Intrinsic Disorder as a Biomimetic Design Paradigm

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This is a timely opinion piece about protein design for disordered proteins. The subject would be of interest to a large number of readers.  There is a minor and a major weakness to this manuscript.

The minor one is about the point made near the beginning that one static structure is not sufficient to represent these proteins.  That problem is much broader than this. This single structure view has detracted from the intrinsic functional dynamics of globular proteins also. Their functions depend on their dynamics, and this single structure perspective has hindered understanding of the functions of globular proteins also.

They also need to mention near the beginning, or somewhere, that the most important characteristic of disordered sequences has long been concluded to be their high charge density.

The major problem, in my opinion, is there are no examples provided of the successes of either the designs themselves, or at least of the characterizations by the approaches mentioned. Nor are there any conclusions to the central questions raised, such as, Does the statistical nature of the characterization mean that the design process is simpler and more forgiving than for globular proteins and does not require the highest levels of stringency?

 

Minor points

Another useful reference to add might be the book  P.J. Flory Statistical Mechanics of Chain Molecules. Even though it is old, much in that book is relevant to the present problem.

On page 2 there is a typo [11?-16]

 

 

 

Author Response

Reviewer Comment 1: 

The minor one is about the point made near the beginning that one static structure is not sufficient to represent these proteins.  That problem is much broader than this. This single structure view has detracted from the intrinsic functional dynamics of globular proteins also. Their functions depend on their dynamics, and this single structure perspective has hindered understanding of the functions of globular proteins also.

Response: We agree that the limitations of a single-structure perspective extend well beyond intrinsically disordered proteins and also affect the understanding of globular proteins. Indeed, a growing body of work has shown that even folded proteins rely on conformational fluctuations, dynamic ensembles, and energy landscape exploration to perform their biological functions.
Our intention in the original text was to highlight how intrinsically disordered regions represent a particularly clear and extreme manifestation of this broader principle, where function is inherently encoded at the level of ensembles rather than a dominant structure. However, we agree that this point would benefit from being placed in a wider context.
Accordingly, we have revised the introduction to explicitly acknowledge that the limitations of the structure-centric paradigm also apply to globular proteins, whose functional mechanisms often depend on dynamical fluctuations around their native states. This addition reinforces the idea that the shift from structure to ensemble is not exclusive to IDRs, but part of a more general paradigm in molecular biophysics.

Changes made: The following sentence was added to the revised manuscript, in the Introduction section:

Importantly, this limitation is not restricted to intrinsically disordered proteins. Even globular proteins rely on conformational dynamics and fluctuations around their native states to perform their functions, highlighting that the structure-centric paradigm is, more generally, an approximation rather than a complete description of molecular function.

Reviewer Comment 2: 

They also need to mention near the beginning, or somewhere, that the most important characteristic of disordered sequences has long been concluded to be their high charge density.

Response: We thank the referee for this important remark. We agree that the high charge density of intrinsically disordered sequences is a key and long-established characteristic, and that it plays a central role in determining their conformational and interaction properties.
We note, however, that while charge density is a fundamental aspect of many disordered sequences, our Perspective aims to highlight a broader design framework in which multiple sequence-level features, such as charge patterning, interaction motifs, and multivalency, collectively define the effective interaction landscape and emergent behavior. The revised text reflects this balance.

Changes made: The second paragraph in the revised Introduction now reads:

This picture has been profoundly revised with the recognition of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs). Once regarded as rare exceptions or experimental artifacts, IDPs are now known to be widespread across proteomes—particularly in eukaryotic organisms—and to play central roles in regulation, signaling, transcription, and cellular organization [3–5]. Rather than representing failures of folding, intrinsic disorder is now better understood as an evolutionarily selected strategy, enabling functional plasticity, tunable affinities, multivalent interactions, and rapid responses to environmental and chemical cues. A key and long-recognized feature of many disordered sequences is their high charge density and low hydrophobic content, which places them in the regime of polyampholytes and polyelectrolytes. This physicochemical signature promotes expanded conformations, strong sensitivity to ionic conditions, and sequence-dependent intramolecular interactions, providing a natural link between IDR behavior and polymer physics descriptions based on charge patterning and electrostatic balance.

 

Reviewer Comment 3: 

The major problem, in my opinion, is there are no examples provided of the successes of either the designs themselves, or at least of the characterizations by the approaches mentioned. Nor are there any conclusions to the central questions raised, such as, Does the statistical nature of the characterization mean that the design process is simpler and more forgiving than for globular proteins and does not require the highest levels of stringency?

Response: We thank the referee for this important and constructive comment. We agree that the original version of the manuscript placed too much emphasis on conceptual framing, without sufficiently highlighting concrete examples and clearly articulated conclusions. We have incorporated the new references 6, 25,26, 61-64 in the revised manuscript

Changes made: The following sentences and new references were added to the manuscript in distinct Sections. In the Introduction:

Importantly, these principles are not merely conceptual: recent experimental work has shown that disorder-driven phase separation can be engineered in synthetic systems, enabling the formation of functional biomolecular condensates with tunable properties  [6].
…
This generality is further supported by synthetic peptide-based systems, where sequence design enables direct control over phase separation behavior and condensate properties, allowing the formation of biomimetic compartments with tunable physicochemical and functional characteristics [25]. The work by Oh et al. has further demonstrated that protein condensates can be engineered with controlled size, stability, and interfacial properties, enabling the design of functional biomaterials with tailored structural and dynamical features [26].

In Section 3:

Minimalist synthetic systems show that key features of intrinsically disordered proteins can be reproduced using simple building blocks [6]. Hybrid constructs combining short peptide “stickers” with flexible polymeric “spacers” undergo liquid–liquid phase separation and form artificial membraneless organelles capable of recruiting biomolecules and enhancing biochemical reactions [61].  These principles extend to cellular contexts, where engineered disordered protein sequences have been used to construct artificial membraneless organelles that improve metabolic efficiency by spatially organizing enzymatic pathways [62]. Peptide-based systems have also been designed to undergo phase separation in response to biochemical triggers, such as enzymatic activity, enabling the formation of functional condensates directly in vivo [63]. More generally, minimal models and biomimetic systems show that controlling a small set of interaction parameters—such as valency, interaction strength, and sequence patterning—is sufficient to reproduce complex assembly pathways and material properties [26,64].

Reviewer Minors Comments: 

Another useful reference to add might be the book  P.J. Flory Statistical Mechanics of Chain Molecules. Even though it is old, much in that book is relevant to the present problem.

On page 2 there is a typo [11?-16]

 

Response: We added the reference and corrected the typo.

Reviewer 2 Report

Comments and Suggestions for Authors

Comments: The work of Puccinelli et. al., performed a comprehensive conceptual synthesis to investigate the framing of intrinsic disorder as a programmable, biomimetic design paradigm for molecular and materials engineering. The present work shows that intrinsically disordered proteins and regions (IDPs/IDRs) function through statistical ensembles, weak multivalent interactions, and collective behaviors, which closely parallel the design principles found in associative polymers and colloidal systems. The work is of interest as it contributes to understanding how disorder-driven phase behavior and liquid-liquid phase separation (LLPS) can be deliberately engineered to create adaptive biomolecular systems and functional condensates. The authors performed a detailed literature review of coarse-grained modeling, machine learning, and inverse design strategies applied to intrinsic disorder; however, more detailed analysis and judgement are required for some of the proposed frameworks and their practical translation into physical applications.

1. Page 4, lines 122-126. The authors adapt theoretical frameworks originally developed for polymer solutions and networks, including Flory-Huggins theory, random phase approximation approaches, and sticker-spacer models, to rationalize condensate formation driven by IDRs. However, there is no quantitative discussion on how the parameters of these models directly translate to the experimental synthesis of biomimetic materials. Providing a specific example of targeted parameters would strengthen this transition from theory to practice.
2. Page 5, lines 180-184. The authors state that agreement at the level of single-molecule observables does not necessarily guarantee accurate prediction of coexistence densities, phase diagrams, or material properties. Furthermore, the authors note that different coarse-grained parameterizations may reproduce similar chain statistics while encoding distinct physical mechanisms. A more explicit discussion on which collective observables should be prioritized during the calibration of these models would help the reader understand how to overcome this barrier.
3. Page 6, lines 222-225. The manuscript asserts that transferability does not emerge automatically from coarse-graining, but must be built in deliberately by deciding which collective observables a model is meant to preserve and under which conditions it is expected to remain valid. While this is a critical insight, the manuscript would benefit from specifying exactly how transferability can be practically integrated into the proposed layered modeling strategies, which combine ML-based screening, CG simulations, and targeted atomistic calculations.
4. Page 7, lines 268-276. The text establishes that function is encoded in a molecular grammar defined by interaction motifs, sequence patterning, and statistical connectivity. Additionally, charge composition and charge patterning regulate long-range electrostatic interactions and overall chain compaction, while aromatic and hydrophobic residues act as interaction hotspots. A diagram or table explicitly mapping these specific sequence features to their corresponding emergent material properties would greatly enhance the clarity of this section.
5. Page 8, lines 345-349. The authors call for closed-loop design workflows that integrate machine learning, physics-based modeling, and experimental validation within a biomimetic framework. Because this concept serves as a major future direction, a schematic workflow outlining the specific steps of this integration, from sequence prediction to physical validation, would solidify the conclusions.

Author Response

Reviewer Comment 1: 

Page 4, lines 122-126. The authors adapt theoretical frameworks originally developed for polymer solutions and networks, including Flory-Huggins theory, random phase approximation approaches, and sticker-spacer models, to rationalize condensate formation driven by IDRs. However, there is no quantitative discussion on how the parameters of these models directly translate to the experimental synthesis of biomimetic materials. Providing a specific example of targeted parameters would strengthen this transition from theory to practice.

 

Response: We thank the referee for this comment. We agree that establishing a clearer connection between theoretical model parameters and experimentally accessible design variables strengthens the link between physical frameworks and biomimetic implementation. Once this Perspective aims to emphasize conceptual and transferable design principles, a fully quantitative treatment is beyond its scope. However, we acknowledge that including a concrete example of parameter mapping improves clarity. In the revised manuscript, we have added a short discussion illustrating how key parameters in commonly used models translate into experimentally tunable quantities.

Changes made: The following discussion was added to the revised manuscript, in Section 2:

In practical terms, the parameters entering these models can be mapped onto experimentally accessible control variables. Within a Flory–Huggins framework, the effective interaction parameter $\chi$ reflects the balance between monomer–monomer and monomer–solvent interactions, and can be tuned experimentally through solvent quality, salt concentration, and sequence composition. In sticker–spacer descriptions, the relevant energy scale associated with sticker–sticker interactions sets the effective attraction strength, while the sticker density along the chain defines the valency and connectivity of the network. Similarly, the overall protein or polymer concentration directly controls the proximity to the phase boundary, as predicted by mean-field and RPA-type approaches. These correspondences provide a direct mapping between model parameters and experimentally tunable design variables in biomimetic systems.

Reviewer Comment 2: 

Page 5, lines 180-184. The authors state that agreement at the level of single-molecule observables does not necessarily guarantee accurate prediction of coexistence densities, phase diagrams, or material properties. Furthermore, the authors note that different coarse-grained parameterizations may reproduce similar chain statistics while encoding distinct physical mechanisms. A more explicit discussion on which collective observables should be prioritized during the calibration of these models would help the reader understand how to overcome this barrier.

Response: We agree that clarifying which observables should be prioritized in the calibration of coarse-grained models is essential to bridge the gap between single-chain accuracy and predictive collective behavior.

Changes made: The following sentence was added to the revised manuscript, in Section 3:

This ambiguity highlights the need to prioritize collective observables in model calibration. In particular, phase coexistence properties, such as binodal concentrations, provide direct constraints on the effective interaction parameters governing phase separation. Structural observables in the dense phase, including pair correlation functions and local density fluctuations, further probe the organization of the condensed state. In addition, thermodynamic response functions related to compressibility and concentration fluctuations offer complementary information on interaction strength and correlations. Together, these quantities provide a more stringent and physically grounded basis for parameterizing coarse-grained models than single-chain observables alone.

Reviewer Comment 3: 

Page 6, lines 222-225. The manuscript asserts that transferability does not emerge automatically from coarse-graining, but must be built in deliberately by deciding which collective observables a model is meant to preserve and under which conditions it is expected to remain valid. While this is a critical insight, the manuscript would benefit from specifying exactly how transferability can be practically integrated into the proposed layered modeling strategies, which combine ML-based screening, CG simulations, and targeted atomistic calculations

Response: We thank the referee for this comment. While the lack of automatic transferability in coarse-grained models is a well-recognized limitation, it is important to clarify how transferability can be systematically incorporated within a multiscale modeling strategy. In the revised manuscript, we have expanded this discussion to explicitly describe how transferability can be built into the proposed layered workflow. 

Changes made: The following sentence was added to the revised manuscript, in Section 3:

In a practical modeling workflow, transferability can be incorporated by assigning complementary roles to different levels of description. Machine learning approaches provide a global exploration of sequence space, identifying robust trends and candidate regions across varying conditions. Coarse-grained models are then calibrated to reproduce selected collective observables—such as phase behavior, structural correlations, and concentration-dependent properties—within a defined thermodynamic regime. Finally, targeted atomistic simulations are used to resolve local interaction mechanisms and validate or refine the effective interactions encoded at the coarse-grained level. In this hierarchical strategy, transferability is not assumed but constructed through consistency across scales, with each level constraining the range of validity of the others.

Reviewer Comment 4: 

Page 7, lines 268-276. The text establishes that function is encoded in a molecular grammar defined by interaction motifs, sequence patterning, and statistical connectivity. Additionally, charge composition and charge patterning regulate long-range electrostatic interactions and overall chain compaction, while aromatic and hydrophobic residues act as interaction hotspots. A diagram or table explicitly mapping these specific sequence features to their corresponding emergent material properties would greatly enhance the clarity of this section.

Response: In the revised manuscript, we have added a summary table that maps key sequence features to their corresponding physical effects on chain conformations, intermolecular interactions, and phase behavior. This provides a more structured overview of the molecular grammar underlying disorder-based design and helps clarify how specific sequence features translate into emergent material properties.

Changes made: The Table 1 was added to Section 4.

Reviewer Comment 5: 

Page 8, lines 345-349. The authors call for closed-loop design workflows that integrate machine learning, physics-based modeling, and experimental validation within a biomimetic framework. Because this concept serves as a major future direction, a schematic workflow outlining the specific steps of this integration, from sequence prediction to physical validation, would solidify the conclusions.

Changes made: The new Figure 2 was added to the manuscript.