ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins

Additional file 2: Table S2. Dipeptide composition distribution between proinflammatory and non proinflammatory data

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibacterial agent and a vaccine in immunization therapies. Due to the advancement in sequence technologies that resulted an avalanche of protein sequence data. Therefore, it is necessary to develop an automated computational method to enable fast and accurate identification of novel PIPs within the vast number of candidate proteins and peptides. To address this, we proposed a new predictor, PIP-EL, for predicting PIPs using the strategy of ensemble learning (EL). Our benchmarking dataset is imbalanced. Thus, we applied a random under-sampling technique to generate 10 balanced models for each composition. Technically, PIP-EL is the fusion of 50 independent random forest (RF) models, where each of the five different compositions, including amino acid, dipeptide, composition–transition–distribution, physicochemical properties, and amino acid index contains 10 RF models. PIP-EL achieves the Matthews’ correlation coefficient (MCC) of 0.435 in a 5-fold cross-validation test, which is ~2–5% higher than that of the individual classifiers and hybrid feature-based classifier. Furthermore, we evaluate the performance of PIP-EL on the independent dataset, showing that our method outperforms the existing method and two different machine learning methods developed in this study, with an MCC of 0.454. These results indicate that PIP-EL will be a useful tool for predicting PIPs and for researchers working in the field of peptide therapeutics and immunotherapy. The user-friendly web server, PIP-EL, is freely accessible.

DiRiboPred: A Web Tool for Classification and Prediction of Ribonucleases

Ribonuclease [commonly abbreviated RNase] is a type of nuclease that catalyzes the degradation of RNA into smaller components and can be divided into endoribonucleases and exoribonucleases. All organisms studied contain many RNases of many different classes, showing that RNA degradation is a very ancient and important process. They are shown to have an important role in cancer, tumor and many neuro degenarative disorders for controlling which, in-silico drug designing can be a valuable tool. In the past machine learning has been used to classify other proteins like GPCRs but no attempt has been made for classification of Ribonucleases. Realizing their importance here an attempt has been made to develop an SVM model to predict, classify and correlate all the major subclasses of ribonucleases with their dipeptide composition. The method was trained and tested on 1857 proteins of ribonucleases. The method discriminated Ribonucleases from other enzymes with Matthew\'s correlation coefficient of 1.00 and 100% accuracy. In classifying different subclasses of Ribonucleases with dipeptide composition, an overall accuracy of 94.534% was achieved. The performance of the method was evaluated using 5-fold cross-validation. A web server DiRiboPred has been developed for predicting Ribonucleases from its amino acid sequence http://www.bif.manit.org/RiboPred2

Prediction of IL4 Inducing Peptides

The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response

Glial Cell Dysfunction in C9orf72-Related Amyotrophic Lateral Sclerosis and Frontotemporal Dementia

Since the discovery of the chromosome 9 open reading frame 72 (C9orf72) repeat expansion mutation in 2011 as the most common genetic abnormality in amyotrophic lateral sclerosis (ALS, also known as Lou Gehrig\u27s disease) and frontotemporal dementia (FTD), progress in understanding the signaling pathways related to this mutation can only be described as intriguing. Two major theories have been suggested-(i) loss of function or haploinsufficiency and (ii) toxic gain of function from either C9orf72 repeat RNA or dipeptide repeat proteins (DPRs) generated from repeat-associated non-ATG (RAN) translation. Each theory has provided various signaling pathways that potentially participate in the disease progression. Dysregulation of the immune system, particularly glial cell dysfunction (mainly microglia and astrocytes), is demonstrated to play a pivotal role in both loss and gain of function theories of C9orf72 pathogenesis. In this review, we discuss the pathogenic roles of glial cells in C9orf72 ALS/FTD as evidenced by pre-clinical and clinical studies showing the presence of gliosis in C9orf72 ALS/FTD, pathologic hallmarks in glial cells, including TAR DNA-binding protein 43 (TDP-43) and p62 aggregates, and toxicity of C9orf72 glial cells. A better understanding of these pathways can provide new insights into the development of therapies targeting glial cell abnormalities in C9orf72 ALS/FTD

Bioinformatic analysis of xenobiotic reactive metabolite target proteins and their interacting partners

<p>Abstract</p> <p>Background</p> <p>Protein covalent binding by reactive metabolites of drugs, chemicals and natural products can lead to acute cytotoxicity. Recent rapid progress in reactive metabolite target protein identification has shown that adduction is surprisingly selective and inspired the hope that analysis of target proteins might reveal protein factors that differentiate target- vs. non-target proteins and illuminate mechanisms connecting covalent binding to cytotoxicity.</p> <p>Results</p> <p>Sorting 171 known reactive metabolite target proteins revealed a number of GO categories and KEGG pathways to be significantly enriched in targets, but in most cases the classes were too large, and the "percent coverage" too small, to allow meaningful conclusions about mechanisms of toxicity. However, a similar analysis of the directlyinteracting partners of 28 common targets of multiple reactive metabolites revealed highly significant enrichments in terms likely to be highly relevant to cytotoxicity (e.g., MAP kinase pathways, apoptosis, response to unfolded protein). Machine learning was used to rank the contribution of 211 computed protein features to determining protein susceptibility to adduction. Protein lysine (but not cysteine) content and protein instability index (i.e., rate of turnover in vivo) were among the features most important to determining susceptibility.</p> <p>Conclusion</p> <p>As yet there is no good explanation for why some low-abundance proteins become heavily adducted while some abundant proteins become only lightly adducted in vivo. Analyzing the directly interacting partners of target proteins appears to yield greater insight into mechanisms of toxicity than analyzing target proteins per se. The insights provided can readily be formulated as hypotheses to test in future experimental studies.</p

An Approach for Identifying Cytokines Based on a Novel Ensemble Classifier

Copyright 2013 Quan Zou et al. his is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

DeepFrag-k: A Fragment-Based Deep Learning Approach for Protein Fold Recognition

Background: One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolutional neural network (CNN) to classify the fragment vector into the corresponding fold. Results: Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition. Conclusions: There is a set of fragments that can serve as structural “keywords” distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition