1.
Plasma profiling of amino acids distinguishes acute gout from asymptomatic hyperuricemia.
Luo, Y, Wang, L, Liu, XY, Chen, X, Song, YX, Li, XH, Jiang, C, Peng, A, Liu, JY
Amino acids. 2018;(11):1539-1548
Abstract
Gout and hyperuricemia are highly prevalent metabolic diseases caused by high level of uric acid. Amino acids (AAs) involve in various biochemical processes including the biosynthesis of uric acid. However, the role of AAs in discriminating gout from hyperuricemia remains unknown. Here, we report that the plasma AAs profile can distinguish acute gout (AG) from asymptomatic hyperuricemia (AHU). We established an LC-MS/MS-based method to measure the plasma AAs without derivatization for the AG and AHU patients, and healthy controls. We found that the plasma profiling of AAs separated the AG patients from AHU patients and controls visually in both principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) models. In addition, L-isoleucine, L-lysine, and L-alanine were suggested as the key mediators to distinguish the AG patients from AHU and control groups based on the S-plot analysis and variable importance in the projection values in the OPLS-DA models, volcano plot, and the receiver operating characteristic curves. In addition, the saturation of monosodium urate in the AA solutions at physiologically mimic status supported the changes in plasma AAs facilitating the precipitation of monosodium urate. This study suggests that L-isoleucine, L-lysine, and L-alanine could be the potential markers to distinguish the AG from AHU when the patients have similar blood levels of uric acid, providing new strategies for the prevention, treatment, and management of acute gout.
2.
Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information.
An, JY, You, ZH, Chen, X, Huang, DS, Yan, G, Wang, DF
Molecular bioSystems. 2016;(12):3702-3710
Abstract
Self-interacting proteins (SIPs) play an essential role in cellular functions and the evolution of protein interaction networks (PINs). Due to the limitations of experimental self-interaction proteins detection technology, it is a very important task to develop a robust and accurate computational approach for SIPs prediction. In this study, we propose a novel computational method for predicting SIPs from protein amino acids sequence. Firstly, a novel feature representation scheme based on Local Binary Pattern (LBP) is developed, in which the evolutionary information, in the form of multiple sequence alignments, is taken into account. Then, by employing the Relevance Vector Machine (RVM) classifier, the performance of our proposed method is evaluated on yeast and human datasets using a five-fold cross-validation test. The experimental results show that the proposed method can achieve high accuracies of 94.82% and 97.28% on yeast and human datasets, respectively. For further assessing the performance of our method, we compared it with the state-of-the-art Support Vector Machine (SVM) classifier, and other existing methods, on the same datasets. Comparison results demonstrate that the proposed method is very promising and could provide a cost-effective alternative for predicting SIPs. In addition, to facilitate extensive studies for future proteomics research, a web server is freely available for academic use at .
3.
Computational methods for DNA-binding protein and binding residue prediction.
Lu, Y, Wang, X, Chen, X, Zhao, G
Protein and peptide letters. 2013;(3):346-51
Abstract
Protein-DNA interactions are involved in many essential biological processes such as transcription, splicing, replication and DNA repair. It is of great value to identify DNA-binding proteins as well as their binding sites in order to study the mechanisms of these biological processes. A number of experimental methods have been developed for the identification of DNA-binding proteins, such as DNAase foot printing, EMSA, X-ray crystallography, NMR spectroscopy and CHIP-on-Chip. However, with the increasingly greater number of suspected protein-DNA interactions, identification by experimental methods is expensive, labor-intensive and time-consuming. Hence, in the past decades researchers have developed many computational approaches to predict in silico the interactions of proteins and DNA. Machine learning technology has been widely used and become dominant in this field. In this article, we focus on reviewing recent machine learning-based progresses in DNA-binding protein and binding residue prediction methods, the most commonly used features in these predictions, machine learning classifier comparison and selection, evaluation method comparison, and existing problems and future directions for the field.