Functional Protein Dynamics Directly from Sequences

The sequence correlations within a protein multiple sequence alignment are routinely being used to predict contacts within its structure, but here we point out that these data can also be used to predict a protein’s dynamics directly. The elastic network protein dynamics models rely directly upon the contacts, and the normal modes of motion are obtained from the decomposition of the inverse of the contact map. To make the direct connection between sequence and dynamics, it is necessary to apply coarse-graining to the structure at the level of one point per amino acid, which has often been done, and protein coarse-grained dynamics from elastic network models has been highly successful, particularly in representing the large-scale motions of proteins that usually relate closely to their functions. The interesting implication of this is that it is not necessary to know the structure itself to obtain its dynamics and instead to use the sequence information directly to obtain the dynamics.蛋白质多重序列比对（protein multiple sequence alignment）中的序列相关性，常被用于预测蛋白质结构内的残基接触。本文指出，这类数据还可直接用于预测蛋白质的动力学特性。弹性网络蛋白质动力学模型（elastic network protein dynamics models）直接依赖于残基接触，其运动简正模式（normal modes of motion）可通过接触图（contact map）的逆矩阵分解获得。为建立序列与动力学之间的直接关联，需对蛋白质结构进行粗粒度化（coarse-graining）处理，即每个氨基酸对应一个建模位点——此类处理方式已被广泛应用。基于弹性网络模型的蛋白质粗粒度动力学研究已取得极高的成功率，尤其在表征通常与蛋白质功能密切相关的蛋白质大规模运动方面。这一发现的有趣启示在于，无需知晓蛋白质的三维结构即可获取其动力学特性，转而可直接利用序列信息来预测蛋白质的动力学特性。