PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

• Published in: Proteins, structure, function, and bioinformatics Vol. 87; no. 1; pp. 62 - 73
• Main Author: Hallen, Mark A.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01-01-2019
• Subjects: Algorithms; biophysics; Computational Biology; Computer Simulation; Design; drug design; Energy; Entropy; Flexibility; Learning algorithms; Linear programming; Lower bounds; Machine learning; Mathematical models; Models, Molecular; molecular conformation; Protein Conformation; Protein Engineering - methods; Proteins; Proteins - chemistry; Pruning; Software; algorithms; linear programming; molecular conformation; entropy; drug design; biophysics
• ISSN: 0887-3585; 1097-0134
• DOI: 10.1002/prot.25623

Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead‐end elimination (DEE). However, the mathematical assumptions of DEE‐based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming‐based method. PLUG's biophysical model consists only of well‐known lower bounds on interatomic distances. PLUG is intended as preprocessing for energy‐based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs—the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced nonpairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE's machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).