Top Scoring Pose Research Articles

Structure-based pose prediction: Non-cognate docking extended to macrocyclic ligands

So-called “cross-docking” is the prediction of the bound configuration of small-molecule ligands that differ from the cognate ligand of a protein co-crystal structure. This is a much more challenging problem than re-docking the cognate ligand, particularly when the new ligand is structurally dissimilar from prior known ones. We have updated the previously introduced PINC (“PINC Is Not Cognate”) benchmark which introduced the idea of temporal segregation to measure cross-docking performance. The temporal set encompasses 846 future ligands for ten targets based on information from the earliest 25% of X-ray co-crystal structures known for each target. Here, we extend the benchmark to include thirteen targets where the bound poses of 128 macrocyclic ligands are to be predicted based on knowledge from structures of bound non-macrocyclic ligands. Performance was roughly equivalent for both the temporally-split non-macrocyclic ligand set and the macrocycle prediction set. Using standard and fully automatic protocols for the Surflex-Dock and ForceGen methods, across the combined 974 non-macrocyclic and macrocyclic ligands, the top-scoring pose family was correct 68% of the time, with the top-two pose families achieving a 79% success rate. Correct poses among all those predicted were identified 92% of the time. These success rates far exceeded those observed for the alternative methods AutoDock Vina and Gnina on both sets.

Relative Binding Energies Predict Crystallographic Binding Modes of Ethionamide Booster Lead Compounds.

Transcriptional repressor EthR from Mycobacterium tuberculosis is a valuable target for antibiotic booster drugs. We previously reported a virtual screening campaign to identify EthR inhibitors for development. Two ligand binding orientations were often proposed, though only the top scoring pose was utilized for filtering of the large data set. We obtained biophysically validated hits, some of which yielded complex crystal structures. In some cases, the crystallized binding mode and top scoring mode agree, while for others an alternate ligand binding orientation was found. In this contribution, we combine rigid docking, molecular dynamics simulations, and the linear interaction energy method to calculate binding free energies and derive relative binding energies for a number of EthR inhibitors in both modes. This strategy allowed us to correctly predict the most favorable orientation. Therefore, this widely applicable approach will be suitable to triage multiple binding modes within EthR and other potential drug targets with similar characteristics.

Protein\u2013ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3

We report the performance of HADDOCK in the 2018 iteration of the Grand Challenge organised by the D3R consortium. Building on the findings of our participation in last year’s challenge, we significantly improved our pose prediction protocol which resulted in a mean RMSD for the top scoring pose of 3.04 and 2.67 Å for the cross-docking and self-docking experiments respectively, which corresponds to an overall success rate of 63% and 71% when considering the top1 and top5 models respectively. This performance ranks HADDOCK as the 6th and 3rd best performing group (excluding multiple submissions from a same group) out of a total of 44 and 47 submissions respectively. Our ligand-based binding affinity predictor is the 3rd best predictor overall, behind only the two leading structure-based implementations, and the best ligand-based one with a Kendall’s Tau correlation of 0.36 for the Cathepsin challenge. It also performed well in the classification part of the Kinase challenges, with Matthews Correlation Coefficients of 0.49 (ranked 1st), 0.39 (ranked 4th) and 0.21 (ranked 4th) for the JAK2, vEGFR2 and p38a targets respectively. Through our participation in last year’s competition we came to the conclusion that template selection is of critical importance for the successful outcome of the docking. This year we have made improvements in two additional areas of importance: ligand conformer selection and initial positioning, which have been key to our excellent pose prediction performance this year.

Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock.

Prediction of the bound configuration of small-molecule ligands that differ substantially from the cognate ligand of a protein co-crystal structure is much more challenging than re-docking the cognate ligand. Success rates for cross-docking in the range of 20–30 % are common. We present an approach that uses structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later. The knowledge-guided docking protocol was tested on a set of ten protein targets using a total of 949 ligands. The benchmark data set, called PINC (“PINC Is Not Cognate”), is publicly available. Protein pocket similarity was used to choose representative structures for ensemble-docking. The docking protocol made use of known ligand poses prior to the cutoff-date, both to help guide the configurational search and to adjust the rank of predicted poses. Overall, the top-scoring pose family was correct over 60 % of the time, with the top-two pose families approaching a 75 % success rate. Correct poses among all those predicted were identified nearly 90 % of the time. The largest improvements came from the use of molecular similarity to improve ligand pose rankings and the strategy for identifying representative protein structures. With the exception of a single outlier target, the knowledge-guided docking protocol produced results matching the quality of cognate-ligand re-docking, but it did so on a very challenging temporally-segregated cross-docking benchmark.

Abstract 5372: A molecular docking strategy for identifying fragment inhibitors of indoleamine 2,3-dioxygenase 1 (IDO1)

Abstract As part of our studies to identify novel inhibitors for the immunosuppressive enzyme indoleamine 2,3-dioxygenase 1 (IDO1) for restoring tumor immunity, we investigated the ability of the enzyme's x-ray crystal structure to predict active molecules when used in molecular docking based virtual screening of fragment libraries. The 352 compound Zenobia Therapeutics fragment library was screened by differential scanning fluorimetry and 74 compounds were identified that altered the thermal stability of the protein. Of these, biochemical analysis showed that only 14 inhibited the enzyme activity by >50% at 1 mM, and hydrazine containing fragments were the most potent inhibitors of recombinant human IDO1 (rhIDO1), with the best being a benzothiazole compound retrieving an IC50 of 8 µM. We recently showed that phenylhydrazine is a more potent inhibitor (IC50 0.3 µM), which provided a dataset for testing various docking strategies to discover fragment inhibitors. The fragment library was docked into the IDO1 active site (PDB code 2D0T) using GOLD driven by the scoring functions ChemPLP, Goldscore, ASP or Chemscore. The three top scoring poses for each compound were kept then rescored by each scoring function. The combination of docking by Goldscore and rescoring by Chemscore to identify the top scoring pose gave the best outcome, an 8.3 fold enrichment of active compounds within the top 5% of the ranked library compared to random selection. Based on this docking and scoring strategy we performed a virtual screening campaign on a 14,000 compound library containing commercially available phenylhydrazine derivatives. Our characterization of phenylhydrazine binding indicated that the hydrazine moiety interacts with the heme iron. Based on this, a set of compounds was selected from the top 30% of poses kept from the docked library for biochemical testing that had predicted hydrazine to heme iron interactions. Of 21 compounds chosen, 10 were tested and 9 inhibited IDO1 with IC50 of < 10 µM. Also, compounds predicted to make a hydrogen bond acceptor to heme iron interaction were selected and an additional 9 compounds chosen of which 6 were tested and only 1 inhibited rhIDO1 with IC50 of < 10 µM. For these latter ones the hydrazine was not predicted to interact with the heme iron. This study indicates that the rhIDO1 structure would be useful for discovering inhibitors in a diverse fragment library, and an additional hydrazine to heme iron binding descriptor is useful for finding actives in a library of compounds containing the phenylhydrazine core. Citation Format: Simon Fung, Jack U. Flanagan, Christopher J. Squire, Brian D. Palmer, Lai-Ming Ching. A molecular docking strategy for identifying fragment inhibitors of indoleamine 2,3-dioxygenase 1 (IDO1). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5372. doi:10.1158/1538-7445.AM2014-5372

Variability in docking success rates due to dataset preparation

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.

Generation of structure-based pharmacophores using energetic analysis – application on fragment docking

We describe a novel method to develop energetically optimized, structure-based pharmacophores for use in rapid in silico screening. The method combines pharmacophore perception and database screening with protein ligand energetic terms computed by the Glide XP scoring function to rank the importance of pharmacophore features. We derive energy-optimized pharmacophore hypotheses for 30 pharmaceutically relevant crystal structures and screen a database to assess the enrichment of active compounds. The method is compared to three other approaches: (1) pharmacophore hypotheses derived from a systematic assessment of receptor ligand contacts, (2) Glide SP docking, and (3) 2D ligand fingerprint similarity. The method developed here shows better enrichments than the other three methods and yields a greater diversity of actives than the contact-based pharmacophores or the 2D ligand similarity. We then apply this method on fragment docking using fragment-specific settings aimed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. We show that the most energetically favourable pharmacophore sites are consistent with features from known tight binding compounds. The derived pharmacophore models are able to recover known active compounds from a database screen and retrieving diverse compounds that are not biased by the co-crystallized ligand.

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation

We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 A for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.

Is it possible to increase hit rates in structure-based virtual screening by pharmacophore filtering? An investigation of the advantages and pitfalls of post-filtering

We have investigated the influence of post-filtering virtual screening results, with pharmacophoric features generated from an X-ray structure, on enrichment rates. This was performed using three docking softwares, zdock+, Surflex and FRED, as virtual screening tools and pharmacophores generated in UNITY from co-crystallized complexes. Sets of known actives along with 9997 pharmaceutically relevant decoy compounds were docked against six chemically diverse protein targets namely CDK2, COX2, ERα, fXa, MMP3, and NA. To try to overcome the inherent limitations of the well-known docking problem, we generated multiple poses for each compound. The compounds were first ranked according to their scores alone and enrichment rates were calculated using only the top scoring pose of each compound. Subsequently, all poses for each compound were passed through the different pharmacophores generated from co-crystallized complexes and the enrichment factors were re-calculated based on the top-scoring passing pose of each compound. Post-filtering with a pharmacophore generated from only one X-ray complex was shown to increase enrichment rates in all investigated targets compared to docking alone. This indicates that this is a general method, which works for diverse targets and different docking softwares.