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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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FusionPDB: a knowledgebase of human fusion proteins

Himansu Kumar1, Lin-Ya Tang1, Chengyuan Yang2

  • 1Department of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

Nucleic Acids Research
|October 23, 2023
PubMed

Insights

FusionPDB provides a comprehensive database of human fusion protein sequences and 3D structures, aiding the development of targeted cancer therapeutics. This resource addresses critical gaps in understanding fusion proteins for novel drug discovery.

Area of Science:

  • Computational biology and bioinformatics
  • Structural biology
  • Cancer genomics

Background:

  • Fusion proteins, resulting from gene fusions, play significant roles in tumorigenesis and other cellular processes.
  • Targeting fusion proteins offers therapeutic potential, but a lack of sequence and structural data hinders drug development.
  • Existing resources lack comprehensive information on the complete sequences and 3D structures of human fusion proteins.

Purpose of the Study:

  • To develop a computational pipeline and resource for human fusion proteins.
  • To provide comprehensive data on human fusion protein sequences and 3D structures.
  • To facilitate the development of targeted therapeutic strategies against fusion proteins.

Main Methods:

  • Development of a computational pipeline for identifying and analyzing fusion proteins.
  • Creation of FusionPDB, a curated database of human fusion protein sequences and 3D structures.
  • Integration of protein structure prediction, quality assessment (pLDDT scores), and virtual screening data.

Main Results:

  • FusionPDB contains over 43,000 fusion protein sequences, including 14,700 in-frame fusion genes.
  • The database includes over 3,500 unique fusion protein 3D structures.
  • pLDDT score analysis and virtual screening outcomes are provided for selected fusion proteins.

Conclusions:

  • FusionPDB is a unique resource offering complete 3D structures and comprehensive data on human fusion proteins.
  • This resource addresses critical knowledge gaps, enabling further research and development of targeted therapeutics.
  • FusionPDB will be continuously updated to encompass all human fusion proteins.

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