Sperm Chromatin-Condensing Protamine Enhances Smyd5 Thermal Stability

BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS(2021)

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摘要
Studying thermal stability of proteins not only provides insight into protein structure but also is instrumental in identifying previously unknown interaction partners. We develop a machine learning strategy that combines orthogonal partial least squares regression and stability screening of Silver Bullets Bio library to identify biologically active molecules that enhance protein stability. This strategy proves effective in extracting the stability-enhancing molecules for SMYD5, a histone lysine methyltransferase that regulates chromosome integrity. Protamine, a histone substitute in chromatin condensation during spermatogenesis, is identified as the most influential molecule to enhance SMYD5 thermal stability. We find that the C-terminal poly-glutamic acid tract (poly-E) and a 30-residue insertion in MYND domain (M-insertion), which are unique to SMYD5, regulate the structural stability. However, protamine plays a dominant role in SMYD5 stability, and in the presence of protamine, the poly-E tract or M-insertion loses its ability to affect the stability. The stability-enhancing effect of protamine is SMYD5 specific, and for SMYD2, a closely related homolog, protamine exhibits opposite, destabilizing effects. We find that both SMYD5 and SMYD2 interact with protamine, where SMYD5 interaction is independent of the poly-E tract and M-insertion. Protamine not only helps provide insight into the structure-stability relationships of SMYD5, but also suggests a potential functional link of SMYD5 to spermatogenesis. SMYD5 is a ubiq-uitously expressed gene with the highest expression in testis, especially in the seminiferous ducts that contain germ cells. Thus, our study opens up avenues that could help delineate major mechanisms underlying chromatin dynamics during spermatogenesis. (c) 2021 Elsevier Inc. All rights reserved.
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关键词
SMYD5, Thermal stability, SET and MYND domain-containing proteins, Protein-protein interaction, Machine learning, Orthogonal partial least squares regression
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