PPCheck: A Webserver for the Quantitative Analysis of Protein-Protein Interfaces and Prediction of Residue Hotspots

Abstract

BACKGROUNDModeling protein–protein interactions (PPIs) using docking algorithms is useful for understanding biomolecular interactions and mechanisms. Typically, a docking algorithm generates a large number of docking poses, and it is often challenging to select the best native-like pose. A further challenge is to recognize key residues, termed as hotspots, at protein–protein interfaces, which contribute more in stabilizing a protein–protein interface.RESULTSWe had earlier developed a computer algorithm, called PPCheck, which ascribes pseudoenergies to measure the strength of PPIs. Native-like poses could be successfully identified in 27 out of 30 test cases, when applied on a separate set of decoys that were generated using FRODOCK. PPCheck, along with conservation and accessibility scores, was able to differentiate ‘native-like and non-native-like poses from 1883 decoys of Critical Assessment of Prediction of Interactions (CAPRI) targets with an accuracy of 60%. PPCheck was trained on a 10-fold mixed dataset and tested on a 10-fold mixed test set for hotspot prediction. We obtain an accuracy of 72%, which is in par with other methods, and a sensitivity of 59%, which is better than most existing methods available for hotspot prediction that uses similar datasets. Other relevant tests suggest that PPCheck can also be reliably used to identify conserved residues in a protein and to perform computational alanine scanning.CONCLUSIONSPPCheck webserver can be successfully used to differentiate native-like and non-native-like docking poses, as generated by docking algorithms. The webserver can also be a convenient platform for calculating residue conservation, for performing computational alanine scanning, and for predicting protein–protein interface hotspots. While PPCheck can differentiate the generated decoys into native-like and non-native-like decoys with a fairly good accuracy, the results improve dramatically when features like conservation and accessibility are included. The method can be successfully used in ranking/scoring the decoys, as obtained from docking algorithms.

Design of inhibitory peptide targeting Toxoplasma gondii RON4‐human β‐tubulin interactions by implementing structural bioinformatics methods

“…The resultant docked complexes were ranked on the basis of HADDOCK score (a weighted sum of van der Waals, electrostatic, desolvation, and restraint violation energies together with buried surface area of the sum of electrostatics) and AIR energy terms. Further, the best docked low energy cluster was determined and validated for its internal energies and binding affinities ΔG (kcal/mol) using PPCheck (http://caps.ncbs.res.in/ppcheck/) and PRODIGY (PROtein binDIng enerGY prediction)(http://milou.science.uu.nl/services/PRODIGY/), respectively. The identified low energy docked complex was further refined by MD simulation, as similar to that of implemented for RON4 model optimization.…”

Bioinformatics comparisons of RNA-binding proteins of pathogenic and non-pathogenic Escherichia coli strains reveal novel virulence factors

“…The head-to-head dimers were randomly selected from both the active and the inactive hexamers of the protein for performing MD simulations, to save computational time. Various energy components of the dimer interface were measured using the in-house algorithm, PPCheck [ 58 ]. This algorithm identifies interface residues in protein-protein interactions on the basis of simple distance criteria, following which the strength of interactions at the interface are quantified.…”

Multiple Roles of the Polycistronic Gene Tarsal-less/Mille-Pattes/Polished-Rice During Embryogenesis of the Kissing Bug Rhodnius prolixus

“…The dockings were performed by HADDOCK (van Zundert et al, 2016). The best interaction complexes were selected after exhausting analyses with DockScore (Malhotra et al, 2015), PPCheck (Sukhwal and Sowdhamini, 2015), and CCharPPI (Moal et al, 2015) programs. After selection of the best complex, further refinements were performed via GalaxyRefineComplex (Heo et al, 2016).…”