Exploring the disruption of SARS-CoV-2 RBD binding to hACE2.

The COVID-19 pandemic was declared due to the spread of the novel coronavirus, SARS-CoV-2. Viral infection is caused by the interaction between the SARS-CoV-2 receptor binding domain (RBD) and the human ACE2 receptor (hACE2). Previous computational studies have identified repurposed small molecules that target the RBD, but very few have screened drugs in the RBD-hACE2 interface. When studies focus solely on the binding affinity between the drug and the RBD, they ignore the effect of hACE2, resulting in an incomplete analysis. We screened ACE inhibitors and previously identified SARS-CoV-2 inhibitors for binding to the RBD-hACE2 interface, and then conducted 500 ns of unrestrained molecular dynamics (MD) simulations of fosinopril, fosinoprilat, lisinopril, emodin, diquafosol, and physcion bound to the interface to assess the binding characteristics of these ligands. Based on MM-GBSA analysis, all six ligands bind favorably in the interface and inhibit the RBD-hACE2 interaction. However, when we repeat our simulation by first binding the drug to the RBD before interacting with hACE2, we find that fosinopril, fosinoprilat, and lisinopril result in a strongly interacting trimeric complex (RBD-drug-hACE2). Hydrogen bonding and pairwise decomposition analyses further suggest that fosinopril is the best RBD inhibitor. However, when lisinopril is bound, it stabilizes the trimeric complex and, therefore, is not an ideal potential drug candidate. Overall, these results reveal important atomistic interactions critical to the binding of the RBD to hACE2 and highlight the significance of including all protein partners in the evaluation of a potential drug candidate.

Transferable Coarse Graining via Contrastive Learning of Graph Neural Networks.

Coarse-grained (CG) force fields are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. These simulations employ simplified models grouping atoms into interaction sites, enabling the study of complex biomolecular systems over biologically relevant timescales. Efforts are underway to develop accurate and transferable CG force fields, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to many-body effects, lack of analytical expressions for the PMF, and limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent CG force fields and potential contrasting for parameterization from atomistic simulation data. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside the training data. Our study offers valuable insights for building accurate coarse-grained models bottom-up.

Atomistic insights into the binding of SARS-CoV-2 spike receptor binding domain with the human ACE2 receptor: The importance of residue 493.

SARS-CoV-2 is a coronavirus that has created a global pandemic. The virus contains a spike protein which has been shown to bind to the ACE2 receptor on the surface of human cells. Vaccines have been developed that recognize elements of the SARS-CoV-2 spike protein and they have been successful in preventing infection. Recently, the Omicron variant of the SARS-CoV-2 virus was reported and quickly became a variant of concern due to its transmissibility. This variant contained an unusually large number (32) of point mutations, of which 15 of those mutations are in the receptor binding domain of the spike protein. While several computational and experimental investigations comparing the binding of the Omicron and wild type RBD to the human ACE2 receptor have been conducted, many of these report contradictory findings. In order to assess the differential binding ability, we conducted 2 µs of classical molecular dynamics (cMD) simulation to estimate the binding affinities and behaviors. Based upon MM-GBSA binding affinity, per-residue energy decomposition analysis, center of mass distance measurements, ensemble clustering, pairwise residue decomposition and hydrogen bonding analysis, our results suggest that a single point mutation is responsible for the enhanced binding of the Omicron mutant relative to the WT. While the 15-point mutations in the receptor binding domain contribute positively and negatively to the affinity of the spike protein for the human ACE2 receptor, it is the point mutation Q493R that confers enhanced binding while the Q493K mutation results in similar binding. The MM-GBSA binding estimations over a 2 µs trajectory, suggest that the wild type binds to ACE2 with a value of -29.69 kcal/mol while the Q493K and Q493R Omicron mutants bind with energy values of -26.67 and -34.56 kcal/mol, respectively. These values are significantly different, given the error estimates associated with the MM-GBSA method. In general, while some mutations increase binding, more mutations diminish binding, leading to an overall similar picture of binding for Q493K and enhanced binding for Q493R.

Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks.

Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective.

Investigating novel thiazolyl-indazole derivatives as scaffolds for SARS-CoV-2 MPro inhibitors.

COVID-19 is a global pandemic caused by infection with the SARS-CoV-2 virus. Remdesivir, a SARS-CoV-2 RNA polymerase inhibitor, is the only drug to have received widespread approval for treatment of COVID-19. The SARS-CoV-2 main protease enzyme (MPro), essential for viral replication and transcription, remains an active target in the search for new treatments. In this study, the ability of novel thiazolyl-indazole derivatives to inhibit MPro is evaluated. These compounds were synthesized via the heterocyclization of phenacyl bromide with (R)-carvone, (R)-pulegone and (R)-menthone thiosemicarbazones. The binding affinity and binding interactions of each compound were evaluated through Schrödinger Glide docking, AMBER molecular dynamics simulations, and MM-GBSA free energy estimation, and these results were compared with similar calculations of MPro binding various 5-mer substrates (VKLQA, VKLQS, VKLQG) and a previously identified MPro tight-binder X77. From these simulations, we can see that binding is driven by residue specific interactions such as π-stacking with His41, and S/π interactions with Met49 and Met165. The compounds were also experimentally evaluated in a MPro biochemical assay and the most potent compound containing a phenylthiazole moiety inhibited protease activity with an IC50 of 92.9 â€‹µM. This suggests that the phenylthiazole scaffold is a promising candidate for the development of future MPro inhibitors.

Identification of Phenazine-Based MEMO1 Small-Molecule Inhibitors: Virtual Screening, Fluorescence Polarization Validation, and Inhibition of Breast Cancer Migration.

Phosphorylation-dependent protein-protein interactions play a significant role in biological signaling pathways; therefore, small molecules that are capable of influencing these interactions can be valuable research tools and have potential as pharmaceutical agents. MEMO1 (mediator of ErbB2-cell driven motility) is a phosphotyrosine-binding protein that interacts with a variety of protein partners and has been found to be upregulated in breast cancer patients. Herein, we report the first small-molecule inhibitors of MEMO1 interactions identified through a virtual screening platform and validated in a competitive fluorescence polarization assay. Initial structure-activity relationships have been investigated for these phenazine-core inhibitors and the binding sites have been postulated using molecular dynamics simulations. The most potent biochemical inhibitor is capable of disrupting the large protein interface with a KI of 2.7 µm. In addition, the most promising phenazine core compounds slow the migration of breast cancer cell lines in a scratch assay.

Understanding the Structure and Apo Dynamics of the Functionally Active JIP1 Fragment.

Recent experiments indicate that the C-Jun amino-terminal kinase-interacting protein 1 (JIP1) binds to and activates the c-Jun N-terminal kinase (JNK) protein. JNK is an integral part of cell apoptosis, and misregulation of this process is a causative factor in diseases such as Alzheimer's disease (AD), obesity, and cancer. It has also been shown that JIP1 may increase the phosphorylation of tau by facilitating the interaction between the tau protein and JNK, which could also be a causative factor in AD. Very little is known about the structure and dynamics of JIP1; however, the amino acid composition of the first 350 residues suggests that it contains an intrinsically disordered region. Molecular dynamics (MD) simulations using AMBER 14 were used to study the structure and dynamics of a functionally active JIP1 10mer fragment to better understand the solution behavior of the fragment. Two microseconds of unbiased MD was performed on the JIP1 10mer fragment in 10 different seeds for a total of 20 µs of simulation time, and from this, seven structurally stable conformations of the 10mer fragment were identified via classical clustering. The 10mer ensemble was also used to build a Markov state model (MSM) that identified four metastable states that encompassed six of the seven conformational families identified by classical dimensional reduction. Based on this MSM, conformational interconversions between the four states occur via two dominant pathways with probability fluxes of 55 and 44% for each individual pathway. Transitions between the initial and final states occur with mean first passage times of 31 (forward) and 16 (reverse) µs.

Analysis of MEMO1 Binding Specificity for ErbB2 Using Fluorescence Polarization and Molecular Dynamics Simulations.

ErbB2 signaling pathways are linked to breast cancer formation, growth, and aggression; therefore, understanding the behavior of proteins associated with these pathways as well as regulatory factors that influence ErbB2 function is essential. MEMO1 is a redox active protein that is shown to associate with phosphorylated ErbB2 and mediate cell motility. We have developed a fluorescence polarization assay to probe the interaction between MEMO1 and an ErbB2-derived peptide containing a phosphorylated tyrosine residue. This interaction is shown to be pH-dependent and stronger with longer peptides as would be expected for protein-protein interactions. We have quantitatively mapped the binding interface of MEMO1 to the peptide using the fluorescence polarization assay and molecular dynamics simulations. We have confirmed that phosphorylation of the peptide is essential for binding and through mutagenesis have identified residues that contribute to favorable interactions. Our results highlight the importance of the protein-protein interactions of MEMO1 that complement the oxidase activity. In the future, these studies will provide a method for screening for selective modulators of MEMO1, which will allow for additional biological investigations.