Mechanistic and thermodynamic characterization of oxathiazolones as potent and selective covalent immunoproteasome inhibitors

Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery

Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out for their theoretical rigor. Despite challenges in force field accuracy and sampling efficiency, advancements in algorithms, software, and hardware have increased the application of free energy perturbation (FEP) calculations in the pharmaceutical industry. Here, we review the practical applications of FEP in drug discovery projects since 2018, covering both ligand-centric and residue-centric transformations. We show that relative binding free energy calculations have steadily achieved chemical accuracy in real-world applications. In addition, we discuss alternative physics-based simulation methods and the incorporation of deep learning into free energy calculations.

Elucidation of the α-Ketoamide Inhibition Mechanism: Revealing the Critical Role of the Electrostatic Reorganization Effect of Asp17 in the Active Site of the 20S Proteasome

The 20S proteasome is an attractive drug target for the development of anticancer agents because it plays an important role in cellular protein degradation. It has a threonine residue that can act as a nucleophile to attack inhibitors with an electrophilic warhead, forming a covalent adduct. Fundamental understanding of the reaction mechanism between covalent inhibitors and the proteasome may assist the design and refinement of compounds with the desired activity. In this study, we investigated the covalent inhibition mechanism of an α-keto phenylamide inhibitor of the proteasome. We calculated the noncovalent binding free energy using the PDLD/S-LRA/β method and the reaction free energy through the empirical valence bond method (EVB). Several possible reaction pathways were explored. Subsequently, we validated the calculated activation and reaction free energies of the most plausible pathways by performing kinetic experiments. Furthermore, the effects of different ionization states of Asp17 on the activation energy at each step were also discussed. The results revealed that the ionization states of Asp17 remarkably affect the activation energies and there is an electrostatic reorganization of Asp17 during the course of the reaction. Our results demonstrate the critical electrostatic effect of Asp17 in the active site of the 20S proteasome.

Challenges in Protein QM/MM Simulations with Intra-Backbone Link Atoms

Hybrid quantum mechanical/molecular mechanical (QM/MM) simulations fuel discoveries in many fields of science including computational biochemistry and enzymology. Development of more convenient tools leads to an increase in the number of works in which mechanical insights into enzymes' mode of operation are obtained. Most commonly, these tools feature hydrogen-capping (link atom) approach to provide coupling between QM and MM subsystems across a covalent bond. Extensive studies were conducted to provide a solid foundation for the correctness of such an approach when a bond to a nonpolar MM atom is considered. However, not every task may be accomplished this way. Certain scenarios of using QM/MM in computational enzymology encourage or even necessitate the incorporation of backbone atoms into the QM region. Two out of three backbone atoms are polar, and in QM/MM with electrostatic embedding, a neighboring link atom will be hyperpolarized. Several schemes to mitigate this effect were previously proposed alongside a rigorous assessment of quantitative effects on model systems. However, it was not clear whether they may translate into qualitatively different results and how link atom hyperpolarization may manifest itself in a real-life enzymological scenario. Here, we show that the consequences of such an artifact may be severe and may completely overturn the conclusions drawn from the simulations. Our case advocates for the use of charge redistribution schemes whenever intra-backbone QM/MM boundaries are considered. Moreover, we addressed how different boundary types and charge redistribution schemes influence backbone dynamics. We showed that the results are heavily dependent on which boundary MM terms are retained, with charge alteration being of secondary importance. In the worst case, only three intra-backbone boundaries may be used with relative confidence in the adequacy of resulting simulations, irrespective of the hyperpolarization mitigation scheme. Thus, advances in the field are certainly needed to fuel new discoveries. As of now, we believe that issues raised in this work might encourage authors in the field to report what boundaries, boundary MM terms, and charge redistribution schemes they are using, so their results may be correctly interpreted.

Targeting immunoproteasome in neurodegeneration: A glance to the future

The immunoproteasome is a specialized form of proteasome equipped with modified catalytic subunits that was initially discovered to play a pivotal role in MHC class I antigen processing and immune system modulation. However, over the last years, this proteolytic complex has been uncovered to serve additional functions unrelated to antigen presentation. Accordingly, it has been proposed that immunoproteasome synergizes with canonical proteasome in different cell types of the nervous system, regulating neurotransmission, metabolic pathways and adaptation of the cells to redox or inflammatory insults. Hence, studying the alterations of immunoproteasome expression and activity is gaining research interest to define the dynamics of neuroinflammation as well as the early and late molecular events that are likely involved in the pathogenesis of a variety of neurological disorders. Furthermore, these novel functions foster the perspective of immunoproteasome as a potential therapeutic target for neurodegeneration. In this review, we provide a brain and retina-wide overview, trying to correlate present knowledge on structure-function relationships of immunoproteasome with the variety of observed neuro-modulatory functions.

Molecular Dynamics Simulations and Diversity Selection by Extended Continuous Similarity Indices

Molecular dynamics (MD) is a core methodology of molecular modeling and computational design for the study of the dynamics and temporal evolution of molecular systems. MD simulations have particularly benefited from the rapid increase of computational power that has characterized the past decades of computational chemical research, being the first method to be successfully migrated to the GPU infrastructure. While new-generation MD software is capable of delivering simulations on an ever-increasing scale, relatively less effort is invested in developing postprocessing methods that can keep up with the quickly expanding volumes of data that are being generated. Here, we introduce a new idea for sampling frames from large MD trajectories, based on the recently introduced framework of extended similarity indices. Our approach presents a new, linearly scaling alternative to the traditional approach of applying a clustering algorithm that usually scales as a quadratic function of the number of frames. When showcasing its usage on case studies with different system sizes and simulation lengths, we have registered speedups of up to 2 orders of magnitude, as compared to traditional clustering algorithms. The conformational diversity of the selected frames is also noticeably higher, which is a further advantage for certain applications, such as the selection of structural ensembles for ligand docking. The method is available open-source at https://github.com/ramirandaq/MultipleComparisons.