Identifying Protein-Protein Interaction Sites Using Covering Algorithm

Identification of protein-protein interface residues is crucial for structural biology. This paper proposes a covering algorithm for predicting protein-protein interface residues with features including protein sequence profile and residue accessible area. This method adequately utilizes the characters of a covering algorithm which have simple, lower complexity and high accuracy for high dimension data. The covering algorithm can achieve a comparable performance (69.62%, Complete dataset; 60.86%, Trim dataset with overall accuracy) to a support vector machine and maximum entropy on our dataset, a correlation coefficient (CC) of 0.2893, 58.83% specificity, 56.12% sensitivity on the Complete dataset and 0.2144 (CC), 53.34% (specificity), 65.59% (sensitivity) on the Trim dataset in identifying interface residues by 5-fold cross-validation on 61 protein chains. This result indicates that the covering algorithm is a powerful and robust protein-protein interaction site prediction method that can guide biologists to make specific experiments on proteins. Examination of the predictions in the context of the 3-dimensional structures of proteins demonstrates the effectiveness of this method

New forming method of manufacturing cylindrical parts with nano/ultrafine grained structures by power spinning based on small plastic strains

A new spinning method to manufacture the cylindrical parts with nano/ultrafine grained structures is proposed, which consists of quenching, power spinning and recrystallization annealing. The microstructural evolution during the different process stages and macroforming quality of the spun parts made of ASTM 1020 steel are investigated. The results show that the microstructures of the ferrites and pearlites in the ASTM 1020 steel are transformed to the lath martensites after quenching. The martensite laths obtained by quenching are refined to 87 nm and a small amount of nanoscale deformation twins with an average thickness of 20 nm is generated after performing a 3-pass stagger spinning with 55% thinning ratio of wall thickness, where the equivalent strain required is only 0.92. The equiaxial ferritic grains with an average size of 160 nm and nano-carbides are generated by subsequent recrystallization annealing at 480°C for 30 min. The spun parts with high dimensional precision and low surface roughness are obtained by the forming method developed in this work, combining quenching with 3-pass stagger spinning and recrystallization annealing

A Novel Feature Extraction Scheme with Ensemble Coding for Protein–Protein Interaction Prediction

Protein–protein interactions (PPIs) play key roles in most cellular processes, such as cell metabolism, immune response, endocrine function, DNA replication, and transcription regulation. PPI prediction is one of the most challenging problems in functional genomics. Although PPI data have been increasing because of the development of high-throughput technologies and computational methods, many problems are still far from being solved. In this study, a novel predictor was designed by using the Random Forest (RF) algorithm with the ensemble coding (EC) method. To reduce computational time, a feature selection method (DX) was adopted to rank the features and search the optimal feature combination. The DXEC method integrates many features and physicochemical/biochemical properties to predict PPIs. On the Gold Yeast dataset, the DXEC method achieves 67.2% overall precision, 80.74% recall, and 70.67% accuracy. On the Silver Yeast dataset, the DXEC method achieves 76.93% precision, 77.98% recall, and 77.27% accuracy. On the human dataset, the prediction accuracy reaches 80% for the DXEC-RF method. We extended the experiment to a bigger and more realistic dataset that maintains 50% recall on the Yeast All dataset and 80% recall on the Human All dataset. These results show that the DXEC method is suitable for performing PPI prediction. The prediction service of the DXEC-RF classifier is available at http://ailab.ahu.edu.cn:8087/ DXECPPI/index.jsp

Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

Protein-protein interactions (PPIs) play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function. Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases. A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive. In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC) for PPIs prediction. A group of metasamples are extracted from the original training samples and then use the l1-regularized least square method to express a new testing sample as the linear combination of these metasamples. PPIs prediction is achieved by using a discrimination function defined in the representation coefficients. The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM) and much higher than naive Bayes (NB), neural networks (NN), and k-nearest neighbor (KNN). The result shows that the MSRC is efficient for PPIs prediction

An integration of deep learning with feature embedding for protein–protein interaction prediction

Protein–protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein–protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein–protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% (recall), 96.45% (precision), 89.62% (Matthew’s correlation coefficient, MCC) and 98.71% (accuracy), 98.54% (recall), 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from https://github.com/xal2019/DeepFE-PPI

Analysis and Prediction of Exon Skipping Events from RNA-Seq with Sequence Information Using Rotation Forest

In bioinformatics, exon skipping (ES) event prediction is an essential part of alternative splicing (AS) event analysis. Although many methods have been developed to predict ES events, a solution has yet to be found. In this study, given the limitations of machine learning algorithms with RNA-Seq data or genome sequences, a new feature, called RS (RNA-seq and sequence) features, was constructed. These features include RNA-Seq features derived from the RNA-Seq data and sequence features derived from genome sequences. We propose a novel Rotation Forest classifier to predict ES events with the RS features (RotaF-RSES). To validate the efficacy of RotaF-RSES, a dataset from two human tissues was used, and RotaF-RSES achieved an accuracy of 98.4%, a specificity of 99.2%, a sensitivity of 94.1%, and an area under the curve (AUC) of 98.6%. When compared to the other available methods, the results indicate that RotaF-RSES is efficient and can predict ES events with RS features

Aqueous phase hydrogenation of furfural to tetrahydrofurfuryl alcohol on alkaline earth metal modified Ni/Al2O3

Al2O3 modified by alkaline earth metals M-Al2O3 (M - Mg, Ca, Sr, Ba) was synthesised by coprecipitation method. The nickel-based catalysts supported by M-Al2O3 were prepared by impregnation method. The catalysts were characterized by TEM, N-2 adsorption/desorption, XRD, H-2-TPR, NH3-TPD and XPS, and used for the direct hydrogenation of furfural to tetrahydrofurfuryl alcohol (THFA) in water. The reaction was demonstrated to proceed through furfuryl alcohol as an intermediate. The modification of Al2O3 by alkaline earth metals has a significant effect on the activity and selectivity of THFA. A high yield of THFA was obtained over Ni/Ba-Al2O3 under optimized conditions. Moreover, the catalyst is recyclable and reusable at least four times without significant loss of the conversion of furfural and selectivity of THFA