Molecular Dynamics of Outer Membrane-Embedded Polysaccharide Secretion Porins Reveals Closed Resting-State Surface Gates Targetable by Virtual Fragment Screening for Drug Hotspot Identification
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Recent advances in iterative neural network analyses (e.g., AlphaFold2 and RoseTTA fold) have been revolutionary for protein 3D structure prediction, especially for difficult-to-manipulate α-helical/β-barrel integral membrane proteins. These model structures are calculated based on the coevolution of amino acids within the protein of interest and similarities to existing protein structures; the local effects of the membrane on folding and stability of the calculated model structures are not considered. We recently reported the discovery, 3D modeling, and characterization of 18-β-stranded outer-membrane (OM) WzpX, WzpS, and WzpB β-barrel secretion porins for the exopolysaccharide (EPS), major spore coat polysaccharide (MASC), and biosurfactant polysaccharide (BPS) pathways (respectively) in the Gram-negative social predatory bacterium Myxococcus xanthus DZ2. However, information was not obtained regarding the dynamic behavior of surface-gating WzpX/S/B loop domains or on potential treatments to inactivate these porins. Herein, we developed a molecular dynamics (MD) protocol to study the core stability and loop dynamism of neural network-based integral membrane protein structure models embedded in an asymmetric OM bilayer, using the M. xanthus WzpX, WzpS, and WzpB proteins as test candidates. This was accomplished through integration of the CHARMM-graphical user interface (GUI) and Molecular Operating Environment (MOE) workflows to allow for a rapid simulation system setup and facilitate data analysis. In addition to serving as a method of model structure validation, our molecular dynamics simulations revealed a minimal movement of extracellular WzpX/S/B loops in the absence of an external stimulus as well as druggable cavities between the loops. Virtual screening of a commercial fragment library against these cavities revealed putative fragment-binding hotspots on the cell-surface face of each β-barrel, along with key interacting residues, and identified promising hits for the design of potential binders capable of plugging the β-barrels and inhibiting polysaccharide secretion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".