Biomolecular recognition is fundamental to the cellular functions via the formation of binding complexes. Functional complex of biomolecular recognition normally has high affinity and specificity which are shaped by the evolution. The affinity determines the stability of the complex while the specificity determines the discrimination of native complex against competitive ones. The affinity can be quantified computationally and experimentally. However, the specificity is challenging to be quantified since the comparison of all possible binding partners for the target biomolecule is a daunting task.
Biological activities are mostly performed by the proteins at the molecular scale. Naturally occurring proteins have a high degree of features different from random mixer of amino acids. They have evolved to be able to spontaneously folding into native structure and specifically bind with their partners for functions. Darwin’s Theory of Evolution suggests that natural selection imposes on the individuals and the fittest survives. A fundamental question is how nature shapes the protein interaction patterns for stable structure and specific functional binding at the molecular scale.
In this lecture, I will introduce these two topics (biomolecular recognition & protein evolution) and the attempt to address the raised challenge and question with physical concept, mathematical statistics and computational approaches. I will focus on the development of the scoring function based on both the affinity and specificity optimizations, and the unification of physical principles for protein folding, binding and evolution.