Protein complex forming ability is favored over the features of interacting partners in determining the evolutionary rates of proteins in the yeast protein-protein interaction networks

<p>Abstract</p> <p>Background</p> <p>Evolutionary rates of proteins in a protein-protein interaction network are primarily governed by the protein connectivity and/or expression level. A recent study revealed the importance of the features of the interacting protein partners, <it>viz</it>., the coefficient of functionality and clustering coefficient in controlling the protein evolutionary rates in a protein-protein interaction (PPI) network.</p> <p>Results</p> <p>By multivariate regression analysis we found that the three parameters: probability of complex formation, expression level and degree of a protein independently guide the evolutionary rates of proteins in the PPI network. The contribution of the complex forming property of a protein and its expression level led to nearly 43% of the total variation as observed from the first principal component. We also found that for complex forming proteins in the network, those which have partners sharing the same functional class evolve faster than those having partners belonging to different functional classes. The proteins in the dense parts of the network evolve faster than their counterparts which are present in the sparse regions of the network. Taking into account the complex forming ability, we found that all the complex forming proteins considered in this study evolve slower than the non-complex forming proteins irrespective of their localization in the network or the affiliation of their partners to same/different functional classes.</p> <p>Conclusions</p> <p>We have shown here that the functionality and clustering coefficient correlated with the degree of the protein in the protein-protein interaction network. We have identified the significant relationship of the complex-forming property of proteins and their evolutionary rates even when they are classified according to the features of their interacting partners. Our study implies that the evolutionarily constrained proteins are actually members of a larger number of protein complexes and this justifies why they have enhanced expression levels.</p

The role of structural pleiotropy in the retention of protein complexes after gene duplication

La duplication de gènes est l’un des plus importants mécanismes évolutifs pour la génération de diversité fonctionelle. Lorsqu’un gène est dupliqué, la nouvelle copie partage toutes ses fonctions avec la copie ancestrale car elles encodent pour des protéines identiques. Donc, les deux protéines, appelées paralogues, auront le même réseau d’interactions physiques protéine-protéine. Cependant, dans le cas de la duplication des gènes qui codent des protéines qui interagissent avec elles-mêmes (homomères), la nouvelle protéine interagira aussi avec la copie ancestrale, ce qui introduit une nouvelle interaction (heteromère) (Kaltenegger and Ober, 2015; Pereira-Leal et al., 2007). Puisque ces interactions peuvent avoir des différents motifs de rétention et de fonction (Ashenberg et al., 2011; Baker et al., 2013; Boncoeur et al., 2012; Bridgham et al., 2008), il est important de mieux comprendre comment ces états sont atteints et quelles forces évolutives les favorisent. Dans ce memoire, je cible ces questions avec des simulations in silico de l’évolution des protéines suite à la duplication de gènes en travaillant avec des structures crystallographiques de haute qualité, provenant de la Protein Data Bank (Berman et al., 2000; Dey et al., 2018). Les simulations montrent que les sous-unités et interfaces partagées entraînent une forte corrélation entre les trajectoires évolutives de ces complexes. Ainsi, les simulations prédisent que la préservation de seulement les deux homomères ou seulement l’hétéromère ne devrait pas être fréquente. Toutefois, la simulation qui applique la sélection seulement sur un homomère montre que l’homomère neutre est destabilisé plus rapidement que l’hétéromère neutre. Nous avons comparé ces prédictions avec des résultats expérimentaux du réseau d’interactions protéine-protéine de la levure. Comme suggéré par les simulations, les patrons d’interactions les plus fréquents ont été la formation des trois complexes (deux homomères et un hétéromère) ou la formation de seulement un homomère. Les patrons correspondants à deux homomères sans hétéromères ou un hétéromère sans homomères sont rares. Nos résultats démontrent l’extension de l’hétéromérisation entre paralogues dans le réseau d’interactions physiques protéine-protéine de la levure, les mécanismes sous-jacents et ses implications.Gene duplication is one of the most important evolutionary mechanisms for the generation of functional diversity. When a gene is duplicated, the new copy shares all of the ancestral copy’s functions because they encode identical proteins. Therefore, the two proteins, called paralogs, will have the same protein-protein interaction network. However, in the case of the duplication of genes encoding proteins that self-interact (homomers), the new protein will also interact with the ancestral copy, introducing a novel interaction (heteromer) (Kaltenegger and Ober, 2015; Pereira-Leal et al., 2007). As these interactions can have different retention and functional patterns (Ashenberg et al., 2011; Baker et al., 2013; Boncoeur et al., 2012; Bridgham et al., 2008), it is important to understand better how these states are reached and what evolutionary forces favor each of them. In this thesis, I approach these questions by means of in silico simulations of protein evolution after gene duplication by working with high-quality crystal structures from the Protein Data Bank (Berman et al., 2000; Dey et al., 2018). The simulations show that the shared subunits and interfaces lead to these complexes having highly correlated evolutionary trajectories. Thus, the simulations predict that the preservation of only the two homomers or only the heteromer is not likely to happen often. Nevertheless, simulating evolution with selection on only one homomer shows that the neutral homomer is destabilized faster than the neutral heteromer. We compared these predictions against experimental results from the yeast protein-protein interaction network. As suggested by the simulations, the most abundant interaction patterns were either the formation of all three complexes (two homomers and one heteromer) or the formation of only one homomer, with motifs corresponding to two homomers without a heteromer or a heteromer without homomers being rare. Our results highlight the extent of heteromerization between paralogs in the yeast protein-protein interaction network, the underlying mechanisms, and its implication

Blessings in disguise: biological benefits of prion-like mechanisms

Prions and amyloids are often associated with disease, but related mechanisms provide beneficial functions in nature. Prion-like mechanisms (PriLiMs) are found from bacteria to humans, where they alter the biological and physical properties of prion-like proteins. We have proposed that prions can serve as heritable bet-hedging devices for diversifying microbial phenotypes. Other, more dynamic proteinaceous complexes may be governed by similar self-templating conformational switches. Additional PriLiMs continue to be identified and many share features of self-templating protein structure (including amyloids) and dependence on chaperone proteins. Here, we discuss several PriLiMs and their functions, intending to spur discussion and collaboration on the subject of beneficial prion-like behaviors.National Science Foundation (U.S.) (NSF Fellowship)Howard Hughes Medical Institute (Investigator

Protein functional features extracted from primary sequences: A focus on disordered sequences.

In this thesis we implement an ensemble of sequence analysis strategies aimed at identifying functional and structural protein features. The first part of this work was dedicated to two case studies of specific proteins analyzed to provide candidate func

Integration of protein binding interfaces and abundance data reveals evolutionary pressures in protein networks

Networks of protein-protein interactions have received considerable interest in the past two decades for their insights about protein function and evolution. Traditionally, these networks only map the functional partners of proteins; they lack further levels of data such as binding affinity, allosteric regulation, competitive vs noncompetitive binding, and protein abundance. Recent experiments have made such data on a network-wide scale available, and in this thesis I integrate two extra layers of data in particular: the binding sites that proteins use to interact with their partners, and the abundance or “copy numbers” of the proteins. By analyzing the networks for the clathrin-mediated endocytosis (CME) system in yeast and the ErbB signaling pathway in humans, I find that this extra data reveals new insights about the evolution of protein networks. The structure of the binding site or interface interaction network (IIN) is optimized to allow higher binding specificity; that is, a high gap in strength between functional binding and nonfunctional mis-binding. This strongly implies that mis-binding is an evolutionary error-load constraint shaping protein network structure. Another method to limit mis-binding is to balance protein copy numbers so that there are no “leftover” proteins available for mis-binding. By developing a new method to quantify balance in IINs, I show that the CME network is significantly balanced when compared to randomly sampled sets of copy numbers. Furthermore, IINs with a biologically realistic structure produce less mis-binding under balanced concentrations, when compared to random networks, but more mis-binding under unbalanced concentrations. This implies strong pressure for copy number balance and that any imbalance should occur for functional reasons. I thus explore some functional consequences of imbalance by constructing dynamic models of two poorly balanced subnetworks of the larger CME network. In general, I find that balanced copy numbers provide higher protein complex yield (number of complete complexes), but imbalance may allow cells to “bottleneck” a functional process, effectively turning complex formation on or off via spatial localization of subunits. Finally, I find that strongly binding proteins are more likely to be balanced, as these “sticky” proteins would be more likely to engage in mid-binding otherwise

Large protein complex interfaces have evolved to promote cotranslational assembly

Assembly pathways of protein complexes should be precise and efficient to minimise misfolding and unwanted interactions with other proteins in the cell. One way to achieve this efficiency is by seeding assembly pathways during translation via the cotranslational assembly of subunits. While recent evidence suggests that such cotranslational assembly is widespread, little is known about the properties of protein complexes associated with the phenomenon. Here, using a combination of proteome-specific protein complex structures and publicly available ribosome profiling data, we show that cotranslational assembly is particularly common between subunits that form large intermolecular interfaces. To test whether large interfaces have evolved to promote cotranslational assembly, as opposed to cotranslational assembly being a non-adaptive consequence of large interfaces, we compared the sizes of first and last translated interfaces of heteromeric subunits in bacterial, yeast, and human complexes. When considering all together, we observe the N-terminal interface to be larger than the C-terminal interface 54% of the time, increasing to 64% when we exclude subunits with only small interfaces, which are unlikely to cotranslationally assemble. This strongly suggests that large interfaces have evolved as a means to maximise the chance of successful cotranslational subunit binding

Computational prediction of protein aggregation : advances in proteomics, conformation-specific algorithms and biotechnological applications

Protein aggregation is a widespread phenomenon that stems from the establishment of non-native intermolecular contacts resulting in protein precipitation. Despite its deleterious impact on fitness, protein aggregation is a generic property of polypeptide chains, indissociable from protein structure and function. Protein aggregation is behind the onset of neurodegenerative disorders and one of the serious obstacles in the production of protein-based therapeutics. The development of computational tools opened a new avenue to rationalize this phenomenon, enabling prediction of the aggregation propensity of individual proteins as well as proteome-wide analysis. These studies spotted aggregation as a major force driving protein evolution. Actual algorithms work on both protein sequences and structures, some of them accounting also for conformational fluctuations around the native state and the protein microenvironment. This toolbox allows to delineate conformation-specific routines to assist in the identification of aggregation-prone regions and to guide the optimization of more soluble and stable biotherapeutics. Here we review how the advent of predictive tools has change the way we think and address protein aggregation

The Amazing World of IDPs in Human Diseases

It is now clearly established that some proteins or protein regions are devoid of any stable secondary and/or tertiary structure under physiological conditions, but still possess fundamental biological functions. These intrinsically disordered proteins (IDPs) or regions (IDRs) have peculiar features due to their plasticity such as the capacity to bind their biological targets with high specificity and low affinity, and the possibility of interaction with numerous partners. A correlation between intrinsic disorder and various human diseases such as cancer, diabetes, amyloidoses and neurodegenerative diseases is now evident, highlighting the great importance of the topic. In this volume, we have collected recent high-quality research about IDPs and human diseases. We have selected nine papers which deal with a wide range of topics, from neurodegenerative disease to cancer, from IDR-mediated interactions to bioinformatics tools, all related to IDP peculiar features. Recent advances in the IDPs/IDRs issue are here presented, contributing to the progress of knowledge of the intrinsic disorder field in human disease