Water-Triggered, Irreversible Conformational Change of SARS-CoV-2 Main Protease on Passing from the Solid State to Aqueous Solution

SARS-CoV-2 Mpro Dihedral Angles Reveal Allosteric Signaling

In allosteric proteins, identifying the pathways that signals take from allosteric ligand-binding sites to enzyme active sites or binding pockets and interfaces remains challenging. This avenue of research is motivated by the goals of understanding particular macromolecular systems of interest and creating general methods for their study. An especially important protein that is the subject of many investigations in allostery is the SARS-CoV-2 main protease (Mpro), which is necessary for coronaviral replication. It is both an attractive drug target and, due to intense interest in it for the development of pharmaceutical compounds, a gauge of the state of the art approaches in studying protein inhibition. Here we develop a computational method for characterizing protein allostery and use it to study Mpro. We propose a role of the protein's C-terminal tail in allosteric modulation and warn of unintuitive traps that can plague studies of the role of protein dihedral angles in transmitting allosteric signals.

Computational advances in discovering cryptic pockets for drug discovery

A number of promising therapeutic target proteins have been considered "undruggable" due to the lack of well-defined ligandable pockets. Substantial research in protein dynamics has elucidated the existence of "cryptic" pockets that only exist transiently and become favorable for binding in the presence of a ligand. These pockets provide an avenue to target challenging proteins, inspiring the development of multiple computational methods. This review highlights established cryptic pocket modeling approaches like mixed solvent molecular dynamics and recent applications of enhanced sampling and AI-based methods in therapeutically relevant proteins.

The Conformational Space of the SARS-CoV-2 Main Protease Active Site Loops Is Determined by Ligand Binding and Interprotomer Allostery

The main protease (Mpro) of SARS-CoV-2 is essential for viral replication and is, therefore, an important drug target. Here, we investigate two flexible loops in Mpro that play a role in catalysis. Using all-atom molecular dynamics simulations, we analyze the structural ensemble of Mpro in an apo state and substrate-bound state. We find that the flexible loops can adopt open, intermediate (partly open), and closed conformations in solution, which differs from the partially closed state observed in crystal structures of Mpro. When the loops are in closed or intermediate states, the catalytic residues are more likely to be in close proximity, which is crucial for catalysis. Additionally, we find that substrate binding to one protomer of the homodimer increases the frequency of intermediate states in the bound protomer while also affecting the structural propensity of the apo protomer's flexible loops. Using dynamic network analysis, we identify multiple allosteric pathways connecting the two active sites of the homodimer. Common to these pathways is an allosteric hotspot involving the N-terminus, a critical region that comprises part of the binding pocket. Taken together, the results of our simulation study provide detailed insight into the relationships between the flexible loops and substrate binding in a prime drug target for COVID-19.

Design of aggregation-induced emission materials for biosensing of molecules and cells

In recent years, aggregation-induced emission (AIE) materials have gained significant attention and have been developed for various applications in different fields including biomedical research, chemical analysis, optoelectronic devices, materials science, and nanotechnology. AIE is a unique luminescence phenomenon, and AIEgens are fluorescent moieties with relatively twisted structures that can overcome the aggregation-caused quenching (ACQ) effect. Additionally, AIEgens offer advantages such as non-washing properties, deep tissue penetration, minimal damage to biological structures, high signal-to-noise ratio, and excellent photostability. Fluorescent probes with AIE characteristics exhibit high sensitivity, short response time, simple operation, real-time detection capability, high selectivity, and excellent biocompatibility. As a result, they have been widely applied in cellular imaging, luminescent sensing, detection of physiological abnormalities in the human body, as well as early diagnosis and treatment of diseases. This review provides a comprehensive summary and discussion of the progress over the past four years regarding the detection of metal ions, small chemical molecules, biomacromolecules, microbes, and cells based on AIE materials, along with discussing their potential applications and future development prospects.

Absolute Binding Free Energies with OneOPES

The calculation of absolute binding free energies (ABFEs) for protein-ligand systems has long been a challenge. Recently, refined force fields and algorithms have improved the quality of the ABFE calculations. However, achieving the level of accuracy required to inform drug discovery efforts remains difficult. Here, we present a transferable enhanced sampling strategy to accurately calculate absolute binding free energies using OneOPES with simple geometric collective variables. We tested the strategy on two protein targets, BRD4 and Hsp90, complexed with a total of 17 chemically diverse ligands, including both molecular fragments and drug-like molecules. Our results show that OneOPES accurately predicts protein-ligand binding affinities with a mean unsigned error within 1 kcal mol-1 of experimentally determined free energies, without the need to tailor the collective variables to each system. Furthermore, our strategy effectively samples different ligand binding modes and consistently matches the experimentally determined structures regardless of the initial protein-ligand configuration. Our results suggest that the proposed OneOPES strategy can be used to inform lead optimization campaigns in drug discovery and to study protein-ligand binding and unbinding mechanisms.

SARS-CoV-2 3CLPro Dihedral Angles Reveal Allosteric Signaling

In allosteric proteins, identifying the pathways that signals take from allosteric ligand-binding sites to enzyme active sites or binding pockets and interfaces remains challenging. This avenue of research is motivated by the goals of understanding particular macromolecular systems of interest and creating general methods for their study. An especially important protein that is the subject of many investigations in allostery is the SARS-CoV-2 main protease (Mpro), which is necessary for coronaviral replication. It is both an attractive drug target and, due to intense interest in it for the development of pharmaceutical compounds, a gauge of the state-of-the-art approaches in studying protein inhibition. Here we develop a computational method for characterizing protein allostery and use it to study Mpro. We propose a role of the protein's C-terminal tail in allosteric modulation and warn of unintuitive traps that can plague studies of the role of protein dihedrals angles in transmitting allosteric signals.

Addressing Suboptimal Poses in Nonequilibrium Alchemical Calculations

Alchemical transformations can be used to quantitatively estimate absolute binding free energies at a reasonable computational cost. However, most of the approaches currently in use require knowledge of the correct (crystallographic) pose. In this paper, we present a combined Hamiltonian replica exchange nonequilibrium alchemical method that allows us to reliably calculate absolute binding free energies, even when starting from suboptimal initial binding poses. Performing a preliminary Hamiltonian replica exchange enhances the sampling of slow degrees of freedom of the ligand and the target, allowing the system to populate the correct binding pose when starting from an approximate docking pose. We apply the method on 6 ligands of the first bromodomain of the BRD4 bromodomain-containing protein. For each ligand, we start nonequilibrium alchemical transformations from both the crystallographic pose and the top-scoring docked pose that are often significantly different. We show that the method produces statistically equivalent binding free energies, making it a useful tool for computational drug discovery pipelines.

Unexpected Single-Ligand Occupancy and Negative Cooperativity in the SARS-CoV-2 Main Protease

Many homodimeric enzymes tune their functions by exploiting either negative or positive cooperativity between subunits. In the SARS-CoV-2 Main protease (Mpro) homodimer, the latter has been suggested by symmetry in most of the 500 reported protease/ligand complex structures solved by macromolecular crystallography (MX). Here we apply the latter to both covalent and noncovalent ligands in complex with Mpro. Strikingly, our experiments show that the occupation of both active sites of the dimer originates from an excess of ligands. Indeed, cocrystals obtained using a 1:1 ligand/protomer stoichiometry lead to single occupation only. The empty binding site exhibits a catalytically inactive geometry in solution, as suggested by molecular dynamics simulations. Thus, Mpro operates through negative cooperativity with the asymmetric activity of the catalytic sites. This allows it to function with a wide range of substrate concentrations, making it resistant to saturation and potentially difficult to shut down, all properties advantageous for the virus' adaptability and resistance.

OneOPES, a Combined Enhanced Sampling Method to Rule Them All

Enhanced sampling techniques have revolutionized molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical degrees of freedom called collective variables (CVs) to accelerate a system's dynamics and recover the original system's statistics. However, encoding all the relevant degrees of freedom in a limited number of CVs is challenging, particularly in large biophysical systems. Another category of techniques, such as parallel tempering, simulates multiple replicas of the system in parallel, without requiring CVs. However, these methods may explore less relevant high-energy portions of the phase space and become computationally expensive for large systems. To overcome the limitations of both approaches, we propose a replica exchange method called OneOPES that combines the power of multireplica simulations and CV-based enhanced sampling. This method efficiently accelerates the phase space sampling without the need for ideal CVs, extensive parameters fine tuning nor the use of a large number of replicas, as demonstrated by its successful applications to protein-ligand binding and protein folding benchmark systems. Our approach shows promise as a new direction in the development of enhanced sampling techniques for molecular dynamics simulations, providing an efficient and robust framework for the study of complex and unexplored problems.

Water regulates the residence time of Benzamidine in Trypsin

The process of ligand-protein unbinding is crucial in biophysics. Water is an essential part of any biological system and yet, many aspects of its role remain elusive. Here, we simulate with state-of-the-art enhanced sampling techniques the binding of Benzamidine to Trypsin which is a much studied and paradigmatic ligand-protein system. We use machine learning methods to determine efficient collective coordinates for the complex non-local network of water. These coordinates are used to perform On-the-fly Probability Enhanced Sampling simulations, which we adapt to calculate also the ligand residence time. Our results, both static and dynamic, are in good agreement with experiments. We find that the presence of a water molecule located at the bottom of the binding pocket allows via a network of hydrogen bonds the ligand to be released into the solution. On a finer scale, even when unbinding is allowed, another water molecule further modulates the exit time.

Protease-Responsive Peptide-Conjugated Mitochondrial-Targeting AIEgens for Selective Imaging and Inhibition of SARS-CoV-2-Infected Cells

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious threat to human health and lacks an effective treatment. There is an urgent need for both real-time tracking and precise treatment of the SARS-CoV-2-infected cells to mitigate and ultimately prevent viral transmission. However, selective triggering and tracking of the therapeutic process in the infected cells remains challenging. Here, we report a main protease (Mpro)-responsive, mitochondrial-targeting, and modular-peptide-conjugated probe (PSGMR) for selective imaging and inhibition of SARS-CoV-2-infected cells via enzyme-instructed self-assembly and aggregation-induced emission (AIE) effect. The amphiphilic PSGMR was constructed with tunable structure and responsive efficiency and validated with recombinant proteins, cells transfected with Mpro plasmid or infected by SARS-CoV-2, and a Mpro inhibitor. By rational construction of AIE luminogen (AIEgen) with modular peptides and Mpro, we verified that the cleavage of PSGMR yielded gradual aggregation with bright fluorescence and enhanced cytotoxicity to induce mitochondrial interference of the infected cells. This strategy may have value for selective detection and treatment of SARS-CoV-2-infected cells.

Exploration vs Convergence Speed in Adaptive-Bias Enhanced Sampling

In adaptive-bias enhanced sampling methods, a bias potential is added to the system to drive transitions between metastable states. The bias potential is a function of a few collective variables and is gradually modified according to the underlying free energy surface. We show that when the collective variables are suboptimal, there is an exploration–convergence tradeoff, and one must choose between a quickly converging bias that will lead to fewer transitions or a slower to converge bias that can explore the phase space more efficiently but might require a much longer time to produce an accurate free energy estimate. The recently proposed on-the-fly probability enhanced sampling (OPES) method focuses on fast convergence, but there are cases where fast exploration is preferred instead. For this reason, we introduce a new variant of the OPES method that focuses on quickly escaping metastable states at the expense of convergence speed. We illustrate the benefits of this approach in prototypical systems and show that it outperforms the popular metadynamics method.

Multiple protonation states in ligand-free SARS-CoV-2 main protease revealed by large-scale quantum molecular dynamics simulations

The main protease (M^pro) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) catalyzes the cleavage of polyproteins for viral replication. Here, large-scale quantum molecular dynamics and metadynamics simulations for ligand-free M^pro were performed, where all the atoms were treated quantum-mechanically, focusing on elucidation of the controversial active-site protonation state. The simulations clarified that the interconverting multiple protonation states exist in unliganded M^pro, and the catalytically relevant ion-pair state is more stable than the neutral state, which is consistent with neutron crystallography. The results highlight the importance of the ion-pair state for repurposing or discovering antiviral drugs that target M^pro.

Discover, Sample, and Refine: Exploring Chemistry with Enhanced Sampling Techniques

Over the last few decades, enhanced sampling methods have been continuously improved. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and determination of reaction paths. In a three-step strategy, at first we use a collective variable derived from spectral graph theory in conjunction with the explore variant of the on-the-fly probability enhanced sampling method to drive reaction discovery runs. Once different chemical products are determined, we construct an ad-hoc neural network-based collective variable to improve sampling, and finally we refine the results using the free energy perturbation theory and a more accurate Hamiltonian. We apply this strategy to both intramolecular and intermolecular reactions. Our workflow requires minimal user input and extends the power of ab initio molecular dynamics to explore and characterize the reaction space.

The dolabellane diterpenes as potential inhibitors of the SARS-CoV-2 main protease: molecular insight of the inhibitory mechanism through computational studies

An investigation has been carried out on natural products from dolabellane derivatives to understand their potential in inhibiting the SARS-CoV-2 main protease (3CLpro) using an in silico approach. Inhibition of the 3CLpro enzyme is a promising target in stopping the replication of the SARS-CoV-2 virus through inhibition of the subsite binding pocket. The redocking process aims to determine the 3CLpro active sites. The redocking requirement showed a good pose with an RMSD value of 1.39 Å. The combination of molecular docking and MD simulation shows the results of DD13 as a candidate which had a good binding affinity (kcal mol-1) to inhibit the 3CLpro enzyme activity. Prediction of binding free energy (kcal mol-1) of DD13 using the Molecular Mechanics-Poisson Boltzmann/Generalized Born Surface Area (MM-PB/GBSA) approach shows the results ΔG bind(MM-GBSA): -52.33 ± 0.34 and ΔG bind(MM-PBSA): -43.52 ± 0.42. The key residues responsible for the inhibition mechanism are Hie41, Ser46, Met49, Asn142, Cys145, Hie163, Met165, and Gln189. Additionally, pharmacokinetic prediction recommended that DD13 had promising criteria as a drug candidate. The results demonstrated in this study provide theoretical information to obtain a potential inhibitor against the SARS-CoV-2 main protease.