Alchemical free energy simulations for biological complexes: powerful but temperamental

Comparative Assessment of Water Models in Protein-Glycan Interaction: Insights from Alchemical Free Energy Calculations and Molecular Dynamics Simulations

Accurate computational simulations of protein-glycan dynamics are crucial for a comprehensive understanding of critical biological mechanisms, including host-pathogen interactions, immune system defenses, and intercellular communication. The accuracy of these simulations, including molecular dynamics (MD) simulation and alchemical free energy calculations, critically relies on the appropriate parameters, including the water model, because of the extensive hydrogen bonding with glycan hydroxyl groups. However, a systematic evaluation of water models' accuracy in simulating protein-glycan interaction at the molecular level is still lacking. In this study, we used full atomistic MD simulations and alchemical absolute binding free energy (ABFE) calculations to investigate the performance of five distinct water models in six protein-glycan complex systems. We evaluated water models' impact on structural dynamics and binding affinity through over 5.8 μs of simulation time per system. Our results reveal that most protein-glycan complexes are stable in the overall structural dynamics regardless of the water model used, while some show obvious fluctuations with specific water models. More importantly, we discover that the stability of the binding motif's conformation is dependent on the water model chosen when its residues form weak hydrogen bonds with the glycan. The water model also influences the conformational stability of the glycan in its bound state according to density functional theory (DFT) calculations. Using alchemical ABFE calculations, we find that the OPC water model exhibits exceptional consistency with experimental binding affinity data, whereas commonly used models such as TIP3P are less accurate. The findings demonstrate how different water models affect protein-glycan interactions and the accuracy of binding affinity calculations, which is crucial in developing therapeutic strategies targeting these interactions.

Conformational Selection of α-Synuclein Tetramers at Biological Interfaces

Controlling the polymorphic assemblies of α-synuclein (αS) oligomers is crucial to reroute toxic protein aggregation implicated in Parkinson's disease (PD). One potential mediator is the interaction of αS tetramers with cell membranes, which may regulate the dynamic balance between aggregation-prone disordered monomers and aggregation-resistant helical tetramers. Here, we model diverse tetramer-cell interactions and compare the structure-function relations at the supramolecular-biological interface with available experimental data. The models predict preferential interaction of compact αS tetramers with highly charged membrane surfaces, which may further stabilize this aggregation-resistant conformer. On moderately charged membranes, extended structures are preferred. In addition to surface charge, curvature influences tetramer thermodynamic stability and aggregation, with potential for selective isolation of tetramers via regio-specific interactions with strongly negatively charged micelles that screen further aggregation. Our modeling data set highlights diverse beneficial nano-bio interactions to redirect biomolecule assembly, supporting new therapeutic approaches for PD based on lipid-mediated conformational selection and inhibition.

Balanced lipase interactions for degradation-controlled paclitaxel release from lipid cubic phase formulations

Lipid cubic phase (LCP) formulations enhance the intestinal solubility and bioavailability of hydrophobic drugs by reducing precipitation and facilitating their mass transport to the intestinal surface for absorption. LCPs with an ester linkage connecting the acyl chain to the glycerol backbone (monoacylglycerols), are susceptible to chemical digestion by several lipolytic enzymes including lipases, accelerating the release of hydrophobic agents from the lipid bilayers of the matrix. Unlike regular enzymes that transform soluble substrates, lipolytic enzymes act at the interface of water and insoluble lipid. Therefore, compounds that bind to this interface can enhance or inhibit the activity of enzymes to varying extent. Here, we explore how the lipolysis rate can be tuned by the interfacial interaction of porcine pancreatic lipase with monoolein LCPs containing a known lipase inhibitor, tetrahydrolipstatin. Release of the Biopharmaceutical Classification System (BCS) class IV drug, paclitaxel, from the inhibitor-modified LCP was examined in the presence of lipase and its effectors colipase and calcium. By combining experimental dynamic digestion studies, thermodynamic measurements and molecular dynamics simulations of the competitive inhibition of lipase by tetrahydrolipstatin, we reveal the role and mode of action of lipase effectors in creating a precisely-balanced degradation-controlled LCP release system for the poorly soluble paclitaxel drug.

Implementing and Assessing an Alchemical Method for Calculating Protein-Protein Binding Free Energy

Protein-protein binding is fundamental to most biological processes. It is important to be able to use computation to accurately estimate the change in protein-protein binding free energy due to mutations in order to answer biological questions that would be experimentally challenging, laborious, or time-consuming. Although nonrigorous free-energy methods are faster, rigorous alchemical molecular dynamics-based methods are considerably more accurate and are becoming more feasible with the advancement of computer hardware and molecular simulation software. Even with sufficient computational resources, there are still major challenges to using alchemical free-energy methods for protein-protein complexes, such as generating hybrid structures and topologies, maintaining a neutral net charge of the system when there is a charge-changing mutation, and setting up the simulation. In the current study, we have used the pmx package to generate hybrid structures and topologies, and a double-system/single-box approach to maintain the net charge of the system. To test the approach, we predicted relative binding affinities for two protein-protein complexes using a nonequilibrium alchemical method based on the Crooks fluctuation theorem and compared the results with experimental values. The method correctly identified stabilizing from destabilizing mutations for a small protein-protein complex, and a larger, more challenging antibody complex. Strong correlations were obtained between predicted and experimental relative binding affinities for both protein-protein systems.

An Active Site Inhibitor Induces Conformational Penalties for ACE2 Recognition by the Spike Protein of SARS-CoV-2

The novel RNA virus, severe acute respiratory syndrome coronavirus II (SARS-CoV-2), is currently the leading cause of mortality in 2020, having led to over 1.6 million deaths and infecting over 75 million people worldwide by December 2020. While vaccination has started and several clinical trials for a number of vaccines are currently underway, there is a pressing need for a cure for those already infected with the virus. Of particular interest in the design of anti-SARS-CoV-2 therapeutics is the human protein angiotensin converting enzyme II (ACE2) to which this virus adheres before entry into the host cell. The SARS-CoV-2 virion binds to cell-surface bound ACE2 via interactions of the spike protein (s-protein) on the viral surface with ACE2. In this paper, we use all-atom molecular dynamics simulations and binding enthalpy calculations to determine the effect that a bound ACE2 active site inhibitor (MLN-4760) would have on the binding affinity of SARS-CoV-2 s-protein with ACE2. Our analysis indicates that the binding enthalpy could be reduced for s-protein adherence to the active site inhibitor-bound ACE2 protein by as much as 1.48-fold as an upper limit. This weakening of binding strength was observed to be due to the destabilization of the interactions between ACE2 residues Glu-35, Glu-37, Tyr-83, Lys-353, and Arg-393 and the SARS-CoV-2 s-protein receptor binding domain (RBD). The conformational changes were shown to lead to weakening of ACE2 interactions with SARS-CoV-2 s-protein, therefore reducing s-protein binding strength. Further, we observed increased conformational lability of the N-terminal helix and a conformational shift of a significant portion of the ACE2 motifs involved in s-protein binding, which may affect the kinetics of the s-protein binding when the small molecule inhibitor is bound to the ACE2 active site. These observations suggest potential new ways for interfering with the SARS-CoV-2 adhesion by modulating ACE2 conformation through distal active site inhibitor binding.

Mechanism of Naphthoquinone Selectivity of Thymidylate Synthase ThyX

Naphthoquinones (NQs) are natural and synthetic compounds with a wide range of biological activities commonly attributed to their redox activity and/or chemical reactivity. However, genetic and biochemical experiments have recently demonstrated that 2-hydroxy-NQs (2-OH-NQs) act as highly specific noncovalent inhibitors of the essential bacterial thymidylate synthase ThyX in a cellular context. We used biochemical experiments and molecular dynamics simulations to elucidate the selective inhibition mechanism of NQ inhibitors of ThyX from Mycobacterium tuberculosis (Mtb). Free energy simulations rationalized how ThyX recognizes the natural substrate dUMP in the N3-ionized form using an arginine, Arg199, in Mtb. The results further demonstrated that 2-OH-NQ, similar to dUMP, binds to ThyX in the ionized form, and the strong and selective binding of 2-OH-NQ to ThyX is also explained by electrostatic interactions with Arg199. The stronger binding of the close analog 5F-dUMP to ThyX and its inhibitory properties compared with dUMP were explained by the stronger acidity of the uracil N3 atom. Our results, therefore, revealed that the ionization of 2-OH-NQs drives their biological activities by mimicking the interactions with the natural substrate. Our observations encourage the rational design of optimized ThyX inhibitors that ultimately may serve as antibiotics.

Structure-based stabilization of insulin as a therapeutic protein assembly via enhanced aromatic-aromatic interactions

Key contributions to protein structure and stability are provided by weakly polar interactions, which arise from asymmetric electronic distributions within amino acids and peptide bonds. Of particular interest are aromatic side chains whose directional π-systems commonly stabilize protein interiors and interfaces. Here, we consider aromatic-aromatic interactions within a model protein assembly: the dimer interface of insulin. Semi-classical simulations of aromatic-aromatic interactions at this interface suggested that substitution of residue TyrB26 by Trp would preserve native structure while enhancing dimerization (and hence hexamer stability). The crystal structure of a [TrpB26]insulin analog (determined as a T3Rf3 zinc hexamer at a resolution of 2.25 Å) was observed to be essentially identical to that of WT insulin. Remarkably and yet in general accordance with theoretical expectations, spectroscopic studies demonstrated a 150-fold increase in the in vitro lifetime of the variant hexamer, a critical pharmacokinetic parameter influencing design of long-acting formulations. Functional studies in diabetic rats indeed revealed prolonged action following subcutaneous injection. The potency of the TrpB26-modified analog was equal to or greater than an unmodified control. Thus, exploiting a general quantum-chemical feature of protein structure and stability, our results exemplify a mechanism-based approach to the optimization of a therapeutic protein assembly.

Combining the polarizable Drude force field with a continuum electrostatic Poisson-Boltzmann implicit solvation model

In this work, we have combined the polarizable force field based on the classical Drude oscillator with a continuum Poisson-Boltzmann/solvent-accessible surface area (PB/SASA) model. In practice, the positions of the Drude particles experiencing the solvent reaction field arising from the fixed charges and induced polarization of the solute must be optimized in a self-consistent manner. Here, we parameterized the model to reproduce experimental solvation free energies of a set of small molecules. The model reproduces well-experimental solvation free energies of 70 molecules, yielding a root mean square difference of 0.8 kcal/mol versus 2.5 kcal/mol for the CHARMM36 additive force field. The polarization work associated with the solute transfer from the gas-phase to the polar solvent, a term neglected in the framework of additive force fields, was found to make a large contribution to the total solvation free energy, comparable to the polar solute-solvent solvation contribution. The Drude PB/SASA also reproduces well the electronic polarization from the explicit solvent simulations of a small protein, BPTI. Model validation was based on comparisons with the experimental relative binding free energies of 371 single alanine mutations. With the Drude PB/SASA model the root mean square deviation between the predicted and experimental relative binding free energies is 3.35 kcal/mol, lower than 5.11 kcal/mol computed with the CHARMM36 additive force field. Overall, the results indicate that the main limitation of the Drude PB/SASA model is the inability of the SASA term to accurately capture non-polar solvation effects. © 2018 Wiley Periodicals, Inc.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

Estimation of relative free energies of binding using pre-computed ensembles based on the single-step free energy perturbation and the site-identification by Ligand competitive saturation approaches

Accurate and rapid estimation of relative binding affinities of ligand-protein complexes is a requirement of computational methods for their effective use in rational ligand design. Of the approaches commonly used, free energy perturbation (FEP) methods are considered one of the most accurate, although they require significant computational resources. Accordingly, it is desirable to have alternative methods of similar accuracy but greater computational efficiency to facilitate ligand design. In the present study relative free energies of binding are estimated for one or two non-hydrogen atom changes in compounds targeting the proteins ACK1 and p38 MAP kinase using three methods. The methods include standard FEP, single-step free energy perturbation (SSFEP) and the site-identification by ligand competitive saturation (SILCS) ligand grid free energy (LGFE) approach. Results show the SSFEP and SILCS LGFE methods to be competitive with or better than the FEP results for the studied systems, with SILCS LGFE giving the best agreement with experimental results. This is supported by additional comparisons with published FEP data on p38 MAP kinase inhibitors. While both the SSFEP and SILCS LGFE approaches require a significant upfront computational investment, they offer a 1000-fold computational savings over FEP for calculating the relative affinities of ligand modifications once those pre-computations are complete. An illustrative example of the potential application of these methods in the context of screening large numbers of transformations is presented. Thus, the SSFEP and SILCS LGFE approaches represent viable alternatives for actively driving ligand design during drug discovery and development. © 2016 Wiley Periodicals, Inc.

Binding affinities controlled by shifting conformational equilibria: opportunities and limitations

Conformational selection is an established mechanism in molecular recognition. Despite its power to explain binding events, it is hardly used in protein/ligand design to modulate molecular recognition. Here, we explore the opportunities and limitations of design by conformational selection. Using appropriate thermodynamic cycles, our approach predicts the effects of a conformational shift on binding affinity and also allows one to disentangle the effects induced by a conformational shift from other effects influencing the binding affinity. The method is assessed and applied to explain the contribution of a conformational shift on the binding affinity of six ubiquitin mutants showing different conformational shifts in six different complexes.

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

Assessment of Mutational Effects on Peptide Stability through Confinement Simulations

The evaluation of free energy differences between specific states of a system is of fundamental interest in the study of (bio)chemical systems. Herein, we examine the use of the recently introduced confinement method (CM) to evaluate relative free energy changes upon protein/peptide mutations. CM is a path-independent technique that involves the transformation of a configurational state of the system into an ideal crystal permitting the direct computation of free energy differences. We illustrate the method by evaluating the differential stabilities between native and mutant sequences of a model peptide that has been extensively characterized by experimental approaches, the GB1 hairpin. We show a good correlation between calculated and experimental relative stabilities and discuss other possible applications of this method in the context of complex molecular conversions.

Free-energy calculations for semi-flexible macromolecules: applications to DNA knotting and looping

We present a method to obtain numerically accurate values of configurational free energies of semiflexible macromolecular systems, based on the technique of thermodynamic integration combined with normal-mode analysis of a reference system subject to harmonic constraints. Compared with previous free-energy calculations that depend on a reference state, our approach introduces two innovations, namely, the use of internal coordinates to constrain the reference states and the ability to freely select these reference states. As a consequence, it is possible to explore systems that undergo substantially larger fluctuations than those considered in previous calculations, including semiflexible biopolymers having arbitrary ratios of contour length L to persistence length P. To validate the method, high accuracy is demonstrated for free energies of prime DNA knots with L/P = 20 and L/P = 40, corresponding to DNA lengths of 3000 and 6000 base pairs, respectively. We then apply the method to study the free-energy landscape for a model of a synaptic nucleoprotein complex containing a pair of looped domains, revealing a bifurcation in the location of optimal synapse (crossover) sites. This transition is relevant to target-site selection by DNA-binding proteins that occupy multiple DNA sites separated by large linear distances along the genome, a problem that arises naturally in gene regulation, DNA recombination, and the action of type-II topoisomerases.

300-Fold increase in production of the Zn2+-dependent dechlorinase TrzN in soluble form via apoenzyme stabilization

Microbial metalloenzymes constitute a large library of biocatalysts, a number of which have already been shown to catalyze the breakdown of toxic chemicals or industrially relevant chemical transformations. However, while there is considerable interest in harnessing these catalysts for biotechnology, for many of the enzymes, their large-scale production in active, soluble form in recombinant systems is a significant barrier to their use. In this work, we demonstrate that as few as three mutations can result in a 300-fold increase in the expression of soluble TrzN, an enzyme from Arthrobacter aurescens with environmental applications that catalyzes the hydrolysis of triazine herbicides, in Escherichia coli. Using a combination of X-ray crystallography, kinetic analysis, and computational simulation, we show that the majority of the improvement in expression is due to stabilization of the apoenzyme rather than the metal ion-bound holoenzyme. This provides a structural and mechanistic explanation for the observation that many compensatory mutations can increase levels of soluble-protein production without increasing the stability of the final, active form of the enzyme. This study provides a molecular understanding of the importance of the stability of metal ion free states to the accumulation of soluble protein and shows that differences between apoenzyme and holoenzyme structures can result in mutations affecting the stability of either state differently.

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 reaction's 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.

Accurate calculation of mutational effects on the thermodynamics of inhibitor binding to p38α MAP kinase: a combined computational and experimental study

A major current challenge for drug design efforts focused on protein kinases is the development of drug resistance caused by spontaneous mutations in the kinase catalytic domain. The ubiquity of this problem means that it would be advantageous to develop fast, effective computational methods that could be used to determine the effects of potential resistance-causing mutations before they arise in a clinical setting. With this long-term goal in mind, we have conducted a combined experimental and computational study of the thermodynamic effects of active-site mutations on a well-characterized and high-affinity interaction between a protein kinase and a small-molecule inhibitor. Specifically, we developed a fluorescence-based assay to measure the binding free energy of the small-molecule inhibitor, SB203580, to the p38α MAP kinase and used it measure the inhibitor's affinity for five different kinase mutants involving two residues (Val38 and Ala51) that contact the inhibitor in the crystal structure of the inhibitor-kinase complex. We then conducted long, explicit-solvent thermodynamic integration (TI) simulations in an attempt to reproduce the experimental relative binding affinities of the inhibitor for the five mutants; in total, a combined simulation time of 18.5 μs was obtained. Two widely used force fields - OPLS-AA/L and Amber ff99SB-ILDN - were tested in the TI simulations. Both force fields produced excellent agreement with experiment for three of the five mutants; simulations performed with the OPLS-AA/L force field, however, produced qualitatively incorrect results for the constructs that contained an A51V mutation. Interestingly, the discrepancies with the OPLS-AA/L force field could be rectified by the imposition of position restraints on the atoms of the protein backbone and the inhibitor without destroying the agreement for other mutations; the ability to reproduce experiment depended, however, upon the strength of the restraints' force constant. Imposition of position restraints in corresponding simulations that used the Amber ff99SB-ILDN force field had little effect on their ability to match experiment. Overall, the study shows that both force fields can work well for predicting the effects of active-site mutations on small molecule binding affinities and demonstrates how a direct combination of experiment and computation can be a powerful strategy for developing an understanding of protein-inhibitor interactions.

CrystalDock: a novel approach to fragment-based drug design

We present a novel algorithm called CrystalDock that analyzes a molecular pocket of interest and identifies potential binding fragments. The program first identifies groups of pocket-lining receptor residues (i.e., microenvironments) and then searches for geometrically similar microenvironments present in publically available databases of ligand-bound experimental structures. Germane fragments from the crystallographic or NMR ligands are subsequently placed within the novel binding pocket. These positioned fragments can be linked together to produce ligands that are likely to be potent; alternatively, they can be joined to an inhibitor with a known or suspected binding pose to potentially improve binding affinity. To demonstrate the utility of the algorithm, CrystalDock is used to analyze the principal binding pockets of influenza neuraminidase and Trypanosoma brucei RNA editing ligase 1, validated drug targets in the fight against pandemic influenza and African sleeping sickness, respectively. In both cases, CrystalDock suggests modifications to known inhibitors that may improve binding affinity.

Free energy simulations of a GTPase: GTP and GDP binding to archaeal initiation factor 2

Archaeal initiation factor 2 (aIF2) is a protein involved in the initiation of protein biosynthesis. In its GTP-bound, "ON" conformation, aIF2 binds an initiator tRNA and carries it to the ribosome. In its GDP-bound, "OFF" conformation, it dissociates from tRNA. To understand the specific binding of GTP and GDP and its dependence on the ON or OFF conformational state of aIF2, molecular dynamics free energy simulations (MDFE) are a tool of choice. However, the validity of the computed free energies depends on the simulation model, including the force field and the boundary conditions, and on the extent of conformational sampling in the simulations. aIF2 and other GTPases present specific difficulties; in particular, the nucleotide ligand coordinates a divalent Mg(2+) ion, which can polarize the electronic distribution of its environment. Thus, a force field with an explicit treatment of electronic polarizability could be necessary, rather than a simpler, fixed charge force field. Here, we begin by comparing a fixed charge force field to quantum chemical calculations and experiment for Mg(2+):phosphate binding in solution, with the force field giving large errors. Next, we consider GTP and GDP bound to aIF2 and we compare two fixed charge force fields to the recent, polarizable, AMOEBA force field, extended here in a simple, approximate manner to include GTP. We focus on a quantity that approximates the free energy to change GTP into GDP. Despite the errors seen for Mg(2+):phosphate binding in solution, we observe a substantial cancellation of errors when we compare the free energy change in the protein to that in solution, or when we compare the protein ON and OFF states. Finally, we have used the fixed charge force field to perform MDFE simulations and alchemically transform GTP into GDP in the protein and in solution. With a total of about 200 ns of molecular dynamics, we obtain good convergence and a reasonable statistical uncertainty, comparable to the force field uncertainty, and somewhat lower than the predicted GTP/GDP binding free energy differences. The sign and magnitudes of the differences can thus be interpreted at a semiquantitative level, and are found to be consistent with the experimental binding preferences of ON- and OFF-aIF2.

A water-swap reaction coordinate for the calculation of absolute protein-ligand binding free energies

The accurate prediction of absolute protein-ligand binding free energies is one of the grand challenge problems of computational science. Binding free energy measures the strength of binding between a ligand and a protein, and an algorithm that would allow its accurate prediction would be a powerful tool for rational drug design. Here we present the development of a new method that allows for the absolute binding free energy of a protein-ligand complex to be calculated from first principles, using a single simulation. Our method involves the use of a novel reaction coordinate that swaps a ligand bound to a protein with an equivalent volume of bulk water. This water-swap reaction coordinate is built using an identity constraint, which identifies a cluster of water molecules from bulk water that occupies the same volume as the ligand in the protein active site. A dual topology algorithm is then used to swap the ligand from the active site with the identified water cluster from bulk water. The free energy is then calculated using replica exchange thermodynamic integration. This returns the free energy change of simultaneously transferring the ligand to bulk water, as an equivalent volume of bulk water is transferred back to the protein active site. This, directly, is the absolute binding free energy. It should be noted that while this reaction coordinate models the binding process directly, an accurate force field and sufficient sampling are still required to allow for the binding free energy to be predicted correctly. In this paper we present the details and development of this method, and demonstrate how the potential of mean force along the water-swap coordinate can be improved by calibrating the soft-core Coulomb and Lennard-Jones parameters used for the dual topology calculation. The optimal parameters were applied to calculations of protein-ligand binding free energies of a neuraminidase inhibitor (oseltamivir), with these results compared to experiment. These results demonstrate that the water-swap coordinate provides a viable and potentially powerful new route for the prediction of protein-ligand binding free energies.

Alchemical free energy methods for drug discovery: progress and challenges

Improved rational drug design methods are needed to lower the cost and increase the success rate of drug discovery and development. Alchemical binding free energy calculations, one potential tool for rational design, have progressed rapidly over the past decade, but still fall short of providing robust tools for pharmaceutical engineering. Recent studies, especially on model receptor systems, have clarified many of the challenges that must be overcome for robust predictions of binding affinity to be useful in rational design. In this review, inspired by a recent joint academic/industry meeting organized by the authors, we discuss these challenges and suggest a number of promising approaches for overcoming them.

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.