Bin Xue

Among the realm of repeat containing proteins that commonly serve as “scaffolds” promoting protein-protein interactions, there is a family of proteins containing between 2 and 20 tetratricopeptide repeats (TPRs), which are functional motifs consisting of 34 amino acids. The most distinguishing feature of TPR domains is their ability to stack continuously one upon the other, with these stacked repeats being able to affect interaction with binding partners either sequentially or in combination. It is known that many repeat-containing proteins are characterized by high levels of intrinsic disorder, and that many protein tandem repeats can be intrinsically disordered. Furthermore, it seems that TPR-containing proteins share many characteristics with hybrid proteins containing ordered domains and intrinsically disordered protein regions. However, there has not been a systematic analysis of the intrinsic disorder status of TPR proteins. To fill this gap, we analyzed 166 human TPR proteins to determine the degree to which proteins containing TPR motifs are affected by intrinsic disorder. Our analysis revealed that these proteins are characterized by different levels of intrinsic disorder and contain functional disordered regions that are utilized for protein-protein interactions and often serve as targets of various posttranslational modifications.

Alzheimer’s disease (AD) is a leading cause of age-related dementia worldwide. Despite more than a century of intensive research, we are not anywhere near the discovery of a cure for this disease or a way to prevent its progression. Among the various molecular mechanisms proposed for the description of the pathogenesis and progression of AD, the amyloid cascade hypothesis, according to which accumulation of a product of amyloid precursor protein (APP) cleavage, amyloid β (Aβ) peptide, induces pathological changes in the brain observed in AD, occupies a unique niche. Although multiple proteins have been implicated in this amyloid cascade signaling pathway, their structure–function relationships are mostly unexplored. However, it is known that two major proteins related to AD pathology, Aβ peptide, and microtubule-associated protein tau belong to the category of intrinsically disordered proteins (IDPs), which are the functionally important proteins characterized by a lack of fixed, ordered three-dimensional structure. IDPs and intrinsically disordered protein regions (IDPRs) play numerous vital roles in various cellular processes, such as signaling, cell cycle regulation, macromolecular recognition, and promiscuous binding. However, the deregulation and misfolding of IDPs may lead to disturbed signaling, interactions, and disease pathogenesis. Often, molecular recognition-related IDPs/IDPRs undergo disorder-to-order transition upon binding to their biological partners and contain specific disorder-based binding motifs, known as molecular recognition features (MoRFs). Knowing the intrinsic disorder status and disorder-based functionality of proteins associated with amyloid cascade signaling pathway may help to untangle the mechanisms of AD pathogenesis and help identify therapeutic targets. In this paper, we have used multiple computational tools to evaluate the presence of intrinsic disorder and MoRFs in 27 proteins potentially relevant to the amyloid cascade signaling pathway. Among these, BIN1, APP, APOE, PICALM, PSEN1 and CD33 were found to be highly disordered. Furthermore, their disorder-based binding regions and associated short linear motifs have also been identified. These findings represent important foundation for the future research, and experimental characterization of disordered regions in these proteins is required to better understand their roles in AD pathogenesis.

APETALA2/ETHYLENE RESPONSE FACTOR transcription factors (AP2/ERFs) play crucial roles in adaptation to stresses such as those caused by pathogens, wounding and cold. Although their name suggests a specific role in ethylene signalling, some ERF members also co-ordinate signals regulated by other key plant stress hormones such as jasmonate, abscisic acid and salicylate. We analysed a set of ERF proteins from three divergent plant species for intrinsically disorder regions containing conserved segments involved in protein–protein interaction known as Molecular Recognition Features (MoRFs). Then we correlated the MoRFs identified with a number of known functional features where these could be identified. Our analyses suggest that MoRFs, with plasticity in their disordered surroundings, are highly functional and may have been shuffled between related protein families driven by selection. A particularly important role may be played by the alpha helical component of the structured DNA binding domain to permit specificity. We also present examples of computationally identified MoRFs that have no known function and provide a valuable conceptual framework to link both disordered and ordered structural features within this family to diverse function.

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

Identifying mRNA targets of miRNAs is critical for studying gene expression regulation at the whole-genome level. Multiple computational tools have been developed to predict miRNA:mRNA interactions. Nonetheless, many of these tools are developed in various small datasets, which each represent a limited sample space. Thus, the prediction accuracy of these tools has not been systematically validated at a larger scale. Accordingly, comparing the prediction accuracy of these tools and determining their applicability become challenging. In addition, the accuracy of these tools, especially in large datasets, needs to be improved for broader applications. In this project, a large dataset containing more than 46,600 miRNA:mRNA interactions was assembled and split into eleven subsets based on the availability of prediction scores of four individual predictors, which are miRanda, miRDB, PITA, and TargetScan. In each of these subsets, the predictive results of four individual predictors were integrated using decision-tree based artificial neural networks to make the meta-prediction. The decision-tree is used here to sort the predictive results of four individual predictors, and artificial neural networks are applied to make meta-prediction based on the outputs of individual predictors. In the decision tree, dual-threshold and two-step significance-voting were incorporated, information gain was analysed to select threshold values. The prediction performance of this new strategy was improved significantly in most of the eleven datasets comparing to the individual predictors and other meta-predictors, such as ComiR, under multi-fold cross-validation, as well as in independent datasets. The overall improvement of prediction accuracy in independent datasets is at least 9 percentile points comparing to the other predictors, and the percentage of improvement of F1 and MCC scores is at least 40% compared to the other predictors. The combination of dual-threshold, two-step significance-voting, and analysis of information gain is very effective in optimizing the outcome of decision-tree, and further integration with artificial neural networks is critical for further improving the performance of meta-predictor. A new pipeline based on this integration for miRNA target prediction has been developed. A strategy using outputs of individual predictors to reorganize large-scale miRNA:mRNA interaction dataset has also been validated and used to evaluate the prediction accuracy of predictors. The predictor is available at: https://github.com/xueLab/mirTarDANN ).

Using computational techniques to identify intrinsically disordered residues is practical and effective in biological studies. Therefore, designing novel high-accuracy strategies is always preferable when existing strategies have a lot of room for improvement. Among many possibilities, a meta-strategy that integrates the results of multiple individual predictors has been broadly used to improve the overall performance of predictors. Nonetheless, a simple and direct integration of individual predictors may not effectively improve the performance. In this project, dual-threshold two-step significance voting and neural networks were used to integrate the predictive results of four individual predictors, including: DisEMBL, IUPred, VSL2, and ESpritz. The new meta-strategy has improved the prediction performance of intrinsically disordered residues significantly, compared to all four individual predictors and another four recently-designed predictors. The improvement was validated using five-fold cross-validation and in independent test datasets.

In microarray/RNAseq experiments, different samples used in the same experiment may have significant levels of heterogeneity. Here, heterogeneity refers to the unique temporospatial composition of various cells in different samples. Consequently, the experimental results and subsequent data analysis may be seriously biased. To minimize the influence of heterogeneous samples in data analysis, the first step is to identify heterogeneous samples by measuring the inter-sample heterogeneity. Principal component analysis (PCA) biplot is often used for this purpose. However, the PCA-biplot is normally obtained from one set of differentially expressed genes, indicating the biplot is static and may not represent the heterogeneity dynamically. Therefore, a novel dynamic PCA biplot was developed in this project, and the advantage of this new method was exemplified in two GEO datasets.