Detection of Farnesyltransferase Interface Hot Spots through Computational Alanine Scanning Mutagenesis

Characterizing the Hot Spots Involved in RON-MSPβ Complex Formation Using In Silico Alanine Scanning Mutagenesis and Molecular Dynamics Simulation

Purpose: Implication of protein-protein interactions (PPIs) in development of many diseases such as cancer makes them attractive for therapeutic intervention and rational drug design. RON (Recepteur d'Origine Nantais) tyrosine kinase receptor has gained considerable attention as promising target in cancer therapy. The activation of RON via its ligand, macrophage stimulation protein (MSP) is the most common mechanism of activation for this receptor. The aim of the current study was to perform in silico alanine scanning mutagenesis and to calculate binding energy for prediction of hot spots in protein-protein interface between RON and MSPβ chain (MSPβ). Methods: In this work the residues at the interface of RON-MSPβ complex were mutated to alanine and then molecular dynamics simulation was used to calculate binding free energy. Results: The results revealed that Gln¹⁹³, Arg²²⁰, Glu²⁸⁷, Pro²⁸⁸, Glu²⁸⁹, and His⁴²⁴ residues from RON and Arg⁵²¹, His⁵²⁸, Ser⁵⁶⁵, Glu⁶⁵⁸, and Arg⁶⁸³ from MSPβ may play important roles in protein-protein interaction between RON and MSP. Conclusion: Identification of these RON hot spots is important in designing anti-RON drugs when the aim is to disrupt RON-MSP interaction. In the same way, the acquired information regarding the critical amino acids of MSPβ can be used in the process of rational drug design for developing MSP antagonizing agents, the development of novel MSP mimicking peptides where inhibition of RON activation is required, and the design of experimental site directed mutagenesis studies.

Discovery of new sites for drug binding to the hypertension-related renin-angiotensinogen complex

Renin (REN) is a key drug target to stop the hypertension cascade, but thus far only one direct inhibitor has been made commercially available. In this study, we assess an innovative REN inhibition strategy, by targeting the interface of the renin:angiotensinogen (REN:ANG) complex. We characterized the energetic role of interfacial residues of REN:ANG and identified the ones responsible for protein:protein binding, which can serve as drug targets for disruption of the REN:ANG association. For this purpose, we applied a computational alanine scanning mutagenesis protocol, which measures the contribution of each side chain for the protein:protein binding free energy with an accuracy of ≈ 1 kcal/mol. As a result, in REN and ANG, six and eight residues were found to be critical for binding, respectively. The leading force behind REN:ANG complexation was found to be the hydrophobic effect. The binding free energy per residue was found to be proportional to the buried area. Residues responsible for binding were occluded from water at the complex, which promotes an efficient pairing between the two proteins. Two druggable pockets involving critical residues for binding were found on the surface of REN, where small druglike molecules can bind and disrupt the ANG:REN association that may provide an efficient way to achieve REN inhibition and control hypertension.

Computational Alanine Scanning Mutagenesis: MM-PBSA vs TI

Understanding protein-protein association and being able to determine the crucial residues responsible for their association (hot-spots) is a key issue with huge practical applications such as rational drug design and protein engineering. A variety of computational methods exist to detect hot-spots residues, but the development of a fast and accurate quantitative alanine scanning mutagenesis (ASM) continues to be crucial. Using four protein-protein complexes, we have compared a variation of the standard computational ASM protocol developed at our group, based on the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM-PBSA) approach, against Thermodynamic Integration (TI), a well-known and accurate but computationally expensive method. To compare the efficiency and the accuracy of the two methods, we have calculated the protein-protein binding free energy differences upon alanine mutation of interfacial residues (ΔΔGbind). In relation to the experimental ΔΔGbind values, the average error obtained with TI was 1.53 kcal/mol, while the ASM protocol resulted in an average error of 1.18 kcal/mol. The results demonstrate that the much faster ASM protocol gives results at the same level of accuracy as the TI method but at a fraction of the computational time required to run TI. This ASM protocol is therefore a strong and efficient alternative to the systematic evaluation of protein-protein interfaces, involving hundreds of amino acid residues in search of hot-spots.