Alexey A. Zeifman

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.

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;

Placental transfer of tyrosine kinase inhibitors used for chronic myeloid leukemia treatment

Abstract Both favorable pregnancy outcomes and fetal abnormalities have been associated with the use of tyrosine kinase inhibitors (TKIs) during pregnancy. The placental transfer of TKIs in humans is poorly understood. We observed women with chronic myeloid leukemia who used imatinib or nilotinib during the late pregnancy stages. The newborns had no birth abnormalities. We evaluated the drug concentrations in maternal blood, umbilical cord blood, and placental samples collected during labor. We found limited placental transfer of the TKIs. The fetal/maternal concentration ratio ranged from 0.5 to 0.58 for nilotinib and from 0.05 to 0.22 for imatinib. The placental/maternal ratio was higher for imatinib than for nilotinib. Theoretical pharmacokinetic modeling of passive placental crossing was insufficient to predict the in vivo data because the calculated fetal/maternal ratio was close to 1 for both drugs. We propose that active placental transport contributes to fetal protection against TKI exposure during pregnancy.

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.