Embrace Disorder as a Function: Bio-Informed Biomaterials Design Advanced by Intrinsically Disordered Proteins

The “lock and key” model of molecular recognition, anchored by the canonical sequence–structure–function paradigm, has successfully served as a basis for designing biomimetic biomaterials for decades. This classical concept associates the function of molecules with a stable three-dimensional structure that recognizes a specific complementary surface. However, biological systems routinely achieve specificity, adaptability, and multifunctionality through protein domains that never adopt a single stable fold. Intrinsically Disordered Proteins (IDPs) and Intrinsically Disordered Regions (IDRs) represent an underutilized functional repertoire in which conformational plasticity, short linear motifs, and phase-separation capacity underpin behaviors that rigid-fold proteins cannot replicate. This Perspective Article extends the molecular biomimetic design framework to the broader range of IDPs/IDRs roles—as effectors triggering downstream signaling, as motion-directing elements interacting with external partners, and as molecular assemblers that organize dynamic complexes while largely avoiding the steric constraints that structured proteins impose. Sequence plasticity, which is greater in disordered domains than in structured proteins, further expands this versatility. Metamorphic and moonlighting proteins further extend the sequence-to-function landscape by encoding multiple folds or functions within a single chain. We discuss how this bio-informed framework guides peptide-based biomaterials design by exploiting the dynamic, multivalent interactions of IDPs. Artificial Intelligence (AI)-guided approaches and machine learning strategies—from supervised classifiers to reinforcement learning loops—that are integrated with experimental feedback to navigate within these high-dimensional design landscapes of intrinsically disordered bio-informed materials systems are provided. Next-generation bio-informed material design must meet challenges driven by clinical, environmental, and industrial needs. Expanding protein matrix in bio-informed material design may enable mimicking complex, dynamic and hierarchical biological functions and materials that are found in Nature. Embracing disorder may even expand functions beyond addressing current challenges. Moving beyond the single-structure paradigm that has dominated the biomimetic field, embracing disorder in biological protein repertoire may give rise to emerging functions in bio-informed biomaterials. These functions are truly adaptive, modular, resourceful, responsive, and sustainable.

Authors

Institutions

Publication Details

Journal
Biomimetics
Published
2026-09-21
DOI
https://doi.org/10.3390/biomimetics11090680
Primary Topic
Supramolecular Self-Assembly in Materials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Embrace Disorder as a Function: Bio-Informed Biomaterials Design Advanced by Intrinsically Disordered Proteins

Candan Tamerler, Malcolm L. Snead
Biomimetics
Supramolecular Self-Assembly in Materials
article

Embrace Disorder as a Function: Bio-Informed Biomaterials Design Advanced by Intrinsically Disordered Proteins

Candan Tamerler, Malcolm L. Snead
article en

Abstract

The “lock and key” model of molecular recognition, anchored by the canonical sequence–structure–function paradigm, has successfully served as a basis for designing biomimetic biomaterials for decades. This classical concept associates the function of molecules with a stable three-dimensional structure that recognizes a specific complementary surface. However, biological systems routinely achieve specificity, adaptability, and multifunctionality through protein domains that never adopt a single stable fold. Intrinsically Disordered Proteins (IDPs) and Intrinsically Disordered Regions (IDRs) represent an underutilized functional repertoire in which conformational plasticity, short linear motifs, and phase-separation capacity underpin behaviors that rigid-fold proteins cannot replicate. This Perspective Article extends the molecular biomimetic design framework to the broader range of IDPs/IDRs roles—as effectors triggering downstream signaling, as motion-directing elements interacting with external partners, and as molecular assemblers that organize dynamic complexes while largely avoiding the steric constraints that structured proteins impose. Sequence plasticity, which is greater in disordered domains than in structured proteins, further expands this versatility. Metamorphic and moonlighting proteins further extend the sequence-to-function landscape by encoding multiple folds or functions within a single chain. We discuss how this bio-informed framework guides peptide-based biomaterials design by exploiting the dynamic, multivalent interactions of IDPs. Artificial Intelligence (AI)-guided approaches and machine learning strategies—from supervised classifiers to reinforcement learning loops—that are integrated with experimental feedback to navigate within these high-dimensional design landscapes of intrinsically disordered bio-informed materials systems are provided. Next-generation bio-informed material design must meet challenges driven by clinical, environmental, and industrial needs. Expanding protein matrix in bio-informed material design may enable mimicking complex, dynamic and hierarchical biological functions and materials that are found in Nature. Embracing disorder may even expand functions beyond addressing current challenges. Moving beyond the single-structure paradigm that has dominated the biomimetic field, embracing disorder in biological protein repertoire may give rise to emerging functions in bio-informed biomaterials. These functions are truly adaptive, modular, resourceful, responsive, and sustainable.

BiomimeticsVol. 11(9)
University of Southern California (US), University of Kansas (US), The University of Kansas Cancer Center (US)
Life in Land
Openalex Percentile: Top 22%
Supramolecular Self-Assembly in Materials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.