Statistical potentials for RNA-protein interactions optimized by CMA-ES

Characterizing RNA-protein interactions remains an important endeavor, complicated by the difficulty in obtaining the relevant structures. Evaluating model structures via statistical potentials is in principle straight-forward and effective. However, given the relatively small size of the existing learning set of RNA-protein complexes optimization of such potentials continues to be problematic. Notably, interaction-based statistical potentials have problems in addressing large RNA-protein complexes. In this study, we adopted a novel strategy with covariance matrix adaptation (CMA-ES) to calculate statistical potentials, successfully identifying native docking poses

Novel Calcium-Binding Ablating Mutations Induce Constitutive RET Activity and Drive Tumorigenesis

がんゲノム医療のさらなる拡大へ向けた一歩 --コンピュータ解析で意義不明変異のなかに治療標的となる新たな遺伝子変異を発見--. 京都大学プレスリリース. 2022-09-29.Distinguishing oncogenic mutations from variants of unknown significance (VUS) is critical for precision cancer medicine. Here, computational modeling of 71, 756 RET variants for positive selection together with functional assays of 110 representative variants identified a three-dimensional cluster of VUSs carried by multiple human cancers that cause amino acid substitutions in the calmodulin-like motif (CaLM) of RET. Molecular dynamics simulations indicated that CaLM mutations decrease interactions between Ca²⁺ and its surrounding residues and induce conformational distortion of the RET cysteine-rich domain containing the CaLM. RET-CaLM mutations caused ligand-independent constitutive activation of RET kinase by homodimerization mediated by illegitimate disulfide bond formation. RET-CaLM mutants possessed oncogenic and tumorigenic activities that could be suppressed by tyrosine kinase inhibitors targeting RET. This study identifies calcium-binding ablating mutations as a novel type of oncogenic mutation of RET and indicates that in silico–driven annotation of VUSs of druggable oncogenes is a promising strategy to identify targetable driver mutations

Biochemical Studies of Mitochondrial Malate: Quinone Oxidoreductase from Toxoplasma gondii

Toxoplasma gondii is a protozoan parasite that causes toxoplasmosis and infects almost one-third of the global human population. A lack of effective drugs and vaccines and the emergence of drug resistant parasites highlight the need for the development of new drugs. The mitochondrial electron transport chain (ETC) is an essential pathway for energy metabolism and the survival of T. gondii. In apicomplexan parasites, malate:quinone oxidoreductase (MQO) is a monotopic membrane protein belonging to the ETC and a key member of the tricarboxylic acid cycle, and has recently been suggested to play a role in the fumarate cycle, which is required for the cytosolic purine salvage pathway. In T. gondii, a putative MQO (TgMQO) is expressed in tachyzoite and bradyzoite stages and is considered to be a potential drug target since its orthologue is not conserved in mammalian hosts. As a first step towards the evaluation of TgMQO as a drug target candidate, in this study, we developed a new expression system for TgMQO in FN102(DE3)TAO, a strain deficient in respiratory cytochromes and dependent on an alternative oxidase. This system allowed, for the first time, the expression and purification of a mitochondrial MQO family enzyme, which was used for steady-state kinetics and substrate specificity analyses. Ferulenol, the only known MQO inhibitor, also inhibited TgMQO at IC50 of 0.822 μM, and displayed different inhibition kinetics compared to Plasmodium falciparum MQO. Furthermore, our analysis indicated the presence of a third binding site for ferulenol that is distinct from the ubiquinone and malate sites

A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified

Screening for inhibitors of main protease in SARS-CoV-2: in silico and in vitro approach avoiding secondary amides

In addition to vaccines, antiviral drugs are essential for suppressing COVID-19. Although several inhibitor candidates were reported for SARS-CoV-2 main protease, most are highly polar peptidomimetics with poor oral bioavailability and cell membrane permeability. Here, we conducted structure-based virtual screening and in vitro assays to obtain hit compounds belonging to a new chemical space excluding secondary amides. In total, 180 compounds were subjected to the primary assay at 20 μM, and nine compounds with inhibition rates higher than 5% were obtained. The IC50 of six compounds was determined in dose-response experiments, with the values on the order of 100 μM. Although nitro groups were enriched in the substructure of the hit compounds, they did not significantly contribute to the binding interaction in the predicted docking poses. Physicochemical properties prediction showed good oral absorption. These new scaffolds are promising candidates for future optimization

Gargoyles: An Open Source Graph-based molecular optimization method based on Deep Reinforcement Learning

Automatic optimization methods for compounds in the vast compound space are important for drug discovery and material design. Several machine learning-based molecular generative models for drug discovery have been proposed, but most of these methods generate compounds from scratch and are not suitable for exploring and optimizing around arbitrary compounds. In this study, we developed a compound optimization method based on molecular graphs using deep reinforcement learning. This method searches for compounds on a fragment-by-fragment basis and at high density by generating fragments to be added atom by atom. Experimental results confirmed that the QED, the optimization target set in this study, was enhanced by searching around the starting compound. This means that the generated compounds are structurally similar to the starting compounds, indicating that the method is suitable for starting generation from a given compound. The source code is available at https://github.com/sekijima-lab/GARGOYLES