Enriching the viral-host interactomes with interactions mediated by SH3 domains

A Systematic Compilation of Human SH3 Domains: A Versatile Superfamily in Cellular Signaling

SRC homology 3 (SH3) domains are fundamental modules that enable the assembly of protein complexes through physical interactions with a pool of proline-rich/noncanonical motifs from partner proteins. They are widely studied modular building blocks across all five kingdoms of life and viruses, mediating various biological processes. The SH3 domains are also implicated in the development of human diseases, such as cancer, leukemia, osteoporosis, Alzheimer's disease, and various infections. A database search of the human proteome reveals the existence of 298 SH3 domains in 221 SH3 domain-containing proteins (SH3DCPs), ranging from 13 to 720 kilodaltons. A phylogenetic analysis of human SH3DCPs based on their multi-domain architecture seems to be the most practical way to classify them functionally, with regard to various physiological pathways. This review further summarizes the achievements made in the classification of SH3 domain functions, their binding specificity, and their significance for various diseases when exploiting SH3 protein modular interactions as drug targets.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Viral proteins and Src family kinases: Mechanisms of pathogenicity from a “liaison dangereuse”

To complete their life cycle and spread, viruses interfere with and gain control of diverse cellular processes, this most often occurring through interaction between viral proteins (VPs) and resident protein partners. Among the latter, Src family kinases (SFKs), a class of non-receptor tyrosine kinases that contributes to the conversion of extracellular signals into intracellular signaling cascades and is involved in virtually all cellular processes, have recently emerged as critical mediators between the cell’s infrastructure and the viral demands. In this scenario, structural or ex novo synthesized VPs are able to bind to the different domains of these enzymes through specific short linear motifs present along their sequences. Proline-rich motifs displaying the conserved minimal consensus PxxP and recognizing the SFK Src homology (SH)3 domain constitute a cardinal signature for the formation of multiprotein complexes and this interaction may promote phosphorylation of VPs by SFKs, thus creating phosphotyrosine motifs that become a docking site for the SH2 domains of SFKs or other SH2 domain-bearing signaling molecules. Importantly, the formation of these assemblies also results in a change in the activity and/or location of SFKs, and these events are critical in perturbing key signaling pathways so that viruses can utilize the cell’s machinery to their own benefit. In the light of these observations, although VPs as such, especially those with enzyme activity, are still regarded as valuable targets for therapeutic strategies, multiprotein complexes composed of viral and host cell proteins are increasingly becoming objects of investigation with a view to deeply characterize the structural aspects that favor their formation and to develop new compounds able to contrast viral diseases in an alternative manner.

Serological prevalence, genetic identification, and characterization of the first strains of avian hepatitis E virus from chickens in Korea

Avian hepatitis E virus (avian HEV) is associated with hepatitis-splenomegaly (HS) syndrome or big liver and spleen disease in chickens. At least three genotypes of avian HEV have been identified from chickens worldwide. A total of 297 serum samples collected from chickens in 35 flocks in Korea were tested for avian HEV antibody with an enzyme-linked immunosorbent assay. The results showed that approximately 57 % of chicken flocks and 28 % of chickens from Korea were positive for antibodies to avian HEV. Thirteen pooled fecal samples from chickens were tested for avian HEV RNA by RT-PCR, and three fecal samples were positive. The partial helicase and capsid genes of the Korean avian HEV isolates were determined, and sequence analyses revealed that the Korean avian HEV isolates were clustered together and closely related to the genotype 1 avian HEV from Australia. The complete genomic sequence of a Korean avian HEV strain HH-F9 from a broiler breeder was determined, and shown to be 6,653 nt in length, excluding the poly (A) tail, which is 1 nt shorter than the prototype avian HEV from chicken with HS syndrome in the United States. Compared to the full-length sequences of other 5 known avian HEV strains worldwide, the Korean avian HEV shared approximately 83-97 % nucleotide sequence identity. The finding that Korean avian HEV belongs to genotype 1 avian HEV which was previously identified only from chickens in Australia has significant implication in understanding the global epidemiology of avian HEV.

Identification of specificity determining residues in peptide recognition domains using an information theoretic approach applied to large-scale binding maps

Background

Peptide Recognition Domains (PRDs) are commonly found in signaling proteins. They mediate protein-protein interactions by recognizing and binding short motifs in their ligands. Although a great deal is known about PRDs and their interactions, prediction of PRD specificities remains largely an unsolved problem.

Results

We present a novel approach to identifying these Specificity Determining Residues (SDRs). Our algorithm generalizes earlier information theoretic approaches to coevolution analysis, to become applicable to this problem. It leverages the growing wealth of binding data between PRDs and large numbers of random peptides, and searches for PRD residues that exhibit strong evolutionary covariation with some positions of the statistical profiles of bound peptides. The calculations involve only information from sequences, and thus can be applied to PRDs without crystal structures. We applied the approach to PDZ, SH3 and kinase domains, and evaluated the results using both residue proximity in co-crystal structures and verified binding specificity maps from mutagenesis studies.

Discussion

Our predictions were found to be strongly correlated with the physical proximity of residues, demonstrating the ability of our approach to detect physical interactions of the binding partners. Some high-scoring pairs were further confirmed to affect binding specificity using previous experimental results. Combining the covariation results also allowed us to predict binding profiles with higher reliability than two other methods that do not explicitly take residue covariation into account.

Conclusions

The general applicability of our approach to the three different domain families demonstrated in this paper suggests its potential in predicting binding targets and assisting the exploration of binding mechanisms.

Virus-host protein interactions in RNA viruses

RNA viruses exhibit small-sized genomes that only encode a limited number of viral proteins, but still establish complex networks of interactions with host cell components. Here we summarize recent reports that aim at understanding general features of RNA virus infection networks at the protein level.