BinderSpace: A package for sequence space analyses for datasets of affinity-selected oligonucleotides and...

Area of Science:

• Computational Biology
• Bioinformatics
• Molecular Biology

Background:

• High-throughput screening generates large datasets of target-binding molecules like aptamers and peptides.
• Identifying high-affinity binders from these datasets often requires extensive low-throughput experiments.
• Bioinformatics approaches can enhance the understanding of these datasets and pinpoint promising sequence spaces.

Purpose of the Study:

• To introduce BinderSpace, an open-source Python package designed for comprehensive analysis of target-binding molecule datasets.
• To facilitate the identification of high-affinity binders by enabling motif analysis, sequence space visualization, and clustering.
• To provide tools for extracting functionally important sequences from large experimental datasets.

Main Methods:

• BinderSpace performs motif analysis with text-based and visual outputs, including heat maps of functional properties.
• It integrates principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) for data visualization and subset analysis.
• The package supports clustering analyses and sequence extraction from identified clusters.

Main Results:

• Demonstrated BinderSpace's utility on datasets of DNA aptamers binding to carbon nanotubes and cyclic peptidomimetics binding to bovine carbonic anhydrase.
• Successfully applied motif analysis, PCA, t-SNE, and clustering to identify key binding sequences.
• Validated the package's capability to analyze diverse molecular binding datasets.

Conclusions:

• BinderSpace offers a powerful, integrated bioinformatics solution for analyzing large-scale molecular binding data.
• The package streamlines the discovery of high-affinity binders by enhancing data interpretation and sequence selection.
• BinderSpace is publicly available on GitHub, promoting accessibility and further research in molecular binding studies.