Amino acid propensities for secondary structures are influenced by the protein structural class

Amino acid propensities for secondary structures were used since the 1970s, when Chou and Fasman evaluated them within datasets of few tens of proteins and developed a method to predict secondary structure of proteins, still in use despite prediction methods having evolved to very different approaches and higher reliability. Propensity for secondary structures represents an intrinsic property of amino acid, and it is used for generating new algorithms and prediction methods, therefore our work has been aimed to investigate what is the best protein dataset to evaluate the amino acid propensities, either larger but not homogeneous or smaller but homogeneous sets, i.e., all-alpha, all-beta, alpha-beta proteins. As a first analysis, we evaluated amino acid propensities for helix, beta-strand, and coil in more than 2000 proteins from the PDBselect dataset. With these propensities, secondary structure predictions performed with a method very similar to that of Chou and Fasman gave us results better than the original one, based on propensities derived from the few tens of X-ray protein structures available in the 1970s. In a refined analysis, we subdivided the PDBselect dataset of proteins in three secondary structural classes, i.e., all-alpha, all-beta, and alpha-beta proteins. For each class, the amino acid propensities for helix, beta-strand, and coil have been calculated and used to predict secondary structure elements for proteins belonging to the same class by using resubstitution and jackknife tests. This second round of predictions further improved the results of the first round. Therefore, amino acid propensities for secondary structures became more reliable depending on the degree of homogeneity of the protein dataset used to evaluate them. Indeed, our results indicate also that all algorithms using propensities for secondary structure can be still improved to obtain better predictive results.     

An evaluation of chemical shift index-based secondary structure determination in proteins: Influence of random coil chemical shifts

Random coil chemical shifts are commonly used to detect protein secondary structural elements in chemical shift index (CSI) calculations. Though this technique is widely used and seems reliable for folded proteins, the choice of reference random coil chemical shift values can significantly alter the outcome of secondary structure estimation. In order to evaluate these effects, we present a comparison of secondary structure content calculated using CSI, based on five different reference random coil chemical shift value sets, to that derived from three-dimensional structures.Our results show that none of the reference random coil data sets chosen for evaluation fully reproduces the actual secondary structures. Among the reference values generally available to date, most tend to be good estimators only of helices. Based on our evaluation, we recommend the experimental values measured by Schwarzinger et al.(2000), and statistical values obtained by Lukin et al. (1997), as good estimators of both helical and sheet content.     

PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines

Secondary structure prediction is a crucial task for understanding the variety of protein structures and performed biological functions. Prediction of secondary structures for new proteins using their amino acid sequences is of fundamental importance in bioinformatics. We propose a novel technique to predict protein secondary structures based on position-specific scoring matrices (PSSMs) and physico-chemical properties of amino acids. It is a two stage approach involving multiclass support vector machines (SVMs) as classifiers for three different structural conformations, viz., helix, sheet and coil. In the first stage, PSSMs obtained from PSI-BLAST and five specially selected physicochemical properties of amino acids are fed into SVMs as features for sequence-to-structure prediction. Confidence values for forming helix, sheet and coil that are obtained from the first stage SVM are then used in the second stage SVM for performing structure-to-structure prediction. The two-stage cascaded classifiers (PSP_MCSVM) are trained with proteins from RS126 dataset. The classifiers are finally tested on target proteins of critical assessment of protein structure prediction experiment-9 (CASP9). PSP_MCSVM with brainstorming consensus procedure performs better than the prediction servers like Predator, DSC, SIMPA96, for randomly selected proteins from CASP9 targets. The overall performance is found to be comparable with the current state-of-the art. PSP_MCSVM source code, train-test datasets and supplementary files are available freely in public domain at: and     

Sequence correction of random coil chemical shifts: correlation between neighbor correction factors and changes in the Ramachandran distribution

Random coil chemical shifts are necessary for secondary chemical shift analysis, which is the main NMR method for identification of secondary structure in proteins. One of the largest challenges in the determination of random coil chemical shifts is accounting for the effect of neighboring residues. The contributions from the neighboring residues are typically removed by using neighbor correction factors determined based on each residue’s effect on glycine chemical shifts. Due to its unusual conformational freedom, glycine may be particularly unrepresentative for the remaining residue types. In this study, we use random coil peptides containing glutamine instead of glycine to determine the random coil chemical shifts and the neighbor correction factors. The resulting correction factors correlate to changes in the populations of the major wells in the Ramachandran plot, which demonstrates that changes in the conformational ensemble are an important source of neighbor effects in disordered proteins. Glutamine derived random coil chemical shifts and correction factors modestly improve our ability to predict ¹³C chemical shifts of intrinsically disordered proteins compared to existing datasets, and may thus improve the identification of small populations of transient structure in disordered proteins.     

TMBpro: secondary structure, beta-contact and tertiary structure prediction of transmembrane beta-barrel proteins

MOTIVATION: Transmembrane beta-barrel (TMB) proteins are embedded in the outer membranes of mitochondria, Gram-negative bacteria and chloroplasts. These proteins perform critical functions, including active ion-transport and passive nutrient intake. Therefore, there is a need for accurate prediction of secondary and tertiary structure of TMB proteins. Traditional homology modeling methods, however, fail on most TMB proteins since very few non-homologous TMB structures have been determined. Yet, because TMB structures conform to specific construction rules that restrict the conformational space drastically, it should be possible for methods that do not depend on target-template homology to be applied successfully. RESULTS: We develop a suite (TMBpro) of specialized predictors for predicting secondary structure (TMBpro-SS), beta-contacts (TMBpro-CON) and tertiary structure (TMBpro-3D) of transmembrane beta-barrel proteins. We compare our results to the recent state-of-the-art predictors transFold and PRED-TMBB using their respective benchmark datasets, and leave-one-out cross-validation. Using the transFold dataset TMBpro predicts secondary structure with per-residue accuracy (Q(2)) of 77.8%, a correlation coefficient of 0.54, and TMBpro predicts beta-contacts with precision of 0.65 and recall of 0.67. Using the PRED-TMBB dataset, TMBpro predicts secondary structure with Q(2) of 88.3% and a correlation coefficient of 0.75. All of these performance results exceed previously published results by 4% or more. Working with the PRED-TMBB dataset, TMBpro predicts the tertiary structure of transmembrane segments with RMSD <6.0 A for 9 of 14 proteins. For 6 of 14 predictions, the RMSD is <5.0 A, with a GDT_TS score greater than 60.0. AVAILABILITY: http://www.igb.uci.edu/servers/psss.html.     

Random coil chemical shift for intrinsically disordered proteins: effects of temperature and pH

Secondary chemical shift analysis is the main NMR method for detection of transiently formed secondary structure in intrinsically disordered proteins. The quality of the secondary chemical shifts is dependent on an appropriate choice of random coil chemical shifts. We report random coil chemical shifts and sequence correction factors determined for a GGXGG peptide series following the approach of Schwarzinger et al. (J Am Chem Soc 123(13):2970–2978, 2001). The chemical shifts are determined at neutral pH in order to match the conditions of most studies of intrinsically disordered proteins. Temperature has a non-negligible effect on the ¹³C random coil chemical shifts, so temperature coefficients are reported for the random coil chemical shifts to allow extrapolation to other temperatures. The pH dependence of the histidine random coil chemical shifts is investigated in a titration series, which allows the accurate random coil chemical shifts to be obtained at any pH. By correcting the random coil chemical shifts for the effects of temperature and pH, systematic biases of the secondary chemical shifts are minimized, which will improve the reliability of detection of transient secondary structure in disordered proteins.     

A reference database for circular dichroism spectroscopy covering fold and secondary structure space

MOTIVATION: Circular Dichroism (CD) spectroscopy is a long-established technique for studying protein secondary structures in solution. Empirical analyses of CD data rely on the availability of reference datasets comprised of far-UV CD spectra of proteins whose crystal structures have been determined. This article reports on the creation of a new reference dataset which effectively covers both secondary structure and fold space, and uses the higher information content available in synchrotron radiation circular dichroism (SRCD) spectra to more accurately predict secondary structure than has been possible with existing reference datasets. It also examines the effects of wavelength range, structural redundancy and different means of categorizing secondary structures on the accuracy of the analyses. In addition, it describes a novel use of hierarchical cluster analyses to identify protein relatedness based on spectral properties alone. The databases are shown to be applicable in both conventional CD and SRCD spectroscopic analyses of proteins. Hence, by combining new bioinformatics and biophysical methods, a database has been produced that should have wide applicability as a tool for structural molecular biology.     

The structure of antimicrobial pexiganan peptide in solution probed by Fourier transform infrared absorption, vibrational circular dichroism, and electronic circular dichroism spectroscopy

Pexiganan (Gly-Ile-Gly-Lys-Phe-Leu-Lys-Lys-Ala-Lys-Lys-Phe-Gly-Lys-Ala-Phe-Val-Lys-Ile-Leu-Lys-Lys), a 22 amino acid peptide, is an analogue of the magainin family of antimicrobial peptides present in the skin of the African clawed frog. Conformational analysis of pexiganan was carried out in different solvent environments for the first time. Organic solvents, trifluoroethanol (TFE) and methanol, were used to study the secondary structural preferences of this peptide in the membrane-mimicking environments. In addition, aqueous (D2O) and dimethyl sulfoxide (DMSO) solutions were also investigated to study the role of hydrogen bonding involved in the secondary structure formation. Fourier transform infrared absorption, vibrational circular dichroism (VCD), and electronic circular dichroism (ECD) measurements were carried out under the same conditions to ascertain the conformational assignments in different solvents. All these spectroscopic measurements suggest that the pexiganan peptide has the tendency to adopt different structures in different environments. Pexiganan appears to adopt an alpha-helical conformation in TFE, a sheet-stabilized beta-turn structure in methanol, a random coil with beta-turn structure in D2O, and a solvated beta-turn structure in DMSO.     

Analysis of secondary metabolites of microscopic fungi of the genus Penicillium by chromatographic techniques

Combinations of various regimens of thin-layer chromatography and high-performance liquid chromatography (HPLC) was efficient in analyzing 39 nitrogen-containing secondary metabolites (alkaloids) produced by 12 strains of microscopic fungi of the genus Penicillium. Chromatographic mobility of alkaloids on Silufol plates was determined in the following systems (following staining with the Ehrlich reagent): (a) chloroform, methanol, and 25% NaOH (90:10:1, 90:10:0.1, or 80:20:0.2); (b) chloroform and acetone (9:1); and (c) ethyl acetate, methanol, and 25% NH4OH (85:15:10). Conditions for separation of clavine alkaloids by HPLC on Spherisorb ODS2 and Supelcosil LC-18 columns (gradient elution) were optimized. Retention values of 22 alkaloids were compared to those of agroclavine and roquefortine.     

Inaccurate secondary structure predictions often indicate protein fold switching

Although most proteins conform to the classical one‐structure/one‐function paradigm, an increasing number of proteins with dual structures and functions have been discovered. In response to cellular stimuli, such proteins undergo structural changes sufficiently dramatic to remodel even their secondary structures and domain organization. This “fold‐switching” capability fosters protein multi‐functionality, enabling cells to establish tight control over various biochemical processes. Accurate predictions of fold‐switching proteins could both suggest underlying mechanisms for uncharacterized biological processes and reveal potential drug targets. Recently, we developed a prediction method for fold‐switching proteins using structure‐based thermodynamic calculations and discrepancies between predicted and experimentally determined protein secondary structure (Porter and Looger, Proc Natl Acad Sci U S A 2018; 115:5968–5973). Here we seek to leverage the negative information found in these secondary structure prediction discrepancies. To do this, we quantified secondary structure prediction accuracies of 192 known fold‐switching regions (FSRs) within solved protein structures found in the Protein Data Bank (PDB). We find that the secondary structure prediction accuracies for these FSRs vary widely. Inaccurate secondary structure predictions are strongly associated with fold‐switching proteins compared to equally long segments of non‐fold‐switching proteins selected at random. These inaccurate predictions are enriched in helix‐to‐strand and strand‐to‐coil discrepancies. Finally, we find that most proteins with inaccurate secondary structure predictions are underrepresented in the PDB compared with their alternatively folded cognates, suggesting that unequal representation of fold‐switching conformers within the PDB could be an important cause of inaccurate secondary structure predictions. These results demonstrate that inconsistent secondary structure predictions can serve as a useful preliminary marker of fold switching.     

Denatured states of ribonuclease A have compact dimensions and residual secondary structure.

Using small-angle X-ray scattering and Fourier transform infrared spectroscopy, we have determined that the thermally denatured state of native ribonuclease A is on average a compact structure having residual secondary structure. Under strongly reducing conditions, the protein further unfolds into a looser structure with larger dimensions but still retains a comparable amount of secondary structure. The dimensions of the thermally and chemically denatured states of the reduced protein are different but both are more compact than is predicted for a random coil of the same length. These results demonstrate that thermal denaturation in ribonuclease A is not a simple two-state transition from a native to a completely disordered random coil state.     

Probability-based protein secondary structure identification using combined NMR chemical-shift data

For a long time, NMR chemical shifts have been used to identify protein secondary structures. Currently, this is accomplished through comparing the observed (1)H(alpha), (13)C(alpha), (13)C(beta), or (13)C' chemical shifts with the random coil values. Here, we present a new protocol, which is based on the joint probability of each of the three secondary structural types (beta-strand, alpha-helix, and random coil) derived from chemical-shift data, to identify the secondary structure. In combination with empirical smooth filters/functions, this protocol shows significant improvements in the accuracy and the confidence of identification. Updated chemical-shift statistics are reported, on the basis of which the reliability of using chemical shift to identify protein secondary structure is evaluated for each nucleus. The reliability varies greatly among the 20 amino acids, but, on average, is in the order of: (13)C(alpha)>(13)C'>(1)H(alpha)>(13)C(beta)>(15)N>(1)H(N) to distinguish an alpha-helix from a random coil; and (1)H(alpha)>(13)C(beta) >(1)H(N) approximately (13)C(alpha) approximately (13)C' approximately (15)N for a beta-strand from a random coil. Amide (15)N and (1)H(N) chemical shifts, which are generally excluded from the application, in fact, were found to be helpful in distinguishing a beta-strand from a random coil. In addition, the chemical-shift statistical data are compared with those reported previously, and the results are discussed. A JAVA User Interface program has been developed to make the entire procedure fully automated and is available via http://ccsr3150-p3.stanford.edu.     

Combining sequence-based prediction methods and circular dichroism and infrared spectroscopic data to improve protein secondary structure determinations

Background  

A number of sequence-based methods exist for protein secondary structure prediction. Protein secondary structures can also be determined experimentally from circular dichroism, and infrared spectroscopic data using empirical analysis methods. It has been proposed that comparable accuracy can be obtained from sequence-based predictions as from these biophysical measurements. Here we have examined the secondary structure determination accuracies of sequence prediction methods with the empirically determined values from the spectroscopic data on datasets of proteins for which both crystal structures and spectroscopic data are available.     

CONFOLD: Residue‐residue contact‐guided ab initio protein folding

Predicted protein residue–residue contacts can be used to build three‐dimensional models and consequently to predict protein folds from scratch. A considerable amount of effort is currently being spent to improve contact prediction accuracy, whereas few methods are available to construct protein tertiary structures from predicted contacts. Here, we present an ab initio protein folding method to build three‐dimensional models using predicted contacts and secondary structures. Our method first translates contacts and secondary structures into distance, dihedral angle, and hydrogen bond restraints according to a set of new conversion rules, and then provides these restraints as input for a distance geometry algorithm to build tertiary structure models. The initially reconstructed models are used to regenerate a set of physically realistic contact restraints and detect secondary structure patterns, which are then used to reconstruct final structural models. This unique two‐stage modeling approach of integrating contacts and secondary structures improves the quality and accuracy of structural models and in particular generates better β‐sheets than other algorithms. We validate our method on two standard benchmark datasets using true contacts and secondary structures. Our method improves TM‐score of reconstructed protein models by 45% and 42% over the existing method on the two datasets, respectively. On the dataset for benchmarking reconstructions methods with predicted contacts and secondary structures, the average TM‐score of best models reconstructed by our method is 0.59, 5.5% higher than the existing method. The CONFOLD web server is available at http://protein.rnet.missouri.edu/confold/ . Proteins 2015; 83:1436–1449. © 2015 Wiley Periodicals, Inc.     

Prediction of protein secondary structure content

All existing algorithms for predicting the content of protein secondary structure elements have been based on the conventional amino-acid-composition, where no sequence coupling effects are taken into account. In this article, an algorithm was developed for predicting the content of protein secondary structure elements that was based on a new amino-acid-composition, in which the sequence coupling effects are explicitly included through a series of conditional probability elements. The prediction was examined by a self-consistency test and an independent dataset test. Both indicated a remarkable improvement obtained when using the current algorithm to predict the contents of alpha-helix, beta-sheet, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend and random coil. Examples of the improved accuracy by introducing the new amino-acid-composition, as well as its impact on the study of protein structural class and biologically function, are discussed.     

SPSSM8: An accurate approach for predicting eight-state secondary structures of proteins

Protein eight-state secondary structure prediction is challenging, but is necessary to determine protein structure and function. Here, we report the development of a novel approach, SPSSM8, to predict eight-state secondary structures of proteins accurately from sequences based on the structural position-specific scoring matrix (SPSSM). The SPSSM has been successfully utilized to predict three-state secondary structures. Now we employ an eight-state SPSSM as a feature that is obtained from sequence structure alignment against a large database of 9 million sequences with putative structural information. The SPSSM8 uses a low sequence identity dataset (9062 entries) as a training set and conditional random field for the classification algorithm. The SPSSM8 achieved an average eight-state secondary structure accuracy (Q8) of 71.7% (Q3, 81.6%) for an independent testing set (463 entries), which had an improved accuracy of 10.1% and 4.6% compared with SSPro8 and CNF, respectively, and significantly improved the accuracy of eight-state secondary structure prediction. For CASP 9 dataset (92 entries) the SPSSM8 achieved a Q8 accuracy of 80.1% (Q3, 83.0%). The SPSSM8 was confirmed as an outstanding predictor for eight-state secondary structures of proteins. SPSSM8 is freely available at http://cal.tongji.edu.cn/SPSSM8.     

Changes in secondary structure follow the dissociation of human insulin hexamers: a circular dichroism study

Vacuum UV circular dichroism spectra measured down to 178 nm for hexameric 2-zinc human insulin, zinc-free human insulin, and the two engineered and biologically active monomeric mutants, [B/S9D] and [B/S9D,T27E] human insulin, show significant differences. The secondary structure analysis of the 2-zinc human insulin (T6) in neutral solution was determined: 57% helix, 1% beta-strand, 18% turn, and 24% random coil. This is very close to the corresponding crystal structure showing that the solution and solid structures are similar. The secondary structure of the monomer shows a 10-15% increase in antiparallel beta-structure and a corresponding reduction in random coil structure. These structural changes are consistent with an independent analysis of the corresponding difference spectra. The advantage of secondary structure analyses of difference spectra is that the contribution of odd spectral features stemming mainly from side chain chromophores is minimized and the sensitivity of the analyses improved. Analysis of the CD spectra of T6 2-zinc, zinc-free human insulin and monomeric mutant insulin by singular value decomposition indicates that the secondary structure changes following the dissociation of hexamers into dimers and monomers are two-state processes.     

Relationship between digestibility and secondary structure of raw and thermally treated legume proteins: a Fourier transform infrared (FT-IR) spectroscopic study

The secondary structure of proteins in legumes, cereals, milk products and chicken meat was studied by diffuse reflectance infrared spectroscopy in the region of the amide I band. Major secondary structure components ( β-sheets, random coil, α-helix, turns), together with the low- and high-frequency side contributions, were resolved and related to the in vitro digestibility behaviour of the different foods. A strong inverse correlation between the relative spectral weights of the β-sheet structures and in vitro protein digestibility values was measured. Structural modifications in legume proteins induced by autoclaving were monitored by the changes in the amide I spectra. The results indicate that the β-sheet structures of raw legume proteins and the intermolecular β-sheet aggregates, arising upon heating, are primary factors in adversely affecting the digestibility.     

Influence of secondary structure of polyglycine on some conformational properties

Values of four conformational properties, namely unperturbed dimension [r2]0, dipole moment [mu 2], mean squared optical anisotropy [gamma 2], and molar Kerr constant [mK], have been calculated for polyglycine chains allowing several combinations of the secondary structure with the aim of studying the dependence of these magnitudes on the secondary structure of the chain. Two different approaches to the secondary structure have been used. In the first, chains with all their units in a given conformation (random coil, alpha-helix or beta-sheet) are interrupted at several positions by one unit in a different conformation. In the second, chains with varying composition of two conformations alpha-helix/beta-sheet and beta-sheet/random coil were allowed and the results obtained compared with previous work for alpha-helix/random coil chains.     

Protein secondary structure prediction using NMR chemical shift data

Accurate determination of protein secondary structure from the chemical shift information is a key step for NMR tertiary structure determination. Relatively few work has been done on this subject. There needs to be a systematic investigation of algorithms that are (a) robust for large datasets; (b) easily extendable to (the dynamic) new databases; and (c) approaching to the limit of accuracy. We introduce new approaches using k-nearest neighbor algorithm to do the basic prediction and use the BCJR algorithm to smooth the predictions and combine different predictions from chemical shifts and based on sequence information only. Our new system, SUCCES, improves the accuracy of all existing methods on a large dataset of 805 proteins (at 86% Q(3) accuracy and at 92.6% accuracy when the boundary residues are ignored), and it is easily extendable to any new dataset without requiring any new training. The software is publicly available at http://monod.uwaterloo.ca/nmr/succes.