Background: Total body clearance of biological drugs is for the most part dependent on the receptor mechanisms (receptor mediated clearance) and the concentration of antibodies aimed at administered drug - anti-drug-antibodies (ADA). One of the significant factors that induces the increase of ADA level after drug administration could be the aggregates present in the finished product or formed in the organism. Numerous attempts have been made to identify the sequence fragments that could be responsible for forming the aggregates - aggregate prone regions (APR).
Purpose: The aim of this study was to find physiochemical parameters specific to APR that would differentiate APR from other sequences present in therapeutic proteins.
Methods: Two groups of amino acid sequences were used in the study. The first one was represented by the sequences separated from the therapeutic proteins (n = 84) able to form APR. A control set (CS) consisted of peptides that were chosen based on 22 tregitope sequences.
Results: Classification model and four classes (A, B, C, D) of sequences were finally presented. For model validation Cooper statistics was presented.
Conclusions: The study proposes a classification model of APR. This consists in a distinction of APR from sequences that do not form aggregates based on the differences in the value of physicochemical parameters. Significant share of electrostatic parameters in relation to classification model was indicated.
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http://dx.doi.org/10.1186/s40203-016-0019-4 | DOI Listing |
Sci China Life Sci
December 2024
State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China.
Salivary proteins serve multifaceted roles in maintaining oral health and hold significant potential for diagnosing and monitoring diseases due to the non-invasive nature of saliva sampling. However, the clinical utility of current saliva biomarker studies is limited by the lack of reference intervals (RIs) to correctly interpret the testing result. Here, we developed a rapid and robust saliva proteome profiling workflow, obtaining coverage of >1,200 proteins from a 50-µL unstimulated salivary flow with 30 min gradients.
View Article and Find Full Text PDFNutr J
December 2024
Department of Nutrition, Center for Big Data and Population Health of IHM, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
Background: Although emerging evidence suggests that indole derivatives, microbial metabolites of tryptophan, may improve cardiometabolic health, the effective metabolites remain unclear. Also, the gut microbiota that involved in producing indole derivatives are less studied. We identified microbial taxa that can predict serum concentrations of the key indole metabolite indole-3-propionic acid (IPA) at population level and investigated the associations of indole derivatives and IPA-predicting microbial genera with cardiometabolic risk markers.
View Article and Find Full Text PDFLipids Health Dis
December 2024
Department of Oncology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China.
Background: Cardiometabolic index (CMI) is a comprehensive clinical parameter which integrates overweight and abnormal lipid metabolism. However, its relationship with all-cause, cardiovascular disease (CVD), and cancer mortality is still obscure. Thus, a large-scale cohort study was conducted to illustrate the causal relation between CMI and CVD, cancer, and all-cause mortality among the common American population.
View Article and Find Full Text PDFBMC Bioinformatics
December 2024
School of Computer Engineering, Jiangsu Ocean University, Lianyungang, 222005, China.
Background: Cancer classification has consistently been a challenging problem, with the main difficulties being high-dimensional data and the collection of patient samples. Concretely, obtaining patient samples is a costly and resource-intensive process, and imbalances often exist between samples. Moreover, expression data is characterized by high dimensionality, small samples and high noise, which could easily lead to struggles such as dimensionality catastrophe and overfitting.
View Article and Find Full Text PDFBMC Genomics
December 2024
School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
Background: The subcellular localization of mRNA plays a crucial role in gene expression regulation and various cellular processes. However, existing wet lab techniques like RNA-FISH are usually time-consuming, labor-intensive, and limited to specific tissue types. Researchers have developed several computational methods to predict mRNA subcellular localization to address this.
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