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PBRpredict-Suite: a suite of models to predict peptide-recognition domain residues from protein sequence

机译:PBR预期套件:一套模型,用于预测蛋白质序列的肽识别结构域残基

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摘要

Motivation: Machine learning plays a substantial role in bioscience owing to the explosive growth in sequence data and the challenging application of computational methods. Peptide-recognition domains (PRDs) are critical as they promote coupled-binding with short peptide-motifs of functional importance through transient interactions. It is challenging to build a reliable predictor of peptide-binding residue in proteins with diverse types of PRDs from protein sequence alone. On the other hand, it is vital to cope up with the sequencing speed and to broaden the scope of study.
机译:由于序列数据的爆炸性增长和计算方法的挑战应用,机器学习在生物科学中发挥了重要作用。 肽 - 识别结构域(PRD)是至关重要的,因为它们通过瞬态相互作用促进与功能性重要性的短肽基序列的偶联结合。 在单独用蛋白质序列中,在蛋白质中构建肽结合残留物的可靠预测因子是挑战性的。 另一方面,应对测序速度并扩大研究范围至关重要。

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