PRISM: protein interactions by structural matching

Prism () is a website for protein interface analysis and prediction of putative protein–protein interactions. It is composed of a database holding protein interface structures derived from the Protein Data Bank (PDB). The server also includes summary information about related proteins and an interactive protein interface viewer. A list of putative protein–protein interactions obtained by running our prediction algorithm can also be accessed. These results are applied to a set of protein structures obtained from the PDB at the time of algorithm execution (January 2004). Users can browse through the non-redundant dataset of representative interfaces on which the prediction algorithm depends, retrieve the list of similar structures to these interfaces or see the results of interaction predictions for a particular protein. Another service provided is interactive prediction. This is done by running the algorithm for user input structures

Enriching Traditional Protein-protein Interaction Networks with Alternative Conformations of Proteins

Traditional Protein-Protein Interaction (PPI) networks, which use a node and edge representation, lack some valuable information about the mechanistic details of biological processes. Mapping protein structures to these PPI networks not only provides structural details of each interaction but also helps us to find the mutual exclusive interactions. Yet it is not a comprehensive representation as it neglects the conformational changes of proteins which may lead to different interactions, functions, and downstream signalling. In this study, we proposed a new representation for structural PPI networks inspecting the alternative conformations of proteins. We performed a large-scale study by creating breast cancer metastasis network and equipped it with different conformers of proteins. Our results showed that although 88% of proteins in our network has at least two structures in Protein Data Bank (PDB), only 22% of them have alternative conformations and the remaining proteins have different regions saved in PDB. However, using even this small set of alternative conformations we observed a considerable increase in our protein docking predictions. Our protein-protein interaction predictions increased from 54% to 76% using the alternative conformations. We also showed the benefits of investigating structural data and alternative conformations of proteins through three case studies

Beyond the heterodimer model for mineralocorticoid and glucocorticoid receptor interactions in nuclei and at DNA.

Glucocorticoid (GR) and mineralocorticoid receptors (MR) are believed to classically bind DNA as homodimers or MR-GR heterodimers to influence gene regulation in response to pulsatile basal or stress-evoked glucocorticoid secretion. Pulsed corticosterone presentation reveals MR and GR co-occupy DNA only at the peaks of glucocorticoid oscillations, allowing interaction. GR DNA occupancy was pulsatile, while MR DNA occupancy was prolonged through the inter-pulse interval. In mouse mammary 3617 cells MR-GR interacted in the nucleus and at a chromatin-associated DNA binding site. Interactions occurred irrespective of ligand type and receptors formed complexes of higher order than heterodimers. We also detected MR-GR interactions ex-vivo in rat hippocampus. An expanded range of MR-GR interactions predicts structural allostery allowing a variety of transcriptional outcomes and is applicable to the multiple tissue types that co-express both receptors in the same cells whether activated by the same or different hormones

Linking Proteins to Signaling Pathways for Experiment Design and Evaluation

Biomedical experimental work often focuses on altering the functions of selected proteins. These changes can hit signaling pathways, and can therefore unexpectedly and non-specifically affect cellular processes. We propose PathwayLinker, an online tool that can provide a first estimate of the possible signaling effects of such changes, e.g., drug or microRNA treatments. PathwayLinker minimizes the users' efforts by integrating protein-protein interaction and signaling pathway data from several sources with statistical significance tests and clear visualization. We demonstrate through three case studies that the developed tool can point out unexpected signaling bias in normal laboratory experiments and identify likely novel signaling proteins among the interactors of known drug targets. In our first case study we show that knockdown of the Caenorhabditis elegans gene cdc-25.1 (meant to avoid progeny) may globally affect the signaling system and unexpectedly bias experiments. In the second case study we evaluate the loss-of-function phenotypes of a less known C. elegans gene to predict its function. In the third case study we analyze GJA1, an anti-cancer drug target protein in human, and predict for this protein novel signaling pathway memberships, which may be sources of side effects. Compared to similar services, a major advantage of PathwayLinker is that it drastically reduces the necessary amount of manual literature searches and can be used without a computational background. PathwayLinker is available at http://PathwayLinker.org. Detailed documentation and source code are available at the website

Computer simulation of leadership, consensus decision making and collective behaviour in humans

The aim of this study is to evaluate the reliability of a crowd simulation model developed by the authors by reproducing Dyer et al.’s experiments(published in Philosophical Transactions in 2009) on human leadership and consensus decision-­making in a computer-­based environment. The theoretical crowd model of the simulation environment is presented, and its results are compared and analysed against Dyer et al.’s original experiments. It is concluded that the results are 11 largely consistent with the experiments, which demonstrates the reliability of the crowd model. Furthermore, the simulation data also reveals several additional new findings, namely: 1) the phenomena of sacrificing accuracy to reach a quicker consensus decision found in ants colonies was also discovered in the simulation; 2) the ability of reaching consensus in groups has a direct impact on the time and accuracy of arriving at the target position; 3) the positions of the informed individuals or leaders in the crowd could have significant impact on the overall crowd movement; 4) the simulation also confirmed Dyer et al.’s anecdotal evidence of the proportion of the leadership in large crowds and its effect on crowd movement. The potential applications of these findings are highlighted in the final discussion of this paper

A Comparative Molecular Dynamics Study of Methylation State Specificity of JMJD2A

Histone modifications have great importance in epigenetic regulation. JMJD2A is a histone demethylase which is selective for di- and trimethyl forms of residues Lys9 and Lys36 of Histone 3 tail (H3K9 and H3K36). We present a molecular dynamics simulations of mono-, di- and trimethylated histone tails in complex with JMJD2A catalytic domain to gain insight into how JMJD2A discriminates between the methylation states of H3K9. The methyl groups are located at specific distances and orientations with respect to Fe(II) in methylammonium binding pocket. For the trimethyllysine the mechanism which provides the effectual orientation of methyl groups is the symmetry, whereas for the dimethyllysine case the determining factors are the interactions between methyllysine head and its environment and subsequently the restriction on angular motion. The occurrence frequency of methyl groups in a certain proximity of Fe(II) comes out as the explanation of the enzyme activity difference on di- and tri-methylated peptides. Energy analysis suggests that recognition is mostly driven by van der Waals and followed by Coulombic interactions in the enzyme-substrate interface. The number (mono, di or tri) and orientations of methyl groups and water molecules significantly affect the extent of van der Waals interaction strengths. Hydrogen bonding analysis suggests that the interaction between JMJD2A and its substrates mainly comes from main chain-side chain interactions. Binding free energy analysis points out Arg8 as an important residue forming an intra-substrate hydrogen bond with tri and dimethylated Lys9 of the H3 chain. Our study provides new insights into how JMJD2A discriminates between its substrates from both a structural and dynamical point of view

Molecular Recognition of H3/H4 Histone Tails by the Tudor Domains of JMJD2A: A Comparative Molecular Dynamics Simulations Study

Background: Histone demethylase, JMJD2A, specifically recognizes and binds to methylated lysine residues at histone H3 and H4 tails (especially trimethylated H3K4 (H3K4me3), trimethylated H3K9 (H3K9me3) and di, trimethylated H4K20 (H4K20me2, H4K20me3)) via its tandem tudor domains. Crystal structures of JMJD2A-tudor binding to H3K4me3 and H4K20me3 peptides are available whereas the others are not. Complete picture of the recognition of the four histone peptides by the tandem tudor domains yet remains to be clarified. Methodology/Principal Findings: We report a detailed molecular dynamics simulation and binding energy analysis of the recognition of JMJD2A-tudor with four different histone tails. 25 ns fully unrestrained molecular dynamics simulations are carried out for each of the bound and free structures. We investigate the important hydrogen bonds and electrostatic interactions between the tudor domains and the peptide molecules and identify the critical residues that stabilize the complexes. Our binding free energy calculations show that H4K20me2 and H3K9me3 peptides have the highest and lowest affinity to JMJD2A-tudor, respectively. We also show that H4K20me2 peptide adopts the same binding mode with H4K20me3 peptide, and H3K9me3 peptide adopts the same binding mode with H3K4me3 peptide. Decomposition of the enthalpic and the entropic contributions to the binding free energies indicate that the recognition of the histone peptides is mainly driven by favourable van der Waals interactions. Residue decomposition of the binding free energies with backbone and side chain contributions as well as their energetic constituents identify the hotspots in the binding interface of the structures. Conclusion: Energetic investigations of the four complexes suggest that many of the residues involved in the interactions are common. However, we found two receptor residues that were related to selective binding of the H3 and H4 ligands. Modifications or mutations on one of these residues can selectively alter the recognition of the H3 tails or the H4 tails

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors