Improving physical realism, stereochemistry, and side‐chain accuracy in homology modeling: Four approaches that performed well in CASP8

TLDR

Accurate alignment is essential for homology modeling, yet bridging the structural gap between template and target often requires high‑resolution refinement to adjust loops, secondary structure elements, and core residues. The study presents four methods that tackle the final stage of protein folding and achieved physically realistic models in CASP8. The four methods employ distinct refinement strategies: YASARA runs explicit‑solvent MD with a knowledge‑based force field; LEE‑SERVER uses conformational space annealing and CHARMM stereochemistry to guide Modeller; ROSETTA combines an all‑atom force field with Monte Carlo minimization; and UNDERTAKER generates candidate models from multiple templates and optimizes them via an adaptive genetic algorithm. Published in *Proteins* (2009) © 2009 Wiley‑Liss, Inc.

Abstract

Abstract A correct alignment is an essential requirement in homology modeling. Yet in order to bridge the structural gap between template and target, which may not only involve loop rearrangements, but also shifts of secondary structure elements and repacking of core residues, high‐resolution refinement methods with full atomic details are needed. Here, we describe four approaches that address this “last mile of the protein folding problem” and have performed well during CASP8, yielding physically realistic models: YASARA, which runs molecular dynamics simulations of models in explicit solvent, using a new partly knowledge‐based all atom force field derived from Amber, whose parameters have been optimized to minimize the damage done to protein crystal structures. The LEE‐SERVER, which makes extensive use of conformational space annealing to create alignments, to help Modeller build physically realistic models while satisfying input restraints from templates and CHARMM stereochemistry, and to remodel the side‐chains. ROSETTA, whose high resolution refinement protocol combines a physically realistic all atom force field with Monte Carlo minimization to allow the large conformational space to be sampled quickly. And finally UNDERTAKER, which creates a pool of candidate models from various templates and then optimizes them with an adaptive genetic algorithm, using a primarily empirical cost function that does not include bond angle, bond length, or other physics‐like terms. Proteins 2009. © 2009 Wiley‐Liss, Inc.