Structural characterization of length-varying peptide sequences for peptide quantitative structure-activity relationship

Peptide quantitative structure-activity relationship (pQSAR) has been widely used in the computational peptidology community to model, predict and explain the activity and function of bioactive peptides. Various amino acid descriptors (AADs) have been developed to characterize the residue building blocks of peptides at sequence level. However, a significant issue is that the total number of AAD-characterized descriptors is proportional to peptide length, thus causing inconsistency in the resulting descriptor vector matrix for a panel of length-varying peptide sequences (LVPSs), which cannot be engaged in pQSAR modelling. Currently, only one AAD-based scaling approach, termed auto-cross covariance (ACC) that was proposed thirty years ago, is available for treating such issue. In this study, we described the second AAD-based multivariate method to do so, namely Residue Descriptor-Distance Vector (RDDV). The strategy characterizes a peptide sequence by using an inter-residue pseudo-interaction potential between different pre-assigned amino acid types involved in the sequence, which results in a given (invariable) number of descriptor parameters for different LVPSs. Here, the RDDV was tested, examined and validated in an in-house pQSAR-oriented bioactive peptide data cluster, which was explored systematically with combinations of different AADs and regression tools. We also compared RDDV with the traditional ACC in multiple aspects.