Residue Folding Degree-Relationship to Secondary Structure Categories and Use as Collective Variable

Homology modeling and molecular docking studies to decrease glutamine affinity of Yarrowia lipolytica L-asparaginase

L-Asparaginase is a key component in the treatment of leukemias and lymphomas. However, the glutamine affinity of this therapeutic enzyme is an off-target activity that causes several side effects. The modeling and molecular docking study of Yarrowia lipolytica L-asparaginase (YL-ASNase) to reduce its l-glutamine affinity and increase its stability was the aim of this study. Protein-ligand interactions of wild-type and different mutants of YL-ASNase against L-asparagine compared to l-glutamine were assessed using AutoDock Vina tools because the crystal structure of YL-ASNase does not exist in the protein data banks. The results showed that three mutants, T171S, T171S-N60A, and T171A-T223A, caused a considerable increase in L-asparagine affinity and a decrease in l-glutamine affinity as compared to the wild-type and other mutants. Then, molecular dynamics simulation and MM/GBSA free energy were applied to assess the stability of protein structure and its interaction with ligands. The three mutated proteins, especially T171S-N60A, had higher stability and interactions with L-asparagine than l-glutamine in comparison with the wild-type. The YL-ASNase mutants could be introduced as appropriate therapeutic candidates that might cause lower side effects. However, the functional properties of these mutated enzymes need to be confirmed by genetic manipulation and in vitro and in vivo studies.

Collective Variable-Based Enhanced Sampling: From Human Learning to Machine Learning

Enhanced-sampling algorithms relying on collective variables (CVs) are extensively employed to study complex (bio)chemical processes that are not amenable to brute-force molecular simulations. The selection of appropriate CVs characterizing the slow movement modes is of paramount importance for reliable and efficient enhanced-sampling simulations. In this Perspective, we first review the application and limitations of CVs obtained from chemical and geometrical intuition. We also introduce path-sampling algorithms, which can identify path-like CVs in a high-dimensional free-energy space. Machine-learning algorithms offer a viable approach to finding suitable CVs by analyzing trajectories from preliminary simulations. We discuss both the performance of machine-learning-derived CVs in enhanced-sampling simulations of experimental models and the challenges involved in applying these CVs to realistic, complex molecular assemblies. Moreover, we provide a prospective view of the potential advancements of machine-learning algorithms for the development of CVs in the field of enhanced-sampling simulations.

Boosting the Full Potential of PyMOL with Structural Biology Plugins

Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses.