Classification of protein quaternary structure by functional domain composition

BACKGROUND: The number and the arrangement of subunits that form a protein are referred to as quaternary structure. Quaternary structure is an important protein attribute that is closely related to its function. Proteins with quaternary structure are called oligomeric proteins. Oligomeric proteins are involved in various biological processes, such as metabolism, signal transduction, and chromosome replication. Thus, it is highly desirable to develop some computational methods to automatically classify the quaternary structure of proteins from their sequences. RESULTS: To explore this problem, we adopted an approach based on the functional domain composition of proteins. Every protein was represented by a vector calculated from the domains in the PFAM database. The nearest neighbor algorithm (NNA) was used for classifying the quaternary structure of proteins from this information. The jackknife cross-validation test was performed on the non-redundant protein dataset in which the sequence identity was less than 25%. The overall success rate obtained is 75.17%. Additionally, to demonstrate the effectiveness of this method, we predicted the proteins in an independent dataset and achieved an overall success rate of 84.11% CONCLUSION: Compared with the amino acid composition method and Blast, the results indicate that the domain composition approach may be a more effective and promising high-throughput method in dealing with this complicated problem in bioinformatics

Protein sequences classification by means of feature extraction with substitution matrices

<p>Abstract</p> <p>Background</p> <p>This paper deals with the preprocessing of protein sequences for supervised classification. Motif extraction is one way to address that task. It has been largely used to encode biological sequences into feature vectors to enable using well-known machine-learning classifiers which require this format. However, designing a suitable feature space, for a set of proteins, is not a trivial task. For this purpose, we propose a novel encoding method that uses amino-acid substitution matrices to define similarity between motifs during the extraction step.</p> <p>Results</p> <p>In order to demonstrate the efficiency of such approach, we compare several encoding methods using some machine learning classifiers. The experimental results showed that our encoding method outperforms other ones in terms of classification accuracy and number of generated attributes. We also compared the classifiers in term of accuracy. Results indicated that SVM generally outperforms the other classifiers with any encoding method. We showed that SVM, coupled with our encoding method, can be an efficient protein classification system. In addition, we studied the effect of the substitution matrices variation on the quality of our method and hence on the classification quality. We noticed that our method enables good classification accuracies with all the substitution matrices and that the variances of the obtained accuracies using various substitution matrices are slight. However, the number of generated features varies from a substitution matrix to another. Furthermore, the use of already published datasets allowed us to carry out a comparison with several related works.</p> <p>Conclusions</p> <p>The outcomes of our comparative experiments confirm the efficiency of our encoding method to represent protein sequences in classification tasks.</p

The RCK1 domain of the human BK_(Ca) channel transduces Ca^(2+) binding into structural rearrangements

Large-conductance voltage- and Ca^(2+)-activated K^+ (BK_(Ca)) channels play a fundamental role in cellular function by integrating information from their voltage and Ca2+ sensors to control membrane potential and Ca^(2+) homeostasis. The molecular mechanism of Ca^(2+)-dependent regulation of BKCa channels is unknown, but likely relies on the operation of two cytosolic domains, regulator of K^+ conductance (RCK)1 and RCK2. Using solution-based investigations, we demonstrate that the purified BK_(Ca) RCK1 domain adopts an α/β fold, binds Ca^(2+), and assembles into an octameric superstructure similar to prokaryotic RCK domains. Results from steady-state and time-resolved spectroscopy reveal Ca^(2+)-induced conformational changes in physiologically relevant [Ca^(2+)]. The neutralization of residues known to be involved in high-affinity Ca^(2+) sensing (D362 and D367) prevented Ca^(2+)-induced structural transitions in RCK1 but did not abolish Ca^(2+) binding. We provide evidence that the RCK1 domain is a high-affinity Ca^(2+) sensor that transduces Ca^(2+) binding into structural rearrangements, likely representing elementary steps in the Ca^(2+)-dependent activation of human BK_(Ca) channels

Small heat-shock proteins: important players in regulating cellular proteostasis

Small heat-shock proteins (sHsps) are a diverse family of intra-cellular molecular chaperone proteins that play a critical role in mitigating and preventing protein aggregation under stress conditions such as elevated temperature, oxidation and infection. In doing so, they assist in the maintenance of protein homeostasis (proteostasis) thereby avoiding the deleterious effects that result from loss of protein function and/or protein aggregation. The chaperone properties of sHsps are therefore employed extensively in many tissues to prevent the development of diseases associated with protein aggregation. Significant progress has been made of late in understanding the structure and chaperone mechanism of sHsps. In this review, we discuss some of these advances, with a focus on mammalian sHsp hetero-oligomerisation, the mechanism by which sHsps act as molecular chaperones to prevent both amorphous and fibrillar protein aggregation, and the role of post-translational modifications in sHsp chaperone function, particularly in the context of disease.SM was supported by a Royal Society Dorothy Hodgkin Fellowship, HE is supported by an Australian Research Council Future Fellowship (FT110100586) and JC is supported by a National Health and Medical Research Council Project Grant (#1068087)

Carnitine metabolism to trimethylamine by an unusual Rieske-type oxygenase from human microbiota

Dietary intake of L-carnitine can promote cardiovascular diseases in humans through microbial production of trimethylamine (TMA) and its subsequent oxidation to trimethylamine N-oxide (TMAO) by hepatic flavin-containing monooxygenases. Although our microbiota are responsible for TMA formation from carnitine, the underpinning molecular and biochemical mechanisms remain unclear. In this study, using bioinformatics approaches, we first identified a two-component Rieske-type oxygenase/reductase (CntAB) and associated gene cluster proposed to be involved in carnitine metabolism in representative genomes of the human microbiota. CntA belongs to a group of previously uncharacterized Rieske-type proteins and has an unusual "bridging" glutamate but not the aspartate residue, which is believed to facilitate inter-subunit electron transfer between the Rieske centre and the catalytic mononuclear iron centre. Using Acinetobacter baumannii as the model, we then demonstrate that cntAB is essential in carnitine degradation to TMA. Heterologous overexpression of cntAB enables Escherichia coli to produce TMA, confirming that these genes are sufficient in TMA formation. Site-directed mutagenesis experiments have confirmed that this unusual "bridging glutamate" residue in CntA is essential in catalysis and neither mutant (E205D, E205A) is able to produce TMA. Together, our study reveals the molecular and biochemical mechanisms underpinning carnitine metabolism to TMA in human microbiota and assigns the role of this novel group of Rieske-type proteins in microbial carnitine metabolism

Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition

<p>Abstract</p> <p>Background</p> <p>Metabolic pathway is a highly regulated network consisting of many metabolic reactions involving substrates, enzymes, and products, where substrates can be transformed into products with particular catalytic enzymes. Since experimental determination of the network of substrate-enzyme-product triad (whether the substrate can be transformed into the product with a given enzyme) is both time-consuming and expensive, it would be very useful to develop a computational approach for predicting the network of substrate-enzyme-product triads.</p> <p>Results</p> <p>A mathematical model for predicting the network of substrate-enzyme-product triads was developed. Meanwhile, a benchmark dataset was constructed that contains 744,192 substrate-enzyme-product triads, of which 14,592 are networking triads, and 729,600 are non-networking triads; i.e., the number of the negative triads was about 50 times the number of the positive triads. The molecular graph was introduced to calculate the similarity between the substrate compounds and between the product compounds, while the functional domain composition was introduced to calculate the similarity between enzyme molecules. The nearest neighbour algorithm was utilized as a prediction engine, in which a novel metric was introduced to measure the "nearness" between triads. To train and test the prediction engine, one tenth of the positive triads and one tenth of the negative triads were randomly picked from the benchmark dataset as the testing samples, while the remaining were used to train the prediction model. It was observed that the overall success rate in predicting the network for the testing samples was 98.71%, with 95.41% success rate for the 1,460 testing networking triads and 98.77% for the 72,960 testing non-networking triads.</p> <p>Conclusions</p> <p>It is quite promising and encouraged to use the molecular graph to calculate the similarity between compounds and use the functional domain composition to calculate the similarity between enzymes for studying the substrate-enzyme-product network system. The software is available upon request.</p

An automatic method for assessing structural importance of amino acid positions

Background: A great deal is known about the qualitative aspects of the sequence-structure relationship, for example that buried residues are usually more conserved between structurally similar homologues, but no attempts have been made to quantitate the relationship between evolutionary conservation at a sequence position and change to global tertiary structure. In this paper we demonstrate that the Spearman correlation between sequence and structural change is suitable for this purpose. Results: Buried residues, bends, cysteines, prolines and leucines were significantly more likely to occupy positions highly correlated with structural change than expected by chance. Some buried residues were found to be less informative than expected, particularly residues involved in active sites and the binding of small molecules. Conclusion: The correlation-based method generates predictions of structural importance for superfamily positions which agree well with previous results of manual analyses, and may be of use in automated residue annotation piplines. A PERL script which implements the method is provided