Comparative Analysis of Protein-Protein Interactions in Cancer-Associated Genes 25

Protein-protein interactions (PPIs) have been widely studied to understand the bi-ological processes or molecular functions associated with different disease systems like cancer. While focused studies on individual cancers have generated valuable in-formation, global and comparative analysis of datasets from different cancer types has not been done. In this work, we carried out bioinformatic analysis of PPIs corresponding to differentially expressed genes from microarrays of various tumor tissues (belonging to bladder, colon, kidney and thyroid cancers) and compared their associated biological processes and molecular functions (based on Gene On-tology terms). We identified a set of processes or functions that are common to all these cancers, as well as those that are specific to only one or partial cancer types. Similarly, protein interaction networks in nucleic acid metabolism were compared to identify the common/specific clusters of proteins across different cancer types. Our results provide a basis for further experimental investigations to study protein interaction networks associated with cancer. The methodology developed in this work can also be applied to study similar disease systems.     

Sequence,Structural and Expression Divergence of Duplicate Genes in the Bovine Genome

Gene duplication is a widespread phenomenon in genome evolution, and it has been proposed to serve as an engine of evolutionary innovation. In the present study, we performed the first comprehensive analysis of duplicate genes in the bovine genome. A total of 3131 putative duplicated gene pairs were identified, including 712 cattle-specific duplicate gene pairs unevenly distributed across the genome, which are significantly enriched for specific biological functions including immunity, growth, digestion, reproduction, embryonic development, inflammatory response, and defense response to bacterium. Around 97.1% (87.8%) of (cattle-specific) duplicate gene pairs were found to have distinct exon-intron structures. Analysis of gene expression by RNA-Seq and sequence divergence (synonymous or non-synonymous) revealed that expression divergence is correlated with sequence divergence, as has been previously observed in other species. This analysis also led to the identification of a subset of cattle-specific duplicate gene pairs exhibiting very high expression divergence. Interestingly, further investigation revealed a significant relationship between structural and expression divergence while controlling for the effect of synonymous sequence divergence. Together these results provide further insight into duplicate gene sequence and expression divergence in cattle, and their potential contributions to phenotypic divergence.     

Divergence of Gene Body DNA Methylation and Evolution of Plant Duplicate Genes

It has been shown that gene body DNA methylation is associated with gene expression. However, whether and how deviation of gene body DNA methylation between duplicate genes can influence their divergence remains largely unexplored. Here, we aim to elucidate the potential role of gene body DNA methylation in the fate of duplicate genes. We identified paralogous gene pairs from Arabidopsis and rice (Oryza sativa ssp. japonica) genomes and reprocessed their single-base resolution methylome data. We show that methylation in paralogous genes nonlinearly correlates with several gene properties including exon number/gene length, expression level and mutation rate. Further, we demonstrated that divergence of methylation level and pattern in paralogs indeed positively correlate with their sequence and expression divergences. This result held even after controlling for other confounding factors known to influence the divergence of paralogs. We observed that methylation level divergence might be more relevant to the expression divergence of paralogs than methylation pattern divergence. Finally, we explored the mechanisms that might give rise to the divergence of gene body methylation in paralogs. We found that exonic methylation divergence more closely correlates with expression divergence than intronic methylation divergence. We show that genomic environments (e.g., flanked by transposable elements and repetitive sequences) of paralogs generated by various duplication mechanisms are associated with the methylation divergence of paralogs. Overall, our results suggest that the changes in gene body DNA methylation could provide another avenue for duplicate genes to develop differential expression patterns and undergo different evolutionary fates in plant genomes.     

Evolution of Specificity in Protein-Protein Interactions

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.     

How Structure Defines Affinity in Protein-Protein Interactions

Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.     

蛋白质相互作用的生物信息学研究进展

生命过程的分子基础在于生物分子之间的相互作用,其中蛋白质分子之间的相互作用占有极其重要的地位。研究蛋白质相互作用对于理解生命的真谛、探讨致病微生物的致病机理,以及研究新药提高人们的健康水平具有重要的作用。用生物信息学的方法研究蛋白质的相互作用已经取得了许多重要的成果,但也有很多问题还需解决。本文从蛋白质相互作用的数据库、预测方法、可预测蛋白质相互作用的网上服务、蛋白质相互作用网络等几方面,对蛋白质相互作用的生物信息学研究成果及其存在的问题做了概述。     

Dynamics of Protein-Protein Interactions at the MscL Periplasmic-Lipid Interface

MscL, the highly conserved bacterial mechanosensitive channel of large conductance, is one of the best studied mechanosensors. It is a homopentameric channel that serves as a biological emergency release valve that prevents cell lysis from acute osmotic stress. We previously showed that the periplasmic region of the protein, particularly a single residue located at the TM1/periplasmic loop interface, F47 of Staphylococcus aureus and I49 of Escherichia coli MscL, plays a major role in both the open dwell time and mechanosensitivity of the channel. Here, we introduced cysteine mutations at these sites and found they formed disulfide bridges that decreased the channel open dwell time. By scanning a likely interacting domain, we also found that these sites could be disulfide trapped by addition of cysteine mutations in other locations within the periplasmic loop of MscL, and this also led to rapid channel kinetics. Together, the data suggest structural rearrangements and protein-protein interactions that occur within this region upon normal gating, and further suggest that locking portions of the channel into a transition state decreases the stability of the open state.     

Dynamics of Protein-Protein Interactions at the MscL Periplasmic-Lipid Interface

MscL, the highly conserved bacterial mechanosensitive channel of large conductance, is one of the best studied mechanosensors. It is a homopentameric channel that serves as a biological emergency release valve that prevents cell lysis from acute osmotic stress. We previously showed that the periplasmic region of the protein, particularly a single residue located at the TM1/periplasmic loop interface, F47 of Staphylococcus aureus and I49 of Escherichia coli MscL, plays a major role in both the open dwell time and mechanosensitivity of the channel. Here, we introduced cysteine mutations at these sites and found they formed disulfide bridges that decreased the channel open dwell time. By scanning a likely interacting domain, we also found that these sites could be disulfide trapped by addition of cysteine mutations in other locations within the periplasmic loop of MscL, and this also led to rapid channel kinetics. Together, the data suggest structural rearrangements and protein-protein interactions that occur within this region upon normal gating, and further suggest that locking portions of the channel into a transition state decreases the stability of the open state.     

Relative Cosolute Size Influences the Kinetics of Protein-Protein Interactions

Protein signaling occurs in crowded intracellular environments, and while high concentrations of macromolecules are postulated to modulate protein-protein interactions, analysis of their impact at each step of the reaction pathway has not been systematically addressed. Potential cosolute-induced alterations in target association are particularly important for a signaling molecule like calmodulin (CaM), where competition among >300 targets governs which pathways are selectively activated. To explore how high concentrations of cosolutes influence CaM-target affinity and kinetics, we methodically investigated each step of the CaM-target binding mechanism under crowded or osmolyte-rich environments mimicked by ficoll-70, dextran-10, and sucrose. All cosolutes stabilized compact conformers of CaM and modulated association kinetics by affecting diffusion and rates of conformational change; however, the results showed that differently sized molecules had variable effects to enhance or impede unique steps of the association pathway. On- and off-rates were modulated by all cosolutes in a compensatory fashion, producing little change in steady-state affinity. From this work insights were gained on how high concentrations of inert crowding agents and osmolytes fit into a kinetic framework to describe protein-protein interactions relevant for cellular signaling.     

Expression of Duplicate msa Genes in the Salmonid Pathogen Renibacteriumsalmoninarum

Renibacterium salmoninarum is a gram-positive bacterium responsible for bacterial kidney disease of salmon and trout. R. salmoninarum has two identical copies of the gene encoding major soluble antigen (MSA), an immunodominant, extracellular protein. To determine whether one or both copies of msa are expressed, reporter plasmids encoding a fusion of MSA and green fluorescent protein controlled by 0.6 kb of promoter region from msa1 or msa2 were constructed and introduced into R. salmoninarum. Single copies of the reporter plasmids integrated into the chromosome by homologous recombination. Expression of mRNA and protein from the integrated plasmids was detected, and transformed cells were fluorescent, demonstrating that both msa1 and msa2 are expressed under in vitro conditions. This is the first report of successful transformation and homologous recombination in R. salmoninarum.     

Computational Prediction of Protein-Protein Interactions in Leishmania Predicted Proteomes

The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree of functional annotation which represents an important contribution since approximately 60% of Leishmania predicted proteomes has no predicted function.     

Prediction of Protein-Protein Interactions Using Protein Signature Profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain inter- actions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as “interacting“. In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis ele- gans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area un- der the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs in- creased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on aver- age 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.     

Prediction of Protein-Protein Interactions Using Protein Signature Profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain inter- actions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis ele- gans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area un- der the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs in- creased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on aver- age 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.