Reconstructing genome-wide protein-protein interaction networks using multiple strategies with homologous mapping

The power of computational proteomics platforms to decipher protein-protein interactions

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.

CrMP-Sol database: classification, bioinformatic analyses and comparison of cancer-related membrane proteins and their water-soluble variant designs

Membrane proteins are critical mediators for tumor progression and present enormous therapeutic potentials. Although gene profiling can identify their cancer-specific signatures, systematic correlations between protein functions and tumor-related mechanisms are still unclear. We present here the CrMP-Sol database ( https://bio-gateway.aigene.org.cn/g/CrMP ), which aims to breach the gap between the two. Machine learning was used to extract key functional descriptions for protein visualization in the 3D-space, where spatial distributions provide function-based predictive connections between proteins and cancer types. CrMP-Sol also presents QTY-enabled water-soluble designs to facilitate native membrane protein studies despite natural hydrophobicity. Five examples with varying transmembrane helices in different categories were used to demonstrate the feasibility. Native and redesigned proteins exhibited highly similar characteristics, predicted structures and binding pockets, and slightly different docking poses against known ligands, although task-specific designs are still required for proteins more susceptible to internal hydrogen bond formations. The database can accelerate therapeutic developments and biotechnological applications of cancer-related membrane proteins.

Membrane protein-regulated networks across human cancers

Alterations in membrane proteins (MPs) and their regulated pathways have been established as cancer hallmarks and extensively targeted in clinical applications. However, the analysis of MP-interacting proteins and downstream pathways across human malignancies remains challenging. Here, we present a systematically integrated method to generate a resource of cancer membrane protein-regulated networks (CaMPNets), containing 63,746 high-confidence protein-protein interactions (PPIs) for 1962 MPs, using expression profiles from 5922 tumors with overall survival outcomes across 15 human cancers. Comprehensive analysis of CaMPNets links MP partner communities and regulated pathways to provide MP-based gene sets for identifying prognostic biomarkers and druggable targets. For example, we identify CHRNA9 with 12 PPIs (e.g., ERBB2) can be a therapeutic target and find its anti-metastasis agent, bupropion, for treatment in nicotine-induced breast cancer. This resource is a study to systematically integrate MP interactions, genomics, and clinical outcomes for helping illuminate cancer-wide atlas and prognostic landscapes in tumor homo/heterogeneity.

Impact of structural prior knowledge in SNV prediction: Towards causal variant finding in rare disease

Can structural information of proteins generate essential features for predicting the deleterious effect of a single nucleotide variant (SNV) independent of the known existence of the SNV in diseases? In this work, we answer the question by examining the performance of features generated from prior knowledge with the goal towards determining the pathogenic effect of rare variants in rare disease. We take the approach of prioritizing SNV loci focusing on protein structure-based features. The proposed structure-based features are generated from geometric, physical, chemical, and functional properties of the variant loci and structural neighbors of the loci utilizing multiple homologous structures. The performance of the structure-based features alone, trained on 80% of HumVar-HumDiv combination (HumVD-train) and tested on 20% of HumVar-HumDiv (HumVD-test), ClinVar and ClinVar rare variant rare disease (ClinVarRVRD) datasets, showed high levels of discernibility in determining the SNV’s pathogenic or benign effects on patients. Combined structure- and sequence-based features generated from prior knowledge on a random forest model further improved the F scores to 0.84 (HumVD-test), 0.75 (ClinVar), and 0.75 (ClinVarRVRD). Including features based on the difference between wild-type in addition to the features based on loci information increased the F score slightly more to 0.90 (HumVD-test), 0.78 (ClinVar), and 0.76 (ClinVarRVRD). The empirical examination and high F scores of the results based on loci information alone suggest that location of SNV plays a primary role in determining functional impact of mutation and that structure-based features can help enhance the prediction performance.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

Background

Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis.

Results

Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous.

Conclusions

The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0346-4) contains supplementary material, which is available to authorized users.