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PubMed1998

Dissociable processes for learning the surface structure and abstract structure of sensorimotor sequences.

A sensorimotor sequence may contain information structure at several different levels. In this study, we investigated the hypothesis that two dissociable processes are required for the learning of surface structure and abstract structure, respectively, of sensorimotor sequences. Surface structure is the simple serial order of the sequence elements, whereas abstract structure is defined by relationships between repeating sequence elements. Thus, sequences ABCBAC and DEFEDF have different surface structures but share a common abstract structure, 123213, and are therefore isomorphic. Our simulations of sequence learning performance in serial reaction time (SRT) tasks demonstrated that (1) an existing model of the primate fronto-striatal system is capable of learning surface structure but fails to learn abstract structure, which requires an additional capability, (2) surface and abstract structure can be learned independently by these independent processes, and (3) only abstract structure transfers to isomorphic sequences. We tested these predictions in human subjects. For a sequence with predictable surface and abstract structure, subjects in either explicit or implicit conditions learn the surface structure, but only explicit subjects learn and transfer the abstract structure. For sequences with only abstract structure, learning and transfer of this structure occurs only in the explicit group. These results are parallel to those from the simulations and support our dissociable process hypothesis. Based on the synthesis of the current simulation and empirical results with our previous neuropsychological findings, we propose a neuro-physiological basis for these dissociable processes: Surface structure can be learned by processes that operate under implicit conditions and rely on the fronto-striatal system, whereas learning abstract structure requires a more explicit activation of dissociable processes that rely on a distributed network that includes the left anterior cortex.

Computer Simulation↗
PubMed1996

Prediction of secondary structural content of proteins from their amino acid composition alone. II. The paradox with secondary structural class.

The success rates reported for secondary structural class prediction with different methods are contradictory. On one side, the problem of recognizing the secondary structural class of a protein knowing only its amino acid composition appears completely solved by simply applying jury decision with an elliptically scaled distance function. Chou and coworkers repeatedly (see Crit. Rev. Biochem. Mol. Biol. 30:275-349, 1995) published prediction accuracies near 100%. On the other hand, traditional secondary structure prediction techniques achieve success rates of about 70% for the secondary structural state per residue and about 75% for structural class only with extensive input information (full sequence of the query protein, its amino acid composition and length, multiple alignments with homologous sequences). In this article, we resolve the paradox and consider (1) the question of the secondary structural class definition, (2) the role of the representativity of the test set of protein tertiary structure for the current state of the Protein Data Bank (PDB); and (3) we estimate the real impact of amino acid composition on secondary structural class. We formulate three objective criteria for a reasonable definition of secondary structural classes and show that only the criterion of Nakashima et al. (J. Biochem. 99:153-162, 1986) complies with all of them. Only this definition matches the distribution of secondary structural content in representative PDB subsets, whereas other criteria leave many proteins (up to 65% of all PDB entries) simply unassigned. We review critically specialized secondary-structural class prediction methods, especially those of Chou and coworkers, which claim almost 100% accuracy using only amino acid composition, and resolve the paradox that these prediction accuracies are better than those from secondary structure predictions from multiple alignments. We show (i) that these techniques rely on a preselection of test sets which removes irregular proteins and other proteins without any class assignment (about 35% of all PDB entries); and (ii) that even for preselected representative test sets, the success rate drops to 60% and lower for a 4-type classification (alpha, beta, alpha + beta, alpha/beta). The prediction accuracies fall to about 50% if the secondary structural class definition of Nakashima et al. is applied and only few irregular proteins are preselected and removed from automatically generated, representative subsets of the PDB. We have applied two new vector decomposition methods for secondary structural content prediction from amino acid composition alone, with and without consideration of amino acid compositional coupling in the learning set of tertiary structures respectively, to the problem of class prediction and achieve about 60% correct assignment among four classes (alpha, beta, mixed, irregular) as well as single sequence-based secondary structure prediction methods like GORIII and COMBI. Our results demonstrate that 60% correctness is the upper limit for a 4-type class prediction from amino acid composition alone for an unknown query protein and that consideration of compositional coupling does not improve the prediction success. The prediction program SSCP offering secondary structural class assignment for query compositions and sequences has been made available as a World Wide Web and E-mail service.

Amino Acids↗
PubMed1992

The solution structure of eglin c based on measurements of many NOEs and coupling constants and its comparison with X-ray structures.

A high-precision solution structure of the elastase inhibitor eglin c was determined by NMR and distance geometry calculations. A large set of 947 nuclear Overhauser (NOE) distance constraints was identified, 417 of which were quantified from two-dimensional NOE spectra at short mixing times. In addition, a large number of homonuclear 1H-1H and heteronuclear 1H-15N vicinal coupling constants were used, and constraints on 42 chi 1 and 38 phi angles were obtained. Structure calculations were carried out using the distance geometry program DG-II. These calculations had a high convergence rate, in that 66 out of 75 calculations converged with maximum residual NOE violations ranging from 0.17 A to 0.47 A. The spread of the structures was characterized with average root mean square deviations ( ) between the structures and a mean structure. To calculate the unbiased toward any single structure, a new procedure was used for structure alignment. A canonical structure was calculated from the mean distances, and all structures were aligned relative to that. Furthermore, an angular order parameter S was defined and used to characterize the spread of structures in torsion angle space. To obtain an accurate estimate of the precision of the structure, the number of calculations was increased until the and the angular order parameters stabilized. This was achieved after approximately 40 calculations. The structure consists of a well-defined core whose backbone deviates from the canonical structure ca. 0.4 A, a disordered N-terminal heptapeptide whose backbone deviates by 0.8-12 A, and a proteinase-binding loop whose backbone deviates up to 3.0 A. Analysis of the angular order parameters and inspection of the structures indicates that a hinge-bending motion of the binding loop may occur in solution. Secondary structures were analyzed by comparison of dihedral angle patterns. The high precision of the structure allows one to identify subtle differences with four crystal structures of eglin c determined in complexes with proteinases.

Amino Acid Sequence↗
PubMed2005

Functional coverage of the human genome by existing structures, structural genomics targets, and homology models.

The bias in protein structure and function space resulting from experimental limitations and targeting of particular functional classes of proteins by structural biologists has long been recognized, but never continuously quantified. Using the Enzyme Commission and the Gene Ontology classifications as a reference frame, and integrating structure data from the Protein Data Bank (PDB), target sequences from the structural genomics projects, structure homology derived from the SUPERFAMILY database, and genome annotations from Ensembl and NCBI, we provide a quantified view, both at the domain and whole-protein levels, of the current and projected coverage of protein structure and function space relative to the human genome. Protein structures currently provide at least one domain that covers 37% of the functional classes identified in the genome; whole structure coverage exists for 25% of the genome. If all the structural genomics targets were solved (twice the current number of structures in the PDB), it is estimated that structures of one domain would cover 69% of the functional classes identified and complete structure coverage would be 44%. Homology models from existing experimental structures extend the 37% coverage to 56% of the genome as single domains and 25% to 31% for complete structures. Coverage from homology models is not evenly distributed by protein family, reflecting differing degrees of sequence and structure divergence within families. While these data provide coverage, conversely, they also systematically highlight functional classes of proteins for which structures should be determined. Current key functional families without structure representation are highlighted here; updated information on the "most wanted list" that should be solved is available on a weekly basis from http://function.rcsb.org:8080/pdb/function_distribution/index.html.

Databases, Protein↗
PubMed2005

Comparison of X-ray and NMR structures: is there a systematic difference in residue contacts between X-ray- and NMR-resolved protein structures?

We have compared structures of 78 proteins determined by both NMR and X-ray methods. It is shown that X-ray and NMR structures of the same protein have more differences than various X-ray structures obtained for the protein, and even more than various NMR structures of the protein. X-ray and NMR structures of 18 of these 78 proteins have obvious large-scale structural differences that seem to reflect a difference of crystal and solution structures. The other 60 pairs of structures have only small-scale differences comparable with differences between various X-ray or various NMR structures of a protein; we have analyzed these structures more attentively. One of the main differences between NMR and X-ray structures concerns the number of contacts per residue: (1) NMR structures presented in PDB have more contacts than X-ray structures at distances below 3.0 A and 4.5-6.5 A, and fewer contacts at distances of 3.0-4.5 A and 6.5-8.0 A; (2) this difference in the number of contacts is greater for internal residues than for external ones, and it is larger for beta-containing proteins than for all-alpha proteins. Another significant difference is that the main-chain hydrogen bonds identified in X-ray and NMR structures often differ. Their correlation is 69% only. However, analogous difference is found for refined and rerefined NMR structures, allowing us to suggest that the observed difference in interresidue contacts of X-ray and NMR structures of the same proteins is due mainly to a difference in mathematical treatment of experimental results.

Animals↗
PubMed2006

Secondary structure spatial conformation footprint: a novel method for fast protein structure comparison and classification.

BACKGROUND: Recently a new class of methods for fast protein structure comparison has emerged. We call the methods in this class projection methods as they rely on a mapping of protein structure into a high-dimensional vector space. Once the mapping is done, the structure comparison is reduced to distance computation between corresponding vectors. As structural similarity is approximated by distance between projections, the success of any projection method depends on how well its mapping function is able to capture the salient features of protein structure. There is no agreement on what constitutes a good projection technique and the three currently known projection methods utilize very different approaches to the mapping construction, both in terms of what structural elements are included and how this information is integrated to produce a vector representation. RESULTS: In this paper we propose a novel projection method that uses secondary structure information to produce the mapping. First, a diverse set of spatial arrangements of triplets of secondary structure elements, a set of structural models, is automatically selected. Then, each protein structure is mapped into a high-dimensional vector of "counts" or footprint, where each count corresponds to the number of times a given structural model is observed in the structure, weighted by the precision with which the model is reproduced. We perform the first comprehensive evaluation of our method together with all other currently known projection methods. CONCLUSION: The results of our evaluation suggest that the type of structural information used by a projection method affects the ability of the method to detect structural similarity. In particular, our method that uses the spatial conformations of triplets of secondary structure elements outperforms other methods in most of the tests.

Computational Biology↗
PubMed1991

Database of homology-derived protein structures and the structural meaning of sequence alignment.

The database of known protein three-dimensional structures can be significantly increased by the use of sequence homology, based on the following observations. (1) The database of known sequences, currently at more than 12,000 proteins, is two orders of magnitude larger than the database of known structures. (2) The currently most powerful method of predicting protein structures is model building by homology. (3) Structural homology can be inferred from the level of sequence similarity. (4) The threshold of sequence similarity sufficient for structural homology depends strongly on the length of the alignment. Here, we first quantify the relation between sequence similarity, structure similarity, and alignment length by an exhaustive survey of alignments between proteins of known structure and report a homology threshold curve as a function of alignment length. We then produce a database of homology-derived secondary structure of proteins (HSSP) by aligning to each protein of known structure all sequences deemed homologous on the basis of the threshold curve. For each known protein structure, the derived database contains the aligned sequences, secondary structure, sequence variability, and sequence profile. Tertiary structures of the aligned sequences are implied, but not modeled explicitly. The database effectively increases the number of known protein structures by a factor of five to more than 1800. The results may be useful in assessing the structural significance of matches in sequence database searches, in deriving preferences and patterns for structure prediction, in elucidating the structural role of conserved residues, and in modeling three-dimensional detail by homology.

Amino Acid Sequence↗
PubMedDate not supplied

[Analysis of structural motifs of proteins using sets of codes, describing local structures].

An amino acid sequence pattern conserved among a family of proteins is called motif. It is usually related to the specific function of the family. On the other hand, functions of proteins are achieved by their 3D structures. Specific local structures, called structural motifs, are considered related to their functions. However, searching for common structural motifs in different proteins is much more difficult than for common sequence motifs. We are attempting in this study to convert the information about the structural motifs into a set of one-dimensional digital strings, i.e., a set of codes, to compare them more easily by computer and to investigate their relationship to functions more quantitatively. By applying the Delaunay tessellation to a 3D structure of a protein, we can assign each local structure to a unique code that is defined so as to reflect its structural feature. Since a structural motif is defined as a set of the local structures in this paper, the structural motif is represented by a set of the codes. In order to examine the ability of the set of the codes to distinguish differences among the sets of local structures with a given PROSITE pattern that contain both true and false positives, we clustered them by introducing a similarity measure among the set of the codes. The obtained clustering shows a good agreement with other results by direct structural comparison methods such as a superposition method. The structural motifs in homologous proteins are also properly clustered according to their sources. These results suggest that the structural motifs can be well characterized by these sets of the codes, and that the method can be utilized in comparing structural motifs and relating them with function.

Amino Acid Motifs↗
PubMed1998

Solution structure and dynamics of linked cell attachment modules of mouse fibronectin containing the RGD and synergy regions: comparison with the human fibronectin crystal structure.

We report the three-dimensional solution structure of the mouse fibronectin cell attachment domain consisting of the linked ninth and tenth type III modules, mFnFn3(9,10). Because the tenth module contains the RGD cell attachment sequence while the ninth contains the synergy region, mFnFn3(9,10) has the cell attachment activity of intact fibronectin. Essentially complete signal assignments and approximately 1800 distance and angle restraints were derived from multidimensional heteronuclear NMR spectra. These restraints were used with a hybrid distance geometry/simulated annealing protocol to generate an ensemble of 20 NMR structures having no distance or angle violations greater than 0.3 A or 3 degrees. Although the beta-sheet core domains of the individual modules are well-ordered structures, having backbone atom rmsd values from the mean structure of 0.51(+/-0.12) and 0.40(+/-0.07) A, respectively, the rmsd of the core atom coordinates increases to 3.63(+/-1.41) A when the core domains of both modules are used to align the coordinates. The latter result is a consequence of the fact that the relative orientation of the two modules is not highly constrained by the NMR restraints. Hence, while structures of the beta-sheet core domains of the NMR structures are very similar to the core domains of the crystal structure of hFnFn3(9,10), the ensemble of NMR structures suggests that the two modules form a less extended and more flexible structure than the fully extended rod-like crystal structure. The radius of gyration, Rg, of mFnFn3(9,10) derived from small-angle neutron scattering measurements, 20.5(+/-0.5) A, agrees with the average Rg calculated for the NMR structures, 20.4 A, and is ca 1 A less than the value of Rg calculated for the X-ray structure. The values of the rotational anisotropy, D ||/D perpendicular, derived from an analysis of 15N relaxation data, range from 1.7 to 2.1, and are significantly less than the anisotropy of 2.67 predicted by hydrodynamic modeling of the crystal coordinates. In contrast, hydrodynamic modeling of the NMR coordinates yields anisotropies in the range of 1.9 to 2.7 (average 2.4(+/-0.2)), with NMR structures bent by more than 20 degrees relative the crystal structure having calculated anisotropies in best agreement with experiment. In addition, the relaxation parameters indicate that several loops in mFnFn3(9,10), including the RGD loop, are flexible on the nanosecond to picosecond time-scale. Taken together, our results suggest that, in solution, the limited set of interactions between the mFnFn3(9,10) modules position the RGD and synergy regions to interact specifically with cell surface integrins, and at the same time permit sufficient flexibility that allows mFnFn3(9,10) to adjust for some variation in integrin structure or environment.

Amino Acid Sequence↗
PubMed2000

Towards a complete description of the structural and dynamic properties of the denatured state of barnase and the role of residual structure in folding.

The detailed characterization of denatured proteins remains elusive due to their mobility and conformational heterogeneity. NMR studies are beginning to provide clues regarding residual structure in the denatured state but the resulting data are too sparse to be transformed into molecular models using conventional techniques. Molecular dynamics simulations can complement NMR by providing detailed structural information for components of the denatured ensemble. Here, we describe three independent 4 ns high-temperature molecular dynamics simulations of barnase in water. The simulated denatured state was conformationally heterogeneous with respect to the conformations populated both within a single simulation and between simulations. Nonetheless, there were some persistent interactions that occurred to varying degrees in all simulations and primarily involved the formation of fluid hydrophobic clusters with participating residues changing over time. The region of the beta(3-4) hairpin contained a particularly high degree of such side-chain interactions but it lacked beta-structure in two of the three denatured ensembles: beta(3-4) was the only portion of the beta-structure to contain significant residual structure in the denatured state. The two principal alpha-helices (alpha1 and alpha2) adopted dynamic helical structure. In addition, there were persistent contacts that pinched off core 2 from the body of the protein. The rest of the protein was unstructured, aside from transient and mostly local side-chain interactions. Overall, the simulated denatured state contains residual structure in the form of dynamic, fluctuating secondary structure in alpha1 and alpha2, as well as fluctuating tertiary contacts in the beta(3-4) region, and between alpha1 and beta(3-4), in agreement with previous NMR studies. Here, we also show that these regions containing residual structure display impaired mobility by both molecular dynamics and NMR relaxation experiments. The residual structure was important in decreasing the conformational states available to the chain and in repairing disrupted regions. For example, tertiary contacts between beta(3-4) and alpha1 assisted in the refolding of alpha1. This contact-assisted helix formation was confirmed in fragment simulations of beta(3-4) and alpha1 alone and complexed, and, as such, alpha1 and beta(3-4) appear to be folding initiation sites. The role of these sites in folding was investigated by working backwards and considering the simulation in reverse, noting that earlier time-points from the simulations provide models of the major intermediate and transition states in quantitative agreement with data from both unfolding and refolding experiments. Both beta(3-4) and alpha1 are dynamic in the denatured state but when they collide and make enough contacts, they provide a loose structural scaffold onto which further beta-strands pack. The beta-structure condenses about beta(3-4), while alpha1 aids in stabilizing beta(3-4) and maintaining its orientation. The resulting beta-structure is relatively planar and loose in the major intermediate. Further packing ensues, and as a result the beta-sheet twists, leading to the major transition state. The structure is still expanded and loops are not well formed at this point. Fine-tuning of the packing interactions and the final condensation of the structure then occurs to yield the native state.

Amino Acid Sequence↗
PubMed2006

Identification of similar regions of protein structures using integrated sequence and structure analysis tools.

BACKGROUND: Understanding protein function from its structure is a challenging problem. Sequence based approaches for finding homology have broad use for annotation of both structure and function. 3D structural information of protein domains and their interactions provide a complementary view to structure function relationships to sequence information. We have developed a web site http://www.sblest.org/ and an API of web services that enables users to submit protein structures and identify statistically significant neighbors and the underlying structural environments that make that match using a suite of sequence and structure analysis tools. To do this, we have integrated S-BLEST, PSI-BLAST and HMMer based superfamily predictions to give a unique integrated view to prediction of SCOP superfamilies, EC number, and GO term, as well as identification of the protein structural environments that are associated with that prediction. Additionally, we have extended UCSF Chimera and PyMOL to support our web services, so that users can characterize their own proteins of interest. RESULTS: Users are able to submit their own queries or use a structure already in the PDB. Currently the databases that a user can query include the popular structural datasets ASTRAL 40 v1.69, ASTRAL 95 v1.69, CLUSTER50, CLUSTER70 and CLUSTER90 and PDBSELECT25. The results can be downloaded directly from the site and include function prediction, analysis of the most conserved environments and automated annotation of query proteins. These results reflect both the hits found with PSI-BLAST, HMMer and with S-BLEST. We have evaluated how well annotation transfer can be performed on SCOP ID's, Gene Ontology (GO) ID's and EC Numbers. The method is very efficient and totally automated, generally taking around fifteen minutes for a 400 residue protein. CONCLUSION: With structural genomics initiatives determining structures with little, if any, functional characterization, development of protein structure and function analysis tools are a necessary endeavor. We have developed a useful application towards a solution to this problem using common structural and sequence based analysis tools. These approaches are able to find statistically significant environments in a database of protein structure, and the method is able to quantify how closely associated each environment is to a predicted functional annotation.

Computational Biology↗
PubMed2003

Chains of [RE6] octahedra coupled by (NCN) links in the network structure of RE2Cl(CN2)N. Synthesis and structure of two novel rare earth chloride carbodiimide nitrides with structures related to the RE2Cl3 type.

Solid state metathesis reactions between RECl(3) (RE = La, Ce) and Li(2)CN(2) at 800 degrees C have led to the discovery of the rare earth chloride carbodiimide nitrides La(2)Cl(CN(2))N (1) and Ce(2)Cl(CN(2))N (2), respectively. Single crystal X-ray diffraction analyses revealed that 1 and 2 crystallize isotypic in an orthorhombic system (Cmmm, Z = 4, a = 13.3914(8) A, b = 9.6345(7) A, c = 3.9568(2) A for 1 and a = 13.340(1) A, b = 9.5267(8) A, c = 3.9402(5) A for 2). The crystal structures of 1 and 2 contain linear chains of edge-sharing octahedra built from rare earth metal atoms. Similar to [M(6)X(8)] type clusters, the [RE(6)] octahedra are capped by eight nitrogen atoms above their faces, of which four are from N(3-) ions and the other four are from (CN(2))(2-) ions. The chains are interconnected by bridging (CN(2))(2-) to form a three-dimensional network with Cl(-) ions in linear channels. Compounds 1 and 2 are surprisingly stable toward air and water. They have been characterized by thermal analysis, infrared spectroscopy, and magnetic susceptibility studies.

Journal Article↗
PubMed2004

Combining evolutionary and structural information for local protein structure prediction.

We study the effects of various factors in representing and combining evolutionary and structural information for local protein structural prediction based on fragment selection. We prepare databases of fragments from a set of non-redundant protein domains. For each fragment, evolutionary information is derived from homologous sequences and represented as estimated effective counts and frequencies of amino acids (evolutionary frequencies) at each position. Position-specific amino acid preferences called structural frequencies are derived from statistical analysis of discrete local structural environments in database structures. Our method for local structure prediction is based on ranking and selecting database fragments that are most similar to a target fragment. Using secondary structure type as a local structural property, we test our method in a number of settings. The major findings are: (1) the COMPASS-type scoring function for fragment similarity comparison gives better prediction accuracy than three other tested scoring functions for profile-profile comparison. We show that the COMPASS-type scoring function can be derived both in the probabilistic framework and in the framework of statistical potentials. (2) Using the evolutionary frequencies of database fragments gives better prediction accuracy than using structural frequencies. (3) Finer definition of local environments, such as including more side-chain solvent accessibility classes and considering the backbone conformations of neighboring residues, gives increasingly better prediction accuracy using structural frequencies. (4) Combining evolutionary and structural frequencies of database fragments, either in a linear fashion or using a pseudocount mixture formula, results in improvement of prediction accuracy. Combination at the log-odds score level is not as effective as combination at the frequency level. This suggests that there might be better ways of combining sequence and structural information than the commonly used linear combination of log-odds scores. Our method of fragment selection and frequency combination gives reasonable results of secondary structure prediction tested on 56 CASP5 targets (average SOV score 0.77), suggesting that it is a valid method for local protein structure prediction. Mixture of predicted structural frequencies and evolutionary frequencies improve the quality of local profile-to-profile alignment by COMPASS.

Algorithms↗
PubMed1990

Understanding structural relationships in proteins of unsolved three-dimensional structure.

The locations of functionally important sequences and general structural motifs have been assigned to Ile-tRNA synthetase. However, a function has not been established for some segments of the protein (e.g., CP1). The method of structural modeling described here cannot establish the details of a 3 A crystal structure, and, in contrast to a crystal structure, the precision of the model varies according to the extent of a sequence similarity or the functional importance of a region. In Ile-tRNA synthetase, the signature sequence and the flanking regions are likely to be similar in structure to the proteins on which the model is based. For other regions, it may be possible to build a three-dimensional model by connecting well defined regions and refining the positions of the connecting elements by energy minimization. Structural modelling of this kind must be done cautiously, because the order and orientation of the elements of a structural motif can change in subtle ways. In the case of Tyr-tRNA synthetase, the beta-strand nearest the N-terminus is the outermost strand of the nucleotide binding fold; in Met-tRNA synthetase, the same strand is innermost. Furthermore, the orientation of this strand may be antiparallel (Tyr-tRNA synthetase) or parallel (Met-tRNA synthetase). Because multiple structures that differ in their orientations of structural elements are possible, the structural analogies between proteins should not be naively extrapolated without independent experimental support. As described above, some regions of proteins tolerate internal deletions and insertions. This provides further experimental support for the practice of allowing for gaps in computer-generated sequence alignments. Nevertheless, because some regions are more tolerant of insertions and deletions than others, the structural and functional significance of a region of broken alignment must be assessed carefully. All gaps in sequence alignments cannot be treated equally, and each must be evaluated within its own context. In the synthetases of known structure, structural analogy can be used to identify important functional elements. For example, the amino acid binding site of Met-tRNA synthetase might be formed, at least in part, by a peptide that encompasses Ala50; this amino acid aligns with Gly94 of the Ile-tRNA synthetase. This is an example in which results on a protein of unknown structure (Ile-tRNA synthetases) can lead to identification of a potential substrate binding site in a protein of known structure (Met-tRNA synthetase).

Amino Acid Sequence↗
PubMed1995

Three-dimensional solution structure of the oxidized high potential iron-sulfur protein from Chromatium vinosum through NMR. Comparative analysis with the solution structure of the reduced species.

The NMR solution structure of the oxidized HiPIP from Chromatium vinosum has been solved. Despite the fact that the protein is paramagnetic, 85% of the 1H and 80% of the 15N signals have been assigned. Through 1537 NOEs, out of which 1142 were found to be relevant for the structure determination, a family of structures has been obtained by distance geometry calculations. These structures have then been subjected to restrained energy minimization (REM) and restrained molecular dynamics (RMD) calculations in vacuum. Finally, the mean structure of the RMD family has been treated through RMD in water. The RMSD values for the backbone and heavy atoms within the RMD family are 0.57 +/- 0.14 and 1.08 +/- 0.16 A, respectively. These values together with other parameters indicate that the structure is of good quality and as good as the structure of the reduced protein. The RMDw structures of the reduced and oxidized proteins are different beyond the experimental indetermination. The set of constraints for the reduced and oxidized forms have been used to treat the available X-ray structure by RMD in water. The two structures generated in this way are quite similar to their respective solution structures, thus confirming that the experimental constraints are capable of yielding two different structures from the same starting structural model. This is the first time that independently determined solution structures of two redox states of a paramagnetic protein are available. Differences between them and the X-ray structure are discussed.

Amino Acid Sequence↗
PubMed1999

Local interactions drive the formation of nonnative structure in the denatured state of human alpha-lactalbumin: a high resolution structural characterization of a peptide model in aqueous solution.

There are a small number of peptides derived from proteins that have a propensity to adopt structure in aqueous solution which is similar to the structure they possess in the parent protein. There are far fewer examples of protein fragments which adopt stable nonnative structures in isolation. Understanding how nonnative interactions are involved in protein folding is crucial to our understanding of the topic. Here we show that a small, 11 amino acid peptide corresponding to residues 101-111 of the protein alpha-lactalbumin is remarkably structured in isolation in aqueous solution. The peptide has been characterized by 1H NMR, and 170 ROE-derived constraints were used to calculate a structure. The calculations yielded a single, high-resolution structure for residues 101-107 that is nonnative in both the backbone and side-chain conformations. In the pH 6.5 crystal structure, residues 101-105 are in an irregular turn-like conformation and residues 106-111 form an alpha-helix. In the pH 4.2 crystal structure, residues 101-105 form an alpha-helix, and residues 106-111 form a loopike structure. Both of these structures are significantly different from the conformation adopted by our peptide. The structure in the peptide model is primarily the result of local side-chain interactions that force the backbone to adopt a nonnative 310/turn-like structure in residues 103-106. The structure in aqueous solution was compared to the structure in 30% trifluoroethanol (TFE), and clear differences were observed. In particular, one of the side-chain interactions, a hydrophobic cluster involving residues 101-105, is different in the two solvents and residues 107-111 are considerably more ordered in 30% TFE. The implications of the nonnative structure for the folding of alpha-lactalbumin is discussed.

Circular Dichroism↗
PubMed1987

Kepiński's functional structures, Kohonen's topological structures, aphasia and apraxia.

The concept of functional structures was introduced by Kepiński. At the Second Scandinavian Congress on Image Analysis in 1981 Kohonen provided evidence that the map of signals has the same topological order as the map of reactions. These topological concepts can be applied also to logical structures. The same is true also of functional structures described by Kepiński in schizophrenia, which may be created or destroyed, but their topological order remains unaltered. Functional structures change in obscuration and in hallucinogen-induced psychodelic states. Fixed pathological structures characterized by an altered topological order occur in paranoia. In the case of amnestic aphasia single elements of the topological functional structures fall out-these elements represent terms which patient is unable to recall. In motor aphasia, on the other hand, damage concerns the topological functional structures of speech related movements. In sensory aphasia damage concerns topological functional structures in the sensory area. In Goldstein's amnestic apraxia there is a loss of some elements of the topological functional structures governing the movements. In ideational apraxia the damage concerns topological functional structures representing entire sets of motor function and especially their creation. In ideomotor apraxia the respective topological functional structures are retained, albeit inaccessible, due to a disturbance in the motor association. In motor apraxia the damage seems to concern the topological functional structures governing precise movements and located at a lower level, i.e., closer to the effector. Constructive apraxia seems to result from the disturbed creation of the respective functional structures. Similarly the hypermotility of a maniacal type has a smooth and purposeful character, as far as the relation to a particular situation is concerned, which suggests that the respective topological functional structures are preserved. On the other hand, in the hyperkinetic phase of catatonic schizophrenia, in which the patient performs sequences of incoherent aimless movements without any relation to the current situation and environment, these structures are supposedly altered.

Aphasia↗
PubMed2004

Recognition of remotely related structural homologues using sequence profiles of aligned homologous protein structures.

In order to bridge the gap between proteins with three-dimensional (3-D) structural information and those without 3-D structures, extensive experimental and computational efforts for structure recognition are being invested. One of the rapid and simple computational approaches for structure recognition makes use of sequence profiles with sensitive profile matching procedures to identify remotely related homologous families. While adopting this approach we used profiles that are generated from structure-based sequence alignment of homologous protein domains of known structures integrated with sequence homologues. We present an assessment of this fast and simple approach. About one year ago, using this approach, we had identified structural homologues for 315 sequence families, which were not known to have any 3-D structural information. The subsequent experimental structure determination for at least one of the members in 110 of 315 sequence families allowed a retrospective assessment of the correctness of structure recognition. We demonstrate that correct folds are detected with an accuracy of 96.4% (106/110). Most (81/106) of the associations are made correctly to the specific structural family. For 23/106, the structure associations are valid at the superfamily level. Thus, profiles of protein families of known structure when used with sensitive profile-based search procedure result in structure association of high confidence. Further assignment at the level of superfamily or family would provide clues to probable functions of new proteins. Importantly, the public availability of these profiles from us could enable one to perform genome wide structure assignment in a local machine in a fast and accurate manner.

Amino Acid Sequence↗