Journal: Bioresources and Bioprocessing

Article Title: Traditional processing unlocks anti-atherogenic potential of perilla fruit via PPAR-γ activation by luteolin

doi: 10.1186/s40643-025-00957-7

Figure Lengend Snippet: A : Molecular dynamics RMSD changes of the complex of luteolin and PPAR-γ within 100 ns; B : The RMSF position of the complex of luteolin and PPAR-γ; C : Protein Ligand contacts of the complex of luteolin and PPAR-γ, where green represents hydrogen bonds, purple represents hydrophobic bonds, and blue represents water bridges; D : Timeline representation of the interactions and contacts

Article Snippet: Fig. 4 A : Molecular dynamics RMSD changes of the complex of luteolin and PPAR-γ within 100 ns; B : The RMSF position of the complex of luteolin and PPAR-γ; C : Protein Ligand contacts of the complex of luteolin and PPAR-γ, where green represents hydrogen bonds, purple represents hydrophobic bonds, and blue represents water bridges; D : Timeline representation of the interactions and contacts

Techniques:

Journal: Drug Design, Development and Therapy

Article Title: Detailed in silico Evaluation of WNV Proteins: Dynamic and Thermodynamic Insights into Doravirine as a Potential Multitarget Agent

doi: 10.2147/DDDT.S551496

Figure Lengend Snippet: Molecular Dynamics analysis assessing protein backbone stability over the course of the simulation, based on Root Mean Square Deviation (RMSD) calculations for the core protein. Stability was evaluated by comparing RMSD profiles of the apo form with those of the protein bound to the analyzed compounds within the C-terminal tunnel ( A ) and the N-terminal hydrophobic pocket ( B ). At the bottom of the panel, the structural superimposition of the final simulation frames shows the apo core protein (gray) overlaid with the structures bound to remdesivir (red), rilpivirine (green), and doravirine (purple) in its N-terminal hydrophobic pocket (outlined in dashed circle).

Article Snippet: Figure 5 Molecular Dynamics analysis assessing protein backbone stability over the course of the simulation, based on Root Mean Square Deviation (RMSD) calculations for the core protein.

Techniques:

Journal: Drug Design, Development and Therapy

Article Title: Detailed in silico Evaluation of WNV Proteins: Dynamic and Thermodynamic Insights into Doravirine as a Potential Multitarget Agent

doi: 10.2147/DDDT.S551496

Figure Lengend Snippet: Molecular Dynamics analysis assessing protein backbone stability over the course of the simulation, based on Root Mean Square Deviation (RMSD) calculations for the RdRp NS5 protein. Stability was evaluated by comparing RMSD profiles of the apo form with those of the protein bound to the analyzed compound within the RdRp domain of NS5.

Article Snippet: Figure 5 Molecular Dynamics analysis assessing protein backbone stability over the course of the simulation, based on Root Mean Square Deviation (RMSD) calculations for the core protein.

Techniques:

Journal: Drug Design, Development and Therapy

Article Title: Detailed in silico Evaluation of WNV Proteins: Dynamic and Thermodynamic Insights into Doravirine as a Potential Multitarget Agent

doi: 10.2147/DDDT.S551496

Figure Lengend Snippet: Molecular Dynamics analysis assessing protein backbone stability over the course of the simulation, based on Root Mean Square Deviation (RMSD) calculations for the MTase NS5 protein. Stability was evaluated by comparing RMSD profiles of the apo form with those of the protein bound to the analyzed compounds within the SAH site ( A ) and KDKE motif ( B ). In panel A (bottom), structural superimposition of the final MD frames shows the apo NS5 MTase (yellow) overlaid with the rilpivirine-bound structure (green), with the cofactor depicted as a purple ball-and-stick. The Asp36–Val49 loop region, exhibiting a conformational shift, is highlighted by a dashed circle.

Article Snippet: Figure 5 Molecular Dynamics analysis assessing protein backbone stability over the course of the simulation, based on Root Mean Square Deviation (RMSD) calculations for the core protein.

Techniques:

Journal: bioRxiv

Article Title: Mutational Robustness Predicts Protein Dynamics Across Natural and Designed Proteins

doi: 10.64898/2026.03.19.713008

Figure Lengend Snippet: Multi-ΔΔ G regression results (5-fold protein-level CV, ThermoMPNN). Two rows: RMSF and B-factor. Left column: cross-validated R 2 for scalar baselines and multi-feature models; error bars show standard deviation across folds. Right column: Ridge coefficients for the 24-feature model (20 per-amino-acid ΔΔ G values + 4 nonlinear summaries, separated by vertical line); error bars show ±2 SE. Datasets: ATLAS (blue), BBFlow (orange), PDB designs (green). Positive coefficients: costly mutation to that amino acid associated with flexibility; negative: rigidity. NMR results are in .

Article Snippet: We test this prediction by defining a per-residue mutational robustness index, the standard deviation of predicted ΔΔ G values across all 19 single-amino-acid substitutions (which we compute using the structure-conditioned predictor ThermoMPNN), and correlating it with molecular-dynamics RMSF, crystallographic B-factors, and NMR-derived order parameters across ∼2,000 natural proteins, ∼400 de novo designs, and 759 NMR-characterized proteins.

Techniques: Standard Deviation, Mutagenesis

Journal: bioRxiv

Article Title: Mutational Robustness Predicts Protein Dynamics Across Natural and Designed Proteins

doi: 10.64898/2026.03.19.713008

Figure Lengend Snippet: Distribution of within-protein Spearman ρ between predictor and dynamics target. Each panel shows one dataset-target pair. Left: histograms of per-protein ρ for ThermoMPNN robustness (blue), ESM-1v robustness (green, where available), and pLDDT (orange). Right: scatter of robustness ρ vs. pLDDT ρ per protein; points below the diagonal indicate pLDDT is the stronger descriptor. (a) ATLAS RMSF ( n = 1,938), (b) BBFlow RMSF ( n = 100), (c) ATLAS B-factor ( n = 1,938), (d) PDB de novo designs B-factor ( n = 306; pLDDT from ESMFold, n = 290). NMR results are in .

Article Snippet: We test this prediction by defining a per-residue mutational robustness index, the standard deviation of predicted ΔΔ G values across all 19 single-amino-acid substitutions (which we compute using the structure-conditioned predictor ThermoMPNN), and correlating it with molecular-dynamics RMSF, crystallographic B-factors, and NMR-derived order parameters across ∼2,000 natural proteins, ∼400 de novo designs, and 759 NMR-characterized proteins.

Techniques:

Journal: bioRxiv

Article Title: Mutational Robustness Predicts Protein Dynamics Across Natural and Designed Proteins

doi: 10.64898/2026.03.19.713008

Figure Lengend Snippet: Pooled 2D density (hex-bin) of per-residue std(ΔΔ G ) (z-scored within protein) vs. dynamics target (z-scored) for ThermoMPNN. Marginal histograms are shown on the top and right axes. The y -axis is clipped at the 1st and 99th percentiles to reduce heavy-tail distortion. (a) ATLAS RMSF, (b) BBFlow RMSF, (c) ATLAS B-factor, (d) PDB de novo designs B-factor. Pooled Spearman ρ and sample size are shown in each panel title. NMR results are in .

Article Snippet: We test this prediction by defining a per-residue mutational robustness index, the standard deviation of predicted ΔΔ G values across all 19 single-amino-acid substitutions (which we compute using the structure-conditioned predictor ThermoMPNN), and correlating it with molecular-dynamics RMSF, crystallographic B-factors, and NMR-derived order parameters across ∼2,000 natural proteins, ∼400 de novo designs, and 759 NMR-characterized proteins.

Techniques: Residue

Journal: bioRxiv

Article Title: Mutational Robustness Predicts Protein Dynamics Across Natural and Designed Proteins

doi: 10.64898/2026.03.19.713008

Figure Lengend Snippet: Pooled per-residue scatter of std(ΔΔ G ) (kcal/mol) vs. dynamics target in raw (unnormalized) units. The data are dominated by between-protein variation: proteins with high overall RMSF or B-factor form distinct vertical bands. The raw Spearman ρ and sample size are shown in each panel title. All main-text analyses use within-protein z-scoring to remove protein-level offsets and isolate the residue-level robustness-dynamics relationship . (a) ATLAS RMSF, (b) BBFlow RMSF, (c) ATLAS B-factor, (d) PDB designs B-factor.

Article Snippet: We test this prediction by defining a per-residue mutational robustness index, the standard deviation of predicted ΔΔ G values across all 19 single-amino-acid substitutions (which we compute using the structure-conditioned predictor ThermoMPNN), and correlating it with molecular-dynamics RMSF, crystallographic B-factors, and NMR-derived order parameters across ∼2,000 natural proteins, ∼400 de novo designs, and 759 NMR-characterized proteins.

Techniques: Residue

Journal: bioRxiv

Article Title: Mutational Robustness Predicts Protein Dynamics Across Natural and Designed Proteins

doi: 10.64898/2026.03.19.713008

Figure Lengend Snippet: Per-residue robustness vs. dynamics on the Zika virus capsid protein (PDB 5YGH, chain A, 76 residues). (a) Z-scored per-residue profiles. (b) - (e) 3D structure colored by each metric: predictors on top (b, c), targets on bottom (d, e). Blue = rigid, red = flexible. pLDDT has a near-zero positive (wrong-sign) correlation with B-factor ( ρ = +0.04), indicating that its per-residue confidence scores carry no useful information about flexibility on this protein, while robustness achieves ρ RMSF = −0.72 and ρ B-factor = −0.67.

Article Snippet: We test this prediction by defining a per-residue mutational robustness index, the standard deviation of predicted ΔΔ G values across all 19 single-amino-acid substitutions (which we compute using the structure-conditioned predictor ThermoMPNN), and correlating it with molecular-dynamics RMSF, crystallographic B-factors, and NMR-derived order parameters across ∼2,000 natural proteins, ∼400 de novo designs, and 759 NMR-characterized proteins.

Techniques: Residue, Virus

Journal: bioRxiv

Article Title: Mutational Robustness Predicts Protein Dynamics Across Natural and Designed Proteins

doi: 10.64898/2026.03.19.713008

Figure Lengend Snippet: Per-residue robustness vs. dynamics on the vaccinia virus immunomodulator A46 N-terminal domain (PDB 5EZU, chain A, 89 residues). Layout as in . Although the pLDDT-colored structure (b) visually resembles the B-factor pattern (e) in the structured core, the high-RMSF N-terminal coil (red in d) is missed by pLDDT, driving the overall correlation gap ( ρ rob,RMSF = −0.75 vs. ρ pLDDT,RMSF = −0.30). The robustness panel (c) correctly identifies this flexible terminus.

Article Snippet: We test this prediction by defining a per-residue mutational robustness index, the standard deviation of predicted ΔΔ G values across all 19 single-amino-acid substitutions (which we compute using the structure-conditioned predictor ThermoMPNN), and correlating it with molecular-dynamics RMSF, crystallographic B-factors, and NMR-derived order parameters across ∼2,000 natural proteins, ∼400 de novo designs, and 759 NMR-characterized proteins.

Techniques: Residue, Virus

Journal: bioRxiv

Article Title: Mutational Robustness Predicts Protein Dynamics Across Natural and Designed Proteins

doi: 10.64898/2026.03.19.713008

Figure Lengend Snippet: Per-residue robustness vs. dynamics on the L27 domain of Drosophila Discs Large 1 (PDB 4RP5, chain A, 98 residues). Layout as in . The two-domain helical bundle shows a clear flexible inter-domain linker (red in c and d). pLDDT assigns uniformly high confidence throughout ( ρ B-factor = +0.01, essentially zero), while robustness captures the flexibility gradient ( ρ RMSF = −0.78, ρ B-factor = −0.33).

Article Snippet: We test this prediction by defining a per-residue mutational robustness index, the standard deviation of predicted ΔΔ G values across all 19 single-amino-acid substitutions (which we compute using the structure-conditioned predictor ThermoMPNN), and correlating it with molecular-dynamics RMSF, crystallographic B-factors, and NMR-derived order parameters across ∼2,000 natural proteins, ∼400 de novo designs, and 759 NMR-characterized proteins.

Techniques: Residue