A novel empirical free energy function that explains and predicts protein–protein binding affinities

Structure-based prediction of protein–protein binding affinity with consideration of allosteric effect

“…With the number of solved protein-protein complex 3D structures growing up rapidly in recent years, interaction analysis and affinity prediction based on complex structures have received much attention in the structural bioinformatics community. Nowadays, the available methods used for structure-based prediction of protein-protein binding affinity can be roughly categorized into three classes: empirical scoring method (Horton and Lewis 1992;Ma et al 2002;Audie and Scarlata 2007), knowledge-based method (Jiang et al 2002;Zhang et al 2005;Su et al 2009), and ab initio prediction method (Brandsdal and Smalås 2000;Gandhi and Mancera 2009;Cole et al 2010). The empirical scoring method defines an energy term-weighed formula on the basis of affinityknown protein complexes (usually used for docking purpose); the knowledge-based method utilizes the frequency of contacts between different residues or atoms in known crystal structures to predict the binding affinity; the ab initio prediction method employs theoretical approaches [such as MM-PB/SA (Gandhi and Mancera 2009), freeenergy perturbation (Brandsdal and Smalås 2000) and linear-scaling analysis (Cole et al 2010)] to directly calculate the interaction energy between two binding partners.…”

Development and validation of an empirical free energy function for calculating protein–protein binding free energy surfaces

“…Furthermore, we argued that the function made basic statistical, theoretical and physical sense and that the function was, at least within certain well-defined limits, explanatory. Importantly, the function can be used to estimate binding free energies in a matter of seconds [1].…”

Prediction of protein–protein binding free energies

“…We also show the same r calculated with several other algorithms that are discussed in detail in the Methods section. These are the protein-protein docking potentials developed in our group (IRAD, 16 ZRANK, 17 and ZDOCK [18][19][20] ), and the three potentials that gave the best results in a previous study using a precursor of the Affinity Benchmark (PyDock, 21 Rosetta, 22 AffinityScore1.0 6,23 ). 10,12 ZAPP has the highest r with experimental binding free energies, 0.63, with Rosetta second, 0.41, and the other functions between 0.22 and 0.27.…”

“…Bur_C/S: This term was presented by Audie and Scarlata, 6 and counts the number of hydrophobic (carbon and sulfur) atoms that exposed in the monomers but buried in the complex. Atoms are considered exposed when the solvent accessible surface, calculated using NACCESS, 32 is larger than 1 Å 2 .…”

“…2 Later works using different methods and datasets also reported high correlation coefficients, ranging from 0.75 to 0.95. [4][5][6][7][8][9] However, as outlined in several recent papers, these high accuracy predictions are due to limitations in the data sets used for training and testing the algorithms. 3,[10][11][12] When a large number of approaches were tested on a larger and more reliable dataset, no correlations higher than 0.53 were observed.…”