Practical Guidance for Consensus Scoring and Force Field Selection in Protein-Ligand Binding Free Energy Simulations

JAK3 Inhibitors: Covalent and Noncovalent Interactions of a Cyanamide Group Investigated by Multiscale Free-Energy Simulations

Janus kinase type 3 (JAK3), an emerging target for treating autoimmune diseases, possesses a front pocket cysteine that is targeted by covalent modifiers, best represented by the marketed drug ritlecitinib (1). Recently, 2,3-dihydro-1H-inden-1-ylcyanamides have been developed as novel JAK3 inhibitors. Among them, the N-(6-(7H-pyrrolo[2,3-d]pyrimidin-4-yl)-2,3-dihydro-1H-inden-1-yl)cyanamide inhibitor (2) and its methylated analogue (3), while being potent inhibitors, displayed different mechanisms of action (covalent vs noncovalent) and binding modes (Casimiro-Garcia et al., J Med Chem 2018). Prompted by this intriguing behavior, we applied a multiscale approach to characterize the reaction mechanism between the JAK3 front-pocket Cys909 and cyanamide-based inhibitors. Quantum mechanics/molecular mechanics simulations showed that 2 can readily form an isothiourea adduct with the Cys909 only when a conserved water molecule assists the reaction as a proton shuttle and that methylation of the 2,3-dihydro-1H-inden-1-ylcyanamide moiety of 2 hampers the isothiourea formation by displacing this water molecule. Metadynamics and thermodynamic integration simulations were applied to investigate the relative abundance of alternative poses accessible to 2,3-dihydro-1H-inden-1-ylcyanamides, explaining the effect of methylation on the relative binding mode preference. This multiscale approach provides new chemical insights into the mechanism of action of cyanamide inhibitors and emerges as an effective protocol to investigate the interaction between drugs and molecular targets.

The evolution of the Amber additive protein force field: History, current status, and future

Molecular dynamics simulations are pivotal in elucidating the intricate properties of biological molecules. Nonetheless, the reliability of their outcomes hinges on the precision of the molecular force field utilized. In this perspective, we present a comprehensive review of the developmental trajectory of the Amber additive protein force field, delving into researchers' persistent quest for higher precision force fields and the prevailing challenges. We detail the parameterization process of the Amber protein force fields, emphasizing the specific improvements and retained features in each version compared to their predecessors. Furthermore, we discuss the challenges that current force fields encounter in balancing the interactions of protein-protein, protein-water, and water-water in molecular dynamics simulations, as well as potential solutions to overcome these issues.

Structural insights into the enzymatic breakdown of azomycin-derived antibiotics by 2-nitroimdazole hydrolase (NnhA)

The antibiotic 2-nitroimidazole (2NI) or azomycin, used for treating drug-resistant tuberculosis and imaging tumor hypoxia, requires activation by bacterial nitroreductases for its antibiotic and cytotoxic effect. Mycobacterium sp. JS330 produces 2-nitroimidazole nitrohydrolase (NnhA) that circumvents 2NI activation, conferring 2NI resistance by hydrolysing it to nitrite and imidazol-2-one (IM2O) instead. This study elucidates NnhA's structure, catalytic mechanism, and evolutionary background within the guanidino-group modifying enzyme (GME) superfamily, aided by a more soluble protein variant engineered through directed evolution. Despite low sequence similarity and limited occurrence in a few soil-dwelling mycobacteria and Actinomycetota, NnhA maintains the α/β propeller fold characteristic of GME superfamily enzymes and forms an unusual hexameric ring structure formed by a trimer of domain-swapped dimers. The similarity of its active site to arginine deiminases (ADIs) and human dimethylarginine dimethylaminohydrolases (DDAHs), along with molecular dynamics simulations, suggests NnhA's catalytic mechanism resembles the hydrolysis reactions of these related enzymes.

Host-Guest Binding Free Energies à la Carte: An Automated OneOPES Protocol

Estimating absolute binding free energies from molecular simulations is a key step in computer-aided drug design pipelines, but the agreement between computational results and experiments is still very inconsistent. Both the accuracy of the computational model and the quality of the statistical sampling contribute to this discrepancy, yet disentangling the two remains a challenge. In this study, we present an automated protocol based on OneOPES, an enhanced sampling method that exploits replica exchange and can accelerate several collective variables to address the sampling problem. We apply this protocol to 37 host-guest systems. The simplicity of setting up the simulations and producing well-converged binding free energy estimates without the need to optimize simulation parameters provides a reliable solution to the sampling problem. This, in turn, allows for a systematic force field comparison and ranking according to the correlation between simulations and experiments, which can inform the selection of an appropriate model. The protocol can be readily adapted to test more force field combinations and study more complex protein-ligand systems, where the choice of an appropriate physical model is often based on heuristic considerations rather than systematic optimization.

Validating Small-Molecule Force Fields for Macrocyclic Compounds Using NMR Data in Different Solvents

Macrocycles are a promising class of compounds as therapeutics for difficult drug targets due to a favorable combination of properties: They often exhibit improved binding affinity compared to their linear counterparts due to their reduced conformational flexibility, while still being able to adapt to environments of different polarity. To assist in the rational design of macrocyclic drugs, there is need for computational methods that can accurately predict conformational ensembles of macrocycles in different environments. Molecular dynamics (MD) simulations remain one of the most accurate methods to predict ensembles quantitatively, although the accuracy is governed by the underlying force field. In this work, we benchmark four different force fields for their application to macrocycles by performing replica exchange with solute tempering (REST2) simulations of 11 macrocyclic compounds and comparing the obtained conformational ensembles to nuclear Overhauser effect (NOE) upper distance bounds from NMR experiments. Especially, the modern force fields OpenFF 2.0 and XFF yield good results, outperforming force fields like GAFF2 and OPLS/AA. We conclude that REST2 in combination with modern force fields can often produce accurate ensembles of macrocyclic compounds. However, we also highlight examples for which all examined force fields fail to produce ensembles that fulfill the experimental constraints.

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed

Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.

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.

Ligand Binding Affinity Prediction for Membrane Proteins with Alchemical Free Energy Calculation Methods

Alchemical relative binding free energy (ΔΔG) calculations have shown high accuracy in predicting ligand binding affinity and have been used as important tools in computer-aided drug discovery and design. However, there has been limited research on the application of ΔΔG methods to membrane proteins despite the fact that these proteins represent a significant proportion of drug targets, play crucial roles in biological processes, and are implicated in numerous diseases. In this study, to predict the binding affinity of ligands to G protein-coupled receptors (GPCRs), we employed two ΔΔG calculation methods: thermodynamic integration (TI) with AMBER and the alchemical transfer method (AToM) with OpenMM. We calculated ΔΔG values for 53 transformations involving four class A GPCRs and evaluated the performance of AMBER-TI and AToM-OpenMM. In addition, we conducted tests using different numbers of windows and varying simulation times to achieve reliable ΔΔG results and to optimize resource utilization. Overall, both AMBER-TI and AToM-OpenMM show good agreement with the experimental data. Our results validate the applicability of AMBER-TI and AToM-OpenMM for optimization of lead compounds targeting membrane proteins.

Adaptive lambda schemes for efficient relative binding free energy calculation

The relative free energy perturbation (RFEP) calculation is one of the most theoretically sound computational chemistry approaches for the binding affinity prediction. However, its application is often hindered by the complexity of the calculation choices and the requirement of expertise in the field. Improper lambda scheme of RFEP may result in deviations from an accurate description of the perturbation process and is prone to erroneous affinity predictions. To address such challenges, an automated adaptive lambda method is proposed where the adaptive lambda schemes are obtained through a split-and-merge algorithm based on the pilot runs. The newly established workflow along with a series of improvements to the perturbation settings increases the consistency of the RFEP calculation results. Comparing the pilot and adaptive lambda schemes, the latter demonstrated improvements in convergence and reproducibility and lowered the mean unsigned error and the root-mean-square error. Overall, the adaptive lambda method is a reliable and robust choice to predict small molecule relative binding free energy and can be capitalized to benefit routine RFEP calculations for drug discovery projects.

Accurate and Efficient Multilevel Free Energy Simulations with Neural Network-Assisted Enhanced Sampling

Free energy differences (ΔF) are essential to quantitative characterization and understanding of chemical and biological processes. Their direct estimation with an accurate quantum mechanical potential is of great interest and yet impractical due to high computational cost and incompatibility with typical alchemical free energy protocols. One promising solution is the multilevel free energy simulation in which the estimate of ΔF at an inexpensive low level of theory is combined with the correction toward a higher level of theory. The poor configurational overlap generally expected between the two levels of theory, however, presents a major challenge. We overcome this challenge by using a deep neural network model and enhanced sampling simulations. An adversarial autoencoder is used to identify a low-dimensional (latent) space that compactly represents the degrees of freedom that encode the distinct distributions at the two levels of theory. Enhanced sampling in this latent space is then used to drive the sampling of configurations that predominantly contribute to the free energy correction. Results for both gas phase and condensed phase systems demonstrate that this data-driven approach offers high accuracy and efficiency with great potential for scalability to complex systems.