Mapping the energetic and allosteric landscapes of protein binding domains.

Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterized, limiting understanding, engineering and drug development1-6. An important reason for this is the lack of methods to comprehensively quantify allostery in diverse proteins. Here we address this shortcoming and present a method that uses deep mutational scanning to globally map allostery. The approach uses an efficient experimental design to infer en masse the causal biophysical effects of mutations by quantifying multiple molecular phenotypes-here we examine binding and protein abundance-in multiple genetic backgrounds and fitting thermodynamic models using neural networks. We apply the approach to two of the most common protein interaction domains found in humans, an SH3 domain and a PDZ domain, to produce comprehensive atlases of allosteric communication. Allosteric mutations are abundant, with a large mutational target space of network-altering 'edgetic' variants. Mutations are more likely to be allosteric closer to binding interfaces, at glycine residues and at specific residues connecting to an opposite surface within the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should enable the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.

Optimization of a deep mutational scanning workflow to improve quantification of mutation effects on protein-protein interactions.

Deep Mutational Scanning (DMS) assays are powerful tools to study sequence-function relationships by measuring the effects of thousands of sequence variants on protein function. During a DMS experiment, several technical artefacts might distort non-linearly the functional score obtained, potentially biasing the interpretation of the results. We therefore tested several technical parameters in the deepPCA workflow, a DMS assay for protein-protein interactions, in order to identify technical sources of non-linearities. We found that parameters common to many DMS assays such as amount of transformed DNA, timepoint of harvest and library composition can cause non-linearities in the data. Designing experiments in a way to minimize these non-linear effects will improve the quantification and interpretation of mutation effects.

Pairwise and higher-order genetic interactions during the evolution of a tRNA.

A central question in genetics and evolution is the extent to which the outcomes of mutations change depending on the genetic context in which they occur1-3. Pairwise interactions between mutations have been systematically mapped within4-18 and between 19 genes, and have been shown to contribute substantially to phenotypic variation among individuals 20 . However, the extent to which genetic interactions themselves are stable or dynamic across genotypes is unclear21, 22. Here we quantify more than 45,000 genetic interactions between the same 87 pairs of mutations across more than 500 closely related genotypes of a yeast tRNA. Notably, all pairs of mutations interacted in at least 9% of genetic backgrounds and all pairs switched from interacting positively to interacting negatively in different genotypes (false discovery rate < 0.1). Higher-order interactions are also abundant and dynamic across genotypes. The epistasis in this tRNA means that all individual mutations switch from detrimental to beneficial, even in closely related genotypes. As a consequence, accurate genetic prediction requires mutation effects to be measured across different genetic backgrounds and the use of  higher-order epistatic terms.

Expression attenuation as a mechanism of robustness against gene duplication.

Gene duplication is ubiquitous and a major driver of phenotypic diversity across the tree of life, but its immediate consequences are not fully understood. Deleterious effects would decrease the probability of retention of duplicates and prevent their contribution to long-term evolution. One possible detrimental effect of duplication is the perturbation of the stoichiometry of protein complexes. Here, we measured the fitness effects of the duplication of 899 essential genes in the budding yeast using high-resolution competition assays. At least 10% of genes caused a fitness disadvantage when duplicated. Intriguingly, the duplication of most protein complex subunits had small to nondetectable effects on fitness, with few exceptions. We selected four complexes with subunits that had an impact on fitness when duplicated and measured the impact of individual gene duplications on their protein-protein interactions. We found that very few duplications affect both fitness and interactions. Furthermore, large complexes such as the 26S proteasome are protected from gene duplication by attenuation of protein abundance. Regulatory mechanisms that maintain the stoichiometric balance of protein complexes may protect from the immediate effects of gene duplication. Our results show that a better understanding of protein regulation and assembly in complexes is required for the refinement of current models of gene duplication.

Systematic identification of signal integration by protein kinase A.

Cellular processes and homeostasis control in eukaryotic cells is achieved by the action of regulatory proteins such as protein kinase A (PKA). Although the outbound signals from PKA directed to processes such as metabolism, growth, and aging have been well charted, what regulates this conserved regulator remains to be systematically identified to understand how it coordinates biological processes. Using a yeast PKA reporter assay, we identified genes that influence PKA activity by measuring protein-protein interactions between the regulatory and the two catalytic subunits of the PKA complex in 3,726 yeast genetic-deletion backgrounds grown on two carbon sources. Overall, nearly 500 genes were found to be connected directly or indirectly to PKA regulation, including 80 core regulators, denoting a wide diversity of signals regulating PKA, within and beyond the described upstream linear pathways. PKA regulators span multiple processes, including the antagonistic autophagy and methionine biosynthesis pathways. Our results converge toward mechanisms of PKA posttranslational regulation by lysine acetylation, which is conserved between yeast and humans and that, we show, regulates protein complex formation in mammals and carbohydrate storage and aging in yeast. Taken together, these results show that the extent of PKA input matches with its output, because this kinase receives information from upstream and downstream processes, and highlight how biological processes are interconnected and coordinated by PKA.

Evidence for the robustness of protein complexes to inter-species hybridization.

Despite the tremendous efforts devoted to the identification of genetic incompatibilities underlying hybrid sterility and inviability, little is known about the effect of inter-species hybridization at the protein interactome level. Here, we develop a screening platform for the comparison of protein-protein interactions (PPIs) among closely related species and their hybrids. We examine in vivo the architecture of protein complexes in two yeast species (Saccharomyces cerevisiae and Saccharomyces kudriavzevii) that diverged 5-20 million years ago and in their F1 hybrids. We focus on 24 proteins of two large complexes: the RNA polymerase II and the nuclear pore complex (NPC), which show contrasting patterns of molecular evolution. We found that, with the exception of one PPI in the NPC sub-complex, PPIs were highly conserved between species, regardless of protein divergence. Unexpectedly, we found that the architecture of the complexes in F1 hybrids could not be distinguished from that of the parental species. Our results suggest that the conservation of PPIs in hybrids likely results from the slow evolution taking place on the very few protein residues involved in the interaction or that protein complexes are inherently robust and may accommodate protein divergence up to the level that is observed among closely related species.

Molecular mechanisms of paralogous compensation and the robustness of cellular networks.

Robustness is the ability of a system to maintain its function despite environmental or genetic perturbation. Genetic robustness is a key emerging property of living systems and is achieved notably by the presence of partially redundant parts that result from gene duplication. Functional overlap between paralogs allows them to compensate for each other's loss, as commonly revealed by aggravating genetic interactions. However, the molecular mechanisms linking the genotype (loss of function of a gene) to the phenotype (genetic buffering by a paralog) are still poorly understood and the molecular aspects of this compensation are rarely addressed in studies of gene duplicates. Here, we review molecular mechanisms of functional compensation between paralogous genes, many of which from studies that were not meant to study this phenomenon. We propose a standardized terminology and, depending on whether or not the molecular behavior of the intact gene is modified in response to the deletion of its paralog, we classify mechanisms of compensation into passive and active events. We further describe three non-exclusive mechanisms of active paralogous compensation for which there is evidence in the literature: changes in abundance, in localization, and in protein interactions. This review will serve as a framework for the genetic and molecular analysis of paralogous compensation, one of the universal features of genetic systems.

The asparagine-transamidosome from Helicobacter pylori: a dual-kinetic mode in non-discriminating aspartyl-tRNA synthetase safeguards the genetic code.

Helicobacter pylori catalyzes Asn-tRNA(Asn) formation by use of the indirect pathway that involves charging of Asp onto tRNA(Asn) by a non-discriminating aspartyl-tRNA synthetase (ND-AspRS), followed by conversion of the mischarged Asp into Asn by the GatCAB amidotransferase. We show that the partners of asparaginylation assemble into a dynamic Asn-transamidosome, which uses a different strategy than the Gln-transamidosome to prevent the release of the mischarged aminoacyl-tRNA intermediate. The complex is described by gel-filtration, dynamic light scattering and kinetic measurements. Two strategies for asparaginylation are shown: (i) tRNA(Asn) binds GatCAB first, allowing aminoacylation and immediate transamidation once ND-AspRS joins the complex; (ii) tRNA(Asn) is bound by ND-AspRS which releases the Asp-tRNA(Asn) product much slower than the cognate Asp-tRNA(Asp); this kinetic peculiarity allows GatCAB to bind and transamidate Asp-tRNA(Asn) before its release by the ND-AspRS. These results are discussed in the context of the interrelation between the Asn and Gln-transamidosomes which use the same GatCAB in H. pylori, and shed light on a kinetic mechanism that ensures faithful codon reassignment for Asn.

Gln-tRNAGln synthesis in a dynamic transamidosome from Helicobacter pylori, where GluRS2 hydrolyzes excess Glu-tRNAGln.

In many bacteria and archaea, an ancestral pathway is used where asparagine and glutamine are formed from their acidic precursors while covalently linked to tRNA(Asn) and tRNA(Gln), respectively. Stable complexes formed by the enzymes of these indirect tRNA aminoacylation pathways are found in several thermophilic organisms, and are called transamidosomes. We describe here a transamidosome forming Gln-tRNA(Gln) in Helicobacter pylori, an ε-proteobacterium pathogenic for humans; this transamidosome displays novel properties that may be characteristic of mesophilic organisms. This ternary complex containing the non-canonical GluRS2 specific for Glu-tRNA(Gln) formation, the tRNA-dependent amidotransferase GatCAB and tRNA(Gln) was characterized by dynamic light scattering. Moreover, we observed by interferometry a weak interaction between GluRS2 and GatCAB (K(D) = 40 ± 5 µM). The kinetics of Glu-tRNA(Gln) and Gln-tRNA(Gln) formation indicate that conformational shifts inside the transamidosome allow the tRNA(Gln) acceptor stem to interact alternately with GluRS2 and GatCAB despite their common identity elements. The integrity of this dynamic transamidosome depends on a critical concentration of tRNA(Gln), above which it dissociates into separate GatCAB/tRNA(Gln) and GluRS2/tRNA(Gln) complexes. Ester bond protection assays show that both enzymes display a good affinity for tRNA(Gln) regardless of its aminoacylation state, and support a mechanism where GluRS2 can hydrolyze excess Glu-tRNA(Gln), ensuring faithful decoding of Gln codons.

mutscan-a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data.

Multiplexed assays of variant effect (MAVE) experimentally measure the effect of large numbers of sequence variants by selective enrichment of sequences with desirable properties followed by quantification by sequencing. mutscan is an R package for flexible analysis of such experiments, covering the entire workflow from raw reads up to statistical analysis and visualization. The core components are implemented in C++ for efficiency. Various experimental designs are supported, including single or paired reads with optional unique molecular identifiers. To find variants with changed relative abundance, mutscan employs established statistical models provided in the edgeR and limma packages. mutscan is available from https://github.com/fmicompbio/mutscan .

Biodiversity matters in a changing world.

It is now widely accepted that the climate of our planet is changing, but it is still hard to predict the consequences of these changes on ecosystems. The impact is worst at the poles, with scientists concerned that impacts at lower latitudes will follow suit. Canada has a great responsibility and potential for studying the effects of climate changes on the ecological dynamics, given its geographical location and its scientific leadership in this field. The 5th annual meeting of the Canadian Society for Ecology and Evolution was held in the International Year of Biodiversity, to share recent advances in a wide variety of disciplines ranging from molecular biology to behavioural ecology, and to integrate them into a general view that will help us preserve biodiversity and limit the impact of climate change on ecosystems.

The genetic landscape of a physical interaction.

A key question in human genetics and evolutionary biology is how mutations in different genes combine to alter phenotypes. Efforts to systematically map genetic interactions have mostly made use of gene deletions. However, most genetic variation consists of point mutations of diverse and difficult to predict effects. Here, by developing a new sequencing-based protein interaction assay - deepPCA - we quantified the effects of >120,000 pairs of point mutations on the formation of the AP-1 transcription factor complex between the products of the FOS and JUN proto-oncogenes. Genetic interactions are abundant both in cis (within one protein) and trans (between the two molecules) and consist of two classes - interactions driven by thermodynamics that can be predicted using a three-parameter global model, and structural interactions between proximally located residues. These results reveal how physical interactions generate quantitatively predictable genetic interactions.

Gene duplication can impart fragility, not robustness, in the yeast protein interaction network.

The maintenance of duplicated genes is thought to protect cells from genetic perturbations, but the molecular basis of this robustness is largely unknown. By measuring the interaction of yeast proteins with their partners in wild-type cells and in cells lacking a paralog, we found that 22 out of 56 paralog pairs compensate for the lost interactions. An equivalent number of pairs exhibit the opposite behavior and require each other's presence for maintaining their interactions. These dependent paralogs generally interact physically, regulate each other's abundance, and derive from ancestral self-interacting proteins. This reveals that gene duplication may actually increase mutational fragility instead of robustness in a large number of cases.

Combining the Dihydrofolate Reductase Protein-Fragment Complementation Assay with Gene Deletions to Establish Genotype-to-Phenotype Maps of Protein Complexes and Interaction Networks.

Systematically measuring the impact of gene deletion on protein-protein interactions is a promising approach to reveal the structural bases of protein interaction networks and to allow a better understanding of how genotypes translate into phenotypes. Genetic and protein-interaction tools in yeast now allow us to explore this third dimension of protein-protein interaction networks. Because it is scalable and quantitative, the protein-fragment complementation assay (PCA) using dihydrofolate reductase (DHFR) as the reporter protein provides an exceptionally powerful tool for such a purpose. Here, we describe a fully automated protocol that combines DHFR PCA for protein-protein interaction measurement and synthetic genetic array (SGA) technology for introducing mutant and other alleles into PCA strains using genetic crosses. In this, PCA strains are crossed with strains carrying a gene deletion and SGA markers, and the recombinant haploid progeny are selected by SGA. The resulting haploid strains, each expressing a DHFR-fragment fusion protein in a gene-specific haploid deletion background, are crossed to measure the interaction between the two recombinant proteins by PCA in a diploid homozygous deletion background. This approach can be used to measure a single protein interaction in a large array of genetic backgrounds or a large number of protein interactions in a small number of genetic backgrounds.

Multi-scale perturbations of protein interactomes reveal their mechanisms of regulation, robustness and insights into genotype-phenotype maps.

Cellular architectures and signaling machineries are organized through protein-protein interactions (PPIs). High-throughput methods to study PPIs in yeast have opened a new perspective on the organization of the cell by allowing the study of whole protein interactomes. Recent investigations have moved from the description of this organization to the analysis of its dynamics by experimenting how protein interaction networks (PINs) are rewired in response to perturbations. Here we review studies that have used the budding yeast as an experimental system to explore these altered networks. Given the large space of possible PPIs and the diversity of potential genetic and environmental perturbations, high-throughput methods are an essential requirement to survey PIN perturbations on a large scale. Network perturbations are typically conceptualized as the removal of entire proteins (nodes), the modification of single PPIs (edges) or changes in growth conditions. These studies have revealed mechanisms of PPI regulation, PIN architectural organization, robustness and sensitivity to perturbations. Despite these major advances, there are still inherent limits to current technologies that lead to a trade-off between the number of perturbations and the number of PPIs that can be considered simultaneously. Nevertheless, as we exemplify here, targeted approaches combined with the existing resources remain extremely powerful to explore the inner organization of cells and their responses to perturbations.

Protein-Fragment Complementation Assays for Large-Scale Analysis, Functional Dissection, and Spatiotemporal Dynamic Studies of Protein-Protein Interactions in Living Cells.

Protein-fragment complementation assays (PCAs) comprise a family of assays that can be used to study protein-protein interactions (PPIs), conformation changes, and protein complex dimensions. We developed PCAs to provide simple and direct methods for the study of PPIs in any living cell, subcellular compartments or membranes, multicellular organisms, or in vitro. Because they are complete assays, requiring no cell-specific components other than reporter fragments, they can be applied in any context. PCAs provide a general strategy for the detection of proteins expressed at endogenous levels within appropriate subcellular compartments and with normal posttranslational modifications, in virtually any cell type or organism under any conditions. Here we introduce a number of applications of PCAs in budding yeast, Saccharomyces cerevisiae These applications represent the full range of PPI characteristics that might be studied, from simple detection on a large scale to visualization of spatiotemporal dynamics.

Genome-wide protein-protein interaction screening by protein-fragment complementation assay (PCA) in living cells.

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein's function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.