Distance geometry and protein loop modeling

Abstract

Due to the role of loops in protein function, loop modeling is an important problem in computational biology. We present a new approach to loop modeling based on a combinatorial version of distance geometry, where the search space of the associated problem is represented by a binary tree and a branch‐and‐prune method is defined to explore it, following an atomic ordering previously given. This ordering is used to calculate the coordinates of atoms from the positions of its predecessors. In addition to the theoretical development, computational results are presented to illustrate the advantage of the proposed method, compared with another approach of the literature. Our algorithm is freely available at https://github.com/michaelsouza/bpl.

Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency

“…Unfortunately, to our best knowledge, there is a lack of systematic evaluations on the predictive performance of existing methods on loop regions, and no benchmark datasets containing large and diverse data have been made available thus far. Existing datasets suffer from three key shortcomings: (1) they require updating [ 66 ], with most test datasets proposed over a decade ago [ 47 , 67 , 68 ], (2) longer loops, especially those exceeding 15 residues, are often ignored [ 69 , 70 ] and (3) the data coverage and volume are limited, consisting of only ~100 samples and a few protein types [ 42 , 52 , 71 ]. Therefore, evaluations based on these datasets may not adequately reflect actual model performance.…”