We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Rethinking peptide developability with sequence-only models: interpretable screening of microplastic-binding peptides with gated query pooling.
Summary
Scientists are designing tiny protein snippets (peptides) that can grab onto microplastics, potentially helping filter them out of water or our bodies—but these peptides need to be safe, not cause blood cell damage, and actually dissolve in water to be useful. This study created a smarter computer model that predicts, just from a peptide's chemical sequence, whether it's likely to be safe and practical, achieving high accuracy without needing expensive lab tests upfront. This matters because it speeds up the search for microplastic-fighting peptides that could one day help reduce our exposure to these pervasive pollutants, while flagging that ke
Designing peptides for microplastic targeting is intrinsically multi-objective: sequence motifs that promote adsorption to hydrophobic polymers frequently elevate developability risks, including hemolysis, non-specific adsorption, and poor aqueous solubility. In this paper, we show that accurate developability screening can be achieved from sequence alone by focusing on the readout that converts token-level foundation model representations into peptide-level decisions. We introduce gated query pooling (GQP), a lightweight, backbone-agnostic evidence-selection head that learns a small set of query vectors to extract complementary signals from protein language model embeddings and gates them adaptively per peptide. With a consistent evaluation protocol and identical splits for all methods, GQP with sequence-only backbones reaches 91.09%, 86.30%, and 75.56% accuracy on hemolysis, non-fouling, and solubility, respectively, outperforming representative sequence-only and AlphaFold-augmented Multi-Peptide baselines. Beyond predictive accuracy, attention diagnostics and controlled counterfactual substitutions enable residue-level, testable design rules that connect model outputs to actionable sequence edits. Finally, integrating these developability constraints with PepBD-derived affinity scores for polyethylene, polypropylene, and polyethylene terephthalate supports scalable multi-objective prioritization of microplastic-binding candidates and reveals non-fouling as a dominant feasibility bottleneck, with coarse-grained molecular dynamics triage providing complementary physical evidence supporting the plausibility of the PepBD-prioritized selections.