Histone deacetylase 6 (HDAC6, Class IIb) is a promising target for anticancer drugs. So far, few nonselective HDAC inhibitors have received regulatory approval as anticancer agents. However, they are associated with cell toxicity. Thus, isoform-selective inhibitors may be desirable. Here, we conducted structure-based virtual screening of multiple libraries containing a total of 2,250,135 compounds against HDAC6. The top hits with good docking scores and potential selectivity over HDAC10 (Class IIb) were submitted to 100 ns molecular dynamics simulation to monitor their dynamic behaviors and stability in the binding pockets of these enzymes. Furthermore, the drug-likeness and ADMET properties of these hits were estimated computationally. Four diverse compounds from different sources, including NCI and ZINC databases (BDH33926500, CID667061, Cromolyn, and ZINC000103531486), show potential selectivity for HDAC6.

This study investigated the effect of low-salt processing on the umami peptide profile of dry-cured hams. Peptidomics data showed 633 umami peptides in the low- and full-salt groups. Among them, 36.2% and 26.5% of shared umami peptides in the low-salt group were significantly down- and up-regulated in relative abundance. Multivariate statistical analysis showed 1011 significantly different umami peptides (SDUPs) in the low- and full-salt groups. Creatine kinase M-type (CKM) and fast skeletal muscle troponin T (TnTf) were the main precursor proteins of these SDUPs. At the end of processing, the relative expression of CKM was lower in the low-salt group than in the full-salt group (P < 0.05), but there was no significant difference in TnTf. More dipeptidyl peptidase cleavage sites were observed in CKM and TnTf proteins in the low-salt group.

Traditional Chinese medicines (TCMs) are popular in clinic because of their safety and efficacy. They contain abundant natural active compounds, which are important sources of new drug discovery. However, how to efficiently identify active compounds from complex ingredients remains a challenge. In this study, a method combining UHPLC-MS/MS characterization and in silico screening was developed to discover compounds with dopamine D2 receptor (D2R) activity in Stephania epigaea (S. epigaea). By combining the compounds identified in S. epigaea by UHPLC-MS/MS with reported compounds, a virtual library of 80 compounds was constructed for in silico screening. Potentially active compounds were chosen based on screening scores and subsequently tested for in vitro activity on a transfected cell line CHO-K1-D2 model using label-free cellular phenotypic assay. Three D2R agonists and five D2R antagonists were identified. (-)-Asimilobine, N-nornuciferine and (-)-roemerine were reported for the first time as D2R agonists, with EC50 values of 0.35 ± 0.04 μM, 1.37 ± 0.10 μM and 0.82 ± 0.22 μM, respectively. Their target specificity was validated by desensitization and antagonism assay. (-)-Isocorypalmine, (-)-tetrahydropalmatine, (-)-discretine, (+)-corydaline and (-)-roemeroline showed strong antagonistic activity on D2R with IC50 values of 92 ± 9.9 nM, 1.73 ± 0.13 μM, 0.34 ± 0.02 μM, 2.09 ± 0.22 μM and 0.85 ± 0.08 μM, respectively. Their kinetic binding profiles were characterized using co-stimulation assay and they were both D2R competitive antagonists. We docked these ligands with human D2R crystal structure and analyzed the structure-activity relationship of aporphine-type D2R agonists and protoberberine-type D2R antagonists. These results would help to elucidate the mechanism of action of S. epigaea for its analgesic and sedative efficacy and benefit for D2R drug design. This study demonstrated the potential of integrating UHPLC-MS/MS with in silico and in vitro screening for accelerating the discovery of active compounds from TCMs.

Nanobodies (Nbs or VHHs) are single-domain antibodies (sdAbs) derived from camelid heavy-chain antibodies. Nbs have special and unique characteristics, such as small size, good tissue penetration, and cost-effective production, making Nbs a good candidate for the diagnosis and treatment of viruses and other pathologies. Identifying effective Nbs against COVID-19 would help us control this dangerous virus or other unknown variants in the future. Herein, we introduce an in silico screening strategy for optimizing stable conformation of anti-SARS-CoV-2 Nbs. Firstly, various complexes containing nanobodies were downloaded from the RCSB database, which were identified from immunized llamas. The primary docking between Nbs and the SARS-CoV-2 spike protein receptor-binding domain was performed through the ClusPro program, with the manual screening leaving the reasonable conformation to the next step. Then, the binding distances of atoms between the antigen-antibody interfaces were measured through the NeighborSearch algorithm. Finally, filtered nanobodies were acquired according to HADDOCK scores through HADDOCK docking the COVID-19 spike protein with nanobodies under restrictions of calculated molecular distance between active residues and antigenic epitopes less than 4.5 Å. In this way, those nanobodies with more reasonable conformation and stronger neutralizing efficacy were acquired. To validate the efficacy ranking of the nanobodies we obtained, we calculated the binding affinities (∆G) and dissociation constants (Kd) of all screened nanobodies using the PRODIGY web tool and predicted the stability changes induced by all possible point mutations in nanobodies using the MAESTROWeb server. Furthermore, we examined the performance of the relationship between nanobodies\' ranking and their number of mutation-sensitive sites (Spearman correlation > 0.68); the results revealed a robust correlation, indicating that the superior nanobodies identified through our screening process exhibited fewer mutation hotspots and higher stability. This correlation analysis demonstrates the validity of our screening criteria, underscoring the suitability of these nanobodies for future development and practical implementation. In conclusion, this three-step screening strategy iteratively in silico greatly improved the accuracy of screening desired nanobodies compared to using only ClusPro docking or default HADDOCK docking settings. It provides new ideas for the screening of novel antibodies and computer-aided screening methods.

Cystic fibrosis (CF) is a monogenetic disease caused by the mutation of CFTR, a cAMP-regulated Cl- channel expressing at the apical plasma membrane (PM) of epithelia. ∆F508-CFTR, the most common mutant in CF, fails to reach the PM due to its misfolding and premature degradation at the endoplasmic reticulum (ER). Recently, CFTR modulators have been developed to correct CFTR abnormalities, with some being used as therapeutic agents for CF treatment. One notable example is Trikafta, a triple combination of CFTR modulators (TEZ/ELX/IVA), which significantly enhances the functionality of ΔF508-CFTR on the PM. However, there\'s room for improvement in its therapeutic effectiveness since TEZ/ELX/IVA doesn\'t fully stabilize ΔF508-CFTR on the PM. To discover new CFTR modulators, we conducted a virtual screening of approximately 4.3 million compounds based on the chemical structures of existing CFTR modulators. This effort led us to identify a novel CFTR ligand named FR3. Unlike clinically available CFTR modulators, FR3 appears to operate through a distinct mechanism of action. FR3 enhances the functional expression of ΔF508-CFTR on the apical PM in airway epithelial cell lines by stabilizing NBD1. Notably, FR3 counteracted the degradation of mature ΔF508-CFTR, which still occurs despite the presence of TEZ/ELX/IVA. Furthermore, FR3 corrected the defective PM expression of a misfolded ABCB1 mutant. Therefore, FR3 may be a potential lead compound for addressing diseases resulting from the misfolding of ABC transporters.

We have previously performed a hierarchical in silico screening of a Mycobacterium tuberculosis shikimic acid kinase [1]. Specifically, 11 compounds were screened from a library of 154,118 compounds provided by ChemBridge [2] using UCSF DOCK [3] and the GOLD [4] program in the first and second steps, respectively. Molecular dynamic simulations were further performed on compound 2 (2-[(5Z)-5-(1-benzyl-5bromo-2-oxoindol-3-(5Z)-5-(1-benzyl-5-bromo-2-oxoindol-3-(5Z)-4-oxo-2 ylidene)-4oxo-2-sulfanylidene-1,3-thiazolidin-3-yl] acetic acid), which showed antimicrobial efficacy. These processes yielded ligand docking scores and trajectories. In this data article, we have added solvent-accessible surface area and PCA analyses, which were calculated from the raw docking scores and trajectories. Data obtained from molecular docking and molecular dynamic simulations are useful in two ways: (1) Further support for previous work (2) Provides a stepping stone for experimental scientists to conduct in silico studies and research ideas for other drug discovery researchers and computational biologists. We believe that this article will provide an opportunity to develop new Mycobacterium tuberculosis therapeutics through searching for analogs and inhibitors against new targets.

Deep-learning-based methods for protein structure prediction have achieved unprecedented accuracy, yet their utility in the engineering of protein-based binders remains constrained due to a gap between the ability to predict the structures of candidate proteins and the ability toprioritize proteins by their potential to bind to a target. To bridge this gap, we introduce Automated Pairwise Peptide-Receptor Analysis for Screening Engineered proteins (APPRAISE), a method for predicting the target-binding propensity of engineered proteins. After generating structural models of engineered proteins competing for binding to a target using an established structure prediction tool such as AlphaFold-Multimer or ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As proof-of-concept cases, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent adeno-associated viral vectors and diverse classes of engineered proteins such as miniproteins targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor or programmed death-ligand 1 (PD-L1). APPRAISE is accessible through a web-based notebook interface using Google Colaboratory (https://tiny.cc/APPRAISE). With its accuracy, interpretability, and generalizability, APPRAISE promises to expand the utility of protein structure prediction and accelerate protein engineering for biomedical applications.

Embedding two boron atoms into a polycyclic aromatic hydrocarbon (PAH) leads to the formation of a neutral analogue that is isoelectronic to the corresponding dicationic PAH skeleton, which can significantly alter its electronic structure. Based on this concept, we explore herein the identification of near-infrared (NIR)-emissive PAHs with the aid of an in silico screening method. Using perylene as the PAH scaffold, we embedded two boron atoms and fused two thiophene rings to it. Based on this design concept, all possible structures (ca. 2500 entities) were generated using a comprehensive structure generator. Time-dependent DFT calculations were conducted on all these structures, and promising candidates were extracted based on the vertical excitation energy, transition dipole moment, and atomization energy per bond. One of the extracted dithieno-diboraperylene candidates was synthesized and indeed exhibited emission at 724 nm with a quantum yield of 0.40 in toluene, demonstrating the validity of this screening method. This modification was further applied to other PAHs, and a series of thienobora-modified PAHs was synthesized.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models\' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.

Autotaxin is a secreted lysophospholipase D which is a member of the ectonucleotide pyrophosphatase/phosphodiesterase family converting extracellular lysophosphatidylcholine and other non-choline lysophospholipids, such as lysophosphatidylethanolamine and lysophosphatidylserine, to the lipid mediator lysophosphatidic acid. Autotaxin is implicated in various fibroproliferative diseases including interstitial lung diseases, such as idiopathic pulmonary fibrosis and hepatic fibrosis, as well as in cancer. In this study, we present an effort of identifying ATX inhibitors that bind to allosteric ATX binding sites using the Enalos Asclepios KNIME Node. All the available PDB crystal structures of ATX were collected, prepared, and aligned. Visual examination of these structures led to the identification of four crystal structures of human ATX co-crystallized with four known inhibitors. These inhibitors bind to five binding sites with five different binding modes. These five binding sites were thereafter used to virtually screen a compound library of 14,000 compounds to identify molecules that bind to allosteric sites. Based on the binding mode and interactions, the docking score, and the frequency that a compound comes up as a top-ranked among the five binding sites, 24 compounds were selected for in vitro testing. Finally, two compounds emerged with inhibitory activity against ATX in the low micromolar range, while their mode of inhibition and binding pattern were also studied. The two derivatives identified herein can serve as \"hits\" towards developing novel classes of ATX allosteric inhibitors.