Protein complex compositions predicted by structural similarity

Proteins function through interactions with other molecules. Thus, the network of physical interactions among proteins is of great interest to both experimental and computational biologists. Here we present structure-based predictions of 3387 binary and 1234 higher order protein complexes in Saccharomyces cerevisiae involving 924 and 195 proteins, respectively. To generate candidate complexes, comparative models of individual proteins were built and combined together using complexes of known structure as templates. These candidate complexes were then assessed using a statistical potential, derived from binary domain interfaces in PIBASE (). The statistical potential discriminated a benchmark set of 100 interface structures from a set of sequence-randomized negative examples with a false positive rate of 3% and a true positive rate of 97%. Moreover, the predicted complexes were also filtered using functional annotation and sub-cellular localization data. The ability of the method to select the correct binding mode among alternates is demonstrated for three camelid VHH domain—porcine α–amylase interactions. We also highlight the prediction of co-complexed domain superfamilies that are not present in template complexes. Through integration with MODBASE, the application of the method to proteomes that are less well characterized than that of S.cerevisiae will contribute to expansion of the structural and functional coverage of protein interaction space. The predicted complexes are deposited in MODBASE ()

RNA Polymerase II Carboxyl-terminal Domain Phosphorylation Regulates Protein Stability of the Set2 Methyltransferase and Histone H3 Di- and Trimethylation at Lysine 36

Methylation of lysine 36 on histone H3 (H3K36) is catalyzed by the Set2 methyltransferase and is linked to transcriptional regulation. Previous studies have shown that trimethylation of H3K36 by Set2 is directed through its association with the phosphorylated repeats of the RNA polymerase C-terminal domain (RNAPII CTD). Here, we show that disruption of this interaction through the use of yeast mutants defective in CTD phosphorylation at serine 2 results in a destabilization of Set2 protein levels and H3K36 methylation. Consistent with this, we find that Set2 has a short half-life and is co-regulated, with RNAPII CTD phosphorylation levels, during logarithmic growth in yeast. To probe the functional consequence of uncoupling Set2-RNAPII regulation, we expressed a truncated and more stable form of Set2 that is capable of dimethylation but not trimethylation in vivo. Results of high throughput synthetic genetic analyses show that this Set2 variant has distinct genetics from either SET2 or set2Δ and is synthetically sick or lethal with a number of transcription elongation mutants. Collectively, these results provide molecular insight into the regulation of Set2 protein levels that influence H3K36 methylation states

MODBASE: a database of annotated comparative protein structure models and associated resources

MODBASE () is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence–structure alignment, model building and model assessment (). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, ), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, ) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, )

High-throughput, quantitative analyses of genetic interactions in E. coli.

Large-scale genetic interaction studies provide the basis for defining gene function and pathway architecture. Recent advances in the ability to generate double mutants en masse in Saccharomyces cerevisiae have dramatically accelerated the acquisition of genetic interaction information and the biological inferences that follow. Here we describe a method based on F factor-driven conjugation, which allows for high-throughput generation of double mutants in Escherichia coli. This method, termed genetic interaction analysis technology for E. coli (GIANT-coli), permits us to systematically generate and array double-mutant cells on solid media in high-density arrays. We show that colony size provides a robust and quantitative output of cellular fitness and that GIANT-coli can recapitulate known synthetic interactions and identify previously unidentified negative (synthetic sickness or lethality) and positive (suppressive or epistatic) relationships. Finally, we describe a complementary strategy for genome-wide suppressor-mutant identification. Together, these methods permit rapid, large-scale genetic interaction studies in E. coli

Systematic Triple-Mutant Analysis Uncovers Functional Connectivity between Pathways Involved in Chromosome Regulation

Genetic interactions reveal the functional relationships between pairs of genes. In this study, we describe a method for the systematic generation and quantitation of triple mutants, termed Triple Mutant Analysis (TMA). We have used this approach to interrogate partially redundant pairs of genes in S. cerevisiae, including ASF1 and CAC1, two histone chaperones. After subjecting asf1Δ cac1Δ to TMA, we found that the Swi/Snf Rdh54 protein, compensates for the absence of Asf1 and Cac1. Rdh54 more strongly associates with the chromatin apparatus and the pericentromeric region in the double mutant. Moreover, Asf1 is responsible for the synthetic lethality observed in cac1Δ strains lacking the HIRA-like proteins. A similar TMA was carried out after deleting both CLB5 and CLB6, cyclins that regulate DNA replication, revealing a strong functional connection to chromosome segregation. This approach can reveal functional redundancies that cannot be uncovered using traditional double mutant analyses

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19

High-Resolution Quantitative Analysis of Genetic Interactions

Genetic interactions describe how the presence of one gene affects the function of a second gene. Negative genetic interactions arise when two mutations together cause a stronger growth defect than expected based on the individual single mutations, while positive genetic interactions correspond to cases where the double mutant is either no sicker (epistatic) or healthier (suppressive) than the sickest single mutant. Our lab has developed a high-throughput technology in budding yeast for quantitative analysis of the entire spectrum of genetic interactions, termed epistatic miniarray profile (E-MAP). The genetic profiles generated by this method provide highly specific readouts that can be used to identify genes that are functionally related. In this dissertation I describe my work related to the employment and extension of the E-MAP technology. With collaborators, I have interrogated several biological processes using E-MAPs, including the phosphorylation network, RNA processing, lipid metabolism, and the plasma membrane. We further extended the technology to facilitate analysis of higher-order genetic interactions by examination of triple mutants, and we developed an adaptation of E-MAP to function in E. coli.My main focus was on the development of an important advance of the E-MAP technology, which allows us to address higher levels of complexity by examining genetic interactions of point mutant alleles of multi-functional genes. The technique, termed point mutant E-MAP (or pE-MAP), greatly increases the achievable resolution as it allows assignment of genetic relationships to individual residues and domains. We applied this system to genetically dissect RNA polymerase II (RNAPII) in budding yeast, and generated a pE-MAP comprising ~60,000 quantitative genetic interactions. Using these data, we assigned functions to RNAPII sub-domains and uncovered connections to protein complexes. The pE-MAP further allowed us to characterize connections related to RNAPII activity. These include an inverse relationship between in vitro transcription rate and in vivo splicing efficiency, classification of fast and slow mutants that shift transcription start upstream and downstream, respectively, and identification of Sub1 as a positive transcription factor that regulates start site selection and influences splicing. The pE-MAP approach provides a powerful strategy to understand other multi-functional machines at amino acid resolution