We report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences. This feature is available in MAFFT versions 7.355 or later at https://mafft.cbrc.jp/alignment/software/mpi.html. Supplementary data are available at Bioinformatics online.

Motivation: In bioinformatics, machine learning-based methods that predict the compound–protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symbolic data (e.g. words in natural language processing) using deep neural networks has demonstrated excellent performance on various difficult problems. For the CPI problem, data are provided as discrete symbolic data, i.e. compounds are represented as graphs where the vertices are atoms, the edges are chemical bonds, and proteins are sequences in which the characters are amino acids. In this study, we investigate the use of end-to-end representation learning for compounds and proteins, integrate the representations, and develop a new CPI prediction approach by combining a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins. Results: Our experiments using three CPI datasets demonstrated that the proposed end-to-end approach achieves competitive or higher performance as compared to various existing CPI prediction methods. In addition, the proposed approach significantly outperformed existing methods on an unbalanced dataset. This suggests that data-driven representations of compounds and proteins obtained by end-to-end GNNs and CNNs are more robust than traditional chemical and biological features obtained from databases. Although analyzing deep learning models is difficult due to their black-box nature, we address this issue using a neural attention mechanism, which allows us to consider which subsequences in a protein are more important for a drug compound when predicting its interaction. The neural attention mechanism also provides effective visualization, which makes it easier to analyze a model even when modeling is performed using real-valued representations instead of discrete features. Availability and implementation: https://github.com/masashitsubaki Supplementary information: Supplementary data are available at Bioinformatics online.

Mitochondria provide numerous essential functions for cells and their dysfunction leads to a variety of diseases. Thus, obtaining a complete mitochondrial proteome should be a crucial step toward understanding the roles of mitochondria. Many mitochondrial proteins have been identified experimentally but a complete list is not yet available. To fill this gap, methods to computationally predict mitochondrial proteins from amino acid sequence have been developed and are widely used, but unfortunately, their accuracy is far from perfect. Here we describe MitoFates, an improved prediction method for cleavable N-terminal mitochondrial targeting signals (presequences) and their cleavage sites. MitoFates introduces novel sequence features including positively charged amphiphilicity, presequence motifs, and position weight matrices modeling the presequence cleavage sites. These features are combined with classical ones such as amino acid composition and physico-chemical properties as input to a standard support vector machine classifier. On independent test data, MitoFates attains better performance than existing predictors in both detection of presequences and in predicting their cleavage sites. We used MitoFates to look for undiscovered mitochondrial proteins from 42,217 human proteins (including isoforms such as alternative splicing or translation initiation variants). MitoFates predicts 1167 genes to have at least one isoform with a presequence. Five-hundred and eighty of these genes were not annotated as mitochondrial in either UniProt or Gene Ontology. Interestingly, these include candidate regulators of parkin translocation to damaged mitochondria, and also many genes with known disease mutations, suggesting that careful investigation of MitoFates predictions may be helpful in elucidating the role of mitochondria in health and disease. MitoFates is open source with a convenient web server publicly available.

Motivation: Large multiple sequence alignments (MSAs), consisting of thousands of sequences, are becoming more and more common, due to advances in sequencing technologies. The MAFFT MSA program has several options for building large MSAs, but their performances have not been sufficiently assessed yet, because realistic benchmarking of large MSAs has been difficult. Recently, such assessments have been made possible through the HomFam and ContTest benchmark protein datasets. Along with the development of these datasets, an interesting theory was proposed: chained guide trees increase the accuracy of MSAs of structurally conserved regions. This theory challenges the basis of progressive alignment methods and needs to be examined by being compared with other known methods including computationally intensive ones. Results: We used HomFam, ContTest and OXFam (an extended version of OXBench) to evaluate several methods enabled in MAFFT: (1) a progressive method with approximate guide trees, (2) a progressive method with chained guide trees, (3) a combination of an iterative refinement method and a progressive method and (4) a less approximate progressive method that uses a rigorous guide tree and consistency score. Other programs, Clustal Omega and UPP, available for large MSAs, were also included into the comparison. The effect of method 2 (chained guide trees) was positive in ContTest but negative in HomFam and OXFam. Methods 3 and 4 increased the benchmark scores more consistently than method 2 for the three datasets, suggesting that they are safer to use. Availability and Implementation:http://mafft.cbrc.jp/alignment/software/ Contact:katoh@ifrec.osaka-u.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

An amino acid index is a set of 20 numerical values representing any of the different physicochemical and biochemical properties of amino adds. As a follow-up to the previous study, we have increased the size of the database, which currently contains 402 published indices, and re-performed the single-linkage cluster analysis. The results basically confirmed the previous findings. Another important feature of amino acids that can be represented numerically is the similarity between them. Thus, a similarity matrix, also called a mutation matrix, is a set of 20×20 numerical values used for protein sequence alignments and similarity searches. We have collected 42 published matrices, performed hierarchical cluster analyses and identified several clusters corresponding to the nature of the data set and the method used for constructing the mutation matrix. Further, we have tried to reproduce each mutation matrix by the combination of amino acid indices in order to understand which properties of amino acids are reflected most. There was a relationship between the PAM units of Dayhoff's mutation matrix and the volume and hydrophobicity of amino adds. The database of 402 amino acid indices and 42 amino acid mutation matrices is made publicly available on the Internet.

The kinetochore forms a dynamic interface with microtubules from the mitotic spindle during mitosis. The Ndc80 complex acts as the key microtubule‐binding complex at kinetochores. However, it is unclear how the Ndc80 complex associates with the inner kinetochore proteins that assemble upon centromeric chromatin. Here, based on a high‐resolution structural analysis, we demonstrate that the N‐terminal region of vertebrate CENP‐T interacts with the ‘RWD' domain in the Spc24/25 portion of the Ndc80 complex. Phosphorylation of CENP‐T strengthens a cryptic hydrophobic interaction between CENP‐T and Spc25 resulting in a phospho‐regulated interaction that occurs without direct recognition of the phosphorylated residue. The Ndc80 complex interacts with both CENP‐T and the Mis12 complex, but we find that these interactions are mutually exclusive, supporting a model in which two distinct pathways target the Ndc80 complex to kinetochores. Our results provide a model for how the multiple protein complexes at kinetochores associate in a phospho‐regulated manner.