Cytochrome P450 CYP89A9 Is Involved in the Formation of Major Chlorophyll Catabolites during Leaf Senescence in Arabidopsis

Nonfluorescent chlorophyll catabolites (NCCs) were described as products of chlorophyll breakdown in Arabidopsis thaliana. NCCs are formyloxobilin-type catabolites derived from chlorophyll by oxygenolytic opening of the chlorin macrocycle. These linear tetrapyrroles are generated from their fluorescent chlorophyll catabolite (FCC) precursors by a nonenzymatic isomerization inside the vacuole of senescing cells. Here, we identified a group of distinct dioxobilin-type chlorophyll catabolites (DCCs) as the major breakdown products in wild-type Arabidopsis, representing more than 90% of the chlorophyll of green leaves. The molecular constitution of the most abundant nonfluorescent DCC (NDCC), At-NDCC-1, was determined. We further identified cytochrome P450 monooxygenase CYP89A9 as being responsible for NDCC accumulation in wild-type Arabidopsis; cyp89a9 mutants that are deficient in CYP89A9 function were devoid of NDCCs but accumulated proportionally higher amounts of NCCs. CYP89A9 localized outside the chloroplasts, implying that FCCs occurring in the cytosol might be its natural substrate. Using recombinant CYP89A9, we confirm FCC specificity and show that fluorescent DCCs are the products of the CYP89A9 reaction. Fluorescent DCCs, formed by this enzyme, isomerize to the respective NDCCs in weakly acidic medium, as found in vacuoles. We conclude that CYP89A9 is involved in the formation of dioxobilin-type catabolites of chlorophyll in Arabidopsis.     

SOD3 R231G polymorphism associated with coronary artery disease and myocardial infarction. The Ludwigshafen Risk and Cardiovascular Health (LURIC) study

This study examined the superoxide dismutase 3 (SOD3) R231G polymorphism in relation to the severity of coronary artery disease (CAD) and the risk of myocardial infarction (MI) in 3211 individuals; 94.4% of study participants were homozygous for SOD3 231RR and 5.5% were heterozygous for SOD3 231RG. The odds ratios of the RG and GG genotype (adjusted for age, gender and for conventional cardiovascular risk factors) were 2.02 (95% CI, 1.23–3.33, p=0.005) for the highest vs the lowest Friesinger coronary score and 1.40 (95% CI, 1.02–1.92, p=0.037) for MI, respectively. Further the SOD3 RG and GG genotype was associated with lower alpha-tocopherol levels than the wild type SOD3 RR genotype. It is concluded that the SOD3 231RG and GG genotype is associated with lower alpha-tocopherol levels and the severity of CAD and the risk of MI.     

A conservation and rigidity based method for detecting critical protein residues

Background

Certain amino acids in proteins play a critical role in determining their structural stability and function. Examples include flexible regions such as hinges which allow domain motion, and highly conserved residues on functional interfaces which allow interactions with other proteins. Detecting these regions can aid in the analysis and simulation of protein rigidity and conformational changes, and helps characterizing protein binding and docking. We present an analysis of critical residues in proteins using a combination of two complementary techniques. One method performs in-silico mutations and analyzes the protein's rigidity to infer the role of a point substitution to Glycine or Alanine. The other method uses evolutionary conservation to find functional interfaces in proteins.

Results

We applied the two methods to a dataset of proteins, including biomolecules with experimentally known critical residues as determined by the free energy of unfolding. Our results show that the combination of the two methods can detect the vast majority of critical residues in tested proteins.

Conclusions

Our results show that the combination of the two methods has the potential to detect more information than each method separately. Future work will provide a confidence level for the criticalness of a residue to improve the accuracy of our method and eliminate false positives. Once the combined methods are integrated into one scoring function, it can be applied to other domains such as estimating functional interfaces.

     

A conservation and biophysics guided stochastic approach to refining docked multimeric proteins

Background

We introduce a protein docking refinement method that accepts complexes consisting of any number of monomeric units. The method uses a scoring function based on a tight coupling between evolutionary conservation, geometry and physico-chemical interactions. Understanding the role of protein complexes in the basic biology of organisms heavily relies on the detection of protein complexes and their structures. Different computational docking methods are developed for this purpose, however, these methods are often not accurate and their results need to be further refined to improve the geometry and the energy of the resulting complexes. Also, despite the fact that complexes in nature often have more than two monomers, most docking methods focus on dimers since the computational complexity increases exponentially due to the addition of monomeric units.

Results

Our results show that the refinement scheme can efficiently handle complexes with more than two monomers by biasing the results towards complexes with native interactions, filtering out false positive results. Our refined complexes have better IRMSDs with respect to the known complexes and lower energies than those initial docked structures.

Conclusions

Evolutionary conservation information allows us to bias our results towards possible functional interfaces, and the probabilistic selection scheme helps us to escape local energy minima. We aim to incorporate our refinement method in a larger framework which also enables docking of multimeric complexes given only monomeric structures.

     

Macrophage inhibitory cytokine‐1 is associated with cognitive impairment and predicts cognitive decline – the Sydney Memory and Aging Study

Higher levels of macrophage inhibitory cytokine‐1, also known as growth differentiation factor 15 (MIC‐1/GDF15), are associated with adverse health outcomes and all‐cause mortality. The aim of this study was to examine the relationships between MIC‐1/GDF15 serum levels and global cognition, five cognitive domains, and mild cognitive impairment (MCI), at baseline (Wave 1) and prospectively at 2 years (Wave 2), in nondemented participants aged 70–90 years. Analyses were controlled for age, sex, education, Framingham risk score, history of cerebrovascular accident, acute myocardial infarction, angina, cancer, depression, C‐reactive protein, tumor necrosis factor‐α, interleukins 6 and 12, and apolipoprotein ε4 genotype. Higher MIC‐1/GDF15 levels were significantly associated with lower global cognition at both waves. Cross‐sectional associations were found between MIC‐1/GDF15 and all cognitive domains in Wave 1 (all P < 0.001) and between processing speed, memory, and executive function in Wave 2 (all P < 0.001). Only a trend was found for the prospective analyses, individuals with high MIC‐1/GDF15 at baseline declined in global cognition, executive function, memory, and processing speed. However, when categorizing MIC‐1/GDF15 by tertiles, prospective analyses revealed statistically significant lower memory and executive function in Wave 2 in those in the upper tertile compared with the lower tertile. Receiver operating characteristics (ROC) analysis was used to determine MIC‐1/GDF15 cutoff values associated with cognitive decline and showed that a MIC‐1/GDF15 level exceeding 2764 pg/ml was associated with a 20% chance of decline from normal to MCI or dementia. In summary, MIC‐1/GDF15 levels are associated with cognitive performance and cognitive decline. Further research is required to determine the pathophysiology of this relationship.     

Predicting beta-turns in proteins using support vector machines with fractional polynomials

Background

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design.

Results

We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features.

Conclusions

In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

     

Integrating domain similarity to improve protein complexes identification in TAP-MS data

Background

Detecting protein complexes in protein-protein interaction (PPI) networks plays an important role in improving our understanding of the dynamic of cellular organisation. However, protein interaction data generated by high-throughput experiments such as yeast-two-hybrid (Y2H) and tandem affinity-purification/mass-spectrometry (TAP-MS) are characterised by the presence of a significant number of false positives and false negatives. In recent years there has been a growing trend to incorporate diverse domain knowledge to support large-scale analysis of PPI networks.

Methods

This paper presents a new algorithm, by incorporating Gene Ontology (GO) based semantic similarities, to detect protein complexes from PPI networks generated by TAP-MS. By taking co-complex relations in TAP-MS data into account, TAP-MS PPI networks are modelled as bipartite graph, where bait proteins consist of one set of nodes and prey proteins are on the other. Similarities between pairs of bait proteins are computed by considering both the topological features and GO-driven semantic similarities. Bait proteins are then grouped in to sets of clusters based on their pair-wise similarities to produce a set of 'seed' clusters. An expansion process is applied to each 'seed' cluster to recruit prey proteins which are significantly associated with the same set of bait proteins. Thus, completely identified protein complexes are then obtained.

Results

The proposed algorithm has been applied to real TAP-MS PPI networks. Fifteen quality measures have been employed to evaluate the quality of generated protein complexes. Experimental results show that the proposed algorithm has greatly improved the accuracy of identifying complexes and outperformed several state-of-the-art clustering algorithms. Moreover, by incorporating semantic similarity, the proposed algorithm is more robust to noises in the networks.

     

M-Finder: Uncovering functionally associated proteins from interactome data integrated with GO annotations

Background

Protein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale.

Results

We propose a computational approach, called M-Finder, for functional association pattern mining. This method employs semantic analytics to integrate the genome-wide PPIs with GO data. We also introduce an interactive web application tool that visualizes a functional association network linked to a protein specified by a user. The proposed approach comprises two major components. First, the PPIs that have been generated by high-throughput methods are weighted in terms of their functional consistency using GO and its annotations. We assess two advanced semantic similarity metrics which quantify the functional association level of each interacting protein pair. We demonstrate that these measures outperform the other existing methods by evaluating their agreement to other biological features, such as sequence similarity, the presence of common Pfam domains, and core PPIs. Second, the information flow-based algorithm is employed to discover a set of proteins functionally associated with the protein in a query and their links efficiently. This algorithm reconstructs a functional association network of the query protein. The output network size can be flexibly determined by parameters.

Conclusions

M-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at http://bionet.ecs.baylor.edu/mfinder

     

Rapid sampling of local minima in protein energy surface and effective reduction through a multi-objective filter

Background

Many problems in protein modeling require obtaining a discrete representation of the protein conformational space as an ensemble of conformations. In ab-initio structure prediction, in particular, where the goal is to predict the native structure of a protein chain given its amino-acid sequence, the ensemble needs to satisfy energetic constraints. Given the thermodynamic hypothesis, an effective ensemble contains low-energy conformations which are similar to the native structure. The high-dimensionality of the conformational space and the ruggedness of the underlying energy surface currently make it very difficult to obtain such an ensemble. Recent studies have proposed that Basin Hopping is a promising probabilistic search framework to obtain a discrete representation of the protein energy surface in terms of local minima. Basin Hopping performs a series of structural perturbations followed by energy minimizations with the goal of hopping between nearby energy minima. This approach has been shown to be effective in obtaining conformations near the native structure for small systems. Recent work by us has extended this framework to larger systems through employment of the molecular fragment replacement technique, resulting in rapid sampling of large ensembles.

Methods

This paper investigates the algorithmic components in Basin Hopping to both understand and control their effect on the sampling of near-native minima. Realizing that such an ensemble is reduced before further refinement in full ab-initio protocols, we take an additional step and analyze the quality of the ensemble retained by ensemble reduction techniques. We propose a novel multi-objective technique based on the Pareto front to filter the ensemble of sampled local minima.

Results and conclusions

We show that controlling the magnitude of the perturbation allows directly controlling the distance between consecutively-sampled local minima and, in turn, steering the exploration towards conformations near the native structure. For the minimization step, we show that the addition of Metropolis Monte Carlo-based minimization is no more effective than a simple greedy search. Finally, we show that the size of the ensemble of sampled local minima can be effectively and efficiently reduced by a multi-objective filter to obtain a simpler representation of the probed energy surface.