Fedor N. Novikov

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.

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.

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;

Molecular modeling of different substrate-binding modes and their role in penicillin acylase catalysis

Molecular modeling was addressed to understand different substrate‐binding modes and clarify the role of two positively charged residues of the penicillin G acylase active site – βR263 and αR145 – in binding of negatively charged substrates. Although the electrostatic contribution to productive substrate binding was dominated by βR263 rather than αR145, it was found that productive binding was not the only possible mode of substrate placement in the active site. Two extra binding modes – nonproductive and preproductive – were located by means of molecular docking and dynamics with binding affinities comparable with the productive one. A unique feature of nonproductive and preproductive complexes was that the substrates acyl group did not penetrate the hydrophobic pocket, but occupied a patch on the protein interface spanning from βR263 to αR145. Nonproductive and preproductive complexes competed with each other and productive binding mode, giving rise to increased apparent substrate binding. Preproductive complex revealed an ability to switch to a productive one during molecular dynamics simulations, and conformational plasticity of the penicillin G acylase active site was shown to be crucial for that. Nonproductive binding observed at molecular modeling corresponded well with experimentally observed substrate inhibition in penicillin acylase catalysis. By combining estimated free energies of substrate binding in each mode, and accounting for two possible conformations of the penicillin G acylase active site (closed and open) quantitative agreement with experimentally measured KM values was achieved. Calculated near‐attack conformation frequencies from corresponding molecular dynamics simulations were in a quantitative correlation with experimental kcat values and demonstrated adequate application of molecular modeling methods.

Novel Antitumor L-Arabinose Derivative of Indolocarbazole with High Affinity to DNA

Novel indolocarbazole derivative 12‐(α‐L‐arabinopyranosyl)indolo[2,3‐α]pyrrolo[3,4‐c]carbazole‐5,7‐dione (AIC) demonstrated high potency (at submicromolar concentrations) against the NCI panel of human tumor cell lines and transplanted tumors in vivo. In search of tentative targets for AIC, we found that the drug formed high affinity intercalative complexes with d(AT)20, d(GC)20 and calf thymus DNA (binding constants (1.6×106) M−1≤Ka≤(3.3×106) M−1). The drug intercalated preferentially into GC pairs of the duplex. Importantly, the concentrations at which AIC formed the intercalative complexes with DNA (C≤1 μM) were identical to the concentrations that triggered p53‐dependent gene reporter transactivation, the replication block, the inhibition of topoisomerase I‐mediated DNA relaxation and death of HCT116 human colon carcinoma cells. We conclude that the formation of high affinity intercalative complexes with DNA is an important factor for anticancer efficacy of AIC.

Improved procedure of the search for poly(ADP-Ribose) polymerase-1 potential inhibitors with the use of the molecular docking approach

A search for poly(ADP-ribose) polymerase-1 inhibitors by virtual screening of a chemical compound database and a subsequent experimental verification of their activities have been performed. It was shown that the most efficient method to predict inhibitory properties implies a combinatorial approach joining molecular docking capabilities with structural filtration. Among more than 300000 low molecular chemical compounds, 9 PARP1 inhibitors were revealed; the most active ones, namely, STK031481, STK056130, and STK265022, displayed biological effect at a micromolar concentration (IC50 = 2.0, 1.0, and 2.6 μM, respectively).

2,3-Dihydroxy-quinoxaline induces ATPase activity of Herpes Simplex Virus thymidine kinase

2,3‐Dihydroxy‐quinoxaline, a small molecule that promotes ATPase catalytic activity of Herpes Simplex Virus thymidine kinase (HSV‐TK), was identified by virtual screening. This compound competitively inhibited HSV‐TK catalyzed phosphorylation of acyclovir with K i = 250 μM (95% CI: 106–405 μM) and dose‐dependently increased the rate of the ATP hydrolysis with K M = 112 μM (95% CI: 28–195 μM). The kinetic scheme consistent with this experimental data is proposed.