Probability-based model of protein-protein interactions on biological timescales

Background  

Simulation methods can assist in describing and understanding complex networks of interacting proteins, providing fresh insights into the function and regulation of biological systems. Recent studies have investigated such processes by explicitly modelling the diffusion and interactions of individual molecules. In these approaches, two entities are considered to have interacted if they come within a set cutoff distance of each other.     

Hinge Atlas: relating protein sequence to sites of structural flexibility

Background  

Relating features of protein sequences to structural hinges is important for identifying domain boundaries, understanding structure-function relationships, and designing flexibility into proteins. Efforts in this field have been hampered by the lack of a proper dataset for studying characteristics of hinges.     

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background  

The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence- and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence.     

A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction

Background  

Predicting which molecules can bind to a given binding site of a protein with known 3D structure is important to decipher the protein function, and useful in drug design. A classical assumption in structural biology is that proteins with similar 3D structures have related molecular functions, and therefore may bind similar ligands. However, proteins that do not display any overall sequence or structure similarity may also bind similar ligands if they contain similar binding sites. Quantitatively assessing the similarity between binding sites may therefore be useful to propose new ligands for a given pocket, based on those known for similar pockets.     

Growth of <Emphasis Type="Italic">Helicobacter pylori</Emphasis> in a long spiral form does not alter expression of immunodominant proteins

Background  

We have previously reported that altered culture conditions (a broth media with shaking) could induce a strain of Helicobacter pylori to assume a long spiral morphology resembling that described for Helicobacter heilmannii. The present study was initiated to determine if other strains of H. pylori could be induced to assume that morphology and if doing so would alter the expression of immunodominant proteins.     

Evolutionary history of histone demethylase families: distinct evolutionary patterns suggest functional divergence

Background  

Histone methylation can dramatically affect chromatin structure and gene expression and was considered irreversible until recent discoveries of two families of histone demethylases, the KDM1 (previously LSD1) and JmjC domain-containing proteins. These two types of proteins have different functional domains and distinct substrate specificities. Although more and more KDM1 and JmjC proteins have been shown to have histone demethylase activity, our knowledge about their evolution history is limited.     

PathEx: a novel multi factors based datasets selector web tool

Background  

Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information.     

Prediction of protein motions from amino acid sequence and its application to protein-protein interaction

Background  

Structural flexibility is an important characteristic of proteins because it is often associated with their function. The movement of a polypeptide segment in a protein can be broken down into two types of motions: internal and external ones. The former is deformation of the segment itself, but the latter involves only rotational and translational motions as a rigid body. Normal Model Analysis (NMA) can derive these two motions, but its application remains limited because it necessitates the gathering of complete structural information.     

Optimised amino acid specific weighting factors for unbound protein docking

Background  

One of the most challenging aspects of protein-protein docking is the inclusion of flexibility into the docking procedure. We developed a postfilter where the grid-representation of proteins for docking is extended by an optimised weighting factor for each amino acid.     

PSSM-based prediction of DNA binding sites in proteins

Background  

Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.     

FlexOracle: predicting flexible hinges by identification of stable domains

Background  

Protein motions play an essential role in catalysis and protein-ligand interactions, but are difficult to observe directly. A substantial fraction of protein motions involve hinge bending. For these proteins, the accurate identification of flexible hinges connecting rigid domains would provide significant insight into motion. Programs such as GNM and FIRST have made global flexibility predictions available at low computational cost, but are not designed specifically for finding hinge points.     

A minimal peach type II chlorophyll <Emphasis Type="Italic">a/b</Emphasis>-binding protein promoter retains tissue-specificity and light regulation in tomato

Background  

Promoters with tissue-specificity are desirable to drive expression of transgenes in crops to avoid accumulation of foreign proteins in edible tissues/organs. Several photosynthetic promoters have been shown to be strong regulators of expression of transgenes in light-responsive tissues and would be good candidates for leaf and immature fruit tissue-specificity, if expression in the mature fruit were minimized.     

Ab initio modeling of small proteins by iterative TASSER simulations

Background  

Predicting 3-dimensional protein structures from amino-acid sequences is an important unsolved problem in computational structural biology. The problem becomes relatively easier if close homologous proteins have been solved, as high-resolution models can be built by aligning target sequences to the solved homologous structures. However, for sequences without similar folds in the Protein Data Bank (PDB) library, the models have to be predicted from scratch. Progress in the ab initio structure modeling is slow. The aim of this study was to extend the TASSER (threading/assembly/refinement) method for the ab initio modeling and examine systemically its ability to fold small single-domain proteins.     

A correlated motif approach for finding short linear motifs from protein interaction networks

Background  

An important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on protein sequence sets each interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches.     

Computational identification of residues that modulate voltage sensitivity of voltage-gated potassium channels

Background  

Studies of the structure-function relationship in proteins for which no 3D structure is available are often based on inspection of multiple sequence alignments. Many functionally important residues of proteins can be identified because they are conserved during evolution. However, residues that vary can also be critically important if their variation is responsible for diversity of protein function and improved phenotypes. If too few sequences are studied, the support for hypotheses on the role of a given residue will be weak, but analysis of large multiple alignments is too complex for simple inspection. When a large body of sequence and functional data are available for a protein family, mature data mining tools, such as machine learning, can be applied to extract information more easily, sensitively and reliably. We have undertaken such an analysis of voltage-gated potassium channels, a transmembrane protein family whose members play indispensable roles in electrically excitable cells.     

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Background  

The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models.     

Expression and purification of ELP-intein-tagged target proteins in high cell density <Emphasis Type="Italic">E. coli</Emphasis> fermentation

Background  

Elastin-like polypeptides (ELPs) are useful tools that can be used to non-chromatographically purify proteins. When paired with self-cleaving inteins, they can be used as economical self-cleaving purification tags. However, ELPs and ELP-tagged target proteins have been traditionally expressed using highly enriched media in shake flask cultures, which are generally not amenable to scale-up.     

Changes in P-glycoprotein activity are mediated by the growth of a tumour cell line as multicellular spheroids

Background  

Expression of P-glycoprotein (P-gp), the multidrug resistance (MDR) 1 gene product, can lead to multidrug resistance in tumours. However, the physiological role of P-gp in tumours growing as multicellular spheroids is not well understood. Recent evidence suggests that P-gp activity may be modulated by cellular components such as membrane proteins, membrane-anchoring proteins or membrane-lipid composition. Since, multicellular spheroids studies have evidenced alterations in numerous cellular components, including those related to the plasma membrane function, result plausible that some of these changes might modulate P-gp function and be responsible for the acquisition of multicellular drug resistance. In the present study, we asked if a human lung cancer cell line (INER-51) grown as multicellular spheroids can modify the P-gp activity to decrease the levels of doxorubicin (DXR) retained and increase their drug resistance.     

Dual targeted mitochondrial proteins are characterized by lower MTS parameters and total net charge

Background

In eukaryotic cells, identical proteins can be located in different subcellular compartments (termed dual-targeted proteins).

Methodology/Principal Findings

We divided a reference set of mitochondrial proteins (published single gene studies) into two groups: i) Dual targeted mitochondrial proteins and ii) Exclusive mitochondrial proteins. Mitochondrial proteins were considered dual-targeted if they were also found or predicted to be localized to the cytosol, the nucleus, the endoplasmic reticulum (ER) or the peroxisome. We found that dual localized mitochondrial proteins have i) A weaker mitochondrial targeting sequence (MitoProtII score, hydrophobic moment and number of basic residues) and ii) a lower whole-protein net charge, when compared to exclusive mitochondrial proteins. We have also generated an annotation list of dual-targeted proteins within the predicted yeast mitochondrial proteome. This considerably large group of dual-localized proteins comprises approximately one quarter of the predicted mitochondrial proteome. We supported this prediction by experimental verification of a subgroup of the predicted dual targeted proteins.

Conclusions/Significance

Taken together, these results establish dual targeting as a widely abundant phenomenon that should affect our concepts of gene expression and protein function. Possible relationships between the MTS/mature sequence traits and protein dual targeting are discussed.