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Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency. Synopsis: Duplicated self-interacting proteins can interact with themselves (homomers) or one another (heteromers). To understand whether natural selection is required to keep homomers over heteromers (or vice versa), the evolution of such duplicate proteins is simulated in the absence of new functions. The dynamic equilibrium of homo- and heteromers is given by physical parameters, such as protein folding energy and binding affinities. Simulations of homodimer evolution from actual structures show a trend towards the increase of the relative concentration of the heterodimer, even when there is no inherent advantage of such an increase. The magnitude of the increase in the concentration of heterodimers is associated with mutational biases, that is, an asymmetry with respect to the effects of mutations on homo- and heterodimer binding affinities. The bias towards heterodimers can be counterbalanced by changes in the protein synthesis rates or the specific activities of the dimers. Duplicated self-interacting proteins can interact with themselves (homomers) or one another (heteromers). To understand whether natural selection is required to keep homomers over heteromers (or vice versa), the evolution of such duplicate proteins is simulated in the absence of new functions. [ABSTRACT FROM AUTHOR]

Protein structure (and thus function) is dictated by non‐covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non‐covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary‐relevant conserved interaction networks across the class A β‐lactamases. [ABSTRACT FROM AUTHOR]

Protein post‐translational modifications and protein interactions are the central research areas in mass‐spectrometry‐based proteomics. Protein post‐translational modifications affect protein structures, stabilities, activities, and all cellular processes are achieved by interactions among proteins and protein complexes. With the continuing advancements of mass spectrometry instrumentations of better sensitivity, speed, and performance, selective enrichment of modifications/interactions of interest from complex cellular matrices during the sample preparation has become the overwhelming bottleneck in the proteomics workflow. Therefore, many strategies have been developed to address this issue by targeting specific modifications/interactions based on their physical properties or chemical reactivities, but only a few have been successfully applied for systematic proteome‐wide study. In this review, we summarized the highlights of recent developments in the affinity enrichment methods focusing mainly on low stoichiometric protein lipidations. Besides, to identify potential glyoxal modified arginines, a small part was added for profiling reactive arginine sites using an enrichment reagent. A detailed section was provided for the enrichment of protein interactions by affinity purification and chemical cross‐linking, to shed light on the potentials of different enrichment strategies, along with the unique challenges in investigating individual protein post‐translational modification or protein interaction network. [ABSTRACT FROM AUTHOR]

Network alignment is an important bridge to understanding human protein–protein interactions (PPIs) and functions through model organisms. However, the underlying subgraph isomorphism problem complicates and increases the time required to align protein interaction networks (PINs). Parallel computing technology is an effective solution to the challenge of aligning large‐scale networks via sequential computing. In this study, the typical Hungarian‐Greedy Algorithm (HGA) is used as an example for PIN alignment. The authors propose a HGA with 2‐nearest neighbours (HGA‐2N) and implement its graphics processing unit (GPU) acceleration. Numerical experiments demonstrate that HGA‐2N can find alignments that are close to those found by HGA while dramatically reducing computing time. The GPU implementation of HGA‐2N optimises the parallel pattern, computing mode and storage mode and it improves the computing time ratio between the CPU and GPU compared with HGA when large‐scale networks are considered. By using HGA‐2N in GPUs, conserved PPIs can be observed, and potential PPIs can be predicted. Among the predictions based on 25 common Gene Ontology terms, 42.8% can be found in the Human Protein Reference Database. Furthermore, a new method of reconstructing phylogenetic trees is introduced, which shows the same relationships among five herpes viruses that are obtained using other methods. [ABSTRACT FROM AUTHOR]

Endocytosis is a vital cellular process maintaining the cell surface, modulating signal transduction and facilitating nutrient acquisition. In metazoa, multiple endocytic modes are recognized, but for many unicellular organisms the process is likely dominated by the ancient clathrin-mediated pathway. The endocytic system of the highly divergent trypanosomatid Trypanosoma brucei exhibits many unusual features, including a restricted site of internalization, dominance of the plasma membrane by GPI-anchored proteins, absence of the AP2 complex and an exceptionally high rate. Here we asked if the proteins subtending clathrin trafficking in trypanosomes are exclusively related to those of higher eukaryotes or if novel, potentially taxon-specific proteins operate. Co-immunoprecipitation identified twelve T. brucei clathrin-associating proteins ( TbCAPs), which partially colocalized with clathrin. Critically, eight TbCAPs are restricted to trypanosomatid genomes and all of these are required for robust cell proliferation. A subset, TbCAP100, TbCAP116, TbCAP161 and TbCAP334, were implicated in distinct endocytic steps by detailed analysis of knockdown cells. Coupled with the absence of orthologs for many metazoan and fungal endocytic factors, these data suggest that clathrin interactions in trypanosomes are highly lineage-specific, and indicate substantial evolutionary diversity within clathrin-mediated endocytosis mechanisms across the eukaryotes. [ABSTRACT FROM AUTHOR]

We introduce a fluorescent reporter for monitoring protein–protein interactions in living cells. The method is based on the Split-Ubiquitin method and uses the ratio of two auto-fluorescent reporter proteins as signal for interaction (SPLIFF). The mating of two haploid yeast cells initiates the analysis and the interactions are followed online by two-channel time-lapse microscopy of the diploid cells during their first cell cycle. Using this approach we could with high spatio-temporal resolution visualize the differences between the interactions of the microtubule binding protein Stu2p with two of its binding partners, monitor the transient association of a Ran-GTPase with its receptors at the nuclear pore, and distinguish between protein interactions at the polar cortical domain at different phases of polar growth. These examples further demonstrate that protein–protein interactions identified from large-scale screens can be effectively followed up by high-resolution single-cell analysis. [ABSTRACT FROM AUTHOR]

Abstract: Peptide-binding domains play a critical role in regulation of cellular processes by mediating protein interactions involved in signalling. In recent years, the development of large-scale technologies has enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. These efforts have provided significant insights into the binding specificities of these modular domains. Many research groups have taken advantage of this unprecedented volume of specificity data and have developed a variety of new algorithms for the prediction of binding specificities of peptide-binding domains and for the prediction of their natural binding targets. This knowledge has also been applied to the design of synthetic peptide-binding domains in order to rewire protein–protein interaction networks. Here, we describe how these experimental technologies have impacted on our understanding of peptide-binding domain specificities and on the elucidation of their natural ligands. We discuss SH3 and PDZ domains as well characterized examples, and we explore the feasibility of expanding high-throughput experiments to other peptide-binding domains. [Copyright &y& Elsevier]

Virtually every process in a cell is carried out by macromolecular complexes whose actions need to be perfectly orchestrated. The synchronization and regulation of these biological functions is indeed critical and is usually carried out by complex networks of transient protein interactions. Here, we review some of the many strategies that proteins in regulatory networks use to achieve the dynamic plasticity necessary to rapidly respond to diverse cellular needs. More specifically, we present recent work on the molecular bases of transient peptide-mediated interactions and the role that post-translational modifications and disordered regions might play. Finally, in light of some recent findings, we speculate on the possibility of a new regulatory code for intrinsically disordered proteins and the potential biophysical and functional advantages that disorder might provide. [ABSTRACT FROM AUTHOR]

Aim: To develop novel methods for identifying new genes that contribute to the risk of developing type 1 diabetes within the Major Histocompatibility Complex (MHC) region on chromosome 6, independently of the known linkage disequilibrium (LD) between human leucocyte antigen ( HLA) -DRB1, - DQA1, - DQB1 genes. Methods: We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein–protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein modules were statistically evaluated using permutation. Results: A total of 151 genes could be mapped to nodes within the protein interaction network and their interaction partners were identified. Five protein interaction modules reached statistical significance using this approach. The identified proteins are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in β-cell development and diabetic complications. Conclusions: The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D. [ABSTRACT FROM AUTHOR]

Recent technological breakthroughs allow the quantification of hundreds of thousands of genetic interactions (GIs) in Saccharomyces cerevisiae. The interpretation of these data is often difficult, but it can be improved by the joint analysis of GIs along with complementary data types. Here, we describe a novel methodology that integrates genetic and physical interaction data. We use our method to identify a collection of functional modules related to chromosomal biology and to investigate the relations among them.We show how the resulting map of modules provides clues for the elucidation of function both at the level of individual genes and at the level of functional modules. [ABSTRACT FROM AUTHOR]

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix-loop-helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks. [ABSTRACT FROM AUTHOR]

The ‘balance hypothesis’ predicts that non-stoichiometric variations in concentrations of proteins participating in complexes should be deleterious. As a corollary, heterozygous deletions and overexpression of protein complex members should have measurable fitness effects. However, genome-wide studies of heterozygous deletions in Saccharomyces cerevisiae and overexpression have been unable to unambiguously relate complex membership to dosage sensitivity. We test the hypothesis that it is not complex membership alone but rather the topology of interactions within a complex that is a predictor of dosage sensitivity. We develop a model that uses the law of mass action to consider how complex formation might be affected by varying protein concentrations given a protein's topological positioning within the complex. Although we find little evidence for combinatorial inhibition of complex formation playing a major role in overexpression phenotypes, consistent with previous results, we show significant correlations between predicted sensitivity of complex formation to protein concentrations and both heterozygous deletion fitness and protein abundance noise levels. Our model suggests a mechanism for dosage sensitivity and provides testable predictions for the effect of alterations in protein abundance noise. [ABSTRACT FROM AUTHOR]

A protein interaction network describes a set of physical associations that can occur between proteins. However, within any particular cell or tissue only a subset of proteins is expressed and so only a subset of interactions can occur. Integrating interaction and expression data, we analyze here this interplay between protein expression and physical interactions in humans. Proteins only expressed in restricted cell types, like recently evolved proteins, make few physical interactions. Most tissue-specific proteins do, however, bind to universally expressed proteins, and so can function by recruiting or modifying core cellular processes. Conversely, most ‘housekeeping’ proteins that are expressed in all cells also make highly tissue-specific protein interactions. These results suggest a model for the evolution of tissue-specific biology, and show that most, and possibly all, ‘housekeeping’ proteins actually have important tissue-specific molecular interactions. [ABSTRACT FROM AUTHOR]

Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community. [ABSTRACT FROM AUTHOR]

In an effort to understand the dynamic organization of the protein interaction network and its role in the regulation of cell behavior, positioning of proteins into specific network localities was studied with respect to their expression dynamics. First, we find that constitutively expressed and dynamically co-regulated proteins cluster in distinct functionally specialized network neighborhoods to form static and dynamic functional modules, respectively. Then, we show that whereas dynamic modules are mainly responsible for condition-dependent regulation of cell behavior, static modules provide robustness to the cell against genetic perturbations or protein expression noise, and therefore may act as buffers of evolutionary as well as population variations in cell behavior. Observations in this study refine the previously proposed model of dynamic modularity in the protein interaction network, and propose a link between the evolution of gene expression regulation and biological robustness. [ABSTRACT FROM AUTHOR]