Protein-protein interactions are at the heart of signal transduction and are central to the function of protein machine in biology. The highly specific protein-protein binding is quantitatively characterized by the binding free energy whose accurate calculation from the first principle is a grand challenge in computational biology. In this paper, we show how the interactionentropy approach, which was recently proposed for protein-ligand binding free energy calculation, can be applied to computing the entropic contribution to the protein-protein binding free energy. Explicit theoretical derivation of the interactionentropy approach for protein-protein interaction system is given in detail from the basic definition. Extensive computational studies for a dozen realistic protein-protein interaction systems are carried out using the present approach and comparisons of the results for these protein-protein systems with those from the standard normal mode method are presented. Analysis of the present method for application in protein-protein binding as well as the limitation of the method in numerical computation is discussed. Our study and analysis of the results provided useful information for extracting correct entropic contribution in protein-protein binding from molecular dynamics simulations.

The indirect method for the construction of quantum mechanics (QM)/molecular mechanics (MM) free energy landscapes provides a cheaper alternative for free energy simulations at the QM level.

The efficiency of alchemical free energy simulations with the staging strategy is improved by adaptively manipulating the significance of each ensemble followed by importance sampling. The OBAR (optimum BAR) method introduced in this work with explicit consideration of the statistical inefficiency in each ensemble outperforms the traditional equal time rule which is used in standard applications of alchemical transformation with the window sampling regime in the sense of minimizing the total variance of the free energy estimate. The Time Derivative of total Variance (TDV) is proposed for the OBAR criterion which is linearly dependent on the variance and is more sensitive to the importance rank than the overlap matrix. The performance of OBAR workflow is demonstrated for solvation of several small molecules and a protein ligand binding system.