Cerny, M. | Search bioRxiv

bioRxiv2018

Effect of methylene blue on the formation of oxidized phospholipid vesicles

Soybean phosphatidylcholine, which is rich in linoleic acid, was oxidized with singlet oxygen through photosensitization with methylene blue. This compound facilitates the oxidation of phospholipids relative to the reaction with free unsaturated fatty acids. A response surface methodology was used to control oxidation, with methylene blue concentration and the amount of available air as independent variables. The conjugated diene-to triene ratio was then monitored. Hydroperoxide yield dependent principally on the amount of air, whereas photosensitizer concentration strongly influenced the size and zeta potential of vesicles formed by the sonication of oxidized phospholipids in water. Methylene blue plays an important role in the surface charge expression and ion permeability of these vesicles.

bioengineering↗

bioRxiv2017

An algorithm for template-based prediction of secondary structures of individual RNA sequences

While understanding the structure of RNA molecules is vital for deciphering their functions, determining RNA structures experimentally is exceptionally hard. At the same time, extant approaches to computational RNA structure prediction have limited applicability and reliability. In this paper we provide a method to solve a simpler yet still biologically relevant problem: prediction of secondary RNA structure using structure of different molecules as a template.\n\nOur method identifies conserved and unconserved subsequences within an RNA molecule. For conserved subsequences, the template structure is directly transferred into the generated structure and combined with de-novo predicted structure for the unconserved subsequences with low evolutionary conservation. The method also determines, when the generated structure is unreliable.\n\nThe method is validated using experimentally identified structures. The accuracy of the method exceeds that of classical prediction algorithms and constrained prediction methods. This is demonstrated by comparison using large number of heterogeneous RNAs. The presented method is fast and robust, and useful for various applications requiring knowledge of secondary structures of individual RNA sequences.

bioinformatics↗