γ-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the central nervous system (CNS). Dysfunctional GABAergic neurotransmission is associated with numerous neurological and neuropsychiatric disorders. The GABAB receptor (GABAB-R) is a heterodimeric class C G protein-coupled receptor (GPCR) comprised of GABAB1a/b and GABAB2 subunits. The orthosteric binding site for GABA is located

De novo drug design actively seeks to use sets of chemical rules for the fast and efficient identification of structurally new chemotypes with the desired set of biological properties. Fragment-based de novo design tools have been successfully applied in the discovery of noncovalent inhibitors. Nevertheless, these tools are rarely applied in the field of covalent inhibitor de-sign. Herein, we present

The need for accurate and efficient force fields for modeling 3D structures of macro-biomolecules and in particular intrinsically disordered proteins (IDPs) has increased with recent findings to associate IDPs and human diseases. However, most conventional protein force fields and recent IDPs specific force fields are limited in reproducing accurate structural features of IDPs. Here, we present an

Structure-based virtual screening (SBVS) relies on classical scoring functions that often fail to reliably discriminate binders from non-binders. In this work, we present a high-throughput protein-ligand complex MD simulations that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in DUD-E (directory of useful decoy, enhanced) dataset. MD trajectories

Cryo-electron microscopy (cryo-EM) density maps at medium resolution (5-10 Å) reveal secondary structural features such as α-helices and β-sheets, but they lack the side chains details that would enable a direct structure determination. Among the more than 800 entries in the Electron Microscopy Data Bank (EMDB) of medium-resolution density maps that are associated with atomic models, a wide variety

Proteins in their native states can be represented as ensembles of conformers in dynamical equilibrium. Thermal fluctuations are responsible for transitions between these conformers. Normal modes analysis (NMA) using elastic network models (ENM) provides an efficient procedure to explore global dynamics of proteins commonly associated to conformational transitions. In the present work, we present an

The number of journal articles in the scientific domain has grown to the point where it has become impossible for researchers to capitalize on all the findings in their relevant discipline. Information is stored in these articles in a number of ways, including figures that describe important results. In organic chemistry, these figures often present chemical schematic diagrams that graphically define

In our publication (10.1021/acs.jcim.9b00751), we investigated the binding mode of the novel, competitive, and specific A3R antagonist K18 (O4-{[3-(2,6-dichlorophenyl)-5-methylisoxazol-4-yl]carbonyl}-2-methyl-1,3-thiazole-4-carbohydroximamide) in complex with the unresolved orthosteric binding area of adenosine receptor A3 (A3R). We applied MM-PBSA and MM-GBSA free energy calculations combined with

Computational high throughput screening (HTS) has emerged as a significant tool in materials science to accelerate the discovery of new materials with target properties in recent years. However, despite many successful cases in which HTS led to the novel discovery, currently, the major bottleneck in HTS is a large computational cost of density functional theory (DFT) calculations that scale cubically

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretic data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal-organic frameworks (MOFs). To augment the data for training

Methods that survey protein surfaces for binding hotspots can help to evaluate tar-get tractability and guide exploration of potential ligand binding regions. Fragment Hotspot Maps builds upon interaction data mined from the CSD (Cambridge Structural Database) and exploits the idea of identifying hotspots using small chemical fragments, which is now widely used to design new drug leads. Prior to this

Recent approaches to the study of biological molecules employ manifold learning to single-particle cryo-EM datasets to map the continuum of states of a molecule into a low-dimensional space spanned by eigenvectors, or “conformational coordinates”. This is done separately for each projection direction (PD) on an angular grid. One important step in deriving a consolidated map of occupancies, from which

Given a particular descriptor/method combination, we find some QSAR datasets are very predictive by random-split cross-validation, while others are not. Recent literature in modelability suggests that the limiting issue for predictivity is in the data, not the QSAR methodology, and the limits are due to activity cliffs. Here we investigate, on in-house data, the relative usefulness of experimental

In general, searching the lowest-energy structures is considerably more time-consuming for bimetallic clusters than for monometallic ones due to the presence of an increasing number of homotops and geometrical isomers. In this article, a basin hopping genetic algorithm (BHGA), in which the genetic algorithm is implanted into the basin hopping method, is proposed to search the lowest-energy structures

The combination of high-end cryogenic transmission electron microscopes (cryo-EM), direct electron detectors and advanced image algorithms allows researchers to obtain the 3D structures of much smaller macromolecules than years ago. However, there are still major challenges for single-particle cryo-EM method to achieve routine structure determinations for macromolecules much smaller than 100 kDa, which

Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of 3D structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural

Cryptic pockets are protein cavities that remain hidden in resolved apo structures and generally require the presence of a co-crystallized ligand to become visible. Finding new cryptic pockets is crucial for structure-based drug discovery (SBDD) in order to identify new ways of modulating protein activity and thus expand the druggable space. We present here a new method and associated web application