Characterization of the Ligand Receptor Encounter Complex and Its Potential for in Silico Kinetics-Based Drug Development

Competitive Interaction of the SGFRKMAF Peptide with 3CLpro Dimerization Intermediates: A Brownian Dynamics Investigation

The SGFRKMAF peptide is known to inhibit the dimerization of 3CLpro monomers, which is essential for SARS-CoV-2 replication. The mechanism behind this, however, is largely unknown. In this work, we used Brownian dynamics simulations to compare and contrast 3CLpro monomer-monomer interactions and 3CLpro monomer-SGFRKMAF peptide interactions. We found that formation of the 3CLpro wild-type dimer could potentially involve formation of three intermediates that are primarily stabilized by G11-G124, S1-S301, and T118-G278 interactions. Analysis of 3CLpro monomer interaction with the SGFRKMAF peptide, however, revealed the presence of eight basins of interactions where the peptide assumes the highest local densities at the 3CLPro monomer surface. The second highest-density basin was found to coincide with the interface region of the wild-type 3CLpro dimer, thereby directly blocking the 3CLpro dimer-dimer interactions. The other basins, however, were found to lie far from the interface region. Notably, we found that only 6% of the BD trajectories end up directly into the basin at the interface region and ∼39% of the trajectories end up into those basins lying away from the interface region, indicating a greater role for peptide binding at sites away from the dimer interface region. Importantly, the locations of the basins lying away from the interface were found to coincide with the 3CLpro residues involved in stabilization of the 3CLpro monomer-monomer intermediates. Given that the rate constant of the peptide reaching the monomer surface was found to be almost an order of magnitude higher than the rate constant of monomer-monomer association, the SGFRKMAF peptide has the potential to inhibit dimerization of 3CLpro monomers not only through blocking the interface region but also through blocking the formation of the intermediates involved in the dimerization process. This could potentially open new avenues for 3CLpro dimerization inhibitors that transcend traditional X-ray-based discovery approaches.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Kinetics for Drug Discovery: an industry-driven effort to target drug residence time

A considerable number of approved drugs show non-equilibrium binding characteristics, emphasizing the potential role of drug residence times for in vivo efficacy. Therefore, a detailed understanding of the kinetics of association and dissociation of a target-ligand complex might provide crucial insight into the molecular mechanism-of-action of a compound. This deeper understanding will help to improve decision making in drug discovery, thus leading to a better selection of interesting compounds to be profiled further. In this review, we highlight the contributions of the Kinetics for Drug Discovery (K4DD) Consortium, which targets major open questions related to binding kinetics in an industry-driven public-private partnership.

Exploration of gated ligand binding recognizes an allosteric site for blocking FABP4-protein interaction

Fatty acid binding protein 4 (FABP4), reversibly binding to fatty acids and other lipids with high affinities, is a potential target for treatment of cancers. The binding site of FABP4 is buried in an interior cavity and thereby ligand binding/unbinding is coupled with opening/closing of FABP4. It is a difficult task both experimentally and computationally to illuminate the entry or exit pathway, especially with the conformational gating. In this report we combine extensive computer simulations, clustering analysis, and the Markov state model to investigate the binding mechanism of FABP4 and troglitazone. Our simulations capture spontaneous binding and unbinding events as well as the conformational transition of FABP4 between the open and closed states. An allosteric binding site on the protein surface is recognized for the development of novel FABP4 inhibitors. The binding affinity is calculated and compared with the experimental value. The kinetic analysis suggests that ligand residence on the protein surface may delay the binding process. Overall, our results provide a comprehensive picture of ligand diffusion on the protein surface, ligand migration into the buried cavity, and the conformational change of FABP4 at an atomic level.

Binding Kinetics in Drug Discovery

Over the last years, researchers have increasingly become interested in measuring and understanding drugs' binding kinetics, namely the time in which drug and its target associate and dissociate. Historically, drug discovery programs focused on the optimization of target affinity as a proxy of in-vivo efficacy. However, often the efficacy of a ligand is not appropriately described by the in-vitro measured drug-receptor affinity, but rather depends on the lifetime of the in-vivo drug-receptor interaction. In this review we review recent works that highlight the importance of binding kinetics, molecular determinants for rational optimization and the recent emergence of computational methods as powerful tools in measuring and understanding binding kinetics.

Advanced molecular dynamics simulation methods for kinase drug discovery

Interest in the application of molecular dynamics (MD) simulations has increased in the field of protein kinase (PK) drug discovery. PKs belong to an important drug target class because they are directly involved in a number of diseases, including cancer. MD methods simulate dynamic biological and chemical events at an atomic level. This information can be combined with other in silico and experimental methods to efficiently target selected receptors. In this review, we present common and advanced methods of MD simulations and we focus on the recent applications of MD-based methodologies that provided significant insights into the elucidation of biological mechanisms involving PKs and into the discovery of novel kinase inhibitors.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Recognition Dynamics of p53 and MDM2: Implications for Peptide Design

Peptides that inhibit MDM2 and attenuate MDM2-p53 interactions, thus activating p53, are currently being pursued as anticancer drug leads for tumors harboring wild type p53. The thermodynamic determinants of peptide-MDM2 interactions have been extensively studied. However, a detailed understanding of the dynamics that underlie these interactions is largely missing. In this study, we explore the kinetics of the binding of a set of peptides using Brownian dynamics simulations. We systematically investigate the effect of peptide C-terminal substitutions (Ser, Ala, Asn, Pro) of a Q16ETFSDLWKLLP27 p53-based peptide and a M1PRFMDYWEGLN12 12/1 phage-derived peptide on their interaction dynamics with MDM2. The substitutions modulate peptide residence times around the MDM2 protein. In particular, the highest affinity peptide, Q16ETFSDLWKLLS27, has the longest residence time (t ∼ 25 μs) around MDM2, suggesting its potentially important contribution to binding affinity. The binding of the p53-based peptides appears to be kinetically driven while that of the phage-derived series appears to be thermodynamically driven. The phage-derived peptides were found to adopt distinctly different modes of interaction with the MDM2 protein compared to their p53-based counterparts. The p53-based peptides approach the N-terminal region of the MDM2 protein with the peptide C-terminal end oriented toward the protein, while the M1PRFMDYWEGLN12-based peptides adopt the reverse orientation. To probe the determinants of this switch in orientation, a designed mutant of the phage-derived peptide, R3E (M1PEFMDYWEGLN12), was simulated and found to adopt the orientation adopted by the p53-based peptides and also to result in almost a 5-fold increase in the peptide residence time (∼120 μs) relative to the p53-based peptides. On this basis, we suggest that the R3E mutant phage-derived peptide has a higher affinity for MDM2 than the p53-based peptides and would therefore, competitively inhibit MDM2-p53. The study, therefore, provides a novel computational framework for kinetics-based lead optimization for anticancer drug development strategies.

A spatiotemporal characterization of the effect of p53 phosphorylation on its interaction with MDM2

The interaction of p53 and MDM2 is modulated by the phosphorylation of p53. This mechanism is key to activating p53, yet its molecular determinants are not fully understood. To study the spatiotemporal characteristics of this molecular process we carried out Brownian dynamics simulations of the interactions of the MDM2 protein with a p53 peptide in its wild type state and when phosphorylated at Thr18 (pThr18) and Ser20 (pSer20). We found that p53 phosphorylation results in concerted changes in the topology of the interaction landscape in the diffusively bound encounter complex domain. These changes hinder phosphorylated p53 peptides from binding to MDM2 well before reaching the binding site. The underlying mechanism appears to involve shift of the peptide away from the vicinity of the MDM2 protein, peptide reorientation, and reduction in peptide residence time relative to wild-type p53 peptide. pThr18 and pSr20 p53 peptides experience reduction in residence times by factors of 13.6 and 37.5 respectively relative to the wild-type p53 peptide, indicating a greater role for Ser20 phosphorylation in abrogating p53 MDM2 interactions. These detailed insights into the effect of phosphorylation on molecular interactions are not available from conventional experimental and theoretical approaches and open up new avenues that incorporate molecular interaction dynamics, for stabilizing p53 against MDM2, which is a major focus of anticancer drug lead development.

SDA 7: A modular and parallel implementation of the simulation of diffusional association software

The simulation of diffusional association (SDA) Brownian dynamics software package has been widely used in the study of biomacromolecular association. Initially developed to calculate bimolecular protein-protein association rate constants, it has since been extended to study electron transfer rates, to predict the structures of biomacromolecular complexes, to investigate the adsorption of proteins to inorganic surfaces, and to simulate the dynamics of large systems containing many biomacromolecular solutes, allowing the study of concentration-dependent effects. These extensions have led to a number of divergent versions of the software. In this article, we report the development of the latest version of the software (SDA 7). This release was developed to consolidate the existing codes into a single framework, while improving the parallelization of the code to better exploit modern multicore shared memory computer architectures. It is built using a modular object-oriented programming scheme, to allow for easy maintenance and extension of the software, and includes new features, such as adding flexible solute representations. We discuss a number of application examples, which describe some of the methods available in the release, and provide benchmarking data to demonstrate the parallel performance.

Towards structural systems pharmacology to study complex diseases and personalized medicine

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

On the origin of the stereoselective affinity of Nutlin-3 geometrical isomers for the MDM2 protein

The stereoselective affinity of small-molecule binding to proteins is typically broadly explained in terms of the thermodynamics of the final bound complex. Using Brownian dynamics simulations, we show that the preferential binding of the MDM2 protein to the geometrical isomers of Nutlin-3, an effective anticancer lead that works by inhibiting the interaction between the proteins p53 and MDM2, can be explained by kinetic arguments related to the formation of the MDM2:Nutlin-3 encounter complex. This is a diffusively bound state that forms prior to the final bound complex. We find that the MDM2 protein stereoselectivity for the Nutlin-3a enantiomer stems largely from the destabilization of the encounter complex of its mirror image enantiomer Nutlin-3b, by the K70 residue that is located away from the binding site. On the other hand, the trans-Nutlin-3a diastereoisomer exhibits a shorter residence time in the vicinity of MDM2 compared with Nutlin-3a due to destabilization of its encounter complex by the collective interaction of pairs of charged residues on either side of the binding site: Glu25 and Lys51 on one side, and Lys94 and Arg97 on the other side. This destabilization is largely due to the electrostatic potential of the trans-Nutlin-3a isomer being largely positive over extended continuous regions around its structure, which are otherwise well-identified into positive and negative regions in the case of the Nutlin-3a isomer. Such rich insight into the binding processes underlying biological selectivity complements the static view derived from the traditional thermodynamic analysis of the final bound complex. This approach, based on an explicit consideration of the dynamics of molecular association, suggests new avenues for kinetics-based anticancer drug development and discovery.

On the interaction mechanisms of a p53 peptide and nutlin with the MDM2 and MDMX proteins: a Brownian dynamics study

The interaction of p53 with its regulators MDM2 and MDMX plays a major role in regulating the cell cycle. Inhibition of this interaction has become an important therapeutic strategy in oncology. Although MDM2 and MDMX share a very high degree of sequence/structural similarity, the small-molecule inhibitor nutlin appears to be an efficient inhibitor only of the p53-MDM2 interaction. Here, we investigate the mechanism of interaction of nutlin with these two proteins and contrast it with that of p53 using Brownian dynamics simulations. In contrast to earlier attempts to examine the bound states of the partners, here we locate initial reaction events in these interactions by identifying the regions of space around MDM2/MDMX, where p53/nutlin experience associative encounters with prolonged residence times relative to that in bulk solution. We find that the initial interaction of p53 with MDM2 is long-lived relative to nutlin, but, unlike nutlin, it takes place at the N- and C termini of the MDM2 protein, away from the binding site, suggestive of an allosteric mechanism of action. In contrast, nutlin initially interacts with MDM2 directly at the clefts of the binding site. The interaction of nutlin with MDMX, however, is very short-lived compared with MDM2 and does not show such direct initial interactions with the binding site. Comparison of the topology of the electrostatic potentials of MDM2 and MDMX and the locations of the initial encounters with p53/nutlin in tandem with structure-based sequence alignment revealed that the origin of the diminished activity of nutlin toward MDMX relative to MDM2 may stem partly from the differing topologies of the electrostatic potentials of the two proteins. Glu25 and Lys51 residues underpin these topological differences and appear to collectively play a key role in channelling nutlin directly toward the binding site on the MDM2 surface and are absent in MDMX. The results, therefore, provide new insight into the mechanism of p53/nutlin interactions with MDM2 and MDMX and could potentially have a broader impact on anticancer drug optimization strategies.