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.

Construction of a -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.

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.

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.

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.

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".

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.

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.

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 (f) and renal clearance (CL), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone.

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.

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.

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

Knowing the value of the unbound drug fraction in the brain () is essential in estimating its effects and toxicity on the central nervous system (CNS); however, no model to predict 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 using only freely available software. By selecting appropriate descriptors, training, and evaluation, our model showed an acceptable performance on a test data set ( = 0.

High-Contrast Facile Imaging with Target-Directing Fluorescent Molecular Rotors, the N-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.

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.

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 (CL ) measurements from an open database and performed extensive manual curation.

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.

Dextran sulfate-resistant A/Puerto Rico/8/34 influenza virus is associated with the emergence of specific mutations in the neuraminidase glycoprotein.

Dextran sulfate (DS) is a negatively charged sulfated polysaccharide that suppresses the replication of influenza A viruses. The suppression was thought to be associated with inhibition of the hemagglutinin-dependent fusion activity. However, we previously showed that suppression by DS was observed not only at the initial stage of viral infection, but also later when virus is released from infected cells due to inhibition of neuraminidase (NA) activity.

Crystal structure of FtsA from Staphylococcus aureus.

The bacterial cell-division protein FtsA anchors FtsZ to the cytoplasmic membrane. But how FtsA and FtsZ interact during membrane division remains obscure. We have solved 2.

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.

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 β-γ-imidoadenosine 5'-phosphate (AMPPNP; a nonhydrolysable ATP analogue) was grown using PEG 3350 as a precipitant at 293 K.

Sagace: a web-based search engine for biomedical databases in Japan.

Background: In the big data era, biomedical research continues to generate a large amount of data, and the generated information is often stored in a database and made publicly available. Although combining data from multiple databases should accelerate further studies, the current number of life sciences databases is too large to grasp features and contents of each database.

Findings: We have developed Sagace, a web-based search engine that enables users to retrieve information from a range of biological databases (such as gene expression profiles and proteomics data) and biological resource banks (such as mouse models of disease and cell lines).

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.

Detecting subtle functional differences in ketopantoate reductase and related enzymes using a rule-based approach with sequence-structure homology recognition scores.

Ketopatoate reductase (KPR) is the second enzyme in the pantothenate (vitamin B(5)) biosynthesis pathway, an essential metabolic pathway identified as a potential target for new antimicrobials. The sequence similarity among putative KPRs is limited and KPR itself belongs to a large superfamily of 6-phosphogluconate dehydrogenases. Therefore, it is necessary to discriminate between true and other enzymes.

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.

Creation of lysine-deficient mutant lymphotoxin-alpha with receptor selectivity by using a phage display system.

The cytokine lymphotoxin-alpha (LT alpha) activates various biological functions through its three receptor subtypes, tumor necrosis factor receptor 1 (TNFR1), TNFR2 and herpes virus entry mediator (HVEM), but the relative contribution of each receptor to each function is unclear. Therefore it is important to create mutant LT alpha with receptor selectivity for optimized cancer therapy and the analysis of receptor function. Here, we attempted to create a lysine-deficient mutant LT alpha with TNFR1-selective bioactivity using a phage display technique.