Protein dynamics and conformational disorder in molecular recognition
Bibliographic record
Abstract
Recognition requires protein flexibility because it facilitates conformational rearrangements and induced-fit mechanisms upon target binding. Intrinsic disorder is an extreme on the continuous spectrum of possible protein dynamics and its role in recognition may seem counterintuitive. However, conformational disorder is widely found in many eukaryotic regulatory proteins involved in processes such as signal transduction and transcription. Disordered protein regions may in fact confer advantages over folded proteins in binding. Rapidly interconverting and diverse conformers may create mean electrostatic fields instead of presenting discrete charges. The resultant "polyelectrostatic" interactions allow for the utilization of post-translational modifications as a means to change the net charge and thereby modify the electrostatic interaction of a disordered region. Plasticity of disordered protein states enables steric advantages over folded proteins and allows for unique binding configurations. Disorder may also have evolutionary advantages, as it facilitates alternative splicing, domain shuffling and protein modularity. As proteins exist in a continuous spectrum of disorder, so do their complexes. Indeed, disordered regions in complexes may control the degree of motion between domains, mask binding sites, be targets of post-translational modifications, permit overlapping binding motifs, and enable transient binding of different binding partners, making them excellent candidates for signal integrators and explaining their prevalence in eukaryotic signaling pathways. "Dynamic" complexes arise if more than two transient protein interfaces are involved in complex formation of two binding partners in a dynamic equilibrium. "Disordered" complexes, in contrast, do not involve significant ordering of interacting protein segments but rely exclusively on transient contacts. The nature of these interactions is not well understood yet but advancements in the structural characterization of disordered states will help us gain insights into their function and their implications for health and disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".