MOTIVATION: Graphical analysis of the molecular structure of proteins can be very complex. Full-atom representations retain most geometric information but are generally crowded, and key structural patterns can be challenging to identify. Non-full-atom representations could be more instructive on physicochemical aspects but be insufficiently detailed regarding shapes (e.g. entity beans-like models in coarse grain approaches) or simple properties of amino acids (e.g. representation of superficial electrostatic properties). In this work, we present TALAIA a visual dictionary that aims to provide another layer of structural representations.TALAIA offers a visual grammar that combines simple representations of amino acids while retaining their general geometry and physicochemical properties. It uses unique objects, with differentiated shapes and colors to represent amino acids. It makes easier to spot crucial molecular information, including patches of amino acids or key interactions between side chains. Most conventions used in TALAIA are standard in chemistry and biochemistry, so experimentalists and modelers can rapidly grasp the meaning of any TALAIA depiction. RESULTS: We propose TALAIA as a tool that renders protein structures and encodes structure and physicochemical aspects as a simple visual grammar. The approach is fast, highly informative, and intuitive, allowing the identification of possible interactions, hydrophobic patches, and other characteristic structural features at first glance. The first implementation of TALAIA can be found at https://github.com/insilichem/talaia.

Motivation: UCSF Chimera is a powerful visualization tool remarkably present in the computational chemistry and structural biology communities. Built on a C++ core wrapped under a Python 2.7 environment, one could expect to easily import UCSF Chimera's arsenal of resources in custom scripts or software projects. Nonetheless, this is not readily possible if the script is not executed within UCSF Chimera due to the isolation of the platform. UCSF ChimeraX, successor to the original Chimera, partially solves the problem but yet major upgrades need to be undergone so that this updated version can offer all UCSF Chimera features. Results: PyChimera has been developed to overcome these limitations and provide access to the UCSF Chimera codebase from any Python 2.7 interpreter, including interactive programming with tools like IPython and Jupyter Notebooks, making it easier to use with additional third-party software. Availability and implementation: PyChimera is LGPL-licensed and available at https://github.com/insilichem/pychimera. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

The relationship between Apolipoprotein E (ApoE) and the aggregation processes of the amyloid beta (A beta) peptide has been shown to be crucial for Alzheimer's disease (AD). The presence of the ApoE4 isoform is considered to be a contributing risk factor for AD. However, the detailed molecular properties of ApoE4 interacting with the A beta peptide are unknown, although various mechanisms have been proposed to explain the physiological and pathological role of this relationship. Here, computer simulations have been used to investigate the process of A beta interaction with the N-terminal domain of the human ApoE isoforms (ApoE2, ApoE3 and ApoE4). Molecular docking combined with molecular dynamics simulations have been undertaken to determine the A beta peptide binding sites and the relative stability of binding to each of the ApoE isoforms. Our results show that from the several ApoE isoforms investigated, only ApoE4 presents a misfolded intermediate when bound to A beta. Moreover, the initial alpha-helix used as the A beta peptide model structure also becomes unstructured due to the interaction with ApoE4. These structural changes appear to be related to a rearrangement of the salt bridge network in ApoE4, for which we propose a model. It seems plausible that ApoE4 in its partially unfolded state is incapable of performing the clearance of A beta, thereby promoting amyloid forming processes. Hence, the proposed model can be used to identify potential drug binding sites in the ApoE4-A beta complex, where the interaction between the two molecules can be inhibited.