Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

Background  

β-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of β-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based β-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor.     

Introduction of a new scheme for classifying β-turns in protein structures

Protein β-turn classification remains an area of ongoing development in structural biology research. While the commonly used nomenclature defining type I, type II and type IV β-turns was introduced in the 1970s and 1980s, refinements of β-turn type definitions have been introduced as recently as 2019 by Dunbrack, Jr and co-workers who expanded the number of β-turn types to 18 (Shapovalov et al, PLOS Computat. Biol., 15, e1006844, 2019). Based on their analysis of 13 030 turns from 1074 ultrahigh resolution (≤1.2 Å) protein structures, they used a new clustering algorithm to expand the definitions used to classify protein β-turns and introduced a new nomenclature system. We recently encountered a specific problem when classifying β-turns in crystal structures of pentapeptide repeat proteins (PRPs) determined in our lab that are largely composed of β-turns that often lie close to, but just outside of, canonical β-turn regions. To address this problem, we devised a new scheme that merges the Klyne-Prelog stereochemistry nomenclature and definitions with the Ramachandran plot. The resulting Klyne-Prelog-modified Ramachandran plot scheme defines 1296 distinct potential β-turn classifications that cover all possible protein β-turn space with a nomenclature that indicates the stereochemistry of i + 1 and i + 2 backbone dihedral angles. The utility of the new classification scheme was illustrated by re-classification of the β-turns in all known protein structures in the PRP superfamily and further assessed using a database of 16 657 high-resolution protein structures (≤1.5 Å) from which 522 776 β-turns were identified and classified.     

Characterization of subcellular localization and stability of a splice variant of G alphai2

Background  

Alternative mRNA splicing of α_i2, a heterotrimeric G protein α subunit, has been shown to produce an additional protein, termed sα_i2. In the sα_i2 splice variant, 35 novel amino acids replace the normal C-terminal 24 amino acids of α_i2. Whereas α_i2 is found predominantly at cellular plasma membranes, sα_i2 has been localized to intracellular Golgi membranes, and the unique 35 amino acids of sα_i2 have been suggested to constitute a specific targeting signal.     

2-Alkyl-2-carboxyazetidines as γ-turn inducers: incorporation into neurotrophin fragments

Conveniently substituted 2-alkyl-2-carboxyazetidine amino acids have been incorporated into NGF and NT3 tetrapeptide sequences to investigate their utility as reverse turn inducers (γ- vs. β-turns). Despite the presence of an Asp residue at i position, highly preferred in β-turns, molecular modeling and NMR studies indicated that the azetidine-containing peptides mainly stabilized γ-turn conformations.     

Motivated Proteins: A web application for studying small three-dimensional protein motifs

Background  

Small loop-shaped motifs are common constituents of the three-dimensional structure of proteins. Typically they comprise between three and seven amino acid residues, and are defined by a combination of dihedral angles and hydrogen bonding partners. The most abundant of these are αβ-motifs, asx-motifs, asx-turns, β-bulges, β-bulge loops, β-turns, nests, niches, Schellmann loops, ST-motifs, ST-staples and ST-turns.     

How many hydrogen-bonded α-turns are possible?

The formation of α-turns is a possibility to reverse the direction of peptide sequences via five amino acids. In this paper, a systematic conformational analysis was performed to find the possible isolated α-turns with a hydrogen bond between the first and fifth amino acid employing the methods of ab initio MO theory in vacuum (HF/6-31G*, B3LYP/6-311?+?G*) and in solution (CPCM/HF/6-31G*). Only few α-turn structures with glycine and alanine backbones fulfill the geometry criteria for the i←(i?+?4) hydrogen bond satisfactorily. The most stable representatives agree with structures found in the Protein Data Bank. There is a general tendency to form additional hydrogen bonds for smaller pseudocycles corresponding to β- and γ-turns with better hydrogen bond geometries. Sometimes, this competition weakens or even destroys the i←(i?+?4) hydrogen bond leading to very stable double β-turn structures. This is also the reason why an “ideal” α-turn with three central amino acids having the perfect backbone angle values of an α-helix could not be localized. There are numerous hints for stable α-turns with a distance between the \( {{\hbox{C}}_\alpha } \)-atoms of the first and fifth amino acid smaller than 6-7 Å, but without an i←(i?+?4) hydrogen bond.     

Improving the performance of β-turn prediction using predicted shape strings and a two-layer support vector machine model

Background

The β-turn is a secondary protein structure type that plays an important role in protein configuration and function. Development of accurate prediction methods to identify β-turns in protein sequences is valuable. Several methods for β-turn prediction have been developed; however, the prediction quality is still a challenge and there is substantial room for improvement. Innovations of the proposed method focus on discovering effective features, and constructing a new architectural model.     

Studying the structural properties of polyalanine and polyglutamine peptides

Poly-(Ala) and poly-(Gln) peptides have important biological effects, and can cause various human illnesses and neurodegenerative diseases. Conformational analysis of these homo-oligopeptides (HOPs) was carried out by simulated annealing in order to identify their structural properties regarding secondary structures and intramolecular H-bonding patterns. Poly-(Ala) and poly-(Gln) peptides composed of 7, 10, 14 or 20 amino acids were modelled in both charged and terminally blocked forms. In the case of conformers derived from simulated annealing calculations, the presence of various secondary structural elements (different types of β-turns, α-helix, 3₁₀-helix, poly-proline II helix, parallel and antiparallel β-strands) was investigated. Moreover, the intramolecular H-bonding patterns formed either between the backbone atoms for both HOPs or between the backbone and side-chain atoms for the poly-(Gln) peptides were examined. Our results showed that different secondary structural elements (type I and type III β-turns, α-helix, 3₁₀-helix, antiparallel β-strand) could be observed in both poly-(Ala) and poly-(Gln) peptides and, according to their presence, characteristic H-bonding patterns formed mainly by i←i+3 and i←i+4 H-bonds could be found.     

Protein secondary structure from circular dichroism spectra

Circular dichroism spectra of proteins are extremely sensitive to secondary structure. Nevertheless, circular dichroism spectra should not be analyzed for protein secondary structure unless they are measured to at least 184 nm. Even if all the various types ofβ-turns are lumped together, there are at least 5 different types of secondary structure in a protein (α-helix, antiparallelβ-sheet, parallelβ-sheet,β-turn, and other structures not included in the first 4 categories). It is not possible to solve for these 5 parameters unless there are 5 equations. Singular value decomposition can be used to show that circular dichroism spectra of proteins measured to 200 nm contain only 2 pieces of information, while spectra measured to 190 nm contain about 4. Adding the constraint that the sum of secondary structures must equal 1 provides another piece of information, but even with this constraint, spectra measured to 190 nm simply do not analyze well for the 5 unknowns in secondary structure. Spectra measured to 184 nm do contain 5 pieces of information and we have used such spectra successfully to analyze a variety of proteins for their component secondary structures.     

Data from necropsy studies and <Emphasis Type="Italic">in vitro</Emphasis> tissue studies lead to a model for allometric scaling of basal metabolic rate

Background  

The basal metabolic rate (BMR) of a mammal of mass M is commonly described by the power function αM ^β where α and β are constants determined by linear regression of the logarithm of BMR on the logarithm of M (i. e., β is the slope and α is the intercept in regression analysis). Since Kleiber's demonstration that, for 13 measurements of BMR, the logarithm of BMR is closely approximated by a straight line with slope 0.75, it has often been assumed that the value of β is exactly 3/4 (Kleiber's law).     

The increase of alpha-melanocyte-stimulating hormone in the plasma of chronic fatigue syndrome patients

Background  

Despite extensive research, no reliable biological marker for chronic fatigue syndrome (CFS) has yet been identified. However, hyperactivation of melanotrophs in the pituitary gland and increased levels of plasma alpha-melanocyte-stimulating hormone (α-MSH) have recently been detected in an animal model of chronic stress. Because CFS is considered to be caused partly by chronic stress events, increased α-MSH plasma levels may also occur in CFS patients. We therefore examined α-MSH levels in CFS patients.     

Predicting <Emphasis Type="Italic">β</Emphasis>-turns and their types using predicted backbone dihedral angles and secondary structures

Background  

β-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains.     

Structure of α-conotoxin BuIA: influences of disulfide connectivity on structural dynamics

Background  

α-Conotoxins have exciting therapeutic potential based on their high selectivity and affinity for nicotinic acetylcholine receptors. The spacing between the cysteine residues in α-conotoxins is variable, leading to the classification of sub-families. BuIA is the only α-conotoxin containing a 4/4 cysteine spacing and thus it is of significant interest to examine the structure of this conotoxin.     

Using predicted shape string to enhance the accuracy of <Emphasis Type="Bold">γ</Emphasis>-turn prediction

Numerous methods for predicting γ-turns in proteins have been developed. However, the results they generally provided are not very good, with a Matthews correlation coefficient (MCC) ≤0.18. Here, an attempt has been made to develop a method to improve the accuracy of γ-turn prediction. First, we employ the geometric mean metric as optimal criterion to evaluate the performance of support vector machine for the highly imbalanced γ-turn dataset. This metric tries to maximize both the sensitivity and the specificity while keeping them balanced. Second, a predictor to generate protein shape string by structure alignment against the protein structure database has been designed and the predicted shape string is introduced as new variable for γ-turn prediction. Based on this perception, we have developed a new method for γ-turn prediction. After training and testing the benchmark dataset of 320 non-homologous protein chains using a fivefold cross-validation technique, the present method achieves excellent performance. The overall prediction accuracy Q _total can achieve 92.2% and the MCC is 0.38, which outperform the existing γ-turn prediction methods. Our results indicate that the protein shape string is useful for predicting protein tight turns and it is reasonable to use the dihedral angle information as a variable for machine learning to predict protein folding. The dataset used in this work and the software to generate predicted shape string from structure database can be obtained from anonymous ftp site freely.     

<Emphasis Type="Italic">Drosophila</Emphasis> Uri,a PP1α binding protein,is essential for viability,maintenance of DNA integrity and normal transcriptional activity

Background  

Protein phosphatase 1 (PP1) is involved in diverse cellular processes, and is targeted to substrates via interaction with many different protein binding partners. PP1 catalytic subunits (PP1c) fall into PP1α and PP1β subfamilies based on sequence analysis, however very few PP1c binding proteins have been demonstrated to discriminate between PP1α and PP1β.     

Position-specific propensities of amino acids in the <Emphasis Type="Italic">β</Emphasis>-strand

Background  

Despite the importance of β -strands as main building blocks in proteins, the propensity of amino acid in β -strands is not well-understood as it has been more difficult to determine experimentally compared to α -helices. Recent studies have shown that most of the amino acids have significantly high or low propensity towards both ends of β -strands. However, a comprehensive analysis of the sequence dependent amino acid propensities at positions between the ends of the β -strand has not been investigated.     

Mapping and mutation of the conserved DNA polymerase interaction motif (DPIM) located in the C-terminal domain of fission yeast DNA polymerase δ subunit Cdc27

Background  

DNA polymerases α and δ play essential roles in the replication of chromosomal DNA in eukaryotic cells. DNA polymerase α (Pol α)-primase is required to prime synthesis of the leading strand and each Okazaki fragment on the lagging strand, whereas DNA polymerase δ (Pol δ) is required for the elongation stages of replication, a function it appears capable of performing on both leading and lagging strands, at least in the absence of DNA polymerase ε (Pol ε).     

Using molecular principal axes for structural comparison: determining the tertiary changes of a FAB antibody domain induced by antigenic binding

Background  

Comparison of different protein x-ray structures has previously been made in a number of different ways; for example, by visual examination, by differences in the locations of secondary structures, by explicit superposition of structural elements, e.g. α-carbon atom locations, or by procedures that utilize a common symmetry element or geometrical feature of the structures to be compared.     

Karyopherin α7 (KPNA7), a divergent member of the importin α family of nuclear import receptors

Background  

Classical nuclear localization signal (NLS) dependent nuclear import is carried out by a heterodimer of importin α and importin β. NLS cargo is recognized by importin α, which is bound by importin β. Importin β mediates translocation of the complex through the central channel of the nuclear pore, and upon reaching the nucleus, RanGTP binding to importin β triggers disassembly of the complex. To date, six importin α family members, encoded by separate genes, have been described in humans.     

Rikkunshi-to attenuates adverse gastrointestinal symptoms induced by fluvoxamine

Background  

The α-EEG anomaly during sleep, originally associated with chronic pain, is noted in several psychiatric and medical conditions and is also present in some normal subjects. The exact significance of the α-EEG anomaly is uncertain, but it has been suggested to be a nonspecific response to a variety of noxious stimuli. We propose that attachment insecurity, which is often associated with a state of hypervigilance during wakefulness, may be associated with the α-EEG anomaly during sleep.