Hot Regions In Protein-protein Interactions Research Articles

Corrigendum

This paper presents a web service named MAGIIC-PRO, which aims to discover functional signatures of a query protein by sequential pattern mining. Automatic discovery of patterns from unaligned biological sequences is an important problem in molecular biology. MAGIIC-PRO is different from several previously established methods performing similar tasks in two major ways. The first remarkable feature of MAGIIC-PRO is its efficiency in delivering long patterns. With incorporating a new type of gap constraints and some of the state-of-the art data mining techniques, MAGIIC-PRO usually identifies satisfied patterns within an acceptable response time. The efficiency of MAGIIC-PRO enables the users to quickly discover functional signatures of which the residues are not from only one region of the protein sequences or are only conserved in few members of a protein family. The second remarkable feature of MAGIIC-PRO is its effort in refining the mining results. Considering large flexible gaps improves the completeness of the derived functional signatures. The users can be directly guided to the patterns with as many blocks as that are conserved simultaneously. In this paper, we show by experiments that MAGIIC-PRO is efficient and effective in identifying ligand-binding sites and hot regions in protein–protein interactions directly from sequences. The web service is available at http://biominer.bime.ntu.edu.tw/magiicpro and a mirror site at http://biominer.cse.yzu.edu.tw/magiicpro.

Identification of hot regions in protein-protein interactions by sequential pattern mining

BackgroundIdentification of protein interacting sites is an important task in computational molecular biology. As more and more protein sequences are deposited without available structural information, it is strongly desirable to predict protein binding regions by their sequences alone. This paper presents a pattern mining approach to tackle this problem. It is observed that a functional region of protein structures usually consists of several peptide segments linked with large wildcard regions. Thus, the proposed mining technology considers large irregular gaps when growing patterns, in order to find the residues that are simultaneously conserved but largely separated on the sequences. A derived pattern is called a cluster-like pattern since the discovered conserved residues are always grouped into several blocks, which each corresponds to a local conserved region on the protein sequence.ResultsThe experiments conducted in this work demonstrate that the derived long patterns automatically discover the important residues that form one or several hot regions of protein-protein interactions. The methodology is evaluated by conducting experiments on the web server MAGIIC-PRO based on a well known benchmark containing 220 protein chains from 72 distinct complexes. Among the tested 218 proteins, there are 900 sequential blocks discovered, 4.25 blocks per protein chain on average. About 92% of the derived blocks are observed to be clustered in space with at least one of the other blocks, and about 66% of the blocks are found to be near the interface of protein-protein interactions. It is summarized that for about 83% of the tested proteins, at least two interacting blocks can be discovered by this approach.ConclusionThis work aims to demonstrate that the important residues associated with the interface of protein-protein interactions may be automatically discovered by sequential pattern mining. The detected regions possess high conservation and thus are considered as the computational hot regions. This information would be useful to characterizing protein sequences, predicting protein function, finding potential partners, and facilitating protein docking for drug discovery.

Hot Regions in Protein–Protein Interactions: The Organization and Contribution of Structurally Conserved Hot Spot Residues

Structurally conserved residues at protein-protein interfaces correlate with the experimental alanine-scanning hot spots. Here, we investigate the organization of these conserved, computational hot spots and their contribution to the stability of protein associations. We find that computational hot spots are not homogeneously distributed along the protein interfaces; rather they are clustered within locally tightly packed regions. Within the dense clusters, they form a network of interactions and consequently their contributions to the stability of the complex are cooperative; however the contributions of independent clusters are additive. This suggests that the binding free energy is not a simple summation of the single hot spot residue contributions. As expected, around the hot spot residues we observe moderately conserved residues, further highlighting the crucial role of the conserved interactions in the local densely packed environment. The conserved occurrence of these organizations suggests that they are advantageous for protein-protein associations. Interestingly, the total number of hydrogen bonds and salt bridges contributed by hot spots is as expected. Thus, H-bond forming residues may use a "hot spot for water exclusion" mechanism. Since conserved residues are located within highly packed regions, water molecules are easily removed upon binding, strengthening electrostatic contributions of charge-charge interactions. Hence, the picture that emerges is that protein-protein associations are optimized locally, with the clustered, networked, highly packed structurally conserved residues contributing dominantly and cooperatively to the stability of the complex. When addressing the crucial question of "what are the preferred ways of proteins to associate", these findings point toward a critical involvement of hot regions in protein-protein interactions.