Leveraging Advanced In Silico Techniques in Early Drug Discovery: A Study of Potent Small-Molecule YAP-TEAD PPI Disruptors.

Disruption of the YAP-TEAD protein-protein interaction is an attractive therapeutic strategy in oncology to suppress tumor progression and cancer metastasis. YAP binds to TEAD at a large flat binding interface (∼3500 Å2) devoid of a well-defined druggable pocket, so it has been difficult to design low-molecular-weight compounds to abrogate this protein-protein interaction directly. Recently, work by Furet and coworkers (ChemMedChem 2022, DOI: 10.1002/cmdc.202200303) reported the discovery of the first class of small molecules able to efficiently disrupt the transcriptional activity of TEAD by binding to a specific interaction site of the YAP-TEAD binding interface. Using high-throughput in silico docking, they identified a virtual screening hit from a hot spot derived from their previously rationally designed peptidic inhibitor. Structure-based drug design efforts led to the optimization of the hit compound into a potent lead candidate. Given advances in rapid high-throughput screening and rational approaches to peptidic ligand discovery for challenging targets, we analyzed the pharmacophore features involved in transferring from the peptidic to small-molecule inhibitor that could enable small-molecule discovery for such targets. Here, we show retrospectively that pharmacophore analysis augmented by solvation analysis of molecular dynamics trajectories can guide the designs, while binding free energy calculations provide greater insight into the binding conformation and energetics accompanying the association event. The computed binding free energy estimates agree well with experimental findings and offer useful insight into structural determinants that influence ligand binding to the TEAD interaction surface, even for such a shallow binding site. Taken together, our results demonstrates the utility of advanced in silico methods in structure-based design efforts for difficult-to-drug targets such as the YAP-TEAD transcription factor complex.

Benchmarking In Silico Tools for Cysteine pKa Prediction.

Accurate estimation of the pKa's of cysteine residues in proteins could inform targeted approaches in hit discovery. The pKa of a targetable cysteine residue in a disease-related protein is an important physiochemical parameter in covalent drug discovery, as it influences the fraction of nucleophilic thiolate amenable to chemical protein modification. Traditional structure-based in silico tools are limited in their predictive accuracy of cysteine pKa's relative to other titratable residues. Additionally, there are limited comprehensive benchmark assessments for cysteine pKa predictive tools. This raises the need for extensive assessment and evaluation of methods for cysteine pKa prediction. Here, we report the performance of several computational pKa methods, including single-structure and ensemble-based approaches, on a diverse test set of experimental cysteine pKa's retrieved from the PKAD database. The dataset consisted of 16 wildtype and 10 mutant proteins with experimentally measured cysteine pKa values. Our results highlight that these methods are varied in their overall predictive accuracies. Among the test set of wildtype proteins evaluated, the best method (MOE) yielded a mean absolute error of 2.3 pK units, highlighting the need for improvement of existing pKa methods for accurate cysteine pKa estimation. Given the limited accuracy of these methods, further development is needed before these approaches can be routinely employed to drive design decisions in early drug discovery efforts.

Identification of a PCSK9-LDLR disruptor peptide with in vivo function.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) regulates plasma low-density lipoprotein cholesterol (LDL-C) levels by promoting hepatic LDL receptor (LDLR) degradation. Therapeutic antibodies that disrupt PCSK9-LDLR binding reduce LDL-C concentrations and cardiovascular disease risk. The epidermal growth factor precursor homology domain A (EGF-A) of the LDLR serves as a primary contact with PCSK9 via a flat interface, presenting a challenge for identifying small molecule PCSK9-LDLR disruptors. We employ an affinity-based screen of 1013in vitro-translated macrocyclic peptides to identify high-affinity PCSK9 ligands that utilize a unique, induced-fit pocket and partially disrupt the PCSK9-LDLR interaction. Structure-based design led to molecules with enhanced function and pharmacokinetic properties (e.g., 13PCSK9i). In mice, 13PCSK9i reduces plasma cholesterol levels and increases hepatic LDLR density in a dose-dependent manner. 13PCSK9i functions by a unique, allosteric mechanism and is the smallest molecule identified to date with in vivo PCSK9-LDLR disruptor function.

Design of Thioether Cyclic Peptide Scaffolds with Passive Permeability and Oral Exposure.

Advances in the design of permeable peptides and in the synthesis of large arrays of macrocyclic peptides with diverse amino acids have evolved on parallel but independent tracks. Less precedent combines their respective attributes, thereby limiting the potential to identify permeable peptide ligands for key targets. Herein, we present novel 6-, 7-, and 8-mer cyclic peptides (MW 774-1076 g·mol-1) with passive permeability and oral exposure that feature the amino acids and thioether ring-closing common to large array formats, including DNA- and RNA-templated synthesis. Each oral peptide herein, selected from virtual libraries of partially N-methylated peptides using in silico methods, reflects the subset consistent with low energy conformations, low desolvation penalties, and passive permeability. We envision that, by retaining the backbone N-methylation pattern and consequent bias toward permeability, one can generate large peptide arrays with sufficient side chain diversity to identify permeability-biased ligands to a variety of protein targets.

Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease.

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics. Recently, many high-resolution apo and inhibitor-bound structures of Mpro, a cysteine protease, have been determined, facilitating structure-based drug design. Mpro plays a central role in the viral life cycle by catalyzing the cleavage of SARS-CoV-2 polyproteins. In addition to the catalytic dyad His41-Cys145, Mpro contains multiple histidines including His163, His164, and His172. The protonation states of these histidines and the catalytic nucleophile Cys145 have been debated in previous studies of SARS-CoV Mpro, but have yet to be investigated for SARS-CoV-2. In this work we have used molecular dynamics simulations to determine the structural stability of SARS-CoV-2 Mpro as a function of the protonation assignments for these residues. We simulated both the apo and inhibitor-bound enzyme and found that the conformational stability of the binding site, bound inhibitors, and the hydrogen bond networks of Mpro are highly sensitive to these assignments. Additionally, the two inhibitors studied, the peptidomimetic N3 and an α-ketoamide, display distinct His41/His164 protonation-state-dependent stabilities. While the apo and the N3-bound systems favored N δ (HD) and N ϵ (HE) protonation of His41 and His164, respectively, the α-ketoamide was not stably bound in this state. Our results illustrate the importance of using appropriate histidine protonation states to accurately model the structure and dynamics of SARS-CoV-2 Mpro in both the apo and inhibitor-bound states, a necessary prerequisite for drug-design efforts.

Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease.

The main protease (M pro ) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics. Recently, many high-resolution apo and inhibitor-bound structures of M pro , a cysteine protease, have been determined, facilitating structure-based drug design. M pro plays a central role in the viral life cycle by catalyzing the cleavage of SARS-CoV-2 polyproteins. In addition to the catalytic dyad His41-Cys145, M pro contains multiple histidines including His163, His164, and His172. The protonation states of these histidines and the catalytic nu-cleophile Cys145 have been debated in previous studies of SARS-CoV M pro , but have yet to be investigated for SARS-CoV-2. In this work we have used molecular dynamics simulations to determine the structural stability of SARS-CoV-2 M pro as a function of the protonation assignments for these residues. We simulated both the apo and inhibitor-bound enzyme and found that the conformational stability of the binding site, bound inhibitors, and the hydrogen bond networks of M pro are highly sensitive to these assignments. Additionally, the two inhibitors studied, the peptidomimetic N3 and an α -ketoamide, display distinct His41/His164 protonation-state-dependent stabilities. While the apo and the N3-bound systems favored N δ (HD) and N ϵ (HE) protonation of His41 and His164, respectively, the α -ketoamide was not stably bound in this state. Our results illustrate the importance of using appropriate histidine protonation states to accurately model the structure and dynamics of SARS-CoV-2 M pro in both the apo and inhibitor-bound states, a necessary prerequisite for drug-design efforts.

Building New Bridges between In Vitro and In Vivo in Early Drug Discovery: Where Molecular Modeling Meets Systems Biology.

Cellular drug targets exist within networked function-generating systems whose constituent molecular species undergo dynamic interdependent non-equilibrium state transitions in response to specific perturbations (i.e.. inputs). Cellular phenotypic behaviors are manifested through the integrated behaviors of such networks. However, in vitro data are frequently measured and/or interpreted with empirical equilibrium or steady state models (e.g. Hill, Michaelis-Menten, Briggs-Haldane) relevant to isolated target populations. We propose that cells act as analog computers, "solving" sets of coupled "molecular differential equations" (i.e. represented by populations of interacting species)via "integration" of the dynamic state probability distributions among those populations. Disconnects between biochemical and functional/phenotypic assays (cellular/in vivo) may arise with targetcontaining systems that operate far from equilibrium, and/or when coupled contributions (including target-cognate partner binding and drug pharmacokinetics) are neglected in the analysis of biochemical results. The transformation of drug discovery from a trial-and-error endeavor to one based on reliable design criteria depends on improved understanding of the dynamic mechanisms powering cellular function/dysfunction at the systems level. Here, we address the general mechanisms of molecular and cellular function and pharmacological modulation thereof. We outline a first principles theory on the mechanisms by which free energy is stored and transduced into biological function, and by which biological function is modulated by drug-target binding. We propose that cellular function depends on dynamic counter-balanced molecular systems necessitated by the exponential behavior of molecular state transitions under non-equilibrium conditions, including positive versus negative mass action kinetics and solute-induced perturbations to the hydrogen bonds of solvating water versus kT.

The mechanism of the translocation step in DNA replication by DNA polymerase I: a computer simulation analysis.

High-fidelity DNA polymerases copy DNA rapidly and accurately by adding correct deoxynucleotide triphosphates to a growing primer strand of DNA. Following nucleotide incorporation, a series of conformational changes translocate the DNA substrate by one base pair step, readying the polymerase for the next round of incorporation. Molecular dynamics simulations indicate that the translocation consists globally of a polymerase fingers-opening transition, followed by the DNA displacement and the insertion of the template base into the preinsertion site. They also show that the pyrophosphate release facilitates the opening transition and that the universally conserved Y714 plays a key role in coupling polymerase opening to DNA translocation. The transition involves several metastable intermediates in one of which the O helix is bent in the vicinity of G711. Completion of the translocation appears to require a gating motion of the O1 helix, perhaps facilitated by the presence of G715. These roles are consistent with the high level of conservation of Y714 and the two glycine residues at these positions. It is likely that a corresponding mechanism is applicable to other polymerases.

Probing polar solvation dynamics in proteins: a molecular dynamics simulation analysis.

Measurements of time-resolved Stokes shifts on picosecond to nanosecond time scales have been used to probe the polar solvation dynamics of biological systems. Since it is difficult to decompose the measurements into protein and solvent contributions, computer simulations are useful to aid in understanding the details of the molecular behavior. Here we report the analysis of simulations of the electrostatic interactions of the rest of the protein and the solvent with 11 residues of the immunoglobulin binding domain B1 of protein G. It is shown that the polar solvation dynamics are position-dependent and highly heterogeneous. The contributions due to interactions with the protein and with the solvent are determined. The solvent contributions are found to vary from negligible after a few picoseconds to dominant on a scale of hundreds of picoseconds. The origin for the latter is found to involve coupled hydration and protein conformational dynamics. The resulting microscopic picture demonstrates that a wide range of possibilities have to be considered in the interpretation of time-resolved Stokes shift measurements.