Wolynes Peter G
Department of Chemistry and Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
Biochimie. 2015 Dec;119:218-30. doi: 10.1016/j.biochi.2014.12.007. Epub 2014 Dec 18.
Protein folding has been viewed as a difficult problem of molecular self-organization. The search problem involved in folding however has been simplified through the evolution of folding energy landscapes that are funneled. The funnel hypothesis can be quantified using energy landscape theory based on the minimal frustration principle. Strong quantitative predictions that follow from energy landscape theory have been widely confirmed both through laboratory folding experiments and from detailed simulations. Energy landscape ideas also have allowed successful protein structure prediction algorithms to be developed. The selection constraint of having funneled folding landscapes has left its imprint on the sequences of existing protein structural families. Quantitative analysis of co-evolution patterns allows us to infer the statistical characteristics of the folding landscape. These turn out to be consistent with what has been obtained from laboratory physicochemical folding experiments signaling a beautiful confluence of genomics and chemical physics.
蛋白质折叠一直被视为分子自组装的一个难题。然而,通过具有漏斗状的折叠能量景观的进化,折叠过程中涉及的搜索问题已得到简化。漏斗假说可以基于最小受挫原则,利用能量景观理论进行量化。能量景观理论得出的强有力的定量预测,已通过实验室折叠实验和详细模拟得到广泛证实。能量景观的理念还使得成功开发出蛋白质结构预测算法成为可能。具有漏斗状折叠景观的选择约束在现有蛋白质结构家族的序列上留下了印记。对协同进化模式的定量分析使我们能够推断折叠景观的统计特征。结果发现,这些特征与从实验室物理化学折叠实验中获得的结果一致,这标志着基因组学与化学物理学的完美融合。