Modifying the DPClus algorithm for identifying protein complexes based on new topological structures

Background  

Identification of protein complexes is crucial for understanding principles of cellular organization and functions. As the size of protein-protein interaction set increases, a general trend is to represent the interactions as a network and to develop effective algorithms to detect significant complexes in such networks.     

A core-attachment based method to detect protein complexes in PPI networks

Background  

How to detect protein complexes is an important and challenging task in post genomic era. As the increasing amount of protein-protein interaction (PPI) data are available, we are able to identify protein complexes from PPI networks. However, most of current studies detect protein complexes based solely on the observation that dense regions in PPI networks may correspond to protein complexes, but fail to consider the inherent organization within protein complexes.     

Detecting internally symmetric protein structures

Background  

Many functional proteins have a symmetric structure. Most of these are multimeric complexes, which are made of non-symmetric monomers arranged in a symmetric manner. However, there are also a large number of proteins that have a symmetric structure in the monomeric state. These internally symmetric proteins are interesting objects from the point of view of their folding, function, and evolution. Most algorithms that detect the internally symmetric proteins depend on finding repeating units of similar structure and do not use the symmetry information.     

Composite functional module inference: detecting cooperation between transcriptional regulation and protein interaction by mantel test

Background  

Functional modules are basic units of cell function, and exploring them is important for understanding the organization, regulation and execution of cell processes. Functional modules in single biological networks (e.g., the protein-protein interaction network), have been the focus of recent studies. Functional modules in the integrated network are composite functional modules, which imply the complex relationships involving multiple biological interaction types, and detect them will help us understand the complexity of cell processes.     

TPRpred: a tool for prediction of TPR-, PPR- and SEL1-like repeats from protein sequences

Background  

Solenoid repeat proteins of the Tetratrico Peptide Repeat (TPR) family are involved as scaffolds in a broad range of protein-protein interactions. Several resources are available for the prediction of TPRs, however, they often fail to detect divergent repeat units.     

Stabilizing mutations increase secretion of functional soluble TCR-Ig fusion proteins

Background  

Whereas T cell receptors (TCRs) detect peptide/major histocompatibility complexes (pMHCs) with exquisite specificity, there are challenges regarding their expression and use as soluble detection molecules due to molecular instability. We have investigated strategies for the production of TCR-immunoglobulin (Ig) fusion proteins. Two different TCRs that are characteristic of a mouse model for idiotype (Id) dependent immune regulation were engineered. They are structurally unrelated with different variable (V), diversity (D) and joining (J) segments, but each share one V gene segment, either V_α or V_β, with the well characterized murine TCR, 2C.     

Structural disorder promotes assembly of protein complexes

Background  

The idea that the assembly of protein complexes is linked with protein disorder has been inferred from a few large complexes, such as the viral capsid or bacterial flagellar system, only. The relationship, which suggests that larger complexes have more disorder, has never been systematically tested. The recent high-throughput analyses of protein-protein interactions and protein complexes in the cell generated data that enable to address this issue by bioinformatic means.     

Deciphering the plasma membrane hallmarks of apoptotic cells: Phosphatidylserine transverse redistribution and calcium entry

Background  

During apoptosis, Ca²⁺-dependent events participate in the regulation of intracellular and morphological changes including phosphatidylserine exposure in the exoplasmic leaflet of the cell plasma membrane. The occurrence of phosphatidylserine at the surface of specialized cells, such as platelets, is also essential for the assembly of the enzyme complexes of the blood coagulation cascade, as demonstrated by hemorrhages in Scott syndrome, an extremely rare genetic deficiency of phosphatidylserine externalization, without other apparent pathophysiologic consequences. We have recently reported a reduced capacitative Ca²⁺ entry in Scott cells which may be part of the Scott phenotype.     

Development and implementation of an algorithm for detection of protein complexes in large interaction networks

Background  

After complete sequencing of a number of genomes the focus has now turned to proteomics. Advanced proteomics technologies such as two-hybrid assay, mass spectrometry etc. are producing huge data sets of protein-protein interactions which can be portrayed as networks, and one of the burning issues is to find protein complexes in such networks. The enormous size of protein-protein interaction (PPI) networks warrants development of efficient computational methods for extraction of significant complexes.     

Sequence-dependent DNA helical rise and nucleosome stability

Background  

Nucleosomes are the basic structural units of eukaryotic chromatin and play a key role in regulation of gene expression. After resolution of the nucleosome structure, the bipartite nature of this particle has revealed itself and has disclosed the presence, on the histone surface, of a symmetric distribution of positive charges, able to interact with their negative DNA phosphate counterpart.     

MCL-CAw: a refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Background  

The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes.     

Environmental stresses inhibit splicing in the aquatic fungus Blastocladiella emersonii

Background  

Exposure of cells to environmental stress conditions can lead to the interruption of several intracellular processes, in particular those performed by macromolecular complexes such as the spliceosome.     

Analysis of the role of PCNA-DNA contacts during clamp loading

Background  

Sliding clamps, such as Proliferating Cell Nuclear Antigen (PCNA) in eukaryotes, are ring-shaped protein complexes that encircle DNA and enable highly processive DNA replication by serving as docking sites for DNA polymerases. In an ATP-dependent reaction, clamp loader complexes, such as the Replication Factor-C (RFC) complex in eukaryotes, open the clamp and load it around primer-template DNA.     

Protein complex detection with semi-supervised learning in protein interaction networks

Background

Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. However, protein complexes are likely to overlap and the interaction data are very noisy. It is a great challenge to effectively analyze the massive data for biologically meaningful protein complex detection.

Results

Many people try to solve the problem by using the traditional unsupervised graph clustering methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. In this paper, we utilize the neural network with the “semi-supervised” mechanism to detect the protein complexes. By retraining the neural network model recursively, we could find the optimized parameters for the model, in such a way we can successfully detect the protein complexes. The comparison results show that our algorithm could identify protein complexes that are missed by other methods. We also have shown that our method achieve better precision and recall rates for the identified protein complexes than other existing methods. In addition, the framework we proposed is easy to be extended in the future.

Conclusions

Using a weighted network to represent the protein interaction network is more appropriate than using a traditional unweighted network. In addition, integrating biological features and topological features to represent protein complexes is more meaningful than using dense subgraphs. Last, the “semi-supervised” learning model is a promising model to detect protein complexes with more biological and topological features available.

     

A switchable light-input, light-output system modelled and constructed in yeast

Background  

Advances in synthetic biology will require spatio-temporal regulation of biological processes in heterologous host cells. We develop a light-switchable, two-hybrid interaction in yeast, based upon the Arabidopsis proteins PHYTOCHROME A and FAR-RED ELONGATED HYPOCOTYL 1-LIKE. Light input to this regulatory module allows dynamic control of a light-emitting LUCIFERASE reporter gene, which we detect by real-time imaging of yeast colonies on solid media.     

High-resolution atomic force microscopy visualization of metalloproteins and their complexes

Background

Metalloproteins myeloperoxidase (MPO), ceruloplasmin (CP) and lactoferrin (LF) play an important role in regulation of inflammation and oxidative stress in vertebrates. It was previously shown that these proteins may work synergetically as antimicrobial and anti-inflammatory agents by forming complexes, such as MPO-CP and LF-CP. However, interaction of metalloprotein molecules with each other has never been characterized at a single-molecule level.