UDP-glucuronosyltransferases (UGTs) are important phase II metabolic enzymes responsible for approximately 40-70% of endo and xenobiotic reactions. It catalyzes the transfer of glucuronic acid to lipophilic substrates, converting them into hydrophilic compounds that are excreted. There are 22 active human UGTs that belong to 4 families. This review focuses on human UGTs, highlighting the most current issues in order to connect all information available and allowing a discussion on the challenges already solved and those in which we need to move forward. Although, several UGTs studies have been conducted, the most recent ones addressing drug-drug interactions and polymorphism issues, there are still bottlenecks to overcome. Tridimensional structure is difficult to obtain due to overexpression, purification, and crystallization problems as well as the action mechanism - since overlapping of substrate specificities renders impasses on the identification of which isoform is responsible for a particular drug metabolic pathway. For this reason, bioinformatic tools are gaining more space, since it is a faster and less expensive reliable methodology that complements in vitro and in vivo researches. Combinations of quantum and molecular methods have become increasingly common, leading to the incorporation of enzyme features comprising their structure, dynamics and chemical reactions. Breakthroughs related to the enzyme, not only enable the discovery of new drugs essential for the treatment of various diseases, but also provide an improved action of the existing drugs.

Dipeptidyl peptidase-4 (DPP-4) is a target to treat type II diabetes mellitus. Therefore, it is important to understand the structural aspects of this enzyme and its interaction with drug candidates. This study involved molecular dynamics simulations, normal mode analysis, binding site detection and analysis of molecular interactions to understand the protein dynamics. We identified some DPP-4 functional motions contributing to the exposure of the binding sites and twist movements revealing how the two enzyme chains are interconnected in their bioactive form, which are defined as chains A (residues 40–767) and B (residues 40–767). By understanding the enzyme structure, its motions and the regions of its binding sites, it will be possible to contribute to the design of new DPP-4 inhibitors as drug candidates to treat diabetes.

While PTEN-induced kinase 1 (PINK1) is well characterized for its role in mitochondrial homeostasis, much less is known concerning its ability to prevent synaptodendritic degeneration. Using unbiased proteomic methods, we identified valosin-containing protein (VCP) as a major PINK1-interacting protein. RNAi studies demonstrate that both VCP and its cofactor NSFL1C/p47 are necessary for the ability of PINK1 to increase dendritic complexity. Moreover, PINK1 regulates phosphorylation of p47, but not the VCP co-factor UFD1. Although neither VCP nor p47 interact directly with PKA, we found that PINK1 binds and phosphorylates the catalytic subunit of PKA at T197 [PKA_cat(pT197)], a site known to activate the PKA holoenzyme. PKA in turn phosphorylates p47 at a novel site (S176) to regulate dendritic complexity. Given that PINK1 physically interacts with both the PKA holoenzyme and the VCP-p47 complex to promote dendritic arborization, we propose that PINK1 scaffolds a novel PINK1-VCP-PKA-p47 signaling pathway to orchestrate dendritogenesis in neurons. These findings highlight an important mechanism by which proteins genetically implicated in Parkinson’s disease (PD; PINK1) and frontotemporal dementia (FTD; VCP) interact to support the health and maintenance of neuronal arbors.

With the explosion of normal mode analyses (NMAs) based on elastic network models (ENMs) in the last decade, and the proven precision of MD simulations for visualizing interactions at atomic scale, many hybrid methods have been proposed in recent years. These aim at exploiting the best of both worlds: the atomic precision of MD that often fall short of exploring time and length scales of biological interest, and the capability of ENM-NMA to predict the cooperative and often functional rearrangements of large structures and assemblies, albeit at low resolution. We present an overview of recent progress in the field with examples of successful applications highlighting the utility of such hybrid methods and pointing to emerging future directions guided by advances in experimental characterization of biomolecular systems structure and dynamics.

Recent years have seen several hybrid simulation methods for exploring the conformational space of proteins and their complexes or assemblies. These methods often combine fast analytical approaches with computationally expensive full atomic molecular dynamics (MD) simulations with the goal of rapidly sampling large and cooperative conformational changes at full atomic resolution. We present here a systematic comparison of the utility and limits of four such hybrid methods that have been introduced in recent years: MD with excited normal modes (MDeNM), collective modes-driven MD (CoMD), and elastic network model (ENM)-based generation, clustering, and relaxation of conformations (ClustENM) as well as its updated version integrated with MD simulations (ClustENMD). We analyzed the predicted conformational spaces using each of these four hybrid methods, applied to four well-studied proteins, triosephosphate isomerase (TIM), 3-phosphoglycerate kinase (PGK), HIV-1 protease (PR) and HIV-1 reverse transcriptase (RT), which provide extensive ensembles of experimental structures for benchmarking and comparing the methods. We show that a rigorous multi-faceted comparison and multiple metrics are necessary to properly assess the differences between conformational ensembles and provide an optimal protocol for achieving good agreement with experimental data. While all four hybrid methods perform well in general, being especially useful as computationally efficient methods that retain atomic resolution, the systematic analysis of the same systems by these four hybrid methods highlights the strengths and limitations of the methods and provides guidance for parameters and protocols to be adopted in future studies.