Thioether acetamides as P3 binding elements for tetrahydropyrido-pyrazole cathepsin S inhibitors

Wiener, Danielle K.; Lee-Dutra, Alice; Bembenek, Scott D.; Nguyen, Steven; Thurmond, Robin L.; Sun, Siquan; Karlsson, Lars; Grice, Cheryl A.; Jones, Todd K.; Edwards, James P.

doi:10.1016/j.bmcl.2010.01.103

Cited by 15 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This allowed us to evaluate the impact our choices had on the quality of our poses. We used four Cathepsin S structures (PDBids: 3IEJ, 3KWN, 3MPE, 3MPF) [ 17 – 19 ] and their respective ligands. After selecting the protein template based on the protocol described in “ Protein template selection ”, we selected the ligand conformers by their TanimotoCombo score and after superimposing them to the site of the crystallographic ligand, proceeded to refine them (see “ Docking ” below).…”

Section: Methodsmentioning

confidence: 99%

Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3

Koukos

Bonvin

2018

J Comput Aided Mol Des

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3

Koukos

Bonvin

2018

J Comput Aided Mol Des

View full text Add to dashboard Cite

show abstract

“…(7). D) cocrystal pose (PDBID: 5QC4 (11)) Ligand carbons are pink; ligand common core carbons are yellow; key binding residues PHE71, VAL163, and PHE212 are green. E) The RMSDs of the ligand core for each pose in each Glide docking method show that blind poses were concentrated farther from the cocrystal position compared to the ligand-core-restrained docking.…”

Section: Resultsmentioning

confidence: 99%

“…A crystal structure of CatS (PDBID: 5QC4 (11)) was obtained from the RCSB PDB database (50). The structure was chosen due to its resolution of 2 Å and similarity of the cocrystallized ligand to those in the D3R dataset.…”

Section: Methodsmentioning

confidence: 99%

“…Glides cross-docking script, xglide.py, was used to perform ensemble docking for each clustering method. The cross-docking script generated receptor grid files for each centroid structure using a 32 Å box centered on the center of mass of the crystal structure’s ligand (BC7 (11)) to define the docking region. Each centroid was then docked to using Glides Standard Precision (SP) docking methodology, which has its own ligand conformer generation steps, and scored with the subsequent Standard Precision GlideScore scoring function (47, 71).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Benchmarking ensemble docking methods as a scientific outreach project

Gan

Kumar²,

Chen

et al. 2020

Preprint

View full text Add to dashboard Cite

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that CatS is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.

show abstract

“…Structures of inhibitor-cathepsin complexes available in the PDB were compared to the enzyme-ligand interactions found in this work. A 4-(methyl sulfonyl) phenyl trifluoroethylamine amide derivative (PDB code 1vsn, an analog of cathepsin K inhibitor odanacatib) in complex with cathepsin K [38]; 4-biphenylacetyl-Cys-(D) Arg-Tyr-N-(2-phenylethyl) amide (PDB code 1mhw) in complex with cathepsin L [39], and thioether acetamide P3 inhibitor (PDB code 3kwn) bound to cathepsin S [40] were used. Negative charges of catalytic thiols in the 3kwn and 1mhw files were imposed; this last step was not necessary for 1vsn, since in this case the ligand is covalently linked to the active-site cysteine of cathepsin K. The geometry of these three structures was optimized in the same way as for the natural and chimeric complexes.…”

Section: Analysis Of Specific Inhibitor Moleculesmentioning

confidence: 99%

What comparisons of natural and chimeric contacts reveal about inhibition of human cathepsins K, L and S by their prosegments

et al. 2019

View full text Add to dashboard Cite

Human cathepsins K, L, and S, which are involved in the development of several serious diseases, are strongly inhibited by their related prosegments, to which they are covalently bound or simply forming complexes. In this work, three-dimensional structures of the three natural complexes of these enzymes with their related proregions were constructed, as well as six chimeric complexes of the same three prosegments with their non-cognate enzymes. We made a comparative study of the contacts in all nine structures throughout their active sites. The analysis was performed looking for a structural parameter that could agree with the values of the inhibition constants reported experimentally for each of the nine complexes. We found that this correlating parameter was the difference of the electrostatic energy (involving hydrogen bonds and ion pairs) at the binding interface of a 13-amino acid fragment of the prosegments. We used the results of this work, on the one hand, to identify the key residues involved in the electrostatic intermolecular recognition in each studied complex and, on the other, to explain some results achieved by different research groups on the inhibition of the same enzymes analyzed here. It was found that the natural cathepsin L complex showed a higher number of electrostatic interactions, some of them interconnected, when compared to the other two natural complexes. In addition, the chimeric contacts revealed binding sites that could be used to achieve a more potent inhibition of these cathepsins, avoiding cross-interactions.

show abstract

Thioether acetamides as P3 binding elements for tetrahydropyrido-pyrazole cathepsin S inhibitors

Cited by 15 publications

References 13 publications

Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3

Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3

Benchmarking ensemble docking methods as a scientific outreach project

What comparisons of natural and chimeric contacts reveal about inhibition of human cathepsins K, L and S by their prosegments

Contact Info

Product

Resources

About