Cascade detection for the extraction of localized sequence features; specificity results for HIV-1 protease and structure-function results for the Schellman loop

Bi-allelic ADARB1 Variants Associated with Microcephaly, Intellectual Disability, and Seizures

The RNA editing enzyme ADAR2 is essential for the recoding of brain transcripts. Impaired ADAR2 editing leads to early-onset epilepsy and premature death in a mouse model. Here, we report bi-allelic variants in ADARB1, the gene encoding ADAR2, in four unrelated individuals with microcephaly, intellectual disability, and epilepsy. In one individual, a homozygous variant in one of the double-stranded RNA-binding domains (dsRBDs) was identified. In the others, variants were situated in or around the deaminase domain. To evaluate the effects of these variants on ADAR2 enzymatic activity, we performed in vitro assays with recombinant proteins in HEK293T cells and ex vivo assays with fibroblasts derived from one of the individuals. We demonstrate that these ADAR2 variants lead to reduced editing activity on a known ADAR2 substrate. We also demonstrate that one variant leads to changes in splicing of ADARB1 transcript isoforms. These findings reinforce the importance of RNA editing in brain development and introduce ADARB1 as a genetic etiology in individuals with intellectual disability, microcephaly, and epilepsy.

Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features

Background

The human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS). Development of HIV-1 protease inhibitors can help understand the specificity of substrates which can restrain the replication of HIV-1, thus antagonize AIDS. However, experimental methods in identification of HIV-1 protease cleavage sites are generally time-consuming and labor-intensive. Therefore, using computational methods to predict cleavage sites has become highly desirable.

Results

In this study, we propose a prediction method in which sequence, structural, and physicochemical features are incorporated in various machine learning algorithms. Then, a bidirectional stepwise selection algorithm is incorporated in feature selection to identify discriminative features. Further, only the selected features are calculated by various encoding schemes and used as input for decision trees, logistic regression, and artificial neural networks. Moreover, a more rigorous three-way data split procedure is applied to evaluate the objective performance of cleavage site prediction. Four benchmark datasets collected from previous studies are used to evaluate the predictive performance.

Conclusions

Experiment results showed that combinations of sequence, structure, and physicochemical features performed better than single feature type for identification of HIV-1 protease cleavage sites. In addition, incorporation of stepwise feature selection is effective to identify interpretable biological features to depict specificity of the substrates. Moreover, artificial neural networks perform significantly better than the other two classifiers. Finally, the proposed method achieved 80.0% ~ 97.4% in accuracy and 0.815 ~ 0.995 evaluated by independent test sets in a three-way data split procedure.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1337-6) contains supplementary material, which is available to authorized users.

The importance of physicochemical characteristics and nonlinear classifiers in determining HIV-1 protease specificity

This paper reviews recent research relating to the application of bioinformatics approaches to determining HIV-1 protease specificity, outlines outstanding issues, and presents a new approach to addressing these issues. Leading machine learning theory for the problem currently suggests that the direct encoding of the physicochemical properties of the amino acid substrates is not required for optimal performance. A number of amino acid encoding approaches which incorporate potentially relevant physicochemical properties of the substrate are identified, and are evaluated using a nonlinear task decomposition based neuroevolution algorithm. The results are evaluated, and compared against a recent benchmark set on a nonlinear classifier using only amino acid sequence and identity information. Ensembles of these nonlinear classifiers using the physicochemical properties of the substrate are demonstrated to consistently outperform the recently published state-of-the-art linear support vector machine based approach in out-of-sample evaluations.

Mapping side chain interactions at protein helix termini

Background

Interactions that involve one or more amino acid side chains near the ends of protein helices stabilize helix termini and shape the geometry of the adjacent loops, making a substantial contribution to overall protein structure. Previous work has identified key helix-terminal motifs, such as Asx/ST N-caps, the capping box, and hydrophobic and electrostatic interactions, but important questions remain, including: 1) What loop backbone geometries are favoured by each motif? 2) To what extent are multi-amino acid motifs likely to represent genuine cooperative interactions? 3) Can new motifs be identified in a large, recent dataset using the latest bioinformatics tools?

Results

Three analytical tools are applied here to answer these questions. First, helix-terminal structures are partitioned by loop backbone geometry using a new 3D clustering algorithm. Next, Cascade Detection, a motif detection algorithm recently published by the author, is applied to each cluster to determine which sequence motifs are overrepresented in each geometry. Finally, the results for each motif are presented in a CapMap, a 3D conformational heatmap that displays the distribution of the motif’s overrepresentation across loop geometries, enabling the rapid isolation and characterization of the associated side chain interaction. This work identifies a library of geometry-specific side chain interactions that provides a new, detailed picture of loop structure near the helix terminus. Highlights include determinations of the favoured loop geometries for the Asx/ST N-cap motifs, capping boxes, “big” boxes, and other hydrophobic, electrostatic, H-bond, and pi stacking interactions, many of which have not been described before.

Conclusions

This work demonstrates that the combination of structural clustering and motif detection in the sequence space can efficiently identify side chain motifs and map them to the loop geometries which they support. Protein designers should find this study useful, because it identifies side chain interactions which are good candidates for inclusion in synthetic helix-terminal loops with specific desired geometries, since they are used in nature to support these geometries. The techniques described here can also be applied to map side chain interactions associated with other structural components of proteins such as beta and gamma turns.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0671-4) contains supplementary material, which is available to authorized users.

State of the art prediction of HIV-1 protease cleavage sites

MOTIVATION

Understanding the substrate specificity of human immunodeficiency virus (HIV)-1 protease is important when designing effective HIV-1 protease inhibitors. Furthermore, characterizing and predicting the cleavage profile of HIV-1 protease is essential to generate and test hypotheses of how HIV-1 affects proteins of the human host. Currently available tools for predicting cleavage by HIV-1 protease can be improved.

RESULTS

The linear support vector machine with orthogonal encoding is shown to be the best predictor for HIV-1 protease cleavage. It is considerably better than current publicly available predictor services. It is also found that schemes using physicochemical properties do not improve over the standard orthogonal encoding scheme. Some issues with the currently available data are discussed.

AVAILABILITY AND IMPLEMENTATION

The datasets used, which are the most important part, are available at the UCI Machine Learning Repository. The tools used are all standard and easily available.

CONTACT

thorsteinn.rognvaldsson@hh.se.

Cleavage entropy as quantitative measure of protease specificity

A purely information theory-guided approach to quantitatively characterize protease specificity is established. We calculate an entropy value for each protease subpocket based on sequences of cleaved substrates extracted from the MEROPS database. We compare our results with known subpocket specificity profiles for individual proteases and protease groups (e.g. serine proteases, metallo proteases) and reflect them quantitatively. Summation of subpocket-wise cleavage entropy contributions yields a measure for overall protease substrate specificity. This total cleavage entropy allows ranking of different proteases with respect to their specificity, separating unspecific digestive enzymes showing high total cleavage entropy from specific proteases involved in signaling cascades. The development of a quantitative cleavage entropy score allows an unbiased comparison of subpocket-wise and overall protease specificity. Thus, it enables assessment of relative importance of physicochemical and structural descriptors in protease recognition. We present an exemplary application of cleavage entropy in tracing substrate specificity in protease evolution. This highlights the wide range of substrate promiscuity within homologue proteases and hence the heavy impact of a limited number of mutations on individual substrate specificity.

Proteases show a broad range of cleavage specificities. Promiscuous proteases as digestive enzymes unspecifically degrade peptides, whereas highly specific proteases are involved in signaling cascades. As a quantitative index of substrate specificity was lacking, we introduce cleavage entropy as a measure of substrate specificity of proteases. This quantitative score allows for straight-forward rationalization of substrate recognition by a subpocket-wise assessment of substrate readout leading to specificity profiles of individual proteases as well as an estimate of overall substrate promiscuity. We present an exemplary application of the descriptor ‘cleavage entropy’ to trace substrate specificity through the evolution of different protease folds. Our score highlights the diversity of substrate specificity within evolutionary related proteases and hence the complex relationship between sequence, structure and substrate recognition. By taking into account the whole distribution of known substrates rather than simple substrate counting, cleavage entropy provides the unique opportunity to dissect the molecular origins of protease substrate specificity.