While GPUs are the main players of the accelerating devices on high performance computing systems, their performance depends on how to utilize a numerous number of cores in parallel on each device. Typically, a loop structure with a number of iterations is assigned to a device to utilize their cores to map calculations in iterations so that there must be enough count of iterations to fill the thousands of GPU cores in the high-end GPUs.

Cyclic peptides are versatile therapeutic agents that boast high binding affinity, minimal toxicity, and the potential to engage challenging protein targets. However, the pharmaceutical utility of cyclic peptides is limited by their low membrane permeability-an essential indicator of oral bioavailability and intracellular targeting. Current machine learning-based models of cyclic peptide permeability show variable performance owing to the limitations of experimental data. Furthermore, these methods use features derived from the whole molecule that have traditionally been used to predict small molecules and ignore the unique structural properties of cyclic peptides. This study presents CycPeptMP: an accurate and efficient method to predict cyclic peptide membrane permeability. We designed features for cyclic peptides at the atom-, monomer-, and peptide-levels and seamlessly integrated these into a fusion model using deep learning technology. Additionally, we applied various data augmentation techniques to enhance model training efficiency using the latest data. The fusion model exhibited excellent prediction performance for the logarithm of permeability, with a mean absolute error of $0.355$ and correlation coefficient of $0.883$. Ablation studies demonstrated that all feature levels contributed and were relatively essential to predicting membrane permeability, confirming the effectiveness of augmentation to improve prediction accuracy. A comparison with a molecular dynamics-based method showed that CycPeptMP accurately predicted peptide permeability, which is otherwise difficult to predict using simulations.

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.

Metagenome techniques allow analyses of microorganisms and their genes present in a given environment without isolation and culture. Thus, metagenomics has become a broadly applied tool to study various environments and elucidate the relationship between diseases and the host microbiota. With continuous improvement in the performance of genome sequencers, the number of sequence reads generated has increased exponentially; thus, methods for the efficient processing of such large numbers of sequences are required. To this end, we developed the pipeline system, GHOSTMEGAN, to speed up the processing of large-scale whole genome shotgun metagenome analysis, which integrates the sequence homology search tool GHOSTZ-GPU and the analyzing tool MEGAN. Assuming a cluster-type computer with a job scheduling system, the multi-node parallel processing of GHOSTZ-GPU and MEGAN was pipelined. Performance evaluation of GHOSTMEGAN with a whole genome sequence dataset, the oral metagenome demonstrated that execution of 128 nodes in parallel, which required 15 h on a single node, could be completed in only 20 min, thereby achieving about 45 times faster calculation. This pipeline is expected to greatly accelerate the field of metagenomics and broaden its application potential.

In this paper, we propose a playful tool to predict protein-protein docking. The tool uses human bodies to explore better protein-protein docking, where it offers natural interactions to dock protein molecules in a flexible way and playful interactions through two users' cooperative body movements. We present the design and implementation of the proposed tool and show potential opportunities and pitfalls of the current tool.

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/ .

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.

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. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Sequence homology searches are used in various fields. New sequencing technologies produce huge amounts of sequence data, which continuously increase the size of sequence databases. As a result, homology searches require large amounts of computational time, especially for metagenomic analysis. RESULTS: We developed a fast homology search method based on database subsequence clustering, and implemented it as GHOSTZ. This method clusters similar subsequences from a database to perform an efficient seed search and ungapped extension by reducing alignment candidates based on triangle inequality. The database subsequence clustering technique achieved an ∼2-fold increase in speed without a large decrease in search sensitivity. When we measured with metagenomic data, GHOSTZ is ∼2.2-2.8 times faster than RAPSearch and is ∼185-261 times faster than BLASTX. AVAILABILITY AND IMPLEMENTATION: The source code is freely available for download at http://www.bi.cs.titech.ac.jp/ghostz/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The application of protein-protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of >97% strong scaling. AVAILABILITY AND IMPLEMENTATION: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Metagenomics is a powerful methodology to study microbial communities, but it is highly dependent on nucleotide sequence similarity searching against sequence databases. Metagenomic analyses with next-generation sequencing technologies produce enormous numbers of reads from microbial communities, and many reads are derived from microbes whose genomes have not yet been sequenced, limiting the usefulness of existing sequence similarity search tools. Therefore, there is a clear need for a sequence similarity search tool that can rapidly detect weak similarity in large datasets. RESULTS: We developed a tool, which we named CLAST (CUDA implemented large-scale alignment search tool), that enables analyses of millions of reads and thousands of reference genome sequences, and runs on NVIDIA Fermi architecture graphics processing units. CLAST has four main advantages over existing alignment tools. First, CLAST was capable of identifying sequence similarities ~80.8 times faster than BLAST and 9.6 times faster than BLAT. Second, CLAST executes global alignment as the default (local alignment is also an option), enabling CLAST to assign reads to taxonomic and functional groups based on evolutionarily distant nucleotide sequences with high accuracy. Third, CLAST does not need a preprocessed sequence database like Burrows-Wheeler Transform-based tools, and this enables CLAST to incorporate large, frequently updated sequence databases. Fourth, CLAST requires <2 GB of main memory, making it possible to run CLAST on a standard desktop computer or server node. CONCLUSIONS: CLAST achieved very high speed (similar to the Burrows-Wheeler Transform-based Bowtie 2 for long reads) and sensitivity (equal to BLAST, BLAT, and FR-HIT) without the need for extensive database preprocessing or a specialized computing platform. Our results demonstrate that CLAST has the potential to be one of the most powerful and realistic approaches to analyze the massive amount of sequence data from next-generation sequencing technologies.

BACKGROUND: Huge numbers of genomes can now be sequenced rapidly with recent improvements in sequencing throughput. However, data analysis methods have not kept up, making it difficult to process the vast amounts of available sequence data. This increased processing time is especially critical in DNA sequence clustering because of the intrinsic difficulty in parallelization. Thus, there is a strong demand for a faster clustering algorithm. RESULTS: We developed a new fast DNA sequence clustering method called LCS-HIT, based on the popular CD-HIT program. The proposed method uses a novel filtering technique based on the longest common subsequence to identify similar sequence pairs. This filtering technique makes the LCS-HIT considerably faster than CD-HIT, without loss of sensitivity. For a dataset of two million DNA sequences, our method was approximately 7.1, 4.4, and 2.2 times faster than CD-HIT for 100, 150, and 400 bases, respectively. CONCLUSIONS: The LCS-HIT clustering program, using a novel filtering technique based on the longest common subsequence, is significantly faster than CD-HIT without compromising clustering accuracy. Moreover, the filtering technique itself is independent from the CD-HIT algorithm. Thus, this technique can be applied to similar clustering algorithms.

MOTIVATION: Homology search is one of the most fundamental tools in Bioinformatics. Typical alignment algorithms use substitution matrices and gap costs. Thus, the improvement of substitution matrices increases accuracy of homology searches. Generally, substitution matrices are derived from aligned sequences whose relationships are known, and gap costs are determined by trial and error. To discriminate relationships more clearly, we are encouraged to optimize the substitution matrices from statistical viewpoints using both positive and negative examples utilizing Bayesian decision theory. RESULTS: Using Cluster of Orthologous Group (COG) database, we optimized substitution matrices. The classification accuracy of the obtained matrix is better than that of conventional substitution matrices to COG database. It also achieves good performance in classifying with other databases.

Abstract Summary: We present FORTE, a profile–profile comparison tool for protein fold recognition. Users can submit a protein sequence to explore the possibilities of structural similarity existing in known structures. Results are reported via email in the form of pairwise alignments. Availability: The server is available at http://www.cbrc.jp/forte/

Abstract Motivation: Protein structure classification has been recognized as one of the most important research issues in protein structure analysis. A substantial number of methods for the classification have been proposed, and several databases have been constructed using these methods. Since some proteins with very similar sequences may exhibit structural diversities, we have proposed PDB-REPRDB: a database of representative protein chains from the Protein Data Bank (PDB), which strategy of selection is based not only on sequence similarity but also on structural similarity. Forty-eight representative sets whose similarity criteria were predetermined were made available over the World Wide Web (WWW). However, the sets were insufficient in number to satisfy users researching protein structures by various methods. Result: We have improved the system for PDB-REPRDB so that the user may obtain a quick selection of representative chains from PDB. The selection of representative chains can be dynamically configured according to the user’s requirement. The WWW interface provides a large degree of freedom in setting parameters, such as cut-off scores of sequence and structural similarity. This paper describes the method we use to classify chains and select the representatives in the system. We also describe the interface used to set the parameters. Availability: The system for PDB-REPRDB is available at the PAPIA WWW server (http://www.rwcp.or.jp/papia/). Contact: [email protected] * To whom correspondence should be addressed. 3 Present address: Information Processing Systems Department, NKK Corporation, 1-1-2 Marunouchi, Chiyoda-ku, Tokyo 100-8202, Japan