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Molecular modeling based on the X-ray crystal structure of the Tang-Ghosh heptapeptide inhibitor 1 (OM99-2) of BACE led to the design and synthesis of a series of constrained P(1)' analogues. A cyclopentane ring was incorporated in 1 spanning the P(1)' Ala methyl group and the adjacent methylene carbon atom of the chain. Progressive truncation at the P(2)'-P(4)' sites led to a potent truncated analogue 5 with good selectivity over Cathepsin D. Using the same backbone replacement concept, a series of cyclopentane, cyclopentanone, tetrahydrofuran, pyrrolidine, and pyrrolidinone analogues were synthesized with considerable variation at the P and P' sites. The cyclopentanone and 2-pyrrolidinone analogues 45 and 57 showed low nM BACE inhibition. X-ray cocrystal structures of two analogues 5 and 45 revealed excellent convergence with the original inhibitor 1 structure while providing new insights into other interactions which could be exploited for future modifications.

The metabolism of xenobioticsand more specifically drugsin the liver is a critical process controlling their half-life. Although there exist experimental methods, which measure the metabolic stability of xenobiotics and identify their metabolites, developing higher throughput predictive methods is an avenue of research. It is expected that predicting the chemical nature of the metabolites would be an asset for designing safer drugs and/or drugs with modulated half-lives. We have developed IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes and Transition States), a computational tool combining docking to metabolic enzymes, transition state modeling, and rule-based substrate reactivity prediction to predict the site of metabolism (SoM) of xenobiotics. Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates and comparison to experts' predictions demonstrates its accuracy and significance. IMPACTS identified an experimentally observed SoM in the top 2 predicted sites for 77% of the substrates, while the accuracy of biotransformation experts' prediction was 65%. Application of IMPACTS to external sets and comparison of its accuracy to those of eleven other methods further validated the method implemented in IMPACTS.

As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.

Inhibition of beta-secretase (BACE 1) has recently been investigated as a promising therapeutic approach in the treatment of Alzheimer's disease, and a growing number of BACE 1 inhibitors and crystal structures of BACE 1/inhibitors complexes have been reported. We report herein a predictive computational method and its application to potential BACE 1 inhibitors. Using a training set of 50 known highly flexible inhibitors, we developed a docking method that accounts for the flexibility of both the protein and the inhibitors. Protein flexibility is accounted for using a specifically designed genetic algorithm. We next developed a scoring function consisting of force field evaluation of the inhibitor/protein interactions and two additional terms for hydrogen bonding and entropy change upon binding. Discarding three outliers from the training set, our protocol was found to perform well with an rmsd of 1.19 kcal/mol. Evaluation of the predictive power was next carried out by virtual screening of 80 synthetic compounds. The significant enrichment at the top of the ranking list in active compounds demonstrated the ability of the docking and scoring protocol to rank the compounds relative to their activities.

Our docking program, Fitted, implemented in our computational platform, Forecaster, has been modified to carry out automated virtual screening of covalent inhibitors. With this modified version of the program, virtual screening and further docking-based optimization of a selected hit led to the identification of potential covalent reversible inhibitors of prolyl oligopeptidase activity. After visual inspection, a virtual hit molecule together with four analogues were selected for synthesis and made in one-five chemical steps. Biological evaluations on recombinant POP and FAPα enzymes, cell extracts, and living cells demonstrated high potency and selectivity for POP over FAPα and DPPIV. Three compounds even exhibited high nanomolar inhibitory activities in intact living human cells and acceptable metabolic stability. This small set of molecules also demonstrated that covalent binding and/or geometrical constraints to the ligand/protein complex may lead to an increase in bioactivity.

Metalloenzymes are ubiquitous proteins which feature one or more metal ions either directly involved in the enzymatic activity and/or structural properties (i.e., zinc fingers). Several members of this class take advantage of the Lewis acidic properties of zinc ions to carry out their various catalytic transformations including isomerization or amide cleavage. These enzymes have been validated as drug targets for a number of diseases including cancer; however, despite their pharmaceutical relevance and the availability of crystal structures, structure-based drug design methods have been poorly and indirectly parametrized for these classes of enzymes. More specifically, the metal coordination component and proton transfers of the process of drugs binding to metalloenzymes have been inadequately modeled by current docking programs, if at all. In addition, several known issues, such as coordination geometry, atomic charge variability, and a potential proton transfer from small molecules to a neighboring basic residue, have often been ignored. We report herein the development of specific functions and parameters to account for zinc-drug coordination focusing on the above-listed phenomena and their impact on docking to zinc metalloenzymes. These atom-type-dependent but atomic charge-independent functions implemented into Fitted 3.1 enable the simulation of drug binding to metalloenzymes, considering an acid-base reaction with a neighboring residue when necessary with good accuracy.

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