Publications by authors named "Heather A Carlson"

74 Publications

Identification of Cryptic Binding Sites Using MixMD with Standard and Accelerated Molecular Dynamics.

J Chem Inf Model 2021 Mar 18;61(3):1287-1299. Epub 2021 Feb 18.

Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, Michigan 48109-1056, United States.

Protein dynamics play an important role in small molecule binding and can pose a significant challenge in the identification of potential binding sites. Cryptic binding sites have been defined as sites which require significant rearrangement of the protein structure to become physically accessible to a ligand. Mixed-solvent MD (MixMD) is a computational protocol which maps the surface of the protein using molecular dynamics (MD) of the unbound protein solvated in a 5% box of probe molecules with explicit water. This method has successfully identified known active and allosteric sites which did not require reorganization. In this study, we apply the MixMD protocol to identify known cryptic sites of 12 proteins characterized by a wide range of conformational changes. Of these 12 proteins, three require reorganization of side chains, five require loop movements, and four require movement of more significant structures such as whole helices. In five cases, we find that standard MixMD simulations are able to map the cryptic binding sites with at least one probe type. In two cases (guanylate kinase and TIE-2), accelerated MD, which increases sampling of torsional angles, was necessary to achieve mapping of portions of the cryptic binding site missed by standard MixMD. For more complex systems where movement of a helix or domain is necessary, MixMD was unable to map the binding site even with accelerated dynamics, possibly due to the limited timescale (100 ns for individual simulations). In general, similar conformational dynamics are observed in water-only simulations and those with probe molecules. This could imply that the probes are not driving opening events but rather take advantage of mapping sites that spontaneously open as part of their inherent conformational behavior. Finally, we show that docking to an ensemble of conformations from the standard MixMD simulations performs better than docking the apo crystal structure in nine cases and even better than half of the bound crystal structures. Poorer performance was seen in docking to ensembles of conformations from the accelerated MixMD simulations.
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http://dx.doi.org/10.1021/acs.jcim.0c01002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8091066PMC
March 2021

Chemical validation of a druggable site on Hsp27/HSPB1 using in silico solvent mapping and biophysical methods.

Bioorg Med Chem 2021 Mar 24;34:115990. Epub 2021 Jan 24.

Department of Pharmaceutical Chemistry and the Institute for Neurodegenerative Disease, University of California at San Francisco, San Francisco, CA 94158, United States. Electronic address:

Destabilizing mutations in small heat shock proteins (sHsps) are linked to multiple diseases; however, sHsps are conformationally dynamic, lack enzymatic function and have no endogenous chemical ligands. These factors render sHsps as classically "undruggable" targets and make it particularly challenging to identify molecules that might bind and stabilize them. To explore potential solutions, we designed a multi-pronged screening workflow involving a combination of computational and biophysical ligand-discovery platforms. Using the core domain of the sHsp family member Hsp27/HSPB1 (Hsp27c) as a target, we applied mixed solvent molecular dynamics (MixMD) to predict three possible binding sites, which we confirmed using NMR-based solvent mapping. Using this knowledge, we then used NMR spectroscopy to carry out a fragment-based drug discovery (FBDD) screen, ultimately identifying two fragments that bind to one of these sites. A medicinal chemistry effort improved the affinity of one fragment by ~50-fold (16 µM), while maintaining good ligand efficiency (~0.32 kcal/mol/non-hydrogen atom). Finally, we found that binding to this site partially restored the stability of disease-associated Hsp27 variants, in a redox-dependent manner. Together, these experiments suggest a new and unexpected binding site on Hsp27, which might be exploited to build chemical probes.
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http://dx.doi.org/10.1016/j.bmc.2020.115990DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968374PMC
March 2021

Predicting binding sites from unbound versus bound protein structures.

Sci Rep 2020 09 28;10(1):15856. Epub 2020 Sep 28.

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, 428 Church Street, Ann Arbor, MI, 48109-1065, USA.

We present the application of seven binding-site prediction algorithms to a meticulously curated dataset of ligand-bound and ligand-free crystal structures for 304 unique protein sequences (2528 crystal structures). We probe the influence of starting protein structures on the results of binding-site prediction, so the dataset contains a minimum of two ligand-bound and two ligand-free structures for each protein. We use this dataset in a brief survey of five geometry-based, one energy-based, and one machine-learning-based methods: Surfnet, Ghecom, LIGSITE, Fpocket, Depth, AutoSite, and Kalasanty. Distributions of the F scores and Matthew's correlation coefficients for ligand-bound versus ligand-free structure performance show no statistically significant difference in structure type versus performance for most methods. Only Fpocket showed a statistically significant but low magnitude enhancement in performance for holo structures. Lastly, we found that most methods will succeed on some crystal structures and fail on others within the same protein family, despite all structures being relatively high-quality structures with low structural variation. We expected better consistency across varying protein conformations of the same sequence. Interestingly, the success or failure of a given structure cannot be predicted by quality metrics such as resolution, Cruickshank Diffraction Precision index, or unresolved residues. Cryptic sites were also examined.
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http://dx.doi.org/10.1038/s41598-020-72906-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7522209PMC
September 2020

Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing.

J Mol Biol 2019 06 22;431(13):2423-2433. Epub 2019 May 22.

Department of Medicinal Chemistry, University of Michigan-Ann Arbor, 428 Church Street, Ann Arbor, MI 48109-1065, USA. Electronic address:

The goal of Binding MOAD is to provide users with a data set focused on high-quality x-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (K, K, K, IC) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9117 protein families and 16,044 unique ligands. The data are freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein-ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.
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http://dx.doi.org/10.1016/j.jmb.2019.05.024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6589129PMC
June 2019

Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations.

J Chem Inf Model 2019 05 2;59(5):2035-2045. Epub 2019 May 2.

Department of Medicinal Chemistry, College of Pharmacy , University of Michigan , 428 Church Street , Ann Arbor , Michigan 48109-1065 , United States.

In our recent efforts to map protein surfaces using mixed-solvent molecular dynamics (MixMD) (Ghanakota, P.; Carlson, H. A. Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems. J. Phys. Chem. B 2016, 120, 8685-8695), we were able to successfully capture active sites and allosteric sites within the top-four most occupied hotspots. In this study, we describe our approach for estimating the thermodynamic profile of the binding sites identified by MixMD. First, we establish a framework for calculating free energies from MixMD simulations, and we compare our approach to alternative methods. Second, we present a means to obtain a relative ranking of the binding sites by their configurational entropy. The theoretical maximum and minimum free energy and entropy values achievable under such a framework along with the limitations of the techniques are discussed. Using this approach, the free energy and relative entropy ranking of the top-four MixMD binding sites were computed and analyzed across our allosteric protein targets: Abl Kinase, Androgen Receptor, Pdk1 Kinase, Farnesyl Pyrophosphate Synthase, Chk1 Kinase, Glucokinase, and Protein Tyrosine Phosphatase 1B.
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http://dx.doi.org/10.1021/acs.jcim.8b00925DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6549510PMC
May 2019

Inherent versus induced protein flexibility: Comparisons within and between apo and holo structures.

PLoS Comput Biol 2019 01 30;15(1):e1006705. Epub 2019 Jan 30.

Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America.

Understanding how ligand binding influences protein flexibility is important, especially in rational drug design. Protein flexibility upon ligand binding is analyzed herein using 305 proteins with 2369 crystal structures with ligands (holo) and 1679 without (apo). Each protein has at least two apo and two holo structures for analysis. The inherent variation in structures with and without ligands is first established as a baseline. This baseline is then compared to the change in conformation in going from the apo to holo states to probe induced flexibility. The inherent backbone flexibility across the apo structures is roughly the same as the variation across holo structures. The induced backbone flexibility across apo-holo pairs is larger than that of the apo or holo states, but the increase in RMSD is less than 0.5 Å. Analysis of χ1 angles revealed a distinctly different pattern with significant influences seen for ligand binding on side-chain conformations in the binding site. Within the apo and holo states themselves, the variation of the χ1 angles is the same. However, the data combining both apo and holo states show significant displacements. Upon ligand binding, χ1 angles are frequently pushed to new orientations outside the range seen in the apo states. Influences on binding-site variation could not be easily attributed to features such as ligand size or x-ray structure resolution. By combining these findings, we find that most binding site flexibility is compatible with the common practice in flexible docking, where backbones are kept rigid and side chains are allowed some degree of flexibility.
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http://dx.doi.org/10.1371/journal.pcbi.1006705DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370239PMC
January 2019

MixMD Probeview: Robust Binding Site Prediction from Cosolvent Simulations.

J Chem Inf Model 2018 07 26;58(7):1426-1433. Epub 2018 Jun 26.

Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique for identifying binding hotspots and specific favorable interactions on a protein's surface. MixMD studies have the ability to identify these biologically relevant sites by examining the occupancy of the cosolvent over the course of the simulation. However, previous MixMD analysis required a great deal of manual inspection to identify relevant sites. To address this limitation, we have developed MixMD Probeview as a plugin for the freely available, open-source version of the molecular visualization program PyMOL. MixMD Probeview incorporates two analysis procedures: (1) identifying and ranking whole binding sites and (2) identifying and ranking local maxima for each probe type. These functionalities were validated using four common benchmark proteins, including two with both active and allosteric sites. In addition, three different cosolvent procedures were compared to examine the impact of including more than one cosolvent in the simulations. For all systems tested, MixMD Probeview successfully identified known active and allosteric sites based on the total occupancy of neutral probe molecules. As an easy-to-use PyMOL plugin, we expect that MixMD Probeview will facilitate identification and analysis of binding sites from cosolvent simulations performed on a wide range of systems.
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http://dx.doi.org/10.1021/acs.jcim.8b00265DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186161PMC
July 2018

Predicting Displaceable Water Sites Using Mixed-Solvent Molecular Dynamics.

J Chem Inf Model 2018 02 16;58(2):305-314. Epub 2018 Jan 16.

Department of Biophysics and ‡Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States.

Water molecules are an important factor in protein-ligand binding. Upon binding of a ligand with a protein's surface, waters can either be displaced by the ligand or may be conserved and possibly bridge interactions between the protein and ligand. Depending on the specific interactions made by the ligand, displacing waters can yield a gain in binding affinity. The extent to which binding affinity may increase is difficult to predict, as the favorable displacement of a water molecule is dependent on the site-specific interactions made by the water and the potential ligand. Several methods have been developed to predict the location of water sites on a protein's surface, but the majority of methods are not able to take into account both protein dynamics and the interactions made by specific functional groups. Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique that explicitly accounts for the interaction of both water and small molecule probes with a protein's surface, allowing for their direct competition. This method has previously been shown to identify both active and allosteric sites on a protein's surface. Using a test set of eight systems, we have developed a method using MixMD to identify conserved and displaceable water sites. Conserved sites can be determined by an occupancy-based metric to identify sites which are consistently occupied by water even in the presence of probe molecules. Conversely, displaceable water sites can be found by considering the sites which preferentially bind probe molecules. Furthermore, the inclusion of six probe types allows the MixMD method to predict which functional groups are capable of displacing which water sites. The MixMD method consistently identifies sites which are likely to be nondisplaceable and predicts the favorable displacement of water sites that are known to be displaced upon ligand binding.
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http://dx.doi.org/10.1021/acs.jcim.7b00268DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6190669PMC
February 2018

Multi-targeting Drug Community Challenge.

Cell Chem Biol 2017 12;24(12):1434-1435

Department of Cell, Developmental and Regenerative Biology and School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY 10029, USA. Electronic address:

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http://dx.doi.org/10.1016/j.chembiol.2017.12.006DOI Listing
December 2017

Are there physicochemical differences between allosteric and competitive ligands?

PLoS Comput Biol 2017 Nov 10;13(11):e1005813. Epub 2017 Nov 10.

Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, United States of America.

Previous studies have compared the physicochemical properties of allosteric compounds to non-allosteric compounds. Those studies have found that allosteric compounds tend to be smaller, more rigid, more hydrophobic, and more drug-like than non-allosteric compounds. However, previous studies have not properly corrected for the fact that some protein targets have much more data than other systems. This generates concern regarding the possible skew that can be introduced by the inherent bias in the available data. Hence, this study aims to determine how robust the previous findings are to the addition of newer data. This study utilizes the Allosteric Database (ASD v3.0) and ChEMBL v20 to systematically obtain large datasets of both allosteric and competitive ligands. This dataset contains 70,219 and 9,511 unique ligands for the allosteric and competitive sets, respectively. Physically relevant compound descriptors were computed to examine the differences in their chemical properties. Particular attention was given to removing redundancy in the data and normalizing across ligand diversity and varied protein targets. The resulting distributions only show that allosteric ligands tend to be more aromatic and rigid and do not confirm the increase in hydrophobicity or difference in drug-likeness. These results are robust across different normalization schemes.
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http://dx.doi.org/10.1371/journal.pcbi.1005813DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5699844PMC
November 2017

Comparing pharmacophore models derived from crystallography and NMR ensembles.

J Comput Aided Mol Des 2017 Nov 19;31(11):979-993. Epub 2017 Oct 19.

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, MI, 48109-1065, USA.

NMR and X-ray crystallography are the two most widely used methods for determining protein structures. Our previous study examining NMR versus X-Ray sources of protein conformations showed improved performance with NMR structures when used in our Multiple Protein Structures (MPS) method for receptor-based pharmacophores (Damm, Carlson, J Am Chem Soc 129:8225-8235, 2007). However, that work was based on a single test case, HIV-1 protease, because of the rich data available for that system. New data for more systems are available now, which calls for further examination of the effect of different sources of protein conformations. The MPS technique was applied to Growth factor receptor bound protein 2 (Grb2), Src SH2 homology domain (Src-SH2), FK506-binding protein 1A (FKBP12), and Peroxisome proliferator-activated receptor-γ (PPAR-γ). Pharmacophore models from both crystal and NMR ensembles were able to discriminate between high-affinity, low-affinity, and decoy molecules. As we found in our original study, NMR models showed optimal performance when all elements were used. The crystal models had more pharmacophore elements compared to their NMR counterparts. The crystal-based models exhibited optimum performance only when pharmacophore elements were dropped. This supports our assertion that the higher flexibility in NMR ensembles helps focus the models on the most essential interactions with the protein. Our studies suggest that the "extra" pharmacophore elements seen at the periphery in X-ray models arise as a result of decreased protein flexibility and make very little contribution to model performance.
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http://dx.doi.org/10.1007/s10822-017-0077-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6549502PMC
November 2017

D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions.

J Comput Aided Mol Des 2016 09 30;30(9):651-668. Epub 2016 Sep 30.

Drug Design Data Resource, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.
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http://dx.doi.org/10.1007/s10822-016-9946-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5562487PMC
September 2016

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets.

Bioinformatics 2016 12 11;32(23):3584-3592. Epub 2016 Aug 11.

Department of Computational Medicine and Bioinformatics.

Motivation: What if you could explain complex chemistry in a simple tree and share that data online with your collaborators? Computational biology often incorporates diverse chemical data to probe a biological question, but the existing tools for chemical data are ill-suited for the very large datasets inherent to bioinformatics. Furthermore, existing visualization methods often require an expert chemist to interpret the patterns. Biologists need an interactive tool for visualizing chemical information in an intuitive, accessible way that facilitates its integration into today's team-based biological research.

Results: ChemTreeMap is an interactive, bioinformatics tool designed to explore chemical space and mine the relationships between chemical structure, molecular properties, and biological activity. ChemTreeMap synergistically combines extended connectivity fingerprints and a neighbor-joining algorithm to produce a hierarchical tree with branch lengths proportional to molecular similarity. Compound properties are shown by leaf color, size and outline to yield a user-defined visualization of the tree. Two representative analyses are included to demonstrate ChemTreeMap's capabilities and utility: assessing dataset overlap and mining structure-activity relationships.

Availability And Implementation: The examples from this paper may be accessed at http://ajing.github.io/ChemTreeMap/ Code for the server and client are available in the Supplementary Information, at the aforementioned github site, and on Docker Hub (https://hub.docker.com) with the nametag ajing/chemtreemap.

Contact: carlsonh@umich.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btw523DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5181537PMC
December 2016

Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics.

J Med Chem 2016 12 17;59(23):10383-10399. Epub 2016 Aug 17.

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States.

Identifying binding hotspots on protein surfaces is of prime interest in structure-based drug discovery, either to assess the tractability of pursuing a protein target or to drive improved potency of lead compounds. Computational approaches to detect such regions have traditionally relied on energy minimization of probe molecules onto static protein conformations in the absence of the natural aqueous environment. Advances in high performance computing now allow us to assess hotspots using molecular dynamics (MD) simulations. MD simulations integrate protein flexibility and the complicated role of water, thereby providing a more realistic assessment of the complex kinetics and thermodynamics at play. In this review, we describe the evolution of various cosolvent-based MD techniques and highlight a myriad of potential applications for such technologies in computational drug development.
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http://dx.doi.org/10.1021/acs.jmedchem.6b00399DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217181PMC
December 2016

Lessons Learned over Four Benchmark Exercises from the Community Structure-Activity Resource.

J Chem Inf Model 2016 06;56(6):951-4

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States.

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http://dx.doi.org/10.1021/acs.jcim.6b00182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217176PMC
June 2016

Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems.

J Phys Chem B 2016 08 17;120(33):8685-95. Epub 2016 Jun 17.

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States.

Mixed-solvent molecular dynamics (MixMD) is a hotspot-mapping technique that relies on molecular dynamics simulations of proteins in binary solvent mixtures. Previous work on MixMD has established the technique's effectiveness in capturing binding sites of small organic compounds. In this work, we show that MixMD can identify both competitive and allosteric sites on proteins. The MixMD approach embraces full protein flexibility and allows competition between solvent probes and water. Sites preferentially mapped by probe molecules are more likely to be binding hotspots. There are two important requirements for the identification of ligand-binding hotspots: (1) hotspots must be mapped at very high signal-to-noise ratio and (2) the hotspots must be mapped by multiple probe types. We have developed our mapping protocol around acetonitrile, isopropanol, and pyrimidine as probe solvents because they allowed us to capture hydrophilic, hydrophobic, hydrogen-bonding, and aromatic interactions. Charged probes were needed for mapping one target, and we introduce them in this work. In order to demonstrate the robust nature and wide applicability of the technique, a combined total of 5 μs of MixMD was applied across several protein targets known to exhibit allosteric modulation. Most notably, all the protein crystal structures used to initiate our simulations had no allosteric ligands bound, so there was no preorganization of the sites to predispose the simulations to find the allosteric hotspots. The protein test cases were ABL Kinase, Androgen Receptor, CHK1 Kinase, Glucokinase, PDK1 Kinase, Farnesyl Pyrophosphate Synthase, and Protein-Tyrosine Phosphatase 1B. The success of the technique is demonstrated by the fact that the top-four sites solely map the competitive and allosteric sites. Lower-ranked sites consistently map other biologically relevant sites, multimerization interfaces, or crystal-packing interfaces. Lastly, we highlight the importance of including protein flexibility by demonstrating that MixMD can map allosteric sites that are not detected in half the systems using FTMap applied to the same crystal structures.
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http://dx.doi.org/10.1021/acs.jpcb.6b03515DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5214548PMC
August 2016

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.

J Chem Inf Model 2016 06 17;56(6):1063-77. Epub 2016 May 17.

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church St., Ann Arbor, Michigan 48109-1065, United States.

The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant's method and the setup protocol we used. This problem is inherent to decoys, and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock,1 Glide-XP,2 PLANTS,3 Wilma,4 Gold,5 SMINA,6 Glide-XP2/PELE,7 FlexX,8 and MedusaDock.9 In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term,11 SMINA6 using Autodock-Vina,12,13 FlexX8 using HYDE,14 and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity.16 Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.
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http://dx.doi.org/10.1021/acs.jcim.5b00523DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5228621PMC
June 2016

Prevalence of CTX-M extended-spectrum beta-lactamases and sequence type 131 in Korean blood, urine, and rectal Escherichia coli isolates.

Infect Genet Evol 2016 07 19;41:292-295. Epub 2016 Apr 19.

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States. Electronic address:

A high proportion of extended-spectrum beta-lactamase (ESBL) producing Escherichia coli are of the ST131 lineage, but there are few estimates of ST131 prevalence among ESBL-negative E. coli. Without this information, it is difficult to evaluate the contribution of the ST131 lineage to the emergence and spread of ESBL E. coli. A total of 1658 E. coli isolates were collected at Gachon University Gil Medical Center in Korea from 2006 to 2008. The antibiotic resistance profile was determined for all isolates, and ESBL-positive isolates were screened for the presence of CTX-M-type ESBLs. All ESBL-positive (n=84) and a representative sample of ESBL-negative (n=100) isolates were screened for O25b-ST131 using a PCR-based assay. The isolates were further classified on the basis of fumC and fimH types, which allowed for a comparison of the two typing methods. 5.7% of isolates were ESBL-positive, 87% of which contained CTX-M-type ESBLs. There was no significant difference in the prevalence of ST131 between ESBL-positive and -negative groups; 14% of ESBL-positive isolates and 9% of tested ESBL-negative isolates were ST131 by CH-typing. ST131-positive isolates harbored CTX-M-1-group ESBLs (including CTX-M-15) more frequently than other CTX-M types, and exhibited greater levels of antibiotic resistance than non-ST131 isolates. Furthermore, a number of isolates identified as O25b-ST131 by PCR corresponded to non-ST131 sequence types by CH-typing, emphasizing the need to consider the testing method when comparing reported prevalences of ST131.
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http://dx.doi.org/10.1016/j.meegid.2016.04.020DOI Listing
July 2016

Dynamic behavior of the post-SET loop region of NSD1: Implications for histone binding and drug development.

Protein Sci 2016 May 31;25(5):1021-9. Epub 2016 Mar 31.

Department of Biophysics, University of Michigan, Ann Arbor, Michigan, 48109-1055.

NSD1 is a SET-domain histone methyltransferase that methylates lysine 36 of histone 3. In the crystal structure of NSD1, the post-SET loop is in an autoinhibitory position that blocks binding of the histone peptide as well as the entrance to the lysine-binding channel. The conformational dynamics preceding histone binding and the mechanism by which the post-SET loop moves to accommodate the target lysine is currently unknown, although potential models have been proposed. Using molecular dynamics simulations, we have identified potential conformations of the post-SET loop differing from those of previous studies, as well as proposed a model of peptide-bound NSD1. Our simulations illustrate the dynamic behavior of the post-SET loop and the presence of a few distinct conformations. In every case, the post-SET loop remains in an autoinhibitory position blocking the peptide-binding cleft, suggesting that another interaction is required to optimally position NSD1 in an active conformation. This finding provides initial evidence for a mechanism by which NSD1 preferentially binds nucleosomal substrates.
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http://dx.doi.org/10.1002/pro.2912DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4838653PMC
May 2016

CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge.

J Chem Inf Model 2016 06 9;56(6):1022-31. Epub 2015 Oct 9.

Department of Medicinal Chemistry, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States.

Community Structure-Activity Resource (CSAR) conducted a benchmark exercise to evaluate the current computational methods for protein design, ligand docking, and scoring/ranking. The exercise consisted of three phases. The first phase required the participants to identify and rank order which designed sequences were able to bind the small molecule digoxigenin. The second phase challenged the community to select a near-native pose of digoxigenin from a set of decoy poses for two of the designed proteins. The third phase investigated the ability of current methods to rank/score the binding affinity of 10 related steroids to one of the designed proteins (pKd = 4.1 to 6.7). We found that 11 of 13 groups were able to correctly select the sequence that bound digoxigenin, with most groups providing the correct three-dimensional structure for the backbone of the protein as well as all atoms of the active-site residues. Eleven of the 14 groups were able to select the appropriate pose from a set of plausible decoy poses. The ability to predict absolute binding affinities is still a difficult task, as 8 of 14 groups were able to correlate scores to affinity (Pearson-r > 0.7) of the designed protein for congeneric steroids and only 5 of 14 groups were able to correlate the ranks of the 10 related ligands (Spearman-ρ > 0.7).
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http://dx.doi.org/10.1021/acs.jcim.5b00387DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6588165PMC
June 2016

Identifying binding hot spots on protein surfaces by mixed-solvent molecular dynamics: HIV-1 protease as a test case.

Biopolymers 2016 Jan;105(1):21-34

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church St., Ann Arbor, MI, 48109-1065.

Mixed-solvent molecular dynamics (MixMD) simulations use full protein flexibility and competition between water and small organic probes to achieve accurate hot-spot mapping on protein surfaces. In this study, we improved MixMD using human immunodeficiency virus type-1 protease (HIVp) as the test case. We used three probe-water solutions (acetonitrile-water, isopropanol-water, and pyrimidine-water), first at 50% w/w concentration and later at 5% v/v. Paradoxically, better mapping was achieved by using fewer probes; 5% simulations gave a superior signal-to-noise ratio and far fewer spurious hot spots than 50% MixMD. Furthermore, very intense and well-defined probe occupancies were observed in the catalytic site and potential allosteric sites that have been confirmed experimentally. The Eye site, an allosteric site underneath the flap of HIVp, has been confirmed by the presence of a 5-nitroindole fragment in a crystal structure. MixMD also mapped two additional hot spots: the Exo site (between the Gly16-Gly17 and Cys67-Gly68 loops) and the Face site (between Glu21-Ala22 and Val84-Ile85 loops). The Exo site was observed to overlap with crystallographic additives such as acetate and dimethyl sulfoxide that are present in different crystal forms of the protein. Analysis of crystal structures of HIVp in different symmetry groups has shown that some surface sites are common interfaces for crystal contacts, which means that they are surfaces that are relatively easy to desolvate and complement with organic molecules. MixMD should identify these sites; in fact, their occupancy values help establish a solid cut-off where "druggable" sites are required to have higher occupancies than the crystal-packing faces.
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http://dx.doi.org/10.1002/bip.22742DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4623995PMC
January 2016

Substrate-Competitive Activity-Based Profiling of Ester Prodrug Activating Enzymes.

Mol Pharm 2015 Sep 17;12(9):3399-407. Epub 2015 Aug 17.

Department of Medicinal Chemistry, College of Pharmacy, ‡Department of Pharmaceutical Sciences, College of Pharmacy, §Department of Chemistry, and ⊥Program in Chemical Biology, University of Michigan , Ann Arbor, Michigan 48109, United States.

Understanding the mechanistic basis of prodrug delivery and activation is critical for establishing species-specific prodrug sensitivities necessary for evaluating preclinical animal models and potential drug-drug interactions. Despite significant adoption of prodrug methodologies for enhanced pharmacokinetics, functional annotation of prodrug activating enzymes is laborious and often unaddressed. Activity-based protein profiling (ABPP) describes an emerging chemoproteomic approach to assay active site occupancy within a mechanistically similar enzyme class in native proteomes. The serine hydrolase enzyme family is broadly reactive with reporter-linked fluorophosphonates, which have shown to provide a mechanism-based covalent labeling strategy to assay the activation state and active site occupancy of cellular serine amidases, esterases, and thioesterases. Here we describe a modified ABPP approach using direct substrate competition to identify activating enzymes for an ethyl ester prodrug, the influenza neuraminidase inhibitor oseltamivir. Substrate-competitive ABPP analysis identified carboxylesterase 1 (CES1) as an oseltamivir-activating enzyme in intestinal cell homogenates. Saturating concentrations of oseltamivir lead to a four-fold reduction in the observed rate constant for CES1 inactivation by fluorophosphonates. WWL50, a reported carbamate inhibitor of mouse CES1, blocked oseltamivir hydrolysis activity in human cell homogenates, confirming CES1 is the primary prodrug activating enzyme for oseltamivir in human liver and intestinal cell lines. The related carbamate inhibitor WWL79 inhibited mouse but not human CES1, providing a series of probes for analyzing prodrug activation mechanisms in different preclinical models. Overall, we present a substrate-competitive activity-based profiling approach for broadly surveying candidate prodrug hydrolyzing enzymes and outline the kinetic parameters for activating enzyme discovery, ester prodrug design, and preclinical development of ester prodrugs.
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http://dx.doi.org/10.1021/acs.molpharmaceut.5b00414DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694564PMC
September 2015

The 12-minute journey.

Narrat Inq Bioeth 2014 ;4(3):192-3

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http://dx.doi.org/10.1353/nib.2014.0060DOI Listing
November 2015

Recent improvements to Binding MOAD: a resource for protein-ligand binding affinities and structures.

Nucleic Acids Res 2015 Jan 6;43(Database issue):D465-9. Epub 2014 Nov 6.

Department of Medicinal Chemistry, University of Michigan, 428 Church St, Ann Arbor, MI 48109-1065, USA

For over 10 years, Binding MOAD (Mother of All Databases; http://www.BindingMOAD.org) has been one of the largest resources for high-quality protein-ligand complexes and associated binding affinity data. Binding MOAD has grown at the rate of 1994 complexes per year, on average. Currently, it contains 23,269 complexes and 8156 binding affinities. Our annual updates curate the data using a semi-automated literature search of the references cited within the PDB file, and we have recently upgraded our website and added new features and functionalities to better serve Binding MOAD users. In order to eliminate the legacy application server of the old platform and to accommodate new changes, the website has been completely rewritten in the LAMP (Linux, Apache, MySQL and PHP) environment. The improved user interface incorporates current third-party plugins for better visualization of protein and ligand molecules, and it provides features like sorting, filtering and filtered downloads. In addition to the field-based searching, Binding MOAD now can be searched by structural queries based on the ligand. In order to remove redundancy, Binding MOAD records are clustered in different families based on 90% sequence identity. The new Binding MOAD, with the upgraded platform, features and functionalities, is now equipped to better serve its users.
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http://dx.doi.org/10.1093/nar/gku1088DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383918PMC
January 2015

An allosteric modulator of HIV-1 protease shows equipotent inhibition of wild-type and drug-resistant proteases.

J Med Chem 2014 Aug 1;57(15):6468-78. Epub 2014 Aug 1.

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States.

NMR and MD simulations have demonstrated that the flaps of HIV-1 protease (HIV-1p) adopt a range of conformations that are coupled with its enzymatic activity. Previously, a model was created for an allosteric site located between the flap and the core of HIV-1p, called the Eye site (Biopolymers 2008, 89, 643-652). Here, results from our first study were combined with a ligand-based, lead-hopping method to identify a novel compound (NIT). NIT inhibits HIV-1p, independent of the presence of an active-site inhibitor such as pepstatin A. Assays showed that NIT acts on an allosteric site other than the dimerization interface. MD simulations of the ligand-protein complex show that NIT stably binds in the Eye site and restricts the flaps. That bound state of NIT is consistent with a crystal structure of similar fragments bound in the Eye site (Chem. Biol. Drug Des. 2010, 75, 257-268). Most importantly, NIT is equally potent against wild-type and a multidrug-resistant mutant of HIV-1p, which highlights the promise of allosteric inhibitors circumventing existing clinical resistance.
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http://dx.doi.org/10.1021/jm5008352DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4136727PMC
August 2014

Parameter choice matters: validating probe parameters for use in mixed-solvent simulations.

J Chem Inf Model 2014 Aug 1;54(8):2190-9. Epub 2014 Aug 1.

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor , 428 Church St., Ann Arbor, Michigan 48109-1065, United States.

Probe mapping is a common approach for identifying potential binding sites in structure-based drug design; however, it typically relies on energy minimizations of probes in the gas phase and a static protein structure. The mixed-solvent molecular dynamics (MixMD) approach was recently developed to account for full protein flexibility and solvation effects in hot-spot mapping. Our first study used only acetonitrile as a probe, and here, we have augmented the set of functional group probes through careful testing and parameter validation. A diverse range of probes are needed in order to map complex binding interactions. A small variation in probe parameters can adversely effect mixed-solvent behavior, which we highlight with isopropanol. We tested 11 solvents to identify six with appropriate behavior in TIP3P water to use as organic probes in the MixMD method. In addition to acetonitrile and isopropanol, we have identified acetone, N-methylacetamide, imidazole, and pyrimidine. These probe solvents will enable MixMD studies to recover hydrogen-bonding sites, hydrophobic pockets, protein-protein interactions, and aromatic hotspots. Also, we show that ternary-solvent systems can be incorporated within a single simulation. Importantly, these binary and ternary solvents do not require artificial repulsion terms like other methods. Within merely 5 ns, layered solvent boxes become evenly mixed for soluble probes. We used radial distribution functions to evaluate solvent behavior, determine adequate mixing, and confirm the absence of phase separation. We recommend that radial distribution functions should be used to assess adequate sampling in all mixed-solvent techniques rather than the current practice of examining the solvent ratios at the edges of the solvent box.
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http://dx.doi.org/10.1021/ci400741uDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4144759PMC
August 2014

Exploring the composition of protein-ligand binding sites on a large scale.

PLoS Comput Biol 2013 21;9(11):e1003321. Epub 2013 Nov 21.

Bioinformatics Graduate Program, University of Michigan, Ann Arbor, Michigan, United States of America.

The residue composition of a ligand binding site determines the interactions available for diffusion-mediated ligand binding, and understanding general composition of these sites is of great importance if we are to gain insight into the functional diversity of the proteome. Many structure-based drug design methods utilize such heuristic information for improving prediction or characterization of ligand-binding sites in proteins of unknown function. The Binding MOAD database if one of the largest curated sets of protein-ligand complexes, and provides a source of diverse, high-quality data for establishing general trends of residue composition from currently available protein structures. We present an analysis of 3,295 non-redundant proteins with 9,114 non-redundant binding sites to identify residues over-represented in binding regions versus the rest of the protein surface. The Binding MOAD database delineates biologically-relevant "valid" ligands from "invalid" small-molecule ligands bound to the protein. Invalids are present in the crystallization medium and serve no known biological function. Contacts are found to differ between these classes of ligands, indicating that residue composition of biologically relevant binding sites is distinct not only from the rest of the protein surface, but also from surface regions capable of opportunistic binding of non-functional small molecules. To confirm these trends, we perform a rigorous analysis of the variation of residue propensity with respect to the size of the dataset and the content bias inherent in structure sets obtained from a large protein structure database. The optimal size of the dataset for establishing general trends of residue propensities, as well as strategies for assessing the significance of such trends, are suggested for future studies of binding-site composition.
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http://dx.doi.org/10.1371/journal.pcbi.1003321DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3836696PMC
July 2014

Identification of key hinge residues important for nucleotide-dependent allostery in E. coli Hsp70/DnaK.

PLoS Comput Biol 2013 21;9(11):e1003279. Epub 2013 Nov 21.

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, United States of America.

DnaK is a molecular chaperone that has important roles in protein folding. The hydrolysis of ATP is essential to this activity, and the effects of nucleotides on the structure and function of DnaK have been extensively studied. However, the key residues that govern the conformational motions that define the apo, ATP-bound, and ADP-bound states are not entirely clear. Here, we used molecular dynamics simulations, mutagenesis, and enzymatic assays to explore the molecular basis of this process. Simulations of DnaK's nucleotide-binding domain (NBD) in the apo, ATP-bound, and ADP/Pi-bound states suggested that each state has a distinct conformation, consistent with available biochemical and structural information. The simulations further suggested that large shearing motions between subdomains I-A and II-A dominated the conversion between these conformations. We found that several evolutionally conserved residues, especially G228 and G229, appeared to function as a hinge for these motions, because they predominantly populated two distinct states depending on whether ATP or ADP/Pi was bound. Consistent with the importance of these "hinge" residues, alanine point mutations caused DnaK to have reduced chaperone activities in vitro and in vivo. Together, these results clarify how sub-domain motions communicate allostery in DnaK.
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http://dx.doi.org/10.1371/journal.pcbi.1003279DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3836694PMC
July 2014

Check your confidence: size really does matter.

J Chem Inf Model 2013 Aug 8;53(8):1837-41. Epub 2013 Aug 8.

Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church St., Ann Arbor, Michigan 48109-1065, USA.

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http://dx.doi.org/10.1021/ci4004249DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3821705PMC
August 2013

CSAR data set release 2012: ligands, affinities, complexes, and docking decoys.

J Chem Inf Model 2013 Aug 10;53(8):1842-52. Epub 2013 May 10.

Department of Medicinal Chemistry, University of Michigan, 428 Church St., Ann Arbor, Michigan 48109-1065, USA.

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) has collected several data sets from industry and added in-house data sets that may be used for this purpose ( www.csardock.org). CSAR has currently obtained data from Abbott, GlaxoSmithKline, and Vertex and is working on obtaining data from several others. Combined with our in-house projects, we are providing a data set consisting of 6 protein targets, 647 compounds with biological affinities, and 82 crystal structures. Multiple congeneric series are available for several targets with a few representative crystal structures of each of the series. These series generally contain a few inactive compounds, usually not available in the literature, to provide an upper bound to the affinity range. The affinity ranges are typically 3-4 orders of magnitude per series. For our in-house projects, we have had compounds synthesized for biological testing. Affinities were measured by Thermofluor, Octet RED, and isothermal titration calorimetry for the most soluble. This allows the direct comparison of the biological affinities for those compounds, providing a measure of the variance in the experimental affinity. It appears that there can be considerable variance in the absolute value of the affinity, making the prediction of the absolute value ill-defined. However, the relative rankings within the methods are much better, and this fits with the observation that predicting relative ranking is a more tractable problem computationally. For those in-house compounds, we also have measured the following physical properties: logD, logP, thermodynamic solubility, and pK(a). This data set also provides a substantial decoy set for each target consisting of diverse conformations covering the entire active site for all of the 58 CSAR-quality crystal structures. The CSAR data sets (CSAR-NRC HiQ and the 2012 release) provide substantial, publically available, curated data sets for use in parametrizing and validating docking and scoring methods.
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http://dx.doi.org/10.1021/ci4000486DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3753885PMC
August 2013