“Visualize, Explore, and Select”: A Protein Language Model-based Approach Enabling Navigation of Protein Sequence Space for Enzyme Discovery and Mining

Abstract The rapid expansion of protein sequence databases continues to outpace functional characterization, creating a persistent bottleneck in enzyme discovery and mining—particularly in large, heterogeneous, and sparsely annotated sequence spaces. This gap is amplified by visualization challenges and the lack of informed strategies for exploration, selection, and mining across sequence spaces. Here, we present an embedding-based workflow, implemented in a computational platform called SelectZyme, for alignment-free visualization and exploration of protein sequence space that combines protein language model (pLM) representations with dimensionality reduction and hierarchical density-based clustering. The approach links complementary visualizations of protein sequence space as low-dimensional landscapes, connectivity projections (minimum spanning trees), and dendrogram-based organization, enabling coherent interactive exploration and candidate selection across global context and local neighborhoods without relying on sequence-identity thresholds, EC numbers, conserved motifs, or predefined functional annotations. Across distinct case studies, we demonstrate that embedding-defined neighborhoods remain structurally conserved even when sequence identity falls within the ‘twilight zone’, and that coherent functional organization emerges also for modular protein segments in a fully unsupervised analysis. We also show how this workflow supports user friendly, interactive and scalable enzyme mining in a sparsely annotated, complex multi-family protein space surpassing > 100,000 sequences, enabling constrained candidate selection around experimentally validated anchors. By enabling interactive exploration across visualizations, supporting informed candidate selection, our workflow streamlines biocatalyst discovery and helps to bridge uncharacterized sequence-space to functional characterization campaigns - thus providing a broad starting point to downstream protein engineering, machine-learning–guided design cycles, and iterative experimental screening campaigns.