Karabencheva-Christova Biomolecular Modelling and Simulations

1. Auflage 2014
ISBN: 978-0-12-800789-1
Verlag: William Andrew Publishing
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band Volume 96, 330 Seiten, Format (B × H): 152 mm x 229 mm

Reihe: Advances in Protein Chemistry and Structural Biology

Published continuously since 1944, the Advances in Protein Chemistry and Structural Biology series is the essential resource for protein chemists. Each volume brings forth new information about protocols and analysis of proteins. Each thematically organized volume is guest edited by leading experts in a broad range of protein-related topics.

- Describes advances in biomolecular modelling and simulations
- Chapters are written by authorities in their field
- Targeted to a wide audience of researchers, specialists, and students
- The information provided in the volume is well supported by a number of high quality illustrations, figures, and tables

Zielgruppe

<p>The intended audiences for this volume are researchers and specialists in biomolecular modelling and simulations</p>

Autoren/Hrsg.

Karabencheva-Christova, Tatyana

Weitere Mitwirkende

Karabencheva-Christova, Tatyana

Fachgebiete

• Naturwissenschaften Chemie Organische Chemie Biochemie
• Naturwissenschaften Biowissenschaften Proteinforschung
• Naturwissenschaften Biowissenschaften Molekularbiologie

Inhaltsverzeichnis

- The Interplay Between Molecular Modeling and Chemoinformatics to Characterize Protein-Ligand and Protein-Protein Interactions Landscapes for Drug Discovery

José L. Medina-Franco, Oscar Méndez-Lucio and Karina Martinez-Mayorga

- Computational Study of Putative Residues Involved in DNA Synthesis Fidelity Checking in Thermus aquaticus DNA Polymerase I

Angela A. Elias and G. Andrés Cisneros

- New Strategies for Integrative Dynamic Modeling of Macromolecular Assembly

Enrico Spiga, Matteo Thomas Degiacomi and Matteo Dal Peraro

- Stability of Amyloid Oligomers

Workalemahu M. Berhanu and Ulrich H. E. Hansmann

- Recent Advances in Transferable Coarse-Grained Modeling of Proteins

Parimal Kar and Michael Feig

- Studying Allosteric Regulation in Metal Sensor Proteins Using Computational Methods

Dhruva K. Chakravorty, and Kenneth M. Merz

- Insights in the Mechanism of Action and Inhibition of N-Acylethanolamine Acid Amidase (NAAA) by means of Computational Methods

Alessio Lodola, Silvia Rivara and Marco Mor

- CHARMM-GUI PDB Manipulator for Advanced Modeling and Simulations of Proteins Containing Nonstandard Residues

Sunhwan Jo, Xi Cheng, Shahidul M. Islam, Lei Huang, Huan Rui, Allen Zhu, Hui Sun Lee, Yifei Qi, Wei Han, Kenno Vanommeslaeghe, Alexander D. MacKerell, Benoît Roux and Wonpil Im

- High-Resolution Modeling of Protein Structures Based on Flexible Fitting of Low-Resolution Structural Data Wenjun Zheng and Mustafa Tekpinar

Leseproben

Chapter One The Interplay Between Molecular Modeling and Chemoinformatics to Characterize Protein–Ligand and Protein–Protein Interactions Landscapes for Drug Discovery
José L. Medina-Franco*,1; Oscar Méndez-Lucio†; Karina Martinez-Mayorga‡    * Mayo Clinic, Scottsdale, Arizona, USA
† Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
‡ Instituto de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico
1 Corresponding author: email address: [e-mail], [e-mail] Abstract
Protein–ligand and protein–protein interactions play a fundamental role in drug discovery. A number of computational approaches have been developed to characterize and use the knowledge of such interactions that can lead to drug candidates and eventually compounds in the clinic. With the increasing structural information of protein–ligand and protein–protein complexes, the combination of molecular modeling and chemoinformatics approaches are often required for the efficient analysis of a large number of such complexes. In this chapter, we review the progress on the developments of in silico approaches that are at the interface between molecular modeling and chemoinformatics. Although the list of methods and applications is not exhaustive, we aim to cover representative cases with a special emphasis on interaction fingerprints and their applications to identify “hot spots.” We also elaborate on proteochemometric modeling and the emerging concept of activity landscape, structure-based interpretation of activity cliffs and structure–protein–ligand interaction relationships. Target–ligand relationships are discussed in the context of chemogenomics data sets. Keywords 2D interaction maps 3D activity cliffs Activity cliff generators Hot spots Interaction fingerprint Pharmacophore Protein–ligand interaction fingerprints Proteochemometric modeling Structure–activity relationships Structure protein–ligand interaction relationships 1 Introduction
Understanding protein–ligand interactions (PLIs) and protein–protein interactions (PPIs) is at the core of molecular recognition and has a fundamental role in many scientific areas. PLIs and PPIs have a broad area of practical applications in drug discovery including but not limited to molecular docking (Bello, Martinez-Archundia, & Correa-Basurto, 2013), structure-based design, virtual screening of molecular fragments, small molecules, and other type of compounds, clustering of complexes, and structural interpretation of activity cliffs, to name a few. Over the years, the scientific community has made significant progress on the understanding of PLIs and PPIs that have led to the development of algorithms to predict the putative interaction of two molecules. For example, Chupakhin et al. recently used a machine learning approach to predict protein–ligand binding modes based on the two-dimensional (2D) structure of the ligand and a previous set of PLIs (Chupakhin, Marcou, Baskin, Varnek, & Rognan, 2013). One of the goals of improving the description of the protein–ligand binding process is, as recently discussed, to reach a point where a more detailed description of protein–ligand complexes can be associated with a more accurate prediction of binding affinity (Ballester, Schreyer, & Blundell, 2014). Indeed, Ballester et al. noted that a typical issue of current scoring functions used in docking is the “difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity.” Ballester et al. also commented that novel scoring functions based on machine learning regression models have shown superior performance over commonly used scoring functions. Finally, the authors of this elegant work concluded that “a more precise chemical description of the protein–ligand complex does not generally lead to a more accurate prediction of binding affinity” (Ballester et al., 2014). In a broad sense, PLIs and PPIs have been characterized using either molecular modeling or chemoinformatic applications. While molecular modeling techniques such as molecular mechanics, quantum mechanics, molecular dynamics, pharmacophore modeling capture, manage, and represent PLIs and PPIs in a three-dimensional (3D) manner, chemoinformatic approaches typically transform those interactions in 2D or one-dimensional (1D) representations for the rapid and easy visualization, clustering, and mining of those interactions. Of course, there is a large overlap between both types of approaches. In-depth reviews of the progress and current status in each of the above mentioned methods have been published in an individual manner (Durrant & McCammon, 2011; Langer, 2010; Scior et al., 2012). In this chapter, our goal is to discuss recent advances and exemplary applications of the integration between molecular modeling and chemoinformatic methods to characterize PLIs and PPIs. We put a special emphasis on the development and application of protein–ligand interaction fingerprints (PLIFs). While the list of applications is not comprehensive, we want to focus on representative combined applications of current interest in drug discovery. The chapter is organized in seven sections. After this introduction, Section 2 discusses an overview and recent advances and selected applications of the characterization of PLIs using fingerprints. Section 3 is dedicated to the visual representation of PLIs with 2D graphs, representation of PLIFs using 3D pharmacophore models, and chemoinformatic approaches used for the visualization of chemical spaces. Section 4 presents studies that aim to explore structure–protein–ligand interaction relationships (SPLIRs). In this section, we put a particular emphasis on the application of the emerging concept of activity landscape. Advances in the characterization of structure-based activity cliffs, structure-based activity cliff generators, and 3D activity cliffs are discussed. Section 5 discusses examples of the analysis of target–ligand relationships in chemogenomics data sets. Section 6 addresses the characterization of PPIs. Section 7 presents summary conclusions. 2 Characterizing PLIs with Fingerprints
PLIFs, also called “structural interaction fingerprints,” are designed to “capture a 1D representation of the interactions between ligand and protein either in complexes of known structure or in docked poses” (Brewerton, 2008). PLIFs are a primary example of combining molecular modeling—that can characterize and describe in detail the interactions at the molecular level—with chemoinformatics that can process large amounts of protein–ligand and protein–protein complexes. PLIFs can also be derived from crystallographic information. As recently pointed out by Desaphy, Raimbaud, Ducrot, and Rognan (2013), fingerprints are a very convenient way to simplify the atomic coordinates of PLIs. Fingerprints are easy to generate, manipulate, and compare a vast number of protein–ligand complexes (Desaphy et al., 2013). For example, PLIFs enable the systematic analysis of large amounts of data and are suitable to evaluate if similar binding sites identically recognize similar ligands, if PLI patterns are conserved across target families, and if different ligand structures or substructures have the same interaction patterns with a single target (Desaphy et al., 2013). There are two general approaches to generate PLIFs: (A) Annotating ligand descriptors with interaction features (Tan, Batista, & Bajorath, 2010). (B) Annotate protein descriptors, typically amino acids in the binding site, with ligand interaction features (Deng, Chuaqui, & Singh, 2004). Both general approaches have been recently summarized by Desaphy et al. (2013). As exemplified below with some representative cases, interaction fingerprints (IFPs) have a number of applications including postprocessing docking results, virtual screening (Chupakhin et al., 2013), data mining and clustering protein–ligand complexes (Weisel, Bitter, Diederich, So, & Kondru, 2012), and library design (Deng, Chuaqui, & Singh, 2006). Representative applications of PLIFs are summarized in Table 1. Table 1 Examples of applications of protein–ligand interaction fingerprints Application Example/representative study Reference Postprocessing docking results Development of APIF (atom-pairs-based interaction fingerprint), an interaction fingerprint tuned for postprocessing protein–ligand docking results Perez-Nueno, Rabal, Borrell, and Teixido (2009) Data mining Development of PROLIX (Protein–Ligand Interaction Explorer), a tool that...