Folding type-specific secondary structure propensities of amino acids,derived from α-helical, β-sheet, α/β, and α+β proteins of known structures

Folding type-specific secondary structure propensities of 20 naturally occurring amino acids have been derived from α-helical, β-sheet, α/β, and α+β proteins of known structures. These data show that each residue type of amino acids has intrinsic propensities in different regions of secondary structures for different folding types of proteins. Each of the folding types shows markedly different rank ordering, indicating folding type-specific effects on the secondary structure propensities of amino acids. Rigorous statistical tests have been made to validate the folding type-specific effects. It should be noted that α and β proteins have relatively small α-helices and β-strands forming propensities respectively compared with those of α+β and α/β proteins. This may suggest that, with more complex architectures than α and β proteins, α+β and α/β proteins require larger propensities to distinguish from interacting α-helices and β-strands. Our finding of folding type-specific secondary structure propensities suggests that sequence space accessible to each folding type may have differing features. Differing sequence space features might be constrained by topological requirement for each of the folding types. Almost all strong β-sheet forming residues are hydrophobic in character regardless of folding types, thus suggesting the hydrophobicities of side chains as a key determinant of β-sheet structures. In contrast, conformational entropy of side chains is a major determinant of the helical propensities of amino acids, although other interactions such as hydrophobicities and charged interactions cannot be neglected. These results will be helpful to protein design, class-based secondary structure prediction, and protein folding. © 1998 John Wiley & Sons, Inc. Biopoly 45: 35–49, 1998     

Prediction of the parallel/antiparallel orientation of beta-strands using amino acid pairing preferences and support vector machines

In principle, structural information of protein sequences with no detectable homology to a protein of known structure could be obtained by predicting the arrangement of their secondary structural elements. Although some ab initio methods for protein structure prediction have been reported, the long-range interactions required to accurately predict tertiary structures of β-sheet containing proteins are still difficult to simulate. To remedy this problem and facilitate de novo prediction of β-sheet containing protein structures, we developed a support vector machine (SVM) approach that classified parallel and antiparallel orientation of β-strands by using the information of interstrand amino acid pairing preferences. Based on a second-order statistics on the relative frequencies of each possible interstrand amino acid pair, we defined an average amino acid pairing encoding matrix (APEM) for encoding β-strands as input in the prediction model. As a result, a prediction accuracy of 86.89% and a Matthew's correlation coefficient value of 0.71 have been achieved through 7-fold cross-validation on a non-redundant protein dataset from PISCES. Although several issues still remain to be studied, the method presented here to some extent could indicate the important contribution of the amino acid pairs to the β-strand orientation, and provide a possible way to further be combined with other algorithms making a full ‘identification’ of β-strands.     

Stability of secondary structural elements in a solvent-free environment. II: The β-pleated sheets

The stability of single β-strands and multistrand β-pleated sheets as elements of secondary structure is examined in the absence of intermolecular interactions. Such experimental conditions (e.g., complete removal of solvent molecules and counterions) are achieved by placing the peptide ions in the gas phase. The metastable multiply- charged peptide ions produced by electrospray ionization undergo unimolecular dissociation. Intercharge repulsion within the precursor ions gives rise to the elevated kinetic energy of fragment ions, which is measured using Mass-analyzed Ion Kinetic Energy (MIKE) spectrometry. Intercharge distances calculated based on these measurements are compared to the numbers derived from molecular mechanics calculations with charge site assignments based on relative proton affinities. Evidence is presented suggesting that single β-strands form collapsed structures in the absence of solvents, while multistrand β-pleated sheets are likely to retain “native-like” secondary structures under the same conditions. These results indicate that intramolecular hydrogen bonds are the major factor determining the three-dimensional arrangements of polypeptides in the gas phase, compensating both long- and short-range electrostatic repulsions. This is in good agreement with our earlier findings (Proteins 27:165–170, 1997) concerning stability of helical conformation of melittin in the absence of solvent. Proteins Suppl. 2:22–27, 1998. © 1998 Wiley-Liss, Inc.     

DNA recognition by β-sheets

The modes of DNA recognition by β-sheets are analyzed by using the known crystal and solution three-dimensional structures of DNA-protein complexes. Close fitting of the protein surface and the DNA surface determines the binding geometry. Interaction takes place so that essentially the N-to-C direction of the β-strands either follows or crosses the DNA groove. Upon following the major groove a two-stranded antiparallel β-sheet dives into the groove and contacts DNA bases with its convex side facing the DNA, while upon following the minor groove, it binds around the sugar-phosphate backbones, with its opposite concave side shielding the DNA. In order for the β-strands crossing the minor groove to interact with the DNA, the dinucleotide steps need to almost totally helically untwist and roll around major groove. The β-sheet, on the other hand, needs to adopt a concave curvature on the binding surface in the direction that follows the DNA minor groove, and a convex surface in the direction that bridges the sugar-phosphate backbones across the groove. The result is to produce a hyperbolic paraboloidal DNA-binding surface. © 1998 John Wiley & Sons, Inc. Biopoly 44: 335–359, 1997     

Five-stranded β-sheet sandwiched with two α-helices: A structural link between restriction endonucleases EcoRI and EcoRV

Examination of crystal structures of restriction endonucleases EcoRI and EcoRV complexes with their cognate DNA revealed a common structural element, which forms the core of both proteins. This element consists of a five-stranded β-sheet and two α-helices packed against it and could be described as α–β sandwich in which helices and β-strands lie in two stacked layers. While the spatial structure of this α–β sandwich is conserved in both enzymes, there are no detectable similarities between amino acid sequences except of a few residues involved in active site formation. Probably, other restriction endonucleases which have similar organization of the active site might possess similar structural element regardless of DNA sequence recognized and recognition elements in the enzyme used. © 1994 Wiley-Liss, Inc.     

Virtual screening on an α‐helix to β‐strand switchable region of the FGFR2 extracellular domain revealed positive and negative modulators

The secondary structure of some protein segments may vary between α‐helix and β‐strand. To predict these switchable segments, we have developed an algorithm, Switch‐P, based solely on the protein sequence. This algorithm was used on the extracellular parts of FGF receptors. For FGFR2, it predicted that β4 and β5 strands of the third Ig‐like domain were highly switchable. These two strands possess a high number of somatic mutations associated with cancer. Analysis of PDB structures of FGF receptors confirmed the switchability prediction for β5. We thus evaluated if compound‐driven α‐helix/β‐strand switching of β5 could modulate FGFR2 signaling. We performed the virtual screening of a library containing 1.4 million of chemical compounds with two models of the third Ig‐like domain of FGFR2 showing different secondary structures for β5, and we selected 32 compounds. Experimental testing using proliferation assays with FGF7‐stimulated SNU‐16 cells and a FGFR2‐dependent Erk1/2 phosphorylation assay with FGFR2‐transfected L6 cells, revealed activators and inhibitors of FGFR2. Our method for the identification of switchable proteinic regions, associated with our virtual screening approach, provides an opportunity to discover new generation of drugs with under‐explored mechanism of action. Proteins 2014; 82:2982–2997. © 2014 Wiley Periodicals, Inc.     

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.     

On the conformation of proteins: The handedness of the β-strand-α-helix-β-strand unit

The β-strand-α-helix-β-strand unit consists of two parallel, but not necessarily adjacent, β-strands which lie in a β-pleated sheet and are connected by one or more α-helices. This unit, which occurs in 17 functionally different globular proteins, may adopt a right- or a left-handed conformation. An analysis of the distribution shows that 57 out of the 58 units are right-handed. If the unit had no right-handed preference, the probability of observing such a distribution by chance is 10^?16. This may be explained in terms of the twist of the β-sheet which is shown to favour a right-handed unit, as otherwise steric hindrance occurs in the loop regions. We show that the right-handed strand-helix-strand unit determines the sense of the super-secondary structure found in the dehydrogenases and of related folds found in other structures. The evolutionary relationships between proteins containing this unit are re-evaluated in terms of this preference. The high probability that the unit will fold with a right-handed conformation has implications for the prediction of tertiary structure.     

An Ion-channel Modulator from the Saliva of the Brown Ear Tick has a Highly Modified Kunitz/BPTI Structure

Ra-KLP, a 75 amino acid protein secreted by the salivary gland of the brown ear tick Rhipicephalus appendiculatus has a sequence resembling those of Kunitz/BPTI proteins. We report the detection, purification and characterization of the function of Ra-KLP. In addition, determination of the three-dimensional crystal structure of Ra-KLP at 1.6 Å resolution using sulphur single-wavelength anomalous dispersion reveals that much of the loop structure of classical Kunitz domains, including the protruding protease-binding loop, has been replaced by β-strands. Even more unusually, the N-terminal portion of the polypeptide chain is pinned to the ”Kunitz head” by two disulphide bridges not found in classical Kunitz/BPTI proteins. The disulphide bond pattern has been further altered by the loss of the bridge that normally stabilizes the protease-binding loop. Consistent with the conversion of this loop into a β-strand, Ra-KLP shows no significant anti-protease activity; however, it activates maxiK channels in an in vitro system, suggesting a potential mechanism for regulating host blood supply during feeding.     

Distinct nuclear import and export pathways mediated by members of the karyopherin β family

Transport of proteins into and out of the nucleus occurs through nuclear pore complexes (NPCs) and is mediated by the interaction of transport factors with nucleoporins at the NPC. Nuclear import of proteins containing classical nuclear localization signals (NLSs) is mediated by a heterodimeric protein complex, composed of karyopherin α and β1, that docks via β1 the NLS-protein to the NPC. The GTPase Ran; the RanGDP binding protein, p10; and the RanGTP binding protein, RanBP1 are involved in translocation of the docked NLS-protein into the nucleus. Recently, new distinct nuclear import and export pathways that are mediated by members of the karyopherin β family have been discovered. Karyopherin β2 mediates import of mRNA binding proteins, whereas karyopherin β3 and β4 mediate import of a set of ribosomal proteins. Two other β karyopherin family members, CRM1 and CAS, mediate export of proteins containing leucine-rich nuclear export signals (NES) and reexport of karyopherin α, respectively. This growing family contains new members that constitute potential transport factors for cargoes yet to be identified in the future. The common features of the members of karyopherin β family are the ability to bind RanGTP and the ability to interact directly with nucleoporins at the NPC. The challenge for the future will be to identify the distinct or, perhaps, overlapping cargo(es) for each member of the karyopherin β superfamily and to characterize the molecular mechanisms of translocation of karyopherins together with their cargoes through the NPC. J. Cell. Biochem. 70:231–239, 1998.© 1998 Wiley-Liss, Inc.     

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.     

Identification and analysis of extended strands and β-sheets in globular proteins

A method to identify β-sheets in globular proteins from extended strands, using only α-carbon positions, has been developed. The strands that form β-sheets are picked up by means of simple distance criteria. The method has been tested by applying it to three proteins with accurately known secondary structures. It has also been applied to ten other proteins wherein only α-carbon coordinates are available, and the list of β-sheets obtained. The following points are worth noting: (i) The sheets identified by the algorithm are found to agree satisfactorily with the reported ones based on backbone hydrogen bonding, wherever this information is available. (ii) β-Strands that do not form parts of any sheet are a common feature of protein structures. (iii) Such isolated β-strands tend to be short. (iv) The conformation corresponding to the preferred right-handed twist of the sheet is overwhelmingly observed in both the sheet-forming and isolated β-strands.     

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.     

The phospho-β-galactosidase and synaptotagmin predictions

Two bona fide consensus predictions of secondary and tertiary structure in a protein family, made and announced before experimental structures were known, are evaluated in light of the subsequently determined experimental structures. The first, for phospho-β-galactosidase, identified the core strands of an 8-fold α–β barrel, and identified the 8-fold α–β barrel itself, which was found in the subsequently determined experimental structure to be the core folding domain. The second, for synaptotagmin, identified seven out of eight β-strands in the structure correctly, missing only a noncore strand. Three preferred “topologies” were selected from several hundred thousand possible topologies of these seven predicted strands using a rule-based analysis. The subsequently determined experimental structure showed that these seven strands in synaptotagmin adopt one of the three preferred topologies. We were unable, however, to identify the correct topology from among these three topologies. © 1995 Wiley-Liss, Inc.     

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.     

Fold recognition and ab initio structure predictions using hidden markov models and β-strand pair potentials

Protein structure predictions were submitted for 9 of the target sequences in the competition that ran during 1994. Targets sequences were selected that had no known homology with any sequence of known structure and were members of a reasonably sized family of related but divergent sequences. The objective was either to recognize a compatible fold for the target sequence in the database of known structures or to predict ab initio its rough 3D topology. The main tools used were Hidden Markov models (HMM) for fold recognition, a β- strand pair potential to predict β-sheet topology, and the PHD server for secondary structure prediction. Compatible folds were correctly identified in a number of cases and the β-strand pair potential was shown to be a useful tool for ab initio topology prediction. © 1995 Wiley-Liss, Inc.     

Binary patterning of polar and nonpolar amino acids in the sequences and structures of native proteins

Protein sequences can be represented as binary patterns of polar (○) and nonpolar (?) amino acids. These binary sequence patterns are categorized into two classes: Class A patterns match the structural repeat of an idealized amphiphilic α-helix (3.6 residues per turn), and class B patterns match the structural repeat of an idealized amphiphilic β-strand (2 residues per turn). The difference between these two classes of sequence patterns has led to a strategy for de novo protein design based on binary patterning of polar and nonpolar amino acids. Here we ask whether similar binary patterning is incorporated in the sequences and structures of natural proteins. Analysis of the Protein Data Bank demonstrates the following. (1) Class A sequence patterns occur considerably more frequently in the sequences of natural proteins than would be expected at random, but class B patterns occur less often than expected. (2) Each pattern is found predominantly in the secondary structure expected from the binary strategy for protein design. Thus, class A patterns are found more frequently in α-helices than in β-strands, and class B patterns are found more frequently in β-strands than in α-helices. (3) Among the α-helices of natural proteins, the most commonly used binary patterns are indeed the class A patterns. (4) Among all β-strands in the database, the most commonly used binary patterns are not the expected class B patterns. (5) However, for solvent-exposed β-strands, the correlation is striking: All β-strands in the database that contain the class B patterns are exposed to solvent. (6) The bias of class A patterns for α-structure over β-structure and the bias of class B patterns for β-structure over α-structure are significant, not merely when compared to other binary patterns of polar (○) and nonpolar (?) amino acids, but also when compared to the full range of sequences in the database. The implications for the design of novel proteins are discussed.     

Prediction of the structure of the replication initiator protein DnaA

The secondary structure of DnaA protein and its interaction with DNA and ribonucleotides has been predicted using biochemical, biophysical techniques, and prediction methods based on multiple-sequence alignment and neural networks. The core of all proteins from the DnaA family consists of an “open twisted α/β structure,” containing five α-helices alternating with five β-strands. In our proposed structural model the interior of the core is formed by a parallel β-sheet, whereas the α-helices are arranged on the surface of the core. The ATP-binding motif is located within the core, in a loop region following the first β-strand. The N-terminal domain (80 aa) is composed of two α-helices, the first of which contains a potential leucine zipper motif for mediating protein-protein interaction, followed by a β-strand and an additional α-helix. The N-terminal domain and the α/β core region of DnaA are connected by a variable loop (45–70 aa); major parts of the loop region can be deleted without loss of protein activity. The C-terminal DNA-binding domain (94 aa) is mostly α-helical and contains a potential helix-loop-helix motif. DnaA protein does not dimerize in solution; instead, the two longest C-terminal α-helices could interact with each other, forming an internal “coiled coil” and exposing highly basic residues of a small loop region on the surface, probably responsible for DNA backbone contacts. © 1997 Wiley-Liss Inc.     

Selective fluorescent labeling at the α-amino group of bovine pancreatic trypsin inhibitor

Experimental investigation of protein structure and dynamics by spectroscopic methods using external probes requires attachment of a probe to a well-defined site and preparation of pure samples. Measurements of efficiency of nonradiative excitation energy transfer can yield very detailed information about the structure of proteins, provided that two different probes are selectively attached to well-defined sites. We have used specific protection of ε-amino groups using tert-butylazidoformate at high pH for covalent attachment of the fluorescent probe 2-naphthoxyacetic acid at the α-amino group of bovine pancreatic trypsin inhibitor (BPTI). The product is a chromatoraphically homogenous protein derivative that contains the probe at a dye to protein ratio of 1:1, specifically located at the N-terminus, and and that retains its full biological activity. The HPLC tryptic peptide map of BPTI has been analyzed, and all the peptide fragments have been identified. Analysis of tryptic fragments of the labled BPTI derivative showed that it was selectively labeled at the N-terminal amino acid. The probe absorbs in the 310–325-nm range, which is spectrally distinct from the absorption of the protein, and has a monoexponetial fluorescence decay. These and other charactristics make this probe a good energy donor in transfer-efficiency measurements.