Index-based similarity search for protein structure databases

We propose new methods for finding similarities in protein structure databases. These methods extract feature vectors on triplets of SSEs (Secondary Structure Elements) of proteins. The feature vectors are then indexed using a multidimensional index structure. Our first technique considers the problem of finding proteins similar to a given query protein in a protein dataset. It quickly finds promising proteins using the index structure. These proteins are then aligned to the query protein using a popular pairwise alignment tool such as VAST. We also develop a novel statistical model to estimate the goodness of a match using the SSEs. Our second technique considers the problem of joining two protein datasets to find an all-to-all similarity. Experimental results show that our techniques improve the pruning time of VAST 3 to 3.5 times, while keeping the sensitivity similar. Our technique can also be incorporated with DALI and CE to improve their running times by a factor of 2 and 2.7 respectively. The software is available online at http://bioserver.cs.ucsb.edu/.     

Searching protein structure databases has come of age

The number of protein structures known in atomic detail has increased from one in 1960 (Kendrew, J. C., Strandberg, B. E., Hart, R. G., Davies, D. R., Phillips, D. C., Shore, V. C. Nature (London) 185:422–427, 1960) to more than 1000 in 1994. The rate at which new structures are being published exceeds one a day as a result of recent advances in protein engineering, crystallography, and spectroscopy. More and more frequently, a newly determined structure is similar in fold to a known one, even when no sequence similarity is detectable. A new generation of computer algorithms has now been developed that allows routine comparison of a protein structure with the database of all known structures. Such structure database searches are already used daily and they are beginning to rival sequence database searches as a tool for discovering biologically interesting relationships. © 1994 Wiley-Liss, Inc.     

Progress in the analysis of membrane protein structure and function

Structural information on membrane proteins is sparse, yet they represent an important class of proteins that is encoded by about 30% of all genes. Progress has primarily been achieved with bacterial proteins, but efforts to solve the structure of eukaryotic membrane proteins are also increasing. Most of the structures currently available have been obtained by exploiting the power of X-ray crystallography. Recent results, however, have demonstrated the accuracy of electron crystallography and the imaging power of the atomic force microscope. These instruments allow membrane proteins to be studied while embedded in the bi-layer, and thus in a functional state. The low signal-to-noise ratio of cryo-electron microscopy is overcome by crystallizing membrane proteins in a two-dimensional protein-lipid membrane, allowing its atomic structure to be determined. In contrast, the high signal-to-noise ratio of atomic force microscopy allows individual protein surfaces to be imaged at sub-nanometer resolution, and their conformational states to be sampled. This review summarizes the steps in membrane protein structure determination and illuminates recent progress.     

Improving classification in protein structure databases using text mining

Background  

The classification of protein domains in the CATH resource is primarily based on structural comparisons, sequence similarity and manual analysis. One of the main bottlenecks in the processing of new entries is the evaluation of 'borderline' cases by human curators with reference to the literature, and better tools for helping both expert and non-expert users quickly identify relevant functional information from text are urgently needed. A text based method for protein classification is presented, which complements the existing sequence and structure-based approaches, especially in cases exhibiting low similarity to existing members and requiring manual intervention. The method is based on the assumption that textual similarity between sets of documents relating to proteins reflects biological function similarities and can be exploited to make classification decisions.     

Searching protein structure databases with DaliLite v.3

The Red Queen said, 'It takes all the running you can do, to keep in the same place.' Lewis Carrol MOTIVATION: Newly solved protein structures are routinely scanned against structures already in the Protein Data Bank (PDB) using Internet servers. In favourable cases, comparing 3D structures may reveal biologically interesting similarities that are not detectable by comparing sequences. The number of known structures continues to grow exponentially. Sensitive-thorough but slow-search algorithms are challenged to deliver results in a reasonable time, as there are now more structures in the PDB than seconds in a day. The brute-force solution would be to distribute the individual comparisons on a massively parallel computer. A frugal solution, as implemented in the Dali server, is to reduce the total computational cost by pruning search space using prior knowledge about the distribution of structures in fold space. This note reports paradigm revisions that enable maintaining such a knowledge base up-to-date on a PC. AVAILABILITY: The Dali server for protein structure database searching at http://ekhidna.biocenter.helsinki.fi/dali_server is running DaliLite v.3. The software can be downloaded for academic use from http://ekhidna.biocenter.helsinki.fi/dali_lite/downloads/v3.     

Plant membrane proteome databases

In all living organisms transmembrane (TM) proteins are crucially involved in many physiological processes and constitute 20-30% of the proteome. An important class of TM proteins are transporters that interconnect biochemical pathways across the plasma membrane and intracellular membranes, e.g. the mitochondrial membranes and chloroplast envelope membranes. In recent years, bioinformatical tools to predict TM domains and subcellular localization were developed and used to analyze the first complete plant genomes of Arabidopsis and rice. This review focuses on plant TM proteome databases that compile topology and intracellular targeting predictions and different kinds of experimental data. In addition, other web sites are discussed that contribute useful experimental and/or bioinformatical data.     

Efficient similarity search in protein structure databases by k-clique hashing

MOTIVATION: Graph-based clique-detection techniques are widely used for the recognition of common substructures in proteins. They permit the detection of resemblances that are independent of sequence or fold homologies and are also able to handle conformational flexibility. Their high computational complexity is often a limiting factor and prevents a detailed and fine-grained modeling of the protein structure. RESULTS: We present an efficient two-step method that significantly speeds up the detection of common substructures, especially when used to screen larger databases. It combines the advantages from both clique-detection and geometric hashing. The method is applied to an established approach for the comparison of protein binding-pockets, and some empirical results are presented. AVAILABILITY: Upon request from the authors.     

Understanding membrane protein structure by design

In contrast to soluble proteins, the primary interactions that specify and stabilize membrane protein structures are still largely a matter of speculation. Although van der Waals interactions have been gaining increasing favor as the dominant player, new results demonstrate the strength of hydrogen bonding in a membrane environment.     

Mapping membrane protein structure with fluorescence

Membrane proteins regulate many cellular processes including signaling cascades, ion transport, membrane fusion, and cell-to-cell communications. Understanding the architecture and conformational fluctuations of these proteins is critical to understanding their regulation and functions. Fluorescence methods including intensity mapping, fluorescence resonance energy transfer (FRET), and photo-induced electron transfer, allow for targeted measurements of domains within membrane proteins. These methods can reveal how a protein is structured and how it transitions between different conformational states. Here, I will review recent work done using fluorescence to map the structures of membrane proteins, focusing on how each of these methods can be applied to understanding the dynamic nature of individual membrane proteins and protein complexes.     

Current status of membrane protein structure classification

For over 2 decades, continuous efforts to organize the jungle of available protein structures have been underway. Although a number of discrepancies between different classification approaches for soluble proteins have been reported, the classification of membrane proteins has so far not been comparatively studied because of the limited amount of available structural data. Here, we present an analysis of α‐helical membrane protein classification in the SCOP and CATH databases. In the current set of 63 α‐helical membrane protein chains having between 1 and 13 transmembrane helices, we observed a number of differently classified proteins both regarding their domain and fold assignment. The majority of all discrepancies affect single transmembrane helix, two helix hairpin, and four helix bundle domains, while domains with more than five helices are mostly classified consistently between SCOP and CATH. It thus appears that the structural constraints imposed by the lipid bilayer complicate the classification of membrane proteins with only few membrane‐spanning regions. This problem seems to be specific for membrane proteins as soluble four helix bundles, not restrained by the membrane, are more consistently classified by SCOP and CATH. Our findings indicate that the structural space of small membrane helix bundles is highly continuous such that even minor differences in individual classification procedures may lead to a significantly different classification. Membrane proteins with few helices and limited structural diversity only seem to be reasonably classifiable if the definition of a fold is adapted to include more fine‐grained structural features such as helix–helix interactions and reentrant regions. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

The progress of membrane protein structure determination

The rate of membrane protein (MP) structure determination has been examined for the 18-year period following the publication of the first high-resolution crystal structure. The growth is solidly exponential, but lags behind the rate for soluble proteins during the equivalent time period.     

Comparative modeling for protein structure prediction

With the progression of structural genomics projects, comparative modeling remains an increasingly important method of choice. It helps to bridge the gap between the available sequence and structure information by providing reliable and accurate protein models. Comparative modeling based on more than 30% sequence identity is now approaching its natural template-based limits and further improvements require the development of effective refinement techniques capable of driving models toward native structure. For difficult targets, for which the most significant progress in recent years has been observed, optimal template selection and alignment accuracy are still the major problems.     

Influence of protein structure databases on the predictive power of statistical pair potentials

A long standing goal in protein structure studies is the development of reliable energy functions that can be used both to verify protein models derived from experimental constraints as well as for theoretical protein folding and inverse folding computer experiments. In that respect, knowledge-based statistical pair potentials have attracted considerable interests recently mainly because they include the essential features of protein structures as well as solvent effects at a low computing cost. However, the basis on which statistical potentials are derived have been questioned. In this paper, we investigate statistical pair potentials derived from protein three-dimensional structures, addressing in particular questions related to the form of these potentials, as well as to the content of the database from which they are derived. We have shown that statistical pair potentials depend on the size of the proteins included in the database, and that this dependence can be reduced by considering only pairs of residue close in space (i.e., with a cutoff of 8 Å). We have shown also that statistical potentials carry a memory of the quality of the database in terms of the amount and diversity of secondary structure it contains. We find, for example, that potentials derived from a database containing α-proteins will only perform best on α-proteins in fold recognition computer experiments. We believe that this is an overall weakness of these potentials, which must be kept in mind when constructing a database. Proteins 31:139–149, 1998. © 1998 Wiley-Liss, Inc.     

Overcoming barriers to membrane protein structure determination

After decades of slow progress, the pace of research on membrane protein structures is beginning to quicken thanks to various improvements in technology, including protein engineering and microfocus X-ray diffraction. Here we review these developments and, where possible, highlight generic new approaches to solving membrane protein structures based on recent technological advances. Rational approaches to overcoming the bottlenecks in the field are urgently required as membrane proteins, which typically comprise ~30% of the proteomes of organisms, are dramatically under-represented in the structural database of the Protein Data Bank.     

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Microscopic assessment of membrane protein structure and function

Membrane proteins represent an important class of proteins that are encoded by about 40% of all genes, but compared to soluble proteins structural information is sparse. Most of the atomic coordinates currently available are from bacterial membrane proteins and have been obtained by X-ray crystallography. Recent results demonstrate the imaging power of the atomic force microscope and the accuracy of electron crystallography. These methods allow membrane proteins to be studied while embedded in the bilayer, and thus in a functional state. The low signal-to-noise ratio of cryoelectron microscopy is overcome by crystallizing membrane proteins in a two-dimensional protein-lipid membrane, allowing its atomic structure to be determined. In contrast, the high signal-to-noise ratio of atomic force microscopy allows individual protein surfaces to be imaged at subnanometer resolution, and their conformational states to be sampled. This review discusses examples of microscopic membrane protein structure determination and illuminates recent progress.     

NMR structure of the integral membrane protein OmpX

The structure of the integral membrane protein OmpX from Escherichia coli reconstituted in 60 kDa DHPC micelles (OmpX/DHPC) was calculated from 526 NOE upper limit distance constraints. The structure determination was based on complete sequence-specific assignments for the amide protons and the Val, Leu, and Ile(delta1) methyl groups in OmpX, which were selectively protonated on a perdeuterated background. The solution structure of OmpX in the DHPC micelles consists of a well-defined, eight-stranded antiparallel beta-barrel, with successive pairs of beta-strands connected by mobile loops. Several long-range NOEs observed outside of the transmembrane barrel characterize an extension of a four-stranded beta-sheet beyond the height of the barrel. This protruding beta-sheet is believed to be involved in intermolecular interactions responsible for the biological functions of OmpX. The present approach for de novo structure determination should be quite widely applicable to membrane proteins reconstituted in mixed micelles with overall molecular masses up to about 100 kDa, and may also provide a platform for additional functional studies.