1.
A Pilot Study of Amino Acids in Unresectable Non-Small-Cell Lung Cancer Patients During Chemotherapy: A Randomized Serial N-of-1 Trials Design.
Liu, L, Zhang, Y, Wei, J, Chen, Z, Yu, J
Nutrition and cancer. 2019;(3):399-408
Abstract
The aim of this study was to evaluate the effect of amino acids (AAs) on immune function and inflammation level in patients with NSCLC receiving chemotherapy. We conducted a series of randomized, multiple-crossover, double-blind, placebo-controlled N-of-1 trials comparing AAs with isocaloric glucose in unresectable NSCLC patients and combined the individual results using Bayesian statistical modeling. 25 patients completed two cycles of chemotherapy. The baseline total blood albumin (ALB) level in all patients was 28 ± 3.3 g/l, and the mean total ALB level in patients receiving AAs supplementation and isocaloric glucose was 29.2 ± 2.2 and 28.1 ± 3.7 g/l, respectively (P = 0.028). Patients' baseline C-reactive protein (CRP) level was 4 ± 1.2 mg/l, the mean total CRP level in patients receiving AAs supplementation and isocaloric glucose was 11 ± 2.8 and 13 ± 3.2 mg/l, respectively (P = 0.028). The baseline total blood CD4+ T cells level was 36 ± 7.8%. The percentage of CD4+ T cells in patients receiving AAs supplementation and isocaloric glucose was 42 ± 6.4 and 33.7 ± 17.3, respectively (P = 0.034). Our preliminary results indicated that AAs improve immune status and suppress inflammation in unresectable NSCLC patients receiving chemotherapy.
2.
hCKSAAP_UbSite: improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties.
Chen, Z, Zhou, Y, Song, J, Zhang, Z
Biochimica et biophysica acta. 2013;(8):1461-7
Abstract
As one of the most common post-translational modifications, ubiquitination regulates the quantity and function of a variety of proteins. Experimental and clinical investigations have also suggested the crucial roles of ubiquitination in several human diseases. The complicated sequence context of human ubiquitination sites revealed by proteomic studies highlights the need of developing effective computational strategies to predict human ubiquitination sites. Here we report the establishment of a novel human-specific ubiquitination site predictor through the integration of multiple complementary classifiers. Firstly, a Support Vector Machine (SVM) classier was constructed based on the composition of k-spaced amino acid pairs (CKSAAP) encoding, which has been utilized in our previous yeast ubiquitination site predictor. To further exploit the pattern and properties of the ubiquitination sites and their flanking residues, three additional SVM classifiers were constructed using the binary amino acid encoding, the AAindex physicochemical property encoding and the protein aggregation propensity encoding, respectively. Through an integration that relied on logistic regression, the resulting predictor termed hCKSAAP_UbSite achieved an area under ROC curve (AUC) of 0.770 in 5-fold cross-validation test on a class-balanced training dataset. When tested on a class-balanced independent testing dataset that contains 3419 ubiquitination sites, hCKSAAP_UbSite has also achieved a robust performance with an AUC of 0.757. Specifically, it has consistently performed better than the predictor using the CKSAAP encoding alone and two other publicly available predictors which are not human-specific. Given its promising performance in our large-scale datasets, hCKSAAP_UbSite has been made publicly available at our server (http://protein.cau.edu.cn/cksaap_ubsite/).