Locally defined protein phylogenetic profiles reveal previously missed protein interactions and functional relationships

Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model

Introduction

The oral microbial group typically represents the human body's highly complex microbial group ecosystem. Oral microorganisms take part in human diseases, including Oral cavity inflammation, mucosal disease, periodontal disease, tooth decay, and oral cancer. On the other hand, oral microbes can also cause endocrine disorders, digestive function, and nerve function disorders, such as diabetes, digestive system diseases, and Alzheimer's disease. It was noted that the proteins of oral microbes play significant roles in these serious diseases. Having a good knowledge of oral microbes can be helpful in analyzing the procession of related diseases. Moreover, the high-dimensional features and imbalanced data lead to the complexity of oral microbial issues, which can hardly be solved with traditional experimental methods.

Methods

To deal with these challenges, we proposed a novel method, which is oral_voting_transfer, to deal with such classification issues in the field of oral microorganisms. Such a method employed three features to classify the five oral microorganisms, including Streptococcus mutans, Staphylococcus aureus, abiotrophy adjacent, bifidobacterial, and Capnocytophaga. Firstly, we utilized the highly effective model, which successfully classifies the organelle's proteins and transfers to deal with the oral microorganisms. And then, some classification methods can be treated as the local classifiers in this work. Finally, the results are voting from the transfer classifiers and the voting ones.

Results and discussion

The proposed method achieved the well performances in the five oral microorganisms. The oral_voting_transfer is a standalone tool, and all its source codes are publicly available at https://github.com/baowz12345/voting_transfer.

Using multi-scale genomics to associate poorly annotated genes with rare diseases

BACKGROUND

Next-generation sequencing (NGS) has significantly transformed the landscape of identifying disease-causing genes associated with genetic disorders. However, a substantial portion of sequenced patients remains undiagnosed. This may be attributed not only to the challenges posed by harder-to-detect variants, such as non-coding and structural variations but also to the existence of variants in genes not previously associated with the patient's clinical phenotype. This study introduces EvORanker, an algorithm that integrates unbiased data from 1,028 eukaryotic genomes to link mutated genes to clinical phenotypes.

METHODS

EvORanker utilizes clinical data, multi-scale phylogenetic profiling, and other omics data to prioritize disease-associated genes. It was evaluated on solved exomes and simulated genomes, compared with existing methods, and applied to 6260 knockout genes with mouse phenotypes lacking human associations. Additionally, EvORanker was made accessible as a user-friendly web tool.

RESULTS

In the analyzed exomic cohort, EvORanker accurately identified the "true" disease gene as the top candidate in 69% of cases and within the top 5 candidates in 95% of cases, consistent with results from the simulated dataset. Notably, EvORanker outperformed existing methods, particularly for poorly annotated genes. In the case of the 6260 knockout genes with mouse phenotypes, EvORanker linked 41% of these genes to observed human disease phenotypes. Furthermore, in two unsolved cases, EvORanker successfully identified DLGAP2 and LPCAT3 as disease candidates for previously uncharacterized genetic syndromes.

CONCLUSIONS

We highlight clade-based phylogenetic profiling as a powerful systematic approach for prioritizing potential disease genes. Our study showcases the efficacy of EvORanker in associating poorly annotated genes to disease phenotypes observed in patients. The EvORanker server is freely available at https://ccanavati.shinyapps.io/EvORanker/ .

RF_phage virion: Classification of phage virion proteins with a random forest model

Introduction: Phages play essential roles in biological procession, and the virion proteins encoded by the phage genome constitute critical elements of the assembled phage particle. Methods: This study uses machine learning methods to classify phage virion proteins. We proposed a novel approach, RF_phage virion, for the effective classification of the virion and non-virion proteins. The model uses four protein sequence coding methods as features, and the random forest algorithm was employed to solve the classification problem. Results: The performance of the RF_phage virion model was analyzed by comparing the performance of this algorithm with that of classical machine learning methods. The proposed method achieved a specificity (Sp) of 93.37%%, sensitivity (Sn) of 90.30%, accuracy (Acc) of 91.84%, Matthews correlation coefficient (MCC) of .8371, and an F1 score of .9196.

GFICLEE: ultrafast tree-based phylogenetic profile method inferring gene function at the genomic-wide level

Background

Phylogenetic profiling is widely used to predict novel members of large protein complexes and biological pathways. Although methods combined with phylogenetic trees have significantly improved prediction accuracy, computational efficiency is still an issue that limits its genome-wise application.

Results

Here we introduce a new tree-based phylogenetic profiling algorithm named GFICLEE, which infers common single and continuous loss (SCL) events in the evolutionary patterns. We validated our algorithm with human pathways from three databases and compared the computational efficiency with current tree-based with 10 different scales genome dataset. Our algorithm has a better predictive performance with high computational efficiency.

Conclusions

The GFICLEE is a new method to infers genome-wide gene function. The accuracy and computational efficiency of GFICLEE make it possible to explore gene functions at the genome-wide level on a personal computer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08070-7.

Expanding the MECP2 network using comparative genomics reveals potential therapeutic targets for Rett syndrome

Inactivating mutations in the Methyl-CpG Binding Protein 2 (MECP2) gene are the main cause of Rett syndrome (RTT). Despite extensive research into MECP2 function, no treatments for RTT are currently available. Here, we used an evolutionary genomics approach to construct an unbiased MECP2 gene network, using 1028 eukaryotic genomes to prioritize proteins with strong co-evolutionary signatures with MECP2. Focusing on proteins targeted by FDA-approved drugs led to three promising targets, two of which were previously linked to MECP2 function (IRAK, KEAP1) and one that was not (EPOR). The drugs targeting these three proteins (Pacritinib, DMF, and EPO) were able to rescue different phenotypes of MECP2 inactivation in cultured human neural cell types, and appeared to converge on Nuclear Factor Kappa B (NF-κB) signaling in inflammation. This study highlights the potential of comparative genomics to accelerate drug discovery, and yields potential new avenues for the treatment of RTT.

Optimization of co-evolution analysis through phylogenetic profiling reveals pathway-specific signals

SUMMARY

The exponential growth in available genomic data is expected to reach full sequencing of a million genomes in the coming decade. Improving and developing methods to analyze these genomes and to reveal their utility is of major interest in a wide variety of fields, such as comparative and functional genomics, evolution and bioinformatics. Phylogenetic profiling is an established method for predicting functional interactions between proteins based on similarities in their evolutionary patterns across species. Proteins that function together (i.e. generate complexes, interact in the same pathways or improve adaptation to environmental niches) tend to show coordinated evolution across the tree of life. The normalized phylogenetic profiling (NPP) method takes into account minute changes in proteins across species to identify protein co-evolution. Despite the success of this method, it is still not clear what set of parameters is required for optimal use of co-evolution in predicting functional interactions. Moreover, it is not clear if pathway evolution or function should direct parameter choice. Here, we create a reliable and usable NPP construction pipeline. We explore the effect of parameter selection on functional interaction prediction using NPP from 1028 genomes, both separately and in various value combinations. We identify several parameter sets that optimize performance for pathways with certain biological annotation. This work reveals the importance of choosing the right parameters for optimized function prediction based on a biological context.

AVAILABILITY AND IMPLEMENTATION

Source code and documentation are available on GitHub: https://github.com/iditam/CompareNPPs.

CONTACT

yuvaltab@ekmd.huji.ac.il.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Proteome-Scale Detection of Differential Conservation Patterns at Protein and Subprotein Levels with BLUR

In the multiomics era, comparative genomics studies based on gene repertoire comparison are increasingly used to investigate evolutionary histories of species, to study genotype–phenotype relations, species adaptation to various environments, or to predict gene function using phylogenetic profiling. However, comparisons of orthologs have highlighted the prevalence of sequence plasticity among species, showing the benefits of combining protein and subprotein levels of analysis to allow for a more comprehensive study of genotype/phenotype correlations. In this article, we introduce a new approach called BLUR (BLAST Unexpected Ranking), capable of detecting genotype divergence or specialization between two related clades at different levels: gain/loss of proteins but also of subprotein regions. These regions can correspond to known domains, uncharacterized regions, or even small motifs. Our method was created to allow two types of research strategies: 1) the comparison of two groups of species with no previous knowledge, with the aim of predicting phenotype differences or specializations between close species or 2) the study of specific phenotypes by comparing species that present the phenotype of interest with species that do not. We designed a website to facilitate the use of BLUR with a possibility of in-depth analysis of the results with various tools, such as functional enrichments, protein–protein interaction networks, and multiple sequence alignments. We applied our method to the study of two different biological pathways and to the comparison of several groups of close species, all with very promising results. BLUR is freely available at http://lbgi.fr/blur/.

Mapping global and local coevolution across 600 species to identify novel homologous recombination repair genes

The homologous recombination repair (HRR) pathway repairs DNA double-strand breaks in an error-free manner. Mutations in HRR genes can result in increased mutation rate and genomic rearrangements, and are associated with numerous genetic disorders and cancer. Despite intensive research, the HRR pathway is not yet fully mapped. Phylogenetic profiling analysis, which detects functional linkage between genes using coevolution, is a powerful approach to identify factors in many pathways. Nevertheless, phylogenetic profiling has limited predictive power when analyzing pathways with complex evolutionary dynamics such as the HRR. To map novel HRR genes systematically, we developed clade phylogenetic profiling (CladePP). CladePP detects local coevolution across hundreds of genomes and points to the evolutionary scale (e.g., mammals, vertebrates, animals, plants) at which coevolution occurred. We found that multiscale coevolution analysis is significantly more biologically relevant and sensitive to detect gene function. By using CladePP, we identified dozens of unrecognized genes that coevolved with the HRR pathway, either globally across all eukaryotes or locally in different clades. We validated eight genes in functional biological assays to have a role in DNA repair at both the cellular and organismal levels. These genes are expected to play a role in the HRR pathway and might lead to a better understanding of missing heredity in HRR-associated cancers (e.g., heredity breast and ovarian cancer). Our platform presents an innovative approach to predict gene function, identify novel factors related to different diseases and pathways, and characterize gene evolution.

Metabolic Pathway Assignment of Plant Genes based on Phylogenetic Profiling-A Feasibility Study

Despite many developed experimental and computational approaches, functional gene annotation remains challenging. With the rapidly growing number of sequenced genomes, the concept of phylogenetic profiling, which predicts functional links between genes that share a common co-occurrence pattern across different genomes, has gained renewed attention as it promises to annotate gene functions based on presence/absence calls alone. We applied phylogenetic profiling to the problem of metabolic pathway assignments of plant genes with a particular focus on secondary metabolism pathways. We determined phylogenetic profiles for 40,960 metabolic pathway enzyme genes with assigned EC numbers from 24 plant species based on sequence and pathway annotation data from KEGG and Ensembl Plants. For gene sequence family assignments, needed to determine the presence or absence of particular gene functions in the given plant species, we included data of all 39 species available at the Ensembl Plants database and established gene families based on pairwise sequence identities and annotation information. Aside from performing profiling comparisons, we used machine learning approaches to predict pathway associations from phylogenetic profiles alone. Selected metabolic pathways were indeed found to be composed of gene families of greater than expected phylogenetic profile similarity. This was particularly evident for primary metabolism pathways, whereas for secondary pathways, both the available annotation in different species as well as the abstraction of functional association via distinct pathways proved limiting. While phylogenetic profile similarity was generally not found to correlate with gene co-expression, direct physical interactions of proteins were reflected by a significantly increased profile similarity suggesting an application of phylogenetic profiling methods as a filtering step in the identification of protein-protein interactions. This feasibility study highlights the potential and challenges associated with phylogenetic profiling methods for the detection of functional relationships between genes as well as the need to enlarge the set of plant genes with proven secondary metabolism involvement as well as the limitations of distinct pathways as abstractions of relationships between genes.

Prediction and redesign of protein-protein interactions

Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function - those mediated by protein-protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI.

Practical aspects of protein co-evolution

Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these.

Human disease locus discovery and mapping to molecular pathways through phylogenetic profiling

By analyzing the conservation of human proteins across 87 species, we sorted proteins into clusters of coevolution. Some clusters are enriched for genes assigned to particular human diseases or molecular pathways; the other genes in the same cluster may function in related pathways and diseases.

Many genes that were thought to map to different diseases are actually coevolved together and mapped into the same phylogenetic clusters.

Many molecular pathways map to the same phylogenetic clusters as genes associated with specific human diseases.

Focusing on proteins coevolved with the microphthalmia-associated transcription factor (MITF), we identified the Notch pathway suppressor of hairless (RBP-Jk/SuH) transcription factor, and showed that RBP-Jk functions as an MITF cofactor.

Our analysis thus establishes a connectivity between different diseases and pathways, linking diseases phenotypes and functional gene groups.

Genes with common profiles of the presence and absence in disparate genomes tend to function in the same pathway. By mapping all human genes into about 1000 clusters of genes with similar patterns of conservation across eukaryotic phylogeny, we determined that sets of genes associated with particular diseases have similar phylogenetic profiles. By focusing on those human phylogenetic gene clusters that significantly overlap some of the thousands of human gene sets defined by their coexpression or annotation to pathways or other molecular attributes, we reveal the evolutionary map that connects molecular pathways and human diseases. The other genes in the phylogenetic clusters enriched for particular known disease genes or molecular pathways identify candidate genes for roles in those same disorders and pathways. Focusing on proteins coevolved with the microphthalmia-associated transcription factor (MITF), we identified the Notch pathway suppressor of hairless (RBP-Jk/SuH) transcription factor, and showed that RBP-Jk functions as an MITF cofactor.

A novel empirical mutual information approach to identify co-evolving amino acid positions of influenza A viruses

Mutual information (MI) is an approach commonly used to estimate the evolutionary correlation of 2 amino acid sites. Although several MI methods exist, prior to our contribution no systematic method had been developed to assess their performance, or to establish numerical thresholds to detect co-evolving amino acid sites. The current study performed a Markov chain Monte Carlo (MCMC) algorithm on influenza viral sequences to capture their evolutionary characteristics. A consensus maximum clade credibility (MCC) tree was estimated from the samples, together with their amino acid substitution statistics, from which we generated synthetic sequences of known dependent and independent paired amino acid sites. A pair-to-pair and influenza-specific amino acid substitution matrix (P2PFLU) incorporated into Bayesian Evolutionary Analysis Sampling Trees (BEAST) enumerated these synthetic sequences. The sequences inherited evolutionary features and co-varying characteristics from the real viral sequences, rendering these synthetic data ideal for exploring their co-evolving features. For the MI measure, we proposed a novel metric called the empirical MI (MI(Em)), which outperformed other MI measures in analysis of receiver operating characteristics (ROC). We implemented our approach on 1086 all-time PB2 sequences of influenza A H5N1 viruses, in which we found 97 sites exhibiting co-evolutionary substitution of one or more amino acid sites. In particular, PB2 451, along with eight other PB2 sites of various MI(Em) scores, was found to co-evolve with PB2 627, a known species-associated amino acid residue which plays a critical role in influenza virus replication.

Proteins with complex architecture as potential targets for drug design: a case study of Mycobacterium tuberculosis

Lengthy co-evolution of Homo sapiens and Mycobacterium tuberculosis, the main causative agent of tuberculosis, resulted in a dramatically successful pathogen species that presents considerable challenge for modern medicine. The continuous and ever increasing appearance of multi-drug resistant mycobacteria necessitates the identification of novel drug targets and drugs with new mechanisms of action. However, further insights are needed to establish automated protocols for target selection based on the available complete genome sequences. In the present study, we perform complete proteome level comparisons between M. tuberculosis, mycobacteria, other prokaryotes and available eukaryotes based on protein domains, local sequence similarities and protein disorder. We show that the enrichment of certain domains in the genome can indicate an important function specific to M. tuberculosis. We identified two families, termed pkn and PE/PPE that stand out in this respect. The common property of these two protein families is a complex domain organization that combines species-specific regions, commonly occurring domains and disordered segments. Besides highlighting promising novel drug target candidates in M. tuberculosis, the presented analysis can also be viewed as a general protocol to identify proteins involved in species-specific functions in a given organism. We conclude that target selection protocols should be extended to include proteins with complex domain architectures instead of focusing on sequentially unique and essential proteins only.

The predicted Arabidopsis interactome resource and network topology-based systems biology analyses

Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the predicted arabidopsis interactome resource (PAIR; ) presents 149,900 potential molecular interactions, which are expected to cover approximately 24% of the entire interactome with approximately 40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks.

Adaptive GDDA-BLAST: fast and efficient algorithm for protein sequence embedding

A major computational challenge in the genomic era is annotating structure/function to the vast quantities of sequence information that is now available. This problem is illustrated by the fact that most proteins lack comprehensive annotations, even when experimental evidence exists. We previously theorized that embedded-alignment profiles (simply "alignment profiles" hereafter) provide a quantitative method that is capable of relating the structural and functional properties of proteins, as well as their evolutionary relationships. A key feature of alignment profiles lies in the interoperability of data format (e.g., alignment information, physio-chemical information, genomic information, etc.). Indeed, we have demonstrated that the Position Specific Scoring Matrices (PSSMs) are an informative M-dimension that is scored by quantitatively measuring the embedded or unmodified sequence alignments. Moreover, the information obtained from these alignments is informative, and remains so even in the "twilight zone" of sequence similarity (<25% identity). Although our previous embedding strategy was powerful, it suffered from contaminating alignments (embedded AND unmodified) and high computational costs. Herein, we describe the logic and algorithmic process for a heuristic embedding strategy named "Adaptive GDDA-BLAST." Adaptive GDDA-BLAST is, on average, up to 19 times faster than, but has similar sensitivity to our previous method. Further, data are provided to demonstrate the benefits of embedded-alignment measurements in terms of detecting structural homology in highly divergent protein sequences and isolating secondary structural elements of transmembrane and ankyrin-repeat domains. Together, these advances allow further exploration of the embedded alignment data space within sufficiently large data sets to eventually induce relevant statistical inferences. We show that sequence embedding could serve as one of the vehicles for measurement of low-identity alignments and for incorporation thereof into high-performance PSSM-based alignment profiles.

Algorithmic and analytical methods in network biology

During the genomic revolution, algorithmic and analytical methods for organizing, integrating, analyzing, and querying biological sequence data proved invaluable. Today, increasing availability of high-throughput data pertaining to functional states of biomolecules, as well as their interactions, enables genome-scale studies of the cell from a systems perspective. The past decade witnessed significant efforts on the development of computational infrastructure for large-scale modeling and analysis of biological systems, commonly using network models. Such efforts lead to novel insights into the complexity of living systems, through development of sophisticated abstractions, algorithms, and analytical techniques that address a broad range of problems, including the following: (1) inference and reconstruction of complex cellular networks; (2) identification of common and coherent patterns in cellular networks, with a view to understanding the organizing principles and building blocks of cellular signaling, regulation, and metabolism; and (3) characterization of cellular mechanisms that underlie the differences between living systems, in terms of evolutionary diversity, development and differentiation, and complex phenotypes, including human disease. These problems pose significant algorithmic and analytical challenges because of the inherent complexity of the systems being studied; limitations of data in terms of availability, scope, and scale; intractability of resulting computational problems; and limitations of reference models for reliable statistical inference. This article provides a broad overview of existing algorithmic and analytical approaches to these problems, highlights key biological insights provided by these approaches, and outlines emerging opportunities and challenges in computational systems biology.

PKC and PLA2: probing the complexities of the calcium network

Lipid signaling and phosphorylation cascades are fundamental to calcium signaling networks. In this review, we will discuss the recent laboratory findings for the phospholipase A(2) (PLA(2))/protein kinase C (PKC) pathway within cellular calcium networks. The complexity and connectivity of these ubiquitous cellular signals make interpretation of experimental results extremely challenging. We present here computational methods which have been developed to conquer such complex data, and how they can be used to make models capable of accurately predicting cellular responses within multiple calcium signaling pathways. We propose that information obtained from network analysis and computational techniques provides a rich source of knowledge which can be directly translated to the laboratory benchtop.

Phylogenetic Profiles Reveal Structural and Functional Determinants of Lipid-binding

One of the major challenges in the genomic era is annotating structure/function to the vast quantities of sequence information now available. Indeed, most of the protein sequence database lacks comprehensive annotation, even when experimental evidence exists. Further, within structurally resolved and functionally annotated protein domains, additional functionalities contained in these domains are not apparent. To add further complication, small changes in the amino-acid sequence can lead to profound changes in both structure and function, underscoring the need for rapid and reliable methods to analyze these types of data. Phylogenetic profiles provide a quantitative method that can relate the structural and functional properties of proteins, as well as their evolutionary relationships. Using all of the structurally resolved Src-Homology-2 (SH2) domains, we demonstrate that knowledge-bases can be used to create single-amino acid phylogenetic profiles which reliably annotate lipid-binding. Indeed, these measures isolate the known phosphotyrosine and hydrophobic pockets as integral to lipid-binding function. In addition, we determined that the SH2 domain of Tec family kinases bind to lipids with varying affinity and specificity. Simulating mutations in Bruton's tyrosine kinase (BTK) that cause X-Linked Agammaglobulinemia (XLA) predict that these mutations alter lipid-binding, which we confirm experimentally. In light of these results, we propose that XLA-causing mutations in the SH3-SH2 domain of BTK alter lipid-binding, which could play a causative role in the XLA-phenotype. Overall, our study suggests that the number of lipid-binding proteins is drastically underestimated and, with further development, phylogenetic profiles can provide a method for rapidly increasing the functional annotation of protein sequences.

Phylogenetic profiles reveal evolutionary relationships within the "twilight zone" of sequence similarity

Inferring evolutionary relationships among highly divergent protein sequences is a daunting task. In particular, when pairwise sequence alignments between protein sequences fall <25% identity, the phylogenetic relationships among sequences cannot be estimated with statistical certainty. Here, we show that phylogenetic profiles generated with the Gestalt Domain Detection Algorithm-Basic Local Alignment Tool (GDDA-BLAST) are capable of deriving, ab initio, phylogenetic relationships for highly divergent proteins in a quantifiable and robust manner. Notably, the results from our computational case study of the highly divergent family of retroelements accord with previous estimates of their evolutionary relationships. Taken together, these data demonstrate that GDDA-BLAST provides an independent and powerful measure of evolutionary relationships that does not rely on potentially subjective sequence alignment. We demonstrate that evolutionary relationships can be measured with phylogenetic profiles, and therefore propose that these measurements can provide key insights into relationships among distantly related and/or rapidly evolving proteins.

Inferring modules of functionally interacting proteins using the Bond Energy Algorithm

BACKGROUND

Non-homology based methods such as phylogenetic profiles are effective for predicting functional relationships between proteins with no considerable sequence or structure similarity. Those methods rely heavily on traditional similarity metrics defined on pairs of phylogenetic patterns. Proteins do not exclusively interact in pairs as the final biological function of a protein in the cellular context is often hold by a group of proteins. In order to accurately infer modules of functionally interacting proteins, the consideration of not only direct but also indirect relationships is required. In this paper, we used the Bond Energy Algorithm (BEA) to predict functionally related groups of proteins. With BEA we create clusters of phylogenetic profiles based on the associations of the surrounding elements of the analyzed data using a metric that considers linked relationships among elements in the data set.

RESULTS

Using phylogenetic profiles obtained from the Cluster of Orthologous Groups of Proteins (COG) database, we conducted a series of clustering experiments using BEA to predict (upper level) relationships between profiles. We evaluated our results by comparing with COG's functional categories, And even more, with the experimentally determined functional relationships between proteins provided by the DIP and ECOCYC databases. Our results demonstrate that BEA is capable of predicting meaningful modules of functionally related proteins. BEA outperforms traditionally used clustering methods, such as k-means and hierarchical clustering by predicting functional relationships between proteins with higher accuracy.

CONCLUSION

This study shows that the linked relationships of phylogenetic profiles obtained by BEA is useful for detecting functional associations between profiles and extending functional modules not found by traditional methods. BEA is capable of detecting relationship among phylogenetic patterns by linking them through a common element shared in a group. Additionally, we discuss how the proposed method may become more powerful if other criteria to classify different levels of protein functional interactions, as gene neighborhood or protein fusion information, is provided.

Strategies for reliable exploitation of evolutionary concepts in high throughput biology

The recent availability of the complete genome sequences of a large number of model organisms, together with the immense amount of data being produced by the new high-throughput technologies, means that we can now begin comparative analyses to understand the mechanisms involved in the evolution of the genome and their consequences in the study of biological systems. Phylogenetic approaches provide a unique conceptual framework for performing comparative analyses of all this data, for propagating information between different systems and for predicting or inferring new knowledge. As a result, phylogeny-based inference systems are now playing an increasingly important role in most areas of high throughput genomics, including studies of promoters (phylogenetic footprinting), interactomes (based on the presence and degree of conservation of interacting proteins), and in comparisons of transcriptomes or proteomes (phylogenetic proximity and co-regulation/co-expression). Here we review the recent developments aimed at making automatic, reliable phylogeny-based inference feasible in large-scale projects. We also discuss how evolutionary concepts and phylogeny-based inference strategies are now being exploited in order to understand the evolution and function of biological systems. Such advances will be fundamental for the success of the emerging disciplines of systems biology and synthetic biology, and will have wide-reaching effects in applied fields such as biotechnology, medicine and pharmacology.

Why Should We Care about Molecular Coevolution?

Non-independent evolution of amino acid sites has become a noticeable limitation of most methods aimed at identifying selective constraints at functionally important amino acid sites or protein regions. The need for a generalised framework to account for non-independence of amino acid sites has fuelled the design and development of new mathematical models and computational tools centred on resolving this problem. Molecular coevolution is one of the most active areas of research, with an increasing rate of new models and methods being developed everyday. Both parametric and non-parametric methods have been developed to account for correlated variability of amino acid sites. These methods have been utilised for detecting phylogenetic, functional and structural coevolution as well as to identify surfaces of amino acid sites involved in protein-protein interactions. Here we discuss and briefly describe these methods, and identify their advantages and limitations.

Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution

The gap between the amount of genome information released by genome sequencing projects and our knowledge about the proteins' functions is rapidly increasing. To fill this gap, various 'genomic-context' methods have been proposed that exploit sequenced genomes to predict the functions of the encoded proteins. One class of methods, phylogenetic profiling, predicts protein function by correlating the phylogenetic distribution of genes with that of other genes or phenotypic characteristics. The functions of a number of proteins, including ones of medical relevance, have thus been predicted and subsequently confirmed experimentally. Additionally, various approaches to measure the similarity of phylogenetic profiles and to account for the phylogenetic bias in the data have been proposed. We review the successful applications of phylogenetic profiling and analyse the performance of various profile similarity measures with a set of one microsporidial and 25 fungal genomes. In the fungi, phylogenetic profiling yields high-confidence predictions for the highest and only the highest scoring gene pairs illustrating both the power and the limitations of the approach. Both practical examples and theoretical considerations suggest that in order to get a reliable and specific picture of a protein's function, results from phylogenetic profiling have to be combined with other sources of evidence.

Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: a comprehensive assessment

BACKGROUND

A widely-used approach for discovering functional and physical interactions among proteins involves phylogenetic profile comparisons (PPCs). Here, proteins with similar profiles are inferred to be functionally related under the assumption that proteins involved in the same metabolic pathway or cellular system are likely to have been co-inherited during evolution.

RESULTS

Our experimentation with E. coli and yeast proteins with 16 different carefully composed reference sets of genomes revealed that the phyletic patterns of proteins in prokaryotes alone could be adequate enough to make reasonably accurate functional linkage predictions. A slight improvement in performance is observed on adding few eukaryotes into the reference set, but a noticeable drop-off in performance is observed with increased number of eukaryotes. Inclusion of most parasitic, pathogenic or vertebrate genomes and multiple strains of the same species into the reference set do not necessarily contribute to an improved sensitivity or accuracy. Interestingly, we also found that evolutionary histories of individual pathways have a significant affect on the performance of the PPC approach with respect to a particular reference set. For example, to accurately predict functional links in carbohydrate or lipid metabolism, a reference set solely composed of prokaryotic (or bacterial) genomes performed among the best compared to one composed of genomes from all three super-kingdoms; this is in contrast to predicting functional links in translation for which a reference set composed of prokaryotic (or bacterial) genomes performed the worst. We also demonstrate that the widely used random null model to quantify the statistical significance of profile similarity is incomplete, which could result in an increased number of false-positives.

CONCLUSION

Contrary to previous proposals, it is not merely the number of genomes but a careful selection of informative genomes in the reference set that influences the prediction accuracy of the PPC approach. We note that the predictive power of the PPC approach, especially in eukaryotes, is heavily influenced by the primary endosymbiosis and subsequent bacterial contributions. The over-representation of parasitic unicellular eukaryotes and vertebrates additionally make eukaryotes less useful in the reference sets. Reference sets composed of highly non-redundant set of genomes from all three super-kingdoms fare better with pathways showing considerable vertical inheritance and strong conservation (e.g. translation apparatus), while reference sets solely composed of prokaryotic genomes fare better for more variable pathways like carbohydrate metabolism. Differential performance of the PPC approach on various pathways, and a weak positive correlation between functional and profile similarities suggest that caution should be exercised while interpreting functional linkages inferred from genome-wide large-scale profile comparisons using a single reference set.

Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins

Background

Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.

Result

In this study, the residue interface propensities of four kinds of complexes (homo-permanent complexes, homo-transient complexes, hetero-permanent complexes and hetero-transient complexes) are investigated. These propensities, combined with sequence profiles and accessible surface areas, are inputted to the support vector machine for the prediction of protein binding sites. Such propensities are further improved by taking evolutional information into consideration, which results in a class of novel propensities at the profile level, i.e. the binary profiles interface propensities. Experiment is performed on the 1139 non-redundant protein chains. Although different residue interface propensities among different complexes are observed, the improvement of the classifier with residue interface propensities can be negligible in comparison with that without propensities. The binary profile interface propensities can significantly improve the performance of binding sites prediction by about ten percent in term of both precision and recall.

Conclusion

Although there are minor differences among the four kinds of complexes, the residue interface propensities cannot provide efficient discrimination for the complicated interfaces of proteins. The binary profile interface propensities can significantly improve the performance of binding sites prediction of protein, which indicates that the propensities at the profile level are more accurate than those at the residue level.

Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions

Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein-protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the non-interacting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain-domain interactions. Given a protein-protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain-domain interactions, and used known domain-domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain-domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites.

Identification and analysis of DNA-binding transcription factors in Bacillus subtilis and other Firmicutes--a genomic approach

Background

Bacillus subtilis is one of the best-characterized organisms in Gram-positive bacteria. It represents a paradigm of gene regulation in bacteria due its complex life style (which could involve a transition between stages as diverse as vegetative cell and spore formation). In order to gain insight into the organization and evolution of the B. subtilis regulatory network and to provide an alternative framework for further studies in bacteria, we identified and analyzed its repertoire of DNA-binding transcription factors in terms of their abundance, family distribution and regulated genes.

Results

A collection of 237 DNA-binding Transcription Factors (TFs) was identified in B. subtilis, half of them with experimental evidence. 59% of them were predicted to be repressors, 17% activators, 17% were putatively identified as dual regulatory proteins and the remaining 6.3% could not be associated with a regulatory role. From this collection 56 TFs were found to be autoregulated, most of them negatively, though a significant proportion of positive feedback circuits were also identified. TFs were clustered into 51 regulatory protein families and then traced on 58 genomes from Firmicutes to detect their presence. From this analysis three families were found conserved in all the Firmicutes; fifteen families were distributed in all Firmicutes except in the phyla Mollicutes; two were constrained to Bacillales and finally two families were found to be specific to B. subtilis, due to their specie specific distribution. Repression seems to be the most common regulatory mechanism in Firmicutes due to the high proportion of repressors in the detected collection in these genomes. In addition, six global regulators were defined in B. subtilis based on the number and function of their regulated genes.

Conclusion

In this work we identified and described the characteristics associated to the repertoire of DNA-binding TFs in B. subtilis. We also quantified their abundance, family distribution, and regulatory roles in the context of Firmicutes. This work should not only contribute to our understanding of the regulation of gene expression in bacteria from the perspective of B. subtilis but also provide us the basis for comprehensive modeling of transcriptional regulatory networks in Firmicutes.