11 research outputs found
Incorporating Hotspot Mapping and Allostery in Structure Based Drug Design.
Hotspots are defined as regions on the protein surface that disproportionately contribute to binding free energy. Mixed-solvent molecular dynamics (MixMD) is a hotspot mapping technique that relies on molecular dynamics simulations of binary solvent mixtures. Previous work in the group on MixMD has established the techniqueâs effectiveness in capturing binding sites of small organic compounds. The MixMD approach embraces full protein flexibility while allowing for competition between probes and water. Sites preferentially mapped by probe molecules are more likely to be hotspots. First, we establish a rigorous protocol for the identification of hotspots on the binding surface. There are two important requirements: 1) the high-ranking hotspots must be mapped at very high signal-to-noise ratio and 2) the hotspots must be mapped by multiple probes. We have focused our probe molecule repertoire to include acetonitrile, isopropanol, and pyrimidine as these probes allowed us to capture a range of interaction types that include hydrophilic, hydrophobic, hydrogen-bonding and aromatic interactions. Second, we use MixMD to identify both competitive and allosteric sites on proteins. The test cases include Abl Kinase, Androgen Receptor, Chk1 Kinase, Glucokinase, Pdk1 Kinase, Protein-Tyrosine Phosphatase 1B, and Farnesyl Pyrophosphate Synthase. The success of the technique is demonstrated by the fact that the top four sites map the competitive and allosteric sites. We then present methodological developments for characterizing the free energies and entropies of binding sites identified by MixMD. Finally, the significance of these findings is strengthened by a successful prospective application of MixMD on Heat Shock Protein 27. Taken together, these studies demonstrate the powerful utility of MixMD in structure based drug design.PHDMedicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113593/1/gphani_1.pd
Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems
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 techniqueâs
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 ÎŒs 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
Large-Scale Validation of Mixed-Solvent Simulations to Assess Hotspots at ProteinâProtein Interaction Interfaces
The
ability to target proteinâprotein interactions (PPIs)
with small molecule inhibitors offers great promise in expanding the
druggable target space and addressing a broad range of untreated diseases.
However, due to their nature and function of interacting with protein
partners, PPI interfaces tend to extend over large surfaces without
the typical pockets of enzymes and receptors. These features present
unique challenges for small molecule inhibitor design. As such, determining
whether a particular PPI of interest could be pursued with a small
molecule discovery strategy requires an understanding of the characteristics
of the PPI interface and whether it has hotspots that can be leveraged
by small molecules to achieve desired potency. Here, we assess the
ability of mixed-solvent molecular dynamic (MSMD) simulations to detect
hotspots at PPI interfaces. MSMD simulations using three cosolvents
(acetonitrile, isopropanol, and pyrimidine) were performed on a large
test set of 21 PPI targets that have been experimentally validated
by small molecule inhibitors. We compare MSMD, which includes explicit
solvent and full protein flexibility, to a simpler approach that does
not include dynamics or explicit solvent (SiteMap) and find that MSMD
simulations reveal additional information about the characteristics
of these targets and the ability for small molecules to inhibit the
PPI interface. In the few cases were MSMD simulations did not detect
hotspots, we explore the shortcomings of this technique and propose
future improvements. Finally, using Interleukin-2 as an example, we
highlight the advantage of the MSMD approach for detecting transient
cryptic druggable pockets that exists at PPI interfaces
Identifying binding hot spots on protein surfaces by mixedâsolvent molecular dynamics: HIVâ1 protease as a test case
Mixedâsolvent molecular dynamics (MixMD) simulations use full protein flexibility and competition between water and small organic probes to achieve accurate hotâspot mapping on protein surfaces. In this study, we improved MixMD using human immunodeficiency virus typeâ1 protease (HIVp) as the test case. We used three probeâwater solutions (acetonitrileâwater, isopropanolâwater, and pyrimidineâwater), 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 signalâtoânoise ratio and far fewer spurious hot spots than 50% MixMD. Furthermore, very intense and wellâdefined 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 5ânitroindole fragment in a crystal structure. MixMD also mapped two additional hot spots: the Exo site (between the Gly16âGly17 and Cys67âGly68 loops) and the Face site (between Glu21âAla22 and Val84âIle85 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 cutâoff where âdruggableâ sites are required to have higher occupancies than the crystalâpacking faces. © 2015 Wiley Periodicals, Inc. Biopolymers 105: 21â34, 2015.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116011/1/bip22742.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116011/2/bip22742-sup-0001-suppinfo.pd
Combining Cloud-Based Free Energy Calculations, Synthetically Aware Enumerations and Goal-Directed Generative Machine Learning for Rapid Large-Scale Chemical Exploration and Optimization
The hit identification process usually involves the profiling of millions to more recently billions of compounds either via traditional experimental high throughput screens (HTS) or computational virtual high throughput screens (vHTS). We have previously demonstrated that by coupling reaction-based enumeration, active learning and free energy calculations, a similarly large-scale exploration of chemical space can be extended to the hit-to-lead process. In this work, we augment that approach by coupling large scale enumeration and cloud-based FEP profiling with goal-directed generative machine learning, which results in a higher enrichment of potent ideas compared to large scale enumeration alone, while simultaneously staying within the bounds of a predefined drug-like property space. We are able to achieve this by building the molecular distribution for generative machine learning from the PathFinder rules-based enumeration and optimizing for a weighted sum QSAR based multi-parameter optimization function. We examine the utility of this combined approach by designing potent inhibitors of cyclin-dependent kinase 2 (CDK2) and demonstrate a coupled workflow that can: (1) provide a 6.4 fold enrichment improvement in identifying 50 50 <100 nM. The reported data suggest combining both reaction-based and generative machine learning for ideation results in a higher enrichment of potent compounds over previously described approaches, and can rapidly accelerate the discovery of novel chemical matter within a predefined potency and property space.<br /
Chemical validation of a druggable site on Hsp27/HSPB1 using in silico solvent mapping and biophysical methods
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 ”M), 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
Substrate-Competitive Activity-Based Profiling of Ester Prodrug Activating Enzymes
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 drugâdrug
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