High-throughput computational structure-based characterization of protein families: START domains and implications for structural genomics

On the use of knowledge-based potentials for the evaluation of models of protein-protein, protein-DNA, and protein-RNA interactions

Proteins are the bricks and mortar of cells, playing structural and functional roles. In order to perform their function, they interact with each other as well as with other biomolecules such as DNA or RNA. Therefore, to fathom the function of a protein, we require knowing its partners and the atomic details of its interactions (i.e., the structure of the complex). However, the amount of protein interactions with an experimentally determined three-dimensional structure is scarce. Therefore, computational techniques such as homology modeling are foremost to fill this gap. Protein interactions can be modeled using as templates the interactions of homologous proteins, if the structure of the complex is known, or using docking methods. In both approaches, the estimation of the quality of models is essential. There are several ways to address this problem. In this review, we focus on the use of knowledge-based potentials for the analysis of protein interactions. We describe the procedure to derive statistical potentials and split them into different energetic terms that can be used for different purposes. We extensively discuss the fields where knowledge-based potentials have been successfully applied to (1) model protein-protein, protein-DNA, and protein-RNA interactions and (2) predict binding sites (in the protein and in the DNA). Moreover, we provide ready-to-use resources for docking and benchmarking protein interactions.

The Lipid Transfer Protein StarD7: Structure, Function, and Regulation

The steroidogenic acute regulatory (StAR) protein-related lipid transfer (START) domain proteins constitute a family of evolutionarily conserved and widely expressed proteins that have been implicated in lipid transport, metabolism, and signaling. The 15 well-characterized mammalian START domain-containing proteins are grouped into six subfamilies. The START domain containing 7 mRNA encodes StarD7, a member of the StarD2/phosphatidylcholine transfer protein (PCTP) subfamily, which was first identified as a gene overexpressed in a choriocarcinoma cell line. Recent studies show that the StarD7 protein facilitates the delivery of phosphatidylcholine to the mitochondria. This review summarizes the latest advances in StarD7 research, focusing on the structural and biochemical features, protein-lipid interactions, and mechanisms that regulate StarD7 expression. The implications of the role of StarD7 in cell proliferation, migration, and differentiation are also discussed.

Using structure to explore the sequence alignment space of remote homologs

Protein structure modeling by homology requires an accurate sequence alignment between the query protein and its structural template. However, sequence alignment methods based on dynamic programming (DP) are typically unable to generate accurate alignments for remote sequence homologs, thus limiting the applicability of modeling methods. A central problem is that the alignment that is “optimal” in terms of the DP score does not necessarily correspond to the alignment that produces the most accurate structural model. That is, the correct alignment based on structural superposition will generally have a lower score than the optimal alignment obtained from sequence. Variations of the DP algorithm have been developed that generate alternative alignments that are “suboptimal” in terms of the DP score, but these still encounter difficulties in detecting the correct structural alignment. We present here a new alternative sequence alignment method that relies heavily on the structure of the template. By initially aligning the query sequence to individual fragments in secondary structure elements and combining high-scoring fragments that pass basic tests for “modelability”, we can generate accurate alignments within a small ensemble. Our results suggest that the set of sequences that can currently be modeled by homology can be greatly extended.

It has been suggested that, for nearly every protein sequence, there is already a protein with a similar structure in current protein structure databases. However, with poor or undetectable sequence relationships, it is expected that accurate alignments and models cannot be generated. Here we show that this is not the case, and that whenever structural relationship exists, there are usually local sequence relationships that can be used to generate an accurate alignment, no matter what the global sequence identity. However, this requires an alternative to the traditional dynamic programming algorithm and the consideration of a small ensemble of alignments. We present an algorithm, S4, and demonstrate that it is capable of generating accurate alignments in nearly all cases where a structural relationship exists between two proteins. Our results thus constitute an important advance in the full exploitation of the information in structural databases. That is, the expectation of an accurate alignment suggests that a meaningful model can be generated for nearly every sequence for which a suitable template exists.

Genome-wide structural analysis reveals novel membrane binding properties of AP180 N-terminal homology (ANTH) domains

An increasing number of cytosolic proteins are shown to interact with membrane lipids during diverse cellular processes, but computational prediction of these proteins and their membrane binding behaviors remains challenging. Here, we introduce a new combinatorial computation protocol for systematic and robust functional prediction of membrane-binding proteins through high throughput homology modeling and in-depth calculation of biophysical properties. The approach was applied to the genomic scale identification of the AP180 N-terminal homology (ANTH) domain, one of the modular lipid binding domains, and prediction of their membrane binding properties. Our analysis yielded comprehensive coverage of the ANTH domain family and allowed classification and functional annotation of proteins based on the differences in local structural and biophysical features. Our analysis also identified a group of plant ANTH domains with unique structural features that may confer novel functionalities. Experimental characterization of a representative member of this subfamily confirmed its unique membrane binding mechanism and unprecedented membrane deforming activity. Collectively, these studies suggest that our new computational approach can be applied to genome-wide functional prediction of other lipid binding domains.