Andrew Schoenrock

Short Co-occurring Polypeptide Regions Can Predict Global Protein Interaction Maps

A goal of the post-genomics era has been to elucidate a detailed global map of protein-protein interactions (PPIs) within a cell. Here, we show that the presence of co-occurring short polypeptide sequences between interacting protein partners appears to be conserved across different organisms. We present an algorithm to automatically generate PPI prediction method parameters for various organisms and illustrate that global PPIs can be predicted from previously reported PPIs within the same or a different organism using protein primary sequences. The PPI prediction code is further accelerated through the use of parallel multi-core programming, which improves its usability for large scale or proteome-wide PPI prediction. We predict and analyze hundreds of novel human PPIs, experimentally confirm protein functions and importantly predict the first genome-wide PPI maps for S. pombe (∼9,000 PPIs) and C. elegans (∼37,500 PPIs).

Binding Site Prediction for Protein-Protein Interactions and Novel Motif Discovery using Re-occurring Polypeptide Sequences

BackgroundWhile there are many methods for predicting protein-protein interaction, very few can determine the specific site of interaction on each protein. Characterization of the specific sequence regions mediating interaction (binding sites) is crucial for an understanding of cellular pathways. Experimental methods often report false binding sites due to experimental limitations, while computational methods tend to require data which is not available at the proteome-scale. Here we present PIPE-Sites, a novel method of protein specific binding site prediction based on pairs of re-occurring polypeptide sequences, which have been previously shown to accurately predict protein-protein interactions. PIPE-Sites operates at high specificity and requires only the sequences of query proteins and a database of known binary interactions with no binding site data, making it applicable to binding site prediction at the proteome-scale.ResultsPIPE-Sites was evaluated using a dataset of 265 yeast and 423 human interacting proteins pairs with experimentally-determined binding sites. We found that PIPE-Sites predictions were closer to the confirmed binding site than those of two existing binding site prediction methods based on domain-domain interactions, when applied to the same dataset. Finally, we applied PIPE-Sites to two datasets of 2347 yeast and 14,438 human novel interacting protein pairs predicted to interact with high confidence. An analysis of the predicted interaction sites revealed a number of protein subsequences which are highly re-occurring in binding sites and which may represent novel binding motifs.ConclusionsPIPE-Sites is an accurate method for predicting protein binding sites and is applicable to the proteome-scale. Thus, PIPE-Sites could be useful for exhaustive analysis of protein binding patterns in whole proteomes as well as discovery of novel binding motifs. PIPE-Sites is available online at http://pipe-sites.cgmlab.org/.

Phosphatase complex Pph3/Psy2 is involved in regulation of efficient non-homologous end-joining pathway in the yeast Saccharomyces cerevisiae.

One of the main mechanisms for double stranded DNA break (DSB) repair is through the non-homologous end-joining (NHEJ) pathway. Using plasmid and chromosomal repair assays, we showed that deletion mutant strains for interacting proteins Pph3p and Psy2p had reduced efficiencies in NHEJ. We further observed that this activity of Pph3p and Psy2p appeared linked to cell cycle Rad53p and Chk1p checkpoint proteins. Pph3/Psy2 is a phosphatase complex, which regulates recovery from the Rad53p DNA damage checkpoint. Overexpression of Chk1p checkpoint protein in a parallel pathway to Rad53p compensated for the deletion of PPH3 or PSY2 in a chromosomal repair assay. Double mutant strains Δpph3/Δchk1 and Δpsy2/Δchk1 showed additional reductions in the efficiency of plasmid repair, compared to both single deletions which is in agreement with the activity of Pph3p and Psy2p in a parallel pathway to Chk1p. Genetic interaction analyses also supported a role for Pph3p and Psy2p in DNA damage repair, the NHEJ pathway, as well as cell cycle progression. Collectively, we report that the activity of Pph3p and Psy2p further connects NHEJ repair to cell cycle progression.

Designing anti-Zika virus peptides derived from predicted human-Zika virus protein-protein interactions

The production of anti-Zika virus (ZIKV) therapeutics has become increasingly important as the propagation of the devastating virus continues largely unchecked. Notably, a causal relationship between ZIKV infection and neurodevelopmental abnormalities has been widely reported, yet a specific mechanism underlying impaired neurological development has not been identified. Here, we report on the design of several synthetic competitive inhibitory peptides against key pathogenic ZIKV proteins through the prediction of protein-protein interactions (PPIs). Often, PPIs between host and viral proteins are crucial for infection and pathogenesis, making them attractive targets for therapeutics. Using two complementary sequence-based PPI prediction tools, we first produced a comprehensive map of predicted human-ZIKV PPIs (involving 209 human protein candidates). We then designed several peptides intended to disrupt the corresponding host-pathogen interactions thereby acting as anti-ZIKV therapeutics. The data generated in this study constitute a foundational resource to aid in the multi-disciplinary effort to combat ZIKV infection, including the design of additional synthetic proteins.

Uncharacterized ORF HUR1 influences the efficiency of non-homologous end-joining repair in Saccharomyces cerevisiae

Non-Homologous End Joining (NHEJ) is a highly conserved pathway that repairs Double-Strand Breaks (DSBs) within DNA. Here we show that the deletion of yeast uncharacterized ORF HUR1, Hydroxyurea Resistance1 affects the efficiency of NHEJ. Our findings are supported by Protein-Protein Interaction (PPI), genetic interaction and drug sensitivity analyses. To assess the activity of HUR1 in DSB repair, we deleted its non-overlapping region with PMR1, referred to as HUR1-A. We observed that similar to deletion of TPK1 and NEJ1, and unlike YKU70 (important for NHEJ of DNA with overhang and not blunt end), deletion of HUR1-A reduced the efficiency of NHEJ in both overhang and blunt end plasmid repair assays. Similarly, a chromosomal repair assay showed a reduction for repair efficiency when HUR1-A was deleted. In agreement with a functional connection for Hur1p with Tpk1p and NEJ1p, double mutant strains Δhur1-A/Δtpk1, and Δhur1-A/Δnej1 showed the same reduction in the efficiency of plasmid repair, compared to both single deletion strains. Also, using a Homologous Recombination (HR) specific plasmid-based DSB repair assay we observed that deletion of HUR1-A influenced the efficiency of HR repair, suggesting that HUR1 might also play additional roles in other DNA repair pathways.

Predicting novel protein-protein interactions between the HIV-1 virus and homo sapiens

The HIV-1 virus affects millions of people around the world. Identifying novel protein-protein interactions (PPIs) between HIV and humans would lead to a better understanding of the virus and possibly to new treatment targets. The Proteinprotein Interaction Prediction Engine (PIPE) is a broadly applicable, highly precise, and computationally efficient method of predicting PPIs. Here, PIPE is used to predict new host-virus protein interactions in order to generate new testable hypotheses and to guide future biological experiments. In total, 229 new interactions were predicted at high confidence, with an estimated recall of 22.5% and specificity of 99.95%. Some of these interactions may be verified experimentally in the future.

Fitness Tradeoffs of Antibiotic Resistance in Extraintestinal Pathogenic Escherichia coli

Abstract Evolutionary trade-offs occur when selection on one trait has detrimental effects on other traits. In pathogenic microbes, it has been hypothesized that antibiotic resistance trades off with fitness in the absence of antibiotic. Although studies of single resistance mutations support this hypothesis, it is unclear whether trade-offs are maintained over time, due to compensatory evolution and broader effects of genetic background. Here, we leverage natural variation in 39 extraintestinal clinical isolates of Escherichia coli to assess trade-offs between growth rates and resistance to fluoroquinolone and cephalosporin antibiotics. Whole-genome sequencing identifies a broad range of clinically relevant resistance determinants in these strains. We find evidence for a negative correlation between growth rate and antibiotic resistance, consistent with a persistent trade-off between resistance and growth. However, this relationship is sometimes weak and depends on the environment in which growth rates are measured. Using in vitro selection experiments, we find that compensatory evolution in one environment does not guarantee compensation in other environments. Thus, even in the face of compensatory evolution and other genetic background effects, resistance may be broadly costly, supporting the use of drug restriction protocols to limit the spread of resistance. Furthermore, our study demonstrates the power of using natural variation to study evolutionary trade-offs in microbes.

Evolution of protein-protein interaction networks in yeast

Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

Engineering inhibitory proteins with InSiPS: the in-silico protein synthesizer

Engineered proteins are synthetic novel proteins (not found in nature) that are designed to fulfill a predetermined biological function. Such proteins can be used as molecular markers, inhibitory agents, or drugs. For example, a synthetic protein could bind to a critical protein of a pathogen, thereby inhibiting the function of the target protein and potentially reducing the impact of the pathogen. In this paper we present the In-Silico Protein Synthesizer (InSiPS), a massively parallel computational tool for the IBM Blue Gene/Q that is aimed at designing inhibitory proteins. More precisely, InSiPS designs proteins that are predicted to interact with a given target protein (and may inhibit the targets cellular functions) while leaving non-target proteins unaffected (to minimize side-effects). As proof-of-concepts, two InSiPS designed proteins have been synthesized in the lab and their inhibitory properties have been experimentally verified through wet-lab experimentation.

Parallel Macro Pipelining on the Intel SCC Many-Core Computer

In this paper we present how Intels Single-Chip-Cloud processor behaves for parallel macro pipeline applications. Subsets of the SCCs available cores can be arranged as a pipeline where each core processes one stage of the overall workload. Each of the independent cores processes a small part of a larger task and feeds the following core with new data after it finishes its work. Our case-study is a parallel rendering system which renders successive images and applies different filters on them. On normal graphics adapters this is usually done in multiple cycles, we do this in a single pipeline pass. We show that we can achieve a significant speedup by using multiple parallel pipelines on the SCC. We show that we can further improve performance by using SCCs controlling PC in conjunction with the SCC. We also identify aspects of the SCC that hinder the overall performance, mainly the lack of local memory banks for each core on the SCC. The results presented in this paper are not limited to only image processing, but users could expect similar experiences where macro pipelining is used in other applications on the SCC.