International School for Advanced Studies (SISSA), Via Bonomea 265, I-34136 Trieste, Italy.
Phys Chem Chem Phys. 2018 Jun 27;20(25):17148-17155. doi: 10.1039/c7cp08170g.
Protein folding and receptor-ligand recognition are fundamental processes for any living organism. Although folding and ligand recognition are based on the same chemistry, the existing empirical scoring functions target just one problem: predicting the correct fold or the correct binding pose. We here introduce a statistical potential which considers moieties as fundamental units. The scoring function is able to deal with both folding and ligand pocket recognition problems with a performance comparable to the scoring functions specifically tailored for one of the two tasks. We foresee that the capability of the new scoring function to tackle both problems in a unified framework will be a key to deal with the induced fit phenomena, in which a target protein changes significantly its conformation upon binding. Moreover, the new scoring function might be useful in docking protocols towards intrinsically disordered proteins, whose flexibility cannot be handled with the available docking software.
蛋白质折叠和受体-配体识别是任何生物体的基本过程。尽管折叠和配体识别基于相同的化学原理，但现有的经验评分函数仅针对一个问题：预测正确的折叠或正确的结合构象。我们在这里引入了一种统计势能，它将部分视为基本单元。该评分函数能够处理折叠和配体口袋识别问题，其性能可与专门针对这两个任务之一的评分函数相媲美。我们预计，新评分函数在统一框架中处理这两个问题的能力将是处理诱导契合现象的关键，在这种现象中，目标蛋白在结合时会发生显著的构象变化。此外，新的评分函数可能对内在无序蛋白的对接协议有用，这些蛋白的灵活性无法用现有的对接软件处理。
