Markov models of the apo-MDM2 lid region reveal diffuse yet two-state binding dynamics and receptor poses for computational docking

Deciphering the Multi-state Conformational Equilibrium of HDM2 in the Regulation of p53 Binding: Perspectives from Molecular Dynamics Simulation and NMR Analysis

HDM2 negatively regulates the activity of the tumor suppressor p53. Previous NMR studies have shown that apo-HDM2 interconverts between an "open" state in which the N-terminal "lid" is disordered and a "closed" state in which the lid covers the p53-binding site in the core region. Molecular dynamics (MD) simulation studies have been performed to elucidate the conformational dynamics of HDM2, but the direct relevance of the experimental and computational analyses is unclear. In addition, how the phosphorylation of S17 in the lid contributes to the inhibition of p53 binding remains controversial. Here, we used both NMR and MD simulations to investigate the conformational dynamics of apo-HDM2. The NMR analysis revealed that apo-HDM2 exists in a fast-exchanging equilibrium within two closed states, closed 1 and closed 2, in addition to a previously demonstrated slow-exchanging "open-closed" equilibrium. MD simulations visualized two characteristic closed states, where the spatial orientation of the key residues corresponds well to the chemical shift changes of the NMR spectra. Furthermore, the phosphorylation of S17 induced an equilibrium shift toward closed 1, thereby suppressing the binding of p53 to HDM2. This study reveals a multi-state equilibrium of apo-HDM2 and provides new insights into the regulation mechanism of HDM2-p53 interactions.

Markov State Model of Solvent Features Reveals Water Dynamics in Protein-Peptide Binding

In this work, we investigate the role of solvent in the binding reaction of the p53 transactivation domain (TAD) peptide to its receptor MDM2. Previously, our group generated 831 μs of explicit-solvent aggregate molecular simulation trajectory data for the MDM2-p53 peptide binding reaction using large-scale distributed computing and subsequently built a Markov State Model (MSM) of the binding reaction (Zhou et al. 2017). Here, we perform a tICA analysis and construct an MSM with similar hyperparameters while using only solvent-based structural features. We find a remarkably similar landscape but accelerated implied timescales for the slowest motions. The solvent shells contributing most to the first tICA eigenvector are those centered on Lys24 and Thr18 of the p53 TAD peptide in the range of 3-6 Å. Important solvent shells were visualized to reveal solvation and desolvation transitions along the peptide-protein binding trajectories. Our results provide a solvent-centric view of the hydrophobic effect in action for a realistic peptide-protein binding scenario.

Multi-Level High-Throughput Screening for Discovery of Ligands That Inhibit Insulin Aggregation

We have developed a multi-level virtual screening protocol to identify lead molecules from the FDA inactives database that can inhibit insulin aggregation. The method is based on the presence of structural and interaction specificity in non-native aggregation pathway protein-protein interactions. Some key challenges specific to the present problem, when compared with native protein association, include structural heterogeneity of the protein species involved, multiple association pathways, and relatively higher probability of conformational rearrangement of the association complex. In this multi-step method, the inactives database was first screened using the dominant pharmacophore features of previously identified molecules shown to significantly inhibit insulin aggregation nucleation by binding to its aggregation-prone conformers. We then performed ensemble docking of several low-energy ligand conformations on these aggregation-prone conformers followed by molecular dynamics simulations and binding affinity calculations on a subset of docked complexes to identify a final set of five potential lead molecules to inhibit insulin aggregation nucleation. Their effect on aggregation inhibition was extensively investigated by incubating insulin under aggregation-prone aqueous buffer conditions (low pH, high temperature). Aggregation kinetics were characterized using size exclusion chromatography and Thioflavin T fluorescence assay, and the secondary structure was determined using circular dichroism spectroscopy. Riboflavin provided the best aggregation inhibition, with 85% native monomer retention after 48 h incubation under aggregation-prone conditions, whereas the no-ligand formulation showed complete monomer loss after 36 h. Further, insulin incubated with two of the screened inactives (aspartame, riboflavin) had the characteristic α-helical dip in CD spectra, while the no-ligand formulation showed a change to β-sheet rich conformations.

Energetics of a protein disorder-order transition in small molecule recognition

Many proteins recognise other proteins via mechanisms that involve the folding of intrinsically disordered regions upon complex formation. Here we investigate how the selectivity of a drug-like small molecule arises from its modulation of a protein disorder-to-order transition. Binding of the compound AM-7209 has been reported to confer order upon an intrinsically disordered ‘lid’ region of the oncoprotein MDM2. Calorimetric measurements revealed that truncation of the lid region of MDM2 increases the apparent dissociation constant of AM-7209 250-fold. By contrast, lid truncation has little effect on the binding of the ligand Nutlin-3a. Insights into these differential binding energetics were obtained via a complete thermodynamic analysis that featured adaptive absolute alchemical free energy of binding calculations with enhanced-sampling molecular dynamics simulations. The simulations reveal that in apo MDM2 the ordered lid state is energetically disfavoured. AM-7209, but not Nutlin-3a, shows a significant energetic preference for ordered lid conformations, thus shifting the balance towards ordering of the lid in the AM-7209/MDM2 complex. The methodology reported herein should facilitate broader targeting of intrinsically disordered regions in medicinal chemistry.

Molecular simulations and biophysical measurements elucidate why the ligand AM-7209 orders a disordered region of the protein MDM2 on binding. This work expands strategies available to medicinal chemists for targeting disordered proteins.

Paratope states in solution improve structure prediction and docking

Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations, we show that a single static X-ray structure is not sufficient to identify determinants of antibody-antigen recognition. Here, we investigate antibodies that undergo substantial conformational changes upon antigen binding and have been classified as difficult cases in an extensive benchmark for antibody-antigen docking. We present thermodynamics and transition kinetics of these conformational rearrangements and show that paratope states can be used to improve antibody-antigen docking. By using the unbound antibody X-ray structure as starting structure for molecular dynamics simulations, we retain a binding competent conformation substantially different to the unbound antibody X-ray structure. We also observe that the kinetically dominant antibody paratope conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that paratope states in solution can improve antibody-antigen docking and structure prediction.

Computational Investigation on the p53-MDM2 Interaction Using the Potential of Mean Force Study

Murine double minute 2 (MDM2) proteins are found to be overproduced by many human tumors in order to inhibit the functioning of p53 molecules, a tumor suppressor protein. Thus, reactivating p53 functioning in cancer cells by disrupting p53-MDM2 interactions may offer a significant approach in cancer treatment. However, the structural characterization of the p53-MDM2 complex at the atomistic level and the mechanism of binding/unbinding of the p53-MDM2 complex still remain unclear. Therefore, we demonstrate here the probable binding (unbinding) pathway of transactivation domain 1 of p53 during the formation (dissociation) of the p53-MDM2 complex in terms of free energy as a function of reaction coordinate from the potential of mean force (PMF) study using two different force fields: ff99SB and ff99SB-ILDN. From the PMF plot, we noticed the PMF to have a minimum value at a p53-MDM2 separation of 12 Å, with a dissociation energy of 30 kcal mol^-1. We also analyzed the conformational dynamics and stability of p53 as a function of its distance of separation from MDM2. The secondary structure content (helix and turns) in p53 was found to vary with its distance of separation from MDM2. The p53-MDM2 complex structure with lowest potential energy was isolated from the ensemble at the reaction coordinate corresponding to the minimum PMF value and subjected to molecular dynamics simulation to identify the interface surface area, interacting residues at the interface, and the stability of the complex. The simulation results highlight the importance of hydrogen bonds and the salt bridge between Lys94 of MDM2 and Glu17 of p53 in the stability of the p53-MDM2 complex. We also carried out the binding free energy calculations and the per residue energy decomposition analyses of the interface residues of the p53-MDM2 complex. We found that the binding affinity between MDM2 and p53 is indeed high [ΔG _bind = -7.29 kcal mol^-1 from molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and ΔG _bind = -53.29 kcal mol^-1 from molecular mechanics/generalized borne surface area]. The total binding energy obtained using the MM/PBSA method was noticed to be closer to the experimental values (-6.4 to -9.0 kcal mol^-1). The p53-MDM2 complex binding profile was observed to follow the same trend even in the duplicate simulation run and also in the simulation carried out with different force fields. We found that Lys51, Leu54, Tyr100, and Tyr104 from MDM2 and the residues Phe19, Trp23, and Leu26 from p53 provide the highest energy contributions for the p53-MDM2 interaction. Our findings highlight the prominent structural and binding characteristics of the p53-MDM2 complex that may be useful in designing potential inhibitors to disrupt the p53-MDM2 interactions.

Bioassay-Guided Isolation and in Silico Study of Antibacterial Compounds From Petroleum Ether Extract of Peperomia blanda (Jacq.) Kunth

Bioassay-guided isolation protocol was performed on petroleum ether extract of Peperomia blanda (Jacq.) Kunth using column chromatographic techniques. Five compounds were isolated and their structures were elucidated via one-dimensional (1D) and two-dimensional (2D) NMR, gas chromatography mass sectroscopy (GCMS), liquid chromatography mass spectroscopy (LCMS), and ultraviolet (UV) and infrared (IR) analyses. Dindygulerione E (a new compound), and two compounds isolated from P. blanda for the first time-namely, dindygulerione A and flavokawain A-are reported herein. Antimicrobial activity was screened against selected pathogenic microbes, and minimum inhibitory concentrations (MIC) were recorded within the range of 62-250 μg/mL. Assessment of the pharmacotherapeutic potential has also been done for the isolated compounds, using the Prediction of Activity spectra for Substances (PASS) software, and different activities of compounds were predicted. Molecular docking, molecular dynamics simulation and molecular mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) calculations have proposed the binding affinity of these compounds toward methylthioadenosine phosphorylase enzyme, which may explain their inhibitory actions.

Choice of Adaptive Sampling Strategy Impacts State Discovery, Transition Probabilities, and the Apparent Mechanism of Conformational Changes

Interest in atomically detailed simulations has grown significantly with recent advances in computational hardware and Markov state modeling (MSM) methods, yet outstanding questions remain that hinder their widespread adoption. Namely, how do alternative sampling strategies explore conformational space and how might this influence predictions generated from the data? Here, we seek to answer these questions for four commonly used sampling methods: (1) a single long simulation, (2) many short simulations run in parallel, (3) adaptive sampling, and (4) our recently developed goal-oriented sampling algorithm, FAST. We first develop a theoretical framework for analytically calculating the probability of discovering select states on simple landscapes, where we uncover the drastic effects of varying the number and length of simulations. We then use kinetic Monte Carlo simulations on a variety of physically inspired landscapes to characterize the probability of discovering particular states and transition pathways for each of the four methods. Consistently, we find that FAST simulations discover each target state with the highest probability, while traversing realistic pathways. Furthermore, we uncover the potential pathology that short parallel simulations sometimes predict an incorrect transition pathway by crossing large energy barriers that long simulations would typically circumnavigate. We refer to this pathology as "pathway tunneling". To protect against this phenomenon when using adaptive-sampling and FAST simulations, we introduce the FAST-string method. This method enhances sampling along the highest-flux transition paths to refine an MSMs transition probabilities and discriminate between competing pathways. Additionally, we compare the performance of a variety of MSM estimators in describing accurate thermodynamics and kinetics. For adaptive sampling, we recommend simply normalizing the transition counts out of each state after adding small pseudocounts to avoid creating sources or sinks. Lastly, we evaluate whether our insights from simple landscapes hold for all-atom molecular dynamics simulations of the folding of the λ-repressor protein. Remarkably, we find that FAST-contacts predicts the same folding pathway as a set of long simulations but with orders of magnitude less simulation time.

Elucidating the inhibition of peptidoglycan biosynthesis in Staphylococcus aureus by albocycline, a macrolactone isolated from Streptomyces maizeus

Antibiotic resistance is a serious threat to global public health, and methicillin-resistant Staphylococcus aureus (MRSA) is a poignant example. The macrolactone natural product albocycline, derived from various Streptomyces strains, was recently identified as a promising antibiotic candidate for the treatment of both MRSA and vancomycin-resistant S. aureus (VRSA), which is another clinically relevant and antibiotic resistant strain. Moreover, it was hypothesized that albocycline's antimicrobial activity was derived from the inhibition of peptidoglycan (i.e., bacterial cell wall) biosynthesis. Herein, preliminary mechanistic studies are performed to test the hypothesis that albocycline inhibits MurA, the enzyme that catalyzes the first step of peptidoglycan biosynthesis, using a combination of biological assays alongside molecular modeling and simulation studies. Computational modeling suggests albocycline exists as two conformations in solution, and computational docking of these conformations to an ensemble of simulated receptor structures correctly predicted preferential binding to S. aureus MurA-the enzyme that catalyzes the first step of peptidoglycan biosynthesis-over Escherichia coli (E. coli) MurA. Albocycline isolated from the producing organism (Streptomyces maizeus) weakly inhibited S. aureus MurA (IC50 of 480 μM) but did not inhibit E. coli MurA. The antimicrobial activity of albocycline against resistant S. aureus strains was superior to that of vancomycin, preferentially inhibiting Gram-positive organisms. Albocycline was not toxic to human HepG2 cells in MTT assays. While these studies demonstrate that albocycline is a promising lead candidate against resistant S. aureus, taken together they suggest that MurA is not the primary target, and further work is necessary to identify the major biological target.

Molecular mechanisms of RNA polymerase II transcription elongation elucidated by kinetic network models

Transcription elongation cycle (TEC) of RNA polymerase II (Pol II) is a process of adding a nucleoside triphosphate to the growing messenger RNA chain. Due to the long timescale events in Pol II TEC, an advanced computational technique, such as Markov State Model (MSM), is needed to provide atomistic mechanism and reaction rates. The combination of MSM and experimental results can be used to build a kinetic network model (KNM) of the whole TEC. This review provides a brief protocol to build MSM and KNM of the whole TEC, along with the latest findings of MSM and other computational studies of Pol II TEC. Lastly, we offer a perspective on potentially using a sequence dependent KNM to predict genome-wide transcription error.

Characterizing the conformational landscape of MDM2-binding p53 peptides using Molecular Dynamics simulations

The conformational landscapes of p53 peptide variants and phage derived peptide (12/1) variants, all known to bind to MDM2, are studied using hamiltonian replica exchange molecular dynamics simulations. Complementing earlier observations, the current study suggests that the p53 peptides largely follow the ‘conformational selection’ paradigm in their recognition of and complexation by MDM2 while the 12/1 peptides likely undergo some element of conformational selection but are mostly driven by ‘binding induced folding’. This hypothesis is further supported by pulling simulations that pull the peptides away from their bound states with MDM2. This data extends the earlier mechanisms proposed to rationalize the entropically driven binding of the p53 set and the enthalpically driven binding of the 12/1 set. Using our hypothesis, we suggest mutations to the 12/1 peptide that increase its helicity in simulations and may, in turn, shift the binding towards conformational selection. In summary, understanding the conformational landscapes of the MDM2-binding peptides may suggest new peptide designs with bespoke binding mechanisms.

Protein-peptide molecular docking with large-scale conformational changes: the p53-MDM2 interaction

Protein-peptide interactions are often associated with large-scale conformational changes that are difficult to study either by classical molecular modeling or by experiment. Recently, we have developed the CABS-dock method for flexible protein-peptide docking that enables large-scale rearrangements of the protein chain. In this study, we use CABS-dock to investigate the binding of the p53-MDM2 complex, an element of the cell cycle regulation system crucial for anti-cancer drug design. Experimental data suggest that p53-MDM2 binding is affected by significant rearrangements of a lid region - the N-terminal highly flexible MDM2 fragment; however, the details are not clear. The large size of the highly flexible MDM2 fragments makes p53-MDM2 intractable for exhaustive binding dynamics studies using atomistic models. We performed extensive dynamics simulations using the CABS-dock method, including large-scale structural rearrangements of MDM2 flexible regions. Without a priori knowledge of the p53 peptide structure or its binding site, we obtained near-native models of the p53-MDM2 complex. The simulation results match well the experimental data and provide new insights into the possible role of the lid fragment in p53 binding. The presented case study demonstrates that CABS-dock methodology opens up new opportunities for protein-peptide docking with large-scale changes of the protein receptor structure.