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 technique’s 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 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

Synthesis, characterization and antimicrobial activity of some new Baylis-Hillman derived benzothiazolo pyrimidinone derivatives

217-227<span style="font-size:10.0pt;font-family: " times="" new="" roman";mso-fareast-font-family:"times="" roman";mso-bidi-font-family:="" mangal;mso-ansi-language:en-us;mso-fareast-language:en-us;mso-bidi-language:="" hi"="" lang="EN-US">A series of Baylis–Hillman derived 22 new benzothiazolo pyrimidinone derivatives have been synthesized from Baylis-Hillman acetates and 2-amino benzothiazole under neat conditions with high yields. All the newly synthesized compounds have been characterized by their spectral data and evaluated for their antibacterial and antifungal activity. Among the 22 new benzothiazolo pyrimidinone compounds, 3o which is having fluoro group at ortho position of phenyl ring has shown excellent activity against gram-positive as well as gram-negative bacteria. Compounds 3f, <b style="mso-bidi-font-weight: normal">3m, 3q exhibit good antibacterial activity compared to the remaining compounds. Among all the compounds, 3o has good antifungal activity and compounds 3f, 3h,<b style="mso-bidi-font-weight: normal"> 3k, 3m, 3q and 3s exhibit comparable antifungal activity. The presence of bezothiazolo pyrimidinone moiety along with-F,-Cl,-CF3 and isopropyl substituent groups on the phenyl ring plays a significant role towards antimicrobial activity.</span

Large-Scale Validation of Mixed-Solvent Simulations to Assess Hotspots at Protein–Protein Interaction Interfaces

The ability to target protein–protein 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

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 /

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

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. © 2015 Wiley Periodicals, Inc. Biopolymers 105: 21–34, 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

CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks

The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands, and also suffers from a relatively low positive detection rate in the early stages of the resultant COVID-19 disease. Hence, there is a need for an alternative approach for repeated large-scale testing of SARS-CoV-2/COVID-19. The emergence of wearable medical sensors (WMSs) and deep neural networks (DNNs) points to a promising approach to address this challenge. WMSs enable continuous and user-transparent monitoring of physiological signals. However, disease detection based on WMSs/DNNs and their deployment on resource-constrained edge devices remain challenging problems. To address these problems, we propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus and the resultant disease. CovidDeep does not depend on manual feature extraction. It directly operates on WMS data and some easy-to-answer questions in a questionnaire whose answers can be obtained through a smartphone application. We collected data from 87 individuals, spanning three cohorts including healthy, asymptomatic (to detect the virus), and symptomatic (to detect the disease) patients. We trained DNNs on various subsets of the features automatically extracted from six WMS and questionnaire categories to perform ablation studies to determine which subsets are most efficacious in terms of test accuracy for a three-way classification. The highest test accuracy obtained was 98.1%. The models were also shown to perform well on other performance measures, such as false positive rate, false negative rate, and F1 score. We augmented the real training dataset with a synthetic training dataset drawn from the same probability distribution to impose a prior on DNN weights and leveraged a grow-and-prune synthesis paradigm to learn both DNN architecture and weights. This boosted the accuracy of the various DNNs further and simultaneously reduced their size and floating-point operations. This makes the CovidDeep DNNs both accurate and efficient, in terms of memory requirements and computations. The resultant DNNs are embedded in a smartphone application, which has the added benefit of preserving patient privacy

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&nbsp;µM), while maintaining good ligand efficiency (~0.32&nbsp;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