Abstract
Predicting the conformations of loops is a critical aspect of protein comparative (homology) modeling. Despite considerable advances in developing loop prediction algorithms, refining loops in homology models remains challenging. In this work, we use antibodies as a model system to investigate strategies for more robustly predicting loop conformations when the protein model contains errors in the conformations of side chains and protein backbone surrounding the loop in question. Specifically, our test system consists of partial models of antibodies in which the โscaffoldโ (i.e., the portion other than the complementarity determining region, CDR, loops) retains native backbone conformation, whereas the CDR loops are predicted using a combination of knowledgeโbased modeling (H1, H2, L1, L2, and L3) and ab initio loop prediction (H3). H3 is the most variable of the CDRs. Using a previously published method, a test set of 10 shorter H3 loops (5โ7 residues) are predicted to an average backbone (N๏ฃฟCฮฑ๏ฃฟC๏ฃฟO) RMSD of 2.7 ร
while 11 longer loops (8โ9 residues) are predicted to 5.1 ร
, thus recapitulating the difficulties in refining loops in models. By contrast, in control calculations predicting the same loops in crystal structures, the same method reconstructs the loops to an average of 0.5 and 1.4 ร
for the shorter and longer loops, respectively. We modify the loop prediction method to improve the ability to sample nearโnative loop conformations in the models, primarily by reducing the sensitivity of the sampling to the loop surroundings, and allowing the other CDR loops to optimize with the H3 loop. The new method improves the average accuracy significantly to 1.3 ร
RMSD and 3.1 ร
RMSD for the shorter and longer loops, respectively. Finally, we present results predicting 8โ10 residue loops within complete comparative models of five nonantibody proteins. While anecdotal, these mixed, fullโmodel results suggest our approach is a promising step toward more accurately predicting loops in homology models. Furthermore, while significant challenges remain, our method is a potentially useful tool for predicting antibody structures based on a known Fv scaffold. Proteins 2010. ยฉ 2010 WileyโLiss, Inc.