The inner workings of an optogenetic tool: Channelrhodopsins are membrane channel proteins whose gating is controlled by light. In their native setting, they allow green algae to move in response to light. Their expression in neurons allows precise control of neural activity, an approach known as optogenetics. Volkov et al. describe the high-resolution structure of channelrhodopsin 2, the most widely used optogenetics tool, as well as the structure of a mutant with a longer open-state lifetime (see the Perspective by Gerwert). Light activation perturbs an intricate hydrogen-bonding network to open the channel. The structures provide a basis for designing better optogenetic tools. Science , this issue p. 10.1126/science.eaan8862 ; see also p. 1000

Two distinct antagonist-bound structures of CysLT ₁ R reveal unique ligand-binding modes and signaling mechanisms.

The light-driven sodium-pumping rhodopsin KR2 from Krokinobacter eikastus is the only non-proton cation active transporter with demonstrated potential for optogenetics. However, the existing structural data on KR2 correspond exclusively to its ground state, and show no sodium inside the protein, which hampers the understanding of sodium-pumping mechanism. Here we present crystal structure of the O-intermediate of the physiologically relevant pentameric form of KR2 at the resolution of 2.1 Å, revealing a sodium ion near the retinal Schiff base, coordinated by N112 and D116 of the characteristic NDQ triad. We also obtained crystal structures of D116N and H30A variants, conducted metadynamics simulations and measured pumping activities of putative pathway mutants to demonstrate that sodium release likely proceeds alongside Q78 towards the structural sodium ion bound between KR2 protomers. Our findings highlight the importance of pentameric assembly for sodium pump function, and may be used for rational engineering of enhanced optogenetic tools.

Cysteinyl leukotriene G protein-coupled receptors CysLT₁ and CysLT₂ regulate pro-inflammatory responses associated with allergic disorders. While selective inhibition of CysLT₁R has been used for treating asthma and associated diseases for over two decades, CysLT₂R has recently started to emerge as a potential drug target against atopic asthma, brain injury and central nervous system disorders, as well as several types of cancer. Here, we describe four crystal structures of CysLT₂R in complex with three dual CysLT₁R/CysLT₂R antagonists. The reported structures together with the results of comprehensive mutagenesis and computer modeling studies shed light on molecular determinants of CysLTR ligand selectivity and specific effects of disease-related single nucleotide variants.

Cysteinyl leukotriene G protein-coupled receptors, CysLT1R and CysLT2R, regulate bronchoconstrictive and pro-inflammatory effects and play a key role in allergic disorders, cardiovascular diseases, and cancer. CysLT1R antagonists have been widely used to treat asthma disorders, while CysLT2R is a potential target against uveal melanoma. However, very few selective antagonist chemotypes for CysLT receptors are available, and the design of such ligands has proved to be challenging. To overcome this obstacle, we took advantage of recently solved crystal structures of CysLT receptors and an ultra-large Enamine REAL library, representing a chemical space of 680 M readily available compounds. Virtual ligand screening employed 4D docking models comprising crystal structures of CysLT1R and CysLT2R and their corresponding ligand-optimized models. Functional assessment of the candidate hits yielded discovery of five novel antagonist chemotypes with sub-micromolar potencies and the best Ki = 220 nM at CysLT1R. One of the hits showed inverse agonism at the L129Q constitutively active mutant of CysLT2R, with potential utility against uveal melanoma.

Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure-function relationship for this particular target. An ultralarge virtual screening approach has emerged recently for rapid discovery of high-affinity hit compounds, but it requires substantial computational resources. This study shows that active learning with simple linear regression models can accelerate virtual screening, retrieving up to 90% of the top-1% of the docking hit list after docking just 10% of the ligands. The results demonstrate that it is unnecessary to use complex models, such as deep learning approaches, to predict the imprecise results of ligand docking with a low sampling depth. Furthermore, we explore active learning meta-parameters and find that constant batch size models with a simple ensembling method provide the best ligand retrieval rate. Finally, our approach is validated on the ultralarge size virtual screening data set, retrieving 70% of the top-0.05% of ligands after screening only 2% of the library. Altogether, this work provides a computationally accessible approach for accelerated virtual screening that can serve as a blueprint for the future design of low-compute agents for exploration of the chemical space via large-scale accelerated docking. With recent breakthroughs in protein structure prediction, this method can significantly increase accessibility for the academic community and aid in the rapid discovery of high-affinity hit compounds for various targets.