Antiferromagnetically coupled iso-structural CrIII,MnIII and FeIII complexes of a tetradentate Schiff base ligand derived from o-phenylenediamine

Transition Metal Chemistry - Three isostructural, Iron(III), Manganese(III) and Chromium complexes, with a tetradentate Schiff base ligand (H2L2) from o-phenylenediamine and o-vanillin having the...     

DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins

The movement of ions across the cell membrane is an essential for many biological processes. This study is focused on ion channels and ion transporters (pumps) as types of border guards control the incessant traffic of ions across cell membranes. Ion channels and ion transporters function to regulate membrane potential and electrical signaling and play important roles in cell proliferation, migration, apoptosis, and differentiation. In their behaviors, it is found that ion channels differ significantly from ion transporters. Therefore, a method for automatically classifying ion transporters and ion channels from membrane proteins is proposed by training deep neural networks and using the position-specific scoring matrix profile as an input. The key of novelty is the three-stage approach, in which five techniques for data normalization are used; next three imbalanced data techniques are applied to the minority classes and then, six classifiers are compared with the proposed method. © 2019 Wiley Periodicals, Inc.