Multiscale Workflow for Modeling Ligand Complexes of Zinc Metalloproteins

Density functional theory (DFT) studies in HDAC-based chemotherapeutics: Current findings, case studies and future perspectives

Density Functional Theory (DFT) is a quantum chemical computational method used to predict and analyze the electronic properties of atoms, molecules, and solids based on the density of electrons rather than wavefunctions. It provides insights into the structure, bonding, and behavior of different molecules, including those involved in the development of chemotherapeutic agents, such as histone deacetylase inhibitors (HDACis). HDACs are a wide group of metalloenzymes that facilitate the removal of acetyl groups from acetyl-lysine residues situated in the N-terminal tail of histones. Abnormal HDAC recruitment has been linked to several human diseases, especially cancer. Therefore, it has been recognized as a prospective target for accelerating the development of anticancer therapies. Researchers have studied HDACs and its inhibitors extensively using a combination of experimental methods and diverse in-silico approaches such as machine learning and quantitative structure-activity relationship (QSAR) methods, molecular docking, molecular dynamics, pharmacophore mapping, and more. In this context, DFT studies can make significant contribution by shedding light on the molecular properties, interactions, reaction pathways, transition states, reactivity and mechanisms involved in the development of HDACis. This review attempted to elucidate the scope in which DFT methodologies may be used to enhance our comprehension of the molecular aspects of HDAC inhibitors, aiding in the rational design and optimization of these compounds for therapeutic applications in cancer and other ailments. The insights gained can guide experimental efforts toward developing more potent and selective HDAC inhibitors.

Modeling Zinc Complexes Using Neural Networks

Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method.

Enhancement by pyrazolones of colistin efficacy against mcr-1-expressing E. coli: an in silico and in vitro investigation

Owing to the emergence of antibiotic resistance, the polymyxin colistin has been recently revived to treat acute, multidrug-resistant Gram-negative bacterial infections. Positively charged colistin binds to negatively charged lipids and damages the outer membrane of Gram-negative bacteria. However, the MCR-1 protein, encoded by the mobile colistin resistance (mcr) gene, is involved in bacterial colistin resistance by catalysing phosphoethanolamine (PEA) transfer onto lipid A, neutralising its negative charge, and thereby reducing its interaction with colistin. Our preliminary results showed that treatment with a reference pyrazolone compound significantly reduced colistin minimal inhibitory concentrations in Escherichia coli expressing mcr-1 mediated colistin resistance (Hanpaibool et al. in ACS Omega, 2023). A docking-MD combination was used in an ensemble-based docking approach to identify further pyrazolone compounds as candidate MCR-1 inhibitors. Docking simulations revealed that 13/28 of the pyrazolone compounds tested are predicted to have lower binding free energies than the reference compound. Four of these were chosen for in vitro testing, with the results demonstrating that all the compounds tested could lower colistin MICs in an E. coli strain carrying the mcr-1 gene. Docking of pyrazolones into the MCR-1 active site reveals residues that are implicated in ligand-protein interactions, particularly E246, T285, H395, H466, and H478, which are located in the MCR-1 active site and which participate in interactions with MCR-1 in ≥ 8/10 of the lowest energy complexes. This study establishes pyrazolone-induced colistin susceptibility in E. coli carrying the mcr-1 gene, providing a method for the development of novel treatments against colistin-resistant bacteria.

Comparison of force fields to study the zinc-finger containing protein NPL4, a target for disulfiram in cancer therapy

Molecular dynamics (MD) simulations are a powerful approach to studying the structure and dynamics of proteins related to health and disease. Advances in the MD field allow modeling proteins with high accuracy. However, modeling metal ions and their interactions with proteins is still challenging. NPL4 is a zinc-binding protein and works as a cofactor for p97 to regulate protein homeostasis. NPL4 is of biomedical importance and has been proposed as the target of disulfiram, a drug recently repurposed for cancer treatment. Experimental studies proposed that the disulfiram metabolites, bis-(diethyldithiocarbamate)‑copper and cupric ions, induce NPL4 misfolding and aggregation. However, the molecular details of their interactions with NPL4 and consequent structural effects are still elusive. Here, biomolecular simulations can help to shed light on the related structural details. To apply MD simulations to NPL4 and its interaction with copper the first important step is identifying a suitable force field to describe the protein in its zinc-bound states. We examined different sets of non-bonded parameters because we want to study the misfolding mechanism and cannot rule out that the zinc may detach from the protein during the process and copper replaces it. We investigated the force-field ability to model the coordination geometry of the metal ions by comparing the results from MD simulations with optimized geometries from quantum mechanics (QM) calculations using model systems of NPL4. Furthermore, we investigated the performance of a force field including bonded parameters to treat copper ions in NPL4 that we obtained based on QM calculations.

Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins

Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data.

Pyrazolones Potentiate Colistin Activity against MCR-1-Producing Resistant Bacteria: Computational and Microbiological Study

The polymyxin colistin is a last line antibiotic for extensively resistant Gram-negative bacteria. Colistin binding to lipid A disrupts the Gram-negative outer membrane, but mobile colistin resistance (mcr) gene family members confer resistance by catalyzing phosphoethanolamine (PEA) transfer onto lipid A, neutralizing its negative charge to reduce colistin interactions. Multiple mcr isoforms have been identified in clinical and environmental isolates, with mcr-1 being the most widespread and mcr-3 being common in South and East Asia. Preliminary screening revealed that treatment with pyrazolones significantly reduced mcr-1, but not mcr-3, mediated colistin resistance. Molecular dynamics (MD) simulations of the catalytic domains of MCR-1 and a homology model of MCR-3, in different protonation states of active site residues H395/H380 and H478/H463, indicate that the MCR-1 active site has greater water accessibility than MCR-3, but that this is less influenced by changes in protonation. MD-optimized structures of MCR-1 and MCR-3 were used in virtual screening of 20 pyrazolone derivatives. Docking of these into the MCR-1/MCR-3 active sites identifies common residues likely to be involved in protein-ligand interactions, specifically the catalytic threonine (MCR-1 T285, MCR-3 T277) site of PEA addition, as well as differential interactions with adjacent amino acids. Minimal inhibitory concentration assays showed that the pyrazolone with the lowest predicted binding energy (ST3f) restores colistin susceptibility of mcr-1, but not mcr-3, expressing Escherichia coli. Thus, simulations indicate differences in the active site structure between MCR-1 and MCR-3 that may give rise to differences in pyrazolone binding and so relate to differential effects upon producer E. coli. This work identifies pyrazolones as able to restore colistin susceptibility of mcr-1-producing bacteria, laying the foundation for further investigations of their activity as phosphoethanolamine transferase inhibitors as well as of their differential activity toward mcr isoforms.

Mechanism of the Early Catalytic Events in the Collagenolysis by Matrix Metalloproteinase-1

Metalloproteinase-1 (MMP-1) catalyzed collagen degradation is essential for a wide variety of normal physiological processes, while at the same time contributing to several diseases in humans. Therefore, a comprehensive understanding of this process is of great importance. Although crystallographic and spectroscopic studies provided fundamental information about the structure and function of MMP-1, the precise mechanism of collagen degradation especially considering the complex and flexible structure of the substrate, remains poorly understood. In addition, how the protein environment dynamically reorganizes at the atomic scale into a catalytically active state capable of collagen hydrolysis remains unknown. In this study, we applied experimentally-guided multiscale molecular modeling methods including classical molecular dynamics (MD), well-tempered (WT) classical metadynamics (MetD), combined quantum mechanics/molecular mechanics (QM/MM) MD and QM/MM MetD simulations to explore and characterize the early catalytic events of MMP-1 collagenolysis. Importantly the study provided a complete atomic and dynamic description of the transition from the open to the closed form of the MMP-1•THP complex. Notably, the formation of catalytically active Michaelis complex competent for collagen cleavage was characterized. The study identified the changes in the coordination state of the catalytic zinc(II) associated with the conformational transformation and the formation of catalytically productive ES complex. Our results confirm the essential role of the MMP-1 catalytic domain's α-helices (hA, hB and hC) and the linker region in the transition to the catalytically competent ES complex. Overall, the results provide unique mechanistic insight into the conformational transformations and associated changes in the coordination state of the catalytic zinc(II) that would be important for the design of effective MMP-1 inhibitors.

Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways

With the increasing popularity of machine learning (ML) applications, the demand for explainable artificial intelligence techniques to explain ML models developed for computational chemistry has also emerged. In this study, we present the development of the Boltzmann-weighted cumulative integrated gradients (BCIG) approach for effective explanation of mechanistic insights into ML models trained on high-level quantum mechanical and molecular mechanical (QM/MM) minimum energy pathways. Using the acylation reactions of the Toho-1 β-lactamase and two antibiotics (ampicillin and cefalexin) as the model systems, we show that the BCIG approach could quantitatively attribute the energetic contribution in one system and the relative reactivity of individual steps across different systems to specific chemical processes such as the bond making/breaking and proton transfers. The proposed BCIG contribution attribution method quantifies chemistry-interpretable insights in terms of contributions from each elementary chemical process, which is in agreement with the validating QM/MM calculations and our intuitive mechanistic understandings of the model reactions.