Combination of Ambiguous and Unambiguous Data in the Restraint-driven Docking of Flexible Peptides with HADDOCK: The Binding of the Spider Toxin PcTx1 to the Acid Sensing Ion Channel (ASIC) 1a

Abstract

Peptides that bind to ion channels have attracted much interest as potential lead molecules for the development of new drugs and insecticides. However, the structure determination of large peptidechannel complexes using experimental methods is challenging. Thus structural models are often derived from combining experimental information with restraint-driven docking approaches. Using the complex formed by the venom peptide PcTx1 and the acid sensing ion channel (ASIC) 1a as a case study, we have examined the effect of different combinations of restraints and input structures on the statistical likelihood of (a) correctly predicting the structure of the binding interface and (b) the ability to predict which residues are involved in specific pairwise peptide-channel interactions. For this, we have analyzed over 200 000 water-refined docked structures obtained with various amounts and types of restraints of the peptidechannel complex predicted using the docking program HADDOCK. We found that increasing the number of restraints or even the use of pairwise interaction data resulted in only a modest improvement in the likelihood of finding a structure within a given accuracy. This suggests that shape complementarity and the force field make a large contribution to the accuracy of the predicted structure. The results also showed that there are large variations in the accuracy of the predicted structure depending on the precise combination of residues used as restraints. Finally, we reflect on the limitations of relying on geometric criteria such as root-mean square deviations to assess the accuracy of docking procedures. We propose that in addition to currently used measures, the likelihood of finding a structure within a given level of accuracy should be also used to evaluate docking methods.

Molecular Simulations of Disulfide-Rich Venom Peptides with Ion Channels and Membranes

“…A number of recent studies used MD simulations to determine the position of the peptide on the membrane surface and identify the residues that govern membrane binding. Data was in good qualitative agreement with structural data from experiments [ 11 , 18 , 106 , 108 ]. The accurate and reliable prediction of binding affinity from free energy calculations remains very challenging as demonstrated by a number of recent studies.…”

“…A commonly used approach to improve the accuracy of the structures from docking and MD simulations is to make use of experimental information regarding the binding interface [ 18 ]. Potential sources of data include alanine scanning mutagenesis, NMR or cross-linking experiments, and bioinformatics predictions based on secondary structure and sequence conservation or even partial electron densities.…”

“…The predicted binding mode was a good first approximation consistent with the experimental data known at the time but a subsequent co-crystal structure of the PcTx1-ASIC1a complex showed that the peptide adopted a different orientation. Later, Deplazes et al [ 18 ] used the same PcTx1-ASIC1a complex as a model system to assess the ability of restraint-driven docking to reliably predict the structure of peptide-channel complexes. By comparing over 240,000 docked structures, the study examined the effect of different combinations of restraints and input structures on the statistical likelihood of a structure predicted by restraint-driven docking to be of sufficient accuracy for rational drug design.…”

“…Most simulation studies of disulfide-rich venom peptides have focused on understanding the interactions of peptides bound to voltage-gated Na V and K V channels [ 14 , 16 , 17 ] as well as ASICs [ 18 , 19 , 20 , 21 ] (see also Figure 2 ). This partially reflects the abundance of peptides that modulate these ion channels and their therapeutic potential but also the availability of high-resolution crystal structures for these proteins.…”

Modelling peptide–protein complexes: docking, simulations and machine learning

“…HPEPDOCK relies on MODPEP to generate an ensemble of unbound peptide conformations. An advantage of HADDOCK over HPEPDOCK is its ability to handle ambiguous data for the binding site (Deplazes et al, 2016; https://doi.org/10.1017/qrd.2022.14 Published online by Cambridge University Press Williamson, 2013). Finally, Rosetta FlexPepDock ab initio, can also produce a diverse set of peptide conformations for binding, but this comes at a higher computationally expense (Raveh et al, 2010(Raveh et al, , 2011.…”