Protein–Protein Docking in Drug Design and Discovery

Abstract

Protein-protein interactions (PPIs) are responsible for a number of key physiological processes in the living cells and underlie the pathomechanism of many diseases. Nowadays, along with the concept of so-called "hot spots" in protein-protein interactions, which are well-defined interface regions responsible for most of the binding energy, these interfaces can be targeted with modulators. In order to apply structure-based design techniques to design PPIs modulators, a three-dimensional structure of protein complex has to be available. In this context in silico approaches, in particular protein-protein docking, are a valuable complement to experimental methods for elucidating 3D structure of protein complexes. Protein-protein docking is easy to use and does not require significant computer resources and time (in contrast to molecular dynamics) and it results in 3D structure of a protein complex (in contrast to sequence-based methods of predicting binding interfaces). However, protein-protein docking cannot address all the aspects of protein dynamics, in particular the global conformational changes during protein complex formation. In spite of this fact, protein-protein docking is widely used to model complexes of water-soluble proteins and less commonly to predict structures of transmembrane protein assemblies, including dimers and oligomers of G protein-coupled receptors (GPCRs). In this chapter we review the principles of protein-protein docking, available algorithms and software and discuss the recent examples, benefits, and drawbacks of protein-protein docking application to water-soluble proteins, membrane anchoring and transmembrane proteins, including GPCRs.

Exploring protein‐protein interactions using the site‐identification by ligand competitive saturation methodology

“…For example, protein‐protein complex structures provide atomic level details of PPIs that are useful for guiding the engineering of proteins or the rational design of therapeutic molecules to enhance or weaken specific PPI in order to achieve a desired pharmacological outcome. In this regard, computational protein docking methods offer valuable alternatives to experimental structure determination methods, especially when experimental limitations or cost considerations are present …”

“…Alternatively, a number of methods carry out local randomized searches in the context of Monte Carlo/Metropolis sampling including RosettaDock and ICM‐DISCO . Scoring functions used by these docking methods vary from empirical, knowledge‐based potentials to physical force field‐based energy functions . To date, most of the methods are based on rigid protein structures or protein models with limited flexibility.…”

“…To date, most of the methods are based on rigid protein structures or protein models with limited flexibility. In addition, desolvation contributions are often not or only empirically included in the scoring function, which may limit the accuracy of PPI prediction . Accordingly, while significant progress has been made in solving the PPI problem, there is still need for more accurate PPI prediction methods, as reflected in the quality of results from the critical assessment of predicted interactions (CAPRI) competitions that aim to promote development of new PPI prediction algorithm and optimization of the current methods.…”

“…A variety of protein-docking algorithms have been developed and evaluated during last two decades. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] Some of these algorithms perform global searches to solve the posing problem and usually utilize fast Fourier transforms (FFT) to expedite the scoring of those generated protein poses. 9 Examples include ZDOCK, 10 FTDock, 11 PIPER,12 and GRAMM, 13 among others.…”

“…9 Examples include ZDOCK, 10 FTDock, 11 PIPER,12 and GRAMM, 13 among others. [6][7][8] Alternatively, a number of methods carry out local randomized searches in the context of Monte Carlo/ Metropolis sampling including RosettaDock 14 and ICM-DISCO. 15 Scoring functions used by these docking methods vary from empirical, knowledge-based potentials to physical force field-based energy functions.…”