PepQSAR: a comprehensive data source and information platform for peptide quantitative structure–activity relationships

Abstract

Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and extrapolative prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, for pQSARs, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, literatures, etc. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB.

Molecular modeling and rational design of disulfide‐stapled self‐inhibitory peptides to target IL‐17A/IL‐17RA interaction

“…In this respect, we considered splitting the two segments from IL‐17A protein context as SIPs, and their primary sequences were characterized by amino acid descriptors. SIPs are therapeutic peptides that mimic the binding hotspot of protein/protein complex by rebinding at the complex interface to competitively disrupt the complex interaction 41,42 . Previously, SIPs have been successfully developed for inhibiting IL‐25/IL‐17Rb interaction 13 .…”

“…SIPs are therapeutic peptides that mimic the binding hotspot of protein/protein complex by rebinding at the complex interface to competitively disrupt the complex interaction. 41,42 Previously, SIPs have been successfully developed for inhibiting IL-25/IL-17Rb interaction. 13 Here, the I-shaped and Ushaped segments were split from the monomers 1 and 2 of IL-17A homodimer to their respective SIPs, namely, I-peptide and U-peptide, respectively, which were separately subjected to 400-ns MD simulation for sufficient relaxation and conformational equilibrium.…”

Targeting peptide‐mediated interaction between the N‐protein and P‐protein of human pediatric respiratory syncytial virus by molecular design of chemically stapled helical peptides

“…Here, the co-crystallized water molecules, ions, and other cofactors were manually removed from the crystal structure, hydrogen atoms were added automatically to the structure using ProteinPlus server [23], and protonation state was assigned for those titratable residues in the structure using H++ server [24]. The sequences of N-protein, P-protein and its C-terminal tail were characterized using amino acid descriptors [25,26]. The complex structure was then subjected to a round of energy minimization by using a 3Drefine refinement protocol [27] to eliminate unreasonable atomic arrangement in local conformation involved in the structure because of the pretreatment.…”

Systematic analysis and comparison of peptide specificity and selectivity between their cognate receptors and noncognate decoys

“…13 Previously, we successfully employed peptide quantitative structure-activity relationship (pQSAR) methodology to carry out the modeling and prediction, of domain-peptide affinities with at sequence level, 14 and also performed systematic comparison and comprehensive evaluation of various amino acid descriptors (AADs) in the pQSAR modeling. 15,16 The peptide affinity and specificity can be regarded as two unityof-opposite aspects of PpIs; the former is an absolute quantity that represents the direct binding strength of a peptide ligand to its cognate protein receptor, while the latter is a relative value that indicates the peptide affinity difference between its cognate receptor and those all noncognate decoys that are potentially encountered by the peptide in cell. In this respect, although peptide affinity has been widely investigated and a variety of experimental and computational methods have been described to do so, the peptide specificity (or selectivity) still remains largely unexplored to date.…”

“…In addition, quantitative prediction of protein–peptide binding affinities has also attracted increased interest in the bioinformatics and drug design areas, 12 which is also an important topic of computational peptidology 13 . Previously, we successfully employed peptide quantitative structure–activity relationship (pQSAR) methodology to carry out the modeling and prediction, of domain–peptide affinities with at sequence level, 14 and also performed systematic comparison and comprehensive evaluation of various amino acid descriptors (AADs) in the pQSAR modeling 15,16 …”