Prediction of the three-dimensional structures of the biotinylated domain from yeast pyruvate carboxylase and of the lipoylated H-protein from the pea leaf glycine cleavage system: a new automated method for the prediction of protein tertiary structure.

A new, automated, knowledge-based method for the construction of three-dimensional models of proteins is described. Geometric restraints on target structures are calculated from a consideration of homologous template structures and the wider knowledge base of unrelated protein structures. Three-dimensional structures are calculated from initial partly folded states by high-temperature molecular dynamics simulations followed slow cooling of the system (simulated annealing) using nonphysical potentials. Three-dimensional models for the biotinylated domain from the pyruvate carboxylase of yeast and the lipoylated H-protein from the glycine cleavage system of pea leaf were constructed, based on the known structures of two lipoylated domains of 2-oxo acid dehydrogenase multienzyme complexes. Despite their weak sequence similarity, the three proteins are predicted to have similar three-dimensional structures, representative of a new protein module. Implications for the mechanisms of posttranslational modification of these proteins and their catalytic function are discussed.     

COMPASS: a tool for comparison of multiple protein alignments with assessment of statistical significance

We present a novel method for the comparison of multiple protein alignments with assessment of statistical significance (COMPASS). The method derives numerical profiles from alignments, constructs optimal local profile-profile alignments and analytically estimates E-values for the detected similarities. The scoring system and E-value calculation are based on a generalization of the PSI-BLAST approach to profile-sequence comparison, which is adapted for the profile-profile case. Tested along with existing methods for profile-sequence (PSI-BLAST) and profile-profile (prof_sim) comparison, COMPASS shows increased abilities for sensitive and selective detection of remote sequence similarities, as well as improved quality of local alignments. The method allows prediction of relationships between protein families in the PFAM database beyond the range of conventional methods. Two predicted relations with high significance are similarities between various Rossmann-type folds and between various helix-turn-helix-containing families. The potential value of COMPASS for structure/function predictions is illustrated by the detection of an intricate homology between the DNA-binding domain of the CTF/NFI family and the MH1 domain of the Smad family.     

Improving fold recognition without folds

The most reliable way to align two proteins of unknown structure is through sequence-profile and profile-profile alignment methods. If the structure for one of the two is known, fold recognition methods outperform purely sequence-based alignments. Here, we introduced a novel method that aligns generalised sequence and predicted structure profiles. Using predicted 1D structure (secondary structure and solvent accessibility) significantly improved over sequence-only methods, both in terms of correctly recognising pairs of proteins with different sequences and similar structures and in terms of correctly aligning the pairs. The scores obtained by our generalised scoring matrix followed an extreme value distribution; this yielded accurate estimates of the statistical significance of our alignments. We found that mistakes in 1D structure predictions correlated between proteins from different sequence-structure families. The impact of this surprising result was that our method succeeded in significantly out-performing sequence-only methods even without explicitly using structural information from any of the two. Since AGAPE also outperformed established methods that rely on 3D information, we made it available through. If we solved the problem of CPU-time required to apply AGAPE on millions of proteins, our results could also impact everyday database searches.     

Retrospective analysis of a secondary structure prediction: the catalytic domain of matrix metalloproteinases.

Secondary structure prediction of the catalytic domain of matrix metalloproteinases is evaluated in the light of recently published experimentally determined structures. The prediction was made by combining conformational propensity, surface probability, and residue conservation calculated for an alignment of 19 sequences. The position of each observed secondary structure element was correctly predicted with a high degree of accuracy, with a single beta-strand falsely predicted. The domain fold was also anticipated from the prediction by analogy with the structural elements found in the distantly related metalloproteinases thermolysin, astacin, and adamalysin.     

Structural relationships of homologous proteins as a fundamental principle in homology modeling

Protein structure prediction is based mainly on the modeling of proteins by homology to known structures; this knowledgebased approach is the most promising method to date. Although it is used in the whole area of protein research, no general rules concerning the quality and applicability of concepts and procedures used in homology modeling have been put forward yet. Therefore, the main goal of the present work is to provide tools for the assessment of accuracy of modeling at a given level of sequence homology. A large set of known structures from different conformational and functional classes, but various degrees of homology was selected. Pairwise structure superpositions were performed. Starting with the definition of the structurally conserved regions and determination of topologically correct sequence alignments, we correlated geometrical properties with sequence homology (defined by the 250 PAM Dayhoff Matrix) and identity. It is shown that both the topological differences of the protein backbones and the relative positions of corresponding side chains diverge with decreasing sequence identity. Below 50% identity, the deviation in regions that are structurally not conserved continually increases, thus implying that with decreasing sequence identity modeling has to take into account more and more structurally diverging loop regions that are difficult to predict. © 1993 Wiley-Liss, Inc.     

Structural model for family 32 of glycosyl-hydrolase enzymes

A structural model is presented for family 32 of the glycosyl-hydrolase enzymes based on the beta-propeller fold. The model is derived from the common prediction of two different threading methods, TOPITS and THREADER. In addition, we used a correlated mutation analysis and prediction of active-site residues to corroborate the proposed model. Physical techniques (circular dichroism and differential scanning calorimetry) confirmed two aspects of the prediction, the proposed all-beta fold and the multi-domain structure. The most reliable three-dimensional model was obtained using the structure of neuraminidase (1nscA) as template. The analysis of the position of the active site residues in this model is compatible with the catalytic mechanism proposed by Reddy and Maley (J. Biol. Chem. 271:13953–13958, 1996), which includes three conserved residues, Asp, Glu, and Cys. Based on this analysis, we propose the participation of one more conserved residue (Asp 162) in the catalytic mechanism. The model will facilitate further studies of the physical and biochemical characteristics of family 32 of the glycosyl-hydrolases. Proteins 33:383–395, 1998. © 1998 Wiley-Liss, Inc.     

Bioinformatics methods to predict protein structure and function. A practical approach

Protein structure prediction by using bioinformatics can involve sequence similarity searches, multiple sequence alignments, identification and characterization of domains, secondary structure prediction, solvent accessibility prediction, automatic protein fold recognition, constructing three-dimensional models to atomic detail, and model validation. Not all protein structure prediction projects involve the use of all these techniques. A central part of a typical protein structure prediction is the identification of a suitable structural target from which to extrapolate three-dimensional information for a query sequence. The way in which this is done defines three types of projects. The first involves the use of standard and well-understood techniques. If a structural template remains elusive, a second approach using nontrivial methods is required. If a target fold cannot be reliably identified because inconsistent results have been obtained from nontrivial data analyses, the project falls into the third type of project and will be virtually impossible to complete with any degree of reliability. In this article, a set of protocols to predict protein structure from sequence is presented and distinctions among the three types of project are given. These methods, if used appropriately, can provide valuable indicators of protein structure and function.     

Recapitulation of protein family divergence using flexible backbone protein design

We use flexible backbone protein design to explore the sequence and structure neighborhoods of naturally occurring proteins. The method samples sequence and structure space in the vicinity of a known sequence and structure by alternately optimizing the sequence for a fixed protein backbone using rotamer based sequence search, and optimizing the backbone for a fixed amino acid sequence using atomic-resolution structure prediction. We find that such a flexible backbone design method better recapitulates protein family sequence variation than sequence optimization on fixed backbones or randomly perturbed backbone ensembles for ten diverse protein structures. For the SH3 domain, the backbone structure variation in the family is also better recapitulated than in randomly perturbed backbones. The potential application of this method as a model of protein family evolution is highlighted by a concerted transition to the amino acid sequence in the structural core of one SH3 domain starting from the backbone coordinates of an homologous structure.     

A simple and fast approach to prediction of protein secondary structure from multiply aligned sequences with accuracy above 70%.

To improve secondary structure predictions in protein sequences, the information residing in multiple sequence alignments of substituted but structurally related proteins is exploited. A database comprised of 70 protein families and a total of 2,500 sequences, some of which were aligned by tertiary structural superpositions, was used to calculate residue exchange weight matrices within alpha-helical, beta-strand, and coil substructures, respectively. Secondary structure predictions were made based on the observed residue substitutions in local regions of the multiple alignments and the largest possible associated exchange weights in each of the three matrix types. Comparison of the observed and predicted secondary structure on a per-residue basis yielded a mean accuracy of 72.2%. Individual alpha-helix, beta-strand, and coil states were respectively predicted at 66.7, and 75.8% correctness, representing a well-balanced three-state prediction. The accuracy level, verified by cross-validation through jack-knife tests on all protein families, dropped, on average, to only 70.9%, indicating the rigor of the prediction procedure. On the basis of robustness, conceptual clarity, accuracy, and executable efficiency, the method has considerable advantage, especially with its sole reliance on amino acid substitutions within structurally related proteins.     

Statistical correlation between protein secondary structure and messenger RNA stem-loop structure

A new integrated sequence-structure database, called IADE (Integrated ASTRAL-DSSP-EMBL), incorporating matching mRNA sequence, amino acid sequence, and protein secondary structural data, is constructed. It includes 648 protein domains. Based on the IADE database, we studied the relation between RNA stem-loop frequencies and protein secondary structure. It was found that the alpha-helices and beta-strands on proteins tend to be preferably "coded" by mRNA stem region, while the coils on proteins tend to be preferably "coded" by mRNA loop region. These tendencies are more obvious if we observe the structural words (SWs). An SW is defined by a four-amino-acid-fragment that shows the pronounced secondary structural (alpha-helix or beta-strand) propensity. It is demonstrated that the deduced correlation between protein and mRNA structure can hardly be explained as the stochastic fluctuation effect.     

Divergent evolution of a β/α-barrel subclass: Detection of numerous phosphate-binding sites by motif search

Study of the most conserved region in many β/α-barrels, the phosphate-binding site, revealed a sequence motif in a few β/α-barrels with known tertiary structure, namely glycolate oxidase (GOX), cytochrome b₂ (Cyb2), tryptophan synthase α subunit (TrpA), and the indoleglycerolphosphate synthase (TrpC). Database searches identified this motif in numerous other enzyme families: (1) IMP dehydrogenase (IMPDH) and GMP reductase (GuaC); (2) phosphoribosylformimino-5-aminoimidazol carboxamide ribotide isomerase (HisA) and the cyclase-producing D-erythro-imidazole-glycerolphosphate (HisF) of the histidine biosynthetic pathway; (3) dihydroorotate dehydrogenase (PyrD); (4) glutamate synthase (GltB); (5) ThiE and ThiG involved in the biosynthesis of thiamine as well as related proteins; (6) an uncharacterized open reading frame from Erwinia herbicola; and (7) a glycerol uptake operon antiterminator regulatory protein (GlpP). Secondary structure predictions of the different families mentioned above revealed an alternating order of β-strands and α-helices in agreement with a β/α-barrel-like topology. The putative phosphate-binding site is always found near the C-terminus of the enzymes, which are all at least about 200 amino acids long. This is compatible with its assumed location between strand 7 and helix 8. The identification of a significant motif in functionally diverse enzymes suggests a divergent evolution of at least a considerable fraction of β/α-barrels. In addition to the known accumulation of β/α-barrels in the tryptophan biosynthetic pathway, we observe clusters of these enzymes in histidine biosynthesis, purine metabolism, and apparently also in thiamine biosynthesis. The substrates are mostly heterocyclic compounds. Although the marginal sequence similarities do not allow a reconstruction of the barrel spreading, they support the idea of pathway evolution by gene duplication.     

Structural classification of αββ and ββα supersecondary structure units in proteins

We present a fully automatic structural classification of supersecondary structure units, consisting of two hydrogen-bonded β strands, preceded or followed by an α helix. The classification is performed on the spatial arrangement of the secondary structure elements, irrespective of the length and conformation of the intervening loops. The similarity of the arrangements is estimated by a structure alignment procedure that uses as similarity measure the root mean square deviation of superimposed backbone atoms. Applied to a set of 141 well-resolved nonhomologous protein structures, the classification yields 11 families of recurrent arrangements. In addition, fragments that are structurally intermediate between the families are found; they reveal the continuity of the classification. The analysis of the families shows that the α helix and β hairpin axes can adopt virtually all relative orientations, with, however, some preferable orientations; moreover, according to the orientation, preferences in the left/right handedness of the α–β connection are observed. These preferences can be explained by favorable side by side packing of the α helix and the β hairpin, local interactions in the region of the α–β connection or stabilizing environments in the parent protein. Furthermore, fold recognition procedures and structure prediction algorithms coupled to database-derived potentials suggest that the preferable nature of these arrangements does not imply their intrinsic stability. They usually accommodate a large number of sequences, of which only a subset is predicted to stabilize the motif. The motifs predicted as stable could correspond to nuclei formed at the very beginning of the folding process. Proteins 30:193–212, 1998. © 1998 Wiley-Liss, Inc.     

Position-specific residue preference features around the ends of helices and strands and a novel strategy for the prediction of secondary structures

It has been many years since position-specific residue preference around the ends of a helix was revealed. However, all the existing secondary structure prediction methods did not exploit this preference feature, resulting in low accuracy in predicting the ends of secondary structures. In this study, we collected a relatively large data set consisting of 1860 high-resolution, non-homology proteins from the PDB, and further analyzed the residue distributions around the ends of regular secondary structures. It was found that there exist position-specific residue preferences (PSRP) around the ends of not only helices but also strands. Based on the unique features, we proposed a novel strategy and developed a tool named E-SSpred that treats the secondary structure as a whole and builds models to predict entire secondary structure segments directly by integrating relevant features. In E-SSpred, the support vector machine (SVM) method is adopted to model and predict the ends of helices and strands according to the unique residue distributions around them. A simple linear discriminate analysis method is applied to model and predict entire secondary structure segments by integrating end-prediction results, tri-peptide composition, and length distribution features of secondary structures, as well as the prediction results of the most famous program PSIPRED. The results of fivefold cross-validation on a widely used data set demonstrate that the accuracy of E-SSpred in predicting ends of secondary structures is about 10% higher than PSIPRED, and the overall prediction accuracy (Q(3) value) of E-SSpred (82.2%) is also better than PSIPRED (80.3%). The E-SSpred web server is available at http://bioinfo.hust.edu.cn/bio/tools/E-SSpred/index.html.     

蛋白质结构型的定义和识别

提出紧结构域的概念,由二级结构序列中一段或几段连续的α螺旋和β折叠构成的空间紧密堆集的最大折叠体称为紧结构域.利用3种紧结构域（α域,β域和α/β域）定义球蛋白的5种结构型：α型蛋白,β型蛋白,α/β型蛋白,多域蛋白和ζ型蛋白.将1 261个代表性的蛋白质(1 022家族)进行分类,并和SCOP库的分类做了比较.进行了删去序列冗余的分析.在此基础上提出结构型的预测方案,成功率在82%～85%.     

DCCP and DICP： Construction and Analyses of Databases for Copper- and Iron-Chelating Proteins

Copper and iron play important roles in a variety of biological processes, especially when being chelated with proteins. The proteins involved in the metal binding, transporting and metabolism have aroused much interest. To facilitate the study on this topic, we constructed two databases （DCCP and DICP） containing the known copper- and iron-chelating proteins~ which are freely available from the website http：//sdbi.sdut.edu.cn/en. Users can conveniently search and browse all of the entries in the databases. Based on the two databases, bioinformatic analyses were performed, which provided some novel insights into metalloproteins.     

Expanding the nitrogen regulatory protein superfamily: Homology detection at below random sequence identity

Nitrogen regulatory (PII) proteins are signal transduction molecules involved in controlling nitrogen metabolism in prokaryots. PII proteins integrate the signals of intracellular nitrogen and carbon status into the control of enzymes involved in nitrogen assimilation. Using elaborate sequence similarity detection schemes, we show that five clusters of orthologs (COGs) and several small divergent protein groups belong to the PII superfamily and predict their structure to be a (betaalphabeta)(2) ferredoxin-like fold. Proteins from the newly emerged PII superfamily are present in all major phylogenetic lineages. The PII homologs are quite diverse, with below random (as low as 1%) pairwise sequence identities between some members of distant groups. Despite this sequence diversity, evidence suggests that the different subfamilies retain the PII trimeric structure important for ligand-binding site formation and maintain a conservation of conservations at residue positions important for PII function. Because most of the orthologous groups within the PII superfamily are composed entirely of hypothetical proteins, our remote homology-based structure prediction provides the only information about them. Analogous to structural genomics efforts, such prediction gives clues to the biological roles of these proteins and allows us to hypothesize about locations of functional sites on model structures or rationalize about available experimental information. For instance, conserved residues in one of the families map in close proximity to each other on PII structure, allowing for a possible metal-binding site in the proteins coded by the locus known to affect sensitivity to divalent metal ions. Presented analysis pushes the limits of sequence similarity searches and exemplifies one of the extreme cases of reliable sequence-based structure prediction. In conjunction with structural genomics efforts to shed light on protein function, our strategies make it possible to detect homology between highly diverse sequences and are aimed at understanding the most remote evolutionary connections in the protein world.     

HOMSTRAD: a database of protein structure alignments for homologous families.

We describe a database of protein structure alignments for homologous families. The database HOMSTRAD presently contains 130 protein families and 590 aligned structures, which have been selected on the basis of quality of the X-ray analysis and accuracy of the structure. For each family, the database provides a structure-based alignment derived using COMPARER and annotated with JOY in a special format that represents the local structural environment of each amino acid residue. HOMSTRAD also provides a set of superposed atomic coordinates obtained using MNYFIT, which can be viewed with a graphical user interface or used for comparative modeling studies. The database is freely available on the World Wide Web at: http://www-cryst.bioc.cam. ac.uk/-homstrad/, with search facilities and links to other databases.     

Prediction of the three-dimensional structure of proteins using the electrostatic screening model and hierarchic condensation

We describe a method for predicting the three-dimensional (3-D) structure of proteins from their sequence alone. The method is based on the electrostatic screening model for the stability of the protein main-chain conformation. The free energy of a protein as a function of its conformation is obtained from the potentials of mean force analysis of high-resolution x-ray protein structures. The free energy function is simple and contains only 44 fitted coefficients. The minimization of the free energy is performed by the torsion space Monte Carlo procedure using the concept of hierarchic condensation. The Monte Carlo minimization procedure is applied to predict the secondary, super-secondary, and native 3-D structures of 12 proteins with 28–110 amino acids. The 3-D structures of the majority of local secondary and super-secondary structures are predicted accurately. This result suggests that control in forming the native-like local structure is distributed along the entire protein sequence. The native 3-D structure is predicted correctly for 3 of 12 proteins composed mainly from the α-helices. The method fails to predict the native 3-D structure of proteins with a predominantly β secondary structure. We suggest that the hierarchic condensation is not an appropriate procedure for simulating the folding of proteins made up primarily from β-strands. The method has been proved accurate in predicting the local secondary and super-secondary structures in the blind ab initio 3-D prediction experiment. Proteins 31:74–96, 1998. © 1998 Wiley-Liss, Inc.     

Predicted secondary and supersecondary structure for the serine–threonine-specific protein phosphatase family

A bona fide consensus prediction for the secondary and supersecondary structure of the serine–threonine specific protein phosphatases is presented. The prediction includes assignments of active site segments, an internal helix, and a region of possible 3₁₀ helical structure. An experimental structure for a member of this family of proteins should appear shortly, allowing this prediction to be evaluated. © 1995 Wiley-Liss, Inc.