Ghermes G. Chilov

Lead Finder: An Approach To Improve Accuracy of Protein−Ligand Docking, Binding Energy Estimation, and Virtual Screening

An innovative molecular docking algorithm and three specialized high accuracy scoring functions are introduced in the Lead Finder docking software. Lead Finders algorithm for ligand docking combines the classical genetic algorithm with various local optimization procedures and resourceful exploitation of the knowledge generated during docking process. Lead Finders scoring functions are based on a molecular mechanics functional which explicitly accounts for different types of energy contributions scaled with empiric coefficients to produce three scoring functions tailored for (a) accurate binding energy predictions; (b) correct energy-ranking of docked ligand poses; and (c) correct rank-ordering of active and inactive compounds in virtual screening experiments. The predicted values of the free energy of protein-ligand binding were benchmarked against a set of experimentally measured binding energies for 330 diverse protein-ligand complexes yielding rmsd of 1.50 kcal/mol. The accuracy of ligand docking was assessed on a set of 407 structures, which included almost all published test sets of the following programs: FlexX, Glide SP, Glide XP, Gold, LigandFit, MolDock, and Surflex. rmsd of 2 A or less was observed for 80-96% of the structures in the test sets (80.0% on the Glide XP and FlexX test sets, 96.0% on the Surflex and MolDock test sets). The ability of Lead Finder to distinguish between active and inactive compounds during virtual screening experiments was benchmarked against 34 therapeutically relevant protein targets. Impressive enrichment factors were obtained for almost all of the targets with the average area under receiver operator curve being equal to 0.92.

Quantitative characterization of the nucleophile reactivity in penicillin acylase-catalyzed acyl transfer reactions.

Nucleophile reactivity of two most known nuclei of penicillins and cephalosporins, 6-aminopenicillanic (6-APA) and 7-aminodesacetoxycephalosporanic (7-ADCA) acids, was quantitatively characterized. In penicillin acylase (PA)-catalyzed acyl transfer reactions the relative reactivity of the added nucleophile compared to the water (i.e. nucleophile reactivity) is defined by two complex kinetic parameters beta(0) and gamma, and depends on the nucleophile concentration. In turn, parameters beta(0) and gamma were shown to be dependent on the structure of both reactants involved: nucleophile and acyl donor. Analysis of the kinetic scheme revealed that nucleophile reactivity is one of a few key parameters controlling efficiency of PA-catalyzed acyl transfer to the added nucleophile in an aqueous medium. Computation of the maximum nucleophile conversion to the product using determined nucleophile reactivity parameters in the synthesis of three different antibiotics, ampicillin, amoxicillin and cephalexin, showed good correlation with the results of corresponding synthetic experiments. Suggested approach can be extended to the quantitative description and optimization of PA-catalyzed acyl transfer reactions in a wide range of experimental conditions.

Resolution of (RS)-phenylglycinonitrile by penicillin acylase-catalyzed acylation in aqueous medium

A new strategy for the biocatalytic resolution of (R,S)-phenylglycinonitrile, a crucial intermediate in the antibiotic industry, has been developed. While former techniques exploit nitrilases or combinations of nitrile hydratases and amidases, manipulating with nitrile functionality, the current approach is based on a highly efficient and enantioselective acylation of the α-amino group with phenylacetic acid catalyzed by a well known enzyme, penicillin acylase from E. coli, in slightly acidic aqueous medium. It is shown that since the condensation product is poorly soluble, removal of (S)-phenylglycinonitrile from the reaction sphere is almost complete and irreversible, favoring kinetics of the process and making high conversion possible. The proposed approach is characterized by high space-time yield and extends the scope of enzymatic synthesis in aqueous medium.

CSAR scoring challenge reveals the need for new concepts in estimating protein-ligand binding affinity.

The dG prediction accuracy by the Lead Finder docking software on the CSAR test set was characterized by R(2)=0.62 and rmsd=1.93 kcal/mol, and the method of preparation of the full-atom structures of the test set did not significantly affect the resulting accuracy of predictions. The primary factors determining the correlation between the predicted and experimental values were the van der Waals interactions and solvation effects. Those two factors alone accounted for R(2)=0.50. The other factors that affected the accuracy of predictions, listed in the order of decreasing importance, were the change of ligands internal energy upon binding with protein, the electrostatic interactions, and the hydrogen bonds. It appears that those latter factors contributed to the independence of the prediction results from the method of full-atom structure preparation. Then, we turned our attention to the other factors that could potentially improve the scoring function in order to raise the accuracy of the dG prediction. It turned out that the ligand-centric factors, including Mw, cLogP, PSA, etc. or protein-centric factors, such as the functional class of protein, did not improve the prediction accuracy. Following that, we explored if the weak molecular interactions such as X-H...Ar, X-H...Hal, CO...Hal, C-H...X, stacking and π-cationic interactions (where X is N or O), that are generally of interest to the medicinal chemists despite their lack of proper molecular mechanical parametrization, could improve dG prediction. Our analysis revealed that out of these new interactions only CO...Hal is statistically significant for dG predictions using Lead FInder scoring function. Accounting for the CO...Hal interaction resulted in the reduction of the rmsd from 2.19 to 0.69 kcal/mol for the corresponding structures. The other weak interaction factors were not statistically significant and therefore irrelevant to the accuracy of dG prediction. On the basis of our findings from our participation in the CSAR scoring challenge we conclude that a significant increase of accuracy predictions necessitates breakthrough scoring approaches. We anticipate that the explicit accounting for water molecules, protein flexibility, and a more thermodynamically accurate method of dG calculation rather than single point energy calculation may lead to such breakthroughs.

Lead Finder docking and virtual screening evaluation with Astex and DUD test sets

Lead Finder is a molecular docking software. Sampling uses an original implementation of the genetic algorithm that involves a number of additional optimization procedures. Lead Finder’s scoring functions employ a set of semi-empiric molecular mechanics functionals that have been parameterized independently for docking, binding energy predictions and rank-ordering for virtual screening. Sampling and scoring both utilize a staged approach, moving from fast but less accurate algorithm versions to computationally more intensive but more accurate versions. Lead Finder includes tools for the preparation of full atom protein and ligand models. In this exercise, Lead Finder achieved 72.9% docking success rate on the Astex test set when the original author-prepared full atom models were used, and 74.1% success rate when the structures were prepared by Lead Finder. The major cause of docking failures were scoring errors resulting from the use of imperfect solvation models. In many cases, docking errors could be corrected by the proper protonation and the use of correct cyclic conformations of ligands. In virtual screening experiments on the DUD test set the early enrichment factor of several tens was achieved on average. However, the area under the ROC curve (“AUC ROC”) ranged from 0.70 to 0.74 depending on the screening protocol used, and the separation from the null model was not perfect—0.12–0.15 units of AUC ROC. We assume that effective virtual screening in the whole range of enrichment curve and not just at the early enrichment stages requires more accurate solvation modeling and accounting for the protein backbone flexibility.

Thermodynamic and kinetic stability of penicillin acylase from Escherichia coli

Thermal denaturation of penicillin acylase (PA) from Escherichia coli has been studied by high-sensitivity differential scanning calorimetry as a function of heating rate, pH and urea concentration. It is shown to be irreversible and kinetically controlled. Upon decrease in the heating rate from 2 to 0.1 K min(-1) the denaturation temperature of PA at pH 6.0 decreases by about 6 degrees C, while the denaturation enthalpy does not change notably giving an average value of 31.6+/-2.1 J g(-1). The denaturation temperature of PA reaches a maximum value of 64.5 degrees C at pH 6.0 and decreases by about of 15 degrees C at pH 3.0 and 9.5. The pH induced changes in the denaturation enthalpy follow changes in the denaturation temperature. Increasing the urea concentration causes a decrease in both denaturation temperature and enthalpy of PA, where denaturation temperature obeys a linear relation. The heat capacity increment of PA is not sensitive to the heating rate, nor to pH, and neither to urea. Its average value is of 0.58+/-0.02 J g(-1) K(-1). The denaturation transition of PA is approximated by the Lumry-Eyring model. The first stage of the process is assumed to be a reversible unfolding of the alpha-subunit. It activates the second stage involving dissociation of two subunits and subsequent denaturation of the beta-subunit. This stage is irreversible and kinetically controlled. Using this model the temperature, enthalpy and free energy of unfolding of the alpha-subunit, and a rate constant of the irreversible stage are determined as a function of pH and urea concentration. Structural features of the folded and unfolded conformation of the alpha-subunit as well as of the transition state of the PA denaturation in aqueous and urea solutions are discussed.

PF-114, a potent and selective inhibitor of native and mutated BCR/ABL is active against Philadelphia chromosome-positive (Ph+) leukemias harboring the T315I mutation.

Targeting BCR/ABL with tyrosine kinase inhibitors (TKIs) is a proven concept for the treatment of Philadelphia chromosome-positive (Ph+) leukemias. Resistance attributable to either kinase mutations in BCR/ABL or nonmutational mechanisms remains the major clinical challenge. With the exception of ponatinib, all approved TKIs are unable to inhibit the ‘gatekeeper’ mutation T315I. However, a broad spectrum of kinase inhibition increases the off-target effects of TKIs and may be responsible for cardiovascular issues of ponatinib. Thus, there is a need for more selective options for the treatment of resistant Ph+ leukemias. PF-114 is a novel TKI developed with the specifications of (i) targeting T315I and other resistance mutations in BCR/ABL; (ii) achieving a high selectivity to improve safety; and (iii) overcoming nonmutational resistance in Ph+ leukemias. PF-114 inhibited BCR/ABL and clinically important mutants including T315I at nanomolar concentrations. It suppressed primary Ph+ acute lymphatic leukemia-derived long-term cultures that either displayed nonmutational resistance or harbor the T315I. In BCR/ABL- or BCR/ABL–T315I-driven murine leukemia as well as in xenograft models of primary Ph+ leukemia harboring the T315I, PF-114 significantly prolonged survival to a similar extent as ponatinib. Our work supports clinical evaluation of PF-114 for the treatment of resistant Ph+ leukemia.

Improving performance of docking-based virtual screening by structural filtration

AbstractIn the current study an innovative method of structural filtration of docked ligand poses is introduced and applied to improve the virtual screening results. The structural filter is defined by a protein-specific set of interactions that are a) structurally conserved in available structures of a particular protein with its bound ligands, and b) that can be viewed as playing the crucial role in protein-ligand binding. The concept was evaluated on a set of 10 diverse proteins, for which the corresponding structural filters were developed and applied to the results of virtual screening obtained with the Lead Finder software. The application of structural filtration resulted in a considerable improvement of the enrichment factor ranging from several folds to hundreds folds depending on the protein target. It appeared that the structural filtration had effectively repaired the deficiencies of the scoring functions that used to overestimate decoy binding, resulting into a considerably lower false positive rate. In addition, the structural filters were also effective in dealing with some deficiencies of the protein structure models that would lead to false negative predictions otherwise. The ability of structural filtration to recover relatively small but specifically bound molecules creates promises for the application of this technology in the fragment-based drug discovery. FigureImprovement of virtual screening performance by structural filtration for ADRB2 as a target. Positions of the native ligands obtained during virtual screening are depicted by vertical bars. Upper part of the plot corresponds to the performance of docking-based screening; lower part - docking-based screening followed by structural filtration. Lower part of the plot contains all (48) native ligands of ADRB2, which fall into the top 0.6% of the screened library.

TSAR, a new graph–theoretical approach to computational modeling of protein side‐chain flexibility: Modeling of ionization properties of proteins

A new graph–theoretical approach called thermodynamic sampling of amino acid residues (TSAR) has been elaborated to explicitly account for the protein side chain flexibility in modeling conformation‐dependent protein properties. In TSAR, a protein is viewed as a graph whose nodes correspond to structurally independent groups and whose edges connect the interacting groups. Each node has its set of states describing conformation and ionization of the group, and each edge is assigned an array of pairwise interaction potentials between the adjacent groups. By treating the obtained graph as a belief‐network—a well‐established mathematical abstraction—the partition function of each node is found. In the current work we used TSAR to calculate partition functions of the ionized forms of protein residues. A simplified version of a semi‐empirical molecular mechanical scoring function, borrowed from our Lead Finder docking software, was used for energy calculations. The accuracy of the resulting model was validated on a set of 486 experimentally determined pKa values of protein residues. The average correlation coefficient (R) between calculated and experimental pKa values was 0.80, ranging from 0.95 (for Tyr) to 0.61 (for Lys). It appeared that the hydrogen bond interactions and the exhaustiveness of side chain sampling made the most significant contribution to the accuracy of pKa calculations. Proteins 2011;

Quantum chemical studies of the catalytic mechanism of N-terminal nucleophile hydrolase.

Modeling of the catalytic mechanism of penicillin acylase, a member of the N-terminal nucleophile hydrolase superfamily, is for the first time conducted at ab initio quantum chemistry level. The uniqueness of this family of enzymes is that their active site lacks His and Asp (Glu) residues, comprising together with a serine residue the classical catalytic triad. The current investigation confirms that the amino group of the N-terminal serine residue in N-terminal hydrolases is capable of activating its own hydroxyl group. Using the MP2/RHF method with the 6−31+G** basis set, stationary points on the potential energy surface of the considered molecular system were located, corresponding to local minima (complexes of reagents, products, intermediate) and to saddle points (transition states). It turned out that the stage of acyl-serine formation proceeds via two transition states; the first one, which separates reagents from the so-called tetrahedral intermediate, has the highest relative energy (30 kcal/mol). In contrast to recently proposed empiric suggestions, we have found that participation of a bridging water molecule in proton shuttling is not necessary for the catalysis. The quantum chemical calculations showed a crucial role of a specific solvation in decreasing the activation barrier of the reaction by approximately 10 kcal/mol.