STS-NLSP

STS-NLSP (Substrate-Transporter Specificity prediction using Natural Language and Sequence Processing) is a computational tool that predicts the specificity of substrate drugs for membrane transport proteins. Key features of STS-NLSP include:

- Aims to streamline the process of profiling a drug's specificity for various transporters, which is important for understanding pharmacokinetics, drug resistance in cancer, and drug discovery
- Utilizes both natural language processing of relevant literature and sequence-based features of the drugs and transporters
- Provides a faster, more efficient alternative to labor-intensive experimental methods for determining drug-transporter specificity
- Could aid in drug development by predicting potential interactions, resistance mechanisms, and targets early in the discovery process
- Relevant for cancer therapeutics, as many anti-cancer drugs are substrates for membrane transporters involved in resistance

Topic

Drug metabolism;Drug discovery;Ontology and terminology;Small molecules;Molecular modelling

Detail

  • Operation: Phasing;Protein fragment weight comparison;Sequence tagged site (STS) mapping

  • Software interface: Command-line user interface

  • Language: Python

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: The National Key Research Program, the National Natural Science Foundation of China, and the Shanghai Jiao Tong University School of Medicine.

  • Input: -

  • Output: -

  • Contact: Yi Xiong xiongyi@sjtu.edu.cn ,Dong-Qing Wei dqwei@sjtu.edu.cn

  • Collection: -

  • Maturity: -

Publications

  • STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity.
  • Wang X, et al. STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity. STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity. 2019; 7:306. doi: 10.3389/fbioe.2019.00306
  • https://doi.org/10.3389/FBIOE.2019.00306
  • PMID: 31781551
  • PMC: PMC6851049

Download and documentation

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