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The study shows that a model-free analysis of motion is derived from NH dipolar couplings. An analysis of the individual spherical harmonics is done which shows that a considerable amount of anisotropy is in motion.

Cross-correlated relaxation was used to determine aliphatic side-chain conformation when applied to stable 2'-aminoethoxy-modified oligonucleotides triplex.

The ROSETTA method which was originally developed for de novo protein structure prediction reviewed to understand its application for biochemical and biomedicinal studies. ROSETTA method could be used to accurately design a novel protein structure, predict the structure of protein-protein complexes, design altered specificity protein-protein and protein-DNA interactions, and stabilize proteins and protein complexes.

The detailed studies and three-dimensional structure of KCNE1, a single-span membrane protein that modulates the voltage-gated potassium channel KCNQ1 by slowing activation and enhancing channel conductance to generate the slow delayed rectifier current, crucial for the repolarization phase of the cardiac action potential are presented. The KCNE1-KCNQ1 model might be used to formulate testable hypotheses of the molecular bases of disease phenotypes associated with the dozens of known inherited mutations in KCNE1 and KCNQ1.

Three-dimensional models are developed for both the open and closed states of human voltage-gated potassium channel KCNQ1 in order to facilitate structurally based hypotheses regarding mutation-phenotype relationships. The gain-of-function mutation sites are localized at the interface between S1 in the voltage-sensor domain of one subunit and the S5 and P-helix segments of the pore domain of another subunit.

The routine work for structure elucidation of molecules produced by organic synthesis is one of the important applications of NMR spectroscopy for which (super13)C NMR spectrum plays an important role. Its fast and correct handling of the three examples and the small database proves the potential of this approach.

It is shown that the residual dipolar couplings (RDC) data of six analyzable N-H(super N) vectors of the central helix in ubiquitin lead to very similar model-free parameters with significant motional anistrophies. This is compatible with a model in which all N-H(super N) vectors of the alpha-helix of ubiquitin exhibit correlate anistropic excursions with amplitudes of the order of 21 degree and 12 degree along the two axes x and y, respectively.

An analysis of the effects of internal motions on residual dipolar NMR couplings of globular proteins is described.

Proves that dipolar couplings in multiple alignments suggest alpha helical motion in ubiquitin. Role of the structural dynamic characterization of biomolecules in solution in the understanding of key biochemical processes; Characterization of the anisotropy of the orientational distribution.

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson's disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a K D lower than 150 μM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 μM. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants adopted strikingly diverging screening strategies. Machine learning-accelerated methods achieved similar results to brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra, resulting in motor dysfunction. Current treatments are primarily centered around enhancing dopamine signaling or providing dopamine replacement therapy and face limitations such as reduced efficacy over time and adverse side effects. To address these challenges, we identified selective dopamine receptor subtype 4 (D 4 R) antagonists not previously reported as potential adjuvants for PD management. In this study, a library screening and artificial neural network quantitative structure-activity relationship (QSAR) modeling with experimentally driven library design resulted in a class of spirocyclic compounds to identify candidate D 4 R antagonists. However, developing selective D 4 R antagonists suitable for clinical translation remains a challenge.

Here, we demonstrate a structure-based small molecule virtual screening and lead optimization pipeline using a homology model of a difficult-to-drug G-protein-coupled receptor (GPCR) target. Protease-activated receptor 4 (PAR4) is activated by thrombin cleavage, revealing a tethered ligand that activates the receptor, making PAR4 a challenging target. A virtual screen of a make-on-demand chemical library yielded a one-hit compound. From the single-hit compound, we developed a novel series of PAR4 antagonists. Subsequent lead optimization via simultaneous virtual library searches and structure-based rational design efforts led to potent antagonists of thrombin-induced activation. Interestingly, this series of antagonists was active against PAR4 activation by the native protease thrombin cleavage but not the synthetic PAR4 agonist peptide AYPGKF., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Positive allosteric modulators targeting the Y 4 receptor (Y 4 R), a G protein-coupled receptor (GPCR) involved in the regulation of satiety, offer great potential in anti-obesity research. In this study, we selected 603 compounds by using quantitative structure-activity relationship (QSAR) models and tested them in high-throughput screening (HTS). Here, the novel positive allosteric modulator (PAM) VU0506013 was identified, which exhibits nanomolar affinity and pronounced selectivity toward the Y 4 R in engineered cell lines and mouse descending colon mucosa natively expressing the Y 4 R. Based on this lead structure, we conducted a systematic SAR study in two regions of the scaffold and presented a series of 27 analogues with modifications in the N - and C -terminal heterocycles of the molecule to obtain insight into functionally relevant positions. By mutagenesis and computational docking, we present a potential binding mode of VU0506013 in the transmembrane core of the Y 4 R. VU0506013 presents a promising scaffold for developing in vivo tools to move toward anti-obesity drug research focused on the Y 4 R.

Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to predict only a specific type of residue-level attribute. In this work, we develop a new deep-learning method, named Membrane Association and Secondary Structure Predictor (MASSP), for accurately predicting both residue-level structural attributes (secondary structure, location, orientation, and topology) and protein-level structural classes (bitopic, α-helical, β-barrel, and soluble). MASSP integrates a multilayer two-dimensional convolutional neural network (2D-CNN) with a long short-term memory (LSTM) neural network into a multitasking framework. Our comparison shows that MASSP performs equally well or better than the state-of-the-art methods in predicting residue-level secondary structures, boundaries of transmembrane segments, and topology. Furthermore, it achieves outstanding accuracy in predicting protein-level structural classes. MASSP automatically distinguishes the structural classes of input sequences and identifies transmembrane segments and topologies if present, making it broadly applicable to different classes of proteins. In summary, MASSP's good performance and broad applicability make it well suited for annotating residue-level attributes and protein-level structural classes at the proteome scale.

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

Human neuropeptide Y receptors (Y 1 R, Y 2 R, Y 4 R, and Y 5 R) belong to the superfamily of G protein-coupled receptors and play an important role in the regulation of food intake and energy metabolism. We identified and characterized the first selective Y 4 R allosteric antagonist ( S ) - VU0637120, an important step toward validating Y receptors as therapeutic targets for metabolic diseases. To obtain insight into the antagonistic mechanism of ( S ) - VU0637120, we conducted a variety of in vitro, ex vivo, and in silico studies. These studies revealed that ( S ) - VU0637120 selectively inhibits native Y 4 R function and binds in an allosteric site located below the binding pocket of the endogenous ligand pancreatic polypeptide in the core of the Y 4 R transmembrane domains. Taken together, our studies provide a first-of-its-kind tool for probing Y 4 R function and improve the general understanding of allosteric modulation, ultimately contributing to the rational development of allosteric modulators for peptide-activated G protein-coupled receptors (GPCRs).

The BioChemical Library (BCL) is an academic open-source cheminformatics toolkit comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative structure-activity/property relationship (QSAR/QSPR) modeling, small molecule flexible alignment, small molecule conformer generation, and more. Here, we expand the capabilities of the BCL to include structure-based virtual screening. We introduce two new score functions, BCL-AffinityNet and BCL-DockANNScore, based on novel distance-dependent signed protein-ligand atomic property correlations. Both metrics are conventional feed-forward dropout neural networks trained on the new descriptors. We demonstrate that BCL-AffinityNet is one of the top performing score functions on the comparative assessment of score functions 2016 affinity prediction and affinity ranking tasks. We also demonstrate that BCL-AffinityNet performs well on the CSAR-NRC HiQ I and II test sets. Furthermore, we demonstrate that BCL-DockANNScore is competitive with multiple state-of-the-art methods on the docking power and screening power tasks. Finally, we show how our models can be decomposed into human-interpretable pharmacophore maps to aid in hit/lead optimization. Altogether, our results expand the utility of the BCL for structure-based scoring to aid small molecule discovery and design. BCL-AffinityNet, BCL-DockANNScore, and the pharmacophore mapping application, as well as the remainder of the BCL cheminformatics toolkit, are freely available with an academic license at the BCL Commons site hosted on http://meilerlab.org/.

RosettaLigand is a protein-small-molecule (ligand) docking software capable of predicting binding poses and is used for virtual screening of medium-sized ligand libraries. Structurally similar small molecules are generally found to bind in the same pose to one binding pocket, despite some prominent exceptions. To make use of this information, we have developed RosettaLigandEnsemble (RLE). RLE docks a superimposed ensemble of congeneric ligands simultaneously. The program determines a well-scoring overall pose for this superimposed ensemble before independently optimizing individual protein-small-molecule interfaces. In a cross-docking benchmark of 89 protein-small-molecule co-crystal structures across 20 biological systems, we found that RLE improved sampling efficiency in 62 cases, with an average change of 18%. In addition, RLE generated more consistent docking results within a congeneric series and was capable of rescuing the unsuccessful docking of individual ligands, identifying a nativelike top-scoring model in 10 additional cases. The improvement in RLE is driven by a balance between having a sizable common chemical scaffold and meaningful modifications to distal groups. The new ensemble docking algorithm will work well in conjunction with medicinal chemistry structure-activity relationship (SAR) studies to more accurately recapitulate protein-ligand interfaces. We also tested whether optimizing the rank correlation of RLE-binding scores to SAR data in the refinement step helps the high-resolution positioning of the ligand. However, no significant improvement was observed., Competing Interests: The authors declare no competing financial interest.