Modelling the binding mode of macrocycles: Docking and conformational sampling

Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general

Investigation of Structural Dynamics of Enzymes and Protonation States of Substrates Using Computational Tools.

This review discusses the use of molecular modeling tools, together with existing experimental findings, to provide a complete atomic-level description of enzyme dynamics and function. We focus on functionally relevant conformational dynamics of enzymes and the protonation states of substrates. The conformational fluctuations of enzymes usually play a crucial role in substrate recognition and catalysis. Protein dynamics can be altered by a tiny change in a molecular system such as different protonation states of various intermediates or by a significant perturbation such as a ligand association. Here we review recent advances in applying atomistic molecular dynamics (MD) simulations to investigate allosteric and network regulation of tryptophan synthase (TRPS) and protonation states of its intermediates and catalysis. In addition, we review studies using quantum mechanics/molecular mechanics (QM/MM) methods to investigate the protonation states of catalytic residues of β-Ketoacyl ACP synthase I (KasA). We also discuss modeling of large-scale protein motions for HIV-1 protease with coarse-grained Brownian dynamics (BD) simulations

Computational study of copper binding to DAHK peptide

Ligand Field Molecular Mechanics (LFMM), Density Functional Theory (DFT) and Semi-Empirical methods are used to study Cu(II) binding to the tetrapeptide Asp-Ala-His-Lys (DAHK). Two conformational searching tools, LFMM/AMBER and CREST/xTB, are used to predict the energy and geometry of Cu-DAHK, using DFT as a benchmark. In addition, DFT-predicted electronic spectra are used to evaluate the binding modes found. LFMM and DFT reproduce the experimentally determined coordination, a distorted square planar arrangement of 4 nitrogen ligands with axial coordination to a fifth, oxygen ligand. However, CREST conformational search was unsuccessful in predicting the coordination mode of Cu-DAHK, changing the bonding equatorial ligands from 4 N to 3N1O

Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery

GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects

Improvement of methods for the structural characterisation of drug metabolites based on collisional cross sections

Tesis Doctoral inédita cotutelada por la Universidade do Porto (Portugal) y la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Química. Fecha de Lectura: 26-07-2022Drug metabolism is a pivotal determining factor for the changes in physiological drug concentration and can determine or modify its toxicological or pharmacological pathway (Iyanagi, T., Int. Rev. Cytol., 2007, 260). Understanding of processes, involving a drug in a living organism, is therefore crucial to study and analyse the action of the drug or its metabolites, as reported by Caldwell, J. et al, Toxicol. Pathol., 1995, vol. 23, no. 2. Drug metabolites are typically identified using various techniques, but lately, Ion-Mobility Mass Spectrometry (IM-MS) has become a widely popular tool for small molecule (which are drug metabolites) structural identification due to its high efficiency and a low amount requirement for samples. A combination of this technique along with a computational approach has proved to deliver reliable identification predictions of investigated compounds by comparing experimental and calculated collisional cross sections (CCS) of structures. However, even though a corresponding experimental field has made some valuable developments over the last couple of years, its theoretical counterpart has seen a rather slow improvement. Recently, Reading, E. et al, Anal. Chem., 2016, 88 (4), have developed a computational protocol for collisional cross section calculations. The first part of this work addresses the issue of efficiency of the proposed protocol along with its large-scale applicability. Additionally, special attention has been paid to the reproducibility of the published results and also to the possible ways of improving the agreement within different sets of theoretical results as well as between newly calculated and experimental values. The second part of this manuscript focuses on studying fragmentation mechanisms that occur during Mass Spectrometry (MS) measurements. Electro Spray Ionisation (ESI) along with Tandem MS and Collision Induced Dissociation (CID) build up a powerful experimental approach, able to deliver a deeper understanding of a collision process and its products (Molina, E. R. et al, J. Mass Spectrom., 2015, 50). It is feasible due to extensive fragmentation that takes place in activated ions (metabolites). A corresponding computational approach developed by Hase, W. L. et al, Quantum Chem. Progr. Exch. Bull., 1996, 16, and Hase, W. L. et al, J. Phys. Chem., 1996, 100.20, is used to run Collision Dynamics Simulations (CDS) to obtain reactive trajectories. These trajectories are further utilised for fragmentation analysis that gives insights about structural information of the fragments and possible reaction pathways and also allows to build a theoretical MS spectru

Self-derived peptides from the SARS-CoV-2 spike glycoprotein disrupting shaping and stability of the homotrimer unit

The structural spike (S) protein from the SARS-CoV-2 β-coronavirus is shown to make different pre- and post-fusion conformations within its homotrimer unit. To support the ongoing novel vaccine design and development strategies, we report the structure-based design approach to develop self-derived S peptides. A dataset of crucial regions from the S protein were transformed into linear motifs that could act as the blockers or stabilizers for the S protein homotrimer unit. Among these distinct S peptides, the pep02 (537-QQFGRDIAD-545) and pep07 (821-RDLICAQKFNGLTVLPPLLTDE-842) were found making stable folded binding with the S protein (550–750 and 950–1050 regions). Upon inserting SARS-CoV-2 S variants in the peptide destabilized the complexed S protein structure, resulting an allosteric effect in different functional regions of the protein. Particularly, the molecular dynamics revealed that A544D mutation in the pep02 peptide induced instability for the complexed S protein, whereas the N943K variant from pep09 exhibited an opposite behavior. An increased protein-peptide binding affinity and the stable structural folding were observed in mutated systems, compared to that of the wild type systems. The presence of mutation has induced an “up” active conformation of the spike (RBD) domain, responsible for interacting the host cell receptor. Among the lower affinity peptide datasets (e.g., pep01), the S1 and S2 subunit in the protein formed an “open” conformation, whereas with higher affinity peptides (e.g., pep07) these domains gained a “closed” conformation. These findings propose that our designed self-derived S peptides could replace a single S protein monomer, blocking the homotrimer formation or inducing stability

Theoretical study of copper binding to GHK peptide

We report ligand field molecular mechanics, density functional theory and semi-empirical studies on the binding of Cu(II) to GlyHisLys (GHK) peptide. Following exhaustive conformational searching using molecular mechanics, we show that relative energy and geometry of conformations are in good agreement between GFN2-xTB semi-empirical and B3LYP-D DFT levels. Conventional molecular dynamics simulation of Cu-GHK shows the stability of the copper-peptide binding over 100 ps trajectory. Four equatorial bonds in 3N1O coordination remain stable throughout simulation, while a fifth in apical position from C-terminal carboxylate is more fluxional. We also show that the automated conformer and rotamer search algorithm CREST is able to correctly predict the metal binding position from a starting point consisting of separated peptide, copper and water

Stem cells are the most sensitive screening tool to identify toxicity of GATA4-targeted novel small-molecule compounds

Safety assessment of drug candidates in numerous in vitro and experimental animal models is expensive, time consuming and animal intensive. More thorough toxicity profiling already in the early drug discovery projects using human cell models, which more closely resemble the physiological cell types, would help to decrease drug development costs. In this study we aimed to compare different cardiac and stem cell models for in vitro toxicity testing and to elucidate structure-toxicity relationships of novel compounds targeting the cardiac transcription factor GATA4. By screening the effects of eight compounds at concentrations ranging from 10 nM up to 30 µM on the viability of eight different cell types, we identified significant cell type- and structure-dependent toxicity profiles. We further characterized two compounds in more detail using high-content analysis. The results highlight the importance of cell type selection for toxicity screening and indicate that stem cells represent the most sensitive screening model, which can detect toxicity that may otherwise remain unnoticed. Furthermore, our structure-toxicity analysis reveals a characteristic dihedral angle in the GATA4-targeted compounds that causes stem cell toxicity and thus helps to direct further drug development efforts towards non-toxic derivatives