Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly curated dataset of 39 experimentally confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) \u3e1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures likely contain cryptic pockets, vastly expanding the potentially druggable proteome

Development and Application of Virtual Screening Methods for G Protein-Coupled Receptors

G protein-coupled receptors (GPCR) constitute one of the largest family of transmembrane proteins that have been implicated in a multitude of diseases, including cancer and diabetes, and have been an important target in drug deve lopment. While experiment-based high-throughput screening for the unearthing of novel chemical compounds remains the de facto standard for drug discovery, virtual screening has been gaining acceptance as an important complementary method due to its high speed and low cost, which instead employs computers. This dissertation is aimed at the development of virtual screening algorithms as applied to GPCR’s, in addition to the construction of GPCR-related databases (GPCR-EXP, GLASS). MAGELLAN is a ligand-based virtual screening algorithm that makes inferences about what a GPCR would potentially bind based on sequence- and structure-based alignments. Building on top of this work, a sequential virtual screening pipeline combining MAGELLAN with AutoDock Vina was constructed for the discovery of novel, bifunctional opioids with mu opioid receptor (MOR) agonist and delta opioid receptor (DOR) antagonist activity. In the process of developing the virtual screening algorithms, two GPCR-related databases were constructed to provide necessary data for the study. GPCR-EXP is a database of experimentally-validated and predicted GPCR structures. Important features include semi-manual curation of data, weekly updates, a user-friendly web interface, and high-resolution structure models with GPCR-I-TASSER, which many of the other GPCR-related databases lack. Additionally, GLASS database was developed in response to the absence of databases dedicated to GPCR experimental data. As a result, pharmacological data was pooled and integrated into a single source, resulting in over 500,000 unique GPCR-ligand associations; this made it the most comprehensive database of its kind thus far, providing the community with an accessible web interface, freely-available data, and ligands ready for docking. MAGELLAN utilized pharmacological data from GLASS to infer from the ligands of sequence- and structure-based homologues what a target GPCR would bind. It was tested on two public virtual screening databases (DUD-E and GPCR-Bench) and achieved an average EF of 9.75 and 13.70, respectively, which compared favorably with AutoDock Vina (1.48/3.16), DOCK 6 (2.12/3.47), and PoLi (2.2). Lastly, case studies with the mu opioid and motilin receptors demonstrated its applicability to virtual screening in general, as well as GPCR de-orphanization. Subsequently, MAGELLAN was combined with AutoDock Vina into a novel, sequential virtual screen pipeline against both MOR and DOR to compensate for the weaknesses of each algorithm. Retrospective virtual screens against both MAGELLAN and AutoDock Vina were established for both receptors, and both methods were reported to have over-random discrimination between actives and decoys using the GPCR-Bench dataset. In conclusion, structure (GPCR-EXP) and pharmacological data (GLASS) databases were constructed to provide users with a comprehensive source of GPCR data. Moreover, GLASS made it possible for MAGELLAN to be developed, providing it a rich source of experimental data. In return, this resulted in greater performance than competing algorithms. Lastly, a prospective sequential virtual screening pipeline was established for the discovery of novel bifunctional opioids, in which the models for both methods were validated to perform well. In future studies, cAMP and β-arrestin assays will be run on a subset of compounds from a prospective virtual screen in the hopes of discovering a novel opioid with reduced tolerance and withdrawal.PHDBiological ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147623/1/wallakin_1.pd

Probing function of unknown proteins by using pharmacophore searching and biophysical techniques

The number of protein structures deposited in the Protein data bank is increasing almost exponentially and among these structures many of the proteins are novel with unknown function. Like Docking, Pharmacophore searching is an In-silico technique which is widely used for drug discovery. In pharmacophore searching the main focus is on the hydrogen bond interactions between the ligand and the target protein. The pharmacophore models are generated either by using the already known actives as templates or by utilizing the significant chemical features of the active site. In this thesis the pharmacophore searching has been used to find potential ligands/substrates for unknown proteins and then ligand binding is confirmed by using different biophysical techniques. In the initial phases the pharmacophore models were generated by using Cerius2 and Weblab Viewer pro programs. While in later stages more sophisticated searches were carried out by using DSV (Discovery studio visualizer, Accelrys®). Procedures were optimized for model building by using DSV, which enabled the pharmacophore searching via both the Vector and the Query atom methods. To validate the technique, it was first used on known enzymes with established function e.g. xylose reductase and shikimate kinase. The optimized pharmacophore model when search through the database successfully identified the true substrates for these enzymes among other ligands thereby demonstrating the attainment. The pharmacophore searching technique has been used to find potential ligands for proteins with unknown function on three test cases e.g. TdcF, HutD and PARI. Of the potential pharmacophore hits obtained through database search, a number of compounds were either purchased or synthesised to be tested for binding affinity. Different biophysical techniques like DSC, ITC, CD and NMR were used for this purpose. Among these techniques NMR proved to be the most sensitive technique to differentiate binders from non-binders and to further detect weak and strong bonding in terms of Kd values. For TdcF among other binders the best binder was 2-ketobutyrate with a Kd value of 200µM. In case of HutD, formyl glutamate (Kd = 92µM) and formimino glutamate (Kd = 500µM) came out to be the best binders and could be the true ligands of the protein at physiological concentration. For PARI L-glutamate appeared to be a potential ligand for the protein as confirmed through the NMR experiments. Pharmacophore modelling has been successful in identifying potential interactions provided by the protein active site which in turns specifies the required features to be present in a ligand and later on the successful binding studies further confirm its applicability. In addition, protein structures from the protein data bank (PDB) with unknown ligands (UNK) were identified and manually screened to find examples that could be used to test the applicability of pharmacophore searching methods. The diversity of structures showed that the definition of an unknown ligand is completely inconsistent with many examples where any non spherical density was labelled as unknown ligand and in most cases a single atom is labelled as an unknown ligand, which most likely can be an ion or a water molecule. It appeared that some compounds like glycerol, phosphate and citrate which co-crystallized with the protein due to their presence in the crystallization conditions were also mistakenly assigned as UNK. The pharmacophore method worked successfully in finding suitable ligand (s) for the protein

Exploring the effects of polymorphic variation on the stability and function of human cytochrome P450 enzymes in silico and in vitro

Includes bibliographical references.Cytochrome P450s are highly polymorphic enzymes responsible for the Phase I metabolism of over 80% of pharmaceutical drugs. Polymorphic variation can result in altered drug efficacy as well as adverse drug reactions so the lack of understanding of the effects of single amino acid substitutions on cytochrome P450 drug metabolism is a major problem for drug development. In order to begin to address this problem, this thesis describes an in silico analysis of over 300 nonsynonymous single nucleotide polymorphisms found across nine of the major human drug metabolising cytochrome P450 isoforms. Information from functional studies - in which regions of the cytochrome P450 structure important for substrate recognition, substrate and product access and egress and interaction with the cytochrome P450 reductase were delineated - was combined with in silico calculations on the effect of mutations on protein stability in order to establish the likely causes of altered drug metabolism observed for cytochrome P450 variants in functional assays carried out to date. This study revealed that 75% of all cytochrome P450 mutations showing altered activity in vitro are either predicted to be damaging to protein structure or are found within regions predicted to be important for catalytic activity. Furthermore, this study showed that 70% of the mutations that showed similar activity to the wild-type enzyme in in vitro studies lie outside of functional regions important for catalytic activity and are predicted to have no effect on protein stability. Based on these results, a cytochrome P450 polymorphic variant map was created that should find utility in predicting the functional effect of uncharacterised variants on drug metabolism. To further test the accuracy of the in silico predictions, in vitro assays were performed on a panel of CYP3A4 and CYP2C9 variants heterogeneously expressed in E.coli. All mutations predicted to alter protein function by stabilising or destabilising the apo-protein structure in silico were found to significantly alter the thermostability of the holo-protein in solution. Thermostability assays also suggest that other mutations may affect stability by disrupting haem binding, changing protein conformation or altering oligomer formation. The utility of a fluorescence-based functional P450 protein microarray platform, previously developed in our laboratory, for generating kinetic data for multiple CYP450 variants in parallel was also examined. Since the microarray platform in its current stage of development was found to be unsuitable for this purpose, kinetic data for the full panel of CYP3A4 and CYP2C9 variants was generated using solution phase assays, revealing several variants with altered catalytic turnover and/or binding affinity for fluorescent substrates

Role of Protein-Protein Interactions in Metabolism: Genetics, Structure, Function

publishersversionpublishe

PPARs as Key Mediators of Metabolic and Inflammatory Regulation

Mounting evidence suggests a bidirectional relationship between metabolism and inflammation. Molecular crosstalk between these processes occurs at different levels with the participation of nuclear receptors, including peroxisome proliferator-activated receptors (PPARs). There are three PPAR isotypes, α, β/δ, and γ, which modulate metabolic and inflammatory pathways, making them key for the control of cellular, organ, and systemic processes. PPAR activity is governed by fatty acids and fatty acid derivatives, and by drugs used in clinics (glitazones and fibrates). The study of PPAR action, also modulated by post-translational modifications, has enabled extraordinary advances in the understanding of the multifaceted roles of these receptors in metabolism, energy homeostasis, and inflammation both in health and disease. This Special Issue of IJMS includes a broad range of basic and translational article, both original research and reviews, focused on the latest developments in the regulation of metabolic and/or inflammatory processes by PPARs in all organs and the microbiomes of different vertebrate species

Senescence

The book "Senescence" is aimed to describe all the phenomena related to aging and senescence of all forms of life on Earth, i.e. plants, animals and the human beings. The book contains 36 carefully reviewed chapters written by different authors, aiming to describe the aging and senescent changes of living creatures, i.e. plants and animals

Mathematical model of interactions immune system with Micobacterium tuberculosis

Tuberculosis (TB) remains a public health problem in the world, because of the increasing prevalence and treatment outcomes are less satisfactory. About 3 million people die each year and an estimated one third of the world's population infected with Mycobacterium Tuberculosis (M.tb) is latent. This is apparently related to incomplete understanding of the immune system in infection M.tb. When this has been known that immune responses that play a role in controlling the development of M.tb is Macrophages, T Lymphocytes and Cytokines as mediators. However, how the interaction between the two populations and a variety of cytokines in suppressing the growth of Mycobacterium tuberculosis germ is still unclear. To be able to better understand the dynamics of infection with M tuberculosis host immune response is required of a model.One interesting study on the interaction of the immune system with M.tb mulalui mathematical model approach. Mathematical model is a good tool in understanding the dynamic behavior of a system. With the mediation of mathematical models are expected to know what variables are most responsible for suppressing the growth of Mycobacterium tuberculosis germ that can be a more appropriate approach to treatment and prevention target is to develop a vaccine. This research aims to create dynamic models of interaction between macrophages (Macrophages resting, macrophages activated and macrophages infected), T lymphocytes (CD4 + T cells and T cells CD8 +) and cytokine (IL-2, IL-4, IL-10,IL-12,IFN-dan TNF-) on TB infection in the lung. To see the changes in each variable used parameter values derived from experimental literature. With the understanding that the variable most responsible for defense against Mycobacterium tuberculosis germs, it can be used as the basis for the development of a vaccine or drug delivery targeted so hopefully will improve the management of patients with tuberculosis. Mathematical models used in building Ordinary Differential Equations (ODE) in the form of differential equation systems Non-linear first order, the equation contains the functions used in biological systems such as the Hill function, Monod function, Menten- Kinetic Function. To validate the system used 4th order Runge Kutta method with the help of software in making the program Matlab or Maple to view the behavior and the quantity of cells of each population