Kauffman S A, Weinberger E D
University of Pennsylvania, Philadelphia 19104-6059.
J Theor Biol. 1989 Nov 21;141(2):211-45. doi: 10.1016/s0022-5193(89)80019-0.
Adaptive evolution is, to a large extent, a complex combinatorial optimization process. Such processes can be characterized as "uphill walks on rugged fitness landscapes". Concrete examples of fitness landscapes include the distribution of any specific functional property such as the capacity to catalyze a specific reaction, or bind a specific ligand, in "protein space". In particular, the property might be the affinity of all possible antibody molecules for a specific antigenic determinant. That affinity landscape presumably plays a critical role in maturation of the immune response. In this process, hypermutation and clonal selection act to select antibody V region mutant variants with successively higher affinity for the immunizing antigen. The actual statistical structure of affinity landscapes, although knowable, is currently unknown. Here, we analyze a class of mathematical models we call NK models. We show that these models capture significant features of the maturation of the immune response, which is currently thought to share features with general protein evolution. The NK models have the important property that, as the parameter K increases, the "ruggedness" of the NK landscape varies from a single peaked "Fujiyama" landscape to a multi-peaked "badlands" landscape. Walks to local optima on such landscapes become shorter as K increases. This fact allows us to choose a value of K that corresponds to the experimentally observed number of mutational "steps", 6-8, taken as an antibody sequence matures. If the mature antibody is taken to correspond to a local optimum in the model, tuning the model requires that K be about 40, implying that the functional contribution of each amino acid in the V region is affected by about 40 others. Given this value of K, the model then predicts several features of "antibody space" that are in qualitative agreement with experiment: (1) The fraction of fitter variants of an initial "roughed in" germ line antibody amplified by clonal selection is about 1-2%. (2) Mutations at some sites of the mature antibody hardly affect antibody function at all, but mutations at other sites dramatically decrease function. (3) The same "roughed in" antibody sequence can "walk" to many mature antibody sequences. (4) Many adaptive walks can end on the same local optimum. (5) Comparison of different mature sequences derived from the same initial V region shows evolutionary hot spots and parallel mutations. All these predictions are open to detailed testing by obtaining monoclonal antibodies early in the immune response and carrying out in vitro mutagenesis and adaptive hill climbing with respect to affinity for the immunizing antigen.
适应性进化在很大程度上是一个复杂的组合优化过程。此类过程可被描述为“在崎岖的适应度景观上向上行走”。适应度景观的具体例子包括在“蛋白质空间”中任何特定功能特性的分布,例如催化特定反应或结合特定配体的能力。特别地,该特性可能是所有可能的抗体分子对特定抗原决定簇的亲和力。这种亲和力景观大概在免疫反应成熟过程中起关键作用。在此过程中,高突变和克隆选择作用于选择对免疫抗原具有相继更高亲和力的抗体V区突变变体。亲和力景观的实际统计结构虽然是可知的,但目前尚不清楚。在这里,我们分析一类我们称为NK模型的数学模型。我们表明这些模型捕捉到了免疫反应成熟的显著特征,目前认为免疫反应成熟与一般蛋白质进化具有共同特征。NK模型具有重要特性,即随着参数K增加,NK景观的“崎岖度”从单峰的“富士山”景观变化到多峰的“荒地”景观。随着K增加,在此类景观上走向局部最优的步数会变短。这一事实使我们能够选择一个与实验观察到的作为抗体序列成熟时的突变“步数”(6 - 8步)相对应的K值。如果将成熟抗体视为对应于模型中的局部最优,那么调整模型需要K约为40,这意味着V区中每个氨基酸的功能贡献受到约40个其他氨基酸的影响。给定这个K值,该模型随后预测了“抗体空间”的几个与实验定性一致的特征:(1)通过克隆选择扩增的初始“大致形成”的种系抗体的更适应变体的比例约为1 - 2%。(2)成熟抗体某些位点的突变几乎根本不影响抗体功能,但其他位点的突变会显著降低功能。(3)相同的“大致形成”抗体序列可以“走向”许多成熟抗体序列。(4)许多适应性行走可以在同一个局部最优处结束。(5)对源自相同初始V区的不同成熟序列进行比较显示出进化热点和平行突变。所有这些预测都可以通过在免疫反应早期获得单克隆抗体,并针对对免疫抗原的亲和力进行体外诱变和适应性爬山来进行详细测试。