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PubMed1992

Predicting protein secondary structure using neural net and statistical methods.

A comparison of neural network methods and Bayesian statistical methods is presented for prediction of the secondary structure of proteins given their primary sequence. The Bayesian method makes the unphysical assumption that the probability of an amino acid occurring in each position in the protein is independent of the amino acids occurring elsewhere. However, we find the predictive accuracy of the Bayesian method to be only minimally less than the accuracy of the most sophisticated methods used to date. We present the relationship of neural network methods to Bayesian statistical methods and show that, in principle, neural methods offer considerable power, although apparently they are not particularly useful for this problem. In the process, we derive a neural formalism in which the output neurons directly represent the conditional probabilities of structure class. The probabilistic formalism allows introduction of a new objective function, the mutual information, which translates the notion of correlation as a measure of predictive accuracy into a useful training measure. Although a similar accuracy to other approaches (utilizing a mean-square error) is achieved using this new measure, the accuracy on the training set is significantly and tantalizingly higher, even though the number of adjustable parameters remains the same. The mutual information measure predicts a greater fraction of helix and sheet structures correctly than the mean-square error measure, at the expense of coil accuracy, precisely as it was designed to do. By combining the two objective functions, we obtain a marginally improved accuracy of 64.4%, with Matthews coefficients C alpha, C beta and Ccoil of 0.40, 0.32 and 0.42, respectively. However, since all methods to date perform only slightly better than the Bayes algorithm, which entails the drastic assumption of independence of amino acids, one is forced to conclude that little progress has been made on this problem, despite the application of a variety of sophisticated algorithms such as neural networks, and that further advances will require a better understanding of the relevant biophysics.

Bayes Theorem↗
PubMed1995

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.

Algorithms↗
PubMed1991

Relationship between nuclear magnetic resonance chemical shift and protein secondary structure.

An analysis of the 1H nuclear magnetic resonance chemical shift assignments and secondary structure designations for over 70 proteins has revealed some very strong and unexpected relationships. Similar studies, performed on smaller databases, for 13C and 15N chemical shifts reveal equally strong correlations to protein secondary structure. Among the more interesting results to emerge from this work is the finding that all 20 naturally occurring amino acids experience a mean alpha-1H upfield shift of 0.39 parts per million (from the random coil value) when placed in a helical configuration. In a like manner, the alpha-1H chemical shift is found to move downfield by an average of 0.37 parts per million when the residue is placed in a beta-strand or extended configuration. Similar changes are also found for amide 1H, carbonyl 13C, alpha-13C and amide 15N chemical shifts. Other relationships between chemical shift and protein conformation are also uncovered; in particular, a correlation between helix dipole effects and amide proton chemical shifts as well as a relationship between alpha-proton chemical shifts and main-chain flexibility. Additionally, useful relationships between alpha-proton chemical shifts and backbone dihedral angles as well as correlations between amide proton chemical shifts and hydrogen bond effects are demonstrated.

Hydrogen Bonding↗
PubMed1996

A note on circular-dichroic-constrained prediction of protein secondary structure.

Circular dichroic (CD) spectra of bovine immunosuppressant binding proteins FKBP12 and FKBP25, and cyclophilins (peptidylprolyl isomerases) A (bCyP-18) and B(bCyP-20), the immunophilins which selectively bind the clinically useful immunosuppressants FK506, rapamycin and cyclosporin A, respectively, were analysed using the singular-value-decomposition algorithm augmented by a simplified variable selection method. The differences between the CD-estimated values of alpha-helix, beta-structure and beta-turn and those predicted by the Chou-Fasman algorithm were minimized using the CD data as constraints of an algorithm which utilizes the method of hierarchical updating of quasi-equipotential peptide segments of the Chou-Fasman prediction. The method allows one to correct the Chou-Fasman prediction of secondary structures in globular proteins.

Amino Acid Isomerases↗
PubMedDate not supplied

[Determination of protein secondary structure from circular dichroism spectra. III. Protein-derived base spectra of circular dichroism for antiparallel and parallel beta-structures].

Protein-derived basic CD spectra for alpha-helix, antiparallel and parallel beta-structures, beta-bends and irregular form of proteins have been determined from the experimental CD spectra of six (myoglobin, lysozyme, ribonuclease A, papain, lactate dehydrogenase, subtilisin BPN') or seven (glyceraldehyde-3-phosphate dehydrogenase added) reference proteins and the analysis of the X-ray data. The secondary structures of thirteen proteins (seven reference and six additional ones) have been analysed using the basic CD spectra thus obtained. The data obtained have been compared with the results of the X-ray data analysis. It is shown that the accuracy of determination of the beta-structure and beta-bends contents using our basic CD spectra is about 2-3 times better than using the basic spectra reported by Chang et al. (Analyt. Biochem. 91, 13-31, 1978).

Circular Dichroism↗
PubMed1999

Novel matrix descriptor for secondary structure segments in proteins: demonstration of predictability from circular dichroism spectra.

An extension to standard protein secondary structure predictions using optical spectra that encompasses the number and average lengths of segments of uniform secondary structure in the sequence is demonstrated. The connectivity and numbers of segments can be described by a matrix descriptor [sij] (i, j representing segment types such as helix and beta-sheet strands). Independent knowledge of the fractional concentration of each secondary structure type and of the total number of residues in the protein then with [sij] yields the average segment length of each type. The physical background for prediction of this extended structural descriptor from spectral data is summarized, rules for its generation from reference X-ray structures are defined, and formal variants of its form are discussed. Using a novel neural network approach to analyze a training set of electronic circular dichroism (ECD) and vibrational circular dichroism (VCD) spectra for 23 proteins, matrix descriptors encompassing helix, sheet, and other forms are predicted. The results show that the matrix descriptor can be predicted to an accuracy comparable to that of conventionally predicted average fractional secondary structures. In this respect the ECD predictions of [sij] were significantly more accurate than the VCD ones, which may result from the longer range length dependence of the ECD bandshape and intensity. Summary results for a parallel analysis using Fourier transform infrared spectra indicate somewhat lower reliability than those for VCD.

Circular Dichroism↗
PubMed2000

Constraint-based assembly of tertiary protein structures from secondary structure elements.

A challenge in computational protein folding is to assemble secondary structure elements-helices and strands-into well-packed tertiary structures. Particularly difficult is the formation of beta-sheets from strands, because they involve large conformational searches at the same time as precise packing and hydrogen bonding. Here we describe a method, called Geocore-2, that (1) grows chains one monomer or secondary structure at a time, then (2) disconnects the loops and performs a fast rigid-body docking step to achieve canonical packings, then (3) in the case of intrasheet strand packing, adjusts the side-chain rotamers; and finally (4) reattaches loops. Computational efficiency is enhanced by using a branch-and-bound search in which pruning rules aim to achieve a hydrophobic core and satisfactory hydrogen bonding patterns. We show that the pruning rules reduce computational time by 10(3)- to 10(5)-fold, and that this strategy is computationally practical at least for molecules up to about 100 amino acids long.

Algorithms↗
PubMed1992

Predicting protein secondary structure content. A tandem neural network approach.

A priori knowledge of secondary structure content can be of great use in theoretical and experimental determination of protein structure. We present a method that uses two computer-simulated neural networks placed in "tandem" to predict the secondary structure content of water-soluble, globular proteins. The first of the two networks, NET1, predicts a protein's helix and strand content given information about the protein's amino acid composition, molecular weight and heme presence. Because NET1 contained more adjustable parameters (network weights) than learning examples, this network experienced problems with memorization, which is the inability to generalize onto new, never-seen-before examples. To overcome this problem, we designed a second network, NET2, which learned to determine when NET1 was in a state of generalization. Together, these two networks produce prediction errors as low as 5.0% and 5.6% for helix and strand content, respectively, on a set of protein crystal structures bearing little homology to those used in network training. A comparison between three other methods including a multiple linear regression analysis, a non-hidden-node network analysis and a secondary structure assignment analysis reveals that our tandem neural network scheme is, indeed, the best method for predicting secondary structure content. The results of our analysis suggest that the knowledge of sequence information is not necessary for highly accurate predictions of protein secondary structure content.

Databases, Bibliographic↗
PubMed1991

Simple techniques for the quantification of protein secondary structure by 1H NMR spectroscopy.

Previous work by Wishart et al. (in press) and others [(1989) J. Magn. Reson. 83, 441-449; (1990) J. Magn. Reson. 90, 165-176] has shown a strong tendency for protein secondary structure to be manifested in 1H NMR chemical shifts. Based on these earlier results, two techniques have been developed for the quantification of secondary structure in proteins. Both methods allow for the rapid and accurate determination of the percent content of helix, coil, and beta-strand based on the integration (or peak enumeration) of selected portions of either 1-D or 2-D 1H NMR spectra. These new and very simple procedures have been found to compare quite favorably to other well established techniques for secondary structure determination such as CD, Raman and IR spectroscopy.

Animals↗
PubMed1994

The origins of protein secondary structure. Effects of packing density and hydrogen bonding studied by a fast conformational search.

Globular proteins fold to create compact structures rich in alpha-helices and beta-sheets. While studies of cubic lattice models of simplified polypeptide chains have concluded that secondary structure is a necessary consequence of chain compactness, different conclusions have been reached from studies of off-lattice models of simplified chains. In an attempt to resolve this controversy, we study an all-atom off-lattice model of a protein subject to a variety of simplified energy functions. A Monte Carlo simulated annealing algorithm is used to search conformational space quickly. The algorithm uses pivot-type moves in which a residue is selected at random and the values of its main-chain dihedral angles are changed. The energy function used to accept or reject moves is taken to be either a term proportional to the volume occupied by a structure (to mimic the hydrophobic effect), a term proportional to the energy of main-chain hydrogen bonding, or a combination of these two terms. Secondary structure content is evaluated using several different definitions. For all the definitions used, compactness alone produces a 10% increase in secondary structure content. However, this is a small fraction of the secondary structure observed in native protein structures. Structures produced by minimizing the hydrogen bond energy have extensive secondary structure but are not densely packed. Structures having both the high density of native structures and extensive secondary structure are produced by minimizing combinations of the volume and hydrogen bond energy terms. Our results emphasize the close relationship between secondary structure and the geometry of main-chain hydrogen bonding. The results are consistent with a description of protein folding in which the hydrophobic effect favors dense packing while hydrogen bonding determines the specific local geometry which generates secondary structure. To make an analogy with lattice studies of packing density and secondary structure, it seems that hydrophobicity provides the packing density while hydrogen bonding provides the lattice.

Algorithms↗
PubMed1988

Role of primary and secondary protein structure in neurotransmitter receptor activation mechanisms.

A proton transfer triggered by a ligand interacting with the receptor had been suggested as the initial step in the activation of a receptor for the neurotransmitter serotonin (5-hydroxy-tryptamine; 5-HT). To evaluate the role of the receptor macromolecule in modulating the primary molecular event in ligand-mediated activation, the process of proton transfer was analysed in the environment of a protein model for the 5-HT receptor. In the absence of a detailed receptor structure, the enzyme actinidin was chosen as the model for the receptor based on criteria obtained from structure-activity considerations on the ligands. The first simulation of a mechanism for receptor activation was performed on this model using methods of theoretical chemistry to study the effect of specific structural elements. The premise is that the role of the elements of secondary structure of soluble proteins (e.g. actinidin) in determining structure-function relations in these macromolecules is maintained when these elements are part of membrane-bound receptor proteins. Results from the calculations of the effects of the six alpha helices of actinidin on the proton transfer process from the imidazolium side chain of His 162 to the thiol side chain of Cys 25 in the protein show that the helices contribute in different ways to modulate the energy of proton transfer. The largest helix, A1, opposes the proton transfer through the effect of the helix dipole. The charged residues (primary structure) in helix A3 favor the proton transfer, and mask the effect of its helix dipole (secondary structure) which opposes the transfer. The direction of the proton transfer simulated for the activation mechanism is opposite to that assumed in the catalytic process of the thiol protease, and the entire protein environment opposes the transfer. This supports the specific role of the ligand in triggering the proton transfer as a response to its binding.

Computer Graphics↗
PubMed2006

Side-chain entropy effects on protein secondary structure formation.

Loss of conformational entropy is one of the primary factors opposing protein folding. Both the backbone and side-chain of each residue in a protein will have their freedom of motion restricted in the final folded structure. The type of secondary structure of which a residue is part will have a significant impact on how much side-chain entropy is lost. Side-chain conformational entropies have previously been determined for folded proteins, simple models of unfolded proteins, alpha-helices, and a dipeptide model for beta-strands, but not for polyproline II (PII) helices. In this work, we present side-chain conformational estimates for the three regular secondary structure types: alpha-helices, beta-strands, and PII helices. Entropies are estimated from Monte Carlo computer simulations. Beta-strands are modeled as two structures, parallel and antiparallel beta-strands. Our data indicate that restraining a residue to the PII helix or antiparallel beta-strand conformations results in side-chain entropies equal to or higher than those obtained by restraining residues to the parallel beta-strand conformation. Side-chains in the alpha-helix conformation have the lowest side-chain entropies. The observation that extended structures retain the most side-chain entropy suggests that such structures would be entropically favored in unfolded proteins under folding conditions. Our data indicate that the PII helix conformation would be somewhat favored over beta-strand conformations, with antiparallel beta-strand favored over parallel. Notably, our data imply that, under some circumstances, residues may gain side-chain entropy upon folding. Implications of our findings for protein folding and unfolded states are discussed.

Amino Acid Sequence↗
PubMed2006

Importance of context in protein folding: secondary structural propensities versus tertiary contact-assisted secondary structure formation.

Molecular dynamics simulations can be used to reveal the detailed conformational behaviors of peptides and proteins. By comparing fragment and full-length protein simulations, we can investigate the role of each peptide segment in the folding process. Here, we take advantage of information regarding the helix formation process from our previous simulations of barnase and protein A as well as new simulations of four helical fragments from these proteins at three different temperatures, starting with both helical and extended structures. Segments with high helical propensity began the folding process by tethering the chain through side chain interactions involving either polar interactions, such as salt bridges, or hydrophobic staples. These tethers were frequently nonnative (i.e., not i --> i + 4 spacing) and provided a scaffold for other residues, thereby limiting the conformational search. The helical structure then propagated on both sides of the tether. Segments with low stability and propensity formed later in the folding process and utilized contacts with other portions of the protein when folding. These helices formed via a tertiary contact-assisted mechanism, primarily via hydrophobic contacts between residues distant in sequence. Thus, segments with different helical propensities appear to play different roles during protein folding. Furthermore, the active role of nonlocal side chains in helix formation highlights why we must move beyond simple hierarchical models of protein folding.

Amino Acid Sequence↗
PubMed1986

Amino acid sequence homology applied to the prediction of protein secondary structures, and joint prediction with existing methods.

The assumption that homologous segments in different proteins may share a similar conformation is applied to the prediction of secondary structures in proteins. Sequences homologous to a target protein are searched, without allowing any gap, and compared against a number of reference proteins of known three-dimensional structure, and then a conformational state (alpha, beta or coil) for each residue of the protein is predicted by looking at the secondary structure of corresponding homologous segments. This prediction is done in a statistical rather than 'deterministic' way, by assigning the most probable conformation state among homologous data to each residue site of a target protein. A test application for 22 sample proteins yields 60% correctness on the average, a better value in comparison with two other existing methods. Joint prediction combining three methods into one is shown to increase the reliability up to 70%, when only the regions identically predicted with the three methods are taken into account. Application of the present method to 10 proteins of unknown structure is demonstrated.

Amino Acid Sequence↗
PubMed1999

New methods for accurate prediction of protein secondary structure.

A primary and a secondary neural network are applied to secondary structure and structural class prediction for a database of 681 non-homologous protein chains. A new method of decoding the outputs of the secondary structure prediction network is used to produce an estimate of the probability of finding each type of secondary structure at every position in the sequence. In addition to providing a reliable estimate of the accuracy of the predictions, this method gives a more accurate Q3 (74.6%) than the cutoff method which is commonly used. Use of these predictions in jury methods improves the Q3 to 74.8%, the best available at present. On a database of 126 proteins commonly used for comparison of prediction methods, the jury predictions are 76.6% accurate. An estimate of the overall Q3 for a given sequence is made by averaging the estimated accuracy of the prediction over all residues in the sequence. As an example, the analysis is applied to the target beta-cryptogein, which was a difficult target for ab initio predictions in the CASP2 study; it shows that the prediction made with the present method (62% of residues correct) is close to the expected accuracy (66%) for this protein. The larger database and use of a new network training protocol also improve structural class prediction accuracy to 86%, relative to 80% obtained previously. Secondary structure content is predicted with accuracy comparable to that obtained with spectroscopic methods, such as vibrational or electronic circular dichroism and Fourier transform infrared spectroscopy.

Algal Proteins↗
PubMed1992

Stoichiometry, selectivity, and exchange dynamics of lipid-protein interaction with bacteriophage M13 coat protein studied by spin label electron spin resonance. Effects of protein secondary structure.

Bacteriophage M13 major coat protein has been isolated with cholate and reconstituted in dimyristoyl- and dioleoylphosphatidylcholine (DMPC and DOPC, respectively) bilayers by dialysis. Fourier transform infrared spectra of DMPC/coat protein recombinants confirmed that, whereas the protein isolated by phenol extraction was predominantly in a beta-sheet conformation, the cholate-isolated coat protein contained a higher proportion of the alpha-helical conformation [cf. Spruijt, R. B., Wolfs, C. J. A. M., & Hemminga, M. A. (1989) Biochemistry 28, 9158-9165]. The cholate-isolated coat protein/lipid recombinants gave different electron spin resonance (ESR) spectral line shapes of incorporated lipid spin labels, as compared with those from recombinants with the phenol-extracted protein that were studied previously [Wolfs, C. J. A. M., Horváth, L. I., Marsh, D., Watts, A., & Hemminga, M. A. (1989) Biochemistry 28, 9995-10001]. Plots of the ratio of the fluid/motionally restricted components in the ESR spectra of spin-labeled phosphatidylglycerol were linear with respect to the lipid/protein ratio in the recombinants up to 20 mol/mol. The corresponding values of the relative association constants, Kr, and number of association sites, N1, on the protein were Kr approximately 1 and N1 approximately 4 for DMPC recombinants and Kr approximately 1 and N1 approximately 5 for DOPC recombinants. Simulation of the two-component lipid spin label ESR spectra with the exchange-coupled Bloch equations gave values for the off-rate of the lipids leaving the protein surface of 2.0 x 10(7) s-1 at 27 degrees C in DMPC recombinants and 3.0 x 10(7) s-1 at 24 degrees C in DOPC recombinants.(ABSTRACT TRUNCATED AT 250 WORDS)

Bacteriophages↗
PubMed1991

Prediction of protein secondary structure by an enhanced neural network.

Computational model of neural network is used for prediction of secondary structure of globular proteins of known sequence. In contrast to earlier works some information about expected tertiary interactions were built in into the neural network. As a result the prediction accuracy was improved by 3% to 5%. Possible applications of this new approach are briefly discussed.

Amino Acid Sequence↗