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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Glycine-induced formation and druggability score prediction of protein surface pockets
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Glycine-induced formation and druggability score prediction of protein surface pockets

机译:甘氨酸诱导的蛋白质表面袋的形成和可药物分数预测

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摘要

Nowadays, it is well established that most of the human diseases which are not related to pathogen infections have their origin from DNA disorders. Thus, DNA mutations, waiting for the availability of CRISPR-like remedies, will propagate into proteomics, offering the possibility to select natural or synthetic molecules to fight against the effects of malfunctioning proteins. Drug discovery, indeed, is a flourishing field of biotechnological research to improve human health, even though the development of a new drug is increasingly more expensive in spite of the massive use of informatics in Medicinal Chemistry. CRISPR technology adds new alternatives to cure diseases by removing DNA defects responsible of genome-related pathologies. In principle, the same technology, however, could also be exploited to induce protein mutations whose effects are controlled by the presence of suitable ligands. In this paper, a new idea is proposed for the realization of mutated proteins, on the surface of which more spacious transient pockets are formed and, therefore, are more suitable for hosting drugs. In particular, new allosteric sites are obtained by replacing amino-acids with bulky side chains with glycine, Gly, the smallest natural amino-acid. We also present a machine learning approach to evaluate the druggability score of new (or enlarged) pockets. Preliminary experimental results are very promising, showing that 10% of the sites created by the Gly-pipe software are druggable.
机译:如今,很好地确定,大多数与病原体感染无关的人类疾病都与DNA疾病的起源。因此,等待Crispr样品的可用性的DNA突变将繁殖到蛋白质组学中,提供了选择自然或合成分子以抵抗蛋白质的影响的可能性。实际上,药物发现是一种改善人类健康的生物技术研究的繁荣领域,尽管新药的发展仍然越来越昂贵,但尽管在药用化学中的信息学大量使用。 CRISPR技术通过去除负责基因组相关病理的DNA缺陷来添加治愈疾病的新替代品。然而,原则上,也可以利用相同的技术来诱导蛋白质突变,其作用通过合适配体的存在来控制。在本文中,提出了一种用于实现突变蛋白的新思路,在形成更宽敞的瞬态口袋的表面上,因此更适合于托管药物。特别地,通过用甘氨酸,甘氨酸,最小的天然氨基酸替换含有庞大的侧链的氨基酸来获得新的变形位点。我们还提出了一种机器学习方法来评估新(或放大)口袋的可耐药性得分。初步实验结果非常有前途,表明甘道软件创建的10%的位点是可脱脂的。

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