Photopharmacology of Ion Channels through the Light of the Computational Microscope

The optical control and investigation of neuronal activity can be achieved and carried out with photoswitchable ligands. Such compounds are designed in a modular fashion, combining a known ligand of the target protein and a photochromic group, as well as an additional electrophilic group for tethered ligands. Such a design strategy can be optimized by including structural data. In addition to experimental structures, computational methods (such as homology modeling, molecular docking, molecular dynamics and enhanced sampling techniques) can provide structural insights to guide photoswitch design and to understand the observed light-regulated effects. This review discusses the application of such structure-based computational methods to photoswitchable ligands targeting voltage- and ligand-gated ion channels. Structural mapping may help identify residues near the ligand binding pocket amenable for mutagenesis and covalent attachment. Modeling of the target protein in a complex with the photoswitchable ligand can shed light on the different activities of the two photoswitch isomers and the effect of site-directed mutations on photoswitch binding, as well as ion channel subtype selectivity. The examples presented here show how the integration of computational modeling with experimental data can greatly facilitate photoswitchable ligand design and optimization. Recent advances in structural biology, both experimental and computational, are expected to further strengthen this rational photopharmacology approach.

Download Full-text

Recent Trends in Drug Design and Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200622150003 ◽

2020 ◽

Vol 20 (19) ◽

pp. 1761-1770

Author(s):

Devadasan Velmurugan ◽

R. Pachaiappan ◽

Chandrasekaran Ramakrishnan

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Molecular Modeling ◽

Drug Design ◽

Computational Methods ◽

Data Analytics ◽

Drug Targets ◽

Structural Data ◽

Target Protein ◽

Structure Based Drug Design

Introduction: Structure-based drug design is a wide area of identification of selective inhibitors of a target of interest. From the time of the availability of three dimensional structure of the drug targets, mostly the proteins, many computational methods had emerged to address the challenges associated with drug design process. Particularly, drug-likeness, druggability of the target protein, specificity, off-target binding, etc., are the important factors to determine the efficacy of new chemical inhibitors. Objective: The aim of the present research was to improve the drug design strategies in field of design of novel inhibitors with respect to specific target protein in disease pathology. Recent statistical machine learning methods applied for structural and chemical data analysis had been elaborated in current drug design field. Methods: As the size of the biological data shows a continuous growth, new computational algorithms and analytical methods are being developed with different objectives. It covers a wide area, from protein structure prediction to drug toxicity prediction. Moreover, the computational methods are available to analyze the structural data of varying types and sizes of which, most of the semi-empirical force field and quantum mechanics based molecular modeling methods showed a proven accuracy towards analysing small structural data sets while statistics based methods such as machine learning, QSAR and other specific data analytics methods are robust for large scale data analysis. Results: In this present study, the background has been reviewed for new drug lead development with respect specific drug targets of interest. Overall approach of both the extreme methods were also used to demonstrate with the plausible outcome. Conclusion: In this chapter, we focus on the recent developments in the structure-based drug design using advanced molecular modeling techniques in conjunction with machine learning and other data analytics methods. Natural products based drug discovery is also discussed.

Download Full-text

Real-time observation of tetrapyrrole binding to an engineered bacterial phytochrome

Communications Chemistry ◽

10.1038/s42004-020-00437-3 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Yusaku Hontani ◽

Mikhail Baloban ◽

Francisco Velazquez Escobar ◽

Swetta A. Jansen ◽

Daria M. Shcherbakova ◽

...

Keyword(s):

Real Time ◽

Rational Design ◽

Near Infrared ◽

Fluorescent Proteins ◽

Covalent Bond ◽

Binding Pocket ◽

Tissue Imaging ◽

Covalent Attachment ◽

Time Resolved

AbstractNear-infrared fluorescent proteins (NIR FPs) engineered from bacterial phytochromes are widely used for structural and functional deep-tissue imaging in vivo. To fluoresce, NIR FPs covalently bind a chromophore, such as biliverdin IXa tetrapyrrole. The efficiency of biliverdin binding directly affects the fluorescence properties, rendering understanding of its molecular mechanism of major importance. miRFP proteins constitute a family of bright monomeric NIR FPs that comprise a Per-ARNT-Sim (PAS) and cGMP-specific phosphodiesterases - Adenylyl cyclases - FhlA (GAF) domain. Here, we structurally analyze biliverdin binding to miRFPs in real time using time-resolved stimulated Raman spectroscopy and quantum mechanics/molecular mechanics (QM/MM) calculations. Biliverdin undergoes isomerization, localization to its binding pocket, and pyrrolenine nitrogen protonation in <1 min, followed by hydrogen bond rearrangement in ~2 min. The covalent attachment to a cysteine in the GAF domain was detected in 4.3 min and 19 min in miRFP670 and its C20A mutant, respectively. In miRFP670, a second C–S covalent bond formation to a cysteine in the PAS domain occurred in 14 min, providing a rigid tetrapyrrole structure with high brightness. Our findings provide insights for the rational design of NIR FPs and a novel method to assess cofactor binding to light-sensitive proteins.

Download Full-text

Three-Dimensional Structures of Carbohydrates and Where to Find Them

International Journal of Molecular Sciences ◽

10.3390/ijms21207702 ◽

2020 ◽

Vol 21 (20) ◽

pp. 7702 ◽

Cited By ~ 1

Author(s):

Sofya I. Scherbinina ◽

Philip V. Toukach

Keyword(s):

Experimental Data ◽

Molecular Modeling ◽

Computational Methods ◽

Three Dimensional ◽

Structural Diversity ◽

Structural Data ◽

Structural Features ◽

Data Validation ◽

Data Generation ◽

Efficient Treatment

Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.

Download Full-text

Computational Ion Channel Research: from the Application of Artificial Intelligence to Molecular Dynamics Simulations.

Cellular Physiology and Biochemistry ◽

10.33594/000000336 ◽

2020 ◽

Vol 55 (S3) ◽

pp. 14-45

Keyword(s):

Artificial Intelligence ◽

Molecular Dynamics ◽

Ion Channels ◽

Ion Channel ◽

Computational Methods ◽

Homology Modelling ◽

Channel Structure ◽

Learning Approaches ◽

Qsar Analysis ◽

Wide Range

Although ion channels are crucial in many physiological processes and constitute an important class of drug targets, much is still unclear about their function and possible malfunctions that lead to diseases. In recent years, computational methods have evolved into important and invaluable approaches for studying ion channels and their functions. This is mainly due to their demanding mechanism of action where a static picture of an ion channel structure is often insufficient to fully understand the underlying mechanism. Therefore, the use of computational methods is as important as chemical-biological based experimental methods for a better understanding of ion channels. This review provides an overview on a variety of computational methods and software specific to the field of ion-channels. Artificial intelligence (or more precisely machine learning) approaches are applied for the sequence-based prediction of ion channel family, or topology of the transmembrane region. In case sufficient data on ion channel modulators is available, these methods can also be applied for quantitative structureactivity relationship (QSAR) analysis. Molecular dynamics (MD) simulations combined with computational molecular design methods such as docking can be used for analysing the function of ion channels including ion conductance, different conformational states, binding sites and ligand interactions, and the influence of mutations on their function. In the absence of a three-dimensional protein structure, homology modelling can be applied to create a model of your ion channel structure of interest. Besides highlighting a wide range of successful applications, we will also provide a basic introduction to the most important computational methods and discuss best practices to get a rough idea of possible applications and risks.

Download Full-text

Properties of the beta 1- and beta 2-adrenergic receptor subtypes revealed by molecular cloning.

Clinical Chemistry ◽

10.1093/clinchem/35.5.721 ◽

1989 ◽

Vol 35 (5) ◽

pp. 721-725 ◽

Cited By ~ 11

Author(s):

T Frielle ◽

M G Caron ◽

R J Lefkowitz

Keyword(s):

Adrenergic Receptor ◽

Radioligand Binding ◽

Binding Pocket ◽

Site Directed Mutagenesis ◽

Xenopus Laevis Oocytes ◽

Receptor Subtypes ◽

Subtype Selectivity ◽

Beta 2 ◽

Transmembrane Regions ◽

Chimeric Rna

Abstract The beta 1- and beta 2-adrenergic receptor subtypes are biochemically and functionally similar, because both receptors mediate the catecholamine-dependent activation of adenylate cyclase through the GTP-binding protein, Gs. Pharmacologically, the two receptors can be distinguished on the basis of their relative affinities for the agonists epinephrine and norepinephrine as well as their affinities for several selective antagonists. The primary structures of the human beta 1- and beta 2-adrenergic receptors have recently been deduced from the cloning of their genes and (or) cDNAs, revealing high sequence homology and a membrane topography of seven putative transmembrane regions similar to that of rhodopsin. Chimeric beta 1/beta 2-adrenergic receptor cDNAs have been constructed by site-directed mutagenesis and the chimeric RNA transcripts expressed in Xenopus laevis oocytes. The pharmacological properties of the expressed chimeric receptor proteins were assessed by radioligand binding utilizing subtype-selective agonists and antagonists. Apparently, several of the putative transmembrane regions contribute significantly to the determination of subtype selectivity, presumably by formation of a ligand-binding pocket, with determinants for agonist and antagonist binding being distinguishable.

Download Full-text

Flavin transferase: the maturation factor of flavin-containing oxidoreductases

Biochemical Society Transactions ◽

10.1042/bst20180524 ◽

2018 ◽

Vol 46 (5) ◽

pp. 1161-1169 ◽

Cited By ~ 11

Author(s):

Alexander V. Bogachev ◽

Alexander A. Baykov ◽

Yulia V. Bertsova

Keyword(s):

Bond Formation ◽

Covalent Bond ◽

Target Protein ◽

Covalent Attachment ◽

Future Research ◽

Post Translational Modification ◽

Nadh:Quinone Oxidoreductase ◽

Maturation Factor ◽

Covalently Linked ◽

Prevailing View

Flavins, cofactors of many enzymes, are often covalently linked to these enzymes; for instance, flavin adenine mononucleotide (FMN) can form a covalent bond through either its phosphate or isoalloxazine group. The prevailing view had long been that all types of covalent attachment of flavins occur as autocatalytic reactions; however, in 2013, the first flavin transferase was identified, which catalyzes phosphoester bond formation between FMN and Na+-translocating NADH:quinone oxidoreductase in certain bacteria. Later studies have indicated that this post-translational modification is widespread in prokaryotes and is even found in some eukaryotes. Flavin transferase can occur as a separate ∼40 kDa protein or as a domain within the target protein and recognizes a degenerate DgxtsAT/S motif in various target proteins. The purpose of this review was to summarize the progress already achieved by studies of the structure, mechanism, and specificity of flavin transferase and to encourage future research on this topic. Interestingly, the flavin transferase gene (apbE) is found in many bacteria that have no known target protein, suggesting the presence of yet unknown flavinylation targets.

Download Full-text

Computational Methods for Structural and Functional Studies of Alzheimer’s Amyloid Ion Channels

Methods in Molecular Biology - Protein Amyloid Aggregation ◽

10.1007/978-1-4939-2978-8_16 ◽

2016 ◽

pp. 251-268 ◽

Cited By ~ 4

Author(s):

Hyunbum Jang ◽

Fernando Teran Arce ◽

Joon Lee ◽

Alan L. Gillman ◽

Srinivasan Ramachandran ◽

...

Keyword(s):

Ion Channels ◽

Computational Methods ◽

Functional Studies

Download Full-text

Insights into the Structural Aspects of the mGlu Receptor Orthosteric Binding Site

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666191011094935 ◽

2019 ◽

Vol 19 (26) ◽

pp. 2421-2446 ◽

Cited By ~ 1

Author(s):

Junliang Hao ◽

Qi Chen

Keyword(s):

Binding Pocket ◽

Receptor Subtypes ◽

X Ray ◽

Mglu Receptor ◽

Metabotropic Glutamate ◽

Amino Terminal ◽

Subtype Selectivity ◽

Agonist And Antagonist ◽

Amino Terminal Domain ◽

Structural Aspects

The amino terminal domain (ATD) of the metabotropic glutamate (mGlu) receptors contains the orthosteric glutamate recognition site, which is highly conserved across the eight mGlu receptor subtypes. In total, 29 X-ray crystal structures of the mGlu ATD proteins have been reported to date. These structures span across 3 subgroups and 6 subtypes, and include apo, agonist- and antagonist-bound structures. We will discuss the insights gained from the analysis of these structures with the focus on the interactions contributing to the observed group and subtype selectivity for select agonists. Furthermore, we will define the full expanded orthosteric ligand binding pocket (LBP) of the mGlu receptors, and discuss the macroscopic features of the mGlu ATD proteins.

Download Full-text