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PubMed1994

Protein structural similarities predicted by a sequence-structure compatibility method.

A method for protein structure prediction has been developed, which evaluates the compatibility of an amino acid sequence with known 3-dimensional structures and identifies the most likely structure. The method was applied to a large number of sequences in a database, and the structures of the following proteins were predicted: (1) shikimate kinase (SKase), (2) the hydrophilic subunit of mannose permease (IIABMan), (3) rat tyrosine aminotransferase (Tyr AT), and (4) threonine dehydratase (TDH). The functional and evolutionary implications of the predictions are discussed. (1) The structural similarity between SKase and adenylate kinase was predicted. Alignment of their sequences reveals that the ATP-binding type A sequence motif and 2 ATP-binding arginine residues are conserved. The prediction suggests a similarity in their functional mechanisms as well as an evolutionary relationship. (2) The structural similarity between IIABMan and galactose/glucose-binding protein (GGBP) was predicted. The IIA and IIB domains are aligned with the N- and C-terminal domains of GGBP, respectively. The 2 phosphorylated residues, His 10 and His 175, of IIABMan are threaded onto loops located in the substrate-binding cleft of GGBP. The prediction accounts for the phosphoryl transfer from His 10 to His 175, and to the sugar substrate. (3) The structural similarity between rat Tyr AT and Escherichia coli aspartate AT was predicted, as well as (4) the structural similarity between TDH and the tryptophan synthase beta subunit. Predictions (3) and (4) support the previous predictions based on observations of the functional similarities between the proteins.

Amino Acid Sequence↗

PubMed2005

ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures.

Key amino acid positions that are important for maintaining the 3D structure of a protein and/or its function(s), e.g. catalytic activity, binding to ligand, DNA or other proteins, are often under strong evolutionary constraints. Thus, the biological importance of a residue often correlates with its level of evolutionary conservation within the protein family. ConSurf (http://consurf.tau.ac.il/) is a web-based tool that automatically calculates evolutionary conservation scores and maps them on protein structures via a user-friendly interface. Structurally and functionally important regions in the protein typically appear as patches of evolutionarily conserved residues that are spatially close to each other. We present here version 3.0 of ConSurf. This new version includes an empirical Bayesian method for scoring conservation, which is more accurate than the maximum-likelihood method that was used in the earlier release. Various additional steps in the calculation can now be controlled by a number of advanced options, thus further improving the accuracy of the calculation. Moreover, ConSurf version 3.0 also includes a measure of confidence for the inferred amino acid conservation scores.

Amino Acid Substitution↗

PubMed1998

Presence of conserved domains in the C-terminus of MARCKS, a major in vivo substrate of protein kinase C: application of ion trap mass spectrometry to the elucidation of protein structures.

MARCKS, the major protein kinase C substrate in various cells and tissues, binds to calmodulin, acidic membrane phospholipids, and actin filaments, and these interactions are regulated by protein phosphorylation. We have previously analyzed MARCKS purified from bovine brain using capillary liquid chromatography/electrospray mass spectrometry and found that the protein structure differed significantly from that deduced from cDNA sequences [Taniguchi, H., Manenti, S., Suzuki, M., and Titani, K. (1994) J. Biol. Chem. 269, 18299-18302]. Moreover, the alignment of the protein from various species showed a lack of any conserved sequences in the C-terminal half of the molecule. This prompted us to reexamine the C-terminal amino-acid sequence of bovine MARCKS. The purified protein was digested with lysyl endoprotease, and the obtained C-terminal peptide was further digested with either Staphylococcus V8 protease or NTCB. The small peptides thus obtained were analyzed by liquid chromatography/electrospray/tandem mass spectrometry. This combined with gas-phase Edman sequencing allowed us to determine the C-terminal primary structure. The sequence obtained differed significantly from that reported previously, and the comparison with other species revealed the presence of a novel conserved domain in the C-terminal region of MARCKS.

Amino Acid Sequence↗

PubMed1984

Molecular homology among the structural proteins of densonucleosis virus from silkworm, Bombyx mori.

Similarities among the four structural proteins of Bombyx densonucleosis virus (DNV) were examined by four independent techniques, peptide mapping, immunodiffusion tests, amino acid analysis and enzyme linked immunosorbent (ELISA) technique. The peptide maps produced by Staphylococcus aureus V8 protease, or chymotrypsin indicated the existence of sequence homology among the proteins. The results of immunodiffusion tests revealed that these four structural proteins share common antigens. The amino acid compositions of these proteins were also very similar to each other. However, the amount of amino acid residues (e.g., serine) in VP3 was not always sufficient to account for the amount found in the smaller structural protein, VP2. These results indicate that the four structural proteins of Bombyx DNV probably originate, at least partially, from a common DNA sequence, and that VP2 is not a direct cleavage product of VP3.

Amino Acids↗

PubMed2005

Improving protein structure prediction with model-based search.

MOTIVATION: De novo protein structure prediction can be formulated as search in a high-dimensional space. One of the most frequently used computational tools to solve such search problems is the Monte Carlo method. We present a novel search technique, called model-based search. This method samples the high-dimensional search space to build an approximate model of the underlying function. This model is incrementally refined in areas of interest, whereas areas that are not of interest are excluded from further exploration. Model-based search derives its efficiency from the fact that the information obtained during the exploration of the search space is used to guide further exploration. In contrast, Monte Carlo-based techniques lack memory and exploration is performed based on random walks, ignoring the information obtained in previous steps. RESULTS: Model-based search is applied to protein structure prediction, where search is employed to find the global minimum of the protein's energy landscape. We show that model-based search uses computational resources more efficiently to find lower-energy conformations of proteins than one of the leading protein structure prediction methods, which relies on a tailored Monte Carlo method to perform a search. The performance improvements become more pronounced as the dimensionality of the search problem increases. We argue that model-based search will enable more accurate protein structure prediction than was previously possible. Furthermore, we believe that similar performance improvements can be expected in other problems that are currently solved using Monte Carlo-based search methods. AVAILABILITY: An implementation of model-based search can be obtained by contacting the authors.

Amino Acids↗

PubMed2001

Defects in extracellular matrix structural proteins in the osteochondrodysplasias.

Mutations in the genes that encode structural proteins of the extracellular matrix affect one or more steps in the diverse set of coordinated events necessary for ordered skeletal development. Depending on the role of the gene product and the severity of the defect, disruption of endochondral ossification and linear growth, the structural integrity and stability of articular cartilage, and/or mineralization can occur. Several themes have emerged from the molecular dissection of these disorders; most of the osteochondrodysplasias that result from defects in structural proteins are inherited in an autosomal dominant fashion; a spectrum of related clinical phenotypes can be produced by distinct mutations in the same gene; haploinsufficiency for the gene product usually produces a milder clinical phenotype than do mutations resulting in synthesis of structurally abnormal proteins. For structural defects, a dominant-negative effect resulting from presence of the abnormal protein in the matrix appears to be the primary determinant of phenotype. Secondary effects on extracellular matrix protein structure can result from defects in post-translational maturation, including hydroxylation, sulfation and proteolytic cleavage, and produce distinct osteochondrodysplasias. Overall, the inherited disorders of skeletogenesis have revealed the exquisite sensitivity of the architecture of the extracellular matrix to the quantity and quality of matrix molecules.

Animals↗

PubMed1998

What is the probability of a chance prediction of a protein structure with an rmsd of 6 A?

BACKGROUND: The root mean square deviation (rmsd) between corresponding atoms of two protein chains is a commonly used measure of similarity between two protein structures. The smaller the rmsd is between two structures, the more similar are these two structures. In protein structure prediction, one needs the rmsd between predicted and experimental structures for which a prediction can be considered to be successful. Success is obvious only when the rmsd is as small as that for closely homologous proteins (< 3 A). To estimate the quality of the prediction in the more general case, one has to compare the native structure not only with the predicted one but also with randomly chosen protein-like folds. One can ask: how many such structures must be considered to find a structure with a given rmsd from the native structure? RESULTS: We calculated the rmsd values between native structures of 142 proteins and all compact structures obtained in the threading of these protein chains over 364 non-homologous structures. The rmsd distributions have a Gaussian form, with the average rmsd approximately proportional to the radius of gyration. CONCLUSIONS: We estimated the number of protein-like structures required to obtain a structure within an rmsd of 6 A to be 10(4)-10(5) for chains of 60-80 residues and 10(11)-10(12) structures for chains of 160-200 residues. The probability of obtaining a 6 A rmsd by chance is so remote that when such structures are obtained from a prediction algorithm, it should be considered quite successful.

Databases, Factual↗

PubMed1999

A rapid method for exploring the protein structure universe.

We have developed an automatic protein fingerprinting method for the evaluation of protein structural similarities based on secondary structure element compositions, spatial arrangements, lengths, and topologies. This method can rapidly identify proteins sharing structural homologies as we demonstrate with five test cases: the globins, the mammalian trypsinlike serine proteases, the immunoglobulins, the cupredoxins, and the actinlike ATPase domain-containing proteins. Principal components analysis of the similarity distance matrix calculated from an all-by-all comparison of 1,031 unique chains in the Protein Data Bank has produced a distribution of structures within a high-dimensional structural space. Fifty percent of the variance observed for this distribution is bounded by six axes, two of which encode structural variability within two large families, the immunoglobulins and the trypsinlike serine proteases. Many aspects of the spatial distribution remain stable upon reduction of the database to 140 proteins with minimal family overlap. The axes correlated with specific structural families are no longer observed. A clear hierarchy of organization is seen in the arrangement of protein structures in the universe. At the highest level, protein structures populate regions corresponding to the all-alpha, all-beta, and alpha/beta superfamilies. Large protein families are arranged along family-specific axes, forming local densely populated regions within the space. The lowest level of organization is intrafamilial; homologous structures are ordered by variations in peripheral secondary structure elements or by conformational shifts in the tertiary structure.

Algorithms↗

PubMed1997

Stabilization of protein structures.

The technique of protein stabilization has been improving steadily in recent years, but it is only in the past year or two that the stability of some protein molecules has been improved to the level of those from extreme thermophilic organisms. This was achieved by multiple mutations and often by utilizing the knowledge gained from the homologous protein structures from extreme thermophiles.

Animals↗

PubMed2000

Logos for amino-acid preferences in different backbone packing density regions of protein structural classes.

A protein sequence can be classified into one of four structural classes, namely alpha, beta, alpha + beta and alpha/beta, based on its amino-acid composition. The present study aims at understanding why a particular sequence with a given amino-acid composition should fold into a specific structural class. In order to answer this question, each amino acid in the protein sequence was classified to a particular neighbor density based on the number of spatial residues surrounding it within a distance of 6.5 A. Each of the four structural classes showed a unique preference of amino acids in each of the neighbor densities. Residues which show a high compositional bias in a structural class are also found to occur in high neighbor densities. This high compositional bias towards specific residues in the four different structural classes of proteins appears to be caused by structural and functional requirements. The distribution of amino acids in different neighbor densities is graphically presented in a novel logo form which incorporates several features such as composition, the frequency of occurrence and color code for amino acids. The spatial neighbors of the residues in different neighbor densities and their secondary structural location are also represented in the form of logos. This representation helped in the identification of specific details of the whole data which may otherwise have gone unnoticed. It is suggested that the data presented in this study may be useful in knowledge-based structure modelling and de novo protein design.

Amino Acid Sequence↗

PubMed1969

The composition of rat liver microsomes. The structural proteins of rat liver microsomes.

1. The structural-protein component of microsomal membranes was isolated by three separate methods. Analysis by polyacrylamide-gel electrophoresis indicated that the microsomal structural component is made up of a heterogeneous group of proteins. These proteins were further characterized by their phospholipid-binding capacity. The electrophoretic patterns of microsomal structural proteins were found to differ significantly from those of mitochondrial structural proteins. 2. The reticulosomal fraction was also characterized by electrophoresis with reference to total microsomal proteins, microsomal structural proteins and ribosomal proteins. The reticulosomes gave an electrophoretic pattern significantly different from those of the other three preparations examined. It is suggested that reticulosomes consist largely of enzymic proteins of the endoplasmic reticulum.

Animals↗

PubMed2005

An effective skeletal muscle prefractionation method to remove abundant structural proteins for optimized two-dimensional gel electrophoresis.

Proteomic analysis of biological samples in disease models or therapeutic intervention studies requires the ability to detect and identify biologically relevant proteins present in relatively low concentrations. The detection and analysis of these low-level proteins is hindered by the presence of a few proteins that are expressed in relatively high concentrations. In the case of muscle tissue, highly abundant structural proteins, such as actin, myosin, and tropomyosin, compromise the detection and analysis of more biologically relevant proteins. We have developed a practical protocol which exploits high-pH extraction to reduce or remove abundant structural proteins from skeletal muscle crude membrane preparations in a manner suitable for two dimensional gel electrophoresis. An initial whole-cell muscle lysate is generated by homogenization of powdered tissue in Tris-base. This lysate is subsequently partitioned into a supernatant and pellet containing the majority of structural proteins. Treatment of the pellet with high-pH conditions effectively releases structural proteins from membrane compartments which are then removed through ultracentrifugation. Mass spectrometric identification shows that the majority of protein spots reduced or removed by high-pH treatment were contractile proteins or contractile-related proteins. Removal of these proteins enabled successful detection and identification of minor proteins. Structural protein removal also results in significant improvement of gel quality and the ability to load higher amounts of total protein for the detection of lower abundant protein classes.

Animals↗

PubMed1998

Assessing protein structures with a non-local atomic interaction energy.

We describe a new approach, based on the energy of non-local interactions, to assess protein structures. The method uses a very sensitive and accurate atomic mean force potential (AMFP) to calculate the non-local energy profile (NL-profile) of a proteins structure. Several protein models, built using the comparative modeling technique and containing several errors, were evaluated. These models exhibit a good stereochemistry and have been previously checked with different, widely used, methods that failed to detect the errors. The AMFP-derived energy profiles are able to correlate high scores with point errors and misalignments in the models. The point errors are frequently found in loops or regions of structural differences between the template and the target protein. The misalignments are clearly detected with very high scores. The performance of the method was also tested for the assessment of X-ray solved protein structures. In a data set of 143 well solved and non-redundant protein structures, we find that the average energy Z-scores, obtained from AMFP, increase as the resolution decreases. In the case of structures that have already been described as having an unusual stereochemistry, very high Z-scores are obtained. Moreover, energy calculations for some pairs of obsolete and replacement proteins always show higher Z-scores for the obsolete proteins. Finally, two particular cases show the usefulness of the profiles in the assessment of X-ray solved protein structures. First, the NL-profile of a protein structure refined in the incorrect space group has very high scores in several regions. One region has already been described to be out-of-register with the density map of the structure. The NL-profile of the re-refined structure with the correct space group is vastly improved. In the second case, the method is able to accurately point out disordered residues, even if the atoms of these residues do not violate the sum of the van der Waals radii. ANOLEA, the program used to calculate the NL-profile of a protein structure containing one or more chains is accessible through the World Wide Web at: http://www.fundp.ac.be/pub/ANOLEA.html.

Crystallography, X-Ray↗

PubMed1992

Accuracy in Laue X-ray diffraction analysis of protein structures.

The accuracy in protein structure analysis based on Laue X-ray diffraction has been investigated for the example of two orthorhombic structures of bovine pancreatic trypsin (BPT). The precision in the Laue structure factors and the contrast in electron-density maps were used as criteria. A comparison with the results of previous analyses based on conventional crystal rotation methods showed that high resolution around 1.4 A may be reached with both monochromatic and polychromatic techniques. Electron-density maps exhibited significantly lower contrast when calculated on the basis of Laue structure amplitudes, due to inefficient exploration of reciprocal space at low resolution by the Laue method even in the case of a broad bandwidth and inclusion of exposures from several different crystal orientations. Laue data were recorded on photographic film and processed using the program LAUEMAD [Bartunik & Borchert (1989). Acta Cryst. A45, 718-726]. The empirically derived wavelength scaling factors based on a comparison of equivalent reflection intensities were in good agreement with theoretical estimates over a broad wavelength range. One BPT structure was refined on the basis of Laue structure amplitudes (current R factor 24% at 1.8 A resolution).

Crystallization↗

PubMed1996

An algorithm for finding maximal common subtopologies in a set of protein structures.

For the comparison and analysis of protein structures, it is of interest to find maximal common substructures in a given set of proteins. This question is also relevant for motif definition and structure classification. In this paper we describe first a new suitable representation of the secondary structure topology of a protein by an undirected labeled graph. Based on this representation we developed a new fast algorithm that finds all common subtopologies in a set of protein structures. Our method is based on the algorithm by Bron and Kerbosch (1973), which enumerates all maximal cliques in a graph. The main improvement of our algorithm is to restrict the search process to cliques that represent connected substructures. This restriction reduces the number of cliques to be considered during the search process and the size of the search tree drastically. Thus we are able to handle large proteins. Experiments show the efficiency and superiority of our algorithm in comparison with other existing algorithms basing on graph-theoretical methods.

Algorithms↗

PubMed2000

Protein structure comparison using the markov transition model of evolution.

A number of automatic protein structure comparison methods have been proposed; however, their similarity score functions are often decided by the researchers' intuition and trial-and-error, and not by theoretical background. We propose a novel theory to evaluate protein structure similarity, which is based on the Markov transition model of evolution. Our similarity score between structures i and j is defined as log P(j --> i)/P(i), where P(j --> i) is the probability that structure j changes to structure i during the evolutionary process, and P(i) is the probability that structure i appears by chance. This is a reasonable definition of structure similarity, especially for finding evolutionarily related (homologous) similarity. The probability P(j --> i) is estimated by the Markov transition model, which is similar to the Dayhoff's substitution model between amino acids. To estimate the parameters of the model, homologous protein structure pairs are collected using sequence similarity, and the numbers of structure transitions within the pairs are counted. Next these numbers are transformed to a transition probability matrix of the Markov transition. Transition probabilities for longer time are obtained by multiplying the probability matrix by itself several times. In this study, we generated three types of structure similarity scores: an environment score, a residue-residue distance score, and a secondary structure elements (SSE) score. Using these scores, we developed the structure comparison program, Matras (MArkovian TRAnsition of protein Structure). It employs a hierarchical alignment algorithm, in which a rough alignment is first obtained by SSEs, and then is improved with more detailed functions. We attempted an all-versus-all comparison of the SCOP database, and evaluated its ability to recognize a superfamily relationship, which was manually assigned to be homologous in the SCOP database. A comparison with the FSSP database shows that our program can recognize more homologous similarity than FSSP. We also discuss the reliability of our method, by studying the disagreement between structural classifications by Matras and SCOP.

Databases, Factual↗

PubMed2004

Rapid 3D protein structure database searching using information retrieval techniques.

MOTIVATION: As the sizes of three-dimensional (3D) protein structure databases are growing rapidly nowadays, exhaustive database searching, in which a 3D query structure is compared to each and every structure in the database, becomes inefficient. We propose a rapid 3D protein structure retrieval system named 'ProtDex2', in which we adopt the techniques used in information retrieval systems in order to perform rapid database searching without having access to every 3D structure in the database. The retrieval process is based on the inverted-file index constructed on the feature vectors of the relationships between the secondary structure elements (SSEs) of all the 3D protein structures in the database. ProtDex2 is a significant improvement, both in terms of speed and accuracy, upon its predecessor system, ProtDex. RESULTS: The experimental results show that ProtDex2 is very much faster than two well-known protein structure comparison methods, DALI and CE, yet not sacrificing on the accuracy of the comparison. When comparing with a similar SSE-based method, namely TopScan, ProtDex2 is much faster with comparable degree of accuracy. AVAILABILITY: The software is available at: http://xena1.ddns.comp.nus.edu.sg/~genesis/PD2.htm

Algorithms↗

PubMed2000

Determining protein structure from electron-density maps using pattern matching.

TEXTAL is an automated system for building protein structures from electron-density maps. It uses pattern recognition to select regions in a database of previously determined structures that are similar to regions in a map of unknown structure. Rotation-invariant numerical values, called features, of the electron density are extracted from spherical regions in an unknown map and compared with features extracted around regions in maps generated from a database of known structures. Those regions in the database that match best provide the local coordinates of atoms and these are accumulated to form a model of the unknown structure. Similarity between the regions in the database and an uninterpreted region is determined firstly by evaluating the numerical difference in feature values and secondly by calculating the electron-density correlation coefficient for those regions with similar feature values. TEXTAL has been successful at building protein structures for a wide range of test electron-density maps and can automatically model entire protein structures in a few hours on a workstation. Models built by TEXTAL from test electron-density maps of known protein structures were accurate to within 0.6-0.7 A root-mean-square deviation, assuming prior knowledge of C(alpha) positions. The system represents a new approach to protein structure determination and has the potential to greatly reduce the time required to interpret electron-density maps in order to build accurate protein models.

Algorithms↗