Computational Studies of Protein Folding and Design

蛋白质折叠是将遗传信息表达为功能分子的关键步骤。然而，蛋白质构象的复杂性，我们对折叠的决定因素的不完全理解，以及大量可能的蛋白质序列，掩盖了对这一过程的理解。正在开发一种统计方法来了解序列-结构兼容性，该方法可以涵盖所有可能的序列。该方法可以帮助研究人员(A)探索蛋白质结构的序列可变性，(B)聚焦蛋白质序列文库，以及(C)设计折叠到预定结构的序列。该理论还将被用来解决观察到的具有共同结构的自然产生的蛋白质序列之间的可变性。最后，基于信息的能量函数在蛋白质设计和结构预测中发挥着核心作用。将开发一类新的此类函数。理解特定结构的序列可变性并确定折叠成所需构象的序列的能力将促进我们对遗传疾病、蛋白质折叠疾病的了解，并可能导致新型的基于蛋白质的疗法。

Protein folding is a crucial step in the expression of genetic information as functional molecules. Understanding this process, however, is obscured by the conformational complexity of proteins, our incomplete understanding of the determinants of folding, and the huge number of possible protein sequences. A statistical approach to understanding sequence-structure compatibility is being developed that can encompass all possible sequences. The method can assist researchers in (a) probing the sequence variability of protein structures, (b) focusing protein sequence libraries, and (c) designing sequences that fold to a predetermined structure. The theory will also be used to address the variability observed among naturally occurring protein sequences having a common structure. Lastly, information-based energy functions play a central role in protein design and structure prediction. A new class of such functions will be developed. The ability to understand the sequence variability of particular structures and to determine sequences that fold to desired conformations will advance our knowledge of genetic disorders, protein folding diseases and could lead to new types of protein based therapeutics.