3D Profile-Based Approach to Proteome-Wide Discovery of Novel Human Chemokines

Chemokines are small secreted proteins with important roles in immune responses. They consist of a conserved three-dimensional (3D) structure, so-called IL8-like chemokine fold, which is supported by disulfide bridges characteristic of this protein family. Sequence- and profile-based computational methods have been proficient in discovering novel chemokines by making use of their sequence-conserved cysteine patterns. However, it has been recently shown that some chemokines escaped annotation by these methods due to low sequence similarity to known chemokines and to different arrangement of cysteines in sequence and in 3D. Innovative methods overcoming the limitations of current techniques may allow the discovery of new remote homologs in the still functionally uncharacterized fraction of the human genome. We report a novel computational approach for proteome-wide identification of remote homologs of the chemokine family that uses fold recognition techniques in combination with a scaffold-based automatic mapping of disulfide bonds to define a 3D profile of the chemokine protein family. By applying our methodology to all currently uncharacterized human protein sequences, we have discovered two novel proteins that, without having significant sequence similarity to known chemokines or characteristic cysteine patterns, show strong structural resemblance to known anti-HIV chemokines. Detailed computational analysis and experimental structural investigations based on mass spectrometry and circular dichroism support our structural predictions and highlight several other chemokine-like features. The results obtained support their functional annotation as putative novel chemokines and encourage further experimental characterization. The identification of remote homologs of human chemokines may provide new insights into the molecular mechanisms causing pathologies such as cancer or AIDS, and may contribute to the development of novel treatments. Besides, the genome-wide applicability of our methodology based on 3D protein family profiles may open up new possibilities for improving and accelerating protein function annotation processes

Plant hormone-mediated regulation of stress responses

10.1186/s12870-016-0771-yBMC Plant Biology1618

Multiple Support Vector Machines for Binary Text Classification Based on Sliding Window Technique

Supervised machine learning algorithms, such as support vector machines (SVMs), are widely used for solving classification tasks. In binary textclassification, linear SVM has shown remarkable efficiency for classifying documents due to its superior performance. It tries to create the best decisionboundary that enables the separation of positive and negative documents with the largest margin hyperplane. However, in most cases there are regions in which positive and negative documents are mixed due to the uncertain boundary. With an uncertain boundary, the learning classifier is more complex, and it often becomes difficult for a single classifier to accurately classify all unknown testing samples into classes. Therefore, more innovative methods and techniques are needed to solve the uncertain boundary problem that was traditionally solved by non-linear SVM. In this paper, multiple support vector machines are proposed that can effectively deal with the uncertain boundary and improve predictive accuracy in linear SVM for data having uncertainties. This is achieved by dividing the training documents into three distinct regions (positive, boundary, and negative regions) based on a sliding window technique to ensure the certainty of extracted knowledge to describe relevant information. The model then derives new training samples to build a multiple SVMs based classifier. The experimental results on the TREC topics and standard dataset Reuters Corpus Volume 1 (RCV1), indicated that the proposed model significantly outperforms six state-of the-art baseline models in binary text classification

Crosslinking renders bacteriophage HK97 capsid maturation irreversible and effects an essential stabilization

In HK97 capsid maturation, structural change (‘expansion') is accompanied by formation of covalent crosslinks, connecting residue K169 in the ‘E-loop' of each subunit with N356 on another subunit. We show by complementation experiments with the K169Y mutant, which cannot crosslink, that crosslinking is an essential function. The precursor Prohead-II passes through three expansion intermediate (EI) states en route to the end state, Head-II. We investigated the effects of expansion and crosslinking on stability by differential scanning calorimetry of wild-type and K169Y capsids. After expansion, the denaturation temperature (T(p)) of K169Y capsids is slightly reduced, indicating that their thermal stability is not enhanced, but crosslinking effects a major stabilization (ΔT(p), +11°C). EI-II is the earliest capsid to form crosslinks. Cryo-electron microscopy shows that for both wild-type and K169Y EI-II, most E-loops are in the ‘up' position, 30 Å from the nearest N356: thus, crosslinking in EI-II represents capture of mobile E-loops in ‘down' positions. At pH 4, most K169Y capsids remain as EI-II, whereas wild-type capsids proceed to EI-III, suggesting that crosslink formation drives maturation by a Brownian ratchet mechanism