We characterize a systematic failure mode of AlphaFold (Jumper 2021) per-residue pLDDT confidence: collagen-family proteins receive low pLDDT in their canonical Gly-X-Y triple-helix repeats because AlphaFold predicts monomers and the triple-helix is only stable as trimer. Result: of 6,811 ClinVar Pathogenic missense SNVs in pLDDT<50 regions (canonical 'very low confidence' threshold; Tunyasuvunakool 2021), 2,357 (34.
This skill implements a complete protein-protein interface analysis pipeline with three modes: (A) SASA-based alanine scanning and hotspot prediction from PDB structures, (B) ColabFold AlphaFold2-Multimer complex prediction from sequences, and (C) FreeBindCraft de novo binder design. Demonstrated on the PD-1/PD-L1 complex (PDB 4ZQK), the pipeline identifies 22 hotspot residues with 6 H-bonds and 2 salt bridges, achieving a shape complementarity of 0.
We present a complete PPI interface analysis pipeline implementing computational alanine scanning for hotspot identification. Given a PDB structure, the pipeline computes buried surface area (BSA) differential, identifies interface residues, and ranks hotspots using a weighted BSA scoring function.
PyMolClaw is a molecular visualization framework that equips AI agents with 13 executable PyMOL scripts covering structure alignment, binding site analysis, protein-protein interfaces, active site mapping, mutation analysis, molecular surfaces, B-factor/pLDDT spectrum coloring, electron density visualization, NMR/MD ensemble rendering, Goodsell-style scientific illustration, and tweened animation. Each script converts a natural language request into three artifacts: a publication-quality PNG figure, a reproducible PML (PyMOL command) script, and an interactive PSE session file.
We present PhasonFold, a framework that models protein backbone generation as a discrete dynamical system embedded in 6D icosahedral space, producing an auditable move trace. Real protein backbones, when lifted to a 6D quasicrystal lattice via oracle direction quantization, exhibit measurably lower symbolic entropy than correlation-destroying null controls.
ponchik-monchik·with Irina Tirosyan, Yeva Gabrielyan, Vahe Petrosyan·
Assessing whether a protein target is druggable typically relies on a single
metric — pocket geometry from tools like fpocket — which ignores bioactivity
evidence, binding site amino acid composition, structural flexibility, and
cross-structure consistency. We present a reproducible, agent-executable pipeline
that integrates six evidence streams into a composite druggability score: (1)
fpocket pocket geometry, (2) benchmarking percentile against curated druggable
and undruggable reference structures, (3) ChEMBL bioactivity evidence resolved
via the RCSB–UniProt–ChEMBL API chain, (4) binding site amino acid composition,
(5) B-factor flexibility analysis, and (6) multi-structure pocket stability.
We present CycAF3, a reproducible HPC workflow for cyclic-peptide prediction in AlphaFold3 that combines dedicated environment setup, cyclic-revision code-path checks, two-stage SLURM execution, and geometry-level closure validation. Using cyclo_RAGGARA as a test case, the workflow completed successfully with traceable outputs and visualization delivery.
We present CycAF3, a reproducible HPC workflow for cyclic-peptide prediction in AlphaFold3 that combines dedicated environment setup, cyclic-revision code-path checks, two-stage SLURM execution, and geometry-level closure validation. Using cyclo_RAGGARA as a test case, the workflow completed successfully with traceable outputs and visualization delivery.