PSC: protein surface classification

Structural and functional insights into ABHD5, a ligand-regulated lipase co-activator

Alpha/beta hydrolase domain-containing protein 5 (ABHD5) is a highly conserved protein that regulates various lipid metabolic pathways via interactions with members of the perilipin (PLIN) and Patatin-like phospholipase domain-containing protein (PNPLA) protein families. Loss of function mutations in ABHD5 result in Chanarin-Dorfman Syndrome (CDS), characterized by ectopic lipid accumulation in numerous cell types and severe ichthyosis. Recent data demonstrates that ABHD5 is the target of synthetic and endogenous ligands that might be therapeutic beneficial for treating metabolic diseases and cancers. However, the structural basis of ABHD5 functional activities, such as protein-protein interactions and ligand binding is presently unknown. To address this gap, we constructed theoretical structural models of ABHD5 by comparative modeling and topological shape analysis to assess the spatial patterns of ABHD5 conformations computed in protein dynamics. We identified functionally important residues on ABHD5 surface for lipolysis activation by PNPLA2, lipid droplet targeting and PLIN-binding. We validated the computational model by examining the effects of mutating key residues in ABHD5 on an array of functional assays. Our integrated computational and experimental findings provide new insights into the structural basis of the diverse functions of ABHD5 as well as pathological mutations that result in CDS.

Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach

Background

Recently, a number of large-scale cancer genome sequencing projects have generated a large volume of somatic mutations; however, identifying the functional consequences and roles of somatic mutations in tumorigenesis remains a major challenge. Researchers have identified that protein pocket regions play critical roles in the interaction of proteins with small molecules, enzymes, and nucleic acid. As such, investigating the features of somatic mutations in protein pocket regions provides a promising approach to identifying new genotype-phenotype relationships in cancer.

Methods

In this study, we developed a protein pocket-based computational approach to uncover the functional consequences of somatic mutations in cancer. We mapped 1.2 million somatic mutations across 36 cancer types from the COSMIC database and The Cancer Genome Atlas (TCGA) onto the protein pocket regions of over 5,000 protein three-dimensional structures. We further integrated cancer cell line mutation profiles and drug pharmacological data from the Cancer Cell Line Encyclopedia (CCLE) onto protein pocket regions in order to identify putative biomarkers for anticancer drug responses.

Results

We found that genes harboring protein pocket somatic mutations were significantly enriched in cancer driver genes. Furthermore, genes harboring pocket somatic mutations tended to be highly co-expressed in a co-expressed protein interaction network. Using a statistical framework, we identified four putative cancer genes (RWDD1, NCF1, PLEK, and VAV3), whose expression profiles were associated with overall poor survival rates in melanoma, lung, or colorectal cancer patients. Finally, genes harboring protein pocket mutations were more likely to be drug-sensitive or drug-resistant. In a case study, we illustrated that the BAX gene was associated with the sensitivity of three anticancer drugs (midostaurin, vinorelbine, and tipifarnib).

Conclusions

This study provides novel insights into the functional consequences of somatic mutations during tumorigenesis and for anticancer drug responses. The computational approach used might be beneficial to the study of somatic mutations in the era of cancer precision medicine.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-014-0081-7) contains supplementary material, which is available to authorized users.

Non-redundant unique interface structures as templates for modeling protein interactions

Improvements in experimental techniques increasingly provide structural data relating to protein-protein interactions. Classification of structural details of protein-protein interactions can provide valuable insights for modeling and abstracting design principles. Here, we aim to cluster protein-protein interactions by their interface structures, and to exploit these clusters to obtain and study shared and distinct protein binding sites. We find that there are 22604 unique interface structures in the PDB. These unique interfaces, which provide a rich resource of structural data of protein-protein interactions, can be used for template-based docking. We test the specificity of these non-redundant unique interface structures by finding protein pairs which have multiple binding sites. We suggest that residues with more than 40% relative accessible surface area should be considered as surface residues in template-based docking studies. This comprehensive study of protein interface structures can serve as a resource for the community. The dataset can be accessed at http://prism.ccbb.ku.edu.tr/piface.

Prediction and experimental validation of enzyme substrate specificity in protein structures

Structural Genomics aims to elucidate protein structures to identify their functions. Unfortunately, the variation of just a few residues can be enough to alter activity or binding specificity and limit the functional resolution of annotations based on sequence and structure; in enzymes, substrates are especially difficult to predict. Here, large-scale controls and direct experiments show that the local similarity of five or six residues selected because they are evolutionarily important and on the protein surface can suffice to identify an enzyme activity and substrate. A motif of five residues predicted that a previously uncharacterized Silicibacter sp. protein was a carboxylesterase for short fatty acyl chains, similar to hormone-sensitive-lipase-like proteins that share less than 20% sequence identity. Assays and directed mutations confirmed this activity and showed that the motif was essential for catalysis and substrate specificity. We conclude that evolutionary and structural information may be combined on a Structural Genomics scale to create motifs of mixed catalytic and noncatalytic residues that identify enzyme activity and substrate specificity.