Protein conformational flexibility modulates kinetics and thermodynamics of drug binding

Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction

Time-lagged independent component analysis (tICA) and the Markov state model (MSM) have been extensively employed for extracting conformational dynamics and kinetic community networks from unbiased trajectory ensembles. However, these techniques may not be the optimal choice for elucidating transition mechanisms within low-dimensional representations, especially for intricate biosystems. Unraveling the association mechanism in such complex systems always necessitates permutations of several essential independent components or collective variables, a process that is inherently obscure and may require empirical knowledge for selection. To address these challenges, we have implemented an integrated unsupervised dimension reduction model: uniform manifold approximation and projection (UMAP) with hierarchy density-based spatial clustering of applications with noise (HDBSCAN). This approach effectively generates low-dimensional configurational embeddings. The hierarchical application of this architecture, in conjunction with MSM, reveals global kinetic connectivity while identifying local conformational states. Consequently, our methodology establishes a multiscale mechanistic elucidation framework. Leveraging the benefits of the uniform sample distribution and a denoising approach, our model demonstrates robustness in preserving global and local data structures compared to traditional dimension reduction methods in the field of MD analysis area. The interpretability of hyperparameter selection and compatibility with downstream tasks are cross-validated across various simulation data sets, utilizing both computational evaluation metrics and experimental kinetic observables. Furthermore, the predicted Mcl1-BH3 association kinetics (0.76 s-1) is in close agreement with surface plasmon resonance experiments (0.12 s-1), affirming the plausibility of the identified pathway composed of representative conformations. We anticipate that the devised workflow will serve as a foundational framework for studying recognition patterns in complex biological systems. Its contributions extend to the exploration of protein functional dynamics and rational drug design, offering a potent avenue for advancing research in these domains.

Confronting heterogeneity in cryogenic electron microscopy data: Innovative strategies and future perspectives with data-driven methods

The surge in the influx of data from cryogenic electron microscopy (cryo-EM) experiments has intensified the demand for robust algorithms capable of autonomously managing structurally heterogeneous datasets. This presents a wealth of exciting opportunities from a data science viewpoint, inspiring the development of numerous innovative, application-specific methods, many of which leverage contemporary data-driven techniques. However, addressing the challenges posed by heterogeneous datasets remains a paramount yet unresolved issue in the field. Here, we explore the subtleties of this challenge and the array of strategies devised to confront it. We pinpoint the shortcomings of existing methodologies and deliberate on prospective avenues for improvement. Specifically, our discussion focuses on strategies to mitigate model overfitting and manage data noise, as well as the effects of constraints, priors, and invariances on the optimization process.

CDK2 and CDK4: Cell Cycle Functions Evolve Distinct, Catalysis-Competent Conformations, Offering Drug Targets

Cyclin-dependent kinases (CDKs), particularly CDK4 and CDK2, are crucial for cell cycle progression from the Gap 1 (G1) to the Synthesis (S) phase by phosphorylating targets such as the Retinoblastoma Protein (Rb). CDK4, paired with cyclin-D, operates in the long G1 phase, while CDK2 with cyclin-E, manages the brief G1-to-S transition, enabling DNA replication. Aberrant CDK signaling leads to uncontrolled cell proliferation, which is a hallmark of cancer. Exactly how they accomplish their catalytic phosphorylation actions with distinct efficiencies poses the fundamental, albeit overlooked question. Here we combined available experimental data and modeling of the active complexes to establish their conformational functional landscapes to explain how the two cyclin/CDK complexes differentially populate their catalytically competent states for cell cycle progression. Our premise is that CDK catalytic efficiencies could be more important for cell cycle progression than the cyclin-CDK biochemical binding specificity and that efficiency is likely the prime determinant of cell cycle progression. We observe that CDK4 is more dynamic than CDK2 in the ATP binding site, the regulatory spine, and the interaction with its cyclin partner. The N-terminus of cyclin-D acts as an allosteric regulator of the activation loop and the ATP-binding site in CDK4. Integrated with a suite of experimental data, we suggest that the CDK4 complex is less capable of remaining in the active catalytically competent conformation, and may have a lower catalytic efficiency than CDK2, befitting their cell cycle time scales, and point to critical residues and motifs that drive their differences. Our mechanistic landscape may apply broadly to kinases, and we propose two drug design strategies: (i) allosteric Inhibition by conformational stabilization for targeting allosteric CDK4 regulation by cyclin-D, and (ii) dynamic entropy-optimized targeting which leverages the dynamic, entropic aspects of CDK4 to optimize drug binding efficacy.

Direct Interaction of Minocycline to p47phox Contributes to its Attenuation of TNF-α-Mediated Neuronal PC12 Cell Death: Experimental and Simulation Validation

Kaempferol illustrates an example of attempting to discover new treatments for neurodegeneration by investigating the potential efficacy of natural products. Despite the identification of several molecular targets for this bio-active compound, the precise underlying pathways are not well elucidated yet. Recently, it has been shown through pulldown assay that kaemferol directly interacts with p47phox, the organizer subunit of NADPH oxidase-2 (NOX2) complex. Hence, in this study, we used homology modelling, computational docking, mutation analysis, molecular dynamics simulations and free energy calculations to determine how kaempferol interacts with p47phox. Firstly, 3D structure of p47phox was generated using x-ray structures of its domains. Then, it was docked with kaempferol, and finally 100-ns molecular dynamics (MD) simulations were performed and the global properties like root-mean square deviation (RMSD) and root-mean square fluctuations (RMSF) were calculated. Literature survey and computational analysis of key interacting amino acid residues of p47phox provided insights into a possible binding site for kaempferol, approximately around Trp193 and Cys196 located within the N-terminal SH3 domain of p47phox. Moreover, free energy calculations indicated that in silico substitution of Trp193 and Cys196 with arginine and alanine, respectively, results in less favorable interaction corroborating their importance in binding with kaempferol. Taken together, these findings suggest that kaempferol directly attaches to N-SH3 domain p47 phox, with a subsequent diminution of p47phox protein-protein interaction and possibly attenuation of NOX2 complex assembly, which reduces reactive oxygen species (ROS) generation. These observations will be beneficial for researchers exploring neuroprotection and for the development of p47phox inhibitors.

Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional Molecular Dynamics (cMD), due to limited simulation timescales. Based on our previously developed Ligand Gaussian accelerated Molecular Dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3", in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 microsecond simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 agreed with available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligand and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Treatment of flexibility of protein backbone in simulations of protein-ligand interactions using steered molecular dynamics

To ensure that an external force can break the interaction between a protein and a ligand, the steered molecular dynamics simulation requires a harmonic restrained potential applied to the protein backbone. A usual practice is that all or a certain number of protein's heavy atoms or Cα atoms are fixed, being restrained by a small force. This present study reveals that while fixing both either all heavy atoms and or all Cα atoms is not a good approach, while fixing a too small number of few atoms sometimes cannot prevent the protein from rotating under the influence of the bulk water layer, and the pulled molecule may smack into the wall of the active site. We found that restraining the Cα atoms under certain conditions is more relevant. Thus, we would propose an alternative solution in which only the Cα atoms of the protein at a distance larger than 1.2 nm from the ligand are restrained. A more flexible, but not too flexible, protein will be expected to lead to a more natural release of the ligand.

In silico molecular study of hepatitis B virus X protein as a therapeutic target

The Hepatitis B virus is a leading cause of liver cirrhosis and hepatocellular carcinoma. HBx viral protein is considered a contributor to pathogenesis and hepatocarcinogenesis. This study aimed to screen the effect of some antiviral compounds to target HBx protein for inhibition of its function. Here, molecular docking, molcular dynsmic simulation, MM/GBSA and T-SNE methods were applied to study the complex stability and to cluster the conformations that generated in the simulation. Among the 179 compounds screened in this study, three antiviral agents (SC75741, Punicalagin, and Ledipasvir) exhibited the lowest docking energy and best interaction. Among these compounds, SC75741 was identified as a potent inhibitor of HBx that showed the best and most stable interaction during molecular dynamic simulation, and blocking a region near to HBx helix resides (aa 88-100) that is associated with cell invasion. The analysis of relative binding free energy through MM/GBSA for molecular dynamic simulation results revealed binding energy -9.9 kcal/mol for SC75741, -11 kcal/mol for Punicalagin, and -10.1 kcal/mol for Ledipasvir. These results elucidate the possible use of these compounds in the research for targeting HBx.Communicated by Ramaswamy H. Sarma.

Accurate Characterization of Binding Kinetics and Allosteric Mechanisms for the HSP90 Chaperone Inhibitors Using AI-Augmented Integrative Biophysical Studies

The binding kinetics of drugs to their targets are gradually being recognized as a crucial indicator of the efficacy of drugs in vivo, leading to the development of various computational methods for predicting the binding kinetics in recent years. However, compared with the prediction of binding affinity, the underlying structure and dynamic determinants of binding kinetics are more complicated. Efficient and accurate methods for predicting binding kinetics are still lacking. In this study, quantitative structure-kinetics relationship (QSKR) models were developed using 132 inhibitors targeting the ATP binding domain of heat shock protein 90α (HSP90α) to predict the dissociation rate constant (koff), enabling a direct assessment of the drug-target residence time. These models demonstrated good predictive performance, where hydrophobic and hydrogen bond interactions significantly influence the koff prediction. In subsequent applications, our models were used to assist in the discovery of new inhibitors for the N-terminal domain of HSP90α (N-HSP90α), demonstrating predictive capabilities on an experimental validation set with a new scaffold. In X-ray crystallography experiments, the loop-middle conformation of apo N-HSP90α was observed for the first time (previously, the loop-middle conformation had only been observed in holo-N-HSP90α structures). Interestingly, we observed different conformations of apo N-HSP90α simultaneously in an asymmetric unit, which was also observed in a holo-N-HSP90α structure, suggesting an equilibrium of conformations between different states in solution, which could be one of the determinants affecting the binding kinetics of the ligand. Different ligands can undergo conformational selection or alter the equilibrium of conformations, inducing conformational rearrangements and resulting in different effects on binding kinetics. We then used molecular dynamics simulations to describe conformational changes of apo N-HSP90α in different conformational states. In summary, the study of the binding kinetics and molecular mechanisms of N-HSP90α provides valuable information for the development of more targeted therapeutic approaches.

Bioactive compounds from Morchella esculenta as potential inhibitors of RNA-binding protein La in ovarian cancer: a molecular modeling and quantum mechanics approach

La protein is significantly expressed in various malignant tumors, including ovarian cancer (OC), which is related to the poor response to platinum-based chemotherapy. Thus, inhibiting La protein could control the expression of the potential downstream genes involved in promoting proliferation and chemotherapy resistance to OC, which could serve as a therapeutic intervention. Through a molecular docking approach, 12 compounds from Morchella esculenta were screened against the crystal structure of La protein and four hit compounds were identified, including beta-carotene, p-hydroxybenzoic acid, gamma-tocopherol, and alpha-tocopherol, with a binding affinity of - 10.7, - 8.1, - 7.9, and - 7.6 kcal/mol, respectively, higher than pyridine-2-carboxylate (control), with a binding affinity of - 5.2 kcal/mol. To explore the interaction of the hit compounds with the target receptor, they were selected for a molecular dynamic simulation and post-simulation analysis for 100 ns. The result showed promising reliability of the ligands due to a stable interaction with the La protein crystal structure. Furthermore, the drug-likeness and physicochemical chemical properties of the compounds were investigated using ADMET study and density functional theory analysis, respectively, and the result shows that the hit compounds could serve as a promising starting for the development of novel LA protein inhibitors for OC therapeutics. Finally, this study compared HOMO and LUMO values from global hybrids with long-range corrected DFAs, and the result from the two followed the same qualitative pattern while calculating HOMO values; however, MO62X/cc-pVTZ could better predict LUMO values when considering a global hybrid.

Discovery of small molecule entry inhibitors targeting the linoleic acid binding pocket of SARS-CoV-2 spike protein

Spike glycoprotein has a significant role in the entry of SARS-CoV-2 to host cells, which makes it a potential drug target. Continued accumulation of non-synonymous mutations in the receptor binding domain of spike protein poses great challenges in identifying antiviral drugs targeting this protein. This study aims to identify potential entry inhibitors of SARS-CoV-2 using virtual screening and molecular dynamics (MD) simulations from three distinct chemical libraries including Pandemic Response Box, Drugbank and DrugCentral, comprising 6971 small molecules. The molecules were screened against a binding pocket identified in the receptor-binding domain (RBD) region of the spike protein which is known as the linoleic acid binding pocket, a highly conserved motif among several SARS-CoV-2 variants. Through virtual screening and binding free energy calculations, we identified four top-scoring compounds, MMV1579787 ([2-Oxo-2-[2-(3-phenoxyphenyl)ethylamino]ethyl]phosphonic acid), Tretinoin, MMV1633963 ((2E,4E)-5-[3-(3,5-dichlorophenoxy)phenyl]penta-2,4-dienoic acid) and Polydatin, which were previously reported to have antibacterial, antifungal or antiviral properties. These molecules showed stable binding on MD simulations over 100 ns and maintained stable interactions with TYR365, PHE338, PHE342, PHE377, TYR369, PHE374 and LEU368 of the spike protein RBD that are found to be conserved among SARS-CoV-2 variants. Our findings were further validated with free energy landscape, principal component analysis and dynamic cross-correlation analysis. Our in silico analysis of binding mode and MD simulation analyses suggest that the identified compounds may impede viral entrance by interacting with the linoleic acid binding site of the spike protein of SARS-CoV-2 regardless of its variants, and they thus demand for further in vitro and in vivo research.Communicated by Ramaswamy H. Sarma.

Chirality Supramolecular Systems: Helical Assemblies, Structure Designs, and Functions

Chirality, as one of the most striking characteristics, exists at various scales in nature. Originating from the interactions of host and guest molecules, supramolecular chirality possesses huge potential in the design of functional materials. Here, an overview of the recent progress in structure designs and functions of chiral supramolecular materials is present. First, three design routes of the chiral supramolecular structure are summarized. Compared with the template-induced and chemical synthesis strategies that depend on accurate molecular identification, the twisted-assembly technique creates chiral materials through the ordered stacking of the nanowire or films. Next, chirality inversion and amplification are reviewed to explain the chirality transfer from the molecular level to the macroscopic scale, where the available external stimuli on the chirality inversion are also given. Lastly, owing to the optical activity and the characteristics of the layer-by-layer stacking structure, the supramolecular chirality materials display various excellent performances, including smart response, shape-memorization, superior mechanical performance, and applications in biomedical fields. To sum up, this work provides a systematic review of the helical assemblies, structure design, and applications of supramolecular chirality systems.

Identification and mechanistic exploration of structural and conformational dynamics of NF-kB inhibitors: rationale insights from in silico and in vitro studies

Increased expression of target genes that code for proinflammatory chemical mediators results from a series of intracellular cascades triggered by activation of dysregulated NF-κB signaling pathway. Dysfunctional NF-kB signaling amplifies and perpetuates autoimmune responses in inflammatory diseases, including psoriasis. This study aimed to identify therapeutically relevant NF-kB inhibitors and elucidate the mechanistic aspects behind NF-kB inhibition. After virtual screening and molecular docking, five hit NF-kB inhibitors opted, and their therapeutic efficacy was examined using cell-based assays in TNF-α stimulated human keratinocyte cells. To investigate the conformational changes of target protein and inhibitor-protein interaction mechanisms, molecular dynamics (MD) simulations, binding free energy calculations together with principal component (PC) analysis, dynamics cross-correlation matrix analysis (DCCM), free energy landscape (FEL) analysis and quantum mechanical calculations were carried out. Among identified NF-kB inhibitors, myricetin and hesperidin significantly scavenged intracellular ROS and inhibited NF-kB activation. Analysis of the MD simulation trajectories of ligand-protein complexes revealed that myricetin and hesperidin formed energetically stabilized complexes with the target protein and were able to lock NF-kB in a closed conformation. Myricetin and hesperidin binding to the target protein significantly impacted conformational changes and internal dynamics of amino acid residues in protein domains. Tyr57, Glu60, Lys144 and Asp239 residues majorly contributed to locking the NF-kB in a closed conformation. The combinatorial approach employing in silico tools integrated with cell-based approaches substantiated the binding mechanism and NF-kB active site inhibition by the lead molecule myricetin, which can be explored as a viable antipsoriatic drug candidate associated with dysregulated NF-kB.Communicated by Ramaswamy H. Sarma.

Thermodynamically Optimized Machine-Learned Reaction Coordinates for Hydrophobic Ligand Dissociation

Ligand unbinding is mediated by its free energy change, which has intertwined contributions from both energy and entropy. It is important, but not easy, to quantify their individual contributions to the free energy profile. We model hydrophobic ligand unbinding for two systems, a methane particle and a C60 fullerene, both unbinding from hydrophobic pockets in all-atom water. Using a modified deep learning framework, we learn a thermodynamically optimized reaction coordinate to describe the hydrophobic ligand dissociation for both systems. Interpretation of these reaction coordinates reveals the roles of entropic and enthalpic forces as the ligand and pocket sizes change. In both cases, we observe that the free-energy barrier to unbinding is dominated by entropy considerations. Furthermore, the process of methane unbinding is driven by methane solvation, while fullerene unbinding is driven first by pocket wetting and then fullerene wetting. For both solutes, the direct importance of the distance from the binding pocket to the learned reaction coordinate is present, but low. Our framework and subsequent feature important analysis thus give useful thermodynamic insight into hydrophobic ligand dissociation problems that are otherwise difficult to glean.

Revealing the Role of Noncovalent Interactions on the Conformation of the Methyl Group in Tricyclic Orthoamide

Tricyclic orthoamides are valuable molecules with wide-ranging applications, including organic synthesis and molecular recognition. Their structural properties make them intriguing, particularly the eclipsed all-trans conformer, which is typically less stable than the alternated conformation and is a rare phenomenon in organic chemistry. However, it gains stability in crystalline and hydrated settings, challenging the existing theoretical explanations. This study investigates which factors make eclipsed conformers more stable using experimentally reported anhydrous (ATO) and hydrated (HTO) crystal structures. Employing the quantum theory of atoms in molecules, noncovalent interaction index, and pairwise energy decomposition analysis, we delve into the noncovalent interaction environment surrounding the molecule of interest. In ATO, dispersive interactions dominate, whereas in HTO, both dispersive and electrostatic contributions are observed due to the presence of water molecules. Anchored to the lone pairs of the nitrogen atom in the orthoamide tricycle, water molecules prompt the methyl group's eclipsing through intermolecular and intramolecular interactions. This work resolves the long-standing conflict behind why tricyclic orthoamide has an eclipsed conformation by establishing the stabilization factors. These insights have implications for crystal engineering and design, enhancing our understanding of structural behavior in both crystalline and hydrated environments.

Induced Circular Dichroism Analysis of Thermally Induced Conformational Changes on Protein Binding Sites Under a Crowding Environment

Protein-ligand interactions in crowded cellular environments play a crucial role in biological functions. The crowded environment can perturb the overall protein structure and local conformation, thereby influencing the binding pathway of protein-ligand reactions within the cellular milieu. Therefore, a detailed understanding of the local conformation is crucial for elucidating the intricacies of protein-ligand interactions in crowded cellular environments. In this study, we investigated the feasibility of induced circular dichroism (ICD) using 8-anilinonaphthalene-1-sulfonic acid (ANS) for local conformational analysis at the binding site in a crowding environment. Bovine serum albumin (BSA) concentration-dependent measurements were performed to assess the feasibility of ANS-ICD for analyzing protein interior binding sites. The results showed distinct changes in the ANS-ICD spectra of BSA solutions, indicating their potential for analyzing the internal conformation of proteins. Moreover, temperature-dependent measurements were performed in dilute and crowding environments, revealing distinct denaturation pathways of BSA binding sites. Principal component analysis of ANS-ICD spectral changes revealed lower temperature pre-denaturation in the crowded solution than that in the diluted solution, suggesting destabilization of binding sites owing to self-crowding repulsive interactions. The established ANS-ICD method can provide valuable conformational insights into protein-ligand interactions in crowded cellular environments.

Identification and functional validation of FZD8-specific antibodies

The Wnt pathway is an evolutionarily conserved pathway involved in stem cell homeostasis and tissue regeneration. Aberrant signaling in the Wnt pathway is highly associated with cancer. Developing antibodies to block overactivation of Frizzled receptors (FZDs), the main receptors in the Wnt pathway, is one of the viable options for treating cancer. However, obtaining isoform-specific antibodies is often challenging due to the high degree of homology among the ten FZDs. In this study, by using a synthetic library, we identified an antibody named pF8_AC3 that preferentially binds to FZD8. Guided by the structure of the complex of pF8_AC3 and FZD8, a second-generation targeted library was further constructed, and finally, the FZD8-specific antibody sF8_AG6 was obtained. Cell-based assays showed that these antibodies could selectively block FZD8-mediated signaling activation. Taken together, these antibodies have the potential to be developed into therapeutic drugs in the future.

QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations

Understanding the interaction of ligands with biomolecules is an integral component of drug discovery and development. Challenges for computing thermodynamic and kinetic quantities for pharmaceutically relevant receptor-ligand complexes include the size and flexibility of the ligands, large-scale conformational rearrangements of the receptor, accurate force field parameters, simulation efficiency, and sufficient sampling associated with rare events. Our recently developed multiscale milestoning simulation approach, SEEKR2 (Simulation Enabled Estimation of Kinetic Rates v.2), has demonstrated success in predicting unbinding (koff) kinetics by employing molecular dynamics (MD) simulations in regions closer to the binding site. The MD region is further subdivided into smaller Voronoi tessellations to improve the simulation efficiency and parallelization. To date, all MD simulations are run using general molecular mechanics (MM) force fields. The accuracy of calculations can be further improved by incorporating quantum mechanical (QM) methods into generating system-specific force fields through reparameterizing ligand partial charges in the bound state. The force field reparameterization process modifies the potential energy landscape of the bimolecular complex, enabling a more accurate representation of the intermolecular interactions and polarization effects at the bound state. We present QMrebind (Quantum Mechanical force field reparameterization at the receptor-ligand binding site), an ORCA-based software that facilitates reparameterizing the potential energy function within the phase space representing the bound state in a receptor-ligand complex. With SEEKR2 koff estimates and experimentally determined kinetic rates, we compare and interpret the receptor-ligand unbinding kinetics obtained using the newly reparameterized force fields for model host-guest systems and HSP90-inhibitor complexes. This method provides an opportunity to achieve higher accuracy in predicting receptor-ligand koff rate constants.

Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 Mpro

Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.

Tuning sterol extraction kinetics yields a renal-sparing polyene antifungal

Decades of previous efforts to develop renal-sparing polyene antifungals were misguided by the classic membrane permeabilization model1. Recently, the clinically vital but also highly renal-toxic small-molecule natural product amphotericin B was instead found to kill fungi primarily by forming extramembraneous sponge-like aggregates that extract ergosterol from lipid bilayers2-6. Here we show that rapid and selective extraction of fungal ergosterol can yield potent and renal-sparing polyene antifungals. Cholesterol extraction was found to drive the toxicity of amphotericin B to human renal cells. Our examination of high-resolution structures of amphotericin B sponges in sterol-free and sterol-bound states guided us to a promising structural derivative that does not bind cholesterol and is thus renal sparing. This derivative was also less potent because it extracts ergosterol more slowly. Selective acceleration of ergosterol extraction with a second structural modification yielded a new polyene, AM-2-19, that is renal sparing in mice and primary human renal cells, potent against hundreds of pathogenic fungal strains, resistance evasive following serial passage in vitro and highly efficacious in animal models of invasive fungal infections. Thus, rational tuning of the dynamics of interactions between small molecules may lead to better treatments for fungal infections that still kill millions of people annually7,8 and potentially other resistance-evasive antimicrobials, including those that have recently been shown to operate through supramolecular structures that target specific lipids9.

Simulation of the ligand-leaving process of the human heat shock protein

The human heat shock protein plays a critical role in various diseases and is an important target for pharmacological modulation. Simulation of conformational changes and free energy profiles of the human heat shock protein derived by the ligand-leaving process is a challenging issue. In this work, steered molecular dynamics simulation was adopted to simulate the ligand-leaving process. Two composite systems of heat shock protein NHSP90 and small molecules 6FJ and 6G7 are selected as research objects. The free energy during the leaving of ligand small molecules is calculated using conventional molecular dynamics simulation, steered molecular dynamics simulation (SMD), and the umbrella sampling method. We found that the a slower pulling velocity (0.001 nm ns-1) will result in 2.19 kcal mol-1, and the umbrella sampling method gives a value of 3.26 kcal mol-1 for the free energy difference for the two systems, which reasonably agrees with experimental results. A faster-pulling velocity (0.01 nm ns-1) leads to a large overestimation of free energy. At the same time, the conformational analysis indicated that the faster pulling velocity may lead to the conformational change of NHSP90, which was proved to be false by the slower pulling velocity and the umbrella sampling method.

Recognition and coacervation of G-quadruplexes by a multifunctional disordered region in RECQ4 helicase

Biomolecular polyelectrolyte complexes can be formed between oppositely charged intrinsically disordered regions (IDRs) of proteins or between IDRs and nucleic acids. Highly charged IDRs are abundant in the nucleus, yet few have been functionally characterized. Here, we show that a positively charged IDR within the human ATP-dependent DNA helicase Q4 (RECQ4) forms coacervates with G-quadruplexes (G4s). We describe a three-step model of charge-driven coacervation by integrating equilibrium and kinetic binding data in a global numerical model. The oppositely charged IDR and G4 molecules form a complex in the solution that follows a rapid nucleation-growth mechanism leading to a dynamic equilibrium between dilute and condensed phases. We also discover a physical interaction with Replication Protein A (RPA) and demonstrate that the IDR can switch between the two extremes of the structural continuum of complexes. The structural, kinetic, and thermodynamic profile of its interactions revealed a dynamic disordered complex with nucleic acids and a static ordered complex with RPA protein. The two mutually exclusive binding modes suggest a regulatory role for the IDR in RECQ4 function by enabling molecular handoffs. Our study extends the functional repertoire of IDRs and demonstrates a role of polyelectrolyte complexes involved in G4 binding.

Conformational Switch of the 250s Loop Enables the Efficient Transglycosylation in GH Family 77

Amylomaltases (AMs) play important roles in glycogen and maltose metabolism. However, the molecular mechanism is elusive. Here, we investigated the conformational dynamics of the 250s loop and catalytic mechanism of Thermus aquaticus TaAM using path-metadynamics and QM/MM MD simulations. The results demonstrate that the transition of the 250s loop from an open to closed conformation promotes polysaccharide sliding, leading to the ideal positioning of the acid/base. Furthermore, the conformational dynamics can also modulate the selectivity of hydrolysis and transglycosylation. The closed conformation of the 250s loop enables the tight packing of the active site for transglycosylation, reducing the energy penalty and efficiently preventing the penetration of water into the active site. Conversely, the partially closed conformation for hydrolysis results in a loosely packed active site, destabilizing the transition state. These computational findings guide mutation experiments and enable the identification of mutants with an improved disproportionation/hydrolysis ratio. The present mechanism is in line with experimental data, highlighting the critical role of conformational dynamics in regulating the catalytic reactivity of GHs.

Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Cross-Linked α-Synuclein as Inhibitor of Amyloid Formation

The aggregation and amyloid formation of α-synuclein is associated with Parkinson's disease and other synucleinopathies. In its native, monomeric form α-synuclein is an intrinsically disordered protein represented by highly dynamic conformational ensembles. Inhibition of α-synuclein aggregation using small molecules, peptides, or proteins has been at the center of interest in recent years. Our aim was to explore the effects of cross-linking on the structure and aggregation/amyloid formation properties of α-synuclein. Comparative analysis of available high-resolution amyloid structures and representative structural models and MD trajectory of monomeric α-synuclein revealed that potential cross-links in the monomeric protein are mostly incompatible with the amyloid forms and thus might inhibit fibrillation. Monomeric α-synuclein has been intramolecularly chemically cross-linked under various conditions using different cross-linkers. We determined the location of cross-links and their frequency using mass spectrometry and found that most of them cannot be realized in the amyloid structures. The inhibitory potential of cross-linked proteins has been experimentally investigated using various methods, including thioflavin-T fluorescence and transmission electron microscopy. We found that conformational constraints applied by cross-linking fully blocked α-synuclein amyloid formation. Moreover, DTSSP-cross-linked molecules exhibited an inhibitory effect on the aggregation of unmodified α-synuclein as well.

Capsaicin binds the N-terminus of Hsp90, induces lysosomal degradation of Hsp70, and enhances the anti-tumor effects of 17-AAG (Tanespimycin)

Heat shock protein 90 (Hsp90) and its co-chaperones promote cancer, and targeting Hsp90 holds promise for cancer treatment. Most of the efforts to harness this potential have focused on targeting the Hsp90 N-terminus ATP binding site. Although newer-generation inhibitors have shown improved efficacy in aggressive cancers, induction of the cellular heat shock response (HSR) by these inhibitors is thought to limit their clinical efficacy. Therefore, Hsp90 inhibitors with novel mechanisms of action and that do not trigger the HSR would be advantageous. Here, we investigated the mechanism by which capsaicin inhibits Hsp90. Through mutagenesis, chemical modifications, and proteomic studies, we show that capsaicin binds to the N-terminus of Hsp90 and inhibits its ATPase activity. Consequently, capsaicin and its analogs inhibit Hsp90 ATPase-dependent progesterone receptor reconstitution in vitro. Capsaicin did not induce the HSR, instead, it promoted the degradation of Hsp70 through the lysosome-autophagy pathway. Remarkably, capsaicin did not induce degradation of the constitutively expressed cognate Hsc70, indicating selectivity for Hsp70. Combined treatments of capsaicin and the Hsp90 inhibitor 17-AAG improved the anti-tumor efficacy of 17-AAG in cell culture and tridimensional tumor spheroid growth assays using breast and prostate cancer models. Consistent with this, in silico docking studies revealed that capsaicin binding to the ATP binding site of Hsp90 was distinct from classical N-terminus Hsp90 inhibitors, indicating a novel mechanism of action. Collectively, these findings support the use of capsaicin as a chemical scaffold to develop novel Hsp90 N-terminus inhibitors as well as its ability to be a potential cancer co-therapeutic.

Molecular Dynamics Simulations of the Human Ecto-5'-Nucleotidase (h-ecto-5'-NT, CD73): Insights into Protein Flexibility and Binding Site Dynamics

Human ecto-5'-nucleotidase (h-ecto-5'-NT, CD73) is a homodimeric Zn2+-binding metallophosphoesterase that hydrolyzes adenosine 5'-monophosphate (5'-AMP) to adenosine and phosphate. h-Ecto-5'-NT is a key enzyme in purinergic signaling pathways and has been recognized as a promising biological target for several diseases, including cancer and inflammatory, infectious, and autoimmune diseases. Despite its importance as a biological target, little is known about h-ecto-5'-NT dynamics, which poses a considerable challenge to the design of inhibitors of this target enzyme. Here, to explore h-ecto-5'-NT flexibility, all-atom unbiased molecular dynamics (MD) simulations were performed. Remarkable differences in the dynamics of the open (catalytically inactive) and closed (catalytically active) conformations of the apo-h-ecto-5'-NT were observed during the simulations, and the nucleotide analogue inhibitor AMPCP was shown to stabilize the protein structure in the closed conformation. Our results suggest that the large and complex domain motion that enables the h-ecto-5'-NT open/closed conformational switch is slow, and therefore, it could not be completely captured within the time scale of our simulations. Nonetheless, we were able to explore the faster dynamics of the h-ecto-5'-NT substrate binding site, which is mainly located at the C-terminal domain and well conserved among the protein's open and closed conformations. Using the TRAPP ("Transient Pockets in Proteins") approach, we identified transient subpockets close to the substrate binding site. Finally, conformational states of the substrate binding site with higher druggability scores than the crystal structure were identified. In summary, our study provides valuable insights into h-ecto-5'-NT structural flexibility, which can guide the structure-based design of novel h-ecto-5'-NT inhibitors.

Binding Mechanism of Inhibitors to Heat Shock Protein 90 Investigated by Multiple Independent Molecular Dynamics Simulations and Prediction of Binding Free Energy

The heat shock protein (HSP90) has been an import target of drug design in the treatment of human disease. An exploration of the conformational changes in HSP90 can provide useful information for the development of efficient inhibitors targeting HSP90. In this work, multiple independent all-atom molecular dynamics (AAMD) simulations followed by calculations of the molecular mechanics generalized Born surface area (MM-GBSA) were performed to explore the binding mechanism of three inhibitors (W8Y, W8V, and W8S) to HSP90. The dynamics analyses verified that the presence of inhibitors impacts the structural flexibility, correlated movements, and dynamics behavior of HSP90. The results of the MM-GBSA calculations suggest that the selection of GB models and empirical parameters has important influences on the predicted results and verify that van der Waals interactions are the main forces that determine inhibitor-HSP90 binding. The contributions of separate residues to the inhibitor-HSP90 binding process indicate that hydrogen-bonding interactions (HBIs) and hydrophobic interactions play important roles in HSP90-inhibitor identifications. Moreover, residues L34, N37, D40, A41, D79, I82, G83, M84, F124, and T171 are recognized as hot spots of inhibitor-HSP90 binding and provide significant target sites of for the design of drugs related to HSP90. This study aims to contribute to the development of efficient inhibitors that target HSP90 by providing an energy-based and theoretical foundation.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Structure-Kinetic Relationship for Drug Design Revealed by a PLS Model with Retrosynthesis-Based Pre-Trained Molecular Representation and Molecular Dynamics Simulation

Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (k_off) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 and obtained the protein-ligand interaction fingerprints (IFPs) on their dissociation pathways and their influencing weights on the k_off value. We observed a high correlation among the simulated, predicted, and experimental -log(k_off) values. Combining ML, molecular dynamics (MD) simulation, and IFPs derived from accelerated MD helps design a drug for specific kinetic properties and selectivity profiles to the target of interest. To further validate our k_off predictive ML model, we tested our model on two new N-HSP90 inhibitors, which have experimental k_off values and are not in our ML training dataset. The predicted k_off values are consistent with experimental data, and the mechanism of their kinetic properties can be explained by IFPs, which shed light on the nature of their selectivity against N-HSP90 protein. We believe that the ML model described here is transferable to predict k_off of other proteins and will enhance the kinetics-based drug design endeavor.

Worth the weight: Sub-Pocket EXplorer (SubPEx), a weighted-ensemble method to enhance binding-pocket conformational sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Structural Manipulations of Marine Natural Products Inspire a New Library of 3-Amino-1,2,4-Triazine PDK Inhibitors Endowed with Antitumor Activity in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the main aggressive types of cancer, characterized by late prognosis and drug resistance. Among the main factors sustaining PDAC progression, the alteration of cell metabolism has emerged to have a key role in PDAC cell proliferation, invasion, and resistance to standard chemotherapeutic agents. Taking into account all these factors and the urgency in evaluating novel options to treat PDAC, in the present work we reported the synthesis of a new series of indolyl-7-azaindolyl triazine compounds inspired by marine bis-indolyl alkaloids. We first assessed the ability of the new triazine compounds to inhibit the enzymatic activity of pyruvate dehydrogenase kinases (PDKs). The results showed that most of derivatives totally inhibit PDK1 and PDK4. Molecular docking analysis was executed to predict the possible binding mode of these derivatives using ligand-based homology modeling technique. Evaluation of the capability of new triazines to inhibit the cell growth in 2D and 3D KRAS-wild-type (BxPC-3) and KRAS-mutant (PSN-1) PDAC cell line, was carried out. The results showed the capacity of the new derivatives to reduce cell growth with a major selectivity against KRAS-mutant PDAC PSN-1 on both cell models. These data demonstrated that the new triazine derivatives target PDK1 enzymatic activity and exhibit cytotoxic effects on 2D and 3D PDAC cell models, thus encouraging further structure manipulation for analogs development against PDAC.

Predicting Protein-Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges

The prediction of drug-target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.

Structural insights into SARS-CoV-2 main protease conformational plasticity

The spread of different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants underscores the need for insights into the structural properties of its structural and non-structural proteins. The highly conserved homo-dimeric chymotrypsin-like protease (3CL M^PRO ), belonging to the class of cysteine hydrolases, plays an indispensable role in processing viral polyproteins that are involved in viral replication and transcription. Studies have successfully demonstrated the role of M^PRO as an attractive drug target for designing antiviral treatments because of its importance in the viral life cycle. Herein, we report the structural dynamics of six experimentally solved structures of M^PRO (i.e., 6LU7, 6M03, 6WQF, 6Y2E, 6Y84, and 7BUY including both ligand-free and ligand-bound states) at different resolutions. We have employed a structure-based balanced forcefield, CHARMM36m through state-of-the-art all-atoms molecular dynamics simulations at µ-seconds scale at room temperature (303K) and pH 7.0 to explore their structure-function relationship. The helical domain-III responsible for dimerization mostly contributes to the altered conformational states and destabilization of M^PRO . A keen observation of the high degree of flexibility in the P5 binding pocket adjoining domain II-III highlights the reason for observation of conformational heterogeneity among the structural ensembles of M^PRO . We also observe a differential dynamics of the catalytic pocket residues His41, Cys145, and Asp187, which may lead to catalytic impairment of the monomeric proteases. Among the highly populated conformational states of the six systems, 6LU7 and 7M03 forms the most stable and compact M^PRO conformation with intact catalytic site and structural integrity. Altogether, our findings from this extensive study provides a benchmark to identify physiologically relevant structures of such promising drug targets for structure-based drug design and discovery of potent drug-like compounds having clinical potential.

Path separation of dissipation-corrected targeted molecular dynamics simulations of protein-ligand unbinding

Protein-ligand (un)binding simulations are a recent focus of biased molecular dynamics simulations. Such binding and unbinding can occur via different pathways in and out of a binding site. Here, we present a theoretical framework on how to compute kinetics along separate paths and on how to combine the path-specific rates into global binding and unbinding rates for comparison with experimental results. Using dissipation-corrected targeted molecular dynamics in combination with temperature-boosted Langevin equation simulations [S. Wolf et al., Nat. Commun. 11, 2918 (2020)] applied to a two-dimensional model and the trypsin-benzamidine complex as test systems, we assess the robustness of the procedure and discuss the aspects of its practical applicability to predict multisecond kinetics of complex biomolecular systems.

Accelerating Cryptic Pocket Discovery Using AlphaFold

Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket openings are often beyond the reach of conventional biomolecular simulations because certain cryptic pocket openings involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate cryptic pocket discovery either by generating structures with open pockets directly or generating structures with partially open pockets that can be used as starting points for simulations. We use AlphaFold to generate ensembles for 10 known cryptic pocket examples, including five that were deposited after AlphaFold's training data were extracted from the PDB. We find that in 6 out of 10 cases AlphaFold samples the open state. For plasmepsin II, an aspartic protease from the causative agent of malaria, AlphaFold only captures a partial pocket opening. As a result, we ran simulations from an ensemble of AlphaFold-generated structures and show that this strategy samples cryptic pocket opening, even though an equivalent amount of simulations launched from a ligand-free experimental structure fails to do so. Markov state models (MSMs) constructed from the AlphaFold-seeded simulations quickly yield a free energy landscape of cryptic pocket opening that is in good agreement with the same landscape generated with well-tempered metadynamics. Taken together, our results demonstrate that AlphaFold has a useful role to play in cryptic pocket discovery but that many cryptic pockets may remain difficult to sample using AlphaFold alone.

Analysis of the structural dynamics of the mutations in the kinase domain of PINK1 protein associated with Parkinson's disease

Parkinson's disease (PD) is a very common neurodegenerative disorder and is considered to be one of the most severe disorders worldwide. Mutations in some PD causing genes are responsible for the early onset of the disease. Pathogenic variants in parkin, PINK1 and DJ1 genes can cause early-onset of PD. Many PINK1 gene mutations have been reported, but not all variants are pathogenic. The gene product of PINK1, also known as PINK1 protein, has 581 amino acid residues in it. Several different mutations are present throughout the kinase domain of PINK1 protein. In this work, we used in silico approaches to analyze the different types of mutations that are distributed in the kinase domain of the PINK1 protein. Based on our results, we categorized the mutations as high, moderate and low pathogenic variants. Furthermore, we performed molecular dynamics simulations of the pathogenic PINK1 variants to decipher their possible impacts on the structure and made a comparison with the wild type PINK1. In conclusion, we suggested the possible mechanistic roles of the pathogenic variants of PINK1 kinase domain that can affect its function. These pathogenic variants are the causative agents of early onset of PD called autosomal recessive Parkinson disease.

1,2,4-Amino-triazine derivatives as pyruvate dehydrogenase kinase inhibitors: Synthesis and pharmacological evaluation

Among the different hallmarks of cancer, deregulation of cellular metabolism turned out to be an essential mechanism in promoting cancer resistance and progression. The pyruvate dehydrogenase kinases (PDKs) are well known as key regulators in cells metabolic process and their activity was found to be overexpressed in different metabolic alerted types of cancer, including the high aggressive pancreatic ductal adenocarcinoma (PDAC). To date few PDK inhibitors have been reported, and the different molecules developed are characterized by structural chemical diversity. In an attempt to find novel classes of potential PDK inhibitors, the molecular hybridization approach, which combine two or more active scaffolds in a single structure, was employed. Herein we report the synthesis and the pharmacological evaluation of the novel hybrid molecules, characterized by the fusion of three different pharmacophoric sub-units such as 1,2,4-amino triazines, 7-azaindoles and indoles, in a single structure. The synthesized derivatives demonstrated a promising ability in hampering the enzymatic activity of PDK1 and 4, further confirmed by docking studies. Interestingly, these derivatives retained a strong antiproliferative activity against pancreatic cancer cells either in 2D and 3D models. Mechanistic studies in highly aggressive PDAC cells confirmed their ability to hamper PDK axis and to induce cancer cell death by apoptosis. Moreover, in vivo translational studies in a murine syngeneic solid tumor model confirmed the ability of the most representative compounds to target the PDK system and highlight the ability to reduce the tumor growth without inducing substantial body weight changes in the treated mice.

Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket

Ligand binding thermodynamics and kinetics are critical parameters for drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics from molecular simulations due to limited simulation timescales. Protein dynamics, especially in the ligand binding pocket, often plays an important role in ligand binding. Based on our previously developed Ligand Gaussian accelerated molecular dynamics (LiGaMD), here we present LiGaMD2 in which a selective boost potential was applied to both the ligand and protein residues in the binding pocket to improve sampling of ligand binding and dissociation. To validate the performance of LiGaMD2, the T4 lysozyme (T4L) mutants with open and closed pockets bound by different ligands were chosen as model systems. LiGaMD2 could efficiently capture repetitive ligand dissociation and binding within microsecond simulations of all T4L systems. The obtained ligand binding kinetic rates and free energies agreed well with available experimental values and previous modeling results. Therefore, LiGaMD2 provides an improved approach to sample opening of closed protein pockets for ligand dissociation and binding, thereby allowing for efficient calculations of ligand binding thermodynamics and kinetics.

Discovery of the 3-Amino-1,2,4-triazine-Based Library as Selective PDK1 Inhibitors with Therapeutic Potential in Highly Aggressive Pancreatic Ductal Adenocarcinoma

Pyruvate dehydrogenase kinases (PDKs) are serine/threonine kinases, that are directly involved in altered cancer cell metabolism, resulting in cancer aggressiveness and resistance. Dichloroacetic acid (DCA) is the first PDK inhibitor that has entered phase II clinical; however, several side effects associated with weak anticancer activity and excessive drug dose (100 mg/kg) have led to its limitation in clinical application. Building upon a molecular hybridization approach, a small library of 3-amino-1,2,4-triazine derivatives has been designed, synthesized, and characterized for their PDK inhibitory activity using in silico, in vitro, and in vivo assays. Biochemical screenings showed that all synthesized compounds are potent and subtype-selective inhibitors of PDK. Accordingly, molecular modeling studies revealed that a lot of ligands can be properly placed inside the ATP-binding site of PDK1. Interestingly, 2D and 3D cell studies revealed their ability to induce cancer cell death at low micromolar doses, being extremely effective against human pancreatic KRAS mutated cancer cells. Cellular mechanistic studies confirm their ability to hamper the PDK/PDH axis, thus leading to metabolic/redox cellular impairment, and to ultimately trigger apoptotic cancer cell death. Remarkably, preliminary in vivo studies performed on a highly aggressive and metastatic Kras-mutant solid tumor model confirm the ability of the most representative compound 5i to target the PDH/PDK axis in vivo and highlighted its equal efficacy and better tolerability profile with respect to those elicited by the reference FDA approved drugs, cisplatin and gemcitabine. Collectively, the data highlights the promising anticancer potential of these novel PDK-targeting derivatives toward obtaining clinical candidates for combatting highly aggressive KRAS-mutant pancreatic ductal adenocarcinomas.

Predicting the effects of rare genetic variants on oncogenic signaling pathways: A computational analysis of HRAS protein function

The HRAS gene plays a crucial role in regulating essential cellular processes for life, and this gene's misregulation is linked to the development of various types of cancers. Nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of HRAS can cause detrimental mutations that disrupt wild-type protein function. In the current investigation, we have employed in-silico methodologies to anticipate the consequences of infrequent genetic variations on the functional properties of the HRAS protein. We have discovered a total of 50 nsSNPs, of which 23 were located in the exon region of the HRAS gene and denoting that they were expected to cause harm or be deleterious. Out of these 23, 10 nsSNPs ([G60V], [G60D], [R123P], [D38H], [I46T], [G115R], [R123G], [P11OL], [A59L], and [G13R]) were identified as having the most delterious effect based on results of SIFT analysis and PolyPhen2 scores ranging from 0.53 to 69. The DDG values -3.21 kcal/mol to 0.87 kcal/mol represent the free energy change associated with protein stability upon mutation. Interestingly, we identified that the three mutations (Y4C, T58I, and Y12E) were found to improve the structural stability of the protein. We performed molecular dynamics (MD) simulations to investigate the structural and dynamic effects of HRAS mutations. Our results showed that the stable model of HRAS had a significantly lower energy value of -18756 kj/mol compared to the initial model of -108915 kj/mol. The RMSD value for the wild-type complex was 4.40 Å, and the binding energies for the G60V, G60D, and D38H mutants were -107.09 kcal/mol, -109.42 kcal/mol, and -107.18 kcal/mol, respectively as compared to wild-type HRAS protein had -105.85 kcal/mol. The result of our investigation presents convincing corroboration for the potential functional significance of nsSNPs in augmenting HRAS expression and adding to the activation of malignant oncogenic signalling pathways.

Recognition Mechanisms between a Nanobody and Disordered Epitopes of the Human Prion Protein: An Integrative Molecular Dynamics Study

Immunotherapy using antibodies to target the aggregation of flexible proteins holds promise for therapeutic interventions in neurodegenerative diseases caused by protein misfolding. Prions or PrP^Sc, the causal agents of transmissible spongiform encephalopathies (TSE), represent a model target for immunotherapies as TSE are prototypical protein misfolding diseases. The X-ray crystal structure of the wild-type (WT) human prion protein (HuPrP) bound to a camelid antibody fragment, denoted as Nanobody 484 (Nb484), has been previously solved. Nb484 was found to inhibit prion aggregation in vitro through a unique mechanism of structural stabilization of two disordered epitopes, that is, the palindromic motif (residues 113-120) and the β2-α2 loop region (residues 164-185). The study of the structural basis for antibody recognition of flexible proteins requires appropriate sampling techniques for the identification of conformational states occurring in disordered epitopes. To elucidate the Nb484-HuPrP recognition mechanisms, here we applied molecular dynamics (MD) simulations complemented with available NMR and X-ray crystallography data collected on the WT HuPrP to describe the conformational spaces occurring on HuPrP prior to Nb484 binding. We observe the experimentally determined binding competent conformations within the ensembles of pre-existing conformational states in solution before binding. We also described the Nb484 recognition mechanisms in two HuPrP carrying a polymorphism (E219K) and a TSE-causing mutation (V210I). Our hybrid approaches allow the identification of dynamic conformational landscapes existing on HuPrP and highly characterized by molecular disorder to identify physiologically relevant and druggable transitions.

In silico identification of potential Hsp90 inhibitors via ensemble docking, DFT and molecular dynamics simulations

The molecular chaperone heat shock protein 90 (Hsp90) has emerged as one of the most exciting targets for anticancer drug development and Hsp90 inhibitors are potentially useful chemotherapeutic agents in cancer. Within the current study, Hsp90 inhibitors that entered different phases of clinical trials were subjected to Zinc15 structure query to find similar compounds (≥ 78%). Obtained small molecules (1-29) with defined similarity cut-off were docked into ensemble of Hsp90-α NTDs. Docked complexes were ranked on the basis of binding modes and Gibbs free energies as Hsp90 binders (cut-off point; ΔG_b ≤ -12 kcal/mol). Top-ranked compounds were subjected to energy decomposition analysis per residue of binding pocket via density functional theory (DFT) calculations in B3LYP level of theory. Subsequent MD simulations of the top-ranked complexes were performed for 100 ns to explore the stable binding modes during a reasonable period in explicit water. Results of molecular docking and intermolecular binding analysis indicated that H-bond, hydrophobic and salt bridge interactions were determinant forces in complex formation. Compounds 19 and 20 were well accommodated in binding pocket of Hsp90 via relatively varied conformations. It was revealed that Asn51 and Phe138 were key residues that interacted stably to 19 and 20. Although primary mechanism of action for proposed molecules are unknown and yet to be explored, results of the present study revealed key structural features for future structure-guided optimization toward potent inhibitors of Hsp90-α NTD. HighlightsHsp90 inhibitors that entered different phases of clinical trials were subjected to Zinc15 based structure query to afford potential enzyme inhibitors 19 and 20.Quantum chemical calculations confirmed docking results and verified pivotal role of a conserved residues (Asn51, Leu103, Phe138 and Tyr139) in making effective hydrogen bonds.MD simulations of top-ranked docked derivatives revealed the achievement of stable binding modes with less conformational variation of 20 than 19 in the active site of Hsp90-α NTD.H-bond, hydrophobic contacts and salt bridge interactions were determinant forces in binding interactions of in silico hits.Resorcinol and isoxazole were important structural motifs of in silico hits in binding to the active site of Hsp90-α NTD.Communicated by Ramaswamy H. Sarma.

Structural Flexibility of Proteins Dramatically Alters Membrane Stability─A Novel Aspect of Lipid-Protein Interaction

Protein isoforms are structural variants with changes in the overall flexibility predominantly at the tertiary level. For membrane associated proteins, such structural flexibility or rigidity affects membrane stability by playing modulatory roles in lipid-protein interaction. Herein, we investigate the protein chain flexibility mediated changes in the mechanistic behavior of phospholipid model membranes in the presence of two well-known isoforms, erythroid (ER) and nonerythroid (NER) spectrin. We show dramatic alterations of membrane elasticity and stability induced by spectrin in the Langmuir monolayers of phosphatidylocholine (PC) and phosphatidylethanolamine (PE) by a combination of isobaric relaxation, surface pressure-area isotherm, X-ray scattering, and microscopy measurements. The NER spectrin drives all monolayers to possess an approximately equal stability, and that required 25-fold increase and 5-fold decrease of stability in PC and PE monolayers, respectively. The untilting transition of the PC membrane in the presence of NER spectrin observed in X-ray measurements can explain better membrane packing and stability.

Toward Reliable and Insightful Entropy Calculations on Flexible Molecules

The absolute entropy of a flexible molecule can be approximated by the sum of a rigid-rotor-harmonic-oscillator (RRHO) entropy and a Gibbs-Shannon entropy associated to the Boltzmann distribution for the occupation of the conformational energy levels. Herein, we show that such partitioning, which has received renewed interest, leads to accurate entropies of single molecules of increasing size provided that the conformational part is estimated by means of a set of discretization and expansion techniques that are able to capture the significant correlation effects among the torsional motions. To ensure a reliable entropy estimation, we rely on extensive sampling as that produced by classical molecular dynamics simulations on the microsecond time scale, which is currently affordable for small- and medium-sized molecules. According to test calculations, the gas-phase entropy of simple organic molecules is predicted with a mean unsigned error of 0.9 cal/(mol K) when the RRHO entropies are computed at the B3LYP-D3/cc-pVTZ level. Remarkably, the same protocol gives small errors [<1 cal/(mol K)] for the extremely flexible linear alkane molecules (C_nH_2n+2, n = 14, 16, and 18). Similarly, we obtain well-converged entropies for a more challenging test of drug molecules, which exhibit more pronounced correlation effects. We also perform equivalent entropy calculations on a 76 amino acid protein, ubiquitin, by taking advantage of the cutoff-dependent formulation of an expansion technique (correlation-consistent multibody local approximation, CC-MLA), which incorporates genuine correlation effects among the neighboring dihedral angles. Moreover, we show that insightful descriptors of the coupled torsional motions can be obtained with the CC-MLA approach.

Practical Guidance for Consensus Scoring and Force Field Selection in Protein-Ligand Binding Free Energy Simulations

The advances in ligand binding affinity prediction have been fostered by system generation tools and improved force fields (FFs). CHARMM-GUI Free Energy Calculator provides input and postprocessing scripts for AMBER-TI free energy calculations with various FFs. In this study, we used 12 different FF combinations (ff14SB and ff19SB for protein, GAFF2.2 and OpenFF for ligand, and TIP3P, TIP4PEW, and OPC for water) to calculate relative binding free energies (ΔΔG_bind) for 80 alchemical transformations (among the JACS benchmark set) with different numbers of λ windows (12 or 21) and simulation times (1, 5, or 10 ns). Our results show that 12 λ windows and 5 ns simulation time for each window are sufficient to obtain reliable ΔΔG_bind with 4 independent runs for the current benchmark set. While there is no statistically noticeable performance difference among 12 different FF combinations compared to the experimental values, a combination of ff14SB + GAFF2.2 + TIP3P FFs appears to be best with a mean unsigned error of 0.87 [0.69, 1.07] kcal/mol, a root-mean-square error of 1.22 [0.94, 1.50] kcal/mol, a Pearson's correlation of 0.64 [0.52, 0.76], a Spearman's correlation of 0.73 [0.58, 0.83], and a Kendell's correlation of 0.54 [0.42, 0.64]. This large-scale ΔΔG_bind calculation study provides useful information about ΔΔG_bind prediction with different AMBER FF combinations and presents valuable suggestions for FF selection in AMBER-TI ΔΔG_bind calculations.

Decoding the Identification Mechanism of an SAM-III Riboswitch on Ligands through Multiple Independent Gaussian-Accelerated Molecular Dynamics Simulations

S-Adenosyl-l-methionine (SAM)-responsive riboswitches play a central role in the regulation of bacterial gene expression at the level of transcription attenuation or translation inhibition. In this study, multiple independent Gaussian-accelerated molecular dynamics simulations were performed to decipher the identification mechanisms of SAM-III (S_MK) on ligands SAM, SAH, and EEM. The results reveal that ligand binding highly affects the structural flexibility, internal dynamics, and conformational changes of SAM-III. The dynamic analysis shows that helices P3 and P4 as well as two junctions J23 and J24 of SAM-III are highly susceptible to ligand binding. Analyses of free energy landscapes suggest that ligand binding induces different free energy profiles of SAM-III, which leads to the difference in identification sites of SAM-III on ligands. The information on ligand-nucleotide interactions not only uncovers that the π-π, cation-π, and hydrogen bonding interactions drive identification of SAM-III on the three ligands but also reveals that different electrostatic properties of SAM, SAH, and EEM alter the active sites of SAM-III. Meanwhile, the results also verify that the adenine group of SAM, SAH, and EEM is well recognized by conserved nucleotides G7, A29, U37, A38, and G48. We expect that this study can provide useful information for understanding the applications of SAM-III in chemical, synthetic RNA biology, and biomedical fields.

Advancing the field of computational drug design using multicanonical molecular dynamics-based dynamic docking

Multicanonical molecular dynamics (McMD)-based dynamic docking is a powerful tool to not only predict the native binding configuration between two flexible molecules, but it can also be used to accurately simulate the binding/unbinding pathway. Furthermore, it can also predict alternative binding sites, including allosteric ones, by employing an exhaustive sampling approach. Since McMD-based dynamic docking accurately samples binding/unbinding events, it can thus be used to determine the molecular mechanism of binding between two molecules. We developed the McMD-based dynamic docking methodology based on the powerful, but woefully underutilized McMD algorithm, combined with a toolset to perform the docking and to analyze the results. Here, we showcase three of our recent works, where we have applied McMD-based dynamic docking to advance the field of computational drug design. In the first case, we applied our method to perform an exhaustive search between Hsp90 and one of its inhibitors to successfully predict the native binding configuration in its binding site, as we refined our analysis methods. For our second case, we performed an exhaustive search of two medium-sized ligands and Bcl-xL, which has a cryptic binding site that differs greatly between the apo and holo structures. Finally, we performed a dynamic docking simulation between a membrane-embedded GPCR molecule and a high affinity ligand that binds deep within its receptor's pocket. These advanced simulations showcase the power that the McMD-based dynamic docking method has, and provide a glimpse of the potential our methodology has to unravel and solve the medical and biophysical issues in the modern world.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01010-z.

Do Go Chasing Waterfalls: Enoyl Reductase (FabI) in Complex with Inhibitors Stabilizes the Tetrameric Structure and Opens Water Channels

The enzyme enoyl-ACP reductase (FabI) is the limiting step of the membrane's fatty acid biosynthesis in bacteria and a druggable target for novel antibacterial agents. The FabI active form is a homotetramer, which displays the highest affinity to inhibitors. Herein, molecular dynamics studies were carried out using the structure of FabI in complex with known inhibitors to investigate their effects on tetramerization. Our results suggest that multimerization is essential for the integrity of the catalytic site and that inhibitor binding enables the multimerization by stabilizing the substrate binding loop (SBL, L:195-200) coupled with changes in the H4/5 (QR interface). We also observed that AFN-1252 (naphtpyridinone derivative) promotes unique conformational changes affecting monomer-monomer interfaces. These changes are induced by AFN-1252 interaction with key residues in the binding sites (Ala95, Tyr146, and Tyr156). In addition, the analysis of water trajectories indicated that AFN-1252 complexes allow more water molecules to enter the binding site than triclosan and MUT056399 complexes. FabI-AFN-1252 simulations show accumulation of water molecules near the Tyr146/147 pocket, which can become a hotspot to the design of novel FabI inhibitors.

Binding pocket stabilization by high-throughput screening of yeast display libraries

Protein dynamics have a great influence on the binding pockets of some therapeutic targets. Flexible protein binding sites can result in transient binding pocket formation which might have a negative impact on drug screening efforts. Here, we describe a protein engineering strategy with FK506-binding protein 51 (FKBP51) as a model protein, which is a promising target for stress-related disorders. High-throughput screening of yeast display libraries of FKBP51 resulted in the identification of variants exhibiting higher affinity binding of conformation-specific FKBP51 selective inhibitors. The gene libraries of a random mutagenesis and site saturation mutagenesis of the FK1 domain of FKBP51 encoding sequence were used to create a yeast surface display library. Fluorescence-activated cell sorting for FKBP51 variants that bind conformation-specific fluorescently labeled ligands with high affinity allowed for the identification of 15 different protein variants with improved binding to either, or both FKBP51-specific ligands used in the screening, with improved affinities up to 34-fold compared to the wild type. These variants will pave the way to a better understanding of the conformational flexibility of the FKBP51 binding pocket and may enable the isolation of new selective ligands that preferably and selectively bind the active site of the protein in its open conformation state.