Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

TFE3 Xp11.2 translocation renal cell carcinoma (TFE3-RCC) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images. In this study, we collect hematoxylin and eosin- stained histopathology whole-slide images of 74 TFE3-RCC cases (the largest cohort to date) and 74 clear cell RCC cases (ccRCC, the most common RCC subtype) with matched gender and tumor grade. An automatic computational pipeline is implemented to extract image features. Comparative study identifies 52 image features with significant differences between TFE3-RCC and ccRCC. Machine learning models are built to distinguish TFE3-RCC from ccRCC. Tests of the classification models on an external validation set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894. Our results suggest that automatically derived image features can capture subtle morphological differences between TFE3-RCC and ccRCC and contribute to a potential guideline for TFE3-RCC diagnosis

Machine Learning in Enzyme Engineering

Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have been increasingly applied to find patterns in data that help predict protein structures, improve enzyme stability, solubility, and function, predict substrate specificity, and guide rational protein design. In this Perspective, we analyze the state of the art in databases and methods used for training and validating predictors in enzyme engineering. We discuss current limitations and challenges which the community is facing and recent advancements in experimental and theoretical methods that have the potential to address those challenges. We also present our view on possible future directions for developing the applications to the design of efficient biocatalysts

iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization

Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development of new predictive models, which depends on the availability of effective tools that support these efforts. We introduce iLearnPlus, the first machine-learning platform with graphical- and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences. iLearnPlus provides a comprehensive set of algorithms and automates sequence-based feature extraction and analysis, construction and deployment of models, assessment of predictive performance, statistical analysis, and data visualization; all without programming. iLearnPlus includes a wide range of feature sets which encode information from the input sequences and over twenty machine-learning algorithms that cover several deep-learning approaches, outnumbering the current solutions by a wide margin. Our solution caters to experienced bioinformaticians, given the broad range of options, and biologists with no programming background, given the point-and-click interface and easy-to-follow design process. We showcase iLearnPlus with two case studies concerning prediction of long noncoding RNAs (lncRNAs) from RNA transcripts and prediction of crotonylation sites in protein chains. iLearnPlus is an open-source platform available at https://github.com/Superzchen/iLearnPlus/ with the webserver at http://ilearnplus.erc.monash.edu/.Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J. Daly, Geoffrey I. Webb, Quanzhi Zhao, Lukasz Kurgan, and Jiangning Son

SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins.

Funder: Mahidol UniversityFunder: College of Arts, Media and Technology, Chiang Mai UniversityFunder: Chiang Mai UniversityFunder: Information Technology Service Center (ITSC) of Chiang Mai UniversityFast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository ( https://github.com/saeed344/SCORPION )

４種類の免疫ペプチド分類問題を解決する機械学習アプローチ

Peptides play an important role in all aspects of the immunological reactions to invading cancer and pathogen cells. It has been known for over 40-years that peptides are critical influences in assembling the immune system against foreign invaders. Since then, new knowledge about the generation and function of peptides in immunology has supported efforts to harness the immune system to treat disease. Yet, with little immunological insight, most of the highly productive treatments, including vaccines, have been developed empirically. Nonetheless, increased knowledge of the biology of antigen processing as well as chemistry and pharmacological properties of antigenic and antimicrobial peptides has now permitted to development of drugs and vaccines. Due to advanced technologies, it is vitally important to develop automatic computational methods for rapidly and accurately predicting immune-peptides. In this thesis, the author focuses on the machine learning approaches for addressing classification problems of four types of immune-peptides (anti-inflammatory, proinflammatory, anti-tuberculosis, and linear B-cell peptides).Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory peptides via biological experiments is often a time consuming and expensive task. The development of novel in silico predictors is desired to classify potential anti-inflammatory peptides prior to in vitro investigation. Herein, an accurate predictor, called PreAIP (Predictor of Anti-Inflammatory Peptides) was developed by integrating multiple complementary features. We systematically investigated different types of features including primary sequence, evolutionary and structural information through a random forest classifier. The final PreAIP model achieved an AUC value of 0.833 in the training dataset via 10-fold cross-validation test, which was better than that of existing models. Moreover, we assessed the performance of the PreAIP with an AUC value of 0.840 on a test dataset to demonstrate that the proposed method outperformed the two existing methods. These results indicated that the PreAIP is an accurate predictor for identifying anti-inflammatory peptides and contributes to the development of anti-inflammatory peptides therapeutics and biomedical research. The curated datasets and the PreAIP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/. A proinflammatory peptide (PIP) is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate a set of informative probabilistic features by making the use of random forest models with eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by the linearly combined models of the informative probabilistic features. The generalization capability of our proposed method evaluated through independent test showed that ProIn-Fuse yielded an accuracy of 0.746, which was over 10% higher than those obtained by the state-of-the-art PIP predictors. Cross-validation and independent results consistently demonstrated that ProIn-Fuse is more precise and promising in the identification of PIPs than existing PIP predictors. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse/. We believe that the proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. Tuberculosis (TB) is a leading killer caused by Mycobacterium tuberculosis. Recently anti-TB peptides have provided an alternative approach to combat antibiotic tolerance. Herein, we have developed an effective computational predictor iAntiTB (identification of anti-tubercular peptides) that integrates multiple feature vectors deriving from the amino acid sequences via Random Forest (RF) and Support Vector Machine (SVM) classifiers. The iAntiTB combined the RF and SVM scores via linear regression to enhance the prediction accuracy. To make a robust and accurate predictor we prepared the two datasets with different types of negative samples. The iAntiTB achieved AUC values of 0.896 and 0.946 on the training datasets of the first and second datasets, respectively. The iAntiTB outperformed the other existing predictors. Thus, the iAntiTB is a robust and accurate predictor that is helpful for researchers working on peptide therapeutics and immunotherapy. All the employed datasets and software application are accessible at http://kurata14.bio.kyutech.ac.jp/iAntiTB/. Linear B-cell peptides are critically important for immunological applications such as vaccine design, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. The accurate identification of linear B-cell peptides remains challenging despite several decades of research. In this work, we have developed a novel predictor, iLBE (Identification of B-Cell Epitope), by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon rank-sum test. Then the random forest (RF) algorithm used the optimal consecutive feature vectors to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. The performance of the final iLBE yielded an AUC score of 0.809 on the training dataset. It outperformed other existing prediction models on a comprehensive independent dataset. The iLBE is suggested to be a powerful computational tool to identify the linear B-cell peptides and development of penetrating diagnostic tests. A web application with curated datasets is freely accessible of iLBE at http://kurata14.bio.kyutech.ac.jp/iLBE/. Taken together, the above results suggest that our proposed predictors (PreAIP, ProIn-Fuse, iAntiTB, and iLBE) would be helpful computational resources for the prediction of anti-inflammatory, pro-inflammatory, tuberculosis, and linear B-cell peptides. / ペプチドは、癌や病原体細胞に対する免疫反応のあらゆる側面で重要な役割を果たす。ペプチドが外来の侵入物に対する免疫系を起動する上で決定的な影響を与えることは40年以上前から知られている。それ以来、免疫学におけるペプチドの生成と機能に関する新しい知見は、病気を治療するために免疫系を利用する研究を支えてきた。依然として、免疫学的洞察がほとんどないため、ワクチンを含む効率的治療法のほとんどは、経験的に開発されている。それでもなお、抗原プロセシングの生物学、ならびに抗原性および抗菌性ペプチドの化学・薬理学に関する知見の増加により、現在、薬物およびワクチンの開発が可能になっている。高度な技術により、免疫ペプチドを迅速かつ正確に予測するためのコンピュータ技術を開発することが非常に重要である。この論文では、著者は4種類の免疫ペプチド（抗炎症、炎症誘発性、抗結核、および線形B細胞エピトープ）の分類問題に対処するための機械学習アプローチに焦点を当てる。炎症性疾患および自己免疫疾患に対する治療用ペプチドは、多くの検討がなされてきた。しかし、生物学的実験による抗炎症ペプチドの探索は、多くの場合、時間と費用のかかる作業である。新しいin siloco予測器の開発は、in vitro実験に先立って、潜在的な抗炎症ペプチドを同定するために望まれている。ここでは、PreAIP（抗炎症ペプチドの予測器）と呼ばれる予測器が、複数の補完的機能を統合することによって開発された。一次配列、進化的および構造的情報を含むさまざまなタイプの特徴量を、ランダムフォレスト分類器を介して抽出した。最終的なPreAIPモデルは、10分割交差検定によるトレーニングデータセットで0.833のAUC値を達成した。これは、既存のモデルよりも優れた値である。さらに、独立の検証用データセットでAUC値0.840を達成し、提案された方法が2つの既存の予測器よりも優れていることを示した。これらの結果は、PreAIPが抗炎症ペプチドを同定するための正確な予測器であり、抗炎症ペプチド治療および生物医学研究の開発に貢献した。用いたデータセットとPreAIPは、http：//kurata14.bio.kyutech.ac.jp/PreAIP/から自由に利用できる。炎症誘発性ペプチド（PIP）は、免疫細胞から分泌されるシグナル伝達分子の一種であり、侵入する病原体に対する防御の第一線を担当する。多くの実験により、PIPはワクチンや免疫療法薬などにおいて重要な役割を果たすことが示されている。ハイスループットな生物実験に時間と費用が掛かることを考えると、効率的なコンピュータ予測は、PIPを短時間にかつ正確に特定するために大きな需要がある。したがって、この研究では、PIP識別性能を向上させるために、ProIn-Fuseと呼ばれる複数の特徴表現を組み合わせた計算モデルを提案した。具体的には、特徴表現学習モデルを利用して、8つのシーケンスエンコーディングスキームを備えたランダムフォレストモデルを利用することにより、確率的予測スコアを計算した。ProIn-Fuseは、確率的予測スコアの線形結合モデルによって構築された。提案手法の汎化性能を独立したテストデータで評価した結果、ProIn-Fuseの精度は0.746であり、これは最新のPIP予測器によって得られた精度よりも10％以上高かった。テストデータによる検証結果は、ProIn-Fuseが既存のPIP予測器よりも正確にPIP識別できることを示した。Webサーバー、データセット、および説明書は、http：//kurata14.bio.kyutech.ac.jp/ProIn-Fuse/から自由にアクセスできる。ProIn-Fuseは、ドラッグデザイン含む幅広いアプリケーションに応用できる。結核（TB）は、結核菌によって引き起こされる疾患である。最近、抗結核ペプチドは抗生物質耐性に対抗するための代替アプローチを提供している。ここでは、ランダムフォレスト（RF）およびサポートベクターマシン（SVM）分類器を用いてアミノ酸配列に由来する複数の特徴ベクトルを統合する効果的な予測器iAntiTB（抗結核ペプチドの識別）を開発した。iAntiTBは、線形回帰を介してRFスコアとSVMスコアを組み合わせて、予測精度を向上させた。ロバストで正確な予測器を作成するために、異なるタイプのネガティブサンプルを使用して2つのデータセットを準備した。iAntiTBは、1番目と2番目のデータセットのトレーニングデータセットでそれぞれ0.896と0.946のAUC値を達成した。iAntiTBは、他の既存の予測器の性能を上回った。このように、iAntiTBは、ペプチド治療および免疫療法に取り組んでいる研究者に役立つロバストで正確な予測器である。利用されたすべてのデータセットとソフトウェアアプリケーションは、http：//kurata14.bio.kyutech.ac.jp/iAntiTB/から自由にアクセスできる。線形B細胞エピトープは、ワクチンの設計、免疫診断テスト、抗体産生、疾患の診断や治療などの免疫学的応用に非常に重要である。線形B細胞エピトープの正確な同定は、数十年の研究にもかかわらず、依然として挑戦的課題のままである。本研究では、配列の進化的特徴や物理化学的特徴等を統合することにより、新規な線形B細胞エピトープ予測モデル（iLBE）を開発した。Wilcoxon順位和検定によって最適化した特徴ベクトル群をランダムフォレスト（RF）アルゴリズムを用いて学習して、線形B細胞エピトープの予測スコアを計算した。ロジスティック回帰を用いてRFスコアを組合せて、予測精度を高めた。iLBEは、トレーニングデータセットで0.809のAUCを達成し、独立のテストデータセットを用いた検定では、既存の予測モデルの性能を超えた。線形B細胞エピトープを同定する強力な計算ツールであるiLBEは、診断テストの開発に有用である。注釈付きデータセットを備えたiLBEモデルのウエブアプリケーションは自由にアクセスできるhttp://kurata14.bio.kyutech.ac.jp/iLBE/。九州工業大学博士学位論文 学位記番号：情工博甲第358号 学位授与年月日：令和3年3月25日1 Introduction|2 Prediction of Anti-Inflammatory Peptides by Integrating Mulptle Complementary Features|3 Prediction of Proinflammatory Peptides by Fusing of Multiple Feature Representations|4 Prediction of Anti-Tubercular Peptides by Exploiting Amino Acid Pattern and Properties|5 Prediction of Linear B-Cell Epitopes by Integrating Sequence and Evolutionary Features|6 Conclusions and Perspectives九州工業大学令和2年

iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets

The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.Zhen Chen, Xuhan Liu, Pei Zhao, Chen Li, Yanan Wang, Fuyi Li, Tatsuya Akutsu, Chris Bain, Robin B. Gasser, Junzhou Li, Zuoren Yang, Xin Gao, Lukasz Kurgan, and Jiangning Son

Systematic identification of the lysine methylome using methyllysine binding domains

Post-translational modifications (PTM) are vital regulators of protein function and homeostasis. The role of dynamic regulations of non-histone lysine methylated proteins (NHKMP) recently began to be recognized in DNA damage repair, apoptosis and transcriptional pathways. My goal was to identify components of the NHKMP network to understand its importance in a healthy versus diseased cellular state. I used membrane peptide arrays to systematically characterize nine naturally occurring lysine methyl binding domains (KMBD). Five KMBDs were chosen based on their overlapping specificities to achieve maximum coverage of lysine methylated peptides. These five KMBDs was used to enrich for methylated lysine peptides from a trypsinized HEK293 cell lysate and followed by mass spectrometry identification. We identified 229 NHKMP and 301 novel sites from HEK293. The amount of NHKMPs and sites that I have identified in total was unprecedented: this allows us to gain valuable insights into components of the lysine methylome network

Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites

As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning (DL) network classifier based on long short-term memory (LSTM) with word embedding (LSTMWE) for the prediction of mammalian malonylation sites. LSTMWE performs better than traditional classifiers developed with common pre-defined feature encodings or a DL classifier based on LSTM with a one-hot vector. The performance of LSTMWE is sensitive to the size of the training set, but this limitation can be overcome by integration with a traditional machine learning (ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTMWE and the random forest classifier with a novel encoding of enhanced amino acid content. LEMP performs not only better than the individual classifiers but also superior to the currently-available malonylation predictors. Additionally, it demonstrates a promising performance with a low false positive rate, which is highly useful in the prediction application. Overall, LEMP is a useful tool for easily identifying malonylation sites with high confidence. LEMP is available at http://www.bioinfogo.org/lemp. Keywords: Deep learning, Recurrent neural network, LSTM, Malonylation, Random fores