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Open research questions in Protein Structure and Dynamics

86 unresolved questions extracted from the limitations and future-work sections of 413 Protein Structure and Dynamics papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Recent advances in generative AI have created new opportunities for learning molecular thermodynamics, kinetics, and conformational evolution directly from simulation data, but progress is limited by the availability of large-scale datasets that combine rigorous sampling, complete phase-space information, and diverse physicochemical perturbations.

    pHaseMD4AI: Phase-Space Dynamics Dataset with Chemical and pH Perturbations for Physically and Kinetically Consistent Biomolecular AI · 2026 · DOI

  • Transmembrane (TM) protein-protein interactions (PPIs) are essential mediators of signal transduction, transport of solutes and communication, yet the biophysical features that characterize their diverse topologies remain poorly understood.

    The topology of transmembrane protein-protein interaction interfaces is encoded in their physicochemical features · 2026 · DOI

  • Although crystal structures have defined the apo inactive and Ca2+-bound active states of Sorcin, the transition pathways connecting these states and the conformational ensembles populated under each condition remain poorly understood.

    Long-Timescale Molecular Dynamics Reveal a Coordination-Biased Conformational Selection Mechanism for Sorcin Activation · 2026 · DOI

  • Accurate prediction of blood-brain barrier permeability (BBBP) is essential for central nervous system drug discovery, yet existing models are often limited by their reliance on predefined physicochemical descriptors, small-molecule-centered training sets, or conformation-dependent representations, which restricts their transferability across chemically diverse modalities especially peptides.

    BBBP_Atlas: Unified Interpretable Modeling of Blood Brain Barrier Permeability across Small Molecules and Peptides · 2026 · DOI

  • The SMN2 exon 7 5 splice-site/U1 snRNA duplex contains an A-1 bulge that weakens splice-site recognition and represents a therapeutically relevant RNA connectivity defect, yet its conformational landscape and coupling to solvation remain poorly understood.

    Solvation Shapes the Conformational Landscape of a Therapeutically Relevant SMN2 Splice-Site Defect · 2026 · DOI

  • While previously developed approaches to reuse validated building-blocks in novel contexts simplify the design task to the generation of a rigid genetic fusion between pre-existing domains (22, 55, 56), they have only been applied to simpler non-responsive PPIs and are even more limited by the scalability of current design methods.

    A generalizable interface-seeded framework for de novo design of functional oligomers · 2026 · DOI

  • 501 Despite the relatively detailed analysis, predicting intrinsic disorder, establishing 502 causal relationships, and understanding evolutionary processes at disordered regions are among 503 the main limitations of this study and are discussed in the next sections.

    Location dependence of protein intrinsic disorder in Drosophila melanogaster · 2026 · DOI

  • Protein kinase activation is driven by conformational changes across multiple structural components, including the conserved Asp-Phe-Gly (DFG) motif, but whether these transitions follow a universal mechanism remains unclear.

    Tyrosine kinases sample unique activation ensembles · 2026 · DOI

  • To provide the flexibility required to appropriately arrange these target proteins, ComplexDesign introduces a specialized masking mechanism that enables exploration of possible relative arrangements rather than being limited to the predefined ones.

    ComplexDesign: sequence-hallucination design of protein binders bridging multiple proteins · 2026 · DOI

  • Many IDRs adopt an ordered structure upon binding to folded protein domains, but the energy landscape for coupled folding and binding (CFB) is poorly understood.

    Electrostatic interactions compensate energetic frustration in the coupled folding and binding of intrinsically disordered proteins · 2026 · DOI

  • While structures of DnaK in complex with nucleotides, co-chaperones, and short peptides have been resolved, structures with larger, stably folded substrates--such as firefly luciferase (Fluc, 61 kDa)--are lacking, limiting mechanistic understanding of how DnaK refolds such proteins.

    DnaK refolds denatured proteins by actively pulling out their misfolded structural elements · 2026 · DOI

  • However, although these tools have improved in structural prediction accuracy, their ability to filter designed binders---an essential use case---remains insufficient; whereas design methods have focused more on unconstrained binder generation rather than capabilities enabled by controllable design.

    Promera: a unified model for biomolecular structure prediction, filtering, and design · 2026 · DOI

  • While recent co-folding models such as AlphaFold-3 achieve accurate structure prediction, they fail to generalize to underexplored binding interfaces - systematically misplacing ligands, particularly for allosteric or structurally novel targets.

    Towards Generalizable Protein-ligand Co-folding with ACER · 2026 · DOI

  • AlphaFold 3 predicts biomolecular structures with unprecedented accuracy, yet the computations transforming sequence and evolutionary data into structural coordinates remain poorly understood.

    AlphaInterp: Mechanistic Interpretability of AlphaFold 3 Reveals How Evolutionary Information Shapes Protein Structure Prediction · 2026 · DOI

  • While structural proteomics techniques like Cross-Linking Mass Spectrometry (XL-MS) and Hydrogen-Deuterium Exchange (HDX-MS) offer valuable spatial and dynamic insights, integrating these sparse, heterogeneous measurements into these models remains an open challenge.

    Co-folding model guided by structural proteomics · 2026

  • 10.1 Summary of Key Contributions The substrate-scaffolding model resolves major paradoxes in protein biology and provides prac- tical routes to constraint-based design and therapeutics. 10.2 Experimental Validation Roadmap Short-, medium-, and long-term validation pathways are detailed, now strengthened by the PHI-base 5.4 empirical foundation. 7 10.3 Technological Development Priorities Computational infrastructure, experimental capabilities, and integration platforms are priori- tised. 10.4 Broader Impact and Vision The framework has transformative potential across materials science, artificial intelligence, sys- tems biology, and biotechnology. 10.5 Final Reflections Biological systems excel at adaptive robustness rather than brittle optimisation. The substrate- scaffolding perspective offers a more biological approach to technology and a deeper appreciation of evolutionary design.

    A Unified Substrate-Mediated Framework for Protein Function · 2026 · DOI

  • Interpretability of deep sequence-based protein-protein interaction predictions from AlphaFold3 remains a black box. Methods to crack the black box and understand which sequence features drive AlphaFold3 predictions of protein complex interfaces are needed for confidence assessment in drug discovery.

    The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology · 2026 · DOI

  • Benchmark sets for statistically correct evaluation of AlphaFold applications require regular updating to prevent overfitting and memorization on standard test sets. Current validation protocols lack agreed-upon, regularly refreshed benchmark datasets for evaluating AlphaFold2 and AlphaFold3 performance across protein classes.

    The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology · 2026 · DOI

  • Tokenization strategies in transformer-based models for biological sequences significantly affect performance, but optimal tokenization approaches for AlphaFold3 and similar sequence models remain undefined. Systematic investigation of tokenization effects on structural prediction accuracy for diverse protein types is needed.

    The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology · 2026 · DOI

  • The reliability of AlphaFold2 and AlphaFold3 models for virtual drug screening on class A GPCRs and other membrane proteins has not been comprehensively validated. Systematic evaluation is needed comparing predicted GPCR structures from AlphaFold3 against experimental structures for structure-based drug design applications.

    The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology · 2026 · DOI

  • Prediction of multiple conformations and dynamic protein ensembles in the post-AlphaFold era requires development of methods beyond single static structure prediction. Current approaches lack standardized protocols for sampling conformational heterogeneity and ensemble modeling from AlphaFold2/3 predictions of intrinsically disordered regions and flexible domains.

    The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology · 2026 · DOI

  • AI-based docking methods including those leveraging AlphaFold predictions fail to generate physically valid poses and do not generalize to novel protein sequences. Specific methodological improvements are needed to ensure predicted macromolecular complexes from AlphaFold3 satisfy chemical and physical constraints rather than relying on memorized training distributions.

    The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology · 2026 · DOI

  • AlphaFold3's performance on D-peptides and non-canonical amino acids remains unresolved. Current validation lacks comprehensive evaluation of whether AlphaFold3 can accurately predict structures of peptidomimetics with non-canonical amino acid building blocks, which are critical for peptide-based drug discovery applications.

    The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology · 2026 · DOI

  • AlphaFold3's predictions for fold-switched proteins are driven by structure memorization rather than genuine conformational prediction capability. The field lacks systematic benchmarking to distinguish whether AlphaFold3 can accurately predict genuine fold-switching transitions or merely reproduces training data patterns for proteins with conditional folding behavior.

    The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology · 2026 · DOI

  • The paper references artifacts in experimental protein structures from crystal packing effects (Eyal et al. 2005, Kleywegt 1999) but does not quantify how these experimental reference structures impact validation benchmarks for rectified AI-generated ensembles or establish acceptance criteria for ensemble quality.

    Rectifying AI-generated protein structure ensembles for equilibrium using physics-based computations · 2026 · DOI

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86 open questions have been extracted from the limitations and future-work passages of 413 Protein Structure and Dynamics papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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