Determination of Multidirectional Pathways for Ligand Release from the Receptor: A New Approach Based on Differential Evolution

ModBind, a Rapid Simulation-Based Predictor of Ligand Binding and Off-Rates

In rational drug discovery, both free energy of binding and the binding half-life (koff) are important factors in determining the efficacy of drugs. Numerous computational methods have been developed to predict these important properties, many of which rely on molecular dynamics (MD) simulations. While binding free-energy methods (thermodynamic equilibrium predictions) have been well validated and have demonstrated the ability to drive daily synthesis decisions in a commercial drug discovery setting, the prediction of koff (kinetics predictions) has had limited validation, and predictive methods have largely not been deployed in drug discovery settings. We developed ModBind, a novel method for MD simulation-based koff predictions. ModBind demonstrated similar accuracy to current state-of-the-art free-energy prediction methods. Additionally, ModBind performs ∼100 times faster than most available MD simulation-based free-energy or koff methods, allowing for widespread use by the molecular modeling community. While most free-energy methods rely on relative free-energy changes and are primarily useful for optimization of a congeneric series, our method requires no structural similarity between ligands, making ModBind an absolute predictor of koff. ModBind is thus a tool that can be used in virtual screening of diverse ligands, making it distinct from relative free-energy methods. We also discuss conditions that enable approximate prediction of ligand efficacy using ModBind and the limitations of this approach.

Reinforcing Tunnel Network Exploration in Proteins Using Gaussian Accelerated Molecular Dynamics

Tunnels are structural conduits in biomolecules responsible for transporting chemical compounds and solvent molecules from the active site. They have been shown to be present in a wide variety of enzymes across all functional and structural classes. However, the study of such pathways is experimentally challenging, because they are typically transient. Computational methods, such as molecular dynamics (MD) simulations, have been successfully proposed to explore tunnels. Conventional MD (cMD) provides structural details to characterize tunnels but suffers from sampling limitations to capture rare tunnel openings on longer time scales. Therefore, in this study, we explored the potential of Gaussian accelerated MD (GaMD) simulations to improve the exploration of complex tunnel networks in enzymes. We used the haloalkane dehalogenase LinB and its two variants with engineered transport pathways, which are not only well-known for their application potential but have also been extensively studied experimentally and computationally regarding their tunnel networks and their importance in multistep catalytic reactions. Our study demonstrates that GaMD efficiently improves tunnel sampling and allows the identification of all known tunnels for LinB and its two mutants. Furthermore, the improved sampling provided insight into a previously unknown transient side tunnel (ST). The extensive conformational landscape explored by GaMD simulations allowed us to investigate in detail the mechanism of ST opening. We determined variant-specific dynamic properties of ST opening, which were previously inaccessible due to limited sampling of cMD. Our comprehensive analysis supports multiple indicators of the functional relevance of the ST, emphasizing its potential significance beyond structural considerations. In conclusion, our research proves that the GaMD method can overcome the sampling limitations of cMD for the effective study of tunnels in enzymes, providing further means for identifying rare tunnels in enzymes with the potential for drug development, precision medicine, and rational protein engineering.

Neuropilin-1 Protein May Serve as a Receptor for SARS-CoV-2 Infection: Evidence from Molecular Dynamics Simulations

The binding of the virus to host cells is the first step in viral infection. Human cell angiotensin converting enzyme 2 (ACE2) is the most popular receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), while other receptors have recently been observed in experiments. Neuropilin-1 protein (NRP1) is one of them, but the mechanism of its binding to the wild type (WT) and different variants of the virus remain unclear at the atomic level. In this work, all-atom umbrella sampling simulations were performed to clarify the binding mechanism of NRP1 to the spike protein fragments 679-685 of the WT, Delta, and Omicron BA.1 variants. We found that the Delta variant binds most strongly to NRP1, while the affinity for Omicron BA.1 slightly decreases for NRP1 compared to that of WT, and the van der Waals interaction plays a key role in stabilizing the studied complexes. The change in the protonation state of the His amino acid results in different binding free energies between variants. Consistent with the experiment, decreasing the pH was shown to increase the binding affinity of the virus to NRP1. Our results indicate that Delta and Omicron mutations not only affect fusogenicity but also affect NRP1 binding. In addition, we argue that viral evolution does not further improve NRP1 binding affinity which remains in the μM range but may increase immune evasion.

Role of UDP-N-acetylmuramic acid in the regulation of MurA activity revealed by molecular dynamics simulations

The peptidoglycan biosynthesis pathway plays a vital role in bacterial cells, and facilitates peptidoglycan layer formation, a fundamental structural component of the bacterial cell wall. The enzymes in this pathway are candidates for antibiotic development, as most do not have mammalian homologues. The UDP-N-acetylglucosamine (UNAG) enolpyruvyl transferase enzyme (MurA) in the peptidoglycan pathway cytoplasmic step is responsible for the phosphoenolpyruvate (PEP)-UNAG catalytic reaction, forming UNAG enolpyruvate and inorganic phosphate. Reportedly, UDP-N-acetylmuramic acid (UNAM) binds tightly to MurA forming a dormant UNAM-PEP-MurA complex and acting as a MurA feedback inhibitor. MurA inhibitors are complex, owing to competitive binding interactions with PEP, UNAM, and UNAG at the MurA active site. We used computational methods to explore UNAM and UNAG binding. UNAM showed stronger hydrogen-bond interactions with the Arg120 and Arg91 residues, which help to stabilize the closed conformation of MurA, than UNAG. Binding free energy calculations using end-point computational methods showed that UNAM has a higher binding affinity than UNAG, when PEP is attached to Cys115. The unbinding process, simulated using τ-random acceleration molecular dynamics, showed that UNAM has a longer relative residence time than UNAG, which is related to several complex dissociation pathways, each with multiple intermediate metastable states. This prevents the loop from opening and exposing the Arg120 residue to accommodate UNAG and potential new ligands. Moreover, we demonstrate the importance of Cys115-linked PEP in closed-state loop stabilization. We provide a basis for evaluating novel UNAM analogues as potential MurA inhibitors. PUBLIC SIGNIFICANCE: MurA is a critical enzyme involved in bacterial cell wall biosynthesis and is involved in antibiotic resistance development. UNAM can remain in the target protein's active site for an extended time compared to its natural substrate, UNAG. The prolonged interaction of this highly stable complex known as the 'dormant complex' comprises UNAM-PEP-MurA and offers insights into antibiotic development, providing potential options against drug-resistant bacteria and advancing our understanding of microbial biology.

Mutability-Landscape-Guided Engineering of l-Threonine Aldolase Revealing the Prelog Rule in Mediating Diastereoselectivity of C-C Bond Formation

l-threonine aldolase (LTA) catalyzes C-C bond synthesis with moderate diastereoselectivity. In this study, with LTA from Cellulosilyticum sp (CpLTA) as an object, a mutability landscape was first constructed by performing saturation mutagenesis at substrate access tunnel amino acids. The combinatorial active-site saturation test/iterative saturation mutation (CAST/ISM) strategy was then used to tune diastereoselectivity. As a result, the diastereoselectivity of mutant H305L/Y8H/V143R was improved from 37.2 %_syn to 99.4 %_syn . Furthermore, the diastereoselectivity of mutant H305Y/Y8I/W307E was inverted to 97.2 %_anti . Based on insight provided by molecular dynamics simulations and coevolution analysis, the Prelog rule was employed to illustrate the diastereoselectivity regulation mechanism of LTA, holding that the asymmetric formation of the C-C bond was caused by electrons attacking the carbonyl carbon atom of the substrate aldehyde from the re or si face. The study would be useful to expand LTA applications and guide engineering of other C-C bond-forming enzymes.

Ligand Unbinding Pathway and Mechanism Analysis Assisted by Machine Learning and Graph Methods

We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of unbinding paths and training a machine learning model for trajectory clustering. The second approach clusters trajectories according to their pairwise mean Euclidean distance employing the neighbor-net algorithm, which takes into account input data bias in the distances set and is superior to dendrogram construction. Finally, we describe a more complex case where the reaction coordinate relevant for path identification is a single intraligand hydrogen bond, highlighting the challenges involved in unbinding path reaction coordinate detection.