The predicted ensemble of low-energy conformations of human somatostatin receptor subtype 5 and the binding of antagonists

Discovery of 3-Phenyl Indazole-Based Novel Chemokine-like Receptor 1 Antagonists for the Treatment of Psoriasis

Chemokine-like receptor 1 (CMKLR1)─a G protein-coupled receptor─has functional roles in the immune system and related diseases, including psoriasis and metabolic diseases. Psoriasis is a chronic inflammatory disease characterized by skin redness, scaliness, and itching. In this study, we sought to develop novel CMKLR1 antagonists by screening our in-house GPCR-targeting compound library. Moreover, we optimized a phenylindazole-based hit compound with antagonistic activities and evaluated its oral pharmacokinetic properties in a murine model. A structure-based design on the human CMKLR1 homology model identified S-26d as an optimized compound that serves as a potent and orally available antagonist with a pIC50 value of 7.44 in hCMKLR1-transfected CHO cells. Furthermore, in the imiquimod-induced psoriasis-like mouse model, oral administration of S-26d for 1 week significantly alleviated modified psoriasis area and severity index scores (severity of erythema, scaliness, skin thickness) compared with the control group.

Development of enhanced conformational sampling methods to probe the activation landscape of GPCRs

G protein-coupled receptors (GPCRs) make up the largest superfamily of integral membrane proteins and play critical signal transduction roles in many physiological processes. Developments in molecular biology, biophysical, biochemical, pharmacological, and computational techniques aimed at these important therapeutic targets are beginning to provide unprecedented details on the structural as well as functional basis of their pleiotropic signaling mediated by G proteins, β arrestins, and other transducers. This pleiotropy presents a pharmacological challenge as the same ligand-receptor interaction can cause a therapeutic effect as well as an undesirable on-target side-effect through different downstream pathways. GPCRs don't function as simple binary on-off switches but as finely tuned shape-shifting machines described by conformational ensembles, where unique subsets of conformations may be responsible for specific signaling cascades. X-ray crystallography and more recently cryo-electron microscopy are providing snapshots of some of these functionally-important receptor conformations bound to ligands and/or transducers, which are being utilized by computational methods to describe the dynamic conformational energy landscape of GPCRs. In this chapter, we review the progress in computational conformational sampling methods based on molecular dynamics and discrete sampling approaches that have been successful in complementing biophysical and biochemical studies on these receptors in terms of their activation mechanisms, allosteric effects, actions of biased ligands, and effects of pathological mutations. Some of the sampled simulation time scales are beginning to approach receptor activation time scales. The list of conformational sampling methods and example uses discussed is not exhaustive but includes representative examples that have pushed the limits of classical molecular dynamics and discrete sampling methods to describe the activation energy landscape of GPCRs.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

BACKGROUND

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts.

OBJECTIVE

A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools.

METHODS

Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data.

RESULTS

Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields.

CONCLUSION

This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Conformational and Thermodynamic Landscape of GPCR Activation from Theory and Computation

We present a hybrid computational methodology to predict multiple energetically accessible conformations for G protein-coupled receptors (GPCRs) that might play a role in binding to ligands and different signaling partners. To our knowledge, this method, termed ActiveGEnSeMBLE, enables the first quantitative energy profile for GPCR activation that is consistent with the qualitative profile deduced from experiments. ActiveGEnSeMBLE starts with a systematic coarse grid sampling of helix tilts/rotations (∼13 trillion transmembrane-domain conformations) and selects the conformational landscape based on energy. This profile identifies multiple potential active-state energy wells, with the TM3-TM6 intracellular distance as an approximate activation coordinate. These energy wells are then sampled locally using a finer grid to find locally minimized conformation in each energy well. We validate this strategy using the inactive and active experimental structures of β2 adrenergic receptor (hβ2AR) and M2 muscarinic acetylcholine receptor. Structures of membrane-embedded hβ2AR along its activation coordinate are subjected to molecular-dynamics simulations for relaxation and interaction energy analysis to generate a quantitative energy landscape for hβ2AR activation. This landscape reveals several metastable states along this coordinate, indicating that for hβ2AR, the agonist alone is not enough to stabilize the active state and that the G protein is necessary, consistent with experimental observations. The method's application to somatostatin receptor SSTR5 (no experimental structure available) shows that to predict an active conformation it is better to start from an inactive structure template based on a close homolog than to start from an active template based on a distant homolog. The energy landscape for hSSTR5 activation is consistent with hβ2AR in the role of the G protein. These results demonstrate the utility of the ActiveGEnSeMBLE method for predicting multiple conformations along the pathways for activating GPCRs and the corresponding energy landscapes, thereby providing detailed structural insights into the initial molecular events of GPCR function that are not easily accessible by experiments.

Computational predictions of corroles as a class of Hsp90 inhibitors

Corroles have been shown experimentally to cause cell cycle arrest, and there is some evidence that this might be attributed to an inhibitory effect of corroles on Heat shock protein 90 (Hsp90), which is known to play a vital role in cancer cell proliferation. In this study, we used molecular dynamics to examine the interaction of gallium corroles with Hsp90, and found that they can bind preferentially to the ATP-binding N-terminal site. We also found that structural variations of the corrole ring can influence the binding energies and affinities of the corrole to Hsp90. We predict that both the bis-carboxylated corrole (4-Ga) and a proposed 3,17-bis-sulfonated corrole (7-Ga) are promising alternatives to Ga(III) 5,10,15-tris(pentafluorophenyl)-2,17-bis(sulfonic acid)-corrole (1-Ga) as anti-cancer agents.

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles. Experimental (nuclear magnetic resonance, small-angle X-ray scattering, single-molecule spectroscopy, and cryo-electron microscopy) and computational (replica-exchange molecular dynamics, metadynamics, and Markov state models) approaches have made great progress in recent years. These address the challenging characterization of the highly flexible and heterogeneous protein ensembles. We focus on structural aspects of protein conformational distributions, from collective motions of single- and multi-domain proteins, intrinsically disordered proteins, to multiprotein complexes. Importantly, we highlight recent studies that illustrate functional adjustment of protein conformational ensembles in the crowded cellular environment. We center on the role of the ensemble in recognition of small- and macro-molecules (protein and RNA/DNA) and emphasize emerging concepts of protein dynamics in enzyme catalysis. Overall, protein ensembles link fundamental physicochemical principles and protein behavior and the cellular network and its regulation.