The results of cognate docking with the prepared Astex dataset provided by the organizers of the “Docking and Scoring: A Review of Docking Programs” session at the 241st ACS national meeting are presented. The MOE software with the newly developed GBVI/WSA dG scoring function is used throughout the study. For 80 % of the Astex targets, the MOE docker produces a top-scoring pose within 2 Å of the X-ray structure. For 91 % of the targets a pose within 2 Å of the X-ray structure is produced in the top 30 poses. Docking failures, defined as cases where the top scoring pose is greater than 2 Å from the experimental structure, are shown to be largely due to the absence of bound waters in the source dataset, highlighting the need to include these and other crucial information in future standardized sets. Docking success is shown to depend heavily on data preparation. A “dataset preparation” error of 0.5 kcal/mol is shown to cause fluctuations of over 20 % in docking success rates.

In this work, four methods are described and validated for generating in silico ensembles of PROTACmediated ternary complexes. Filters based on characteristics of the proposed ternary complexes are developed to identify those that resemble known crystal structures. We then show how to use these modeling techniques a priori to discriminate the PROTAC-mediated degradation behavior of a mutant protein vs its wild type, of three closely related targets, and among three different PROTAC molecules.

The sensitivity of docking calculations to the geometry of the input ligand was studied. It was found that even small changes in the ligand input conformation can lead to large differences in the geometries and scores of the resulting docked poses. The accuracy of docked poses produced from different ligand input structures-the X-ray structure, the minimized Corina structure, and structures generated from conformational searches and molecular dynamics ensembles-were also assessed. It was found that using the X-ray ligand conformation as docking input does not always produce the most accurate docked pose when compared with other sources of ligand input conformations. Furthermore, no one method of conformer generation is guaranteed to always produce the most accurate docking pose. The docking scores are also highly sensitive to the source of the input conformation, which might introduce some noise in compound ranking and in binding affinity predictions. It is concluded that for the purposes of reproducibility and optimal performance, the most prudent procedure is to use multiple input structures for docking. The implications of these results on docking validation studies are discussed.

The use of high throughput screening (HTS) to identify lead compounds has greatly challenged conventional quantitative structure-activity relationship (QSAR) techniques that typically correlate structural variations in similar compounds with continuous changes in biological activity. A new QSAR-like methodology that can correlate less quantitative assay data (i.e., "active" versus "inactive"), as initially generated by HTS, has been introduced. In the present study, we have, for the first time, applied this approach to a drug discovery problem; that is, the study of the estrogen receptor ligands. The binding affinities of 463 estrogen analogues were transformed into a binary data format, and a predictive binary QSAR model was derived using 410 estrogen analogues as a training set. The model was applied to predict the activity of 53 estrogen analogues not included in the training set. An overall accuracy of 94% was obtained.

This work examines the sensitivity of docking programs to tiny changes in ligand input files. The results show that nearly identical ligand input structures can produce dramatically different top-scoring docked poses. Even changing the atom order in a ligand input file can produce significantly different poses and scores. In well-behaved cases the docking variations are small and follow a normal distribution around a central pose and score, but in many cases the variations are large and reflect wildly different top scores and binding modes. The docking variations are characterized by statistical methods, and the sensitivity of high-throughput and more precise docking methods are compared. The results demonstrate that part of docking variation is due to numerical sensitivity and potentially chaotic effects in current docking algorithms and not solely due to incomplete ligand conformation and pose searching. These results have major implications for the way docking is currently used for pose prediction, ranking, and virtual screening.