Can Protein Chains Lock?

这项研究探讨了一个理论问题，对蛋白质折叠模拟具有潜在的重要意义。如果研究人员能够通过计算从氨基酸序列中预测蛋白质的折叠，而不是在体外探索各种选择，那么药物设计（例如，阻止致命病毒的作用）将会快得多。然而，这个“蛋白质折叠问题”的进展一直很缓慢，因为长蛋白质可以折叠的方式有很多种。 这些不同的方式被抽象地收集到一个可能的折叠路径的“空间”中。研究者和他的合作者证明了一个几何定理，在一些简化的假设下，这意味着这个可能性的空间比以前想象的要小得多。现在的目标是证明这种简化在没有这些简化假设的情况下也成立。特别是，空间维度的减少取决于“锁定”多边形链（模拟蛋白质骨架）的存在。 已知确实存在具有任意连接长度的这种锁定链，但是当所有连接具有大约相同的长度时，锁定链是否存在是一个未解决的问题，其跟踪蛋白质的情况。 如果这个问题可以得到肯定的回答，下一步将是利用降维来加速蛋白质折叠模拟，从而缩短药物设计周期。这项研究是在一所女子学院进行的，研究者的五名学生合作者都是女性本科生，她们都是计算机科学、数学和工程专业的学生。有几个人将进入研究生院，他们在这项赠款方面的培训是宝贵的准备经验。

This research explores a theoretical question that has potentially important implications for protein folding simulations. Drug design (for example, blocking the action of a deadly virus) would be significantly faster if researchers could predict computationally the folding of a protein from its sequence of amino acids, rather than having to explore options *in vitro*. However, progress on this "protein folding problem" has been slow because of the vast number of different ways a long protein can fold. These different ways are abstractly collected into a "space" of possible folding paths. The investigator and his collaborators proved a geometric theorem that, under some simplifying assumptions, implies that this space of possibilities is significantly smaller than previously thought.The goal now is to show that this reduction holds without those simplifying assumptions. In particular, the reduction in the dimensions of the space depends on the existence of "locked" polygonal chains (which model protein backbones). It is known that there do exist such locked chains with arbitrary link lengths, but it is an unsolved problem whether locked chains exist when all links have about the same length, which tracks the situation for proteins. If this question can be answered positively, the next step will be to exploit the dimension reduction to accelerate protein folding simulations, and thereby reduce the drug-design cycle time. The research is conducted at a women's college, and all five of the investigator's student collaborators are female undergraduates in areas with significant national under-representation of women: they are computer science, mathematics, and engineering majors. Several are headed to graduate school, and their training on this grant serves as invaluable preparatory experience.