Synthetic aporphine alkaloids are potential therapeutics for Leigh syndrome

Mitochondrial diseases are mainly caused by dysfunction of mitochondrial respiratory chain complexes and have a variety of genetic variants or phenotypes. There are only a few approved treatments, and fundamental therapies are yet to be developed. Leigh syndrome (LS) is the most severe type of progressive encephalopathy. We previously reported that apomorphine, an anti- "off" agent for Parkinson's disease, has cell-protective activity in patient-derived skin fibroblasts in addition to strong dopamine agonist effect. We obtained 26 apomorphine analogs, synthesized 20 apomorphine derivatives, and determined their anti-cell death effect, dopamine agonist activity, and effects on the mitochondrial function. We found three novel apomorphine derivatives with an active hydroxy group at position 11 of the aporphine framework, with a high anti-cell death effect without emetic dopamine agonist activity. These synthetic aporphine alkaloids are potent therapeutics for mitochondrial diseases without emetic side effects and have the potential to overcome the low bioavailability of apomorphine. Moreover, they have high anti-ferroptotic activity and therefore have potential as a therapeutic agent for diseases related to ferroptosis.

QUBO Problem Formulation of Fragment-Based Protein-Ligand Flexible Docking

Protein-ligand docking plays a significant role in structure-based drug discovery. This methodology aims to estimate the binding mode and binding free energy between the drug-targeted protein and candidate chemical compounds, utilizing protein tertiary structure information. Reformulation of this docking as a quadratic unconstrained binary optimization (QUBO) problem to obtain solutions via quantum annealing has been attempted. However, previous studies did not consider the internal degrees of freedom of the compound that is mandatory and essential. In this study, we formulated fragment-based protein-ligand flexible docking, considering the internal degrees of freedom of the compound by focusing on fragments (rigid chemical substructures of compounds) as a QUBO problem. We introduced four factors essential for fragment-based docking in the Hamiltonian: (1) interaction energy between the target protein and each fragment, (2) clashes between fragments, (3) covalent bonds between fragments, and (4) the constraint that each fragment of the compound is selected for a single placement. We also implemented a proof-of-concept system and conducted redocking for the protein-compound complex structure of Aldose reductase (a drug target protein) using SQBM+, which is a simulated quantum annealer. The predicted binding pose reconstructed from the best solution was near-native (RMSD = 1.26 Å), which can be further improved (RMSD = 0.27 Å) using conventional energy minimization. The results indicate the validity of our QUBO problem formulation.

Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

Amide-to-ester substitution as a stable alternative to N-methylation for increasing membrane permeability in cyclic peptides

Naturally occurring peptides with high membrane permeability often have ester bonds on their backbones. However, the impact of amide-to-ester substitutions on the membrane permeability of peptides has not been directly evaluated. Here we report the effect of amide-to-ester substitutions on the membrane permeability and conformational ensemble of cyclic peptides related to membrane permeation. Amide-to-ester substitutions are shown to improve the membrane permeability of dipeptides and a model cyclic hexapeptide. NMR-based conformational analysis and enhanced sampling molecular dynamics simulations suggest that the conformational transition of the cyclic hexapeptide upon membrane permeation is differently influenced by an amide-to-ester substitution and an amide N-methylation. The effect of amide-to-ester substitution on membrane permeability of other cyclic hexapeptides, cyclic octapeptides, and a cyclic nonapeptide is also investigated to examine the scope of the substitution. Appropriate utilization of amide-to-ester substitution based on our results will facilitate the development of membrane-permeable peptides.

CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides

Recently, cyclic peptides have been considered breakthrough drugs because they can interact with "undruggable" targets such as intracellular protein-protein interactions. Membrane permeability is an essential indicator of oral bioavailability and intracellular targeting, and the development of membrane-permeable peptides is a bottleneck in cyclic peptide drug discovery. Although many experimental data on membrane permeability of cyclic peptides have been reported, a comprehensive database is not yet available. A comprehensive membrane permeability database is essential for developing computational methods for cyclic peptide drug design. In this study, we constructed CycPeptMPDB, the first web-accessible database of cyclic peptide membrane permeability. We collected information on a total of 7334 cyclic peptides, including the structure and experimentally measured membrane permeability, from 45 published papers and 2 patents from pharmaceutical companies. To unambiguously represent cyclic peptides larger than small molecules, we used the hierarchical editing language for macromolecules notation to generate a uniform sequence representation of peptides. In addition to data storage, CycPeptMPDB provides several supporting functions such as online data visualization, data analysis, and downloading. CycPeptMPDB is expected to be a valuable platform to support membrane permeability research on cyclic peptides. CycPeptMPDB can be freely accessed at http://cycpeptmpdb.com.

Lipid Composition Is Critical for Accurate Membrane Permeability Prediction of Cyclic Peptides by Molecular Dynamics Simulations

Cyclic peptides have attracted attention as a promising pharmaceutical modality due to their potential to selectively inhibit previously undruggable targets, such as intracellular protein-protein interactions. Poor membrane permeability is the biggest bottleneck hindering successful drug discovery based on cyclic peptides. Therefore, the development of computational methods that can predict membrane permeability and support elucidation of the membrane permeation mechanism of drug candidate peptides is much sought after. In this study, we developed a protocol to simulate the behavior in membrane permeation steps and estimate the membrane permeability of large cyclic peptides with more than or equal to 10 residues. This protocol requires the use of a more realistic membrane model than a single-lipid phospholipid bilayer. To select a membrane model, we first analyzed the effect of cholesterol concentration in the model membrane on the potential of mean force and hydrogen bonding networks along the direction perpendicular to the membrane surface as predicted by molecular dynamics simulations using cyclosporine A. These results suggest that a membrane model with 40 or 50 mol % cholesterol was suitable for predicting the permeation process. Subsequently, two types of membrane models containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine and 40 and 50 mol % cholesterol were used. To validate the efficiency of our protocol, the membrane permeability of 18 ten-residue peptides was predicted. Correlation coefficients of R > 0.8 between the experimental and calculated permeability values were obtained with both model membranes. The results of this study demonstrate that the lipid membrane is not just a medium but also among the main factors determining the membrane permeability of molecules. The computational protocol proposed in this study and the findings obtained on the effect of membrane model composition will contribute to building a schematic view of the membrane permeation process. Furthermore, the results of this study will eventually aid the elucidation of design rules for peptide drugs with high membrane permeability.

Effective Protein-Ligand Docking Strategy via Fragment Reuse and a Proof-of-Concept Implementation

Virtual screening is a commonly used process to search for feasible drug candidates from a huge number of compounds during the early stages of drug design. As the compound database continues to expand to billions of entries or more, there remains an urgent need to accelerate the process of docking calculations. Reuse of calculation results is a possible way to accelerate the process. In this study, we first propose yet another virtual screening-oriented docking strategy by combining three factors, namely, compound decomposition, simplified fragment grid storing k-best scores, and flexibility consideration with pregenerated conformers. Candidate compounds contain many common fragments (chemical substructures). Thus, the calculation results of these common fragments can be reused among them. As a proof-of-concept of the aforementioned strategies, we also conducted the development of REstretto, a tool that implements the three factors to enable the reuse of calculation results. We demonstrated that the speed and accuracy of REstretto were comparable to those of AutoDock Vina, a well-known free docking tool. The implementation of REstretto has much room for further performance improvement, and therefore, the results show the feasibility of the strategy. The code is available under an MIT license at https://github.com/akiyamalab/restretto.

Solving Generalized Polyomino Puzzles Using the Ising Model

In the polyomino puzzle, the aim is to fill a finite space using several polyomino pieces with no overlaps or blanks. Because it is an NP-complete combinatorial optimization problem, various probabilistic and approximated approaches have been applied to find solutions. Several previous studies embedded the polyomino puzzle in a QUBO problem, where the original objective function and constraints are transformed into the Hamiltonian function of the simulated Ising model. A solution to the puzzle is obtained by searching for a ground state of Hamiltonian by simulating the dynamics of the multiple-spin system. However, previous methods could solve only tiny polyomino puzzles considering a few combinations because their Hamiltonian designs were not efficient. We propose an improved Hamiltonian design that introduces new constraints and guiding terms to weakly encourage favorable spins and pairs in the early stages of computation. The proposed model solves the pentomino puzzle represented by approximately 2000 spins with >90% probability. Additionally, we extended the method to a generalized problem where each polyomino piece could be used zero or more times and solved it with approximately 100% probability. The proposed method also appeared to be effective for the 3D polycube puzzle, which is similar to applications in fragment-based drug discovery.

Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning

MOTIVATION

In recent years, cyclic peptide drugs have been receiving increasing attention because they can target proteins that are difficult to be tackled by conventional small-molecule drugs or antibody drugs. Plasma protein binding rate (%PPB) is a significant pharmacokinetic property of a compound in drug discovery and design. However, due to structural differences, previous computational prediction methods developed for small-molecule compounds cannot be successfully applied to cyclic peptides, and methods for predicting the PPB rate of cyclic peptides with high accuracy are not yet available.

RESULTS

Cyclic peptides are larger than small molecules, and their local structures have a considerable impact on PPB; thus, molecular descriptors expressing residue-level local features of cyclic peptides, instead of those expressing the entire molecule, as well as the circularity of the cyclic peptides should be considered. Therefore, we developed a prediction method named CycPeptPPB using deep learning that considers both factors. First, the macrocycle ring of cyclic peptides was decomposed residue by residue. The residue-based descriptors were arranged according to the sequence information of the cyclic peptide. Furthermore, the circular data augmentation method was used, and the circular convolution method CyclicConv was devised to express the cyclic structure. CycPeptPPB exhibited excellent performance, with mean absolute error (MAE) of 4.79% and correlation coefficient (R) of 0.92 for the public drug dataset, compared to the prediction performance of the existing PPB rate prediction software (MAE=15.08%, R=0.63).

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the online supplementary material. The source code of CycPeptPPB is available at https://github.com/akiyamalab/cycpeptppb.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Large-Scale Membrane Permeability Prediction of Cyclic Peptides Crossing a Lipid Bilayer Based on Enhanced Sampling Molecular Dynamics Simulations

Membrane permeability is a significant obstacle facing the development of cyclic peptide drugs. However, membrane permeation mechanisms are poorly understood. To investigate common features of permeable (and nonpermeable) designs, it is necessary to reproduce the membrane permeation process of cyclic peptides through the lipid bilayer. We simulated the membrane permeation process of 100 six-residue cyclic peptides across the lipid bilayer based on steered molecular dynamics (MD) and replica-exchange umbrella sampling simulations and predicted membrane permeability using the inhomogeneous solubility-diffusion model and a modified version of it. Furthermore, we confirmed the effectiveness of this protocol by predicting the membrane permeability of 56 eight-residue cyclic peptides with diverse chemical structures, including some confidential designs from a pharmaceutical company. As a result, a reasonable correlation between experimentally assessed and calculated membrane permeability of cyclic peptides was observed for the peptide libraries, except for strongly hydrophobic peptides. Our analysis of the MD trajectory demonstrated that most peptides were stabilized in the boundary region between bulk water and membrane and that for most peptides, the process of crossing the center of the membrane is the main obstacle to membrane permeation. The height of this barrier is well correlated with the electrostatic interaction between the peptide and the surrounding media. The structural and energetic features of the representative peptide at each vertical position within the membrane were also analyzed, revealing that peptides permeate the membrane by changing their orientation and conformation according to the surrounding environment.

Taxonomic and Gene Category Analyses of Subgingival Plaques from a Group of Japanese Individuals with and without Periodontitis

Periodontitis is an inflammation of tooth-supporting tissues, which is caused by bacteria in the subgingival plaque (biofilm) and the host immune response. Traditionally, subgingival pathogens have been investigated using methods such as culturing, DNA probes, or PCR. The development of next-generation sequencing made it possible to investigate the whole microbiome in the subgingival plaque. Previous studies have implicated dysbiosis of the subgingival microbiome in the etiology of periodontitis. However, details are still lacking. In this study, we conducted a metagenomic analysis of subgingival plaque samples from a group of Japanese individuals with and without periodontitis. In the taxonomic composition analysis, genus Bacteroides and Mycobacterium demonstrated significantly different compositions between healthy sites and sites with periodontal pockets. The results from the relative abundance of functional gene categories, carbohydrate metabolism, glycan biosynthesis and metabolism, amino acid metabolism, replication and repair showed significant differences between healthy sites and sites with periodontal pockets. These results provide important insights into the shift in the taxonomic and functional gene category abundance caused by dysbiosis, which occurs during the progression of periodontal disease.

Functional Restoration of Bacteriomes and Viromes by Fecal Microbiota Transplantation

BACKGROUND & AIMS

Fecal microbiota transplantation (FMT) is an effective therapy for recurrent Clostridioides difficile infection (rCDI). However, the overall mechanisms underlying FMT success await comprehensive elucidation, and the safety of FMT has recently become a serious concern because of the occurrence of drug-resistant bacteremia transmitted by FMT. We investigated whether functional restoration of the bacteriomes and viromes by FMT could be an indicator of successful FMT.

METHODS

The human intestinal bacteriomes and viromes from 9 patients with rCDI who had undergone successful FMT and their donors were analyzed. Prophage-based and CRISPR spacer-based host bacteria-phage associations in samples from recipients before and after FMT and in donor samples were examined. The gene functions of intestinal microorganisms affected by FMT were evaluated.

RESULTS

Metagenomic sequencing of both the viromes and bacteriomes revealed that FMT does change the characteristics of intestinal bacteriomes and viromes in recipients after FMT compared with those before FMT. In particular, many Proteobacteria, the fecal abundance of which was high before FMT, were eliminated, and the proportion of Microviridae increased in recipients. Most temperate phages also behaved in parallel with the host bacteria that were altered by FMT. Furthermore, the identification of bacterial and viral gene functions before and after FMT revealed that some distinctive pathways, including fluorobenzoate degradation and secondary bile acid biosynthesis, were significantly represented.

CONCLUSIONS

The coordinated action of phages and their host bacteria restored the recipients' intestinal flora. These findings show that the restoration of intestinal microflora functions reflects the success of FMT.

Design of Temperature-Responsive Cell Culture Surfaces for Cell Sheet Engineering

Temperature-responsive cell culture surfaces, which modulate cell attachment/detachment characteristics with temperature, have been used to fabricate cell sheets. Extensive study on fabrication of cell sheet with the temperature-responsive cell culture surface, manipulation, and transplantation of the cell sheet has established the interdisciplinary field of cell sheet engineering, in which engineering, biological, and medical fields closely collaborate. Such collaboration has pioneered cell sheet engineering, making it a promising and attractive technology in tissue engineering and regenerative medicine. This review introduces concepts of cell sheet engineering, followed by designs for the fabrication of various types of temperature-responsive cell culture surfaces and technologies for cell sheet manipulation. The development of various methods for the fabrication of temperature-responsive cell culture surfaces was also summarized. The availability of cell sheet engineering for the treatment and regeneration of damaged human tissue has also been described, providing examples of the clinical application of cell sheet transplantation in humans.

Clinical characteristics of adult T-cell leukemia/lymphoma infiltration in the gastrointestinal tract

Background

Adult T-cell leukemia/lymphoma (ATLL) is a peripheral T-cell malignancy caused by human T-cell leukemia virus type 1. The clinical course of ATLL is very heterogeneous, and many organs, including the gastrointestinal (GI) tract, can be involved. However, there are few detailed reports on ATLL infiltration in the GI tract. We investigated the clinical characteristics of ATLL infiltration in the GI tract.

Methods

This retrospective observational single-center study included 40 consecutive ATLL patients who underwent GI endoscopy. The patients’ demographic and clinical characteristics and endoscopic findings were analyzed retrospectively. Patients with ATLL who were diagnosed by histological examination were divided into two groups based on GI tract infiltration.

Results

Multivariate analysis revealed that the absence of skin lesions was significantly associated with GI infiltration (P < 0.05). Furthermore, the infiltration group tended to have similar macroscopic lesions in the upper and lower GI tracts, such as diffuse type, tumor-forming type, and giant-fold type.

Conclusions

GI endoscopy may be considered for ATLL patients without skin lesions.

Comprehensive Fungal Community Analysis of House Dust Using Next-Generation Sequencing

Fungal community analyses in homes have been attracting attention because fungi are now generally considered to be allergens. Currently, these analyses are generally conducted using the culture method, although fungal communities in households often contain species that are difficult to culture. In contrast, next-generation sequencing (NGS) represents a comprehensive, labor- and time-saving approach that can facilitate species identification. However, the reliability of the NGS method has not been compared to that of the culture method. In this study, in an attempt to demonstrate the reliability of this application, we used the NGS method to target the internal transcribed spacer 1 (ITS1) in the fungal genome, conducted fungal community analyses for 18 house-dust samples and analyzed fungal community structures. The NGS method positively correlated with the culture method regarding the relative abundance of Aspergillus, Penicillium, Cladosporium and yeasts, which represent the major fungal components found in houses. Furthermore, several genera, such as Malassezia, could be sensitively detected. Our results imply that the reliability of the NGS method is comparable to that of the culture method and indicates that easily available databases may require modifications, including the removal of registrations that have not been sufficiently classified at the genus level.

Metagenome Data on Intestinal Phage-Bacteria Associations Aids the Development of Phage Therapy against Pathobionts

The application of bacteriophages (phages) is proposed as a highly specific therapy for intestinal pathobiont elimination. However, the infectious associations between phages and bacteria in the human intestine, which is essential information for the development of phage therapies, have yet to be fully elucidated. Here, we report the intestinal viral microbiomes (viromes), together with bacterial microbiomes (bacteriomes), in 101 healthy Japanese individuals. Based on the genomic sequences of bacteriomes and viromes from the same fecal samples, the host bacteria-phage associations are illustrated for both temperate and virulent phages. To verify the usefulness of the comprehensive host bacteria-phage information, we screened Clostridioides difficile-specific phages and identified antibacterial enzymes whose activity is confirmed both in vitro and in vivo. These comprehensive metagenome analyses reveal not only host bacteria-phage associations in the human intestine but also provide vital information for the development of phage therapies against intestinal pathobionts.

Multidomain protein structure prediction using information about residues interacting on multimeric protein interfaces

Protein functions can be predicted based on their three-dimensional structures. However, many multidomain proteins have unstable structures, making it difficult to determine the whole structure in biological experiments. Additionally, multidomain proteins are often decomposed and identified based on their domains, with the structure of each domain often found in public databases. Recent studies have advanced structure prediction methods of multidomain proteins through computational analysis. In existing methods, proteins that serve as templates are used for three-dimensional structure prediction. However, when no protein template is available, the accuracy of the prediction is decreased. This study was conducted to predict the structures of multidomain proteins without the need for whole structure templates.

We improved structure prediction methods by performing rigid-body docking from the structure of each domain and reranking a structure closer to the correct structure to have a higher value. In the proposed method, the score for the domain-domain interaction obtained without a structural template of the multidomain protein and score for the three-dimensional structure obtained during docking calculation were newly incorporated into the score function. We successfully predicted the structures of 50 of 55 multidomain proteins examined in the test dataset.

Interaction residue pair information of the protein-protein complex interface contributes to domain reorganizations even when a structural template for a multidomain protein cannot be obtained. This approach may be useful for predicting the structures of multidomain proteins with important biochemical functions.

A rare association between true thymic hyperplasia and thyroid follicular tumor: a case report

Background

True thymic hyperplasia is a rare condition characterized by enlargement of the thymus while its normal structure is retained. True thymic hyperplasia is known to accompany Graves’ disease, but no association between true thymic hyperplasia and thyroid follicular tumor has been reported so far. We report a case of true thymic hyperplasia in a patient with a thyroid follicular tumor.

Case presentation

A 52-year-old Japanese man was referred to our hospital for evaluation of a thyroid mass and a mediastinal mass. His serum thyroglobulin level was high, and hemithyroidectomy was performed to remove the thyroid mass. The resected mass was diagnosed as a follicular tumor of uncertain malignant potential. After resection of the thyroid lesion, the patient’s serum thyroglobulin levels were markedly decreased. Seven months later, the patient underwent resection of the mediastinal mass. On pathological examination, the mass was found to consist of lobules, which formed a corticomedullary structure with Hassall’s bodies, indicating a normal thymic mass with hyperplastic thymic tissue, less organized cellular cords, and intermingled adipose tissue. Immunostaining for cytokeratin 19 and cytokeratin 7 indicated that the lesion was consistent with thymic tissue. The lesion was diagnosed as true thymic hyperplasia, and the histological findings suggested that secondary atrophy had occurred. No evidence of recurrence was observed at 24 months after surgery.

Conclusions

We present a case of a combination of true thymic hyperplasia and thyroidal follicular tumors that, to our knowledge, has not been reported previously. High serum thyroglobulin levels might play a role in hyperplasia of the thymus. Although true thymic hyperplasia is a rare disorder, it should be included in the differential diagnosis of a mediastinal mass in patients with thyroid disease.

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.

A case of carcinoma ex pleomorphic adenoma arising from multinodular pleomorphic adenoma of the buccal region

In multinodular lesions or tumors with mixed benign and malignant components, malignant elements may be undetectable using fine-needle aspiration biopsy/cytology in preoperative pathological diagnosis of some cases, because of sampling error.

NRLMFβ: Beta-distribution-rescored neighborhood regularized logistic matrix factorization for improving the performance of drug-target interaction prediction

Techniques for predicting interactions between a drug and a target (protein) are useful for strategic drug repositioning. Neighborhood regularized logistic matrix factorization (NRLMF) is one of the state-of-the-art drug-target interaction prediction methods; it is based on a statistical model using the Bernoulli distribution. However, the prediction is not accurate when drug-target interaction pairs have less interaction information (e.g., the sum of the number of ligands for a target and the number of target proteins for a drug). This study aimed to address this issue by proposing NRLMF with beta distribution rescoring (NRLMFβ), which is an algorithm to improve the score of NRLMF. The score of NRLMFβ is equivalent to the value of the original NRLMF score when the concentration of the beta distribution becomes infinity. The beta distribution is known as a conjugative prior distribution of the Bernoulli distribution and can reflect the amount of interaction information to its shape based on Bayesian inference. Therefore, in NRLMFβ, the beta distribution was used for rescoring the NRLMF score. In the evaluation experiment, we measured the average values of area under the receiver operating characteristics and area under precision versus recall and the 95% confidence intervals. The performance of NRLMFβ was found to be better than that of NRLMF in the four types of benchmark datasets. Thus, we concluded that NRLMFβ improved the prediction accuracy of NRLMF. The source code is available at https://github.com/akiyamalab/NRLMFb.

Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques

BACKGROUND

Cyclic peptide-based drug discovery is attracting increasing interest owing to its potential to avoid target protein depletion. In drug discovery, it is important to maintain the biostability of a drug within the proper range. Plasma protein binding (PPB) is the most important index of biostability, and developing a computational method to predict PPB of drug candidate compounds contributes to the acceleration of drug discovery research. PPB prediction of small molecule drug compounds using machine learning has been conducted thus far; however, no study has investigated cyclic peptides because experimental information of cyclic peptides is scarce.

RESULTS

First, we adopted sparse modeling and small molecule information to construct a PPB prediction model for cyclic peptides. As cyclic peptide data are limited, applying multidimensional nonlinear models involves concerns regarding overfitting. However, models constructed by sparse modeling can avoid overfitting, offering high generalization performance and interpretability. More than 1000 PPB data of small molecules are available, and we used them to construct a prediction models with two enumeration methods: enumerating lasso solutions (ELS) and forward beam search (FBS). The accuracies of the prediction models constructed by ELS and FBS were equal to or better than those of conventional non-linear models (MAE = 0.167-0.174) on cross-validation of a small molecule compound dataset. Moreover, we showed that the prediction accuracies for cyclic peptides were close to those for small molecule compounds (MAE = 0.194-0.288). Such high accuracy could not be obtained by a simple method of learning from cyclic peptide data directly by lasso regression (MAE = 0.286-0.671) or ridge regression (MAE = 0.244-0.354).

CONCLUSION

In this study, we proposed a machine learning techniques that uses low-dimensional sparse modeling to predict the PPB value of cyclic peptides computationally. The low-dimensional sparse model not only exhibits excellent generalization performance but also improves interpretation of the prediction model. This can provide common an noteworthy knowledge for future cyclic peptide drug discovery studies.

In Silico Prediction of Major Clearance Pathways of Drugs among 9 Routes with Two-Step Support Vector Machines

PURPOSE

The clearance pathways of drugs are critical elements for understanding the pharmacokinetics of drugs. We previously developed in silico systems to predict the five clearance pathway using a rectangular method and a support vector machine (SVM). In this study, we improved our classification system by increasing the number of clearance pathways available for our prediction (CYP1A2, CYP2C8, CYP2C19, and UDP-glucuronosyl transferases (UGTs)) and by accepting multiple major pathways.

METHODS

Using the four default descriptors (charge, molecular weight, logD at pH 7.0, and unbound fraction in plasma), three kinds of SVM-based predictors based on traditional single-step approach or two-step focusing approaches with subset or partition clustering were developed. The two-step approach with subset clustering resulted in the highest prediction performance. The feature-selection of additional descriptors based on a greedy algorithm was employed to further improve the predictability.

RESULTS

The prediction accuracy for each pathway was increased to more than 0.83 with the exception of CYP2C19 and UGTs pathways, whose accuracies were below 0.7. Prediction performance of CYP1A2, CYP3A4 and renal excretion pathways were found to be acceptable using external dataset.

CONCLUSIONS

We successfully constructed a novel SVM-based predictor for the multiple major clearance pathways based on chemical structures.

Spresso: an ultrafast compound pre-screening method based on compound decomposition

Motivation

Recently, the number of available protein tertiary structures and compounds has increased. However, structure-based virtual screening is computationally expensive owing to docking simulations. Thus, methods that filter out obviously unnecessary compounds prior to computationally expensive docking simulations have been proposed. However, the calculation speed of these methods is not fast enough to evaluate ≥ 10 million compounds.

Results

In this article, we propose a novel, docking-based pre-screening protocol named Spresso (Speedy PRE-Screening method with Segmented cOmpounds). Partial structures (fragments) are common among many compounds; therefore, the number of fragment variations needed for evaluation is smaller than that of compounds. Our method increases calculation speeds by ∼200-fold compared to conventional methods.

Availability and Implementation

Spresso is written in C ++ and Python, and is available as an open-source code (http://www.bi.cs.titech.ac.jp/spresso/) under the GPLv3 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions

Background

Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs.

Results

In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer.

Conclusion

MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2073-x) contains supplementary material, which is available to authorized users.

Optimization of memory use of fragment extension-based protein-ligand docking with an original fast minimum cost flow algorithm

The need to accelerate large-scale protein-ligand docking in virtual screening against a huge compound database led researchers to propose a strategy that entails memorizing the evaluation result of the partial structure of a compound and reusing it to evaluate other compounds. However, the previous method required frequent disk accesses, resulting in insufficient acceleration. Thus, more efficient memory usage can be expected to lead to further acceleration, and optimal memory usage could be achieved by solving the minimum cost flow problem. In this research, we propose a fast algorithm for the minimum cost flow problem utilizing the characteristics of the graph generated for this problem as constraints. The proposed algorithm, which optimized memory usage, was approximately seven times faster compared to existing minimum cost flow algorithms.

The correlation of urinary podocytes and podocalyxin with histological features of lupus nephritis

Objectives The objective of this study was to test the correlation of urinary podocyte number (U-Pod) and urinary podocalyxin levels (U-PCX) with histology of lupus nephritis. Methods This was an observational, cross-sectional study. Sixty-four patients were enrolled: 40 with lupus nephritis and 24 without lupus nephritis (12 lupus nephritis patients in complete remission and 12 systemic lupus erythematosus patients without lupus nephritis). Urine samples were collected before initiating treatment. U-Pod was determined by counting podocalyxin-positive cells, and U-PCX was measured by sandwich ELISA, normalized to urinary creatinine levels (U-Pod/Cr, U-PCX/Cr). Results Lupus nephritis patients showed significantly higher U-Pod/Cr and U-PCX/Cr compared with patients without lupus nephritis. U-Pod/Cr was high in proliferative lupus nephritis (class III±V/IV±V), especially in pure class IV (4.57 (2.02-16.75)), but low in pure class V (0.30 (0.00-0.71)). U-Pod/Cr showed a positive correlation with activity index ( r=0.50, P=0.0012) and was independently associated with cellular crescent formation. In contrast, U-PCX/Cr was high in both proliferative and membranous lupus nephritis. Receiver operating characteristic analysis revealed significant correlation of U-Pod/Cr with pure class IV, class IV±V and cellular crescent formation, and the combined values of U-Pod/Cr and U-PCX/Cr were shown to be associated with pure class V. Conclusions U-Pod/Cr and U-PCX/Cr correlate with histological features of lupus nephritis.

A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data

Sequence similarity searches have been widely used in the analyses of metagenomic sequencing data. Finding homologous sequences in a reference database enables the estimation of taxonomic and functional characteristics of each query sequence. Because current metagenomic sequencing data consist of a large number of nucleotide sequences, the time required for sequence similarity searches account for a large proportion of the total time. This time-consuming step makes it difficult to perform large-scale analyses. To analyze large-scale metagenomic data, such as those found in the human oral microbiome, we developed GHOST-MP (Genome-wide HOmology Search Tool on Massively Parallel system), a parallel sequence similarity search tool for massively parallel computing systems. This tool uses a fast search algorithm based on suffix arrays of query and database sequences and a hierarchical parallel search to accelerate the large-scale sequence similarity search of metagenomic sequencing data. The parallel computing efficiency and the search speed of this tool were evaluated. GHOST-MP was shown to be scalable over 10,000 CPU (Central Processing Unit) cores, and achieved over 80-fold acceleration compared with mpiBLAST using the same computational resources. We applied this tool to human oral metagenomic data, and the results indicate that the oral cavity, the oral vestibule, and plaque have different characteristics based on the functional gene category.

In silico, in vitro, X-ray crystallography, and integrated strategies for discovering spermidine synthase inhibitors for Chagas disease

Chagas disease results from infection by Trypanosoma cruzi and is a neglected tropical disease (NTD). Although some treatment drugs are available, their use is associated with severe problems, including adverse effects and limited effectiveness during the chronic disease phase. To develop a novel anti-Chagas drug, we virtually screened 4.8 million small molecules against spermidine synthase (SpdSyn) as the target protein using our super computer “TSUBAME2.5” and conducted in vitro enzyme assays to determine the half-maximal inhibitory concentration values. We identified four hit compounds that inhibit T. cruzi SpdSyn (TcSpdSyn) by in silico and in vitro screening. We also determined the TcSpdSyn–hit compound complex structure using X-ray crystallography, which shows that the hit compound binds to the putrescine-binding site and interacts with Asp171 through a salt bridge.

Stability of cervical esophagogastrostomy via hand-sewn anastomosis after esophagectomy for esophageal cancer

The aim of the present study is to evaluate the outcome of hand-sewn esophagogastric anastomosis during radical esophagectomy for esophageal cancer. The outcomes of 467 consecutive esophageal cancer patients who underwent cervical esophagogastric anastomosis using interrupted and double-layered sutures after radical esophagectomy via right thoracotomy or thoracoscopic surgery were retrospectively reviewed. Anastomotic leakage, including conduit necrosis, occurred in 11 of 467 patients (2.4%); 7 of 11 (63.6%) cases experienced only minor leakage, whereas the other four (36.4%) patients had major leakage that required surgical or radiologic intervention, including two patients of conduit necrosis. Anastomotic leakages were more frequently observed after retrosternal reconstruction compared with the posterior mediastinal route (P < 0.0001). The median time to healing of leakage was 40 days (range: 14-97 days). Two patients (2/467, 0.4%) died in the hospital due to sepsis caused by the leakage and conduit necrosis. Twelve patients (2.6%) developed anastomotic stenosis, which was improved by dilatation in all patients. Hand-sewn cervical esophagogastric anastomosis is a stable and highly safe method of radical esophagectomy for esophageal cancer.

Thoracoscopic enucleation in the left decubitus position for leiomyoma of the upper thoracic esophagus: Utility of preoperative diagnosis applying endoscopic ultrasound-guided fine needle aspiration

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We report a case of esophageal leiomyoma enucleated by thoracoscopic procedures.

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Preoperative EUS-FNA is useful to decide the operative procedure.

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Preoperative EUS-FNA does not adversely influence subsequent enucleation.

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Precise preoperative diagnosis is necessary to avoid excessive surgery.

Introduction

We report a relatively rare case of esophageal leiomyoma in the upper thoracic esophagus enucleated by thoracoscopic procedures. The usefulness of preoperative diagnosis and an adequate surgical approach are described along with a review of the relevant literature.

Presentaion of case

A submucosal tumor 45 mm in diameter was detected in the upper thoracic esophagus of a 69-year-old man. The tumor was preoperatively diagnosed from histopathological biopsy under endoscopic ultrasound-guided fine needle aspiration. Thoracoscopic enucleation was therefore preoperatively scheduled under the left decubitus position in consideration of the low risk of malignant disease. Histopathological diagnosis of the resected specimen was benign leiomyoma and patient outcomes were good.

Discussion

The need for preoperative biopsy of esophageal submucosal tumor is a controversial issue. However, preoperative biopsy provided the benefits to decide the operative procedure or confirm adequate resection, and our experience suggested that preoperative biopsy did not adversely influence subsequent enucleation.

Conclusion

Precise preoperative diagnosis is necessary to avoid excessive surgery when managing esophageal submucosal tumor.

Myopericytoma in urinary bladder: a case report

BACKGROUND

Myopericytoma is reported to occur mainly in the skin and superficial soft tissue of the extremities. In contrast, occurrence in the urinary bladder is extremely rare.

CASE PRESENTATION

We describe a 75-year-old Japanese man who developed a submucosal tumor at the right trigone of his bladder that led to interference with the discharge of right ureteral calculus. No invasive growth was observed by magnetic resonance imaging. Transurethral resection was successfully performed; histopathological analysis revealed perivascular proliferation of spindle-shaped to oval-shaped, cytologically bland tumor cells with eosinophilic cytoplasm. On immunohistochemical examination, the tumor cells were positive for alpha-smooth muscle actin, desmin, CD34 and h-caldesmon.

CONCLUSION

Cystoscopic and pathological findings were compatible with a diagnosis of myopericytoma of the urinary bladder.

GHOSTX: A Fast Sequence Homology Search Tool for Functional Annotation of Metagenomic Data

Metagenomic analysis based on whole genome shotgun sequencing data requires fast protein sequence homology searches for predicting the function of proteins coded on metagenome short reads. However, huge amounts of sequence data cause even general homology search analyses using BLASTX to become difficult in terms of computational cost. GHOSTX is a sequence homology search tool specifically developed for functional annotation of metagenome sequences. The tool is more than 160 times faster than BLASTX and has sufficient search sensitivity for metagenomic analysis. Using this tool, user can perform functional annotation of metagenomic data within a short time and infer metabolic pathways within an environment.

Orbital Fat Volumetry and Water Fraction Measurements Using T2-Weighted FSE-IDEAL Imaging in Patients with Thyroid-Associated Orbitopathy

BACKGROUND AND PURPOSE

The quantitative evaluation of orbital fat proliferation and edema and the assessment of extraocular muscles are useful for diagnosing and monitoring thyroid-associated orbitopathy. To evaluate therapy-induced quantitative changes in the orbital fat of patients with thyroid-associated orbitopathy, we performed volumetric and water fraction measurements by using T2-weighted FSE iterative decomposition of water and fat with echo asymmetry and least-squares estimation (FSE-IDEAL) imaging.

MATERIALS AND METHODS

Orbital FSE-IDEAL images of 30 volunteers were acquired twice within 1 week. Nine patients with thyroid-associated orbitopathy underwent FSE-IDEAL imaging before and after methylprednisolone pulse therapy, and the treatment results were assessed by using their pre- and post-methylprednisolone pulse therapy clinical activity scores. We performed volumetric and water fraction measurements of orbital fat by using FSE-IDEAL imaging and evaluated interscan differences in the volunteers. In patients with thyroid-associated orbitopathy, we compared pre- and posttherapy orbital fat measurements and assessed the correlation between the pretherapy values and clinical activity score improvement.

RESULTS

The reproducibility of results obtained by the quantitative evaluation of orbital fat in volunteers was acceptable. After methylprednisolone pulse therapy, the water fraction in the orbital fat of patients with thyroid-associated orbitopathy was significantly decreased (P < .001). There was a significant positive correlation between the pretherapy water fraction and clinical activity score improvement (right, r = 0.82; left, r = 0.79) and a significant negative correlation between the pretherapy volume and clinical activity score improvement (bilateral, r = -0.84).

CONCLUSIONS

Volumetric and water fraction measurements of orbital fat by using FSE-IDEAL imaging are feasible and useful for monitoring the effects of therapy and for predicting the response of patients with thyroid-associated orbitopathy to methylprednisolone pulse therapy.