Protein B-factor modeling with local magnitude
Topological magnitude characterizes the effective size and spatial organization of metric spaces, offering a geometric approach with potential applications in molecular biology and materials science. Previous work has demonstrated its utility in modeling protein flexibility from structure, but global magnitude weighting requires dense linear systems whose computational cost increases with protein size. We introduce a local magnitude representation that retains residue-level structural information through nearest-neighbor environments. The construction is well posed and geometrically invariant, with central weighting distinguished from scalar neighborhood magnitude. Across 364 proteins, the mean within-protein fitting Pearson correlation coefficient (PCC) increases from 0.725 for global magnitude to 0.728 for local magnitude. Blind-prediction experiments on 345 proteins combine each representation with identical auxiliary descriptors and evaluate random forests and gradient boosting under three validation protocols. Local features improve the primary PCC in all six model--protocol comparisons, with a maximum absolute increase of 0.008 in protein-level ten-fold gradient boosting. For a fixed feature bank, local factorization costs $O(Nk^3)$ compared with $O(N^3)$ for global factorization, where $N$ is the number of C$_α$ atoms and $k$ the neighborhood size. Timing measurements show a computational advantage of local magnitude on large structures. Within the tested single-seed configuration and distinct global and local scale banks, these results support local magnitude as a compact representation that preserves useful B-factor information while reducing feature-extraction costs for large proteins.