Familial Episodic Pain Syndrome: A Japanese Family Harboring the Novel Variant c.2431C>T (p.Leu811Phe) in SCN11A.

Familial episodic pain syndrome (FEPS) is an autosomal-dominant inherited disorder characterized by paroxysmal pain episodes. FEPS appears in early childhood, gradually disappearing with age, and pain episodes can be triggered by fatigue, bad weather, and cold temperatures. Several gain-of-function variants have been reported for SCN9A, SCN10A, or SCN11A, which encode the voltage-gated sodium channel α subunits Nav1.7, Nav1.8, and Nav1.9, respectively. In this study, we conducted genetic analysis in a four-generation Japanese pedigree. The proband was a 7-year-old girl, and her brother, sister, mother, and grandmother were also experiencing or had experienced pain episodes and were considered to be affected. The father was unaffected. Sequencing of SCN9A, SCN10A, and SCN11A in the proband revealed a novel heterozygous variant of SCN11A: g.38894937G>A (c.2431C>T, p.Leu811Phe). This variant was confirmed in other affected members but not in the unaffected father. The affected residue, Leu811, is located within the DII/S6 helix of Nav1.9 and is important for signal transduction from the voltage-sensing domain and pore opening. On the other hand, the c.2432T>C (p.Leu811Pro) variant is known to cause congenital insensitivity to pain (CIP). Molecular dynamics simulations showed that p.Leu811Phe increased the structural stability of Nav1.9 and prevented the necessary conformational changes, resulting in changes in the dynamics required for function. By contrast, CIP-related p.Leu811Pro destabilized Nav1.9. Thus, we speculate that p.Leu811Phe may lead to current leakage, while p.Leu811Pro can increase the current through Nav1.9.

High-Contrast Facile Imaging with Target-Directing Fluorescent Molecular Rotors, the N3-Modified Thioflavin T Derivatives.

Immunostaining methods have generally been used not only for biological studies but also for clinical diagnoses for decades. However, recently, for these methods, improved rapidity and simplicity have been required for relevant techniques in laboratory research and medical applications. To this end, we present here a novel approach for designing fluorescent molecular rotor probes, i.e., N3-modified thioflavin T (ThT) derivatives, which enabled specific detection of interesting protein targets with sensitive fluorescence turn-on. As an example, we synthesized N3-( d-desthiobiotinyl-PEGylated) thioflavin T (ThT-PD) and N3-(cortisolyl-PEGylated) thioflavin T (ThT-PC) that carried d-desthiobioin and cortisol, respectively, via PEG linkers. Compared to those of the probes without the targets, ThT-PD and ThT-PC exhibited around 27- and 8-fold fluorescence intensities, respectively, with the target streptavidin and anti-cortisol antibody in excess of saturation, enabling quantitative detection of the targets. Furthermore, we successfully demonstrated the feasibility of ligand-tethering N3-ThT derivatives by the rapid specific staining of glucocorticoid receptors in cells, which was completed within only several minutes using ThT-PC after cell fixation, whereas it took ∼24 h for immunostaining to capture the corresponding fluorescence images.

Computational Model To Predict the Fraction of Unbound Drug in the Brain.

Knowing the value of the unbound drug fraction in the brain (fu,brain) is essential in estimating its effects and toxicity on the central nervous system (CNS); however, no model to predict fu,brain without experimental procedures is publicly available. In this study, we collected 253 measurements from the literature and an open database and built in silico models to predict fu,brain using only freely available software. By selecting appropriate descriptors, training, and evaluation, our model showed an acceptable performance on a test data set (R2 = 0.630, percentage of compounds predicted within a 3-fold error: 69.4%) using chemical structure alone. Our model is available at https://drumap.nibiohn.go.jp/fubrain/ , and all of our data sets can be obtained from the Supporting Information.

Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges.

Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed several machine learning techniques and built prediction models trained on the largest ever data set of 2738 experimental values. The classification model showed a high true positive rate of 0.826 for the low fraction unbound class on the test set. The strongly biased distribution of the fraction unbound in plasma was mitigated by a logarithmic transformation in the regression model, leading to improved accuracy at lower values. Overall, our models showed better performance than those of previously published methods, including commercial software. Our prediction tool can be used on its own or integrated into other pharmacokinetic modeling systems.

Improved pose and affinity predictions using different protocols tailored on the basis of data availability.

The D3R 2015 grand drug design challenge provided a set of blinded challenges for evaluating the applicability of our protocols for pose and affinity prediction. In the present study, we report the application of two different strategies for the two D3R protein targets HSP90 and MAP4K4. HSP90 is a well-studied target system with numerous co-crystal structures and SAR data. Furthermore the D3R HSP90 test compounds showed high structural similarity to existing HSP90 inhibitors in BindingDB. Thus, we adopted an integrated docking and scoring approach involving a combination of both pharmacophoric and heavy atom similarity alignments, local minimization and quantitative structure activity relationships modeling, resulting in the reasonable prediction of pose [with the root mean square deviation (RMSD) values of 1.75 Å for mean pose 1, 1.417 Å for the mean best pose and 1.85 Å for the mean all poses] and affinity (ROC AUC = 0.702 at 7.5 pIC50 cut-off and R = 0.45 for 180 compounds). The second protein, MAP4K4, represents a novel system with limited SAR and co-crystal structure data and little structural similarity of the D3R MAP4K4 test compounds to known MAP4K4 ligands. For this system, we implemented an exhaustive pose and affinity prediction protocol involving docking and scoring using the PLANTS software which considers side chain flexibility together with protein-ligand fingerprints analysis assisting in pose prioritization. This protocol through fares poorly in pose prediction (with the RMSD values of 4.346 Å for mean pose 1, 4.69 Å for mean best pose and 4.75 Å for mean all poses) and produced reasonable affinity prediction (AUC = 0.728 at 7.5 pIC50 cut-off and R = 0.67 for 18 compounds, ranked 1st among 80 submissions).

Construction of a T m-value prediction model and molecular dynamics study of AmNA-containing gapmer antisense oligonucleotide.

RNase H-dependent antisense oligonucleotides (gapmer ASOs) represent a class of nucleic acid therapeutics that bind to target RNA to facilitate RNase H-mediated RNA cleavage, thereby regulating the expression of disease-associated proteins. Integrating artificial nucleic acids into gapmer ASOs enhances their therapeutic efficacy. Among these, amido-bridged nucleic acid (AmNA) stands out for its potential to confer high affinity and stability to ASOs. However, a significant challenge in the design of gapmer ASOs incorporating artificial nucleic acids, such as AmNA, is the accurate prediction of their melting temperature (T m ) values. The T m is a critical parameter for designing effective gapmer ASOs to ensure proper functioning. However, predicting accurate T m values for oligonucleotides containing artificial nucleic acids remains problematic. We developed a T m prediction model using a library of AmNA-containing ASOs to address this issue. We measured the T m values of 157 oligonucleotides through differential scanning calorimetry, enabling the construction of an accurate prediction model. Additionally, molecular dynamics simulations were used to elucidate the molecular mechanisms by which AmNA modifications elevate T m , thereby informing the design strategies of gapmer ASOs.

Phosphoproteomic subtyping of gastric cancer reveals dynamic transformation with chemotherapy and guides targeted cancer treatment.

There are only a few effective molecular targeted agents for advanced unresectable or recurrent advanced gastric cancer (AGC), which has a poor prognosis with a median survival time of less than 14 months. Focusing on phosphorylation signaling in cancer cells, we have been developing deep phosphoproteome analysis from minute endoscopic biopsy specimens frozen within 20 s of collection. Phosphoproteomic analysis of 127 fresh-frozen endoscopic biopsy samples from untreated patients with AGC revealed three subtypes reflecting different cellular signaling statuses. Subsequent serial biopsy analysis has revealed the dynamic mesenchymal transitions within cancer cells, along with the concomitant rewiring of the kinome network, ultimately resulting in the conversion to the epithelial-mesenchymal transition (EMT) subtype throughout treatment. We present our investigation of intracellular signaling related to the EMT in gastric cancer and propose therapeutic approaches targeting AXL. This study also provides a wealth of resources for the future development of treatments and biomarkers for AGC.

Expression, purification, crystallization and preliminary crystallographic study of FtsA from methicillin-resistant Staphylococcus aureus.

FtsA from methicillin-resistant Staphylococcus aureus (MRSA) was cloned, overexpressed and purified. The protein was crystallized using the sitting-drop vapour-diffusion technique. A cocrystal with ß-γ-imidoadenosine 5'-phosphate (AMPPNP; a nonhydrolysable ATP analogue) was grown using PEG 3350 as a precipitant at 293 K. X-ray diffraction data were collected to a resolution of 2.3 Å at 100 K. The crystal belonged to the monoclinic space group P21, with unit-cell parameters a = 75.31, b = 102.78, c = 105.90 Å, ß = 96.54°. The calculated Matthews coefficient suggested that the asymmetric unit contained three or four monomers.

Attention network for predicting T-cell receptor-peptide binding can associate attention with interpretable protein structural properties.

Understanding how a T-cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining an insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR-peptide-major histocompatibility complex (TCR-pMHC) interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few researchers have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of Transformer, a source-target attention neural network, to predict the TCR-pMHC interaction solely from the amino acid sequences of the TCR complementarity-determining region (CDR) 3 and the peptide. This model achieved competitive performance on a benchmark dataset of the TCR-pMHC interaction, as well as on a truly new external dataset. Additionally, by analyzing the results of binding predictions, we associated the neural network weights with protein structural properties. By classifying the residues into large- and small-attention groups, we identified statistically significant properties associated with the largely attended residues such as hydrogen bonds within CDR3. The dataset that we created and the ability of our model to provide an interpretable prediction of TCR-peptide binding should increase our knowledge about molecular recognition and pave the way for designing new therapeutics.

DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform.

We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and in-house data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/.

Computational design, construction, and characterization of a set of specificity determining residues in protein-protein interactions.

Proteins interact with different partners to perform different functions and it is important to elucidate the determinants of partner specificity in protein complex formation. Although methods for detecting specificity determining positions have been developed previously, direct experimental evidence for these amino acid residues is scarce, and the lack of information has prevented further computational studies. In this article, we constructed a dataset that is likely to exhibit specificity in protein complex formation, based on available crystal structures and several intuitive ideas about interaction profiles and functional subclasses. We then defined a "structure-based specificity determining position (sbSDP)" as a set of equivalent residues in a protein family showing a large variation in their interaction energy with different partners. We investigated sequence and structural features of sbSDPs and demonstrated that their amino acid propensities significantly differed from those of other interacting residues and that the importance of many of these residues for determining specificity had been verified experimentally.

Relationships between functional subclasses and information contained in active-site and ligand-binding residues in diverse superfamilies.

To investigate the relationships between functional subclasses and sequence and structural information contained in the active-site and ligand-binding residues (LBRs), we performed a detailed analysis of seven diverse enzyme superfamilies: aldolase class I, TIM-barrel glycosidases, alpha/beta-hydrolases, P-loop containing nucleotide triphosphate hydrolases, collagenase, Zn peptidases, and glutamine phosphoribosylpyrophosphate, subunit 1, domain 1. These homologous superfamilies, as defined in CATH, were selected from the enzyme catalytic-mechanism database. We defined active-site and LBRs based solely on the literature information and complex structures in the Protein Data Bank. From a structure-based multiple sequence alignment for each CATH homologous superfamily, we extracted subsequences consisting of the aligned positions that were used as an active-site or a ligand-binding site by at least one sequence. Using both the subsequences and full-length alignments, we performed cluster analysis with three sequence distance measures. We showed that the cluster analysis using the subsequences was able to detect functional subclasses more accurately than the clustering using the full-length alignments. The subsequences determined by only the literature information and complex structures, thus, had sufficient information to detect the functional subclasses. Detailed examination of the clustering results provided new insights into the mechanism of functional diversification for these superfamilies.

Developing a Kinase-Specific Target Selection Method Using a Structure-Based Machine Learning Approach.

INTRODUCTION: Despite recent advances in the drug discovery field, developing selective kinase inhibitors remains a complicated issue for a number of reasons, one of which is that there are striking structural similarities in the ATP-binding pockets of kinases. OBJECTIVE: To address this problem, we have designed a machine learning model utilizing various structure-based and energy-based descriptors to better characterize protein-ligand interactions. METHODS: In this work, we use a dataset of 104 human kinases with available PDB structures and experimental activity data against 1202 small-molecule compounds from the PubChem BioAssay dataset "Navigating the Kinome". We propose structure-based interaction descriptors to build activity predicting machine learning model. RESULTS AND DISCUSSION: We report a ligand-oriented computational method for accurate kinase target prioritizing. Our method shows high accuracy compared to similar structure-based activity prediction methods, and more importantly shows the same prediction accuracy when tested on the special set of structurally remote compounds, showing that it is unbiased to ligand structural similarity in the training set data. We hope that our approach will be useful for the development of novel highly selective kinase inhibitors.

Novel anti-flavivirus drugs targeting the nucleolar distribution of core protein.

The risk of infectious diseases caused by Flavivirus is increasing globally. Here, we developed a novel high-throughput screening (HTS) system to evaluate the inhibitory effects of compounds targeting the nuclear localization of the flavivirus core protein. We screened 4000 compounds based on their ability to inhibit the nuclear localization of the core protein, and identified over 20 compounds including inhibitors for cyclin dependent kinase and glycogen synthase kinase. The efficacy of the identified compounds to suppress viral growth was validated in a cell-based infection system. Remarkably, the nucleolus morphology was affected by the treatment with the compounds, suggesting that the nucleolus function is critical for viral propagation. The present HTS system provides a useful strategy for the identification of antivirals against flavivirus by targeting the nucleolar localization of the core protein.

Prediction of the secondary structure of short DNA aptamers.

Aptamers have a spectrum of applications in biotechnology and drug design, because of the relative simplicity of experimental protocols and advantages of stability and specificity associated with their structural properties. However, to understand the structure-function relationships of aptamers, robust structure modeling tools are necessary. Several such tools have been developed and extensively tested, although most of them target various forms of biological RNA. In this study, we tested the performance of three tools in application to DNA aptamers, since DNA aptamers are the focus of many studies, particularly in drug discovery. We demonstrated that in most cases, the secondary structure of DNA can be reconstructed with acceptable accuracy by at least one of the three tools tested (Mfold, RNAfold, and CentroidFold), although the G-quadruplex motif found in many of the DNA aptamer structures complicates the prediction, as well as the pseudoknot interaction. This problem should be addressed more carefully to improve prediction accuracy.

Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance.

A key consideration at the screening stages of drug discovery is in vitro metabolic stability, often measured in human liver microsomes. Computational prediction models can be built using a large quantity of experimental data available from public databases, but these databases typically contain data measured using various protocols in different laboratories, raising the issue of data quality. In this study, we retrieved the intrinsic clearance (CLint ) measurements from an open database and performed extensive manual curation. Then, chemical descriptors were calculated using freely available software, and prediction models were built using machine learning algorithms. The models trained on the curated data showed better performance than those trained on the non-curated data and achieved performance comparable to previously published models, showing the importance of manual curation in data preparation. The curated data were made available, to make our models fully reproducible.

Constructing an In Silico Three-Class Predictor of Human Intestinal Absorption With Caco-2 Permeability and Dried-DMSO Solubility.

Absorption of drugs is the first step after dosing, and it largely affects drug bioavailability. Hence, estimating the fraction of absorption (Fa) in humans is important in the early stages of drug discovery. To achieve correct exclusion of low Fa compounds and retention of potential compounds, we developed a freely available model to classify compounds into 3 levels of Fa capacity using only the chemical structure. To improve Fa prediction, we added predicted binary classification results of membrane permeability measured using Caco-2 cell line (Papp) and dried-dimethyl sulfoxide solubility (accuracy, 0.836; kappa, 0.560). The constructed models can be accessed via a web application.

Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor.

Prediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

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.

Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.

Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.