摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-调查人员发布了关于人工智能的新报告。根据纽约布朗克斯的新闻报道，Ne wsRx编辑称，“蛋白质之间的物理相互作用主要取决于它们结合界面的结构特性。我们发现，不同蛋白质复合物中的结合界面非常相似。”这项研究的资助者包括美国国立卫生研究院(NIH)，阿尔伯特爱因斯坦医学院。我们的新闻记者从阿尔伯特·爱因斯坦医学院的研究中获得了一句话：“人工智能可以进一步捕捉到界面上不同结合的结构特征。为了验证这一假设，我们将蛋白-蛋白结合界面分解为相互作用的片段对，我们使用生成模型在低维潜空间中编码这些界面片段对。经过训练，我们发现，我们发现，仅使用少量人工智能产生的界面片段对，就可以获得更多的界面片段对。这些结果表明，蛋白质-蛋白质结合界面上片段对的构象是高度简并的，简并空间中的特征可以用人工智能很好地表征。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ar tificial Intelligence. According to news reporting out of Bronx, New York, by Ne wsRx editors, research stated, "The physical interactions between proteins are l argely determined by the structural properties at their binding interfaces. It w as found that the binding interfaces in distinctive protein complexes are highly similar." Funders for this research include National Institutes of Health (NIH) - USA, Alb ert Einstein College of Medicine. Our news journalists obtained a quote from the research from the Albert Einstein College of Medicine, "The structural properties underlying different binding in terfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of inter acting fragments. We employed a generative model to encode these interface fragm ent pairs in a lowdimensional latent space. After training, new conformations o f interface fragment pairs were generated. We found that, by only using a small number of interface fragment pairs that were generated by artificial intelligenc e, we were able to guide the assembly of protein complexes into their native con formations. These results demonstrate that the conformational space of fragment pairs at protein-protein binding interfaces is highly degenerate. Features in th is degenerate space can be well characterized by artificial intelligence."
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