Xulvi-Brunet Ramon, Campbell Gregory W, Rajamani Sudha, Jiménez José I, Chen Irene A
Departamento de Física, Facultad de Ciencias, Escuela Politécnica Nacional, Quito, Ecuador; Department of Chemistry and Biochemistry, Program in Biomolecular Science and Engineering, University of California, Santa Barbara, USA.
Department of Chemistry and Biochemistry, Program in Biomolecular Science and Engineering, University of California, Santa Barbara, USA.
Methods. 2016 Aug 15;106:86-96. doi: 10.1016/j.ymeth.2016.05.012. Epub 2016 May 19.
In vitro selection experiments in biochemistry allow for the discovery of novel molecules capable of specific desired biochemical functions. However, this is not the only benefit we can obtain from such selection experiments. Since selection from a random library yields an unprecedented, and sometimes comprehensive, view of how a particular biochemical function is distributed across sequence space, selection experiments also provide data for creating and analyzing molecular fitness landscapes, which directly map function (phenotypes) to sequence information (genotypes). Given the importance of understanding the relationship between sequence and functional activity, reliable methods to build and analyze fitness landscapes are needed. Here, we present some statistical methods to extract this information from pools of RNA molecules. We also provide new computational tools to construct and study molecular fitness landscapes.
生物化学中的体外筛选实验有助于发现具有特定所需生化功能的新型分子。然而，这并非我们能从此类筛选实验中获得的唯一益处。由于从随机文库中进行筛选能以前所未有的、有时甚至是全面的视角展现特定生化功能在序列空间中的分布情况，筛选实验还为创建和分析分子适应度景观提供了数据，分子适应度景观可直接将功能（表型）映射到序列信息（基因型）。鉴于理解序列与功能活性之间关系的重要性，需要有可靠的方法来构建和分析适应度景观。在此，我们介绍一些从RNA分子库中提取此类信息的统计方法。我们还提供了用于构建和研究分子适应度景观的新计算工具。
