Accurate alignment of protein-protein binding sites can aid in protein docking studies and constructing templates for predicting structure of protein complexes, along with in-depth understanding of evolutionary and functional relationships. However, over the past three decades, structural alignment algorithms have focused predominantly on global alignments with little effort on the alignment of local interfaces. In this paper, we introduce the PBSalign (Protein-protein Binding Site alignment) method, which integrates techniques in graph theory, 3D localized shape analysis, geometric scoring, and utilization of physicochemical and geometrical properties. Computational results demonstrate that PBSalign is capable of identifying similar homologous and analogous binding sites accurately and performing alignments with better geometric match measures than existing protein-protein interface comparison tools. The proportion of better alignment quality generated by PBSalign is 46, 56, and 70 percent more than iAlign as judged by the average match index (MI), similarity index (SI), and structural alignment score (SAS), respectively. PBSalign provides the life science community an efficient and accurate solution to binding-site alignment while striking the balance between topological details and computational complexity.

Accurate alignment of protein-protein binding sites can aid in protein docking studies and constructing templates for predicting structure of protein complexes, along with in-depth understanding of evolutionary and functional relationships. However, over the past three decades, structural alignment algorithms have focused predominantly on global alignments with little effort on the alignment of local interfaces. In this paper, we introduce the PBSalign (Protein-protein Binding Site alignment) method, which integrates techniques in graph theory, 3D localized shape analysis, geometric scoring, and utilization of physiochemical and geometrical properties. Computational results demonstrate that PBSalign is capable of identifying similar homologous and analogous binding sites accurately and performing alignments with better geometric match measures than existing protein-protein interface comparison tools. The proportion of better alignment quality generated by PBSalign is 46%, 56%, and 70% more than Align as judged by the average match index (MI), similarity index (SI), and structural alignment score (SAS), respectively. PBSalign provides the life science community a fast and accurate solution to binding-site alignment while striking the balance between topological details and computational complexity.

MOTIVATION: Finding geometrically similar protein binding sites is crucial for understanding protein functions and can provide valuable information for protein-protein docking and drug discovery. As the number of known protein-protein interaction structures has dramatically increased, a high-throughput and accurate protein binding site comparison method is essential. Traditional alignment-based methods can provide accurate correspondence between the binding sites but are computationally expensive. RESULTS: In this article, we present a novel method for the comparisons of protein binding sites using a 'visual words' representation (PBSword). We first extract geometric features of binding site surfaces and build a vocabulary of visual words by clustering a large set of feature descriptors. We then describe a binding site surface with a high-dimensional vector that encodes the frequency of visual words, enhanced by the spatial relationships among them. Finally, we measure the similarity of binding sites by utilizing metric space operations, which provide speedy comparisons between protein binding sites. Our experimental results show that PBSword achieves a comparable classification accuracy to an alignment-based method and improves accuracy of a feature-based method by 36% on a non-redundant dataset. PBSword also exhibits a significant efficiency improvement over an alignment-based method.

The progress in experimental and computational structural biology has led to a rapid growth of experimentally resolved structures and computational models of protein-protein interactions. However, distinguishing between the physiological and non-physiological interactions remains a challenging problem. In this work, two related problems of interface classification have been addressed. The first problem is concerned with classification of the physiological and crystal-packing interactions. The second problem deals with the classification of the physiological interactions, or their accurate models, and decoys obtained from the inaccurate docking models. We have defined a universal set of interface features and employed supervised and semi-supervised learning approaches to accurately classify the interactions in both problems. Furthermore, we formulated the second problem as a semi-supervised learning problem and employed a transductive SVM to improve the accuracy of classification. Finally, we showed that using the scoring functions from the obtained classifiers, one can improve the accuracy of the docking methods.

MOTIVATION: To investigate structure-function relationships, life sciences researchers usually retrieve and classify proteins with similar substructures into the same fold. A manually constructed database, SCOP, is believed to be highly accurate; however, it is labor intensive. Another known method, DALI, is also precise but computationally expensive. We have developed an efficient algorithm, namely, index-based protein substructure alignment (IPSA), for protein-fold classification. IPSA constructs a two-layer indexing tree to quickly retrieve similar substructures in proteins and suggests possible folds by aligning these substructures. RESULTS: Compared with known algorithms, such as DALI, CE, MultiProt and MAMMOTH, on a sample dataset of non-redundant proteins from SCOP v1.73, IPSA exhibits an efficiency improvement of 53.10, 16.87, 3.60 and 1.64 times speedup, respectively. Evaluated on three different datasets of non-redundant proteins from SCOP, average accuracy of IPSA is approximately equal to DALI and better than CE, MAMMOTH, MultiProt and SSM. With reliable accuracy and efficiency, this work will benefit the study of high-throughput protein structure-function relationships. AVAILABILITY: IPSA is publicly accessible at http://ProteinDBS.rnet.missouri.edu/IPSA.php