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1. Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors

   https://doi.org/10.1093/bioinformatics/btz336  

   Wu, Jiansheng ; Liu, Ben ; Chan, Wallace K. B. ; Wu, Weijian ; Pang, Tao ; Hu, Haifeng ; Yan, Shancheng ; Ke, Xiaoyan ; Zhang, Yang ( July 2019 , Bioinformatics)

   Accurate prediction and interpretation of ligand bioactivities are essential for virtual screening and drug discovery. Unfortunately, many important drug targets lack experimental data about the ligand bioactivities; this is particularly true for G protein-coupled receptors (GPCRs), which account for the targets of about a third of drugs currently on the market. Computational approaches with the potential of precise assessment of ligand bioactivities and determination of key substructural features which determine ligand bioactivities are needed to address this issue.

   A new method, SED, was proposed to predict ligand bioactivities and to recognize key substructures associated with GPCRs through the coupling of screening for Lasso of long extended-connectivity fingerprints (ECFPs) with deep neural network training. The SED pipeline contains three successive steps: (i) representation of long ECFPs for ligand molecules, (ii) feature selection by screening for Lasso of ECFPs and (iii) bioactivity prediction through a deep neural network regression model. The method was examined on a set of 16 representative GPCRs that cover most subfamilies of human GPCRs, where each has 300–5000 ligand associations. The results show that SED achieves excellent performance in modelling ligand bioactivities, especially for those in the GPCR datasets without sufficient ligand associations, where SED improved the baseline predictors by 12% in correlation coefficient (r2) and 19% in root mean square error. Detail data analyses suggest that the major advantage of SED lies on its ability to detect substructures from long ECFPs which significantly improves the predictive performance.

   The source code and datasets of SED are freely available at https://zhanglab.ccmb.med.umich.edu/SED/.

   Supplementary data are available at Bioinformatics online.

    

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2. DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites

   https://doi.org/10.1093/bioinformatics/btz721  

   Li, Fuyi ; Chen, Jinxiang ; Leier, André ; Marquez-Lago, Tatiana ; Liu, Quanzhong ; Wang, Yanze ; Revote, Jerico ; Smith, A. Ian ; Akutsu, Tatsuya ; Webb, Geoffrey I. ; et al ( September 2019 , Bioinformatics)

   Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the ‘life and death’ cellular processes, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases’ functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events.

   We propose DeepCleave, the first deep learning-based predictor of protease-specific substrates and cleavage sites. DeepCleave uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of our models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites.

   The DeepCleave webserver and source code are freely available at http://deepcleave.erc.monash.edu/.

   Supplementary data are available at Bioinformatics online.

    

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3. KGML-xDTD: a knowledge graph–based machine learning framework for drug treatment prediction and mechanism description

   https://doi.org/10.1093/gigascience/giad057  

   Ma, Chunyu ; Zhou, Zhihan ; Liu, Han ; Koslicki, David ( August 2023 , GigaScience)

   Computational drug repurposing is a cost- and time-efficient approach that aims to identify new therapeutic targets or diseases (indications) of existing drugs/compounds. It is especially critical for emerging and/or orphan diseases due to its cheaper investment and shorter research cycle compared with traditional wet-lab drug discovery approaches. However, the underlying mechanisms of action (MOAs) between repurposed drugs and their target diseases remain largely unknown, which is still a main obstacle for computational drug repurposing methods to be widely adopted in clinical settings.

   In this work, we propose KGML-xDTD: a Knowledge Graph–based Machine Learning framework for explainably predicting Drugs Treating Diseases. It is a 2-module framework that not only predicts the treatment probabilities between drugs/compounds and diseases but also biologically explains them via knowledge graph (KG) path-based, testable MOAs. We leverage knowledge-and-publication–based information to extract biologically meaningful “demonstration paths” as the intermediate guidance in the Graph-based Reinforcement Learning (GRL) path-finding process. Comprehensive experiments and case study analyses show that the proposed framework can achieve state-of-the-art performance in both predictions of drug repurposing and recapitulation of human-curated drug MOA paths.

   KGML-xDTD is the first model framework that can offer KG path explanations for drug repurposing predictions by leveraging the combination of prediction outcomes and existing biological knowledge and publications. We believe it can effectively reduce “black-box” concerns and increase prediction confidence for drug repurposing based on predicted path-based explanations and further accelerate the process of drug discovery for emerging diseases.

    

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4. GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

   https://doi.org/10.1186/s13321-021-00540-0  

   Liu, Guannan ; Singha, Manali ; Pu, Limeng ; Neupane, Prasanga ; Feinstein, Joseph ; Wu, Hsiao-Chun ; Ramanujam, J. ; Brylinski, Michal ( December 2021 , Journal of Cheminformatics)

   Abstract Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioning opportunities. 

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5. Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

   https://doi.org/10.1093/bioinformatics/btz328  

   Chen, Muhao ; Ju, Chelsea J. -T ; Zhou, Guangyu ; Chen, Xuelu ; Zhang, Tianran ; Chang, Kai-Wei ; Zaniolo, Carlo ; Wang, Wei ( July 2019 , Bioinformatics)

   Sequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.

   We present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.

   The implementation is available at https://github.com/muhaochen/seq_ppi.git.

   Supplementary data are available at Bioinformatics online.

    

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