Alexey Aleksandrov

Molecular Dynamics Simulations Show That Conformational Selection Governs the Binding Preferences of Imatinib for Several Tyrosine Kinases

Tyrosine kinases transmit cellular signals through a complex mechanism, involving their phosphorylation and switching between inactive and active conformations. The cancer drug imatinib binds tightly to several homologous kinases, including Abl, but weakly to others, including Src. Imatinib specifically targets the inactive, so-called “DFG-out” conformation of Abl, which differs from the preferred, “DFG-in” conformation of Src in the orientation of a conserved Asp-Phe-Gly (DFG) activation loop. However, recent x-ray structures showed that Src can also adopt the DFG-out conformation and uses it to bind imatinib. The Src/Abl-binding free energy difference can thus be decomposed into two contributions. Contribution i measures the different protein-imatinib interactions when either kinase is in its DFG-out conformation. Contribution ii depends on the ability of imatinib to select or induce this conformation, i.e. on the relative stabilities of the DFG-out and DFG-in conformations of each kinase. Neither contribution has been measured experimentally. We use molecular dynamics simulations to show that contribution i is very small, 0.2 ± 0.6 kcal/mol; imatinib interactions are very similar in the two kinases, including long range electrostatic interactions with the imatinib positive charge. Contribution ii, deduced using the experimental binding free energy difference, is much larger, 4.4 ± 0.9 kcal/mol. Thus, conformational selection, easy in Abl, difficult in Src, underpins imatinib specificity. Contribution ii has a simple interpretation; it closely approximates the stability difference between the DFG-out and DFG-in conformations of apo-Src. Additional calculations show that conformational selection also governs the relative binding of imatinib to the kinases c-Kit and Lck. These results should help clarify the current framework for engineering kinase signaling.

An Overview of Electrostatic Free Energy Computations for Solutions and Proteins.

Free energy simulations for electrostatic and charging processes in complex molecular systems encounter specific difficulties owing to the long-range, 1/r Coulomb interaction. To calculate the solvation free energy of a simple ion, it is essential to take into account the polarization of nearby solvent but also the electrostatic potential drop across the liquid-gas boundary, however distant. The latter does not exist in a simulation model based on periodic boundary conditions because there is no physical boundary to the system. An important consequence is that the reference value of the electrostatic potential is not an ion in a vacuum. Also, in an infinite system, the electrostatic potential felt by a perturbing charge is conditionally convergent and dependent on the choice of computational conventions. Furthermore, with Ewald lattice summation and tinfoil conducting boundary conditions, the charges experience a spurious shift in the potential that depends on the details of the simulation system such as the volume fraction occupied by the solvent. All these issues can be handled with established computational protocols, as reviewed here and illustrated for several small ions and three solvated proteins.

Alchemical free energy simulations for biological complexes: powerful but temperamental …

Free energy simulations compare multiple ligand:receptor complexes by “alchemically” transforming one into another, yielding binding free energy differences. Since their introduction in the 1980s, many technical and theoretical obstacles were surmounted, and the method (“MDFE,” since molecular dynamics are often used) has matured into a powerful tool. We describe its current status, its effectiveness, and the challenges it faces. MDFE has provided chemical accuracy for many systems but remains expensive, with significant human overhead costs. The bottlenecks have shifted, partly due to increased computer power. To study diverse sets of ligands, force field availability and accuracy can be a major difficulty. Another difficulty is the frequent need to consider multiple states, related to sidechain protonation or buried waters, for example. Sophisticated, automated methods to sample these states are maturing, such as constant pH simulations. Meanwhile, combinations of MDFE and simpler approaches, like continuum dielectric models, can be very effective. As illustrations, we show how, with careful force field parameterization, MDFE accurately predicts binding specificities between complex tetracycline ligands and their targets. We describe substrate binding to the aspartyl‐tRNA synthetase enzyme, where many distinct electrostatic states play a role, and a histidine and a Mg2+ ion act as coupled switches that help enforce a strict preference for the aspartate substrate, relative to several analogs. Overall, MDFE has achieved a predictive status, where novel ligands can be studied and molecular recognition elucidated in depth. It should play an increasing role in the analysis of complex cellular processes and biomolecular engineering. Copyright

Protonation Patterns in Tetracycline:Tet Repressor Recognition: Simulations and Experiments

Resistance to the antibiotic tetracycline (Tc) is regulated by its binding as a Tc:Mg2+ complex to the Tet Repressor protein (TetR). Tc:TetR recognition is a complex problem, with the protein and ligand each having several possible conformations and protonation states, which are difficult to elucidate by experiment alone. We used a combination of free‐energy simulations and crystallographic analysis to investigate the electrostatic interactions between protein and ligand and the possible role of induced fit in Tc binding. Tc in solution was described quantum mechanically, while Tc:TetR interactions were described by a recent, high‐quality molecular‐mechanics model. The orientations of the amide and imidazole groups were determined experimentally by a careful analysis of Debye–Waller factors in alternate crystallographic models. The agreement with experiment for these orientations suggested that the simulations and their more detailed, thermodynamic predictions were reliable. We found that the ligand prefers an extended, zwitterionic state both in solution and in complexation with the protein. Tc is thus preorganized for binding, while the protein combines lock‐and‐key behavior for regions close to the ligands amide, enolate, and ammonium groups, with an induced fit for regions close to the Mg2+ ion. These insights and the modeling techniques employed should be of interest for engineering improved TetR ligands and improved TetR proteins for gene regulation, as well as for drug design.

Predicting the acid/base behavior of proteins: a constant-pH Monte Carlo approach with generalized born solvent.

The acid/base properties of proteins are essential in biochemistry, and proton binding is a valuable reporter on electrostatic interactions. We propose a constant-pH Monte Carlo strategy to compute protonation free energies and pK(a)s. The solvent is described implicitly, through a generalized Born model. The electronic polarizability and backbone motions of the protein are included through the protein dielectric constant. Side chain motions are described explicitly, by the Monte Carlo scheme. An efficient computational algorithm is described, which allows us to treat the fluctuating shape of the protein/solvent boundary in a way that is numerically exact (within the GB framework); this contrasts with several previous constant-pH approaches. For a test set of six proteins and 78 titratable groups, the model performs well, with an rms error of 1.2 pH units. While this is slightly greater than a simple Null model (rms error of 1.1) and a fully empirical model (rms error of 0.9), it is obtained using physically meaningful model parameters, including a low protein dielectric of four. Importantly, similar performance is obtained for side chains with large and small pK(a) shifts (relative to a standard model compound). The titration curve slopes and the conformations sampled are reasonable. Several directions to improve the method further are discussed.

Nonantibiotic properties of tetracyclines: structural basis for inhibition of secretory phospholipase A2.

Secretory phospholipase A(2) is involved in inflammatory processes and was previously shown to be inhibited by lipophilic tetracyclines such as minocycline (minoTc) and doxycycline. Lipophilic tetracyclines might be a new lead compound for the design of specific inhibitors of secretory phospholipase A(2), which play a crucial role in inflammatory processes. Our X-ray crystal structure analysis at 1.65 A resolution of the minoTc complex of phospholipase A(2) (PLA(2)) of the Indian cobra (Naja naja naja) is the first example of nonantibiotic tetracycline interactions with a protein. MinoTc interferes with the conformation of the active-site Ca(2+)-binding loop, preventing Ca(2)(+) binding, and shields the active site from substrate entrance, resulting in inhibition of the enzyme. MinoTc binding to PLA(2) is dominated by hydrophobic interactions quite different from antibiotic recognition of tetracyclines by proteins or the ribosome. The affinity of minoTc for PLA(2) was determined by surface plasmon resonance, resulting in a dissociation constant K(d)=1.8 x 10(-)(4) M.

Mechanism of activation of elongation factor Tu by ribosome: Catalytic histidine activates GTP by protonation

Elongation factor Tu (EF-Tu) is central to prokaryotic protein synthesis as it has the role of delivering amino-acylated tRNAs to the ribosome. Release of EF-Tu, after correct binding of the EF-Tu:aa-tRNA complex to the ribosome, is initiated by GTP hydrolysis. This reaction, whose mechanism is uncertain, is catalyzed by EF-Tu, but requires activation by the ribosome. There have been a number of mechanistic proposals, including those spurred by a recent X-ray crystallographic analysis of a ribosome:EF-Tu:aa-tRNA:GTP-analog complex. In this work, we have investigated these and alternative hypotheses, using high-level quantum chemical/molecular mechanical simulations for the wild-type protein and its His85Gln mutant. For both proteins, we find previously unsuggested mechanisms as being preferred, in which residue 85, either His or Gln, directly assists in the reaction. Analysis shows that the RNA has a minor catalytic effect in the wild-type reaction, but plays a significant role in the mutant by greatly stabilizing the reactions transition state. Given the similarity between EF-Tu and other members of the translational G-protein family, it is likely that these mechanisms of ribosome-activated GTP hydrolysis are pertinent to all of these proteins.

A molecular mechanics model for imatinib and imatinib:kinase binding

Imatinib is an important anticancer drug, which binds specifically to the Abl kinase and blocks its signalling activity. To model imatinib:protein interactions, we have developed a molecular mechanics force field for imatinib and four close analogues, which is consistent with the CHARMM force field for proteins and nucleic acids. Atomic charges and Lennard‐Jones parameters were derived from a supermolecule ab initio approach. We considered the ab initio energies and geometries of a probe water molecule interacting with imatinib fragments at 32 different positions. We considered both a neutral and a protonated imatinib. The final RMS deviation between the ab initio and force field energies, averaged over both forms, was 0.2 kcal/mol. The model also reproduces the ab initio geometry and flexibility of imatinib. To apply the force field to imatinib:Abl simulations, it is also necessary to determine the most likely imatinib protonation state when it binds to Abl. This was done using molecular dynamics free energy simulations, where imatinib is reversibly protonated during a series of MD simulations, both in solution and in complex with Abl. The simulations indicate that imatinib binds to Abl in its protonated, positively‐charged form. To help test the force field and the protonation prediction, we did MD free energy simulations that compare the Abl binding affinities of two imatinib analogs, obtaining good agreement with experiment. Finally, two new imatinib variants were considered, one of which is predicted to have improved Abl binding. This variant could be of interest as a potential drug.

Binding of tetracyclines to elongation factor Tu, the Tet repressor, and the ribosome: a molecular dynamics simulation study.

Tetracycline (Tc) is a broad-spectrum antibiotic that kills bacteria by interrupting protein biosynthesis. It is thought that the bacteriostatic action of Tc is associated with its binding to the acceptor site (or A site) in the bacterial ribosome, interfering with the attachment of aminoacyl-tRNA. Recently, however, the crystal structure of a complex between Tc and trypsin-modified elongation factor Tu (tm-EF-Tu) was determined, raising the question of whether Tc binding to EF-Tu has a role in its inhibition of protein synthesis. We address this question using computer simulations. As controls, we first compute relative ribosome binding free energies for seven Tc variants for which experimental data are available, obtaining good agreement. We then consider the binding of Tc to both the trypsin-modified and unmodified EF-Tu-GDP complexes. We show that the direct contribution of EF-Tu to the binding free energy is negligible; rather, the binding can be solely attributed to interactions of Tc with a bridging Mg(2+) ion and the GDP phosphate groups. The effects of trypsin modification are modest. Further, our calculations show that EF-Tu does not exhibit any binding preference for Tc over the nonantibiotic, 4-dedimethyl-Tc, and EF-Tu does not bind the Tc analogue tigecycline, which is a potent antibiotic. In contrast, both the ribosome and the Tet Repressor protein (involved in Tc resistance) do show a binding preference for Tc over 4-dedimethyl-Tc, and the ribosome prefers to bind tigecycline over Tc. Overall, our results provide insights into the binding properties of tetracyclines and support the idea that EF-Tu is not their primary target.

Molecular mechanics models for tetracycline analogs

Tetracyclines (Tcs) are an important family of antibiotics that bind to the ribosome and several proteins. To model Tc interactions with protein and RNA, we have developed a molecular mechanics force field for 12 tetracyclines, consistent with the CHARMM force field. We considered each Tc variant in its zwitterionic tautomer, with and without a bound Mg2+. We used structures from the Cambridge Crystallographic Data Base to identify the conformations likely to be present in solution and in biomolecular complexes. A conformational search by simulated annealing was undertaken, using the MM3 force field, for tetracycline, anhydrotetracycline, doxycycline, and tigecycline. Resulting, low‐energy structures were optimized with an ab initio method. We found that Tc and its analogs all adopt an extended conformation in the zwitterionic tautomer and a twisted one in the neutral tautomer, and the zwitterionic‐extended state is the most stable in solution. Intermolecular force field parameters were derived from a standard supermolecule approach: we considered the ab initio energies and geometries of a water molecule interacting with each Tc analog at several different positions. The final, rms deviation between the ab initio and force field energies, averaged over all forms, was 0.35 kcal/mol. Intramolecular parameters were adopted from either the standard CHARMM force field, the ab initio structure, or the earlier, plain Tc force field. The model reproduces the ab initio geometry and flexibility of each Tc. As tests, we describe MD and free energy simulations of a solvated complex between three Tcs and the Tet repressor protein.