Yuxing Liao

The WAVE Regulatory Complex Links Diverse Receptors to the Actin Cytoskeleton

The WAVE regulatory complex (WRC) controls actin cytoskeletal dynamics throughout the cell by stimulating the actin-nucleating activity of the Arp2/3 complex at distinct membrane sites. However, the factors that recruit the WRC to specific locations remain poorly understood. Here, we have identified a large family of potential WRC ligands, consisting of ∼120 diverse membrane proteins, including protocadherins, ROBOs, netrin receptors, neuroligins, GPCRs, and channels. Structural, biochemical, and cellular studies reveal that a sequence motif that defines these ligands binds to a highly conserved interaction surface of the WRC formed by the Sra and Abi subunits. Mutating this binding surface in flies resulted in defects in actin cytoskeletal organization and egg morphology during oogenesis, leading to female sterility. Our findings directly link diverse membrane proteins to the WRC and actin cytoskeleton and have broad physiological and pathological ramifications in metazoans.

CASP9 assessment of free modeling target predictions

We present an overview of the ninth round of Critical Assessment of Protein Structure Prediction (CASP9) “Template free modeling” category (FM). Prediction models were evaluated using a combination of established structural and sequence comparison measures and a novel automated method designed to mimic manual inspection by capturing both global and local structural features. These scores were compared to those assigned manually over a diverse subset of target domains. Scores were combined to compare overall performance of participating groups and to estimate rank significance. Moreover, we discuss a few examples of free modeling targets to highlight the progress and bottlenecks of current prediction methods. Notably, a server prediction model for a single target (T0581) improved significantly over the closest structure template (44% GDT increase). This accomplishment represents the “winner” of the CASP9 FM category. A number of human expert groups submitted slight variations of this model, highlighting a trend for human experts to act as “meta predictors” by correctly selecting among models produced by the top‐performing automated servers. The details of evaluation are available at http://prodata.swmed.edu/CASP9/.

Large-scale determination of previously unsolved protein structures using evolutionary information

The prediction of the structures of proteins without detectable sequence similarity to any protein of known structure remains an outstanding scientific challenge. Here we report significant progress in this area. We first describe de novo blind structure predictions of unprecendented accuracy we made for two proteins in large families in the recent CASP11 blind test of protein structure prediction methods by incorporating residue–residue co-evolution information in the Rosetta structure prediction program. We then describe the use of this method to generate structure models for 58 of the 121 large protein families in prokaryotes for which three-dimensional structures are not available. These models, which are posted online for public access, provide structural information for the over 400,000 proteins belonging to the 58 families and suggest hypotheses about mechanism for the subset for which the function is known, and hypotheses about function for the remainder. DOI: http://dx.doi.org/10.7554/eLife.09248.001

ECOD: an evolutionary classification of protein domains.

Understanding the evolution of a protein, including both close and distant relationships, often reveals insight into its structure and function. Fast and easy access to such up-to-date information facilitates research. We have developed a hierarchical evolutionary classification of all proteins with experimentally determined spatial structures, and presented it as an interactive and updatable online database. ECOD (Evolutionary Classification of protein Domains) is distinct from other structural classifications in that it groups domains primarily by evolutionary relationships (homology), rather than topology (or “fold”). This distinction highlights cases of homology between domains of differing topology to aid in understanding of protein structure evolution. ECOD uniquely emphasizes distantly related homologs that are difficult to detect, and thus catalogs the largest number of evolutionary links among structural domain classifications. Placing distant homologs together underscores the ancestral similarities of these proteins and draws attention to the most important regions of sequence and structure, as well as conserved functional sites. ECOD also recognizes closer sequence-based relationships between protein domains. Currently, approximately 100,000 protein structures are classified in ECOD into 9,000 sequence families clustered into close to 2,000 evolutionary groups. The classification is assisted by an automated pipeline that quickly and consistently classifies weekly releases of PDB structures and allows for continual updates. This synchronization with PDB uniquely distinguishes ECOD among all protein classifications. Finally, we present several case studies of homologous proteins not recorded in other classifications, illustrating the potential of how ECOD can be used to further biological and evolutionary studies.

Manual classification strategies in the ECOD database

ECOD (Evolutionary Classification Of protein Domains) is a comprehensive and up‐to‐date protein structure classification database. The majority of new structures released from the PDB (Protein Data Bank) each week already have close homologs in the ECOD hierarchy and thus can be reliably partitioned into domains and classified by software without manual intervention. However, those proteins that lack confidently detectable homologs require careful analysis by experts. Although many bioinformatics resources rely on expert curation to some degree, specific examples of how this curation occurs and in what cases it is necessary are not always described. Here, we illustrate the manual classification strategy in ECOD by example, focusing on two major issues in protein classification: domain partitioning and the relationship between homology and similarity scores. Most examples show recently released and manually classified PDB structures. We discuss multi‐domain proteins, discordance between sequence and structural similarities, difficulties with assessing homology with scores, and integral membrane proteins homologous to soluble proteins. By timely assimilation of newly available structures into its hierarchy, ECOD strives to provide a most accurate and updated view of the protein structure world as a result of combined computational and expert‐driven analysis. Proteins 2015; 83:1238–1251.

ECOD: New developments in the evolutionary classification of domains

Evolutionary Classification Of protein Domains (ECOD) (http://prodata.swmed.edu/ecod) comprehensively classifies protein with known spatial structures maintained by the Protein Data Bank (PDB) into evolutionary groups of protein domains. ECOD relies on a combination of automatic and manual weekly updates to achieve its high accuracy and coverage with a short update cycle. ECOD classifies the approximately 120 000 depositions of the PDB into more than 500 000 domains in ∼3400 homologous groups. We show the performance of the weekly update pipeline since the release of ECOD, describe improvements to the ECOD website and available search options, and discuss novel structures and homologous groups that have been classified in the recent updates. Finally, we discuss the future directions of ECOD and further improvements planned for the hierarchy and update process.

Classification of proteins with shared motifs and internal repeats in the ECOD database.

Proteins and their domains evolve by a set of events commonly including the duplication and divergence of small motifs. The presence of short repetitive regions in domains has generally constituted a difficult case for structural domain classifications and their hierarchies. We developed the Evolutionary Classification Of protein Domains (ECOD) in part to implement a new schema for the classification of these types of proteins. Here we document the ways in which ECOD classifies proteins with small internal repeats, widespread functional motifs, and assemblies of small domain‐like fragments in its evolutionary schema. We illustrate the ways in which the structural genomics project impacted the classification and characterization of new structural domains and sequence families over the decade.

An ancient autoproteolytic domain found in GAIN, ZU5 and Nucleoporin98

A large family of G protein-coupled receptors (GPCRs) involved in cell adhesion has a characteristic autoproteolysis motif of HLT/S known as the GPCR proteolysis site (GPS). GPS is also shared by polycystic kidney disease proteins and it precedes the first transmembrane segment in both families. Recent structural studies have elucidated the GPS to be part of a larger domain named GPCR autoproteolysis inducing (GAIN) domain. Here we demonstrate the remote homology relationships of GAIN domain to ZU5 domain and Nucleoporin98 (Nup98) C-terminal domain by structural and sequence analysis. Sequence homology searches were performed to extend ZU5-like domains to bacteria and archaea, as well as new eukaryotic families. We found that the consecutive ZU5-UPA-death domain domain organization is commonly used in human cytoplasmic proteins with ZU5 domains, including CARD8 (caspase recruitment domain-containing protein 8) and NLRP1 (NACHT, LRR and PYD domain-containing protein 1) from the FIIND (Function to Find) family. Another divergent family of extracellular ZU5-like domains was identified in cartilage intermediate layer proteins and FAM171 proteins. Current diverse families of GAIN domain subdomain B, ZU5 and Nup98 C-terminal domain likely evolved from an ancient autoproteolytic domain with an HFS motif. The autoproteolytic site was kept intact in Nup98, p53-induced protein with a death domain and UNC5C-like, deteriorated in many ZU5 domains and changed in GAIN and FIIND. Deletion of the strand after the cleavage site was observed in zonula occluden-1 and some Nup98 homologs. These findings link several autoproteolytic domains, extend our understanding of GAIN domain origination in adhesion GPCRs and provide insights into the evolution of an ancient autoproteolytic domain.

Searching ECOD for Homologous Domains by Sequence and Structure

ECOD is a database of evolutionary domains from structures deposited in the PDB. Domains in ECOD are classified by a mixed manual/automatic method wherein the bulk of newly deposited structures are classified automatically by protein‐protein BLAST. Those structures that cannot be classified automatically are referred to manual curators who use a combination of alignment results, functional analysis, and close reading of the literature to generate novel assignments. ECOD differs from other structural domain resources in that it is continually updated, classifying thousands of proteins per week. ECOD recognizes homology as its key organizing concept, rather than structural or sequence similarity alone. Such a classification scheme provides functional information about proteins of interest by placing them in the correct evolutionary context among all proteins of known structure. This unit demonstrates how to access ECOD via the Web and how to search the database by sequence or structure. It also details the distributable data files available for large‐scale bioinformatics users.

A sequence family database built on ECOD structural domains

Motivation The ECOD database classifies protein domains based on their evolutionary relationships, considering both remote and close homology. The family group in ECOD provides classification of domains that are closely related to each other based on sequence similarity. Due to different perspectives on domain definition, direct application of existing sequence domain databases, such as Pfam, to ECOD struggles with several shortcomings. Results We created multiple sequence alignments and profiles from ECOD domains with the help of structural information in alignment building and boundary delineation. We validated the alignment quality by scoring structure superposition to demonstrate that they are comparable to curated seed alignments in Pfam. Comparison to Pfam and CDD reveals that 27 and 16% of ECOD families are new, but they are also dominated by small families, likely because of the sampling bias from the PDB database. There are 35 and 48% of families whose boundaries are modified comparing to counterparts in Pfam and CDD, respectively. Availability and implementation The new families are now integrated in the ECOD website. The aggregate HMMER profile library and alignment are available for download on ECOD website (http://prodata.swmed.edu/ecod). Supplementary information Supplementary data are available at Bioinformatics online.