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A Tool to Predict Affinity and Specificity of GAG Binding to Proteins
Author(s) -
Desai Umesh R.
Publication year - 2020
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2020.34.s1.01998
Subject(s) - serpin , computational biology , proteases , binding selectivity , chemistry , biology , biochemistry , microbiology and biotechnology , gene , enzyme
Glycosaminoglycans (GAGs) are unusual biopolymers that interact with a large number of proteins that reside intracellularly, extracellularly or on cell membranes. These include growth factors, chemokines/cytokines, serine proteases, serpins, metalloproteases, histone acyltransferases, integrins and other cell membrane receptors. Antithrombin is the only serpin believed to bind GAGs with high specificity. However, our computational algorithm is beginning to challenge this prejudicial assumption. In fact, our computational tool shows that GAG display selectivity from both – chemical as well as biological – perspectives. Alternatively, distinct group of GAG sequences selectively recognize a target protein from the horde of sequences (chemical specificity) and distinct binding sites on a protein uniquely recognize a group of GAG sequences (biological specificity). This understanding has come about from the computational tool we have developed to study GAG – protein interactions. The use of this novel combinatorial virtual library screening (CVLS) tool to predict affinity and specificity of GAG sequences for target proteins will be presented. In principle, the tool could be applied to any protein – GAG system of interest to identify the precise GAG sequence that offers high selectivity and high affinity. Support or Funding Information U01 CA241951; K12 HL141954; R25 HL128639; R01 HL090586