Publications by authors named "M A Sanner"

87 Publications

Improving Docking Power for Short Peptides Using Random Forest.

J Chem Inf Model 2021 Jun 14;61(6):3074-3090. Epub 2021 Jun 14.

Koliber Biosciences, Inc., 12265 World Trade Drive, Suite G, San Diego, California 92128, United States.

In recent years, therapeutic peptides have gained a lot interest as demonstrated by the 60 peptides approved as drugs in major markets and 150+ peptides currently in clinical trials. However, while small molecule docking is routinely used in rational drug design efforts, docking peptides has proven challenging partly because docking scoring functions, developed and calibrated for small molecules, perform poorly for these molecules. Here, we present random forest classifiers trained to discriminate correctly docked peptides. We show that, for a testing set of 47 protein-peptide complexes, structurally dissimilar from the training set and previously used to benchmark AutoDock Vina's ability to dock short peptides, these random forest classifiers improve docking power from ∼25% for AutoDock scoring functions to an average of ∼70%. These results pave the way for peptide-docking success rates comparable to those of small molecule docking. To develop these classifiers, we compiled the ProptPep37_2021 data set, a curated, high-quality set of 322 crystallographic protein-peptides complexes annotated with structural similarity information. The data set also provides a collection of high-quality putative poses with a range of deviations from the crystallographic pose, providing correct and incorrect poses (i.e., decoys) of the peptide for each entry. The ProptPep37_2021 data set as well as the classifiers presented here are freely available.
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http://dx.doi.org/10.1021/acs.jcim.1c00573DOI Listing
June 2021

Cyclic Peptides as Protein Kinase Inhibitors: Structure-Activity Relationship and Molecular Modeling.

J Chem Inf Model 2021 Jun 17;61(6):3015-3026. Epub 2021 May 17.

Center for Targeted Drug Delivery, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Harry and Diane Rinker Health Science Campus, Irvine, California 92618, United States.

Under-expression or overexpression of protein kinases has been shown to be associated with unregulated cell signal transduction in cancer cells. Therefore, there is major interest in designing protein kinase inhibitors as anticancer agents. We have previously reported [WR], a peptide containing alternative arginine (R) and tryptophan (W) residues as a non-competitive c-Src tyrosine kinase inhibitor. A number of larger cyclic peptides containing alternative hydrophobic and positively charged residues [WR] ( = 6-9) and hybrid cyclic-linear peptides, [RK]W and [RK]W, containing R and W residues were evaluated for their protein kinase inhibitory potency. Among all the peptides, cyclic peptide [WR] was found to be the most potent tyrosine kinase inhibitor. [WR] showed higher inhibitory activity (IC = 0.21 μM) than [WR], [WR], [WR], and [WR] with IC values of 0.81, 0.57, 0.35, and 0.33 μM, respectively, against c-Src kinase as determined by a radioactive assay using [γ-P]ATP. Consistent with the result above, [WR] inhibited other protein kinases such as Abl kinase activity with an IC value of 0.35 μM, showing 2.2-fold higher inhibition than [WR] (IC = 0.79 μM). [WR] also inhibited PKCa kinase activity with an IC value of 2.86 μM, approximately threefold higher inhibition than [WR] (IC = 8.52 μM). A similar pattern was observed against Braf, c-Src, Cdk2/cyclin A1, and Lck. [WR] exhibited IC values of <0.25 μM against Akt1, Alk, and Btk. These data suggest that [WR] is consistently more potent than other cyclic peptides with a smaller ring size and hybrid cyclic-linear peptides [RK]W and [RK]W against selected protein kinases. Thus, the presence of R and W residues in the ring, ring size, and the number of amino acids in the structure of the cyclic peptide were found to be critical in protein kinase inhibitory potency. We identified three putative binding pockets through automated blind docking of cyclic peptides [WR]. The most populated pocket is located between the SH2, SH3, and N-lobe domains on the opposite side of the ATP binding site. The second putative pocket is formed by the same domains and located on the ATP binding site side of the protein. Finally, a third pocket was identified between the SH2 and SH3 domains. These results are consistent with the non-competitive nature of the inhibition displayed by these molecules. Molecular dynamics simulations of the protein-peptide complexes indicate that the presence of either [WR] or [WR] affects the plasticity of the protein and in particular the volume of the ATP binding site pocket in different ways. These results suggest that the second pocket is most likely the site where these peptides bind and offer a plausible rationale for the increased affinity of [WR].
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http://dx.doi.org/10.1021/acs.jcim.1c00320DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238896PMC
June 2021

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.

J Chem Theory Comput 2021 Feb 6;17(2):1060-1073. Epub 2021 Jan 6.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States.

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.
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http://dx.doi.org/10.1021/acs.jctc.0c01006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8063785PMC
February 2021

The AutoDock suite at 30.

Protein Sci 2021 01 12;30(1):31-43. Epub 2020 Sep 12.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.
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http://dx.doi.org/10.1002/pro.3934DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737764PMC
January 2021

C-terminal residues of activated protein C light chain contribute to its anticoagulant and cytoprotective activities.

J Thromb Haemost 2020 05 5;18(5):1027-1038. Epub 2020 Mar 5.

Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, USA.

Background: Activated protein C (APC) is an important homeostatic blood coagulation protease that conveys anticoagulant and cytoprotective activities. Proteolytic inactivation of factors Va and VIIIa facilitated by cofactor protein S is responsible for APC's anticoagulant effects, whereas cytoprotective effects of APC involve primarily the endothelial protein C receptor (EPCR), protease activated receptor (PAR)1 and PAR3.

Objective: To date, several binding exosites in the protease domain of APC have been identified that contribute to APC's interaction with its substrates but potential contributions of the C-terminus of the light chain have not been studied in detail.

Methods: Site-directed Ala-scanning mutagenesis of six positively charged residues within G142-L155 was used to characterize their contributions to APC's anticoagulant and cytoprotective activities.

Results And Conclusions: K151 was involved in protein S dependent-anticoagulant activity of APC with some contribution of K150. 3D structural analysis supported that these two residues were exposed in an extended protein S binding site on one face of APC. Both K150 and K151 were important for PAR1 and PAR3 cleavage by APC, suggesting that this region may also mediate interactions with PARs. Accordingly, APC's cytoprotective activity as determined by endothelial barrier protection was impaired by Ala substitutions of these residues. Thus, both K150 and K151 are involved in APC's anticoagulant and cytoprotective activities. The differential contribution of K150 relative to K151 for protein S-dependent anticoagulant activity and PAR cleavage highlights that binding exosites for protein S binding and for PAR cleavage in the C-terminal region of APC's light chain overlap.
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http://dx.doi.org/10.1111/jth.14756DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7380734PMC
May 2020
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