Detecting Non-Trivial Protein Structure Relationships

Automated methods for protein three-dimensional structure comparison play an important role in understanding protein function, evolution and biochemical reaction mechanisms. Since the tertiary structure of proteins is more conserved than their amino-acid sequences, accurately aligning three-dimensional structures allows to detect homology between proteins in the "twilight zone", those sharing less than similar to 25% sequence identity. Unfortunately, existing methods for protein structure comparison are often unable to properly compare and align proteins related by complex structural modifications, such as circular permutations, large conformational changes and large residue insertions and deletions. In this paper, we present an algorithm capable of computing biologically meaningful alignments from structurally homologous but spatially distant fragments. Accurate alignments of proteins that have undergone large conformational variations are derived from multiple spatial superpositions. For mild to moderate conformational variations, approximate rigid body superpositions are recursively relaxed to allow matching of spatially distant regions. The algorithm incorporates an exact procedure for computing alignments of proteins related by circular permutations. We used two benchmarking datasets to demonstrate that our algorithm compares favorably to some of the most accurate methods available today. In the most difficult RIPC test set, the median accuracy of our method is 100%. The algorithm is freely available as a Web service at http://bioinfo.cs.uni.edu.