DNA
计算生物学
DNA结合位点
生物
结合位点
线程(蛋白质序列)
DNA测序
DNA结合蛋白
蛋白质结构
遗传学
生物物理学
生物化学
基因
发起人
转录因子
基因表达
作者
Ajay Arya,Dana Mary Varghese,Ajay Verma,Shandar Ahmad
标识
DOI:10.1016/j.jmb.2022.167640
摘要
Sequence-based prediction of DNA-binding residues in a protein is a widely studied problem for which machine learning methods with continuously improving predictive power have been developed. Concatenated rows within a sliding window of a Position Specific Substitution Matrix (PSSM) of the protein are currently used as the primary feature set in almost all the methods of predicting DNA-binding residues. Here we report that these evolutionary profiles are powerful, only for identifying conserved binding sites and fall short for the residue positions which undergo binding to non-binding transitions in closely related proteins. We created a database of highly similar protein pairs with known protein-DNA complexes and investigated differential predictability of conserved and transient binding residues within each pair. Retraining machine learning models uniformly, we compared the predictive powers of the models trained on PSSMs against similarly trained models on sparse-encoded single sequences. We found that the transient binding site predictions from evolutionary profiles are outperformed by single-sequence based models under controlled experiments by as much as 8 percentage points. Thus, we conclude that the PSSM-based models are inadequate to predict high-specificity DNA-binding residues. These findings are of critical significance for the design of mutant- and species-specific DNA ligands and for homology based modeling of protein-DNA complexes.
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