Computational Studies of Protein-ligand Interaction Through QM/MM Methods and Virtual Screening PDF Download
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Author: Seth A. Hayik
Publisher:
ISBN:
Category :
Languages : en
Pages :
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The solvation model is based on a finite-difference Poisson-Boltzmann solver available in the DivCon program. This method was validated through calculation of solvation energy on a set of pentapeptides as well as small proteins. The QM/MM solvation energy was compared to the full QM solvation energy for various sized QM regions and only small differences were found. The QM/MM scoring function made use of this solvation method to calculate the solvation free energy of 23 zinc containing proteins. This QM/MM scoring function was validated against the set of proteins by comparing the predicted binding free energies of these proteins to the experimental binding free energies. Following this, the next chapter discusses the use of virtual screening methods to find a new inhibitor of a deubiquitinating cysteine protease protein, HAUSP/USP7. DOCK and AutoDock were utilized to screen a commercially available subset of compounds from the ZINC database. First, a potential active site was identified, and then the ligands were docked and ranked. These ranked compounds were then analyzed, purchased and tested experimentally to determine their potency for the target protein. Several ligands bound with low micromolar efficiency and the results were examined. Future directions for this study are also suggested at the end of this chapter to determine a more specific stronger binding ligand.
Author: Seth A. Hayik
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The solvation model is based on a finite-difference Poisson-Boltzmann solver available in the DivCon program. This method was validated through calculation of solvation energy on a set of pentapeptides as well as small proteins. The QM/MM solvation energy was compared to the full QM solvation energy for various sized QM regions and only small differences were found. The QM/MM scoring function made use of this solvation method to calculate the solvation free energy of 23 zinc containing proteins. This QM/MM scoring function was validated against the set of proteins by comparing the predicted binding free energies of these proteins to the experimental binding free energies. Following this, the next chapter discusses the use of virtual screening methods to find a new inhibitor of a deubiquitinating cysteine protease protein, HAUSP/USP7. DOCK and AutoDock were utilized to screen a commercially available subset of compounds from the ZINC database. First, a potential active site was identified, and then the ligands were docked and ranked. These ranked compounds were then analyzed, purchased and tested experimentally to determine their potency for the target protein. Several ligands bound with low micromolar efficiency and the results were examined. Future directions for this study are also suggested at the end of this chapter to determine a more specific stronger binding ligand.
Author: Benjamin Michael Samudio
Publisher:
ISBN: 9781369201079
Category :
Languages : en
Pages :
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Proteins are fascinating biomolecular "machines" that enable the chemistry of life to occur. They underlie such diverse processes as energy transduction and immunity. Science continuous to unravel how these proteins work and many exciting questions remain to be answered. A paramount goal in the study of proteins is the understanding of how protein structure and dynamics facilitate chemistry. Proteins undergo conformational changes which make certain chemistry more probable. Elucidating these conformational changes is a major challenge to which both experimental and computational methods are applied. Computational methods can complement experimental ones by modeling protein conformational changes at an atomic level of detail. In addition, more elaborate computational methods can model chemical changes and reactions. This provides a link between structure and chemistry which is central to descriptions of protein function. This dissertation describes my research involving proteins which has been carried out at two institutions, the University of California in Davis (UC Davis) and the Novartis Institutes for Biomedical Research (NIBR), and correspondingly is divided into two parts. Part I: Unidirectional Proton Translocation Involving Glu-242 of Cytochrome c Oxidase (UC Davis) Cytochrome c oxidase (CcO) is the fourth protein complex in the electron transport chain (ETC) of mitochondria and some bacteria. This protein is embedded within the proton impermeable inner membrane of mitochondria and outer membrane in bacteria. CcO functions to: 1) reduce dioxygen to water and 2) move protons in a scalar manner from a lower concentration of protons in the mitochondrial matrix to a higher concentration of protons in the intermembrane space in a process known as proton pumping. Proton pumping establishes an electrochemical gradient across the inner membrane which is essential to aerobic life. CcO is remarkable because it is able to pump protons against the electrochemical gradient via a thermodynamically unfavorable but unidirectional and productive trajectory. CcO is unique as of this writing, in that it is designated as a "true" proton pump meaning that the protons which are pumped through CcO are not also substrates in the redox reactions which occur within this enzyme. Though a wealth of knowledge has been generated regarding CcO, much uncertainty remains about the microscopic details of the proton pumping process. Experimental methods have produced a detailed framework describing many aspects of CcO structure and function, however, probing this enzyme at the molecular level can be difficult. To this end, computational molecular modeling offers a complement to experimental efforts. The methods that are a part of computational molecular modeling can provide keen insight into biophysical processes. There are many different methods and ways to apply them, however, and it is not always straightforward how to best develop and deploy a model for a particular system. CcO presents an especially challenging system since the process of proton translocation involves levels of detail spanning electronic structure dynamics to large protein conformational changes. Computational methods must therefore be systematically tested and validated in order to increase confidence that their results are meaningful for investigations of CcO. In the current work, several computational models of CcO are compared. These models differ from one another in the level of detail describing a key region of the proton pumping pathway within CcO. This region contains a highly conserved residue, Glu-242 (bovine heart mitochondria numbering), which has been shown to be pivotal in relaying protons across the proton pumping pathway. The results of this work indicate that there are differences regarding the energetics and dynamics of Glu-242 side chain isomerization depending on the level of detail used in the model. These differences lead to differing descriptions of proton translocation as it involves Glu-242 and underscores the need to thoroughly examine the application of computational models. In Chapter 1, the major structures and functioning of CcO is outlined. The analogy that underlies this chapter is of CcO functioning similar to a macroscopic pump in moving protons from one side of the membrane to the other. The CcO reaction cycle is akin to the repetitive motions of a piston as it operates to pump material. In Chapter 2, the proton pump pathway through CcO is characterized. The focus then collects on a region of this pathway which is instrumental in the process of proton pumping named the "motif" region. Finally a four-state model is used to describe the participation in proton pumping of Glu-242 or its physiochemical analogue at this region. In Chapter 3, proton leaks and proton leak prevention are described. Proton leaks are thought to occur in some structural variants of CcO. In these cases, the unidirectional and productive movement of protons through CcO is compromised as indicated by abnormal proton pumping stoichiometry. Kinetic gating is a conceptual framework whereby the prevention of these leaks may be rationalized. This chapter ends with the description of a criterion that must be met in order to prevent protons from leaking. Chapter 4 introduces common methods used in molecular modeling. Finally, in Chapter 5 computational molecular models involving the motif region are compared. These models employ varying levels of detail. This offers a test of how increasing levels of model detail effects the conclusions which might be drawn regarding proton pumping. A proposal for how unidirectional proton translocation may occur in CcO is offered based on the results of the molecular models at the higher level of detail. In conclusion, these models are used to speculate on how proton leaks occur in structural variants of CcO and how unidirectional proton translocation may occur in CcO enzymes which lack Glu-242 or its equivalent. Part II: Ensemble Surrogate AutoShim: Probing Sensitivity to Parameter Modification (NIBR) Ensemble Surrogate AutoShim (ESA) is a powerful and versatile virtual docking and screening method which has proven to be useful in drug discovery and design. ESA is powerful in that it transforms general all-purpose scoring functions into target-tailored scoring functions using a combined 2D and 3D-QSAR based approach that involves virtual docking. These target-tailored scoring functions are then trained to reproduce bioactivity data against a given target resulting in a knowledge-based model that is used in virtual screening. Held-out test set validations of ESA models show that they routinely outperforms exclusively all-purpose scoring function based approaches. Chapter 1 outlines the ESA method in general. The versatility of the ESA method stems from the fact that various ligand preparation, docking and scoring, and pose filtering and refinement schemes may be implemented within the ESA framework. For example, the ESA method is open to the inclusion of any conventional all-purpose scoring function and docking program within its framework. This versatility offers flexibility in the implementation of the ESA method and invites an exploration of the parameters underlying this method. In Chapter 2, a systematic evaluation of ESA parameter modification is undertaken and quantified through statistical analysis. The results of this work indicate that several parameters significantly influence the quality of ESA models. Based on these results, a protocol is proposed which produces the most predictive ESA models on average of any parameter configuration and protocol studied so far on the data set evaluated.
Author: Holger Gohlke
Publisher: John Wiley & Sons
ISBN: 3527329668
Category : Medical
Languages : en
Pages : 361
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Book Description
Innovative and forward-looking, this volume focuses on recent achievements in this rapidly progressing field and looks at future potential for development. The first part provides a basic understanding of the factors governing protein-ligand interactions, followed by a comparison of key experimental methods (calorimetry, surface plasmon resonance, NMR) used in generating interaction data. The second half of the book is devoted to insilico methods of modeling and predicting molecular recognition and binding, ranging from first principles-based to approximate ones. Here, as elsewhere in the book, emphasis is placed on novel approaches and recent improvements to established methods. The final part looks at unresolved challenges, and the strategies to address them. With the content relevant for all drug classes and therapeutic fields, this is an inspiring and often-consulted guide to the complexity of protein-ligand interaction modeling and analysis for both novices and experts.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 4
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Author: Vasanthanathan Poongavanam
Publisher: John Wiley & Sons
ISBN: 3527840737
Category : Science
Languages : en
Pages : 882
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Book Description
Computational Drug Discovery A comprehensive resource that explains a wide array of computational technologies and methods driving innovation in drug discovery Computational Drug Discovery: Methods and Applications (2 volume set) covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design. Additionally, it covers key technological advancements in computing such as quantum and cloud computing that are driving innovations in drug discovery. Furthermore, dedicated chapters that addresses the recent trends in the field of computer aided drug design, including ultra-large-scale virtual screening for hit identification, computational strategies for designing new therapeutic modalities like PROTACs and covalent inhibitors that target residues beyond cysteine are also presented. To offer the most up-to-date information on computational methods utilized in computational drug discovery, it covers chapters highlighting the use of molecular dynamics and other related methods, application of QM and QM/MM methods in computational drug design, and techniques for navigating and visualizing the chemical space, as well as leveraging big data to drive drug discovery efforts. The book is thoughtfully organized into eight thematic sections, each focusing on a specific computational method or technology applied to drug discovery. Authored by renowned experts from academia, pharmaceutical industry, and major drug discovery software providers, it offers an overview of the latest advances in computational drug discovery. Key topics covered in the book include: Application of molecular dynamics simulations and related approaches in drug discovery The application of QM, hybrid approaches such as QM/MM, and fragment molecular orbital framework for understanding protein-ligand interactions Adoption of artificial intelligence in pre-clinical drug discovery, encompassing protein structure prediction, generative modeling for de novo design, and virtual screening. Techniques for navigating and visualizing the chemical space, along with harnessing big data to drive drug discovery efforts. Methods for performing ultra-large-scale virtual screening for hit identification. Computational strategies for designing new therapeutic models, including PROTACs and molecular glues. In silico ADMET approaches for predicting a variety of pharmacokinetic and physicochemical endpoints. The role of computing technologies like quantum computing and cloud computing in accelerating drug discovery This book will provide readers an overview of the latest advancements in computational drug discovery and serve as a valuable resource for professionals engaged in drug discovery.
Author: Zaheer Ul-Haq
Publisher: Elsevier
ISBN: 1608058646
Category : Science
Languages : en
Pages : 364
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Book Description
Frontiers in Computational Chemistry, originally published by Bentham and now distributed by Elsevier, presents the latest research findings and methods in the diverse field of computational chemistry, focusing on molecular modeling techniques used in drug discovery and the drug development process. This includes computer-aided molecular design, drug discovery and development, lead generation, lead optimization, database management, computer and molecular graphics, and the development of new computational methods or efficient algorithms for the simulation of chemical phenomena including analyses of biological activity. In Volume 1, the leading researchers in the field have collected eight different perspectives in the application of computational methods towards drug design to provide an up-to-date rendering of the current field. This volume covers a variety of topics from G protein-coupled receptors, to the use of cheminformatics and bioinformatics, computational tools such as Molecular Mechanics Poisson-Boltzmann Surface Area, protein-protein interactions, the use of computational methods on large biological data sets, various computational methods used to identify pharmaceutically relevant targets, and more. Brings together a wide range of research into a single collection to help researchers keep up with new methods Uniquely focuses on computational chemistry approaches that can accelerate drug design Makes a solid connection between experiment and computation and the novel application of computational methods in the fields of biology, chemistry, biochemistry, physics, and biophysics, with particular focus on the integration of computational methods with experimental data
Author: Daniel Mucs
Publisher:
ISBN:
Category :
Languages : en
Pages :
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This thesis contains three main sections. In the first section, we examine methodologies to discriminate Type II protein kinase inhibitors from the Type I inhibitors. We have studied the structure of 55 Type II kinase inhibitors and have notice specific descriptive geometric features. Using this information we have developed a pharmacophore and a shape based screening approach. We have found that these methods did not effectively discriminate between the two inhibitor types used independently, but when combined in a consecutive way - pharmacophore search first, then shape based screening, we have found a method that successfully filtered out all Type I molecules. The effect of protonation states and using different conformer generators were studied as well. This method was then tested on a freely available database of decoy molecules and again shown to be discriminative. In the second section of the thesis, we implement and assess swarm-based docking methods. We implement a repulsive particle swarm optimization (RPSO) based conformational search approach into Autodock 3.05. The performance of this approach with different parameters was then tested on a set of 51 protein ligand complexes. The effect of using different factoring for the cognitive, social and repulsive terms and the importance of the inertia weight were explored. We found that the RPSO method gives similar performance to the particle swarm optimization method. Compared to the genetic algorithm approach used in Autodock 3.05, our RPSO method gives better results in terms of finding lower energy conformations. In the final, third section we have implemented a Monte Carlo (MC) based conformer searching approach into Gaussian03. This enables high level quantum mechanics/molecular mechanics (QM/MM) potentials to be used in docking molecules in a protein active site. This program was tested on two Zn2+ ion-containing complexes, carbonic anhydrase II and cytidine deaminase. The effects of different QM region definitions were explored in both systems. A consecutive and a parallel docking approach were used to study the volume of the active site explored by the MC search algorithm. In case of the carbonic anhydrase II complex, we have used 1,2-difluorobenzene as a ligand to explore the favourable interactions within the binding site. With the cytidine deaminase complex, we have evaluated the ability of the approach to discriminate the native pose from other higher energy conformations during the exploration of the active site of the protein. We find from our initial calculations, that our program is able to perform a conformational search in both cases, and the effect of QM region definition is noticeable, especially in the description of the hydrophobic interactions within the carbonic anhydrase II system. Our approach is also able to find poses of the cytidine deaminase ligand within 1 Å of the native pose.
Author: Hans-Joachim Böhm
Publisher: John Wiley & Sons
ISBN: 3527605517
Category : Science
Languages : en
Pages : 262
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Book Description
The lock-and-key principle formulated by Emil Fischer as early as the end of the 19th century has still not lost any of its significance for the life sciences. The basic aspects of ligand-protein interaction may be summarized under the term 'molecular recognition' and concern the specificity as well as stability of ligand binding. Molecular recognition is thus a central topic in the development of active substances, since stability and specificity determine whether a substance can be used as a drug. Nowadays, computer-aided prediction and intelligent molecular design make a large contribution to the constant search for, e. g., improved enzyme inhibitors, and new concepts such as that of pharmacophores are being developed. An up-to-date presentation of an eternally young topic, this book is an indispensable information source for chemists, biochemists and pharmacologists dealing with the binding of ligands to proteins.
Author:
Publisher: Academic Press
ISBN: 012813917X
Category : Science
Languages : en
Pages : 154
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Book Description
Computational Molecular modelling in Structural Biology, Volume 113, the latest release in the Advances in Protein Chemistry and Structural Biology, highlights new advances in the field, with this new volume presenting interesting chapters on charting the Bromodomain BRD4: Towards the Identification of Novel Inhibitors with Molecular Similarity and Receptor Mapping, and Computational Methods to Discover Compounds for the Treatment of Chagas Disease. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Advances in Protein Chemistry and Structural Biology series Updated, with the latest information on Computational Molecular Modelling in Structural Biology
Author: Bing Wang
Publisher: Academic Press
ISBN: 0128244356
Category : Science
Languages : en
Pages : 280
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Book Description
Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognizing protein interaction sites, and identifying protein drug targets. The book emphasizes novel approaches to protein-ligand interactions, including machine learning and deep learning, presenting a state-of-the-art suite of skills for researchers. The volume represents a resource for scientists, detailing the fundamentals of computational methods, showing how to use computational algorithms to study protein interaction data, and giving scientific explanations for biological data through computational intelligence. Fourteen chapters offer a comprehensive guide to protein interaction data and computational intelligence methods for protein-ligand interactions. Presents a guide to computational techniques for protein-ligand interaction analysis Guides researchers in developing advanced computational intelligence methods for the protein-ligand problem Identifies appropriate computational tools for various problems Demonstrates the use of advanced techniques such as vector machine, neural networks, and machine learning Offers the computational, mathematical and statistical skills researchers need
