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Enzyme Kinetics and Drugs as Enzyme Inhibitors
Published in Peter Grunwald, Pharmaceutical Biocatalysis, 2019
In this subchapter a variety of enzyme inhibitors and (allosteric) activators—alone or in combination with other APIs—are discussed that entered the market in recent years or about which new findings were published. Inhibitors act either reversible or irreversible (Section 21.2.2). They are discovered by high-throughput screening of large libraries of compounds generated by combinatorial chemistry approaches against a target enzyme. Alternatively, rational design based on the knowledge of the enzyme’s structure is employed together with computational methods as, e.g., molecular docking, molecular mechanics, free energy calculation methods, etc. (e.g., De Vivo and Cavalli, 2017; Abel et al., 2017). Examples are treated in other volumes of this Series, too (e.g., Chapter 19 in Volume 4 of this Series).
Hits and Lead Discovery in the Identification of New Drugs against the Trypanosomatidic Infections
Published in Venkatesan Jayaprakash, Daniele Castagnolo, Yusuf Özkay, Medicinal Chemistry of Neglected and Tropical Diseases, 2019
Theodora Calogeropoulou, George E. Magoulas, Ina Pöhner, Joanna Panecka-Hofman, Pasquale Linciano, Stefania Ferrari, Nuno Santarem, Ma Dolores Jiménez-Antón, Ana Isabel Olías-Molero, José María Alunda, Anabela Cordeiro da Silva, Rebecca C. Wade, Maria Paola Costi
To gain more insight into cruzain dynamics and its interactions with inhibitors, MD simulations have been applied together with other molecular modelling techniques. Durrant et al. (2010) used sequence analysis and MD simulations to explore additional binding sites in cruzain. Hoelz et al. (2016) studied free and liganded cruzain dynamics at acidic pH, which corresponds to its environment in the cell. Very recently, Cianni et al. (2018) also used MD simulations to study the binding mode of reversible covalent inhibitors to the less frequently studied specific subsite S3 of cruzain. Finally, Martins et al. (2018) demonstrated a comprehensive computational approach including docking, MD, ab initio and MM/PBSA calculations for binding mode prediction and estimation of the contributions of specific amino acids to binding. Finally furthermore, more mechanistic approaches have been employed, for example by Arafet et al. (2015, 2017) who studied the MoA of peptidyl-epoxyketone- or peptidyl-halomethyl-ketone-based cruzain inhibitors with a QM/MM method.
Computer-Aided Drug Design for the Identification of Multi-Target Directed Ligands (MTDLs) in Complex Diseases: An Overview
Published in Peter Grunwald, Pharmaceutical Biocatalysis, 2019
The scoring functions of existing docking methodologies help in ranking or prioritization of the compounds but are not accurate enough to compute reliable binding free energy values. Molecular dynamics simulation study helps in investigating the ligand–protein interactions in detail, which includes understanding the most stable or key interactions and to find significantly accurate binding free energy. Usually Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) or Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) simulation approaches are employed to compute the binding free energy between ligand and target protein (Hou et al., 2011). However, the molecular dynamics studies should be performed on the finally screened compounds with potential multi-target characteristics, as this technique is highly time consuming (Ambure et al., 2018). Moreover, the issue of protein flexibility and false-positive hits sometimes observed in molecular docking and (SBP) technique can be resolved by performing molecular dynamics analysis.
Deciphering deamidation and isomerization in therapeutic proteins: Effect of neighboring residue
Published in mAbs, 2022
Flaviyan Jerome Irudayanathan, Jonathan Zarzar, Jasper Lin, Saeed Izadi
Multiple computational strategies, from physics-based approaches (e.g., quantum mechanics/molecular mechanics (QM/MM), molecular dynamics (MD) simulations)29,30,35,36 along with machine-learning models,17,20,37 have been developed over the past two decades to predict deamidation and isomerization in peptides and proteins. Several detailed computational models explored site-specific effects using QM/MM calculations at the peptide level. For example, ab initio calculations on Gly-containing peptide mimetic model compound (N-formyl-glycinamide) revealed how the conformation of the molecule affects the proton-affinity of the backbone amide (Figure 2). 38 However, it is unclear if this trend is generalizable to Asp and Asn residues where the n + 1 residue is not Gly. Multiple machine learning (ML) models have been proposed that were trained on a number of structure-based features such as secondary structure, local flexibility, size of the neighboring residue, and solvent exposure.17,20,37,38 While computationally facile and practical in nature, ML-based models often focus exclusively on prediction without necessarily offering mechanistic insight into causality. Sometimes important mechanistic and structural details such as conformational dynamics, key for the reaction’s feasibility, are neglected. Lastly, given that these models are trained on a limited set of data points using pre-assumed input features, their generalizability to new datasets remain unclear.
The discovery and development of transmembrane serine protease 2 (TMPRSS2) inhibitors as candidate drugs for the treatment of COVID-19
Published in Expert Opinion on Drug Discovery, 2022
Christiana Mantzourani, Sofia Vasilakaki, Velisaria-Eleni Gerogianni, George Kokotos
Escalante et al. used the plasma kallikrein A (PDB: 2ANY) as the template for building the homology model in Prime [106]. They studied the interactions of the residues close to the catalytic center and they documented a triad of His296, Ser441 and Asp345 residues, which forms a network of hydrogen bonds, contributing to the activation of the serine for a nucleophilic attack to the substrate. As soon as His296 is positively charged, Asp45 can stabilize it via the formation of a hydrogen bond. The Glide algorithm and then the cdock algorithm were used to simulate the nucleophilic attack of Ser441 to the ligand, and based on the results, it is more likely that Ser441 attacks the carbonyl group from below and it is not a top side attack. The authors used quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulations to study the apo structure as well as complexes of protein-camostat and protein-GBPA. QM was used to model the residues of the catalytic triad in the apo structure and included all atoms of the ligand in the complex protein-camostat. A strength of the Ser441 to His296 H-bonding interaction was measured upon insertion of the ligand. Hydrogen bonds between the ligand, especially the guanidino donor, and Asp435, Ser436, Ser463 and Gly464 in combination with polar interactions contribute to the stabilization of the complex. In the GBPA complex, the benzoyl guanidino moiety behaves similar to camostat, while a number of π-π interactions formed and could guide the design of new inhibitors toward an enhancement of their binding affinity against TMPRSS2.
Exploring space-energy matching via quantum-molecular mechanics modeling and breakage dynamics-energy dissipation via microhydrodynamic modeling to improve the screening efficiency of nanosuspension prepared by wet media milling
Published in Expert Opinion on Drug Delivery, 2021
Jing Tian, Fangxia Qiao, Yanhui Hou, Bin Tian, Jianhong Yang
In nanosuspensions, both the structures of low molecular weight compounds and complex drug systems may need to be optimized and established. We need to combine the advantages of quantum mechanics and molecular mechanics methods to facilitate investigations of nanosuspensions [31,38]. The quantum mechanics methods have high precision, but they are usually employed for calculating the states of motion, energy, etc. of small systems. The molecular mechanics methods can be applied to large complex systems. The accuracy is not as high as those of the quantum mechanics methods, but the modeling speed is quicker [26]. Therefore, systems with small molecular structures are optimized using quantum mechanics methods, and other complex structures are constructed with molecular mechanics methods for improving the accuracy and speed of modeling. For example, in the Materials Studios software, the atomic coordinates and crystal cell parameters are optimized by using the CASTEP module [39], and the Amorphous Cells module is used to build amorphous structures.