Comprehensive Evaluation of Fourteen Docking Programs on Protein–Peptide Complexes

Abstract

A large number of protein−protein interactions (PPIs) are mediated by the interactions between proteins and peptide segments binding partners, and therefore determination of protein−peptide interactions (PpIs) is quite crucial to elucidate important biological processes and design peptides or peptidomimetic drugs that can modulate PPIs. Nowadays, as a powerful computation tool, molecular docking has been widely utilized to predict the binding structures of protein−peptide complexes. However, although a number of docking programs have been available, the systematic study on the assessment of their performance for PpIs has never been reported. In this study, a benchmark data set called PepSet consisting of 185 protein− peptide complexes with peptide length ranging from 5 to 20 residues was employed to evaluate the performance of 14 docking programs, including three protein−protein docking programs (ZDOCK, FRODOCK, and HawkDock), three small molecule docking programs (GOLD, Surflex-Dock, and AutoDock Vina), and eight protein−peptide docking programs (GalaxyPepDock, MDockPeP, HPEPDOCK, CABS-dock, pepATTRACT, DINC, AutoDock CrankPep (ADCP), and HADDOCK peptide docking). A new evaluation parameter, named IL_RMSD, was proposed to measure the docking accuracy with f nat (the fraction of native contacts). In global docking, HPEPDOCK performs the best for the entire data set and yields the success rates of 4.3%, 24.3%, and 55.7% at the top 1, 10, and 100 levels, respectively. In local docking, overall, ADCP achieves the best predictions and reaches the success rates of 11.9%, 37.3%, and 70.3% at the top 1, 10, and 100 levels, respectively. It is expected that our work can provide some helpful insights into the selection and development of improved docking programs for PpIs. The benchmark data set is freely available at http://cadd.zju.edu.cn/pepset/.

TheAutoDocksuite at 30

“…ADCP, which currently allows consistent docking of peptides with up to 20 amino acids 25 (Figure 7), is at the cutting edge of this field in a recent evaluation of 14 peptide docking programs. 61 ADCP can also cyclize peptides head-to-tail ( Figure 7a) and/or by forming up to two disulfide bridges when cysteines are present (Figure 7b) thus supporting molecules with multiple cycles. 27 Cyclization is achieved on-the-fly during the docking simulation by using potentials to pull the N-and C-termini or cysteine sulfur atoms together while ignoring steric repulsion between these atoms.…”

“…We are currently tackling these challenges by exploring simplified representations of the conformational space based on backbone crankshaft transformations 26 (Figure 3). ADCP, which currently allows consistent docking of peptides with up to 20 amino acids 25 (Figure 7), is at the cutting edge of this field in a recent evaluation of 14 peptide docking programs 61 . ADCP can also cyclize peptides head‐to‐tail (Figure 7a) and/or by forming up to two disulfide bridges when cysteines are present (Figure 7b) thus supporting molecules with multiple cycles 27 .…”

Mining peptides against SARS-CoV-2 entry from constructed RBMs-hACE2 isomer libraries using machine learning and molecular docking

“…Typical hydrophobic face is not seen in Lead 1, 3 and 4 though it seems to be in W1, W2 and L2 ( Figure 7C). As emphasized before, the 4 distinct isomers were determined and selected by the experiential thresholds of RMSD and binding affinity 36,40 though there were no statistical differences of RMSD and binding affinity among the isomers and wild types ( Figure 8). In line with early studies 42 , various physicochemical properties were found in the millions of isomers (data not shown).…”

“…Using the reference ligands and the docking receptor SARS-CoV-2 S chain E ( Figure 4B, 4D-E), individual affinity maps were computed by the ARDF model in AutoDock CrankPep. As shown in Figure 5 and in Table S2 Supplementary data, following 618 docking assays (all dots), 4 peptide ligands (red dots) were finally determined as the most promising leads against SARS-CoV-2 S because the mean values of all atoms Root-Mean-Square Deviation (RMSD) and binding affinity were less than 2.5 Å and -10 kcal/mol respectively among all the first 10 docking ranks 36,40 . These outputs were the first demonstration of the effective binding activities between RBMs-hACE2 isomers and SARS-CoV-2 S and suggested that these 4 peptide isomers retained inhibitory potentials against SARS-CoV-2 infection.…”

“…As indicated in Figure 2 and 3, based on the 3D structures of RBMs-hACE2, millions of the homolog isomers were generated by computational traversal and permutation methods. These candidates were screened through a feature filter, a Python program using modlamp and AutoDock CrankPep 35,36,40 , 4 peptide leads with potential inhibitory activity against SARS CoV-2 S were identified by their distinct binding activity, stability, binding affinity, effective conformation and transmembrane potentials (Table 1-2, Figure 3 and 5). Interface analysis unveiled that these peptide leads exhibited specific binding activities against SARS-CoV-2 S, encompassing whole RBM regions through K417, Y453, L455, F456, A475, N487, Y489, Q493, S494 to Y505.…”