LabCaS for Ranking Potential Calpain Substrate Cleavage Sites from Amino Acid Sequence.

Calpains are a family of Ca2+-dependent cysteine proteases involved in many important biological processes, where they selectively cleave relevant substrates at specific cleavage sites to regulate the function of the substrate proteins. Presently, our knowledge about the function of calpains and the mechanism of substrate cleavage is still limited due to the fact that the experimental determination and validation on calpain bindings are usually laborious and expensive. This chapter describes LabCaS, an algorithm that is designed for predicting the calpain substrate cleavage sites from amino acid sequences. LabCaS is built on a conditional random field (CRF) statistic model, which trains the cleavage site prediction on multiple features of amino acid residue preference, solvent accessibility information, pair-wise alignment similarity score, secondary structure propensity, and physical-chemistry properties. Large-scale benchmark tests have shown that LabCaS can achieve a reliable recognition of the cleavage sites for most calpain proteins with an average AUC score of 0.862. Due to the fast speed and convenience of use, the protocol should find its usefulness in large-scale calpain-based function annotations of the newly sequenced proteins. The online web server of LabCaS is freely available at http://www.csbio.sjtu.edu.cn/bioinf/LabCaS .

IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction.

BACKGROUND: Non-coding RNAs (ncRNAs) play crucial roles in many biological processes, such as post-transcription of gene regulation. ncRNAs mainly function through interaction with RNA binding proteins (RBPs). To understand the function of a ncRNA, a fundamental step is to identify which protein is involved into its interaction. Therefore it is promising to computationally predict RBPs, where the major challenge is that the interaction pattern or motif is difficult to be found. RESULTS: In this study, we propose a computational method IPMiner (Interaction Pattern Miner) to predict ncRNA-protein interactions from sequences, which makes use of deep learning and further improves its performance using stacked ensembling. One of the IPMiner's typical merits is that it is able to mine the hidden sequential interaction patterns from sequence composition features of protein and RNA sequences using stacked autoencoder, and then the learned hidden features are fed into random forest models. Finally, stacked ensembling is used to integrate different predictors to further improve the prediction performance. The experimental results indicate that IPMiner achieves superior performance on the tested lncRNA-protein interaction dataset with an accuracy of 0.891, sensitivity of 0.939, specificity of 0.831, precision of 0.945 and Matthews correlation coefficient of 0.784, respectively. We further comprehensively investigate IPMiner on other RNA-protein interaction datasets, which yields better performance than the state-of-the-art methods, and the performance has an increase of over 20 % on some tested benchmarked datasets. In addition, we further apply IPMiner for large-scale prediction of ncRNA-protein network, that achieves promising prediction performance. CONCLUSION: By integrating deep neural network and stacked ensembling, from simple sequence composition features, IPMiner can automatically learn high-level abstraction features, which had strong discriminant ability for RNA-protein detection. IPMiner achieved high performance on our constructed lncRNA-protein benchmark dataset and other RNA-protein datasets. IPMiner tool is available at http://www.csbio.sjtu.edu.cn/bioinf/IPMiner .

Synthesis and antifungal evaluation of a series of maleimides.

BACKGROUND: Maleimides, both natural and synthesised, have good biological activities. In a continuous effort to discover new maleimides with good antifungal activities, the authors have synthesised a series of 3,4-dichloro-, 3-methyl and non-substituted maleimides based on previous studies. The compounds were biologically evaluated against the fungal pathogen Sclerotinia sclorotiorum. RESULTS: Of the 63 compounds evaluated, 25 compounds had interesting inhibitory potency with EC50 < 10 µg mL(-1). N-(3,5-Dichlorophenyl)-3,4-dichloromaleimide (EC50 = 1.11 µg mL(-1)) and N-octyl-3-methylmaleimide (EC50 = 1.01 µg mL(-1)) were more potent than the commercial fungicide dicloran (EC50 = 1.72 µg mL(-1)). The results showed that compounds exhibiting log P values within the range 2.4-3.0 displayed the best results in terms of fungicidal activity, and this seemed, therefore, to be the optimum range for this physicochemical parameter. CONCLUSION: The present work demonstrates that some maleimides can be used as potential lead compounds for developing novel antifungal agents against S. sclerotiorum.

Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection.

Protein-RNA interaction plays a very crucial role in many biological processes, such as protein synthesis, transcription and post-transcription of gene expression and pathogenesis of disease. Especially RNAs always function through binding to proteins. Identification of binding interface region is especially useful for cellular pathways analysis and drug design. In this study, we proposed a novel approach for binding sites identification in proteins, which not only integrates local features and global features from protein sequence directly, but also constructed a balanced training dataset using sub-sampling based on submodularity subset selection. Firstly we extracted local features and global features from protein sequence, such as evolution information and molecule weight. Secondly, the number of non-interaction sites is much more than interaction sites, which leads to a sample imbalance problem, and hence biased machine learning model with preference to non-interaction sites. To better resolve this problem, instead of previous randomly sub-sampling over-represented non-interaction sites, a novel sampling approach based on submodularity subset selection was employed, which can select more representative data subset. Finally random forest were trained on optimally selected training subsets to predict interaction sites. Our result showed that our proposed method is very promising for predicting protein-RNA interaction residues, it achieved an accuracy of 0.863, which is better than other state-of-the-art methods. Furthermore, it also indicated the extracted global features have very strong discriminate ability for identifying interaction residues from random forest feature importance analysis.

LabCaS: labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields.

The calpain family of Ca(2+) -dependent cysteine proteases plays a vital role in many important biological processes which is closely related with a variety of pathological states. Activated calpains selectively cleave relevant substrates at specific cleavage sites, yielding multiple fragments that can have different functions from the intact substrate protein. Until now, our knowledge about the calpain functions and their substrate cleavage mechanisms are limited because the experimental determination and validation on calpain binding are usually laborious and expensive. In this work, we aim to develop a new computational approach (LabCaS) for accurate prediction of the calpain substrate cleavage sites from amino acid sequences. To overcome the imbalance of negative and positive samples in the machine-learning training which have been suffered by most of the former approaches when splitting sequences into short peptides, we designed a conditional random field algorithm that can label the potential cleavage sites directly from the entire sequences. By integrating the multiple amino acid features and those derived from sequences, LabCaS achieves an accurate recognition of the cleave sites for most calpain proteins. In a jackknife test on a set of 129 benchmark proteins, LabCaS generates an AUC score 0.862. The LabCaS program is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/LabCaS. Proteins 2013. © 2012 Wiley Periodicals, Inc.

Chiral resolution of racemic p-methylsulfonylphenyl serine ethyl ester with lipases: the mechanism of side reaction and its suppression.

The D-threo form of p-methylsulfonylphenyl serine ethyl ester (MPSE) is a key intermediate for the synthesis of florfenicol. In this study, chiral resolution of DL-threo-p-MPSE with lipases was investigated. Among a series of lipases, Novzyme 435 was the best to resolve DL-threo-p-MPSE with the conversion rate of 36.83% and ee value of 35.13%. To improve the conversion rate and ee value, a number of byproducts were identified and characterized using reverse-phase HPLC, normal-phase HPLC, (1)H NMR, and LC-MS when threo-p-MPSE was hydrolyzed by lipases in organic medium. Mechanisms of generating main byproducts are proposed, and a suppressing method is provided. The results showed that byproduct p-methylsulfonyl benzaldehyde serves as the key intermediate during the whole side reaction process. It was also observed that threo-p-MPSE with a proper hydrolytic velocity served as a driving force to generate p-methylsulfonyl benzaldehyde and accelerated the side reactions. Finally, a feasible approach to suppress side reactions in enzymatic catalysis is offered. The conversion rate and ee value were greatly improved by 69.29 and 46.26%, respectively, using Zn(2+) compared to those without Zn(2+).

Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features.

BACKGROUND: Adenosine-5'-triphosphate (ATP) is one of multifunctional nucleotides and plays an important role in cell biology as a coenzyme interacting with proteins. Revealing the binding sites between protein and ATP is significantly important to understand the functionality of the proteins and the mechanisms of protein-ATP complex. RESULTS: In this paper, we propose a novel framework for predicting the proteins' functional residues, through which they can bind with ATP molecules. The new prediction protocol is achieved by combination of sequence evolutional information and bi-profile sampling of multi-view sequential features and the sequence derived structural features. The hypothesis for this strategy is single-view feature can only represent partial target's knowledge and multiple sources of descriptors can be complementary. CONCLUSIONS: Prediction performances evaluated by both 5-fold and leave-one-out jackknife cross-validation tests on two benchmark datasets consisting of 168 and 227 non-homologous ATP binding proteins respectively demonstrate the efficacy of the proposed protocol. Our experimental results also reveal that the residue structural characteristics of real protein-ATP binding sites are significant different from those normal ones, for example the binding residues do not show high solvent accessibility propensities, and the bindings prefer to occur at the conjoint points between different secondary structure segments. Furthermore, results also show that performance is affected by the imbalanced training datasets by testing multiple ratios between positive and negative samples in the experiments. Increasing the dataset scale is also demonstrated useful for improving the prediction performances.

Conotoxin superfamily prediction using diffusion maps dimensionality reduction and subspace classifier.

Conotoxins are disulfide-rich small peptides that are invaluable channel-targeted peptides and target neuronal receptors, which have been demonstrated to be potent pharmaceuticals in the treatment of Alzheimer's disease, Parkinson's disease, and epilepsy. Accurate prediction of conotoxin superfamily would have many important applications towards the understanding of its biological and pharmacological functions. In this study, a novel method, named dHKNN, is developed to predict conotoxin superfamily. Firstly, we extract the protein's sequential features composed of physicochemical properties, evolutionary information, predicted secondary structures and amino acid composition. Secondly, we use the diffusion maps for dimensionality reduction, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain efficient representation of data geometric descriptions. Finally, an improved K-local hyperplane distance nearest neighbor subspace classifier method called dHKNN is proposed for predicting conotoxin superfamilies by considering the local density information in the diffusion space. The overall accuracy of 91.90% is obtained through the jackknife cross-validation test on a benchmark dataset, indicating the proposed dHKNN is promising.

PredCSF: an integrated feature-based approach for predicting conotoxin superfamily.

Conotoxins are small disulfide-rich peptides that are invaluable channel-targeted peptides and target neuronal receptors. They show prospects for being potent pharmaceuticals in the treatment of Alzheimer's disease, Parkinson's disease, and epilepsy. Accurate and fast prediction of conotoxin superfamily is very helpful towards the understanding of its biological and pharmacological functions especially in the post-genomic era. In the present study, we have developed a novel approach called PredCSF for predicting the conotoxin superfamily from the amino acid sequence directly based on fusing different kinds of sequential features by using modified one-versus-rest SVMs. The input features to the PredCSF classifiers are composed of physicochemical properties, evolutionary information, predicted second structure and amino acid composition, where the most important features are further screened by random forest feature selection to improve the prediction performance. The prediction results show that PredCSF can obtain an overall accuracy of 90.65% based on a benchmark dataset constructed from the most recent database, which consists of 4 main conotoxin superfamilies and 1 class of non-conotoxin class. Systematic experiments also show that combing different features is helpful for enhancing the prediction power when dealing with complex biological problems. PredCSF is expected to be a powerful tool for in silico identification of novel conotonxins and is freely available for academic use at http://www.csbio.sjtu.edu.cn/bioinf/PredCSF.

Chemoselective catalytic conversion of glycerol as a biorenewable source to valuable commodity chemicals.

New opportunities for the conversion of glycerol into value-added chemicals have emerged in recent years as a result of glycerol's unique structure, properties, bioavailability, and renewability. Glycerol is currently produced in large amounts during the transesterification of fatty acids into biodiesel and as such represents a useful by-product. This paper provides a comprehensive review and critical analysis on the different reaction pathways for catalytic conversion of glycerol into commodity chemicals, including selective oxidation, selective hydrogenolysis, selective dehydration, pyrolysis and gasification, steam reforming, thermal reduction into syngas, selective transesterification, selective etherification, oligomerization and polymerization, and conversion of glycerol into glycerol carbonate.