Exploration of the Dynamic Properties of Protein Complexes Predicted from Spatially Constrained Protein-Protein Interaction Networks

Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and visualization of the hidden dynamics within protein interaction networks.     

Use of Transgenic Systems to Investigate Oligodendrocyte Differentiation and Function

Transgenic techniques are generating new strains of animals that are of great importance for many neurological research projects. This includes new animal models of human diseases that should allow analysis of disease etiology and treatment. The insertion of new genetic material into the mouse genome enables the investigator to study the effects of overexpression of normal or mutated genes under a variety of experimental conditions. The use of cell-specific and/or developmentally regulated promoters permits studies on the expression of the specific DNA in selected cells within the nervous system at important developmental stages. This article focuses on the techniques for generating transgenic mice, noting specific advantages or problems that should be considered when designing a transgenic project. The use of reporter genes such as the LacZ gene is discussed, using the particular example of the myelin proteolipid protein promoter directing expression of the LacZ gene in differentiating oligodendrocytes.     

双杂交和质谱技术在蛋白质-蛋白质相互作用研究中的应用

基因的功能是由蛋白质来执行的,而蛋白质要通过与其他生物分子相互作用来完成其各种生物功能。因此,如果能够快速做出蛋白质在不同时间、空间和不同环境中的相互作用图谱,就会帮助我们了解这些蛋白质的功能,进而了解许多生命活动的机制。目前,用于大规模研究蛋白质间相互作用的方法主要有酵母双杂交系统及其衍生系统、亲和纯化与质谱分析联用技术,前者用于研究蛋白分子间的两两相互作用,后者用于研究蛋白质复合物间的相互作用。本文主要阐述了酵母双杂交、细菌双杂交、哺乳动物细胞双杂交、亲和纯化与质谱联用技术在大规模蛋白质相互作用研究中的应用。     

Analysis of Human Nuclear Protein Complexes by Quantitative Mass Spectrometry Profiling

Analysis of protein complexes provides insights into how the ensemble of expressed proteome is organized into functional units. While there have been advances in techniques for proteome‐wide profiling of cytoplasmic protein complexes, information about human nuclear protein complexes are very limited. To close this gap, we combined native size exclusion chromatography (SEC) with label‐free quantitative MS profiling to characterize hundreds of nuclear protein complexes isolated from human glioblastoma multiforme T98G cells. We identified 1794 proteins that overlapped between two biological replicates of which 1244 proteins were characterized as existing within stably associated putative complexes. co‐IP experiments confirmed the interaction of PARP1 with Ku70/Ku80 proteins and HDAC1 (histone deacetylase complex 1) and CHD4. HDAC1/2 also co‐migrated with various SIN3A and nucleosome remodeling and deacetylase components in SEC fractionation including SIN3A, SAP30, RBBP4, RBBP7, and NCOR1. Co‐elution of HDAC1/2/3 with both the KDM1A and RCOR1 further confirmed that these proteins are integral components of human deacetylase complexes. Our approach also demonstrated the ability to identify potential moonlighting complexes and novel complexes containing uncharacterized proteins. Overall, the results demonstrated the utility of SEC fractionation and LC–MS analysis for system‐wide profiling of proteins to predict the existence of distinct forms of nuclear protein complexes.     

Synchrotron Protein Footprinting: A Technique to Investigate Protein-Protein Interactions

Abstract

Traditional approaches for macromolecular structure elucidation, including NMR, crystallography and cryo-EM have made significant progress in defining the structures of protein-protein complexes. A substantial number of macromolecular structures, however, have not been examined with atomic detail due to sample size and heterogeneity, or resolution limitations of the technique; therefore, the general applicability of each method is greatly reduced. Synchrotron footprinting attempts to bridge the gap in these methods by monitoring changes in accessible surface areas of discrete macromolecular moieties. As evidenced by our previous studies on RNA folding and DNA-protein interactions, the three-dimensional structure is probed by examining the reactions of these moieties with hydroxyl radicals generated by synchrotron X-rays. Here we report the application of synchrotron foot- printing to the investigation of protein-protein interactions, as the novel technique has been utilized to successfully map the contact sites of gelsolin segment-1 in the gelsolin segment 1/actin complex. Footprinting results demonstrate that phenylalanine 104, located on the actin binding helix of gelsolin segment 1, is protected from hydroxyl radical modification in the presence of actin. This change in reactivity results from the specific protection of gel- solin segment-1, consistent with the substantial decrease in solvent accessibility of F104 upon actin binding, as calculated from the crystal structural of the gelsolin segment 1/actin complex. The results presented here establish synchrotron footprinting as a broadly applicable method to probe structural features of macromolecular complexes that are not amenable to conventional approaches.     

ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein Structures

The advance of next-generation sequencing technologies has made exome sequencing rapid and relatively inexpensive. A major application of exome sequencing is the identification of genetic variations likely to cause Mendelian diseases. This requires processing large amounts of sequence information and therefore computational approaches that can accurately and efficiently identify the subset of disease-associated variations are needed. The accuracy and high false positive rates of existing computational tools leave much room for improvement. Here, we develop a boosted tree regression machine-learning approach to predict human disease-associated amino acid variations by utilizing a comprehensive combination of protein sequence and structure features. On comparing our method, ENTPRISE, to the state-of-the-art methods SIFT, PolyPhen-2, MUTATIONASSESSOR, MUTATIONTASTER, FATHMM, ENTPRISE exhibits significant improvement. In particular, on a testing dataset consisting of only proteins with balanced disease-associated and neutral variations defined as having the ratio of neutral/disease-associated variations between 0.3 and 3, the Mathews Correlation Coefficient by ENTPRISE is 0.493 as compared to 0.432 by PPH2-HumVar, 0.406 by SIFT, 0.403 by MUTATIONASSESSOR, 0.402 by PPH2-HumDiv, 0.305 by MUTATIONTASTER, and 0.181 by FATHMM. ENTPRISE is then applied to nucleic acid binding proteins in the human proteome. Disease-associated predictions are shown to be highly correlated with the number of protein-protein interactions. Both these predictions and the ENTPRISE server are freely available for academic users as a web service at http://cssb.biology.gatech.edu/entprise/.     

Human Induced Pluripotent Stem Cell-Derived Models to Investigate Human Cytomegalovirus Infection in Neural Cells

Human cytomegalovirus (HCMV) infection is one of the leading prenatal causes of congenital mental retardation and deformities world-wide. Access to cultured human neuronal lineages, necessary to understand the species specific pathogenic effects of HCMV, has been limited by difficulties in sustaining primary human neuronal cultures. Human induced pluripotent stem (iPS) cells now provide an opportunity for such research. We derived iPS cells from human adult fibroblasts and induced neural lineages to investigate their susceptibility to infection with HCMV strain Ad169. Analysis of iPS cells, iPS-derived neural stem cells (NSCs), neural progenitor cells (NPCs) and neurons suggests that (i) iPS cells are not permissive to HCMV infection, i.e., they do not permit a full viral replication cycle; (ii) Neural stem cells have impaired differentiation when infected by HCMV; (iii) NPCs are fully permissive for HCMV infection; altered expression of genes related to neural metabolism or neuronal differentiation is also observed; (iv) most iPS-derived neurons are not permissive to HCMV infection; and (v) infected neurons have impaired calcium influx in response to glutamate.     

Transmembrane Protein Alignment and Fold Recognition Based on Predicted Topology

Background

Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from soluble proteins, current protein structure prediction tools for soluble proteins may not work well for TMPs. With the increasing number of experimental TMP structures available, template-based methods have the potential to become broadly applicable for TMP structure prediction. However, the current fold recognition methods for TMPs are not as well developed as they are for soluble proteins.

Methodology

We developed a novel TMP Fold Recognition method, TMFR, to recognize TMP folds based on sequence-to-structure pairwise alignment. The method utilizes topology-based features in alignment together with sequence profile and solvent accessibility. It also incorporates a gap penalty that depends on predicted topology structure segments. Given the difference between α-helical transmembrane protein (αTMP) and β-strands transmembrane protein (βTMP), parameters of scoring functions are trained respectively for these two protein categories using 58 αTMPs and 17 βTMPs in a non-redundant training dataset.

Results

We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs. Our method achieved 10% and 9% better accuracies than HHalign in αTMPs and βTMPs, respectively. The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition. The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.     

Histone H1 as a Reporter Protein to Investigate Glycation in Bacteria

Nonenzymatic glycosylation (glycation) of proteins is a multistage chemical process starting as a condensation reaction between reducing sugars and primary amino groups (mainly from the side chains of Lis and Arg) and ending up with formation of complex heterocyclic compounds called advanced glycation end products (AGEs). For a long time, glycation has been attributed to the long-lived eukaryotes (including in humans) only. In a recent study, we showed that glycation also occurs in bacteria. The present study aims to prove that bacterial cytoplasm contains soluble glycating compounds. To this end, Lis/Arg-rich histone H1 isolated from rat liver was treated with deproteinized Escherichia coli cytoplasm through a dialysis membrane. This treatment leads to accumulation of AGEs as well as to a remarkable degradation of the reporter protein on storage at 4°C. Our results indicate also that glycation can be inhibited by acetylsalicylic acid (aspirin), thiamine (vitamin B1), and pyridoxine (vitamin B6).     

基于生物信息学技术筛选影响胶质母细胞瘤化疗敏感性相关基因的研究

目的：应用生物信息学技术筛选影响胶质母细胞瘤（GBM）化疗敏感性的相关基因。方法：对2批胶质瘤患者BIOSTAR基因芯片进行分析。通过随访完善临床资料,筛选芯片中胶质母细胞瘤患者生存期长、短两组间的差异基因,明确差异基因参与的功能和通路,并构建与烷化剂相关基因的信号传导网络,结合芯片数据、患者预后和信号传导网络,筛选GBM化疗敏感性的相关基因。结果：两组芯片中间差异基因有503条。2批芯片的差异基因主要参与62项基因功能,主要参与31条信号传导通路。通过对差异基因功能、通路,烷化剂信号转导网络的分析,得到影响胶质母细胞瘤化疗敏感性的核心的差异基因IFNGR2、IL8、ITGA5、TNFRSF1B。结论：通过严谨的实验设计和科学的统计学判别,结合患者完整的生存资料,本研究成功地应用生物信息学技术对基因芯片的大量数据进行挖掘和分析,并筛选出了可能影响GBM患者预后和化疗药物敏感性的基因,为进一步功能实验和患者个体化治疗奠定了基础。     

Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers

Sub-micrometer carriers (nanocarriers; NCs) enhance efficacy of drugs by improving solubility, stability, circulation time, targeting, and release. Additionally, traversing cellular barriers in the body is crucial for both oral delivery of therapeutic NCs into the circulation and transport from the blood into tissues, where intervention is needed. NC transport across cellular barriers is achieved by: (i) the paracellular route, via transient disruption of the junctions that interlock adjacent cells, or (ii) the transcellular route, where materials are internalized by endocytosis, transported across the cell body, and secreted at the opposite cell surface (transyctosis). Delivery across cellular barriers can be facilitated by coupling therapeutics or their carriers with targeting agents that bind specifically to cell-surface markers involved in transport. Here, we provide methods to measure the extent and mechanism of NC transport across a model cell barrier, which consists of a monolayer of gastrointestinal (GI) epithelial cells grown on a porous membrane located in a transwell insert. Formation of a permeability barrier is confirmed by measuring transepithelial electrical resistance (TEER), transepithelial transport of a control substance, and immunostaining of tight junctions. As an example, ~200 nm polymer NCs are used, which carry a therapeutic cargo and are coated with an antibody that targets a cell-surface determinant. The antibody or therapeutic cargo is labeled with ¹²⁵I for radioisotope tracing and labeled NCs are added to the upper chamber over the cell monolayer for varying periods of time. NCs associated to the cells and/or transported to the underlying chamber can be detected. Measurement of free ¹²⁵I allows subtraction of the degraded fraction. The paracellular route is assessed by determining potential changes caused by NC transport to the barrier parameters described above. Transcellular transport is determined by addressing the effect of modulating endocytosis and transcytosis pathways.     

基于生物信息学技术筛选影响胶质母细胞瘤化疗敏感性 相关基因的研究

目的:应用生物信息学技术筛选影响胶质母细胞瘤(GBM)化疗敏感性的相关基因。方法:对2批胶质瘤患者BIOSTAR基因芯片进行分析。通过随访完善临床资料,筛选芯片中胶质母细胞瘤患者生存期长、短两组间的差异基因,明确差异基因参与的功能和通路,并构建与烷化剂相关基因的信号传导网络,结合芯片数据、患者预后和信号传导网络,筛选GBM化疗敏感性的相关基因。结果:两组芯片中间差异基因有503条。2批芯片的差异基因主要参与62项基因功能,主要参与31条信号传导通路。通过对差异基因功能、通路,烷化剂信号转导网络的分析,得到影响胶质母细胞瘤化疗敏感性的核心的差异基因IFNGR2、IL8、ITGA5、TNFRSF1B。结论:通过严谨的实验设计和科学的统计学判别,结合患者完整的生存资料,本研究成功地应用生物信息学技术对基因芯片的大量数据进行挖掘和分析,并筛选出了可能影响GBM患者预后和化疗药物敏感性的基因,为进一步功能实验和患者个体化治疗奠定了基础。     

GWYRE: A Resource for Mapping Variants onto Experimental and Modeled Structures of Human Protein Complexes

Rapid progress in structural modeling of proteins and their interactions is powered by advances in knowledge-based methodologies along with better understanding of physical principles of protein structure and function. The pool of structural data for modeling of proteins and protein–protein complexes is constantly increasing due to the rapid growth of protein interaction databases and Protein Data Bank. The GWYRE (Genome Wide PhYRE) project capitalizes on these developments by advancing and applying new powerful modeling methodologies to structural modeling of protein–protein interactions and genetic variation. The methods integrate knowledge-based tertiary structure prediction using Phyre2 and quaternary structure prediction using template-based docking by a full-structure alignment protocol to generate models for binary complexes. The predictions are incorporated in a comprehensive public resource for structural characterization of the human interactome and the location of human genetic variants. The GWYRE resource facilitates better understanding of principles of protein interaction and structure/function relationships. The resource is available at http://www.gwyre.org.     

The Use of Edge-Betweenness Clustering to Investigate Biological Function in Protein Interaction Networks

Background  

This paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters.