Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach
บทความในวารสาร
ผู้เขียน/บรรณาธิการ
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Bhukrit Ruengsrichaiya, Chakarida Nukoolkit, Saowalak Kalapanulak, and Treenut Saithong
ผู้เผยแพร่: Frontiers Media
ปีที่เผยแพร่ (ค.ศ.): 2022
Volume number: 13
Issue number: -
หน้าแรก: 1
หน้าสุดท้าย: 16
จำนวนหน้า: 16
eISSN: 1664-462X
URL: https://www.frontiersin.org/articles/10.3389/fpls.2022.970018/full
ภาษา: English-United States (EN-US)
บทคัดย่อ
As a sessile organism, plants hold elaborate transcriptional regulatory systems that allow them to adapt to variable surrounding environments. Current understanding of plant regulatory mechanisms is greatly constrained by limited knowledge of transcription factor (TF)–DNA interactions. To mitigate this problem, a Plant-DTI predictor (Plant DBD-TFBS Interaction) was developed here as the first machine-learning model that covered the largest experimental datasets of 30 plant TF families, including 7 plant-specific DNA binding domain (DBD) types, and their transcription factor binding sites (TFBSs). Plant-DTI introduced a novel TFBS feature construction, called TFBS base-preference, which enhanced the specificity of TFBS to DBD types. The proposed model showed better predictive performance with the TFBS basepreference than the simple binary representation. Plant-DTI was validated with 22 independent ChIP-seq datasets. It accurately predicted the measured DBD-TFBS pairs along with their TFBS motifs, and effectively predicted interactions of other TFs containing similar DBD types. Comparing to the existing state-of-art methods, Plant-DTI prediction showed a figure of merit in sensitivity and specificity with respect to the position weight matrix (PWM) and TSPTFBS methods. Finally, the proposed Plant-DTI model helped to fill the knowledge gap in the regulatory mechanisms of the cassava sucrose synthase 1 gene (MeSUS1). Plant-DTI predicted MeERF72 as a regulator of MeSUS1 in consistence with the yeast one-hybrid (Y1H) experiment. Taken together, Plant-DTI would help facilitate the prediction of TF-TFBS and TF-target gene (TG) interactions, thereby accelerating the study of transcriptional regulatory systems in plant species.
คำสำคัญ
DNA binding domain, machine learning, plants, TF-TFBS interactions, Transcriptional regulation, transcription factor, transcription factor binding site