In Silico Conotoxin Studies: Progress and Prospects

Abstract

Cone snails of the genus Conus have evolved to produce structurally distinct and functionally diverse venom peptides for defensive and predatory purposes. This nature-devised delicacy enlightened drug discovery and for decades, the bioactive cone snail venom peptides, known as conotoxins, have been widely explored for their therapeutic potential, yet we know very little about them. With the augmentation of computational algorithms from the realms of bioinformatics and machine learning, in silico strategies have made substantial contributions to facilitate conotoxin studies although still with certain limitations. In this review, we made a bibliometric analysis of in silico conotoxin studies from 2004 to 2024 and then discussed in silico strategies to not only efficiently classify conotoxin superfamilies but also speed up drug discovery from conotoxins, reveal binding modes of known conotoxin-ion channel interactions at a microscopic level and relate the mechanisms of ion channel modulation to its underlying molecular structure. We summarized the current progress of studies in this field and gave an outlook on prospects.

Keywords: binding modes; drug discovery; in silico strategies; venom peptides.

Figure 1

Year-by-year changes in the number of published documents on in silico conotoxin studies in the period from 2004 to 2024.

Figure 2

The keyword co-occurrence of bibliometric analysis of in silico conotoxin studies from 2004 to 2024 made by VOSviewer. Some parameters in this figure are listed as follows: Type of analysis—Co-occurrence; Unit of analysis—All keywords; Counting method—Full counting; Minimum number of occurrences of a keyword—5. The normalization method is association strength. The layout is set with Attraction = 2 and Repulsion = 0. Among the 1560 keywords, 134 meet the threshold.

Figure 3

Top 12 keywords with strong citation bursts of in silico conotoxin studies from 2004 to 2024 made by CiteSpace. Some parameters used are listed as follows: Term Source—Title, Abstract and Author Keywords; Node Types—Keyword. The detection model is configured with γ = 1.0 and Minimum duration = 2, resulting in 12 burst items found. Some labels in the figure require to be explained: The blue lines indicate the period when the keywords were used and the red ones represent the burst periods. The “Strength” column reveals the frequency of each keyword. The higher the strength, the more popular the corresponding keyword.