Zhongyang Liu

Toward an understanding of the protein interaction network of the human liver.

Proteome‐scale protein interaction maps are available for many organisms, ranging from bacteria, yeast, worms and flies to humans. These maps provide substantial new insights into systems biology, disease research and drug discovery. However, only a small fraction of the total number of human protein–protein interactions has been identified. In this study, we map the interactions of an unbiased selection of 5026 human liver expression proteins by yeast two‐hybrid technology and establish a human liver protein interaction network (HLPN) composed of 3484 interactions among 2582 proteins. The data set has a validation rate of over 72% as determined by three independent biochemical or cellular assays. The network includes metabolic enzymes and liver‐specific, liver‐phenotype and liver‐disease proteins that are individually critical for the maintenance of liver functions. The liver enriched proteins had significantly different topological properties and increased our understanding of the functional relationships among proteins in a liver‐specific manner. Our data represent the first comprehensive description of a HLPN, which could be a valuable tool for understanding the functioning of the protein interaction network of the human liver.

PRINCESS, a Protein Interaction Confidence Evaluation System with Multiple Data Sources

Advances in proteomics technologies have enabled novel protein interactions to be detected at high speed, but they come at the expense of relatively low quality. Therefore, a crucial step in utilizing the high throughput protein interaction data is evaluating their confidence and then separating the subsets of reliable interactions from the background noise for further analyses. Using Bayesian network approaches, we combine multiple heterogeneous biological evidences, including model organism protein-protein interaction, interaction domain, functional annotation, gene expression, genome context, and network topology structure, to assign reliability to the human protein-protein interactions identified by high throughput experiments. This method shows high sensitivity and specificity to predict true interactions from the human high throughput protein-protein interaction data sets. This method has been developed into an on-line confidence scoring system specifically for the human high throughput protein-protein interactions. Users may submit their protein-protein interaction data on line, and the detailed information about the supporting evidence for query interactions together with the confidence scores will be returned. The Web interface of PRINCESS (protein interaction confidence evaluation system with multiple data sources) is available at the website of China Human Proteome Organisation.

CAPER: a chromosome-assembled human proteome browsER.

High-throughput mass spectrometry and antibody-based experiments have begun to produce a large amount of proteomic data sets. Chromosome-based visualization of these data sets and their annotations can help effectively integrate, organize, and analyze them. Therefore, we developed a web-based, user-friendly Chromosome-Assembled human Proteome browsER (CAPER). To display proteomic data sets and related annotations comprehensively, CAPER employs two distinct visualization strategies: track-view for the sequence/site information and the correspondence between proteome, transcriptome, genome, and chromosome and heatmap-view for the qualitative and quantitative functional annotations. CAPER supports data browsing at multiple scales through Google Map-like smooth navigation, zooming, and positioning with chromosomes as the reference coordinate. Both track-view and heatmap-view can mutually switch, providing a high-quality user interface. Taken together, CAPER will greatly facilitate the complete annotation and functional interpretation of the human genome by proteomic approaches, thereby making a significant contribution to the Chromosome-Centric Human Proteome Project and even the human physiology/pathology research. CAPER can be accessed at http://www.bprc.ac.cn/CAPE .

BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine

Traditional Chinese Medicine (TCM), with a history of thousands of years of clinical practice, is gaining more and more attention and application worldwide. And TCM-based new drug development, especially for the treatment of complex diseases is promising. However, owing to the TCM’s diverse ingredients and their complex interaction with human body, it is still quite difficult to uncover its molecular mechanism, which greatly hinders the TCM modernization and internationalization. Here we developed the first online Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM). Its main functions include 1) TCM ingredients’ target prediction; 2) functional analyses of targets including biological pathway, Gene Ontology functional term and disease enrichment analyses; 3) the visualization of ingredient-target-pathway/disease association network and KEGG biological pathway with highlighted targets; 4) comparison analysis of multiple TCMs. Finally, we applied BATMAN-TCM to Qishen Yiqi dripping Pill (QSYQ) and combined with subsequent experimental validation to reveal the functions of renin-angiotensin system responsible for QSYQ’s cardioprotective effects for the first time. BATMAN-TCM will contribute to the understanding of the “multi-component, multi-target and multi-pathway” combinational therapeutic mechanism of TCM, and provide valuable clues for subsequent experimental validation, accelerating the elucidation of TCM’s molecular mechanism. BATMAN-TCM is available at http://bionet.ncpsb.org/batman-tcm.

Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs

BackgroundHigh-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance.ResultsHere we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current protein interaction networks, proteins of the same age class tend to form motifs and such co-origins of motif constituents are affected by their topologies and biological functions. Further, we find that the proteins within motifs whose constituents are of the same age class tend to be densely interconnected, co-evolve and share the same biological functions, and these motifs tend to be within protein complexes.ConclusionsOur findings provide novel evidence for the hypothesis of the additions of clustered interacting nodes and point out network motifs, especially the motifs with the dense topology and specific function may play important roles during this process. Our results suggest functional constraints may be the underlying driving force for such additions of clustered interacting nodes.

Proteome-wide Prediction of Self-interacting Proteins Based on Multiple Properties

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions.

CAPER 2.0: An Interactive, Configurable, and Extensible Workflow-Based Platform to Analyze Data Sets from the Chromosome-centric Human Proteome Project

The Chromosome-centric Human Proteome Project (C-HPP) aims to map and annotate the entire human proteome by the chromosome-by-chromosome strategy. As the C-HPP proceeds, the increasing volume of proteomic data sets presents a challenge for customized and reproducible bioinformatics data analyses for mining biological knowledge. To address this challenge, we updated the previous static proteome browser CAPER into a higher version, CAPER 2.0 - an interactive, configurable and extensible workflow-based platform for C-HPP data analyses. In addition to the previous visualization functions of track-view and heatmap-view, CAPER 2.0 presents a powerful toolbox for C-HPP data analyses and also integrates a configurable workflow system that supports the view, construction, edit, run, and share of workflows. These features allow users to easily conduct their own C-HPP proteomic data analyses and visualization by CAPER 2.0. We illustrate the usage of CAPER 2.0 with four specific workflows for finding missing proteins, mapping peptides to chromosomes for genome annotation, integrating peptides with transcription factor binding sites from ENCODE data sets, and functionally annotating proteins. The updated CAPER is available at http://www.bprc.ac.cn/CAPE.

CAPER 3.0: A Scalable Cloud-Based System for Data-Intensive Analysis of Chromosome-Centric Human Proteome Project Data Sets.

The Chromosome-centric Human Proteome Project (C-HPP) aims to catalog genome-encoded proteins using a chromosome-by-chromosome strategy. As the C-HPP proceeds, the increasing requirement for data-intensive analysis of the MS/MS data poses a challenge to the proteomic community, especially small laboratories lacking computational infrastructure. To address this challenge, we have updated the previous CAPER browser into a higher version, CAPER 3.0, which is a scalable cloud-based system for data-intensive analysis of C-HPP data sets. CAPER 3.0 uses cloud computing technology to facilitate MS/MS-based peptide identification. In particular, it can use both public and private cloud, facilitating the analysis of C-HPP data sets. CAPER 3.0 provides a graphical user interface (GUI) to help users transfer data, configure jobs, track progress, and visualize the results comprehensively. These features enable users without programming expertise to easily conduct data-intensive analysis using CAPER 3.0. Here, we illustrate the usage of CAPER 3.0 with four specific mass spectral data-intensive problems: detecting novel peptides, identifying single amino acid variants (SAVs) derived from known missense mutations, identifying sample-specific SAVs, and identifying exon-skipping events. CAPER 3.0 is available at http://prodigy.bprc.ac.cn/caper3.

General Trends in the Utilization of Structural Factors Contributing to Biological Complexity

During evolution, proteins containing newly emerged domains and the increasing proportion of multidomain proteins in the full genome-encoded proteome (GEP) have substantially contributed to increasing biological complexity. However, it is not known how these two potential structural factors are preferentially utilized at given physiological states. Here, we classified proteins according to domain number and domain age and explored the general trends across species for the utilization of proteins from GEP to various certain-state proteomes (CSPs, i.e., all the proteins expressed at certain physiological states). We found that multidomain proteins or only older domain-containing proteins are significantly overrepresented in CSPs compared with GEP, which is a trend that is stronger in multicellular organisms than in unicellular organisms. Interestingly, the strengths of overrepresentation decreased during evolution of multicellular eukaryotes. When comparing across CSPs, we found that multidomain proteins are more overrepresented in complex tissues than in simpler ones, whereas no difference among proteins with domains of different ages is evident between complex and simple tissues. Thus, biological complexity under certain conditions is more significantly realized by diverse domain organization than by the emergence of new types of domain. In addition, we found that multidomain or only older domain-containing proteins tend to evolve slowly and generally are under stronger purifying selection, which may partly result from their general overrepresentation trends in CSPs.

Functional constraints on adaptive evolution of protein ubiquitination sites

It is still unclear whether there exist functional constraints on the evolution of protein ubiquitination sites, because most previous studies regarded all protein ubiquitination sites as a whole or only focused on limited structural properties. We tried to clarify the relation between functional constraints and ubiquitination sites evolution. We investigated the evolutionary conservation of human ubiquitination sites in a broad evolutionary scale from G. gorilla to S. pombe, and we found that in organisms originated after the divergence of vertebrate, ubiquitination sites are more conserved than their flanking regions, while the opposite tendency is observed before this divergence time. By grouping the ubiquitination proteins into different functional categories, we confirm that many functional constraints like certain molecular functions, protein tissue expression specificity and protein connectivity in protein-protein interaction network enhance the evolutionary conservation of ubiquitination sites. Furthermore, by analyzing the gains of ubiquitination sites at different divergence time and their functional characters, we validate that the emergences of ubiquitination sites at different evolutionary time were also affected by the uncovered functional constraints. The above results suggest that functional constraints on the adaptive evolution of ubiquitination sites increase the opportunity for ubiquitination to synthetically regulate various cellular and developmental processes during evolution.