Protein structural domain identification

A simple method for the definition of protein structural domains is described that requires only alpha-carbon coordinate data. The basic method, which encodes no specific aspects of protein structure, captures the essence of most domains but does not give high enough priority to the integrity of beta-sheet structure. This aspect was encouraged both by a bias toward attaining intact beta-sheets and also as an acceptance condition on the final result. The method has only one variable parameter, reflecting the granularity level of the domains, and an attempt was made to set this level automatically for each protein based on the best agreement attained between the domains predicted on the native structure and a set of smoothed coordinates. While not perfect, this feature allowed some tightly packed domains to be separated that would have remained undivided had the best fixed granularity level been used. The quality of the results was high and, when compared with a large collection of accepted domain definitions, only a few could be said to be clearly incorrect. The simplicity of the method allowed its easy extension to the simultaneous definition of domains across related structures in a way that does not involve loss of detail through averaging the structures. This was found to be a useful approach to reconciling differences among structural family members. The method is fast, taking less than 1 s per 100 residues for medium-sized proteins.     

Signaling to the Rho GTPases: networking with the DH domain

The Dbl homology (DH) domain was first identified in the Dbl oncogene product as the limit region required for mediating guanine nucleotide exchange on the Rho family GTPase Cdc42. Since the initial biochemical characterization of the DH domain, this conserved motif has been identified in a large family of proteins. In each case, a pleckstrin homology (PH) domain immediately follows the DH domain and this tandem DH-PH module is the signature motif of the Dbl family of guanine nucleotide exchange factors (GEFs). Recent structural studies have provided significant insight into the molecular basis of guanine nucleotide exchange by Dbl family GEFs, opening the door for understanding the specificity of the DH/GTPase interaction as well as providing a starting point for understanding how the exchange activity of these proteins is modulated to achieve specific biological outcomes in the cell.     

Protein networking: insights into global functional organization of proteomes

The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields. This approach has provided many insights into the functional organization of the proteome so far and will likely continue to do so. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow different insights into functional organization. One such concept is the protein interaction network, which contains proteins as nodes and undirected edges representing the occurrence of binding in large-scale protein-protein interaction studies. A second concept is the protein-signaling network, in which the nodes correspond to levels of post-translationally modified forms of proteins and directed edges to causal effects through post-translational modification, such as phosphorylation. Several other network concepts were introduced for proteomics. Although all formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We review recent literature formulating and analyzing such networks.     

Protein transduction domain delivery of therapeutic macromolecules

Owing to their unprecedented selectivity, specific activity and potential for 1000+ fold amplification of signal, macromolecules, such as peptides, catalytic protein domains, complete proteins, and oligonucleotides, offer great potential as therapeutic molecules. However, therapeutic use of macromolecules is limited by their poor penetration in tissues and their inability to cross the cellular membrane. The discovery of small cationic peptides that cross the membrane, called Protein Transduction Domains (PTDs) or Cell Penetrating Peptides (CPPs), in the late 1980s opened the door to cellular delivery of large, bioactive molecules. Now, PTDs are widely used as research tools, and impressively, multiple clinical trials are testing PTD-mediated delivery of macromolecular drug conjugates in patients with a variety of diseases.     

Protein domain decomposition using a graph-theoretic approach

MOTIVATION: Automatic decomposition of a multi-domain protein into individual domains represents a highly interesting and unsolved problem. As the number of protein structures in PDB is growing at an exponential rate, there is clearly a need for more reliable and efficient methods for protein domain decomposition simply to keep the domain databases up-to-date. RESULTS: We present a new algorithm for solving the domain decomposition problem, using a graph-theoretic approach. We have formulated the problem as a network flow problem, in which each residue of a protein is represented as a node of the network and each residue--residue contact is represented as an edge with a particular capacity, depending on the type of the contact. A two-domain decomposition problem is solved by finding a bottleneck (or a minimum cut) of the network, which minimizes the total cross-edge capacity, using the classical Ford--Fulkerson algorithm. A multi-domain decomposition problem is solved through repeatedly solving a series of two-domain problems. The algorithm has been implemented as a computer program, called DomainParser. We have tested the program on a commonly used test set consisting of 55 proteins. The decomposition results are 78.2% in agreement with the literature on both the number of decomposed domains and the assignments of residues to each domain, which compares favorably to existing programs. On the subset of two-domain proteins (20 in number), the program assigned 96.7% of the residues correctly when we require that the number of decomposed domains is two.     

Protein domain annotation with predicted domain-domain interaction networks

This paper presents a framework for annotating protein domains with predicted domain-domain interaction networks. Specially, domain annotation is formalized as a multi-class classification problem in this work. The numerical experiments on InterPro domains show promising results, which proves the efficiency of our proposed methods.     

Protein domain definition should allow for conditional disorder

Abstract: Proteins are often classified in a binary fashion as either structured or disordered. However this approach has several deficits. Firstly, protein folding is always conditional on the physiochemical environment. A protein which is structured in some circumstances will be disordered in others. Secondly, it hides a fundamental asymmetry in behavior. While all structured proteins can be unfolded through a change in environment, not all disordered proteins have the capacity for folding. Failure to accommodate these complexities confuses the definition of both protein structural domains and intrinsically disordered regions. We illustrate these points with an experimental study of a family of small binding domains, drawn from the RNA polymerase of mumps virus and its closest relatives. Assessed at face value the domains fall on a structural continuum, with folded, partially folded, and near unstructured members. Yet the disorder present in the family is conditional, and these closely related polypeptides can access the same folded state under appropriate conditions. Any heuristic definition of the protein domain emphasizing conformational stability divides this domain family in two, in a way that makes no biological sense. Structural domains would be better defined by their ability to adopt a specific tertiary structure: a structure that may or may not be realized, dependent on the circumstances. This explicitly allows for the conditional nature of protein folding, and more clearly demarcates structural domains from intrinsically disordered regions that may function without folding.     

Bacterial networking

A report of the ESF-EMBO Symposium Bacterial Networks (BacNet08), Sant Feliu de Guixols, Spain, 13-18 September 2008.     

Protein domain analysis in the era of complete genomes

Domains present one of the most useful levels at which to understand protein function, and domain family-based analysis has had a profound impact on the study of individual proteins. Protein domain discovery has been progressing steadily over the past 30 years. What are the realistically achievable goals of sequence-based domain analysis, and how far off are they for the sequences encoded in eukaryotic genomes? Here we address some of the issues involved in better coverage of sequence-based domain annotation, and the integration of these results within the wider context of genomes, structures and function.     

Protein fold analysis of the B30.2-like domain

The B30.2-like domain occurs in some members of a diverse and growing family of proteins containing zinc-binding B-box motifs, whose functions include regulation of cell growth and differentiation. The B30.2-like domain is also found in proteins without the zinc-binding motifs, such as butyrophilin (a transmembrane glycoprotein) and stonustoxin (a secreted cytolytic toxin). Currently, the function for the B30.2-like domain is not clear and the structure of a protein containing this domain has not been solved. The secondary structure prediction methods indicate that the B30.2-like domain consists of fifteen or fewer beta-strands. Fold recognition methods identified different structural topologies for the B30.2-like domains. Secondary structure prediction, deletion and lack of local sequence identity at the C-terminal region for certain members of the family, and packing of known core structures suggest that a structure containing two beta domains is the most probable of these folds. The most C-terminal sequence motif predicted to be a beta-strand in all B30.2-like domains is a potential subdomain boundary based on the sequence-structure alignments. Models of the B30.2-like domains were built based on immunoglobulin-like folds identified by the fold recognition methods to evaluate the possibility of the B30.2 domain adopting known folds and infer putative functional sites. The SPRY domain has been identified as a subdomain within the B30.2-like domain. If the B30.2-like domain is a subclass of the SPRY domain family, then this analysis would suggest that the SPRY domains are members of the immunoglobulin superfamily.     

Protein domain annotation with integration of heterogeneous information sources

Domains are structural and functional units of proteins and play an important role in functional genomics. Theoretically, the functions of a protein can be directly inferred if the biological functions of its component domains are determined. Despite the important role that domains play, only a small number of domains have been annotated so far, and few works have been performed to predict the functions of domains. Hence, it is necessary to develop automatic methods for predicting domain functions based on various available data. In this article, two new methods, that is, the threshold-based classification method and the support vector machines method, are proposed for protein domain function prediction by integrating heterogeneous information sources, including protein-domain mapping features, domain-domain interactions, and domain coexisting features. We show that the integration of heterogeneous information sources improves not only prediction accuracy but also annotation reliability when compared with the methods using only individual information sources.     

Protein domain mapping by lambda phage display: the minimal lactose-binding domain of galectin-3

Mapping of protein domains having a distinct function is essential to understanding the protein's structure-function relationship. We used a bacteriophage lambda surface expression vector, lambdafoo, in order to determine the minimal carbohydrate-binding domain of human galectin-3 (Gal-3). Gal-3 cDNA was randomly digested by DNase I and cloned into the phage vector. The library generated was screened by affinity selection using lactose immobilized on agarose beads. DNA sequence analysis of a set of isolated clones defined the minimal folding domain of Gal-3 required for lactose binding, which consisted of 136 amino-acid residues. Using the phage clones isolated, we also determined relative dissociation constants in solution between lactose and the minimal domain expressed on the phage surface. This technique does not require either purified or labeled proteins, and bacteriophage lambda surface display may, therefore, be useful for protein domain mapping and in vitro studies of various macromolecular interactions.     

Protein domain assignment from the recurrence of locally similar structures

Domains are basic units of protein structure and essential for exploring protein fold space and structure evolution. With the structural genomics initiative, the number of protein structures in the Protein Databank (PDB) is increasing dramatically and domain assignments need to be done automatically. Most existing structural domain assignment programs define domains using the compactness of the domains and/or the number and strength of intra-domain versus inter-domain contacts. Here we present a different approach based on the recurrence of locally similar structural pieces (LSSPs) found by one-against-all structure comparisons with a dataset of 6373 protein chains from the PDB. Residues of the query protein are clustered using LSSPs via three different procedures to define domains. This approach gives results that are comparable to several existing programs that use geometrical and other structural information explicitly. Remarkably, most of the proteins that contribute the LSSPs defining a domain do not themselves contain the domain of interest. This study shows that domains can be defined by a collection of relatively small locally similar structural pieces containing, on average, four secondary structure elements. In addition, it indicates that domains are indeed made of recurrent small structural pieces that are used to build protein structures of many different folds as suggested by recent studies.     

Protein domain identification and improved sequence similarity searching using PSI-BLAST

Protein sequences containing more than one structural domain are problematic when used in homology searches where they can either stop an iterative database search prematurely or cause an explosion of a search to common domains. We describe a method, DOMAINATION, that infers domains and their boundaries in a query sequence from local gapped alignments generated using PSI-BLAST. Through a new technique to recognize domain insertions and permutations, DOMAINATION submits delineated domains as successive database queries in further iterative steps. Assessed over a set of 452 multidomain proteins, the method predicts structural domain boundaries with an overall accuracy of 50% and improves finding distant homologies by 14% compared with PSI-BLAST. DOMAINATION is available as a web based tool at http://mathbio.nimr.mrc.ac.uk, and the source code is available from the authors upon request.     

Protein kinase D interacts with Golgi via its cysteine-rich domain

Protein kinase D (PKD)/protein kinase Cmu is a serine/threonine protein kinase that has been localized in the cytosol and in several intracellular compartments including Golgi, mitochondria and plasma membrane. Using real time imaging of fluorescent protein (GFP)-tagged PKD, we have found that the accumulation of PKD in the Golgi compartment, following a temperature shift from 37 to 20 degrees C, was mediated by the cysteine-rich domain (CRD) of PKD. The CRD of PKD also mediates its interaction with the plasma membrane, further supporting the conclusion that the CRD of PKD may act as a subcellular localization signal.     

Protein 4.1R self-association: identification of the binding domain

Erythroid protein 4.1 (4.1R) stabilizes the spectrin-actin network and anchors it to the plasma membrane. To contribute to the characterization of non-erythroid protein 4.1R, we used sedimentation, pull-down and co-immunoprecipitation assays to investigate the ability of protein 4.1R to establish inter-/intra-molecular associations. We demonstrated that the small 4.1R isoforms of 60 kDa (4.1R60), but not the larger isoforms of 80 and 135 kDa (4.1R80 and 4.1R135), were self-associated, and that a domain contained in all 4.1R isoforms, the core region, was responsible for 4.1R self-association. Results from denaturing-renaturing experiments, in which an initially non-self-associated 4.1R80 isoform became self-associated, suggested that an initially hidden core region was subsequently exposed. This hypothesis was supported by results from pull-down assays, which showed that the core region interacted with the N-terminal end of the FERM (4.1, ezrin, radixin, moesin) domain that is present in 4.1R80 and 4.1R135 isoforms but absent from 4.1R60 isoforms. Consistently, 4.1R80 isoforms bound neither to each other nor to 4.1R60 isoforms. We propose that 4.1R60 isoforms are constitutively self-associated, whereas 4.1R80 and 4.1R135 self-association is prevented by intramolecular interactions.