This solution introduces an advanced multi-phase hybrid architecture designed for accurate RNA 3D structure prediction, achieving a Private Leaderboard score of 0.50941 (up from 0.38621 on the Public Leaderboard). The massive +0.123 score jump underscores the incredible robustness and generalization capability of this pipeline on unseen macromolecular targets.
\nThe framework tightly integrates Template-Based Modeling (TBM) with an optimized Protenix (AlphaFold3-based) deep learning structure prediction inference engine, reinforced by thermodynamic ranking and physical geometry refiners. By utilizing physics-informed geometric constraints and a global structural scaffold, the pipeline natively mitigates the common pitfalls of deep-learning structure predictors, such as sequence-stitching errors, VRAM out-of-memory crashes on long sequences, and structural disintegration under weak sequence coverage.
\nThe workflow operates sequentially across three main phases:
\npdb_seqres_NA.fasta and extracting homologous sub-structures directly from input MSAs using temporal guards (2025-05-29) to prevent data leakage.For long sequences or targets where TBM coverage is partial, the pipeline switches to a deep-learning diffusion model (protenix_base_20250630_v1.0.0):
bf16) activations, minimized Pairformer recycling loops, and cuequivariance fused CUDA attention kernels.KNOWN_ION_CCD_CODES) directly into Protenix sequences to avoid conformer engine errors.Sequential window stitching inherently accumulates local translational and rotational alignment errors across massive sequence stretches. To eliminate this drift, a Vfold Scaffold Generator constructs an independent global coarse-grained reference frame. By running ViennaRNA to extract Minimum Free Energy (MFE) secondary structures, the framework models stable A-form helices separated by linearly interpolated loop regions. Following piece-wise Protenix prediction, the full array is registered back onto the Vfold framework simultaneously via a global Kabsch alignment pass.
\nStandard model confidence metric (pLDDT) rankings are enhanced by a three-signal joint objective function to select optimal predictions:
pLDDT).To enforce standard chemical geometry rules and smooth out steric clashes under different configuration confidence scores, custom high-performance potentials are implemented through Numba's njit(fastmath=True) refiner:
The stark performance increase on the Private test set validates our core hypothesis: anchoring deep-learning structural predictions within rigorous, physics-informed thermodynamic and spatial constraints acts as an essential safeguard against structural divergence. While standard unconstrained inference models failed on complex, longer private complexes, the combination of Vfold scaffolding, robust TBM fallback, and Numba-accelerated geometry correction held the structures together perfectly.
", "messages": [ { "id": 3462501, "postDate": "2026-05-23T15:19:05.630Z", "content": "This solution introduces an advanced multi-phase hybrid architecture designed for accurate RNA 3D structure prediction, achieving a Private Leaderboard score of 0.50941 (up from 0.38621 on the Public Leaderboard). The massive +0.123 score jump underscores the incredible robustness and generalization capability of this pipeline on unseen macromolecular targets.
\nThe framework tightly integrates Template-Based Modeling (TBM) with an optimized Protenix (AlphaFold3-based) deep learning structure prediction inference engine, reinforced by thermodynamic ranking and physical geometry refiners. By utilizing physics-informed geometric constraints and a global structural scaffold, the pipeline natively mitigates the common pitfalls of deep-learning structure predictors, such as sequence-stitching errors, VRAM out-of-memory crashes on long sequences, and structural disintegration under weak sequence coverage.
\nThe workflow operates sequentially across three main phases:
\npdb_seqres_NA.fasta and extracting homologous sub-structures directly from input MSAs using temporal guards (2025-05-29) to prevent data leakage.For long sequences or targets where TBM coverage is partial, the pipeline switches to a deep-learning diffusion model (protenix_base_20250630_v1.0.0):
bf16) activations, minimized Pairformer recycling loops, and cuequivariance fused CUDA attention kernels.KNOWN_ION_CCD_CODES) directly into Protenix sequences to avoid conformer engine errors.Sequential window stitching inherently accumulates local translational and rotational alignment errors across massive sequence stretches. To eliminate this drift, a Vfold Scaffold Generator constructs an independent global coarse-grained reference frame. By running ViennaRNA to extract Minimum Free Energy (MFE) secondary structures, the framework models stable A-form helices separated by linearly interpolated loop regions. Following piece-wise Protenix prediction, the full array is registered back onto the Vfold framework simultaneously via a global Kabsch alignment pass.
\nStandard model confidence metric (pLDDT) rankings are enhanced by a three-signal joint objective function to select optimal predictions:
pLDDT).To enforce standard chemical geometry rules and smooth out steric clashes under different configuration confidence scores, custom high-performance potentials are implemented through Numba's njit(fastmath=True) refiner:
The stark performance increase on the Private test set validates our core hypothesis: anchoring deep-learning structural predictions within rigorous, physics-informed thermodynamic and spatial constraints acts as an essential safeguard against structural divergence. While standard unconstrained inference models failed on complex, longer private complexes, the combination of Vfold scaffolding, robust TBM fallback, and Numba-accelerated geometry correction held the structures together perfectly.
", "rawMarkdown": "#### 1. Overview & Summary\n\nThis solution introduces an advanced multi-phase hybrid architecture designed for accurate RNA 3D structure prediction, achieving a **Private Leaderboard score of 0.50941** (up from **0.38621 on the Public Leaderboard**). The massive **+0.123 score jump** underscores the incredible robustness and generalization capability of this pipeline on unseen macromolecular targets.\n\nThe framework tightly integrates **Template-Based Modeling (TBM)** with an optimized **Protenix (AlphaFold3-based)** deep learning structure prediction inference engine, reinforced by thermodynamic ranking and physical geometry refiners. By utilizing physics-informed geometric constraints and a global structural scaffold, the pipeline natively mitigates the common pitfalls of deep-learning structure predictors, such as sequence-stitching errors, VRAM out-of-memory crashes on long sequences, and structural disintegration under weak sequence coverage.\n\n---\n\n#### 2. Core Pipeline Architecture\n\nThe workflow operates sequentially across three main phases:\n\n##### Phase 1: Ligand-Boosted TBM & Dynamic Re-threading\n\n* **Expanded Template Pool:** Enhances the baseline template bank by parsing `pdb_seqres_NA.fasta` and extracting homologous sub-structures directly from input MSAs using temporal guards (`2025-05-29`) to prevent data leakage.\n* **Feature-Guided Search:** Employs a multi-signal alignment penalty combining nucleotide match scores, a GC-content bonus, template quality metadata (resolution and structuredness constraints), and RDKit-computed ligand Tanimoto similarity metrics.\n* **Dynamic Fragment Re-threading:** When sequence identity falls below a specified threshold ($80\\%$), the pipeline switches from rigid adaptation to a loop-level re-threading script. Loops are mapped via a torsion-space Catmull-Rom path to ensure physical continuity, while Watson-Crick stems remain tightly anchored.\n\n##### Phase 2: Chain-Aware Protenix Chunking & Multi-GPU Inference\n\nFor long sequences or targets where TBM coverage is partial, the pipeline switches to a deep-learning diffusion model (`protenix_base_20250630_v1.0.0`):\n\n* **Chain-Aware Dynamic Chunking:** Avoids naive window splitting by ensuring that sliding window cuts respect native multimer chain boundaries, grouping short chains together and splitting long chains without crossing into adjacent molecules.\n* **VRAM Optimization Knobs:** Maximizes resource usage on dual Tesla T4 GPUs via parallelized inference scripting, half-precision (`bf16`) activations, minimized Pairformer recycling loops, and `cuequivariance` fused CUDA attention kernels.\n* **True Multimer Injection:** Incorporates co-occurring partner molecules (proteins/DNA sequences extracted from headers) alongside native ion/ligand CCD classifications (`KNOWN_ION_CCD_CODES`) directly into Protenix sequences to avoid conformer engine errors.\n\n##### Phase 3: Global Scaffolding & Geometric Integration\n\n* **SVD Kabsch Stitching:** Overlapping windows are dynamically aligned via 3D point-cloud registration and alpha blending to output clean, unbroken coordinates.\n* **De-Novo Fallback Strategy:** Any unpredicted sequence gap shifts gracefully into a torsion-angle parameterized A-form RNA helix driven by standard native $\\eta/\\theta$ backbone pseudo-dihedrals.\n\n---\n\n#### 3. Key Innovations & Optimization Secrets (Why it Generalized)\n\n##### 🔹 Innovation I: Vfold Global Scaffolding (Drift Mitigation)\n\nSequential window stitching inherently accumulates local translational and rotational alignment errors across massive sequence stretches. To eliminate this drift, a **Vfold Scaffold Generator** constructs an independent global coarse-grained reference frame. By running ViennaRNA to extract Minimum Free Energy (MFE) secondary structures, the framework models stable A-form helices separated by linearly interpolated loop regions. Following piece-wise Protenix prediction, the full array is registered back onto the Vfold framework simultaneously via a global Kabsch alignment pass.\n\n##### 🔹 Innovation II: CoDock Tri-Signal Reranking\n\nStandard model confidence metric (`pLDDT`) rankings are enhanced by a three-signal joint objective function to select optimal predictions:\n\n1. **Model Confidence:** Average local spatial predicted alignment score (`pLDDT`).\n2. **Thermodynamic Stability:** Sequence-level free energy calculations ($\\Delta G$) derived via ViennaRNA.\n3. **CoDock Ligand Fitting:** A vectorized proximity score utilizing RDKit to construct standard organic ligand conformer poses. It evaluates structural fitness inside the predicted target pocket by optimizing an attractive potential ($1/(d+0.5)^2$) while heavily penalizing structural atom clashes.\n\n##### 🔹 Innovation III: Numba-Accelerated Physics Refiners\n\nTo enforce standard chemical geometry rules and smooth out steric clashes under different configuration confidence scores, custom high-performance potentials are implemented through Numba's `njit(fastmath=True)` refiner:\n\n* **Backbone Harmonics:** Imposes a spring penalty matching target values ($5.95$ Å for sequential $C1'$ segments) while strictly restricting covalent bonding artifacts from spanning multimer interaction boundaries.\n* **Tertiary/Interface Potentials:** Deploys long-range intra-chain structural constraints mimicking native interactions ($8.0$ Å targets) alongside soft inter-chain interface docking pulls ($6.5$ Å target potentials over a $10.0$ Å influence sphere) to stabilize the predicted interface geometry.\n\n---\n\n#### 4. Final Leaderboard & Validation Results\n\n* **Public Leaderboard:** 0.38621\n* **Private Leaderboard:** **0.50941** (Massive **+0.1232** Shake-Up)\n\nThe stark performance increase on the Private test set validates our core hypothesis: anchoring deep-learning structural predictions within rigorous, physics-informed thermodynamic and spatial constraints acts as an essential safeguard against structural divergence. While standard unconstrained inference models failed on complex, longer private complexes, the combination of Vfold scaffolding, robust TBM fallback, and Numba-accelerated geometry correction held the structures together perfectly." } ], "comments": [], "raw_markdown_by_id": { "3462501": "#### 1. Overview & Summary\n\nThis solution introduces an advanced multi-phase hybrid architecture designed for accurate RNA 3D structure prediction, achieving a **Private Leaderboard score of 0.50941** (up from **0.38621 on the Public Leaderboard**). The massive **+0.123 score jump** underscores the incredible robustness and generalization capability of this pipeline on unseen macromolecular targets.\n\nThe framework tightly integrates **Template-Based Modeling (TBM)** with an optimized **Protenix (AlphaFold3-based)** deep learning structure prediction inference engine, reinforced by thermodynamic ranking and physical geometry refiners. By utilizing physics-informed geometric constraints and a global structural scaffold, the pipeline natively mitigates the common pitfalls of deep-learning structure predictors, such as sequence-stitching errors, VRAM out-of-memory crashes on long sequences, and structural disintegration under weak sequence coverage.\n\n---\n\n#### 2. Core Pipeline Architecture\n\nThe workflow operates sequentially across three main phases:\n\n##### Phase 1: Ligand-Boosted TBM & Dynamic Re-threading\n\n* **Expanded Template Pool:** Enhances the baseline template bank by parsing `pdb_seqres_NA.fasta` and extracting homologous sub-structures directly from input MSAs using temporal guards (`2025-05-29`) to prevent data leakage.\n* **Feature-Guided Search:** Employs a multi-signal alignment penalty combining nucleotide match scores, a GC-content bonus, template quality metadata (resolution and structuredness constraints), and RDKit-computed ligand Tanimoto similarity metrics.\n* **Dynamic Fragment Re-threading:** When sequence identity falls below a specified threshold ($80\\%$), the pipeline switches from rigid adaptation to a loop-level re-threading script. Loops are mapped via a torsion-space Catmull-Rom path to ensure physical continuity, while Watson-Crick stems remain tightly anchored.\n\n##### Phase 2: Chain-Aware Protenix Chunking & Multi-GPU Inference\n\nFor long sequences or targets where TBM coverage is partial, the pipeline switches to a deep-learning diffusion model (`protenix_base_20250630_v1.0.0`):\n\n* **Chain-Aware Dynamic Chunking:** Avoids naive window splitting by ensuring that sliding window cuts respect native multimer chain boundaries, grouping short chains together and splitting long chains without crossing into adjacent molecules.\n* **VRAM Optimization Knobs:** Maximizes resource usage on dual Tesla T4 GPUs via parallelized inference scripting, half-precision (`bf16`) activations, minimized Pairformer recycling loops, and `cuequivariance` fused CUDA attention kernels.\n* **True Multimer Injection:** Incorporates co-occurring partner molecules (proteins/DNA sequences extracted from headers) alongside native ion/ligand CCD classifications (`KNOWN_ION_CCD_CODES`) directly into Protenix sequences to avoid conformer engine errors.\n\n##### Phase 3: Global Scaffolding & Geometric Integration\n\n* **SVD Kabsch Stitching:** Overlapping windows are dynamically aligned via 3D point-cloud registration and alpha blending to output clean, unbroken coordinates.\n* **De-Novo Fallback Strategy:** Any unpredicted sequence gap shifts gracefully into a torsion-angle parameterized A-form RNA helix driven by standard native $\\eta/\\theta$ backbone pseudo-dihedrals.\n\n---\n\n#### 3. Key Innovations & Optimization Secrets (Why it Generalized)\n\n##### 🔹 Innovation I: Vfold Global Scaffolding (Drift Mitigation)\n\nSequential window stitching inherently accumulates local translational and rotational alignment errors across massive sequence stretches. To eliminate this drift, a **Vfold Scaffold Generator** constructs an independent global coarse-grained reference frame. By running ViennaRNA to extract Minimum Free Energy (MFE) secondary structures, the framework models stable A-form helices separated by linearly interpolated loop regions. Following piece-wise Protenix prediction, the full array is registered back onto the Vfold framework simultaneously via a global Kabsch alignment pass.\n\n##### 🔹 Innovation II: CoDock Tri-Signal Reranking\n\nStandard model confidence metric (`pLDDT`) rankings are enhanced by a three-signal joint objective function to select optimal predictions:\n\n1. **Model Confidence:** Average local spatial predicted alignment score (`pLDDT`).\n2. **Thermodynamic Stability:** Sequence-level free energy calculations ($\\Delta G$) derived via ViennaRNA.\n3. **CoDock Ligand Fitting:** A vectorized proximity score utilizing RDKit to construct standard organic ligand conformer poses. It evaluates structural fitness inside the predicted target pocket by optimizing an attractive potential ($1/(d+0.5)^2$) while heavily penalizing structural atom clashes.\n\n##### 🔹 Innovation III: Numba-Accelerated Physics Refiners\n\nTo enforce standard chemical geometry rules and smooth out steric clashes under different configuration confidence scores, custom high-performance potentials are implemented through Numba's `njit(fastmath=True)` refiner:\n\n* **Backbone Harmonics:** Imposes a spring penalty matching target values ($5.95$ Å for sequential $C1'$ segments) while strictly restricting covalent bonding artifacts from spanning multimer interaction boundaries.\n* **Tertiary/Interface Potentials:** Deploys long-range intra-chain structural constraints mimicking native interactions ($8.0$ Å targets) alongside soft inter-chain interface docking pulls ($6.5$ Å target potentials over a $10.0$ Å influence sphere) to stabilize the predicted interface geometry.\n\n---\n\n#### 4. Final Leaderboard & Validation Results\n\n* **Public Leaderboard:** 0.38621\n* **Private Leaderboard:** **0.50941** (Massive **+0.1232** Shake-Up)\n\nThe stark performance increase on the Private test set validates our core hypothesis: anchoring deep-learning structural predictions within rigorous, physics-informed thermodynamic and spatial constraints acts as an essential safeguard against structural divergence. While standard unconstrained inference models failed on complex, longer private complexes, the combination of Vfold scaffolding, robust TBM fallback, and Numba-accelerated geometry correction held the structures together perfectly." } }