Dataset Information

A simple, yet highly accurate, QSAR model captures the complement inhibitory activity of compstatin.

ABSTRACT: Compstatin is a 13-residue cyclic peptide inhibitor of complement activation that was originally identified through phage-mediated presentation of a peptide library to C3b. Recent efforts to improve its activity have led to a rich dataset of complement analogs, with the most active analog being approximately 260 times more active than the parent compstatin. In the present work, a highly transparent quantitative structure-activity relationship model (Radj2=0.89) with four parameters is presented that captures important physico-chemical and geometrical properties of the analog molecules with regard to activity. The number of aromatic bonds and hydrophobicity of the fourth residue of compstatin correlated strongly with activity. Also important were the hydrophobic patch size near the disulfide bond and the solvent-accessible surface area occupied by nitrogen atoms of basic amino acid residues.

SUBMITTER: Mulakala C

PROVIDER: S-EPMC1995432 | biostudies-literature | 2007 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A simple, yet highly accurate, QSAR model captures the complement inhibitory activity of compstatin.

Mulakala Chandrika C Lambris John D JD Kaznessis Yiannis Y

Bioorganic & medicinal chemistry 20061213 4

Compstatin is a 13-residue cyclic peptide inhibitor of complement activation that was originally identified through phage-mediated presentation of a peptide library to C3b. Recent efforts to improve its activity have led to a rich dataset of complement analogs, with the most active analog being approximately 260 times more active than the parent compstatin. In the present work, a highly transparent quantitative structure-activity relationship model (Radj2=0.89) with four parameters is presented ...[more]

PMID: 17188878

Similar Datasets

Project description:The ability of many proteins to convert from their functional soluble state to amyloid fibrils can be attributed to inter-molecular beta strand formation. Such amyloid formation is associated with neurodegenerative disorders like Alzheimer's and Parkinson's. Molecular modelling can play a key role in providing insight into the factors that make proteins prone to fibril formation. However, fully atomistic models are computationally too expensive to capture the length and time scales associated with fibril formation. As the ability to form fibrils is the rule rather than the exception, much insight can be gained from the study of coarse-grained models that capture the key generic features associated with amyloid formation. Here we present a simple lattice model that can capture both protein folding and beta strand formation. Unlike standard lattice models, this model explicitly incorporates the formation of hydrogen bonds and the directionality of side chains. The simplicity of our model makes it computationally feasible to investigate the interplay between folding, amorphous aggregation and fibril formation, and maintains the capability of classic lattice models to simulate protein folding with high specificity. In our model, the folded proteins contain structures that resemble naturally occurring beta-sheets, with alternating polar and hydrophobic amino acids. Moreover, fibrils with intermolecular cross-beta strand conformations can be formed spontaneously out of multiple short hydrophobic peptide sequences. Both the formation of hydrogen bonds in folded structures and in fibrils is strongly dependent on the amino acid sequence, indicating that hydrogen-bonding interactions alone are not strong enough to initiate the formation of beta sheets. This result agrees with experimental observations that beta sheet and amyloid formation is strongly sequence dependent, with hydrophobic sequences being more prone to form such structures. Our model should open the way to a systematic study of the interplay between the factors that lead to amyloid formation.

Dataset Information

A simple, yet highly accurate, QSAR model captures the complement inhibitory activity of compstatin.

Publications

A simple, yet highly accurate, QSAR model captures the complement inhibitory activity of compstatin.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets