SHARP2: protein-protein interaction predictions using patch analysis

SHARP2 is a flexible web-based bioinformatics tool for predicting potential protein-protein interaction sites on protein structures. It implements a predictive algorithm that calculates multiple parameters for overlapping patches of residues on the surface of a protein. Six parameters are calculated: solvation potential, hydrophobicity, accessible surface area, residue interface propensity, planarity and protrusion (SHARP2). Parameter scores for each patch are combined, and the patch with the highest combined score is predicted as a potential interaction site. SHARP2 enables users to upload 3D protein structure files in PDB format, to obtain information on potential interaction sites as downloadable HTML tables and to view the location of the sites on the 3D structure using Jmol. The server allows for the input of multiple structures and multiple combinations of parameters. Therefore predictions can be made for complete datasets, as well as individual structures. AVAILABILITY: http://www.bioinformatics.sussex.ac.uk/SHARP2.     

False positive reduction in protein-protein interaction predictions using gene ontology annotations

Background  

Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.     

Genome studies and molecular genetics

A selection of World Wide Web sites relevant to papers published in this issue of Current Opinion in Plant Biology.     

Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks

Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub‐disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species’ presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change.     

Assessing predictions of protein-protein interaction: the CAPRI experiment

The Critical Assessment of PRedicted Interactions (CAPRI) experiment was designed in 2000 to test protein docking algorithms in blind predictions of the structure of protein-protein complexes. In four years, 17 complexes offered by crystallographers as targets prior to publication, have been subjected to structure prediction by docking their two components. Models of these complexes were submitted by predictor groups and assessed by comparing their geometry to the X-ray structure and by evaluating the quality of the prediction of the regions of interaction and of the pair wise residue contacts. Prediction was successful on 12 of the 17 targets, most of the failures being due to large conformation changes that the algorithms could not cope with. Progress in the prediction quality observed in four years indicates that the experiment is a powerful incentive to develop new procedures that allow for flexibility during docking and incorporate nonstructural information. We therefore call upon structural biologists who study protein-protein complexes to provide targets for further rounds of CAPRI predictions.     

Modelling molecular interaction pathways using a two-stage identification algorithm

In systems biology, molecular interactions are typically modelled using white-box methods, usually based on mass action kinetics. Unfortunately, problems with dimensionality can arise when the number of molecular species in the system is very large, which makes the system modelling and behavior simulation extremely difficult or computationally too expensive. As an alternative, this paper investigates the identification of two molecular interaction pathways using a black-box approach. This type of method creates a simple linear-in-the-parameters model using regression of data, where the output of the model at any time is a function of previous system states of interest. One of the main objectives in building black-box models is to produce an optimal sparse nonlinear one to effectively represent the system behavior. In this paper, it is achieved by applying an efficient iterative approach, where the terms in the regression model are selected and refined using a forward and backward subset selection algorithm. The method is applied to model identification for the MAPK signal transduction pathway and the Brusselator using noisy data of different sizes. Simulation results confirm the efficacy of the black-box modelling method which offers an alternative to the computationally expensive conventional approach.     

Genome analysis and signature discovery for diving and sensory properties of the endangered Chinese alligator

Crocodilians are diving reptiles that can hold their breath under water for long periods of time and are crepuscular animals with excellent sensory abilities. They comprise a sister lineage of birds and have no sex chromosome. Here we report the genome sequence of the endangered Chinese alligator (Alligator sinensis) and describe its unique features. The next-generation sequencing generated 314 Gb of raw sequence, yielding a genome size of 2.3 Gb. A total of 22 200 genes were predicted in Alligator sinensis using a de novo, homology- and RNA-based combined model. The genetic basis of long-diving behavior includes duplication of the bicarbonate-binding hemoglobin gene, co-functioning of routine phosphate-binding and special bicarbonate-binding oxygen transport, and positively selected energy metabolism, ammonium bicarbonate excretion and cardiac muscle contraction. Further, we elucidated the robust Alligator sinensis sensory system, including a significantly expanded olfactory receptor repertoire, rapidly evolving nerve-related cellular components and visual perception, and positive selection of the night vision-related opsin and sound detection-associated otopetrin. We also discovered a well-developed immune system with a considerable number of lineage-specific antigen-presentation genes for adaptive immunity as well as expansion of the tripartite motif-containing C-type lectin and butyrophilin genes for innate immunity and expression of antibacterial peptides. Multifluorescence in situ hybridization showed that alligator chromosome 3, which encodes DMRT1, exhibits significant synteny with chicken chromosome Z. Finally, population history analysis indicated population admixture 0.60-1.05 million years ago, when the Qinghai-Tibetan Plateau was uplifted.     

Challenging AQP4 druggability for NMO-IgG antibody binding using molecular dynamics and molecular interaction fields

Neuromyelitis optica (NMO) is a multiple sclerosis-like immunopathology disease affecting optic nerves and the spinal cord. Its pathological hallmark is the deposition of a typical immunoglobulin, called NMO-IgG, against the water channel Aquaporin-4 (AQP4). Preventing NMO-IgG binding would represent a valuable molecular strategy for a focused NMO therapy. The recent observation that aspartate in position 69 (D69) is determinant for the formation of NMO-IgG epitopes prompted us to carry out intensive Molecular Dynamics (MD) studies on a number of single-point AQP4 mutants. Here, we report a domino effect originating from the point mutation at position 69: we find that the side chain of T62 is reoriented far from its expected position leaning on the lumen of the pore. More importantly, the strength of the H-bond interaction between L53 and T56, at the basis of the loop A, is substantially weakened. These events represent important pieces of a clear-cut mechanistic rationale behind the failure of the NMO-IgG binding, while the water channel function as well as the propensity to aggregate into OAPs remains unaltered. The molecular interaction fields (MIF)-based analysis of cavities complemented MD findings indicating a putative binding site comprising the same residues determining epitope reorganization. In this respect, docking studies unveiled an intriguing perspective to address the future design of small drug-like compounds against NMO. In agreement with recent experimental observations, the present study is the first computational attempt to elucidate NMO-IgG binding at the molecular level, as well as a first effort toward a less elusive AQP4 druggability.     

Motional timescale predictions by molecular dynamics simulations: Case study using proline and hydroxyproline sidechain dynamics

We propose a new approach for force field optimizations which aims at reproducing dynamics characteristics using biomolecular MD simulations, in addition to improved prediction of motionally averaged structural properties available from experiment. As the source of experimental data for dynamics fittings, we use ¹³C NMR spin‐lattice relaxation times T₁ of backbone and sidechain carbons, which allow to determine correlation times of both overall molecular and intramolecular motions. For structural fittings, we use motionally averaged experimental values of NMR J couplings. The proline residue and its derivative 4‐hydroxyproline with relatively simple cyclic structure and sidechain dynamics were chosen for the assessment of the new approach in this work. Initially, grid search and simplexed MD simulations identified large number of parameter sets which fit equally well experimental J couplings. Using the Arrhenius‐type relationship between the force constant and the correlation time, the available MD data for a series of parameter sets were analyzed to predict the value of the force constant that best reproduces experimental timescale of the sidechain dynamics. Verification of the new force‐field (termed as AMBER99SB‐ILDNP) against NMR J couplings and correlation times showed consistent and significant improvements compared to the original force field in reproducing both structural and dynamics properties. The results suggest that matching experimental timescales of motions together with motionally averaged characteristics is the valid approach for force field parameter optimization. Such a comprehensive approach is not restricted to cyclic residues and can be extended to other amino acid residues, as well as to the backbone. Proteins 2014; 82:195–215. © 2013 Wiley Periodicals, Inc.     

Self-organizing-map-based molecular signature representing the development of hepatocellular carcinoma

Using high-density oligonucleotide array, we comprehensively analyzed expression levels of 12600 genes in 50 hepatocellular carcinoma (HCC) samples with positive hepatitis C virus (HCV) serology (well (G1), moderately (G2), and poorly (G3) differentiated tumors) and 11 non-tumorous livers (L1 and L0) with and without HCV infection. We searched for discriminatory genes of transition (L0 vs. L1, L1 vs. G1, G1 vs. G2, G2 vs. G3) with a supervised learning method, and then arranged the samples by self-organizing map (SOM) with the discriminatory gene sets. The SOM arranged the five clusters on a unique sigmoidal curve in the order L0, L1, G1, G2, and G3. The sample arrangement reproduced development-related features of HCC such as p53 abnormality. Strikingly, G2 tumors without venous invasion were located closer to the G1 cluster, and most G2 tumors with venous invasion were located closer to the G3 cluster (P=0.001 by Fisher's exact test). Our present profiling data will serve as a framework to understand the relation between the development and dedifferentiation of HCC.     

Identification of a molecular signature in human type 1 diabetes mellitus using serum and functional genomics

Understanding active proinflammatory mechanisms at and before type 1 diabetes mellitus (T1DM) onset is hindered in humans, given that the relevant tissues are inaccessible and pancreatic immune responses are difficult to measure in the periphery by traditional approaches. Therefore, we investigated the use of a sensitive and comprehensive genomics strategy to investigate the presence of proinflammatory factors in serum. The sera of recent onset diabetes patients (n = 15, 12 possessing and 3 lacking islet cell autoantibodies), long-standing diabetes patients (n = 12), "at risk" siblings of diabetes patients (n = 9), and healthy controls (n = 12) were used to induce gene expression in unrelated, healthy PBMC. After culture, gene expression was measured with microarrays and normalized expression data were subjected to hierarchical clustering and multidimensional scaling. All recent onset sera induced an expression signature (192 UniGenes; fold change: >1.5, p < 0.01; false discovery rate: <0.01) that included IL-1 cytokine family members and chemokines involved in monocyte/macrophage and neutrophil chemotaxis, as well as numerous receptors and signaling molecules. This molecular signature was not induced with the sera of healthy controls or long standing diabetes patients, where longitudinal analysis of "at risk" siblings (n = 3) before and after onset support the hypothesis that the signature emerges years before onset. This study supports prior investigations of serum that reflect disease processes associated with progression to T1DM. Identification of unique inflammatory mediators may improve disease prediction beyond current islet autoantibodies. Furthermore, proinflammatory serum markers may be used as inclusion criteria or endpoint measures in clinical trials aimed at preventing T1DM.