Roediger, S. | bioRxiv Science

bioRxiv2023

Immobilization of Lipophilic and Amphiphilic Biomarker on Hydrophobic Microbeads

BackgroundLipids and amphiphilic molecules are ubiquitous and play a central role in cell signalling, cell membrane structure, and lipid transport in the human body. However, they also appear in many diseases such as atherosclerosis, cardiovascular diseases, infections, inflammatory diseases, cancer, and autoimmune diseases. Thus, it is necessary to have detection systems for lipids and amphiphilic molecules. Microbeads can be one of these systems for the simultaneous detection of different lipophilic biomarkers. MethodsBased on the fundamentals of microbead development, novel hydrophobic microbeads were produced. These not only have a hydrophobic surface, but are also fluorescently encoded and organic solvent resistant. The challenge after the development of the hydrophobic microbeads was to immobilise the amphiphilic molecules, in this study phospholipids, on the microbead surface in an oriented direction. After successful immobilisation of the biomarkers, a suitable antibody based detection assay was established. ResultsBy passive adsorption, the phospholipids cardiolipin, phosphatidylethanolamine and phosphatidylcholine could be bound to the microbead surface. With the application of the enzymes phospholipase A2 and phospholipase C, the directional binding of the phospholipids to the microbead surface was demonstrated. The detection of directional binding indicated the need for the hydrophobic surface. Microbeads with no hydrophobic surface bound the phospholipids non-directionally (with the hydrophilic head) and were thus no longer reactively accessible for detection. ConclusionWith the newly developed hydrophobic, dual coded and solvent stable microbeads it is possible to bind amphiphilic biomolecules directionally onto the microbead surfaces.

biochemistry↗

bioRxiv2022

The impact of negative data sampling on antimicrobial peptide prediction

Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared to traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.

bioinformatics↗