Knowledge of the structures formed by proteins and small molecules is key to understand the molecular principles of chemotherapy and for designing new and more effective drugs. During the early stage of a drug discovery program, it is customary to predict ligand-protein complexes in silico, particularly when screening large compound databases. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology to generate bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites by exploiting only information on the unbound structure and the putative binding sites. The protocol is validated on the paradigm enzyme adenylate kinase, for which we generated a significant fraction of bound-like structures. A fraction of these conformations, employed in ensemble-docking calculations, allowed to find native-like poses of substrates and inhibitors (binding to the active form of the enzyme), as well as catalytically incompetent analogs (binding the inactive form). Our protocol provides a general framework for the generation of bound-like conformations of challenging drug targets that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening, in the prediction of the impact of amino acid mutations on structure and dynamics, and in protein engineering.
Protein interactions with various other macromolecules is a key biological phenomenon for the molecular recognition process leading to various physiological functions. Throughout decades, researchers have proposed various methods for the investigation of such binding mechanism, starting from static, rigid docking to flexible docking approaches. Rational drug designing approaches were improvised by introducing semi- to full-flexibility in the protein-ligand molecular recognition process, conformational dynamics, and binding kinetics and thermodynamics of conserved waters in the binding site. A better understanding of ligand-binding is quintessential to gain more quantitative and accurate information about molecular recognition for drug and therapeutic interventions. To address these issues, Ensemble docking approaches were introduced, which include protein flexibility through a different set of protein conformations either experimentally or with computational simulations i.e., molecular dynamics simulations. MD simulations enable ensemble construction which generates an array of binding site conformations for multiple docking trials of the same protein, though sometimes poorly sampled. To overcome the same, enhanced sampling was introduced. In this chapter, the theoretical background of molecular docking, classical MD simulations, MD-based enhanced sampling methods and hybrid docking-MD based methods are highlighted, demonstrating how protein flexibility has been introduced to optimize and enhance accurate protein-ligand binding predictions. Overall, the evolution of various computational strategies is discussed, from molecular docking to molecular dynamics simulations, to improve the overall drug discovery and development process.
This chapter gives a comprehensive overview of design and fabrication of a special kind of colloidal dispersion which do not exhibit macroscopic behavior. The term "nanoparticles" suites better to describe such class of materials, which are of nanoscale dimension and differ from their bulk counterpart in every respect. The spectacular and unprecedented features of nanoparticles give them significant recognition in almost every field of contemporary sciences. The fascinating applications of nanoparticles have been increasing in innovative areas of research and development. We have structured this chapter in five parts: introduction, synthetic and fabrication strategies of nanoparticles using calixarenes, theoretical approach for mechanistic insights, and concluding remarks. One part covers a critical review on what are nanoparticles, their formation, and typical synthetic strategies. Another important part of this chapter covers the fabrication of nanoparticles using calixarenes, with some superlative properties and applications. The newest trend of computational modelling has been incorporated to emphasize a break point in the evolution of fabricated nanoparticles. Collectively, this chapter frames the state-of-the-art information of this crucial topic of reliability and significant trends of nanoscience.
Herein, a fluorescent oxacalix[4]arene-based receptor, DAQTNOC(5,17-di(N-(9,10-dioxo-9,10-dihydroanthracen-1-yl)acetamide) tetranitrooxacalix[4]arene), was described for the specific recognition of N-methyl-p-nitroaniline (MNA).
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