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STAM: simple transmembrane alignment method.

Yinon Shafrir1, H Robert Guy

  • 1National Cancer Institute, LECB, MSC 5677, 12 South Drive, Bethesda, MD 20892-5677, USA. yinon@nih.gov

Bioinformatics (Oxford, England)
|January 31, 2004
PubMed
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A new alignment program, STAM, improves transmembrane protein modeling by accounting for distinct segment properties. This specialized tool enhances accuracy in homology modeling for transmembrane proteins.

Area of Science:

  • Biochemistry
  • Structural Biology
  • Bioinformatics

Background:

  • The limited number of known transmembrane protein (TMP) structures hinders accurate molecular modeling.
  • Existing alignment algorithms, developed for globular proteins, perform poorly on TMP sequences.
  • Homology modeling requires accurate sequence alignment to template structures.

Purpose of the Study:

  • To develop an automated alignment procedure specifically designed for transmembrane proteins.
  • To improve the accuracy of homology modeling for TMPs by addressing their unique structural characteristics.

Main Methods:

  • Identification of potential transmembrane alpha-helical segments.
  • Application of distinct alignment criteria for transmembrane and non-transmembrane regions.

Related Experiment Videos

  • Utilizing different substitution matrices and penalties for insertions/deletions based on segment type.

    • Main Results:

    • The developed program, STAM, is the first multisequence alignment tool tailored for TMPs.
    • STAM improves protein models by recognizing and applying different physical properties to various protein segments.
    • This approach leads to more accurate models compared to treating the protein as a uniform entity.

    Conclusions:

    • STAM offers a significant advancement in TMP sequence alignment.
    • The program enhances the accuracy of homology modeling for transmembrane proteins.
    • Specialized algorithms are crucial for effectively modeling proteins with distinct structural domains.