Hydrophobic interactions at subsite S1′ of human dipeptidyl peptidase IV contribute significantly to the inhibitory effect of tripeptides

Functional inhibitory peptides of human dipeptidyl peptidase 4 (hDPP4) have been highly anticipated as the active ingredient of functional food for type II diabetes; however, the molecular mechanism of hDPP4 inhibition remains unclear. In this study, we focused on dipeptides and tripeptides, which display structure-function correlations that are relatively easy to analyze, and examined their interactions with hDPP4 on an atomic level using a combination of docking studies and an hDPP4 inhibition assay. First, we performed comprehensive binding mode analysis of the dipeptide library and demonstrated that the formation of a tight interaction with the S1 subsite composing part of the substrate pocket is essential for dipeptides to compete with the substrate and strongly inhibit hDPP4. Next, we synthesized tripeptides by adding various amino acids to the C-terminus of Ile-Pro and Val-Pro, which have especially high inhibitory activity among compounds in the dipeptide library, and measured the hDPP4 inhibitory activity of the tripeptides. When hydrophobic amino acids (Ile, Met, Val, Trp) were added, the inhibitory activity increased several-fold. This phenomenon could be explained as follows: the C-terminal amino acid of the tripeptide formed hydrophobic interactions with Tyr547 and Trp629, which compose the S1′ subsite located relatively outside the substrate pocket, thereby stabilizing the hDPP4-peptide binding. The structural information on the interaction between hDPP4 and peptide inhibitors attained in this study is anticipated to be useful in the development of a more potent hDPP4 competitive inhibitor

Exploring ligand binding pathways on proteins using hypersound-accelerated molecular dynamics

生体分子の動きを効率的に捉えるシミュレーション技術を開発 --超高周波超音波照射によってタンパク質と医薬品の結合計算を加速--. 京都大学プレスリリース. 2021-05-28.Capturing the dynamic processes of biomolecular systems in atomistic detail remains difficult despite recent experimental advances. Although molecular dynamics (MD) techniques enable atomic-level observations, simulations of “slow” biomolecular processes (with timescales longer than submilliseconds) are challenging because of current computer speed limitations. Therefore, we developed a method to accelerate MD simulations by high-frequency ultrasound perturbation. The binding events between the protein CDK2 and its small-molecule inhibitors were nearly undetectable in 100-ns conventional MD, but the method successfully accelerated their slow binding rates by up to 10–20 times. Hypersound-accelerated MD simulations revealed a variety of microscopic kinetic features of the inhibitors on the protein surface, such as the existence of different binding pathways to the active site. Moreover, the simulations allowed the estimation of the corresponding kinetic parameters and exploring other druggable pockets. This method can thus provide deeper insight into the microscopic interactions controlling biomolecular processes

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

Calculation of absolute binding free energies between the hERG channel and structurally diverse drugs

The human ether-a-go-go-related gene (hERG) encodes a voltage-gated potassium channel that plays an essential role in the repolarization of action potentials in cardiac muscle. However, various drugs can block the ion current by binding to the hERG channel, resulting in potentially lethal cardiac arrhythmia. Accordingly, in silico studies are necessary to clarify the mechanisms of how these drugs bind to the hERG channel. Here, we used the experimental structure of the hERG channel, determined by cryo-electron microscopy, to perform docking simulations to predict the complex structures that occur between the hERG channel and structurally diverse drugs. The absolute binding free energies for the models were calculated using the MP-CAFEE method; calculated values were well correlated with experimental ones. By applying the regression equation obtained here, the affinity of a drug for the hERG channel can be accurately predicted from the calculated value of the absolute binding free energy

Machine learning accelerates MD-based binding pose prediction between ligands and proteins

Motivation: Fast and accurate prediction of protein–ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short Molecular Dynamics (MD) simulations, such as MM-PBSA and MM-GBSA, among generated docking poses have been used. Since molecular structures obtained from MD simulation depend on the initial condition, taking the average over different initial conditions leads to better accuracy. Prediction accuracy of protein–ligand binding poses can be improved with multiple runs at different initial velocity.Results: This paper shows that a machine learning method, called Best Arm Identification, can optimally control the number of MD runs for each binding pose. It allows us to identify a correct binding pose with a minimum number of total runs. Our experiment using three proteins and eight inhibitors showed that the computational cost can be reduced substantially without sacrificing accuracy. This method can be applied for controlling all kinds of molecular simulations to obtain best results under restricted computational resources

Brigatinib combined with anti-EGFR antibody overcomes osimertinib resistance in EGFR-mutated non-small-cell lung cancer

EGFR変異陽性肺がんに対する新規耐性克服療法を発見 --今後予想されるオシメルチニブ耐性の克服へ--. 京都大学プレスリリース. 2017-04-03.Osimertinib has been demonstrated to overcome the epidermal growth factor receptor (EGFR)-T790M, the most relevant acquired resistance to first-generation EGFR–tyrosine kinase inhibitors (EGFR–TKIs). However, the C797S mutation, which impairs the covalent binding between the cysteine residue at position 797 of EGFR and osimertinib, induces resistance to osimertinib. Currently, there are no effective therapeutic strategies to overcome the C797S/T790M/activating-mutation (triple-mutation)-mediated EGFR–TKI resistance. In the present study, we identify brigatinib to be effective against triple-mutation-harbouring cells in vitro and in vivo. Our original computational simulation demonstrates that brigatinib fits into the ATP-binding pocket of triple-mutant EGFR. The structure–activity relationship analysis reveals the key component in brigatinib to inhibit the triple-mutant EGFR. The efficacy of brigatinib is enhanced markedly by combination with anti-EGFR antibody because of the decrease of surface and total EGFR expression. Thus, the combination therapy of brigatinib with anti-EGFR antibody is a powerful candidate to overcome triple-mutant EGFR

Structure-Based De Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations

Recently, molecular generation models based on deep learning have attracted significant attention in drug discovery. However, most existing molecular generation models have a serious limitation in the context of drug design wherein they do not sufficiently consider the effect of the three-dimensional (3D) structure of the target protein in the generation process. In this study, we developed a new deep learning-based molecular generator, SBMolGen, that integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations. The results of an evaluation using four target proteins (two kinases and two G protein-coupled receptors) showed that the generated molecules had a better binding affinity score (docking score) than the known active compounds, and they possessed a broader chemical space distribution. SBMolGen not only generates novel binding active molecules but also presents 3D docking poses with target proteins, which will be useful in subsequent drug design

Fulvestrant 500 mg in postmenopausal patients with metastatic breast cancer : the initial clinical experience

Background: Fulvestrant 500 mg is currently approved for the treatment of postmenopausal women with hormone receptor-positive metastatic breast cancer after failure of prior endocrine therapies. Methods: A total of 117 postmenopausal women with metastatic breast cancer, who experienced progression after previous endocrine therapies, were treated with fulvestrant 500 mg between January 2012 and June 2014. Clinical response, time to progression (TTP) and adverse events were investigated. Results: Ninety-nine patients had recurrent breast cancer and 18 patients had stage IV disease. Patients had received a median of two endocrine therapies and a median of two chemotherapies, prior to fulvestrant. There were 10 patients with partial response, 39 patients with long stable disease, 18 patients with stable disease, and 50 patients with progressive disease, so that the objective response rate was 8.5 %, with a clinical benefit rate of 41.9 %. The median TTP was 6.1 months. The absence of liver metastases, a small number of previous chemotherapies, and the longer duration of first-line endocrine therapy were positively correlated with TTP in univariate analysis. In multivariate analysis, a significant association was observed between TTP and duration of first-line endocrine therapy. Serious adverse events were observed in one patient with pulmonary embolism and in one patient with psychiatric symptoms. Conclusions: Fulvestrant 500 mg is an effective and well-tolerated treatment for postmenopausal women with metastatic breast cancer that had progressed after prior endocrine therapies. Patients with acquired resistance to endocrine therapies might be good candidates for fulvestrant therapy regardless of the number of prior endocrine treatments