Abstract
Conotoxins are short, cysteine-rich peptides of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. Currently, of the approximately 6000 known conotoxin sequences, only about 3% have associated structural characterization, which leads to a bottleneck in rapid high-throughput screening (HTS) for identification of potential leads or threats. In this work, we combine a graph-based approach with homology modeling to expand the library of conotoxin structures and to identify those conotoxin sequences that are of the greatest value for experimental structural characterization. The latter would allow for the rapid expansion of the known structural space for generating high quality template-based models. Our approach generalizes to other evolutionarily-related, short, cysteine-rich venoms of interest. Overall, we present and validate an approach for venom structure modeling and experimental guidance and employ it to produce a 290%-larger library of approximate conotoxin structures for HTS. We also provide a set of ranked conotoxin sequences for experimental structure determination to further expand this library.
Highlights
Toxins have for a long time been considered a rich natural source of therapeutic leads because of their high specificity and binding affinity for various receptors involved in different biological pathways [1,2]
We initialize the algorithm by separating a set of over 2000 known conotoxin sequences into databases containing four, six, eight, and ten cysteines, respectively
We refer to the set of sequences that may be homology modeled based on set {Lex } as set {C (Lex }) that are covered by {Lex }
Summary
Toxins have for a long time been considered a rich natural source of therapeutic leads because of their high specificity and binding affinity for various receptors involved in different biological pathways [1,2]. In general, are expected to be a good source of potential therapeutic candidates, and the computational advancements in various HTS strategies make it possible to apply approaches such as docking to more than just small molecules [5,6,7]. In one recent study of note, for example, the authors employed a docking approach to identify α-conotoxin BuIA, produced by species Conus bullatus, as a competitive agonist for the lysophosphatidic acid receptor 6, a G-protein coupled receptor involved in the development
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