Estimating binding affinities by docking/scoring methods using variable protonation states

A multiple-step screening protocol to identify norepinephrine and dopamine reuptake inhibitors for depression

Depression severely impairs the health of people all over the world. Cognitive dysfunction due to depression has resulted in a severe economic burden to family and society induced by the reduction of social functioning of patients. Norepinephrine-dopamine reuptake inhibitors (NDRIs) targeted with the human norepinephrine transporter (hNET) and distributed with the human dopamine transporter (hDAT) simultaneously treat depression and improve cognitive function, and they effectively prevent sexual dysfunction and other side effects. Because many patients continue to poorly respond to NDRIs, it is urgent to discover novel NDRI antidepressants that do not interfere with cognitive function. The aim of this work was to selectively identify novel NDRI candidates acting against hNET and hDAT from large compound libraries by a comprehensive strategy integrating support vector machine (SVM) models, ADMET, molecular docking, in vitro binding assays, molecular dynamics simulation, and binding energy calculation. First, 6522 compounds that do not inhibit the human serotonin transporter (hSERT) were obtained by SVM models of hNET, hDAT, and non-target hSERT with similarity analyses from compound libraries. ADMET and molecular docking were then used to identify compounds that could potently bind to the hNET and hDAT with satisfactory ADMET, and 4 compounds were successfully identified. According to their docking scores and ADMET information, 3719810 was advanced for profiling by in vitro assays as a novel NDRI lead compound due to its strongest druggability and balancing activities. Encouragingly, 3719810 performed comparative activities on two targets, with Ki values of 7.32 μM for hNET and 5.23 μM for hDAT. To obtain candidates with additional activities and balance the activities of 2 targets, 5 analogs were optimized, and 2 novel scaffold compounds were successively designed. By assessment of molecular docking, molecular dynamics simulations, and binding energy calculations, 5 compounds were validated as NDRI candidates with high activities, and 4 of them performed acceptable balancing activities acting on hNET and hDAT. This work supplied promising novel NDRIs for treatment of depression with cognitive dysfunction or other related neurodegenerative disorders, and also provided a strategy for highly efficient and cost-effective identification of inhibitors for dual targets with homologous non-targets.

Structural And Computational Perspectives of Selectively Targeting Mutant Proteins

Diseases are often caused by mutant proteins. Many drugs have limited effectiveness and/or toxic side effects because of a failure to selectively target the disease-causing mutant variant, rather than the functional wild type protein. Otherwise, the drugs may even target different proteins with similar structural features. Designing drugs that successfully target mutant proteins selectively represents a major challenge. Decades of cancer research have led to an abundance of potential therapeutic targets, often touted to be "master regulators". For many of these proteins, there are no FDA-approved drugs available; for others, off-target effects result in dose-limiting toxicity. Cancer-related proteins are an excellent medium to carry the story of mutant-specific targeting, as the disease is both initiated and sustained by mutant proteins; furthermore, current chemotherapies generally fail at adequate selective distinction. This review discusses some of the challenges associated with selective targeting from a structural biology perspective, as well as some of the developments in algorithm approach and computational workflow that can be applied to address those issues. One of the most widely researched proteins in cancer biology is p53, a tumor suppressor. Here, p53 is discussed as a specific example of a challenging target, with contemporary drugs and methodologies used as examples of burgeoning successes. The oncogene KRAS, which has been described as "undruggable", is another extensively investigated protein in cancer biology. This review also examines KRAS to exemplify progress made towards selective targeting of diseasecausing mutant proteins. Finally, possible future directions relevant to the topic are discussed.

Molecular modeling of four Dermaseptin-related peptides of the gliding tree frog Agalychnis spurrelli

In this research, we present a preliminary computational study of four Dermaseptin-related peptides from the skin exudate of the gliding tree frog Agalychnis spurrelli. Experimentally, the amino acid sequence of these peptides was elucidated through molecular cloning and tandem mass spectrometry and synthetic peptides were assayed against E. coli, S. aureus, and C. albicans to determine their antimicrobial properties. With the sequences on hand, a computational study of the structures was carried out, obtaining their physicochemical properties, secondary structure, and their similarity to other known peptides. A molecular docking study of these peptides was also performed against cell membrane and several enzymes are known to be vital for the organisms. Results showed that Dermaseptin-related peptides are α-helical cationic peptides with an isoelectric point above 9.70 and a positive charge of physiological pH. Introducing theses peptides in a database, it was determined that their identity compared with known peptides range from 36 to 82% meaning these four Dermaseptins are novel peptides. This preliminary study of molecular docking suggests the mechanism of action of this peptide is not given by the inhibition of essential enzymatic pathways, but by cell lysis. Graphical abstract.

Approaching Pharmacological Space: Events and Components

With a view to introducing the concept of pharmacological space and its potential applications in investigating and predicting the toxic mechanisms of xenobiotics, this opening chapter describes the logical relations between conformational behavior, physicochemical properties and binding spaces, which are seen as the three key elements composing the pharmacological space. While the concept of conformational space is routinely used to encode molecular flexibility, the concepts of property spaces and, particularly, of binding spaces are more innovative. Indeed, their descriptors can find fruitful applications (a) in describing the dynamic adaptability a given ligand experiences when inserted into a specific environment, and (b) in parameterizing the flexibility a ligand retains when bound to a biological target. Overall, these descriptors can conveniently account for the often disregarded entropic factors and as such they prove successful when inserted in ligand- or structure-based predictive models. Notably, and although binding space parameters can clearly be derived from MD simulations, the chapter will illustrate how docking calculations, despite their static nature, are able to evaluate ligand's flexibility by analyzing several poses for each ligand. Such an approach, which represents the founding core of the binding space concept, can find various applications in which the related descriptors show an impressive enhancing effect on the statistical performances of the resulting predictive models.

Identification of Novel Non-secosteroidal Vitamin D Receptor Agonists with Potent Cardioprotective Effects and devoid of Hypercalcemia

Vitamin D regulates many biological processes, but its clinical utility is limited by its hypercalcemic effect. Using a virtual screening platform to search novel chemical probes that activate the vitamin D signaling, we report discovery of novel non-steroidal small-molecule compounds that activate the vitamin D receptor (VDR), but are devoid of hypercalcemia. A lead compound (known as VDR 4-1) demonstrated potent transcriptional activities in a VDR reporter gene assay, and significantly ameliorated cardiac hypertrophy in cell culture studies and in animal models. VDR 4-1 also effectively suppressed secondary hyperparathyroidism in 1α-hydroxylase knockout mice. In contrast to 1α,25-dihydroxyvitamin D₃ (1,25-D₃ or calcitriol), a naturally occurring VDR agonist, VDR 4-1 therapy even at high doses did not induce hypercalcemia. These findings were accompanied by a lack of upregulation of calcium transport genes in kidney and in the gut providing a mechanism for the lack of hypercalcemia. Furthermore, VDR 4-1 therapy significantly suppressed cardiac hypertrophy and progression to heart failure in both vitamin D deficient and normal mice without inducing significant hypercalcemia. In conclusion, we have identified a unique VDR agonist compound with beneficial effects in mouse models of hyperparathyroidism and heart failure without inducing significant hypercalcemia.

Molecular Docking: From Lock and Key to Combination Lock

Accurate modeling of protein ligand binding is an important step in structure-based drug design, is a useful starting point for finding new lead compounds or drug candidates. The 'Lock and Key' concept of protein-ligand binding has dominated descriptions of these interactions, and has been effectively translated to computational molecular docking approaches. In turn, molecular docking can reveal key elements in protein-ligand interactions-thereby enabling design of potent small molecule inhibitors directed against specific targets. However, accurate predictions of binding pose and energetic remain challenging problems. The last decade has witnessed more sophisticated molecular docking approaches to modeling protein-ligand binding and energetics. However, the complexities that confront accurate modeling of binding phenomena remain formidable. Subtle recognition and discrimination patterns governed by three-dimensional features and microenvironments of the active site play vital roles in consolidating the key intermolecular interactions that mediates ligand binding. Herein, we briefly review contemporary approaches and suggest that future approaches treat protein-ligand docking problems in the context of a 'combination lock' system.

Homology modeling and virtual screening studies of FGF-7 protein-a structure-based approach to design new molecules against tumor angiogenesis

Keratinocyte growth factor (KGF) protein is a member of the fibroblast growth factor (FGF) family, which is also known as FGF-7. The FGF-7 plays an important role in tumor angiogenesis. In the present work, FGF-7 is treated as a potential therapeutic target to prevent angiogenesis in cancerous tissue. Computational techniques are applied to evaluate and validate the 3D structure of FGF-7 protein. The active site region of the FGF-7 protein is identified based on hydrophobicity calculations using CASTp and Q-site Finder active site prediction tools. The protein-protein docking study of FGF-7 with its natural receptor FGFR2b is carried out to confirm the active site region in FGF-7. The amino acid residues Asp34, Arg67, Glu116, and Thr194 in FGF-7 interact with the receptor protein (FGFR2b). A grid is generated at the active site region of FGF-7 using Glide module of Schrödinger suite. Subsequently, a virtual screening study is carried out at the active site using small molecular structural databases to identify the ligand molecules. The binding interactions of the ligand molecules, with piperazine moiety as a pharmacophore, are observed at Arg67 and Glu149 residues of the FGF-7 protein. The identified ligand molecules against the FGF-7 protein show permissible pharmacokinetic properties (ADME). The ligand molecules with good docking scores and satisfactory pharmacokinetic properties are prioritized and identified as novel ligands for the FGF-7 protein in cancer therapy.

A Novel Multi-step Virtual Screening for the Identification of Human and Mouse mPGES-1 Inhibitors

We present here the development of a novel virtual screening protocol combining Structure-based and Ligand-based drug design approaches for the identification of mouse mPGES-1 inhibitors. We used the existing 3D structural data of the murine enzyme to hypothesize the inhibitors binding mode, which was the starting point for docking simulations, shape screening, and pharmacophore hypothesis screening. The protocol allowed the identification of 16 mouse mPGES-1 inhibitors with low micromolar activity, which, notably, also inhibit the human enzyme in the same concentration range. The inhibitors predicted binding mode is expected to be the base for the rational drug design of new potent dual species inhibitors of human and murine mPGES-1.

Measured and predicted affinities of binding and relative potencies to activate the AhR of PAHs and their alkylated analogues

Polycyclic aromatic hydrocarbons (PAHs) and their alkylated forms are important components of crude oil. Both groups of PAHs have been reported to cause dioxin-like responses, mediated by aryl hydrocarbon receptor (AhR). Thus, characterization of binding affinity to the AhR of unsubstituted or alkylated PAHs is important to understand the toxicological consequences of oil contamination on ecosystems. We investigated the potencies of major PAHs of crude oil, e.g., chrysene, phenanthrene and dibenzothiophene, and their alkylated forms (n=17) to upregulate expression of AhR-mediated processes by use of the H4IIE-luc transactivation bioassay. In addition, molecular descriptors of different AhR activation potencies among PAHs were investigated by use of computational molecular docking models. Based on responses of the H4IIE-luc in vitro assay, it was shown that potencies of PAHs were determined by alkylation in addition to the number and conformation of rings. Potencies of AhR-mediated processes were generally greater when a chrysene group was substituted, especially in 1-methyl-chrysene. Significant negative correlations were observed between the in vitro dioxin-like potency measured in H4IIE-luc cells and the binding distance estimated from the in silico modeling. The difference in relative potency for AhR activation observed among PAHs and their alkylated forms could be explained by differences among binding distances in the ligand binding domain of the AhR caused by alkylation. The docking model developed in the present study may have utility in predicting risks of environmental contaminants of which toxicities are mediated by AhR binding.

Charting a Path to Success in Virtual Screening

Docking is commonly applied to drug design efforts, especially high-throughput virtual screenings of small molecules, to identify new compounds that bind to a given target. Despite great advances and successful applications in recent years, a number of issues remain unsolved. Most of the challenges and problems faced when running docking experiments are independent of the specific software used, and can be ascribed to either improper input preparation or to the simplified approaches applied to achieve high-throughput speed. Being aware of approximations and limitations of such methods is essential to prevent errors, deal with misleading results, and increase the success rate of virtual screening campaigns. In this review, best practices and most common issues of docking and virtual screening will be discussed, covering the journey from the design of the virtual experiment to the hit identification.

Protein-protein docking with dynamic residue protonation states

Protein-protein interactions depend on a host of environmental factors. Local pH conditions influence the interactions through the protonation states of the ionizable residues that can change upon binding. In this work, we present a pH-sensitive docking approach, pHDock, that can sample side-chain protonation states of five ionizable residues (Asp, Glu, His, Tyr, Lys) on-the-fly during the docking simulation. pHDock produces successful local docking funnels in approximately half (79/161) the protein complexes, including 19 cases where standard RosettaDock fails. pHDock also performs better than the two control cases comprising docking at pH 7.0 or using fixed, predetermined protonation states. On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock. Addition of backbone flexibility using a computationally-generated conformational ensemble further improves native contact and hydrogen bond recovery in the top-ranked structures. Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc–FcRn complex, suggesting that it can be exploited to improve affinity predictions. The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.

Protein-protein interactions are fundamental for biological function and are strongly influenced by their local environment. Cellular pH is tightly controlled and is one of the critical environmental factors that regulates protein-protein interactions. Three-dimensional structures of the protein complexes can help us understand the mechanism of the interactions. Since experimental determination of the structures of protein-protein complexes is expensive and time-consuming, computational docking algorithms are helpful to predict the structures. However, none of the current protein-protein docking algorithms account for the critical environmental pH effects. So we developed a pH-sensitive docking algorithm that can dynamically pick the favorable protonation states of the ionizable amino-acid residues. Compared to our previous standard docking algorithm, the new algorithm improves docking accuracy and generates higher-quality predictions over a large dataset of protein-protein complexes. We also use a case study to demonstrate efficacy of the algorithm in predicting a large pH-dependent binding affinity change that cannot be captured by the other methods that neglect pH effects. In principle, the approaches in the study can be used for rational design of pH-dependent protein inhibitors or industrial enzymes that are active over a wide range of pH values.

Predicting the functions and specificity of triterpenoid synthases: a mechanism-based multi-intermediate docking approach

Terpenoid synthases construct the carbon skeletons of tens of thousands of natural products. To predict functions and specificity of triterpenoid synthases, a mechanism-based, multi-intermediate docking approach is proposed. In addition to enzyme function prediction, other potential applications of the current approach, such as enzyme mechanistic studies and enzyme redesign by mutagenesis, are discussed.

The rapid growth in the number of protein sequences presents challenges for enzyme function assignment. Computational methods, such as bioinformatics, homology modeling and docking, are becoming increasingly important for predicting of enzyme functions from protein sequences. Terpenoids are one of largest classes of natural products, and many drugs (e.g. taxol) consist of terpenoids or terpenoid derivatives. Understanding the biosynthesis of the terpenoids is of great interest. Terpenoid synthases catalyze the key cyclization steps of the biosynthesis of terpenoids via carbocation rearrangements, generating numerous multiple-ring carbon skeletons. Triterpenoid synthases, as an important class of terpenoid synthases, catalyze the cyclization of either squalene or oxido-squalene into cyclized products such as sterols (e.g. lanosterol). In this work, we propose a computational approach that can be used to predict product specificity of the triterpenoid synthases. Our approach provides insight into the ‘design principles’ of these fascinating enzymes, and may become a practical approach for function prediction and enzyme engineering.

Lipid receptor S1P₁ activation scheme concluded from microsecond all-atom molecular dynamics simulations

Sphingosine 1-phosphate (S1P) is a lysophospholipid mediator which activates G protein–coupled sphingosine 1-phosphate receptors and thus evokes a variety of cell and tissue responses including lymphocyte trafficking, endothelial development, integrity, and maturation. We performed five all-atom 700 ns molecular dynamics simulations of the sphingosine 1-phosphate receptor 1 (S1P₁) based on recently released crystal structure of that receptor with an antagonist. We found that the initial movements of amino acid residues occurred in the area of highly conserved W269^6.48 in TM6 which is close to the ligand binding location. Those residues located in the central part of the receptor and adjacent to kinks of TM helices comprise of a transmission switch. Side chains movements of those residues were coupled to the movements of water molecules inside the receptor which helped in the gradual opening of intracellular part of the receptor. The most stable parts of the protein were helices TM1 and TM2, while the largest movement was observed for TM7, possibly due to the short intracellular part starting with a helix kink at P^7.50, which might be the first helix to move at the intracellular side. We show for the first time the detailed view of the concerted action of the transmission switch and Trp (W^6.48) rotamer toggle switch leading to redirection of water molecules flow in the central part of the receptor. That event is a prerequisite for subsequent changes in intracellular part of the receptor involving water influx and opening of the receptor structure.

The activation of G-protein-coupled receptors (GPCRs) depends on small differences in agonist and antagonist structures resulting in specific forces they impose on the helical bundle of the receptor. Having the crystal structures of GPCRs in different stages of activation it is possible to investigate the successive conformational changes leading to full activation. The long molecular dynamics simulations can fill the gap spanning between those structures and provide an overview of the activation processes. The water molecules are recognized to be crucial in the activation process which link shifting of ligand in the binding site, the actions of molecular switches and finally the movements of fragments of TM helices. Here, we present five 700 ns MD simulations of lipid S1P₁ receptor, either in Apo form, or bound to antagonist ML056 or natural agonist S1P. The Apo and antagonist-bound receptor structures exhibited similar behavior, with their TM bundles nearly intact, while in the case of the agonist-bound receptor we observed movements of intracellular ends of some of TM helices.

Protonation and pK changes in protein-ligand binding

Formation of protein-ligand complexes causes various changes in both the receptor and the ligand. This review focuses on changes in pK and protonation states of ionizable groups that accompany protein-ligand binding. Physical origins of these effects are outlined, followed by a brief overview of the computational methods to predict them and the associated corrections to receptor-ligand binding affinities. Statistical prevalence, magnitude and spatial distribution of the pK and protonation state changes in protein-ligand binding are discussed in detail, based on both experimental and theoretical studies. While there is no doubt that these changes occur, they do not occur all the time; the estimated prevalence varies, both between individual complexes and by method. The changes occur not only in the immediate vicinity of the interface but also sometimes far away. When receptor-ligand binding is associated with protonation state change at particular pH, the binding becomes pH dependent: we review the interplay between sub-cellular characteristic pH and optimum pH of receptor-ligand binding. It is pointed out that there is a tendency for protonation state changes upon binding to be minimal at physiologically relevant pH for each complex (no net proton uptake/release), suggesting that native receptor-ligand interactions have evolved to reduce the energy cost associated with ionization changes. As a result, previously reported statistical prevalence of these changes - typically computed at the same pH for all complexes - may be higher than what may be expected at optimum pH specific to each complex. We also discuss whether proper account of protonation state changes appears to improve practical docking and scoring outcomes relevant to structure-based drug design. An overview of some of the existing challenges in the field is provided in conclusion.

A QM/MM investigation of the catalytic mechanism of metal-ion-independent core 2 β1,6-N-acetylglucosaminyltransferase

β1,6-GlcNAc-transferase (C2GnT) is an important controlling factor of biological functions for many glycoproteins and its activity has been found to be altered in breast, colon, and lung cancer cells, in leukemia cells, in the lymhomonocytes of multiple sclerosis patients, leukocytes from diabetes patients, and in conditions causing an immune deficiency. The result of the action of C2GnT is the core 2 structure that is essential for the further elongation of the carbohydrate chains of O-glycans. The catalytic mechanism of this metal-ion-independent glycosyltransferase is of paramount importance and is investigated here by using quantum mechanical (QM) (density functional theory (DFT))/molecular modeling (MM) methods with different levels of theory. The structural model of the reaction site used in this report is based on the crystal structures of C2GnT. The entire enzyme-substrate system was subdivided into two different subsystems: the QM subsystem containing 206 atoms and the MM region containing 5914 atoms. Three predefined reaction coordinates were employed to investigate the catalytic mechanism. The calculated potential energy surfaces discovered the existence of a concerted SN 2-like mechanism. In this mechanism, a nucleophilic attack by O6 facilitated by proton transfer to the catalytic base and the separation of the leaving group all occur almost simultaneously. The transition state for the proposed reaction mechanism at the M06-2X/6-31G** (with diffuse functions on the O1', O5', OGlu , and O6 atoms) level was located at C1-O6=1.74 Å and C1-O1=2.86 Å. The activation energy for this mechanism was estimated to be between 20 and 29 kcal mol⁻¹, depending on the method used. These calculations also identified a low-barrier hydrogen bond between the nucleophile O6H and the catalytic base Glu320, and a hydrogen bond between the N-acetamino group and the glycosidic oxygen of the donor in the TS. It is proposed that these interactions contribute to a stabilization of TS and participate in the catalytic mechanism.

Effects of histidine protonation and rotameric states on virtual screening of M. tuberculosis RmlC

While it is well established that protonation and tautomeric states of ligands can significantly affect the results of virtual screening, such effects of ionizable residues of protein receptors are less well understood. In this study, we focus on histidine protonation and rotameric states and their impact on virtual screening of Mycobacterium tuberculosis enzyme RmlC. Depending on the net charge and the location of proton(s), a histidine can adopt three states: HIP (+1 charged, both δ- and ε-nitrogens protonated), HID (neutral, δ-nitrogen protonated), and HIE (neutral, ε-nitrogen protonated). Due to common ambiguities in X-ray crystal structures, a histidine may also be resolved as three additional states with its imidazole ring flipped. Here, we systematically investigate the predictive power of 36 receptor models with different protonation and rotameric states of two histidines in the RmlC active site by using results from a previous high-throughput screening. By measuring enrichment factors and area under the receiver operating characteristic curves, we show that virtual screening results vary depending on hydrogen bonding networks provided by the histidines, even in the cases where the ligand does not obviously interact with the side chain. Our results also suggest that, even with the help of widely used pKa prediction software, assigning histidine protonation and rotameric states for virtual screening can still be challenging and requires further examination and systematic characterization of the receptor-ligand complex.

Rapid calculation of protein pKa values using Rosetta

We developed a Rosetta-based Monte Carlo method to calculate the pK(a) values of protein residues that commonly exhibit variable protonation states (Asp, Glu, Lys, His, and Tyr). We tested the technique by calculating pK(a) values for 264 residues from 34 proteins. The standard Rosetta score function, which is independent of any environmental conditions, failed to capture pK(a) shifts. After incorporating a Coulomb electrostatic potential and optimizing the solvation reference energies for pK(a) calculations, we employed a method that allowed side-chain flexibility and achieved a root mean-square deviation (RMSD) of 0.83 from experimental values (0.68 after discounting 11 predictions with an error over 2 pH units). Additional degrees of side-chain conformational freedom for the proximal residues facilitated the capture of charge-charge interactions in a few cases, resulting in an overall RMSD of 0.85 pH units. The addition of backbone flexibility increased the overall RMSD to 0.93 pH units but improved relative pK(a) predictions for proximal catalytic residues. The method also captures large pK(a) shifts of lysine and some glutamate point mutations in staphylococcal nuclease. Thus, a simple and fast method based on the Rosetta score function and limited conformational sampling produces pK(a) values that will be useful when rapid estimation is essential, such as in docking, design, and folding.

Substrate-assisted catalytic mechanism of O-GlcNAc transferase discovered by quantum mechanics/molecular mechanics investigation

In higher eukaryotes, a variety of proteins are post-translationally modified by adding O-linked N-acetylglucosamine (GlcNAc) residue to serine or threonine residues. Misregulation of O-GlcNAcylation is linked to a wide variety of diseases, such as diabetes, cancer, and neurodegenerative diseases, including Alzheimer's disease. GlcNAc transfer is catalyzed by an inverting glycosyltransferase O-GlcNAc transferase (uridine diphospho-N-acetylglucosamine:polypeptide β-N-acetylaminyltransferase, OGT) that belongs to the GT-B superfamily. The catalytic mechanism of this metal-independent glycosyltransferase is of primary importance and is investigated here using QM(DFT)/MM methods. The structural model of the reaction site used in this paper is based on the crystal structures of OGT. The entire enzyme-substrate system was partitioned into two different subsystems: the QM subsystem containing 198 atoms, and the MM region containing 11,326 atoms. The catalytic mechanism was monitored by means of three two-dimensional potential energy maps calculated as a function of three predefined reaction coordinates at different levels of theory. These potential energy surfaces revealed the existence of a concerted S(N)2-like mechanism, in which a nucleophilic attack by O(Ser), facilitated by proton transfer to the catalytic base, and the dissociation of the leaving group occur almost simultaneously. The transition state for the proposed reaction mechanism at the MPW1K level was located at C1-O(Ser) = 1.92 Å and C1-O1 = 3.11 Å. The activation energy for this passage was estimated to be ~20 kcal mol(-1). These calculations also identified, for the first time for glycosyltransferases, the substrate-assisted mechanism in which the N-acetamino group of the donor participates in the catalytic mechanism.

Recognition and reactivity in the binding between Raf kinase inhibitor protein and its small-molecule inhibitor locostatin

The present work is aimed to provide detail on the binding process between Raf kinase inhibitor protein (RKIP) and locostatin, the only exogenous compound known to alter the function of RKIP. Understanding the basis of RKIP inhibition for use in pharmacological applications is of considerable interest, as dysregulated RKIP expression has the potential to contribute to pathophysiological processes. Herein, we report a series of atomistic models to describe the protein-ligand recognition step and the subsequent reactivity steps. Modeling approaches include ligand docking, molecular dynamics, and quantum mechanics/molecular mechanics calculations. We expect that such a computational assay will serve to study similar complexes in which potency is associated with recognition and reactivity. Although previous data suggested a single amino acid residue (His86) to be involved in the binding of locostatin, the actual ligand conformation and the steps involved in the reactivity process remain elusive from a detailed atomistic description. We show that the first reaction step, consisting of a nucleophilic attack of the nitrogen (Nε) of His86 at the sp(2)-hybridized carbon (C2) of locostatin, presents a late transition state (almost identical to the product). The reaction is followed by a hydrogen abstraction and hydrolysis. The theoretically predicted overall rate constant (6 M(-1) s(-1)) is in a very good agreement with the experimentally determined rate constant (13 M(-1) s(-1)).

Thermodynamics of transport through the ammonium transporter Amt-1 investigated with free energy calculations

Amt-1 from Archaeoglobus fulgidus (AfAmt-1) belongs to the Amt/Rh family of ammonium/ammonia transporting membrane proteins. The transport mode and the precise microscopic permeation mechanism utilized by these proteins are intensely debated. Open questions concern the identity of the transported substrate (ammonia and/or ammonium) and whether the transport is passive or active. To address these questions, we studied the overall thermodynamics of the different transport modes as a function of the environmental conditions. Then, we investigated the thermodynamics of the underlying microscopic transport mechanisms with free energy calculations within a continuum electrostatics model. The formalism developed for this purpose is of general utility in the calculation of binding free energies for ligands with multiple protonation forms or other binding forms. The results of our calculations are compared to the available experimental and theoretical data on Amt/Rh proteins and discussed in light of the current knowledge on the physiological conditions experienced by microorganisms and plants. We found that microscopic models of electroneutral and electrogenic transport modes are in principle thermodynamically viable. However, only the electrogenic variants have a net thermodynamic driving force under the physiological conditions experienced by microorganisms and plants. Thus, the transport mechanism of AfAmt-1 is most likely electrogenic.

Comparative evaluation of pK(a) prediction tools on a drug discovery dataset

Due to their impact on pharmacokinetic and pharmacodynamic properties the accurate prediction of dissociation constants is of outmost importance in drug discovery settings. The prediction accuracy, however, is typically assessed on public datasets most likely included in the training sets of the available tools. In this work we therefore tested five pK(a) prediction softwares such as ACD, Epik, Marvin, PharmaAlgorithm and Pallas on novel, never-published compounds. Our dataset consists of 177 pK(a) values of 95 structurally diverse in-house compounds prepared for real-life drug discovery programs. The thorough analysis of prediction accuracy allowed us identifying the best practice and exploring the limitations of the current methods. Mean absolute errors (0.86-1.28) obtained for this set of discovery compounds indicates the potential in the improvement of the available pK(a) prediction approaches. Limitations were further characterized by measuring and evaluating 39 pK(a) values of additional 28 commercially available compounds representing the most challenging chemotypes. We believe that these results would facilitate further developments and hopefully contribute to the necessary improvement of the prediction accuracy.

Prediction of trypsin/molecular fragment binding affinities by free energy decomposition and empirical scores

Two families of binding affinity estimation methodologies are described which were utilized in the SAMPL3 trypsin/fragment binding affinity challenge. The first is a free energy decomposition scheme based on a thermodynamic cycle, which included separate contributions from enthalpy and entropy of binding as well as a solvent contribution. Enthalpic contributions were estimated with PM6-DH2 semiempirical quantum mechanical interaction energies, which were modified with a statistical error correction procedure. Entropic contributions were estimated with the rigid-rotor harmonic approximation, and solvent contributions to the free energy were estimated with several different methods. The second general methodology is the empirical score LISA, which contains several physics-based terms trained with the large PDBBind database of protein/ligand complexes. Here we also introduce LISA+, an updated version of LISA which, prior to scoring, classifies systems into one of four classes based on a ligand's hydrophobicity and molecular weight. Each version of the two methodologies (a total of 11 methods) was trained against a compiled set of known trypsin binders available in the Protein Data Bank to yield scaling parameters for linear regression models. Both raw and scaled scores were submitted to SAMPL3. Variants of LISA showed relatively low absolute errors but also low correlation with experiment, while the free energy decomposition methods had modest success when scaling factors were included. Nonetheless, re-scaled LISA yielded the best predictions in the challenge in terms of RMS error, and six of these models placed in the top ten best predictions by RMS error. This work highlights some of the difficulties of predicting binding affinities of small molecular fragments to protein receptors as well as the benefit of using training data.

The aminopeptidase inhibitor CHR-2863 is an orally bioavailable inhibitor of murine malaria

Malaria remains a significant risk in many areas of the world, with resistance to the current antimalarial pharmacopeia an ever-increasing problem. The M1 alanine aminopeptidase (PfM1AAP) and M17 leucine aminopeptidase (PfM17LAP) are believed to play a role in the terminal stages of digestion of host hemoglobin and thereby generate a pool of free amino acids that are essential for parasite growth and development. Here, we show that an orally bioavailable aminopeptidase inhibitor, CHR-2863, is efficacious against murine malaria.

Chromone, a privileged scaffold for the development of monoamine oxidase inhibitors

Two series of novel chromone derivatives were synthesized and investigated for their ability to inhibit the activity of monoamine oxidase. The SAR data indicate that chromone derivatives with substituents in position 3 of γ-pyrone nucleus act preferably as MAO-B inhibitors, with IC(50) values in the nanomolar to micromolar range. Almost all chromone 3-carboxamides display selectivity toward MAO-B. Identical substitutions on position 2 of γ-pyrone nucleus result in complete loss of activity in both isoforms (chromones 2-12 except 3 and 5). Notably, chromone (19) exhibits an MAO-B IC(50) of 63 nM, greater than 1000-fold selectivity over MAO-A, and behaves as a quasi-reversible inhibitor. Docking experiments onto the MAO binding of the most active compound highlight different interaction patterns among the isoforms A and B. The differential analysis of the solvation effects among the chromone isomers gave additional insight about the superior outline of the 3-substituted chromone derivatives.