A Database of Domain Definitions for Proteins with Complex Interdomain Geometry

Protein structural domains are necessary for understanding evolution and protein folding, and may vary widely from functional and sequence based domains. Although, various structural domain databases exist, defining domains for some proteins is non-trivial, and definitions of their domain boundaries are not available. Here, we present a novel database of manually defined structural domains for a representative set of proteins from the SCOP “multi-domain proteins” class. (http://prodata.swmed.edu/multidom/). We consider our domains as mobile evolutionary units, which may rearrange during protein evolution. Additionally, they may be visualized as structurally compact and possibly independently folding units. We also found that representing domains as evolutionary and folding units do not always lead to a unique domain definition. However, unlike existing databases, we retain and refine these “alternate” domain definitions after careful inspection of structural similarity, functional sites and automated domain definition methods. We provide domain definitions, including actual residue boundaries, for proteins that well known databases like SCOP and CATH do not attempt to split. Our alternate domain definitions are suitable for sequence and structure searches by automated methods. Additionally, the database can be used for training and testing domain delineation algorithms. Since our domains represent structurally compact evolutionary units, the database may be useful for studying domain properties and evolution

Improved general regression network for protein domain boundary prediction

Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy. Conclusion: The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. © 2008 Yoo et al; licensee BioMed Central Ltd

Implications from a Network-Based Topological Analysis of Ubiquitin Unfolding Simulations

BACKGROUND: The architectural organization of protein structures has been the focus of intense research since it can hopefully lead to an understanding of how proteins fold. In earlier works we had attempted to identify the inherent structural organization in proteins through a study of protein topology. We obtained a modular partitioning of protein structures with the modules correlating well with experimental evidence of early folding units or "foldons". Residues that connect different modules were shown to be those that were protected during the transition phase of folding. METHODOLOGY/PRINCIPAL FINDINGS: In this work, we follow the topological path of ubiquitin through molecular dynamics unfolding simulations. We observed that the use of recurrence quantification analysis (RQA) could lead to the identification of the transition state during unfolding. Additionally, our earlier contention that the modules uncovered through our graph partitioning approach correlated well with early folding units was vindicated through our simulations. Moreover, residues identified from native structure as connector hubs and which had been shown to be those that were protected during the transition phase of folding were indeed more stable (less flexible) well beyond the transition state. Further analysis of the topological pathway suggests that the all pairs shortest path in a protein is minimized during folding. CONCLUSIONS: We observed that treating a protein native structure as a network by having amino acid residues as nodes and the non-covalent interactions among them as links allows for the rationalization of many aspects of the folding process. The possibility to derive this information directly from 3D structure opens the way to the prediction of important residues in proteins, while the confirmation of the minimization of APSP for folding allows for the establishment of a potentially useful proxy for kinetic optimality in the validation of sequence-structure predictions

Altering APP Proteolysis: Increasing sAPPalpha Production by Targeting Dimerization of the APP Ectodomain

One of the events associated with Alzheimer's disease is the dysregulation of α- versus β-cleavage of the amyloid precursor protein (APP). The product of α-cleavage (sAPPα) has neuroprotective properties, while Aβ1-42 peptide, a product of β-cleavage, is neurotoxic. Dimerization of APP has been shown to influence the relative rate of α- and β- cleavage of APP. Thus finding compounds that interfere with dimerization of the APP ectodomain and increase the α-cleavage of APP could lead to the development of new therapies for Alzheimer's disease. Examining the intrinsic fluorescence of a fragment of the ectodomain of APP, which dimerizes through the E2 and Aβ-cognate domains, revealed significant changes in the fluorescence of the fragment upon binding of Aβ oligomers—which bind to dimers of the ectodomain— and Aβ fragments—which destabilize dimers of the ectodomain. This technique was extended to show that RERMS-containing peptides (APP695 328–332), disulfiram, and sulfiram also inhibit dimerization of the ectodomain fragment. This activity was confirmed with small angle x-ray scattering. Analysis of the activity of disulfiram and sulfiram in an AlphaLISA assay indicated that both compounds significantly enhance the production of sAPPα by 7W-CHO and B103 neuroblastoma cells. These observations demonstrate that there is a class of compounds that modulates the conformation of the APP ectodomain and influences the ratio of α- to β-cleavage of APP. These compounds provide a rationale for the development of a new class of therapeutics for Alzheimer's disease

Assembly and function of the Photosystem II manganese stabilizing protein: lessons from its natively unfolded behavior

The Photosystem II (PS II) manganese stabilizing protein (MSP) possesses characteristics, including thermostability, ascribed to the natively unfolded class of proteins (Lydakis-Simantiris et al. (1999) Biochemistry 38: 404–414). A site-directed mutant of MSP, C28A, C51A, which lacks the -S–S- bridge, also binds to PS II at wild-type levels and reconstitutes oxygen evolution activity [Betts et al. (1996) Biochim Biophys Acta 1274: 135–142], although the mutant protein is even more disordered in solution. Both WT and C28A, C51A MSP aggregate upon heating, but an examination of the effects of protein concentration and pH on heat-induced aggregation showed that each MSP species exhibited greater resistance to aggregation at a pH near their p I (5.2) than do either bovine serum albumin (BSA) or carbonic anhydrase, which were used as model water soluble proteins. Increases in pH above the p I of the MSPs and BSA enhanced their aggregation resistance, a behavior which can be predicted from their charge (MSP) or a combination of charge and stabilization by -S–S- bonds (BSA). In the case of aggregation resistance by MSP, this is likely to be an important factor in its ability to avoid unproductive self-association reactions in favor of formation of the protein–protein interactions that lead to formation of the functional oxygen evolving complex.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43544/1/11120_2004_Article_7759.pd

Intrinsically disordered domains: Sequence ➔ disorder ➔ function relationships

Disordered domains are long regions of intrinsic disorder that ideally have conserved sequences, conserved disorder, and conserved functions. These domains were first noticed in protein–protein interactions that are distinct from the interactions between two structured domains and the interactions between structured domains and linear motifs or molecular recognition features (MoRFs). So far, disordered domains have not been systematically characterized. Here, we present a bioinformatics investigation of the sequence–disorder–function relationships for a set of probable disordered domains (PDDs) identified from the Pfam database. All the Pfam seed proteins from those domains with at least one PDD sequence were collected. Most often, if a set contains one PDD sequence, then all members of the set are PDDs or nearly so. However, many seed sets have sequence collections that exhibit diverse proportions of predicted disorder and structure, thus giving the completely unexpected result that conserved sequences can vary substantially in predicted disorder and structure. In addition to the induction of structure by binding to protein partners, disordered domains are also induced to form structure by disulfide bond formation, by ion binding, and by complex formation with RNA or DNA. The two new findings, (a) that conserved sequences can vary substantially in their predicted disorder content and (b) that homologues from a single domain can evolve from structure to disorder (or vice versa), enrich our understanding of the sequence ➔ disorder ensemble ➔ function paradigm