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Article ; Online: MolSHAP: Interpreting Quantitative Structure-Activity Relationships Using Shapley Values of R-Groups.

Tian, Tingzhong / Li, Shuya / Fang, Meng / Zhao, Dan / Zeng, Jianyang

Journal of chemical information and modeling

2023 Volume 64, Issue 7, Page(s) 2236–2249

Abstract: Optimizing the activities and properties of lead compounds is an essential step in the drug discovery process. Despite recent advances in machine learning-aided drug discovery, most of the existing methods focus on making predictions for the desired ... ...

Abstract	Optimizing the activities and properties of lead compounds is an essential step in the drug discovery process. Despite recent advances in machine learning-aided drug discovery, most of the existing methods focus on making predictions for the desired objectives directly while ignoring the explanations for predictions. Although several techniques can provide interpretations for machine learning-based methods such as feature attribution, there are still gaps between these interpretations and the principles commonly adopted by medicinal chemists when designing and optimizing molecules. Here, we propose an interpretation framework, named MolSHAP, for quantitative structure-activity relationship analysis by estimating the contributions of R-groups. Instead of attributing the activities to individual input features, MolSHAP regards the R-group fragments as the basic units of interpretation, which is in accordance with the fragment-based modifications in molecule optimization. MolSHAP is a model-agnostic method that can interpret activity regression models with arbitrary input formats and model architectures. Based on the evaluations of numerous representative activity regression models on a specially designed R-group ranking task, MolSHAP achieved significantly better interpretation power compared with other methods. In addition, we developed a compound optimization algorithm based on MolSHAP and illustrated the reliability of the optimized compounds using an independent case study. These results demonstrated that MolSHAP can provide a useful tool for accurately interpreting the quantitative structure-activity relationships and rationally optimizing the compound activities in drug discovery.
MeSH term(s)	Quantitative Structure-Activity Relationship ; Reproducibility of Results ; Drug Discovery/methods ; Algorithms ; Machine Learning
Language	English
Publishing date	2023-08-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.3c00465
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A probabilistic knowledge graph for target identification.

Liu, Chang / Xiao, Kaimin / Yu, Cuinan / Lei, Yipin / Lyu, Kangbo / Tian, Tingzhong / Zhao, Dan / Zhou, Fengfeng / Tang, Haidong / Zeng, Jianyang

PLoS computational biology

2024 Volume 20, Issue 4, Page(s) e1011945

Abstract: Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On ... ...

Abstract	Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.
MeSH term(s)	Humans ; Computational Biology/methods ; Drug Discovery/methods ; Machine Learning ; Neural Networks, Computer ; Algorithms ; Melanoma ; Probability ; Colorectal Neoplasms
Language	English
Publishing date	2024-04-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1011945
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Article ; Online: PocketAnchor: Learning structure-based pocket representations for protein-ligand interaction prediction.

Li, Shuya / Tian, Tingzhong / Zhang, Ziting / Zou, Ziheng / Zhao, Dan / Zeng, Jianyang

Cell systems

2023 Volume 14, Issue 8, Page(s) 692–705.e6

Abstract: Protein-ligand interactions are essential for cellular activities and drug discovery processes. Appropriately and effectively representing protein features is of vital importance for developing computational approaches, especially data-driven methods, ... ...

Abstract	Protein-ligand interactions are essential for cellular activities and drug discovery processes. Appropriately and effectively representing protein features is of vital importance for developing computational approaches, especially data-driven methods, for predicting protein-ligand interactions. However, existing approaches may not fully investigate the features of the ligand-occupying regions in the protein pockets. Here, we design a structure-based protein representation method, named PocketAnchor, for capturing the local environmental and spatial features of protein pockets to facilitate protein-ligand interaction-related learning tasks. We define "anchors" as probe points reaching into the cavities and those located near the surface of proteins, and we design a specific message passing strategy for gathering local information from the atoms and surface neighboring these anchors. Comprehensive evaluation of our method demonstrated its successful applications in pocket detection and binding affinity prediction, which indicated that our anchor-based approach can provide effective protein feature representations for improving the prediction of protein-ligand interactions.
MeSH term(s)	Binding Sites ; Ligands ; Algorithms ; Proteins/metabolism
Chemical Substances	Ligands ; Proteins
Language	English
Publishing date	2023-07-28
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2854138-8
ISSN	2405-4720 ; 2405-4712
ISSN (online)	2405-4720
ISSN	2405-4712
DOI	10.1016/j.cels.2023.05.005
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Article ; Online: Full-length ribosome density prediction by a multi-input and multi-output model.

Tian, Tingzhong / Li, Shuya / Lang, Peng / Zhao, Dan / Zeng, Jianyang

PLoS computational biology

2021 Volume 17, Issue 3, Page(s) e1008842

Abstract: Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at ... ...

Abstract	Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at codon resolution, the regulatory codes of elongation dynamics are still not fully understood. Most of the existing computational approaches for modeling translation elongation from ribosome profiling data mainly focus on local contextual patterns, while ignoring the continuity of the elongation process and relations between ribosome densities of remote codons. Modeling the translation elongation process in full-length coding sequence (CDS) level has not been studied to the best of our knowledge. In this paper, we developed a deep learning based approach with a multi-input and multi-output framework, named RiboMIMO, for modeling the ribosome density distributions of full-length mRNA CDS regions. Through considering the underlying correlations in translation efficiency among neighboring and remote codons and extracting hidden features from the input full-length coding sequence, RiboMIMO can greatly outperform the state-of-the-art baseline approaches and accurately predict the ribosome density distributions along the whole mRNA CDS regions. In addition, RiboMIMO explores the contributions of individual input codons to the predictions of output ribosome densities, which thus can help reveal important biological factors influencing the translation elongation process. The analyses, based on our interpretable metric named codon impact score, not only identified several patterns consistent with the previously-published literatures, but also for the first time (to the best of our knowledge) revealed that the codons located at a long distance from the ribosomal A site may also have an association on the translation elongation rate. This finding of long-range impact on translation elongation velocity may shed new light on the regulatory mechanisms of protein synthesis. Overall, these results indicated that RiboMIMO can provide a useful tool for studying the regulation of translation elongation in the range of full-length CDS.
MeSH term(s)	Codon/genetics ; Codon/metabolism ; Computational Biology/methods ; Deep Learning ; Escherichia coli/genetics ; Models, Genetic ; Peptide Chain Elongation, Translational/genetics ; RNA, Messenger/chemistry ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; Ribosomes/genetics ; Ribosomes/metabolism ; Saccharomyces cerevisiae/genetics
Chemical Substances	Codon ; RNA, Messenger
Language	English
Publishing date	2021-03-26
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1008842
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Article ; Online: Network-based screening identifies sitagliptin as an antitumor drug targeting dendritic cells.

Ng, Ian-Ian / Zhang, Jiaqi / Tian, Tingzhong / Peng, Qi / Huang, Zheng / Xiao, Kaimin / Yao, Xiyue / Ng, Lui / Zeng, Jianyang / Tang, Haidong

Journal for immunotherapy of cancer

2024 Volume 12, Issue 3

Abstract: Background: Dendritic cell (DC)-mediated antigen presentation is essential for the priming and activation of tumor-specific T cells. However, few drugs that specifically manipulate DC functions are available. The identification of drugs targeting DC ... ...

Abstract	Background: Dendritic cell (DC)-mediated antigen presentation is essential for the priming and activation of tumor-specific T cells. However, few drugs that specifically manipulate DC functions are available. The identification of drugs targeting DC holds great promise for cancer immunotherapy. Methods: We observed that type 1 conventional DCs (cDC1s) initiated a distinct transcriptional program during antigen presentation. We used a network-based approach to screen for cDC1-targeting therapeutics. The antitumor potency and underlying mechanisms of the candidate drug were investigated in vitro and in vivo. Results: Sitagliptin, an oral gliptin widely used for type 2 diabetes, was identified as a drug that targets DCs. In mouse models, sitagliptin inhibited tumor growth by enhancing cDC1-mediated antigen presentation, leading to better T-cell activation. Mechanistically, inhibition of dipeptidyl peptidase 4 (DPP4) by sitagliptin prevented the truncation and degradation of chemokines/cytokines that are important for DC activation. Sitagliptin enhanced cancer immunotherapy by facilitating the priming of antigen-specific T cells by DCs. In humans, the use of sitagliptin correlated with a lower risk of tumor recurrence in patients with colorectal cancer undergoing curative surgery. Conclusions: Our findings indicate that sitagliptin-mediated DPP4 inhibition promotes antitumor immune response by augmenting cDC1 functions. These data suggest that sitagliptin can be repurposed as an antitumor drug targeting DC, which provides a potential strategy for cancer immunotherapy.
MeSH term(s)	Mice ; Animals ; Humans ; Dipeptidyl Peptidase 4/metabolism ; Dendritic Cells ; Sitagliptin Phosphate/pharmacology ; Sitagliptin Phosphate/therapeutic use ; Sitagliptin Phosphate/metabolism ; Diabetes Mellitus, Type 2 ; Neoplasms ; Antigen Presentation ; Antineoplastic Agents/pharmacology ; Antineoplastic Agents/therapeutic use
Chemical Substances	Dipeptidyl Peptidase 4 (EC 3.4.14.5) ; Sitagliptin Phosphate (TS63EW8X6F) ; Antineoplastic Agents
Language	English
Publishing date	2024-03-07
Publishing country	England
Document type	Journal Article
ZDB-ID	2719863-7
ISSN	2051-1426 ; 2051-1426
ISSN (online)	2051-1426
ISSN	2051-1426
DOI	10.1136/jitc-2023-008254
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Modeling multi-species RNA modification through multi-task curriculum learning.

Xiong, Yuanpeng / He, Xuan / Zhao, Dan / Tian, Tingzhong / Hong, Lixiang / Jiang, Tao / Zeng, Jianyang

Nucleic acids research

2021 Volume 49, Issue 7, Page(s) 3719–3734

Abstract: N6-methyladenosine (m6A) is the most pervasive modification in eukaryotic mRNAs. Numerous biological processes are regulated by this critical post-transcriptional mark, such as gene expression, RNA stability, RNA structure and translation. Recently, ... ...

Abstract	N6-methyladenosine (m6A) is the most pervasive modification in eukaryotic mRNAs. Numerous biological processes are regulated by this critical post-transcriptional mark, such as gene expression, RNA stability, RNA structure and translation. Recently, various experimental techniques and computational methods have been developed to characterize the transcriptome-wide landscapes of m6A modification for understanding its underlying mechanisms and functions in mRNA regulation. However, the experimental techniques are generally costly and time-consuming, while the existing computational models are usually designed only for m6A site prediction in a single-species and have significant limitations in accuracy, interpretability and generalizability. Here, we propose a highly interpretable computational framework, called MASS, based on a multi-task curriculum learning strategy to capture m6A features across multiple species simultaneously. Extensive computational experiments demonstrate the superior performances of MASS when compared to the state-of-the-art prediction methods. Furthermore, the contextual sequence features of m6A captured by MASS can be explained by the known critical binding motifs of the related RNA-binding proteins, which also help elucidate the similarity and difference among m6A features across species. In addition, based on the predicted m6A profiles, we further delineate the relationships between m6A and various properties of gene regulation, including gene expression, RNA stability, translation, RNA structure and histone modification. In summary, MASS may serve as a useful tool for characterizing m6A modification and studying its regulatory code. The source code of MASS can be downloaded from https://github.com/mlcb-thu/MASS.
MeSH term(s)	Adenosine/analogs & derivatives ; Adenosine/chemistry ; Animals ; Databases, Genetic ; Datasets as Topic ; Gene Expression Regulation ; Humans ; Machine Learning ; RNA/chemistry ; RNA-Binding Proteins ; Sequence Analysis, RNA ; Software ; Transcriptome
Chemical Substances	RNA-Binding Proteins ; RNA (63231-63-0) ; N-methyladenosine (CLE6G00625) ; Adenosine (K72T3FS567)
Language	English
Publishing date	2021-03-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkab124
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Zs.A 1067: Show issues

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Article: EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction.

Wan, Fangping / Li, Shuya / Tian, Tingzhong / Lei, Yipin / Zhao, Dan / Zeng, Jianyang

Frontiers in pharmacology

2020 Volume 11, Page(s) 112

Abstract: Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be ...

Abstract	Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be conserved in humans, most SL interactions remain cell-line specific. Here, we demonstrated that the cell-line-specific gene expression profiles derived from the shRNA perturbation experiments performed in the LINCS L1000 project can provide useful features for predicting SL interactions in human. In this paper, we developed a semi-supervised neural network-based method called EXP2SL to accurately identify SL interactions from the L1000 gene expression profiles. Through a systematic evaluation on the SL datasets of three different cell lines, we demonstrated that our model achieved better performance than the baseline methods and verified the effectiveness of using the L1000 gene expression features and the semi-supervise training technique in SL prediction.
Language	English
Publishing date	2020-02-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587355-6
ISSN	1663-9812
ISSN	1663-9812
DOI	10.3389/fphar.2020.00112
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Article ; Online: A deep-learning framework for multi-level peptide-protein interaction prediction.

Lei, Yipin / Li, Shuya / Liu, Ziyi / Wan, Fangping / Tian, Tingzhong / Li, Shao / Zhao, Dan / Zeng, Jianyang

Nature communications

2021 Volume 12, Issue 1, Page(s) 5465

Abstract: Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide- ... ...

Abstract	Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Here, we present a deep learning framework for multi-level peptide-protein interaction prediction, called CAMP, including binary peptide-protein interaction prediction and corresponding peptide binding residue identification. Comprehensive evaluation demonstrated that CAMP can successfully capture the binary interactions between peptides and proteins and identify the binding residues along the peptides involved in the interactions. In addition, CAMP outperformed other state-of-the-art methods on binary peptide-protein interaction prediction. CAMP can serve as a useful tool in peptide-protein interaction prediction and identification of important binding residues in the peptides, which can thus facilitate the peptide drug discovery process.
MeSH term(s)	Algorithms ; Binding Sites ; Computational Biology/methods ; Deep Learning ; Models, Molecular ; Peptides/chemistry ; Peptides/metabolism ; Protein Binding ; Protein Domains ; Proteins/chemistry ; Proteins/metabolism ; Reproducibility of Results
Chemical Substances	Peptides ; Proteins
Language	English
Publishing date	2021-09-15
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-25772-4
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Article ; Online: Understanding the phase separation characteristics of nucleocapsid protein provides a new therapeutic opportunity against SARS-CoV-2.

Protein & cell

2021 Volume 12, Issue 9, Page(s) 734–740

MeSH term(s)	COVID-19 ; Coronavirus Nucleocapsid Proteins ; Humans ; Nucleocapsid Proteins ; Phosphoproteins ; SARS-CoV-2
Chemical Substances	Coronavirus Nucleocapsid Proteins ; Nucleocapsid Proteins ; Phosphoproteins
Language	English
Publishing date	2021-03-26
Publishing country	Germany
Document type	Letter ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	2543451-2
ISSN	1674-8018 ; 1674-8018
ISSN (online)	1674-8018
ISSN	1674-8018
DOI	10.1007/s13238-021-00832-z
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Article ; Online: A community challenge for a pancancer drug mechanism of action inference from perturbational profile data.

Douglass, Eugene F / Allaway, Robert J / Szalai, Bence / Wang, Wenyu / Tian, Tingzhong / Fernández-Torras, Adrià / Realubit, Ron / Karan, Charles / Zheng, Shuyu / Pessia, Alberto / Tanoli, Ziaurrehman / Jafari, Mohieddin / Wan, Fangping / Li, Shuya / Xiong, Yuanpeng / Duran-Frigola, Miquel / Bertoni, Martino / Badia-I-Mompel, Pau / Mateo, Lídia /

Guitart-Pla, Oriol / Chung, Verena / Tang, Jing / Zeng, Jianyang / Aloy, Patrick / Saez-Rodriguez, Julio / Guinney, Justin / Gerhard, Daniela S / Califano, Andrea

Cell reports. Medicine

2022 Volume 3, Issue 1, Page(s) 100492

Abstract: The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific ... ...

Abstract	The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for
MeSH term(s)	Algorithms ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Humans ; Neoplasms/drug therapy ; Neural Networks, Computer ; Polypharmacology ; Protein Kinases/metabolism ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; Transcription, Genetic
Chemical Substances	RNA, Messenger ; Protein Kinases (EC 2.7.-)
Language	English
Publishing date	2022-01-18
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2666-3791
ISSN (online)	2666-3791
DOI	10.1016/j.xcrm.2021.100492
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