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.

中文翻译：

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甘氨酸诱导的蛋白质表面口袋的形成和成药性评分预测

如今，已确定大多数与病原体感染无关的人类疾病起源于 DNA 疾病。因此，等待类似 CRISPR 疗法的 DNA 突变将传播到蛋白质组学中，从而提供选择天然或合成分子来对抗故障蛋白质的影响的可能性。事实上，药物发现是改善人类健康的生物技术研究的一个蓬勃发展领域，尽管在药物化学中大量使用信息学的情况下，新药的开发成本越来越高。CRISPR 技术通过去除与基因组相关的病理相关的 DNA 缺陷，增加了治愈疾病的新方法。然而，原则上，同样的技术，也可用于诱导蛋白质突变，其作用受合适配体的存在控制。在本文中，提出了一种实现突变蛋白的新思路，在突变蛋白的表面形成了更宽敞的瞬态口袋，因此更适合容纳药物。特别是，通过用最小的天然氨基酸甘氨酸、Gly 替换具有庞大侧链的氨基酸来获得新的变构位点。我们还提出了一种机器学习方法来评估新（或扩大）口袋的成药性得分。初步的实验结果非常有希望，表明 Gly-pipe 软件创建的 10% 的站点是可成药的。在其表面上形成更宽敞的瞬时口袋，因此更适合容纳药物。特别是，通过用最小的天然氨基酸甘氨酸、Gly 替换具有庞大侧链的氨基酸来获得新的变构位点。我们还提出了一种机器学习方法来评估新（或扩大）口袋的成药性得分。初步的实验结果非常有希望，表明 Gly-pipe 软件创建的 10% 的站点是可成药的。在其表面上形成更宽敞的瞬时口袋，因此更适合容纳药物。特别是，通过用最小的天然氨基酸甘氨酸、Gly 替换具有庞大侧链的氨基酸来获得新的变构位点。我们还提出了一种机器学习方法来评估新（或扩大）口袋的成药性得分。初步的实验结果非常有希望，表明 Gly-pipe 软件创建的 10% 的站点是可成药的。