Characterization of the Bottlenecks and Pathways for Inhibitor Dissociation from [NiFe] Hydrogenase.

[NiFe] hydrogenases can act as efficient catalysts for hydrogen oxidation and biofuel production. However, some [NiFe] hydrogenases are inhibited by gas molecules present in the environment, such as O2 and CO. One strategy to engineer [NiFe] hydrogenases and achieve O2- and CO-tolerant enzymes is by introducing point mutations to block the access of inhibitors to the catalytic site. In this work, we characterized the unbinding pathways of CO in the complex with the wild-type and 10 different mutants of [NiFe] hydrogenase from Desulfovibrio fructosovorans using τ-random accelerated molecular dynamics (τRAMD) to enhance the sampling of unbinding events. The ranking provided by the relative residence times computed with τRAMD is in agreement with experiments. Extensive data analysis of the simulations revealed that from the two bottlenecks proposed in previous studies for the transit of gas molecules (residues 74 and 122 and residues 74 and 476), only one of them (residues 74 and 122) effectively modulates diffusion and residence times for CO. We also computed pathway probabilities for the unbinding of CO, O2, and H2 from the wild-type [NiFe] hydrogenase, and we observed that while the most probable pathways are the same, the secondary pathways are different. We propose that introducing mutations to block the most probable paths, in combination with mutations to open the main secondary path used by H2, can be a feasible strategy to achieve CO and O2 resistance in the [NiFe] hydrogenase from Desulfovibrio fructosovorans.

pH-dependence of the Plasmodium falciparum chloroquine resistance transporter is linked to the transport cycle.

The chloroquine resistance transporter, PfCRT, of the human malaria parasite Plasmodium falciparum is sensitive to acidic pH. Consequently, PfCRT operates at 60% of its maximal drug transport activity at the pH of 5.2 of the digestive vacuole, a proteolytic organelle from which PfCRT expels drugs interfering with heme detoxification. Here we show by alanine-scanning mutagenesis that E207 is critical for pH sensing. The E207A mutation abrogates pH-sensitivity, while preserving drug substrate specificity. Substituting E207 with Asp or His, but not other amino acids, restores pH-sensitivity. Molecular dynamics simulations and kinetics analyses suggest an allosteric binding model in which PfCRT can accept both protons and chloroquine in a partial noncompetitive manner, with increased proton concentrations decreasing drug transport. Further simulations reveal that E207 relocates from a peripheral to an engaged location during the transport cycle, forming a salt bridge with residue K80. We propose that the ionized carboxyl group of E207 acts as a hydrogen acceptor, facilitating transport cycle progression, with pH sensing as a by-product.

Advances in computational methods for ligand binding kinetics.

Binding kinetic parameters can be correlated with drug efficacy, which in recent years led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity, and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.

Diffusion of small molecule drugs is affected by surface interactions and crowder proteins.

Crowded environments are known to affect the diffusion of macromolecules, but their effects on the diffusion of small molecules are largely uncharacterized. We investigate how three protein crowders, bovine serum albumin (BSA), hen egg-white lysozyme, and myoglobin, influence the diffusion rates and interactions of four small molecules: fluorescein, and three drugs, doxorubicin, glycogen synthase kinase-3 inhibitor SB216763, and quinacrine. Using Line-FRAP measurements, Brownian dynamics simulations, and molecular docking, we find that the diffusion rates of the small molecules are highly affected by self-aggregation, interactions with the proteins, and surface adsorption. The diffusion of fluorescein is decreased because of its interactions with the protein crowders and their surface adsorption. Protein crowders increase the diffusion rates of doxorubicin and SB216763 by reducing surface interactions and self-aggregation, respectively. Quinacrine diffusion was not affected by protein crowders. The mechanistic insights gained here may assist in optimization of compounds for higher mobility in complex macromolecular environments.

The Non-phosphorylating Glyceraldehyde-3-Phosphate Dehydrogenase GapN Is a Potential New Drug Target in Streptococcus pyogenes.

The strict human pathogen Streptococcus pyogenes causes infections of varying severity, ranging from self-limiting suppurative infections to life-threatening diseases like necrotizing fasciitis or streptococcal toxic shock syndrome. Here, we show that the non-phosphorylating glyceraldehyde-3-phosphate dehydrogenase GapN is an essential enzyme for S. pyogenes. GapN converts glyceraldehyde 3-phosphate into 3-phosphoglycerate coupled to the reduction of NADP to NADPH. The knock-down of gapN by antisense peptide nucleic acids (asPNA) significantly reduces viable bacterial counts of S. pyogenes laboratory and macrolide-resistant clinical strains in vitro. As S. pyogenes lacks the oxidative part of the pentose phosphate pathway, GapN appears to be the major NADPH source for the bacterium. Accordingly, other streptococci that carry a complete pentose phosphate pathway are not prone to asPNA-based gapN knock-down. Determination of the crystal structure of the S. pyogenes GapN apo-enzyme revealed an unusual cis-peptide in proximity to the catalytic binding site. Furthermore, using a structural modeling approach, we correctly predicted competitive inhibition of S. pyogenes GapN by erythrose 4-phosphate, indicating that our structural model can be used for in silico screening of specific GapN inhibitors. In conclusion, the data provided here reveal that GapN is a potential target for antimicrobial substances that selectively kill S. pyogenes and other streptococci that lack the oxidative part of the pentose phosphate pathway.

Prediction of the Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis.

There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each ligand-protein complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of ligand-protein complexes to predict binding parameters. We introduce the option of using multiple structures to describe each ligand-protein complex in COMBINE analysis and apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (koff) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression, and they allowed for the computation of koff values. The QSKR model obtained using single, energy-minimized crystal structures for each ligand-protein complex had higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, incorporation of ligand-protein flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, such as interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures. These results show that COMBINE analysis is a promising method to guide the design of compounds that bind to flexible proteins with improved binding kinetics.

Ligand unbinding mechanisms and kinetics for T4 lysozyme mutants from τRAMD simulations.

The protein-ligand residence time, τ, influences molecular function in biological networks and has been recognized as an important determinant of drug efficacy. To predict τ, computational methods must overcome the problem that τ often exceeds the timescales accessible to conventional molecular dynamics (MD) simulation. Here, we apply the τ-Random Acceleration Molecular Dynamics (τRAMD) method to a set of kinetically characterized complexes of T4 lysozyme mutants with small, engineered binding cavities. τRAMD yields relative ligand dissociation rates in good accordance with experiments across this diverse set of complexes that differ with regard to measurement temperature, ligand identity, protein mutation and binding cavity. τRAMD thereby allows a comprehensive characterization of the ligand egress routes and determinants of τ. Although ligand dissociation by multiple egress routes is observed, we find that egress via the predominant route determines the value of τ. We also find that the presence of a greater number of metastable states along egress pathways leads to slower protein-ligand dissociation. These physical insights could be exploited in the rational optimization of the kinetic properties of drug candidates.

A Protocol to Use Comparative Binding Energy Analysis to Estimate Drug-Target Residence Time.

Comparative Binding Energy (COMBINE) analysis is an approach for deriving a target-specific scoring function to compute binding free energy, drug-binding kinetics, or a related property by exploiting the information contained in the three-dimensional structures of receptor-ligand complexes. Here, we describe the process of setting up and running COMBINE analysis to derive a Quantitative Structure-Kinetics Relationship (QSKR) for the dissociation rate constants (koff) of inhibitors of a drug target. The derived QSKR model can be used to estimate residence times (τ, τ=1/koff) for similar inhibitors binding to the same target, and it can also help to identify key receptor-ligand interactions that distinguish inhibitors with short and long residence times. Herein, we demonstrate the protocol for the application of COMBINE analysis on a dataset of 70 inhibitors of heat shock protein 90 (HSP90) belonging to 11 different chemical classes. The procedure is generally applicable to any drug target with known structural information on its complexes with inhibitors.

Line-FRAP, A Versatile Method to Measure Diffusion Rates In Vitro and In Vivo.

The crowded cellular milieu affect molecular diffusion through hard (occluded space) and soft (weak, non-specific) interactions. Multiple methods have been developed to measure diffusion coefficients at physiological protein concentrations within cells, each with its limitations. Here, we show that Line-FRAP, combined with rigours data analysis, is able to determine diffusion coefficients in a variety of environments, from in vitro to in vivo. The use of Line mode greatly improves time resolution of FRAP data acquisition, from 20-100 Hz in the classical mode to 800 Hz in the line mode. This improves data analysis, as intensity and radius of the bleach at the first post-bleach frame is critical. We evaluated the method on different proteins labelled chemically or fused to YFP in a wide range of environments. The diffusion coefficients measured in HeLa and in E. coli were ~2.5-fold and 15-fold slower than in buffer, and were comparable to previously published data. Increasing the osmotic pressure on E. coli further decreases diffusion, to the point at which proteins virtually stop moving. The method presented here, which requires a confocal microscope equipped with dual scanners, can be applied to study a large range of molecules with different sizes, and provides robust results in a wide range of environments and protein concentrations for fast diffusing molecules.

Structure-kinetic relationship reveals the mechanism of selectivity of FAK inhibitors over PYK2.

There is increasing evidence of a significant correlation between prolonged drug-target residence time and increased drug efficacy. Here, we report a structural rationale for kinetic selectivity between two closely related kinases: focal adhesion kinase (FAK) and proline-rich tyrosine kinase 2 (PYK2). We found that slowly dissociating FAK inhibitors induce helical structure at the DFG motif of FAK but not PYK2. Binding kinetic data, high-resolution structures and mutagenesis data support the role of hydrophobic interactions of inhibitors with the DFG-helical region, providing a structural rationale for slow dissociation rates from FAK and kinetic selectivity over PYK2. Our experimental data correlate well with computed relative residence times from molecular simulations, supporting a feasible strategy for rationally optimizing ligand residence times. We suggest that the interplay between the protein structural mobility and ligand-induced effects is a key regulator of the kinetic selectivity of inhibitors of FAK versus PYK2.

Recent progress in molecular simulation methods for drug binding kinetics.

Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.

Impact of the Journal of Chemical Information and Modeling Special Issue on Women in Computational Chemistry.

In this Viewpoint, we provide a commentary on the impact of the Journal of Chemical Information and Modeling Special Issue on Women in Computational Chemistry published in May 2019 and the feedback we received.

Myotubularin-related protein 7 activates peroxisome proliferator-activated receptor-gamma.

Peroxisome proliferator-activated receptor-gamma (PPARγ) is a transcription factor drugable by agonists approved for treatment of type 2 diabetes, but also inhibits carcinogenesis and cell proliferation in vivo. Activating mutations in the Kirsten rat sarcoma viral oncogene homologue (KRAS) gene mitigate these beneficial effects by promoting a negative feedback-loop comprising extracellular signal-regulated kinase 1/2 (ERK1/2) and mitogen-activated kinase kinase 1/2 (MEK1/2)-dependent inactivation of PPARγ. To overcome this inhibitory mechanism, we searched for novel post-translational regulators of PPARγ. Phosphoinositide phosphatase Myotubularin-Related-Protein-7 (MTMR7) was identified as cytosolic interaction partner of PPARγ. Synthetic peptides were designed resembling the regulatory coiled-coil (CC) domain of MTMR7, and their activities studied in human cancer cell lines and C57BL6/J mice. MTMR7 formed a complex with PPARγ and increased its transcriptional activity by inhibiting ERK1/2-dependent phosphorylation of PPARγ. MTMR7-CC peptides mimicked PPARγ-activation in vitro and in vivo due to LXXLL motifs in the CC domain. Molecular dynamics simulations and docking predicted that peptides interact with the steroid receptor coactivator 1 (SRC1)-binding site of PPARγ. Thus, MTMR7 is a positive regulator of PPARγ, and its mimicry by synthetic peptides overcomes inhibitory mechanisms active in cancer cells possibly contributing to the failure of clinical studies targeting PPARγ.

Structural characterization of an Arf dimer interface: molecular mechanism of Arf-dependent membrane scission.

Dimerization of the small GTPase Arf is prerequisite for the scission of COPI-coated transport vesicles. Here, we quantify the monomer/dimer equilibrium of Arf within the membrane and show that after membrane scission, Arf dimers are restricted to donor membranes. By hydrogen exchange mass spectrometry, we define the interface of activated dimeric Arf within its switch II region. Single amino acid exchanges in this region reduce the propensity of Arf to dimerize. We suggest a mechanism of membrane scission by which the dimeric form of Arf is segregated to the donor membrane. Our data are consistent with the previously reported absence of dimerized Arf in COPI vesicles and could explain the presence of one single scar-like noncoated region in each COPI vesicle.

From Brazil to Germany: Challenges and Advantages.

Moving to a new country, with a different culture and a new environment, is not an easy decision. In this perspective, I present some reasons that made me, a Brazilian computational biochemist, move abroad to do postdoctoral research and some of the challenges I faced before and after moving.

Escape of a Small Molecule from Inside T4 Lysozyme by Multiple Pathways.

The T4 lysozyme L99A mutant is often used as a model system to study small-molecule binding to proteins, but pathways for ligand entry and exit from the buried binding site and the associated protein conformational changes have not been fully resolved. Here, molecular dynamics simulations were employed to model benzene exit from its binding cavity using the weighted ensemble (WE) approach to enhance sampling of low-probability unbinding trajectories. Independent WE simulations revealed four pathways for benzene exit, which correspond to transient tunnels spontaneously formed in previous simulations of apo T4 lysozyme. Thus, benzene unbinding occurs through multiple pathways partially created by intrinsic protein structural fluctuations. Motions of several α-helices and side chains were involved in ligand escape from metastable microstates. WE simulations also provided preliminary estimates of rate constants for each exit pathway. These results complement previous works and provide a semiquantitative characterization of pathway heterogeneity for binding of small molecules to proteins.

Mechanical Unfolding of Macromolecules Coupled to Bond Dissociation.

Single-molecule force spectroscopy has become a powerful tool to investigate molecular mechanisms in biophysics and materials science. In particular, the new field of polymer mechanochemistry has emerged to study how tension may induce chemical reactions in a macromolecule. A rich example is the mechanical unfolding of the metalloprotein rubredoxin coupled to dissociation of iron-sulfur bonds that has recently been studied in detail by atomic force microscopy. Here, we present a simple molecular model composed of a classical all-atom force field description, implicit solvation, and steered molecular dynamics simulation to describe the mechanical properties and mechanism of forced unfolding coupled to covalent bond dissociation of macromolecules. We apply this model and test it extensively to simulate forced rubredoxin unfolding, and we dissect the sensitivity of the calculated mechanical properties with model parameters. The model provides a detailed molecular explanation of experimental observables such as force-extension profiles and contour length increments. Changing the points of force application along the macromolecule results in different unfolding mechanisms, characterized by disruption of hydrogen bonds and secondary protein structure, and determines the degree of solvent access to the reactive center. We expect that this molecular model will be broadly applicable to simulate (bio)polymer mechanochemistry.