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Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as ‘nodes’ and ‘edges’, respectively. Better understanding of genotype‐to‐phenotype relationships in human disease will require modeling of how disease‐causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products (‘node removal’) and interaction‐specific or edge‐specific (‘edgetic’) alterations. Global computational analyses of ∼50 000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies. Synopsis Genotype‐to‐phenotype relationships in human genetic disease are often modeled as: ‘mutation in gene X leads to loss of gene product X, which leads to disease A’. However, single ‘gene‐loss’ models cannot explain the increasingly appreciated prevalence of complex genotype‐to‐phenotype relationships, particularly with instances of allelic or locus hetrogeneity (Goh et al , 2007 ). Genes and gene products function not in isolation but as components of complex networks of macromolecules (DNA, RNA, or proteins) and metabolites linked through biochemical or physical interactions, often represented in ‘interactome’ network models as ‘nodes’ and ‘edges’, respectively. Here we use network perturbation models to explain molecular dysfunctions underlying human disease in addition to the gene‐loss model. We hypothesize that different mutations leading to different molecular defects to proteins may cause distinct perturbations of cellular networks, giving rise to distinct phenotypic outcomes (Figure 1 ). For example, truncations close to the start of an open‐reading frame, or mutations that grossly destabilize a protein structure, can be modeled as removing a protein node from the network (‘node removal’). Alternatively, single amino‐acid substitutions that affect specific binding sites, or truncations that preserve certain domains of a protein, may give rise to partially functional gene products with specific changes in distinct molecular interaction(s) (edge‐specific or ‘edgetic’ perturbations) (Figure 1B ). Taking advantage of the large number of known disease‐causing allelic variations in human Mendelian disorders, we investigated how disease‐associated mutations may cause complete loss of gene products or, alternatively, may cause specific loss or gain of individual molecular interaction(s). We examined ∼50 000 Mendelian disease‐causing alleles, affecting over 1900 protein‐coding genes, altogether associated with more than 2000 human disorders available in the Human Gene Mutation Database (HGMD) (Stenson et al , 2003 ), that can be subdivided into two subsets: truncating’ alleles (truncations or frameshifts caused by stop codons, out‐of‐frame insertions or deletions, or defective splicing) versus ‘in‐frame’ alleles (missense mutations and in‐frame insertions or deletions). Over 50% (27 919/52 491) of Mendelian alleles in HGMD correspond to ‘in‐frame’ mutations. Our hypothesis is that, ‘in‐frame’ alleles may affect specific interactions of a given gene product while leaving most other interactions unperturbed. Although exceptions may apply, our hypothesis has several predictions. First, ‘truncating’ versus ‘in‐frame’ alleles may distribute differently among autosomal dominant and autosomal recessive disease, given that dominant mutations are more likely to be edgetic than recessive ones. Indeed, autosomal dominant and autosomal recessive traits annotated in the Online Mendelian Inheritance in Man (OMIM) database (Hamosh et al , 2005 ) show a clear separation with respect to the associated ‘in‐frame’ versus ‘truncating’ mutations. Among genes affected solely by ‘in‐frame’ mutations, the proportion of dominant diseases is ∼10‐fold higher than that of recessive ones, supporting ‘in‐frame’ mutations causing distinct molecular defects as opposed to ‘truncating’ mutations. A proof‐of‐principle characterization of binary protein interaction defects of mutant alleles associated with five genetic disorders supports our hypothesis that ‘in‐frame’ alleles indeed produce mostly functional proteins, preserving many specific protein interactions. As grossly disruptive mutations versus mutations leading to loss or gain of specific interaction(s) probably distribute differently on protein structures, we examined available three‐dimensional structures of all disease proteins. Mutated residues in autosomal dominant disease are significantly more exposed to the surface of the structure than those in autosomal recessive disease, consistent with the idea that disease with distinct modes of inheritance probably involves distinct network perturbations. A second testable prediction of our edgetic perturbation model is that edgetic perturbation versus gene loss for a given gene product might in some cases cause different diseases. We examined 142 genes associated with two or more distinct diseases in which at least five distinct alleles have been reported for each disease. We found ∼30% of the cases for which distribution of ‘in‐frame’ versus ‘truncating’ mutations is significantly different between the two diseases linked to the same gene ( P <0.05). Hence, when affecting the same gene, node removal versus edgetic perturbation can confer strikingly different phenotypes. A third testable prediction is that different edgetic perturbations for a given gene product might cause phenotypically distinguishable diseases (Figure 6 ). We used predicted Pfam domains (Finn et al , 2006 ) as surrogates for functional interaction domains, assuming that ‘in‐frame’ mutations located in distinct Pfam domain‐encoding sequences probably alter distinct interactions. Among 169 genes associated with two or more diseases and encoding proteins containing at least two Pfam domains, nine proteins have at least two Pfam domains significantly enriched with ‘in‐frame’ mutations ( P <0.05). For each of the nine proteins, we found a striking pattern of near mutual exclusivity, whereby different Pfam domains seem to be specifically affected in distinct disorders (Figure 6B ). We conclude that edgetic alleles probably underlie many complex genotype‐to‐phenotype relationships in human disease, such as incomplete penetrance or variable expressivity, as well as allele‐specific phenotypic variations among patients. Edgetic perturbation of human inherited disorders might help explain how seemingly devastating alleles have appeared and persevered in human populations. We present alternative models to explain molecular dysfunctions underlying human inherited disorders based on interaction‐specific or “edgetic” perturbations rather than complete loss of gene products. We find that a substantial fraction of known genetic variants in human Mendelian disorders likely cause edgetic perturbations. We find frequent situations where edgetic perturbation models can explain how different mutations in a single gene can cause distinct disorders. Edgetic perturbation models should provide alternative explanations to complex genotype‐to‐phenotype relationships

This chapter contains sections titled: Introduction Methods and approaches Troubleshooting References

Heat shock protein 90 (Hsp90) is an ATP-dependent molecular chaperone which is essential in eukaryotes. It is required for the activation and stabilization of a wide variety of client proteins and many of them are involved in important cellular pathways. Since Hsp90 affects numerous physiological processes such as signal transduction, intracellular transport, and protein degradation, it became an interesting target for cancer therapy. Structurally, Hsp90 is a flexible dimeric protein composed of three different domains which adopt structurally distinct conformations. ATP binding triggers directionality in these conformational changes and leads to a more compact state. To achieve its function, Hsp90 works together with a large group of cofactors, termed co-chaperones. Co-chaperones form defined binary or ternary complexes with Hsp90, which facilitate the maturation of client proteins. In addition, posttranslational modifications of Hsp90, such as phosphorylation and acetylation, provide another level of regulation. They influence the conformational cycle, co-chaperone interaction, and inter-domain communications. In this review, we discuss the recent progress made in understanding the Hsp90 machinery.

Background Protein–protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming. Results In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database with M proteins can be transformed into a much more simpler problem: to find a number inside a sorted array of length M . This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship. Conclusions The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from O ( M 2 ) to O ( M log M ) for performing an all-against-all PPI prediction for a database with M proteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.

Flavonoids are a class of plant specialized metabolites with more than 6,900 known structures and play important roles in plant survival and reproduction. These metabolites are derived from -coumaroyl-CoA the sequential actions of a variety of flavonoid enzymes, which have been proposed to form weakly bound, ordered protein complexes termed flavonoid metabolons. This review discusses the impacts of the formation of flavonoid metabolons on the chemodiversity of flavonoids. Specific protein-protein interactions in the metabolons of and other plant species have been studied for two decades. In many cases, metabolons are associated with the ER membrane, with ER-bound cytochromes P450 hypothesized to serve as nuclei for metabolon formation. Indeed, cytochromes P450 have been found to be components of flavonoid metabolons in rice, snapdragon, torenia, and soybean. Recent studies illustrate the importance of specific interactions for the efficient production and temporal/spatial distribution of flavonoids. For example, in diverse plant species, catalytically inactive type-IV chalcone isomerase-like protein serves as an enhancer of flavonoid production its involvement in flavonoid metabolons. In soybean roots, a specific isozyme of chalcone reductase (CHR) interacts with 2-hydroxyisoflavanone synthase, to which chalcone synthase (CHS) can also bind, providing a mechanism to prevent the loss of the unstable CHR substrate during its transfer from CHS to CHR. Thus, diversification in chemical structures and temporal/spatial distribution patterns of flavonoids in plants is likely to be mediated by the formation of specific flavonoid metabolons specific protein-protein interactions.

A framework based on numerous empirical data, including protein-protein interaction reference sets, provides parameters for assessing the quality and coverage of protein-protein interaction datasets and estimation of the size of the human interactome. Braun et al ., also in this issue, use the reference sets to help derive confidence scores for individual protein-protein interactions. Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains ∼130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.

BackgroundRice (Oryza sativa) is the main food for half of the world’s population, and is considered the model for molecular biology studies of monocotyledon species. Although the rice genome was completely sequenced about 15 years ago, the function of most rice genes is still unknown.ResultsIn this study, we developed a vector toolkit that contains 42 vectors for transient expression studies in rice protoplasts and stable expression analysis in transgenic rice. These vectors have been successfully used to study protein subcellular localization, protein-protein interaction, gene overexpression, and the CRISPR/Cas9-mediated gene editing. A novel feature of these vectors is that they contain a universal multiple cloning site, which enables more than 99% of the rice coding sequences to be conveniently transferred between vectors.ConclusionsThe versatile vectors represent a highly efficient and high-throughput toolkit for functional analysis of rice genes.

Background The order of genes in bacterial genomes is not random; for example, the products of genes belonging to an operon work together in the same pathway. The cotranslational assembly of protein complexes is deemed to conserve genomic neighborhoods even stronger than a common function. This is why a conserved genomic neighborhood can be utilized to predict, whether gene products form protein complexes. Results We were interested to assess the performance of a neighborhood-based classifier that analyzes a large number of genomes. Thus, we determined for the genes encoding the subunits of 494 experimentally verified hetero-dimers their local genomic context. In order to generate phylogenetically comprehensive genomic neighborhoods, we utilized the tools offered by the Enzyme Function Initiative. For each subunit, a sequence similarity network was generated and the corresponding genome neighborhood network was analyzed to deduce the most frequent gene product. This was predicted as interaction partner, if its abundance exceeded a threshold, which was the frequency giving rise to the maximal Matthews correlation coefficient. For the threshold of 16%, the true positive rate was 45%, the false positive rate 0.06%, and the precision 55%. For approximately 20% of the subunits, the interaction partner was not found in a neighborhood of ± 10 genes. Conclusions Our phylogenetically comprehensive analysis confirmed that complex formation is a strong evolutionary factor that conserves genome neighborhoods. On the other hand, for 55% of the cases analyzed here, classification failed. Either, the interaction partner was not present in a ± 10 gene window or was not the most frequent gene product.

Proteins play essential roles in almost all life processes. The prediction of protein function is of significance for the understanding of molecular function and evolution. Network alignment provides a fast and effective framework to automatically identify functionally conserved proteins in a systematic way. However, due to the fast growing genomic data, interactions and annotation data, there is an increasing demand for more accurate and efficient tools to deal with multiple PPI networks. Here, we present a novel global alignment algorithm NetCoffee2 based on graph feature vectors to discover functionally conserved proteins and predict function for unknown proteins. To test the algorithm performance, NetCoffee2 and three other notable algorithms were applied on eight real biological datasets. Functional analyses were performed to evaluate the biological quality of these alignments. Results show that NetCoffee2 is superior to existing algorithms IsoRankN, NetCoffee and multiMAGNA++ in terms of both coverage and consistency. The binary and source code are freely available under the GNU GPL v3 license at https://github.com/screamer/NetCoffee2.

Novel therapeutics are needed to treat pathologies associated with the Clostridioides difficile binary toxin (CDT), particularly when C. difficile infection (CDI) occurs in the elderly or in hospitalized patients having illnesses, in addition to CDI, such as cancer. While therapies are available to block toxicities associated with the large clostridial toxins (TcdA and TcdB) in this nosocomial disease, nothing is available yet to treat toxicities arising from strains of CDI having the binary toxin. Like other binary toxins, the active CDTa catalytic subunit of CDT is delivered into host cells together with an oligomeric assembly of CDTb subunits via host cell receptor-mediated endocytosis. Once CDT arrives in the host cell’s cytoplasm, CDTa catalyzes the ADP-ribosylation of G-actin leading to degradation of the cytoskeleton and rapid cell death. Although a detailed molecular mechanism for CDT entry and host cell toxicity is not yet fully established, structural and functional resemblances to other binary toxins are described. Additionally, unique conformational assemblies of individual CDT components are highlighted herein to refine our mechanistic understanding of this deadly toxin as is needed to develop effective new therapeutic strategies for treating some of the most hypervirulent and lethal strains of CDT-containing strains of CDI.