Deep learning for de-convolution of Smad2 versus Smad3 binding sites.

BACKGROUND: The transforming growth factor beta-1 (TGF ß-1) cytokine exerts both pro-tumor and anti-tumor effects in carcinogenesis. An increasing body of literature suggests that TGF ß-1 signaling outcome is partially dependent on the regulatory targets of downstream receptor-regulated Smad (R-Smad) proteins Smad2 and Smad3. However, the lack of Smad-specific antibodies for ChIP-seq hinders convenient identification of Smad-specific binding sites. RESULTS: In this study, we use localization and affinity purification (LAP) tags to identify Smad-specific binding sites in a cancer cell line. Using ChIP-seq data obtained from LAP-tagged Smad proteins, we develop a convolutional neural network with long-short term memory (CNN-LSTM) as a deep learning approach to classify a pool of Smad-bound sites as being Smad2- or Smad3-bound. Our data showed that this approach is able to accurately classify Smad2- versus Smad3-bound sites. We use our model to dissect the role of each R-Smad in the progression of breast cancer using a previously published dataset. CONCLUSIONS: Our results suggests that deep learning approaches can be used to dissect binding site specificity of closely related transcription factors.

R248Q mutation--Beyond p53-DNA binding.

R248 in the DNA binding domain (DBD) of p53 interacts directly with the minor groove of DNA. Earlier nuclear magnetic resonance (NMR) studies indicated that the R248Q mutation resulted in conformation changes in parts of DBD far from the mutation site. However, how information propagates from the mutation site to the rest of the DBD is still not well understood. We performed a series of all-atom molecular dynamics (MD) simulations to dissect sterics and charge effects of R248 on p53-DBD conformation: (i) wild-type p53 DBD; (ii) p53 DBD with an electrically neutral arginine side-chain; (iii) p53 DBD with R248A; (iv) p53 DBD with R248W; and (v) p53 DBD with R248Q. Our results agree well with experimental observations of global conformational changes induced by the R248Q mutation. Our simulations suggest that both charge- and sterics are important in the dynamics of the loop (L3) where the mutation resides. We show that helix 2 (H2) dynamics is altered as a result of a change in the hydrogen bonding partner of D281. In turn, neighboring L1 dynamics is altered: in mutants, L1 predominantly adopts the recessed conformation and is unable to interact with the major groove of DNA. We focused our attention the R248Q mutant that is commonly found in a wide range of cancer and observed changes at the zinc-binding pocket that might account for the dominant negative effects of R248Q. Furthermore, in our simulations, the S6/S7 turn was more frequently solvent exposed in R248Q, suggesting that there is a greater tendency of R248Q to partially unfold and possibly lead to an increased aggregation propensity. Finally, based on the observations made in our simulations, we propose strategies for the rescue of R248Q mutants.