower in TPD trees compared to healthy trees. Many TPD associated genes were also up-regulated in TPD trees suggesting that they may be involved in triggering programmed cell death (PCD) during the onset of TPD syndrome. The results presented here demonstrate that SSH technique provides a powerful complementary approach for the identification of TPD related genes from rubber tree.

Suppression subtractive hybridization-mediated transcriptome analysis from multiple tissues of aspen (Populus tremuloides) altered in phenylpropanoid metabolism.

A PCR-based suppression subtractive hybridization (SSH) technique was used to identify differentially expressed genes in developing tissues of control and transgenic aspen (Populus tremuloides Michx.) with down-regulated 4CL1 (4-coumarate:coenzyme A ligase) expression and enhanced growth. A total of 11,308 expressed sequence tags (ESTs) representing 5,028 non-redundant transcripts encoding 4,224 unique proteins was obtained from shoot apex, young stem, young leaf and root tip SSH libraries. Putative functions can be assigned to 60% of these transcripts. Approximately 14% of the ESTs are not represented among the 111,000 entries already present in Populus EST databases. In general, ESTs of the metabolism class occurred at a higher frequency in control- than transgenic-enriched libraries of all tissues, whereas protein synthesis and protein fate ESTs were over-represented in meristematic tissues of transgenics where 4CL1 was relatively strongly suppressed. Among all tissues, leaves yielded the highest percentage of ESTs with either unknown protein function or insignificant similarity to other protein/DNA/EST sequences in existing databases. Of particular interest was a large number of ESTs (16%) associated with signal transduction in transgenic leaves. Among these were several leucine-rich-repeat receptor-like protein kinases with markedly elevated expression in transgenic leaves. We also identified homologs of transposable elements that were up-regulated in transgenic tissues, providing the first experimental data for active expression of DNA mobile elements in long-lived tree species.

Bauplan of Urmetazoa: Basis for Genetic Complexity of Metazoa

Sponges were first grouped to the animal-plants or plant-animals then to the Zoophyta or Mesozoa and finally to the Parazoa. Only after the application of molecular biological techniques was it possible to place the Porifera monophyletically with the other metazoan phyla, justifying a unification of all multicellular animals to only one kingdom, the Metazoa. The first strong support came from the discovery that cell-cell and cell-matrix adhesion molecules that were cloned from sponges and were subsequently expressed share a high DNA sequence and protein function similarity with the corresponding molecules of other metazoans. Besides these evolutionary novelties for Metazoa, sponges also have morphogens and transcription factors in common with other metazoan phyla. Surprisingly, even those elements exist in Porifera, which are characteristic for pattern and axis formation. Recent studies showed that epithelial layers of sponges are sealed against the extracellular milieu through tight-junction proteins. The cell culture system from sponges, the primmorphs, was suitable for understanding morphogenetic events. Finally, stem cell marker genes were isolated, which underscored that sponge cells have the capacity to differentiate. In the relatively short period of time, approximately 200 million years, the basic pathways had to be established that allowed the transition for multicellular organisms to a colonial system through the formation of adhesion molecules; based on the development of a complex immune system and the apoptotic machinery of an integrated system, the Urmetazoa, which evolved approximately 800 million years ago, could be reached. Hence, the Bauplan of the hypothetical Urmetazoa can now be constructed according to genomic regulatory systems similar to those found in higher Metazoa. These data caused a paradigmatic change; the Porifera are complex and simple but by far not primitive.

Protein folds and functional similarity; The Greek key/immunoglobulin fold

Investigations on a number of proteins in the Greek key/immunoglobulin superfold group using computer methods suggested that proteins containing modified Greek keys might exhibit some protein-protein interactions similar to those seen with immunoglobulins. The two domain β-sheet modified Greek key structure of the β γ - crystallins is shared by other proteins, for example the chaperone protein PapD. Based on computer analysis, we tested for protein-protein interactions using protein A from Staphylococcus aureus, and showed that γ-crystallin, unlike α- or γs-crystallins, exhibits weak protein A binding. β-Crystallin exhibits much weaker binding than γ-crystallin. Protein A interaction may involve hydrophobic interactions around the interconnecting peptide region. We discuss the implications for the molecular evolution of the crystallins, the superfold, and for the molecular interactions in the mammalian lens that could be important in maintaining transparency.