Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins

• 1.(1) Co-operation between a laboratory interested in developing the theory for protein secondary structure prediction methods and a laboratory interested in applying and comparing such methods has led to the development of a simple predictive algorithm.
• 2.(2) Four-state predictions, in which each residue is unambiguously assigned one conformational state of α-helix, extended chain, reverse turn or coil, predict 49% of residue states correctly (in a sample of 26 proteins) when the overall helix and extended-chain content is not taken into account.
• 3.(3) When the relative abundances of helix, extended chain, reverse turn and coil observed by X-ray crystallography are taken into account, a single constant for each protein and type of conformation can be used to bias the prediction. When predictions are optimized in this way, 63% of all residue states are unambiguously and correctly assigned.
• 4.(4) By analysing the nature of the bias required, proteins can be classified into helix-rich types, pleated-sheet-rich types, and so on. It is shown that, if the type of protein can be determined even approximately by circular dichroism, 57% of residue states can be correctly predicted without taking into account the X-ray structure. Further, comparable predictions can be obtained if, instead of circular dichroism, preliminary predictions are made to assess the protein type.
• 5.(5) It is emphasized that the numbers quoted here depend on the method used to assess accuracy, and the algorithm is shown to be at least as good as, and usually superior to, the reported prediction methods assessed in the same way.
• 6.(6) Ways of further enhancing predictions by the use of additional information from hydrophobic triplets and homologous sequences are also explored. Hydro-phobic triplet information does not significantly improve predictive power and it is concluded that this information is used by proteins in the next stage of folding. On the other hand, the use of homologous sequences appears to be very promising.
• 7.(7) The implication of these results in protein folding is discussed.

     

A method of calculating the discrete secondary structures of globular proteins

The model of formation of alpha-helices and beta-structures determined by joint action of the three elements: N-terminal, internal and C-terminal fragments are presented. Algorithm for calculation of their localization in a given amino acid sequence was constructed on the base of this model. The preference of the fragments of the amino acid sequence to a definite type of the secondary structure was estimated on the base of corresponding average values of linear discriminant functions dsk (s = alpha, beta, k = N, in, C). The latter were constructed in the previous paper on the base of the revealed significant characteristics. These integral characteristics are used for calculating the localisation of discrete secondary structures. The total prediction for 3 states (alpha, beta, c) given 71% correctly predicted residues (for 4 states alpha, beta, c, t) 62% for the training set, consisting of 72 proteins. For the control set (15 proteins) the accuracy of prediction is about 65%. The essential advantages of this method are: 1) the possibility to localize the discrete secondary structures; 2) the high accuracy of prediction of long secondary structures (for alpha-helices approximately 90%, for beta-structures approximately 80%), which is important for the determination of the protein folding. The influence of mutation on the secondary structure of proteins was investigated. The anormally high stability of the secondary structures of immunoglobulins to mutations was revealed. This probably results from the selection during evolution of such variants of amino acid sequences, which are able to provide the functional variability of antigenic determinants, but keep invariant the tertially structure of protein.     

Automatic identification of secondary structure in globular proteins

A computer program is used to analyse automatically and objectively the atomic co-ordinates of a large number of globular proteins in order to identify the regions of α-helix, β-sheet and reverse-turn secondary structure. Several different criteria for the assignment of secondary structure are tested for accuracy, reproducibility and efficiency. The most successful criterion, which is based on patterns of peptide hydrogen bonds, inter-C^α distances and inter-C^α torsion angles, is used to find the secondary structure of all the proteins studied. The accuracy of the derived assignments is assessed by comparing them with the secondary structure reported in the literature for each protein. The reliability of the methods is assessed by comparing the secondary structures derived from the independently determined sets of co-ordinates available for some proteins.We provide the first objective and consistent compilation of α-helix, β-sheet and reverse-turn secondary structure in almost all globular proteins of known tertiary structure. These data will be invaluable for analysing the relative tendencies of different amino acids to occur in different types of secondary structure, for analysing the regularity of the secondary structure itself, and for analysing how the pieces of secondary structure fit together to form the globular tertiary structure of each protein.     

Improving the accuracy of predicting secondary structure for aligned RNA sequences

Considerable attention has been focused on predicting the secondary structure for aligned RNA sequences since it is useful not only for improving the limiting accuracy of conventional secondary structure prediction but also for finding non-coding RNAs in genomic sequences. Although there exist many algorithms of predicting secondary structure for aligned RNA sequences, further improvement of the accuracy is still awaited. In this article, toward improving the accuracy, a theoretical classification of state-of-the-art algorithms of predicting secondary structure for aligned RNA sequences is presented. The classification is based on the viewpoint of maximum expected accuracy (MEA), which has been successfully applied in various problems in bioinformatics. The classification reveals several disadvantages of the current algorithms but we propose an improvement of a previously introduced algorithm (CentroidAlifold). Finally, computational experiments strongly support the theoretical classification and indicate that the improved CentroidAlifold substantially outperforms other algorithms.     

A simple method for predicting transmembrane alpha helices with better accuracy.

The prediction of a protein's structure from its amino acid sequence has been a long-standing goal of molecular biology. In this work, a new set of conformational parameters for membrane spanning alpha helices was developed using the information from the topology of 70 membrane proteins. Based on these conformational parameters, a simple algorithm has been formulated to predict the transmembrane alpha helices in membrane proteins. A FORTRAN program has been developed which takes the amino acid sequence as input and gives the predicted transmembrane alpha-helices as output. The present method correctly identifies 295 transmembrane helical segments in 70 membrane proteins with only two overpredictions. Furthermore, this method predicts all 45 transmembrane helices in the photosynthetic reaction center, bacteriorhodopsin and cytochrome c oxidase to an 86% level of accuracy and so is better than all other methods published to date.     

Application of expert networks for predicting proteins secondary structure

The present study utilizes expert neural networks for the prediction of proteins secondary structure. We use three independent networks, one for each structure (alpha, beta and coil) as the first-level processing unit; decision upon the chosen structure for each residue is carried out by a second-level, post-processing unit, which utilizes the Chou and Fasman frequency values Falpha and Fbeta in order to strengthen and/or deplete the probability of the specific structure under investigation. The highest prediction case was 76%. Our method requires primitive computational means and a relatively small training set, while still been comparable to previous work. It is not meant to be an alternative to the determination of secondary structure by means of free energy minimization, integration of dynamic equations of motion or crystallography, which are expensive, time-consuming and complicated, but to provide additional constrains, which might be considered and incorporated into larger computing setups in order to reduce the initial search space for the above methods.     

Predicting the secondary structure of globular proteins using neural network models

We present a new method for predicting the secondary structure of globular proteins based on non-linear neural network models. Network models learn from existing protein structures how to predict the secondary structure of local sequences of amino acids. The average success rate of our method on a testing set of proteins non-homologous with the corresponding training set was 64.3% on three types of secondary structure (alpha-helix, beta-sheet, and coil), with correlation coefficients of C alpha = 0.41, C beta = 0.31 and Ccoil = 0.41. These quality indices are all higher than those of previous methods. The prediction accuracy for the first 25 residues of the N-terminal sequence was significantly better. We conclude from computational experiments on real and artificial structures that no method based solely on local information in the protein sequence is likely to produce significantly better results for non-homologous proteins. The performance of our method of homologous proteins is much better than for non-homologous proteins, but is not as good as simply assuming that homologous sequences have identical structures.     

Influence of drying on the secondary structure of intrinsically disordered and globular proteins

Circular dichroism (CD) spectroscopy of five Arabidopsis late embryogenesis abundant (LEA) proteins constituting the plant specific families LEA_5 and LEA_6 showed that they are intrinsically disordered in solution and partially fold during drying. Structural predictions were comparable to these results for hydrated LEA_6, but not for LEA_5 proteins. FTIR spectroscopy showed that verbascose, but not sucrose, strongly affected the structure of the dry proteins. The four investigated globular proteins were only mildly affected by drying in the absence, but strongly in the presence of sugars. These data highlight the larger structural flexibility of disordered compared to globular proteins and the impact of sugars on the structure of both disordered and globular proteins during drying.     

A simple statistical method for discriminating outer membrane proteins with better accuracy

MOTIVATION: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for identifying outer membrane proteins from genomic sequences and for the successful prediction of their secondary and tertiary structures. RESULTS: We have systematically analyzed the amino acid composition of globular proteins from different structural classes and outer membrane proteins. We found that the residues, Glu, His, Ile, Cys, Gln, Asn and Ser, show a significant difference between globular and outer membrane proteins. Based on this information, we have devised a statistical method for discriminating outer membrane proteins from other globular and membrane proteins. Our approach correctly picked up the outer membrane proteins with an accuracy of 89% for the training set of 337 proteins. On the other hand, our method has correctly excluded the globular proteins at an accuracy of 79% in a non-redundant dataset of 674 proteins. Furthermore, the present method is able to correctly exclude alpha-helical membrane proteins up to an accuracy of 80%. These accuracy levels are comparable to other methods in the literature, and this is a simple method, which could be used for dissecting outer membrane proteins from genomic sequences. The influence of protein size, structural class and specific residues for discrimination is discussed.     

Analysis of the relationship between side-chain conformation and secondary structure in globular proteins

The relationship between the preferred side-chain dihedral angles and the secondary structure of a residue was examined. The structures of 61 proteins solved to a resolution of 2.0 A (1 A = 0.1 nm) or better were analysed using a relational database to store the information. The strongest feature observed was that the chi 1 distribution for most side-chains in an alpha-helix showed an absence of the g- conformation and a shift towards the t conformation when compared to the non-alpha/beta structures. The exceptions to this tendency were for short polar side-chains that form hydrogen bonds with the main-chain which prefer g+. Shifts in the chi 1 preferences for residues in the beta-sheet were observed. Other side-chain dihedral angles (chi 2, chi 3, chi 4) were found to be influenced by the main-chain. This paper presents more accurate distributions for the side-chain dihedral angles which were obtained from the increased number of proteins determined to high resolution. The means and standard deviations for chi 1 and chi 2 angles are presented for all residues according to the secondary structure of the main-chain. The means and standard deviations are given for the most popular conformations for side-chains in which chi 3 and chi 4 rotations affect the position of C atoms.     

Determination of secondary structure of globular proteins using circular dichroism spectra

A ridge regression method is presented for prediction of the secondary structure of proteins by the circular dichroism spectra (CD) from 190 to 236 nm. Eight types of the secondary structure were calculated on a microcalculator. The method is based on the X-ray data of Kabsh and Sander. The teaching rule is constructed on CD spectra of 30 proteins of all structural classes of the globular proteins (alpha, alpha/beta, alpha + beta and beta-proteins). The errors of the methods are analysed by removing each protein from the reference set and analyzing its structure in terms of the remaining proteins. Correlation coefficients and root-mean square deviations between CD and X-ray data were: 0.99 and 0.03 for alpha-helix, 0.86 and 0.02 for 3(10)-helix, 0.92 and 0.06 for antiparallel beta-sheet, 0.86 and 0.03 for parallel beta-sheet, 0.94 and 0.01 for T3 beta-turn, 0.85 and 0.02 for other beta-turn, 0.84 and 0.03 for S-bends, 0.83 and 0.04 for "random" structure.     

Computer method for predicting the secondary structure of single-stranded RNA.

We present a computer method utilizing published values for base pairing energies to compute the most energetically favorable secondary structure of an RNA from its primary nucleotide sequence. After listing all possible double-helical regions, every pair of mutally incompatible regions (whose nucleotides overlap) is examined to determine whether parts of those two regions can be combined by branch migration to form a pair of compatible new subregions which together are more stable than either of the original regions separately. These subregions are added to the list of base pairing regions which will compete to form the best overall structure. Then, a 'hyperstructure matrix' is generated, containing the unique topological relationship between every pair of regions. We have shown that the best structure can be chosen directly from this matrix, without the necessity of creating and examing every possible secondary structure. We have included the results from our solution of the 5S rRNA of the cyanobacterium Anacystis nidulans as an example of our program's capabilities.     

Prediction of the secondary structure content of globular proteins based on structural classes

The prediction of the secondary structure content (-helix and-strand content) of a globular protein may play an important complementary role in the prediction of the protein's structure. We propose a new prediction algorithm based on Chou's database [Chou (1995),Proteins Struct. Fund Genet. 21, 319]. The new algorithm is an improved multiple linear regression method, taking the nonlinear and coupling terms of the frequencies of different amino acids into account. The prediction is also based on the structural classes of proteins. A resubstitution examination for the algorithm shows that the average errors are 0.040 and 0.033 for the prediction of-helix content and-strand content, respectively. The examination of cross-validation, the jackknife analysis, shows that the average errors are 0.051 and 0.044 for the prediction of-helix content and-strand content, respectively. Both examinations indicate the self-consistency and the extrapolative effectiveness of the new algorithm. Compared with the other methods available currently, our method has the merits of simplicity and convenience for use, as well as a high prediction accuracy. By incorporating the prediction of the structural classes, the only input of our method is the amino acid composition of the protein to be predicted.     

A simple electrostatic criterion for predicting the thermal stability of proteins

The enhancement of protein thermostability is an important issue for both basic science and biotechnology purposes. We have developed a thermostability criterion for a protein in terms of a quasi-electric dipole moment (contributed by its charged residues) defined for an electric charge distribution whose total charge is not zero. It was found that minimization of the modulus of this dipole moment increased its thermal stability, as demonstrated by surveying these values in pairs of mesostable-thermostable homologous proteins and in mutations described in the literature. The analysis of these observations provides criteria for thermostabilization of a protein, by computing its dipole profile. This profile is obtained by direct substitution of each amino acid of the sequence by either a positive, negative or neutral amino acid, followed by a recalculation of the dipole moment. As an experimental example, these criteria were applied to a beta-glucanase to enhance its thermal stability.