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.     

PackHelix: a tool for helix-sheet packing during protein structure prediction

The three‐dimensional structure of a protein is organized around the packing of its secondary structure elements. Predicting the topology and constructing the geometry of structural motifs involving α‐helices and/or β‐strands are therefore key steps for accurate prediction of protein structure. While many efforts have focused on how to pack helices and on how to sample exhaustively the topologies and geometries of multiple strands forming a β‐sheet in a protein, there has been little progress on generating native‐like packings of helices on sheets. We describe a method that can generate the packing of multiple helices on a given β‐sheet for αβα sandwich type protein folds. This method mines the results of a statistical analysis of the conformations of αβ₂ motifs in protein structures to provide input values for the geometric attributes of the packing of a helix on a sheet. It then proceeds with a geometric builder that generates multiple arrangements of the helices on the sheet of interest by sampling through these values and performing consistency checks that guarantee proper loop geometry between the helices and the strands, minimal number of collisions between the helices, and proper formation of a hydrophobic core. The method is implemented as a module of ProteinShop. Our results show that it produces structures that are within 4–6 Å RMSD of the native one, regardless of the number of helices that need to be packed, though this number may increase if the protein has several helices between two consecutive strands in the sequence that pack on the sheet formed by these two strands. Proteins 2011; Published 2011 Wiley‐Liss, Inc.     

Structural trees for proteins containing φ-motifs

In the present study, a novel structural motif of proteins referred to as the phi-motif is considered, and two novel structural trees in which the phi-motif is taken as the root structure have been constructed. The simplest phi-motif is formed by three adjacent beta-strands connected by loops and packed in one beta-sheet so that its overall fold resembles the Greek letter phi. Construction of the structural trees and modeling of folding pathways have shown that all structures of the protein superfamilies can be obtained by stepwise addition of alpha-helices and/or beta-strands to the root phi-motif taking into account a restricted set of rules inferred from known principles of protein structure. The structural trees are a good tool for structure comparison, structural classification of proteins, as well as for searching for all possible protein folds and folding pathways.     

Refolding molecular dynamics simulations of small- and middle-sized proteins in an explicit solvent

In order to elucidate the protein folding problem, we performed molecular dynamics simulations for small- and middle-sized two unfolding and six refolding proteins in an explicit solvent. Histidine-containing phosphocarrier protein and small designed protein were chosen for the simulations. We found that the protein folding process of these proteins was divided into three phases: an α -helix formation phase, a packing phase and a β -sheet formation phase. In the α -helix formation phase, an α -helix was developed from a β -turn structure through a 3₁₀-helix state. In the packing phase, proteins became compact, and tertiary structures (α / α or pre- β / β packing) were formed. Formation of a hydrophobic nucleus occurred concomitant with the α -helix formation and packing phase. Finally, in the β -sheet formation phase, a β -sheet was developed owing to the sequential formation of hydrogen bonds between two neighbouring strands, just like a "closing zipper".     

大鼠MUPP1蛋白PDZ8结构域的生化及晶体结构特性

多重PDZ结构域蛋白1型（MUPP1）是一种存在于上皮细胞和神经细胞内含有13个PDZ结构域的重要支架蛋白.在上皮细胞中,MUPP1蛋白在紧密连接结构的形成和上皮细胞的极化过程中发挥重要作用.而在中枢神经系统中,MUPP1基因的1个提前终止突变导致了其最后12个PDZ结构域的缺失,以及严重的先天性脑积水.此外,MUPP1蛋白的表达水平与酒精依赖性和药物戒断的严重性也具有显著的相关性.因此,对MUPP1蛋白所含的PDZ结构域进行纯化和性质鉴定,将有助于深入研究MUPP1蛋白的功能和分子机制.在本文研究中,利用亲和纯化和分子筛技术,对大鼠来源的MUPP1蛋白的第8个PDZ结构域进行了表达和纯化.多角度激光光散射的数据表明: MUPP1-PDZ8结构域在溶液中为单体,分子量为16.4 kD.圆二色谱结果表明,MUPP1-PDZ8结构域具有较好的二级结构折叠,测得其熔解温度为71.6摄氏度,暗示该PDZ结构域在溶液中非常稳定.最后,MUPP1-PDZ8结构域的晶体结构显示,该结构域属于I 型PDZ 结构域,包含3个α螺旋和6个β折叠.其中GLGL模块、β折叠B上的1 351位亮氨酸,以及α螺旋B上的1 405位异亮氨酸/1 398位组氨酸形成的PDZ结合口袋,可以特异性地与其目标蛋白质的羧基末端相结合.综上所述,本文的研究提供了MUPP1-PDZ8结构域的生化特性,以及该结构域与其目标蛋白质相互作用的分子机制,这将为MUPP1蛋白的功能研究提供生物化学与结构生物学的理论基础.     

Helix‐sheet packing in proteins

The three‐dimensional structure of a protein is organized around the packing of its secondary structure elements. Although much is known about the packing geometry observed between α‐helices and between β‐sheets, there has been little progress on characterizing helix–sheet interactions. We present an analysis of the conformation of αβ₂ motifs in proteins, corresponding to all occurrences of helices in contact with two strands that are hydrogen bonded. The geometry of the αβ₂ motif is characterized by the azimuthal angle θ between the helix axis and an average vector representing the two strands, the elevation angle ψ between the helix axis and the plane containing the two strands, and the distance D between the helix and the strands. We observe that the helix tends to align to the two strands, with a preference for an antiparallel orientation if the two strands are parallel; this preference is diminished for other topologies of the β‐sheet. Side‐chain packing at the interface between the helix and the strands is mostly hydrophobic, with a preference for aliphatic amino acids in the strand and aromatic amino acids in the helix. From the knowledge of the geometry and amino acid propensities of αβ₂ motifs in proteins, we have derived different statistical potentials that are shown to be efficient in picking native‐like conformations among a set of non‐native conformations in well‐known decoy datasets. The information on the geometry of αβ₂ motifs as well as the related statistical potentials have applications in the field of protein structure prediction. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Description and recognition of regular and distorted secondary structures in proteins using the automated protein structure analysis method

The Automated Protein Structure Analysis (APSA) method, which describes the protein backbone as a smooth line in three‐dimensional space and characterizes it by curvature κ and torsion τ as a function of arc length s, was applied on 77 proteins to determine all secondary structural units via specific κ(s) and τ(s) patterns. A total of 533 α‐helices and 644 β‐strands were recognized by APSA, whereas DSSP gives 536 and 651 units, respectively. Kinks and distortions were quantified and the boundaries (entry and exit) of secondary structures were classified. Similarity between proteins can be easily quantified using APSA, as was demonstrated for the roll architecture of proteins ubiquitin and spinach ferridoxin. A twenty‐by‐twenty comparison of all α domains showed that the curvature‐torsion patterns generated by APSA provide an accurate and meaningful similarity measurement for secondary, super secondary, and tertiary protein structure. APSA is shown to accurately reflect the conformation of the backbone effectively reducing three‐dimensional structure information to two‐dimensional representations that are easy to interpret and understand. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

A consensus prediction of the secondary structure for the 6-phospho-β-D-galactosidase superfamily

Two separate unrefined models for the secondary structure of two subfamilies of the 6-phospho-β-D -galactosidase superfamily were independently constructed by examining patterns of variation and conservation within homologous protein sequences, assigning surface, interior, parsing, and active site residues to positions in the alignment, and identifying periodicities in these. A consensus model for the secondary structure of the entire superfamily was then built. The prediction tests the limits of an unrefined prediction made using this approach in a large protein with substantial functional and sequence divergence within the family. The protein belongs to the (α–β class), with the core β strands aligned parallel. The supersecondary structural elements that are readily identified in this model is a parallel β sheet built by strands C, D, and E, with helices 2 and 3 connecting strands (C + D) and (D + E), respectively, and an analogous α–β unit (strand G and helix 7) toward the end of the sequence. The resemblance of the supersecondary model to the tertiary structure formed by 8-fold α–β barrel proteins is almost certainly not coincidental. © 1995 Wiley-Liss, Inc.     

BuildBeta—A system for automatically constructing beta sheets

We describe a method that can thoroughly sample a protein conformational space given the protein primary sequence of amino acids and secondary structure predictions. Specifically, we target proteins with β‐sheets because they are particularly challenging for ab initio protein structure prediction because of the complexity of sampling long‐range strand pairings. Using some basic packing principles, inverse kinematics (IK), and β‐pairing scores, this method creates all possible β‐sheet arrangements including those that have the correct packing of β‐strands. It uses the IK algorithms of ProteinShop to move α‐helices and β‐strands as rigid bodies by rotating the dihedral angles in the coil regions. Our results show that our approach produces structures that are within 4–6 Å RMSD of the native one regardless of the protein size and β‐sheet topology although this number may increase if the protein has long loops or complex α‐helical regions. Proteins 2010. © Published 2009 Wiley‐Liss, Inc.     

X-ray structures of ferritins and related proteins

Ferritins are members of a much larger superfamily of proteins, which are characterised by a structural motif consisting of a bundle of four parallel and anti-parallel α helices. The ferritin superfamily itself is widely distributed across all three living kingdoms, in both aerobic and anaerobic organisms, and a considerable number of X-ray structures are available, some at extremely high resolution. We describe first of all the subunit structure of mammalian H and L chain ferritins and then discuss intersubunit interactions in the 24-subunit quaternary structure of these ferritins. Bacteria contain two types of ferritins, FTNs, which like mammalian ferritins do not contain haem, and the haem-containing BFRs. The characteristic carboxylate-bridged di-iron ferroxidase sites of H chain ferritins, FTNs and BFRs are compared, as are the potential entry sites for iron and the ‘nucleation’ site of L chain ferritins. Finally we discuss the three-dimensional structures of the 12-subunit bacterial Dps (DNA-binding protein from starved cells) proteins as well as their intersubunit di-iron ferroxidase site.     

Amyloidogenic sequences in native protein structures

Numerous short peptides have been shown to form β‐sheet amyloid aggregates in vitro. Proteins that contain such sequences are likely to be problematic for a cell, due to their potential to aggregate into toxic structures. We investigated the structures of 30 proteins containing 45 sequences known to form amyloid, to see how the proteins cope with the presence of these potentially toxic sequences, studying secondary structure, hydrogen‐bonding, solvent accessible surface area and hydrophobicity. We identified two mechanisms by which proteins avoid aggregation: Firstly, amyloidogenic sequences are often found within helices, despite their inherent preference to form β structure. Helices may offer a selective advantage, since in order to form amyloid the sequence will presumably have to first unfold and then refold into a β structure. Secondly, amyloidogenic sequences that are found in β structure are usually buried within the protein. Surface exposed amyloidogenic sequences are not tolerated in strands, presumably because they lead to protein aggregation via assembly of the amyloidogenic regions. The use of α‐helices, where amyloidogenic sequences are forced into helix, despite their intrinsic preference for β structure, is thus a widespread mechanism to avoid protein aggregation.     

An automatic method involving cluster analysis of secondary structures for the identification of domains in proteins.

With a growing number of structures available in the Brookhaven Protein Data Bank, automatic methods for domain identification are required for the construction of databases. Domains are considered to be clusters of secondary structure elements. Thus, helices and strands are first clustered using intersecondary structural distances between C alpha positions, and dendrograms based on this distance measure are used to identify domains. Individual domains are recognized by a disjoint factor, which enables the automatic identification and classification into disjoint, interacting, and conjoint domains. Application to a database of 83 protein families and 18 unique structures shows that the approach provides an effective delineation of boundaries and identifies those proteins that can be considered as a single domain. A quantitative estimate of the interaction between domains has been proposed. The database of protein domains is a useful tool for understanding protein folding, for recognizing protein folds, and for understanding structure-activity relationships.     

Structural similarity to bridge sequence space: finding new families on the bridges

Structures for protein domains have increased rapidly in recent years owing to advances in structural biology and structural genomics projects. New structures are often similar to those solved previously, and such similarities can give insights into function by linking poorly understood families to those that are better characterized. They also allow the possibility of combing information to find still more proteins adopting a similar structure and sometimes a similar function, and to reprioritize families in structural genomics pipelines. We explore this possibility here by preparing merged profiles for pairs of structurally similar, but not necessarily sequence-similar, domains within the SMART and Pfam database by way of the Structural Classification of Proteins (SCOP). We show that such profiles are often able to successfully identify further members of the same superfamily and thus can be used to increase the sensitivity of database searching methods like HMMer and PSI-BLAST. We perform detailed benchmarks using the SMART and Pfam databases with four complete genomes frequently used as annotation benchmarks. We quantify the associated increase in structural information in Swissprot and discuss examples illustrating the applicability of this approach to understand functional and evolutionary relationships between protein families.     

Analysis of the three-alpha-helix motif in the spectrin superfamily of proteins.

Members of the spectrin superfamily of proteins contain different numbers of homologous repeats arranged in tandem. Each of these consists of a three-alpha-helix motif, comprising two similarly and one oppositely directed alpha-helical segment joined by nonhelical linkers of characteristic length. The right-handed alpha-helices each display a heptad repeat in their amino acid sequences indicative of left-handed coiled-coil-like packing. We have calculated the potential number of inter-helix ionic interactions that specify the spatial arrangement of the helices in the motif in terms of both the handedness of helix connectivity (left or right) and the relative axial stagger between the three alpha-helices. All of the models examined were constrained to have optimal coiled-coil packing. For alpha-spectrin and alpha-actinin the results provide strong support for a left-handed connectivity of the three helices and axial repeat lengths of 5.05 and 6.24 nm, respectively. Furthermore, the axial staggers between homologous segments in the preferred models are identical. The insights provided into the topography of this widespread tertiary fold may prove of value to those concerned with the problem of de novo protein design.     

Crystal structure of cat muscle pyruvate kinase at a resolution of 2.6 A

The structure of pyruvate kinase (EC 2.7.1.40) has been determined from a 2.6 Å resolution electron density map. This map shows more detail than the previous 3.1 Å map (Stammers &; Muirhead, 1977) and has enabled a detailed chain folding to be established for two out of the three domains which make up each of the four identical subunits. A provisional chain folding has been established for the third domain. The results have been briefly reported in a previous paper (Levine et al., 1978). Details of the structure determination and a further discussion of the results are presented in this paper.Domain A (the three domains of pyruvate kinase are referred to as A, B and C) can be described in terms of a cylindrical eight-stranded parallel β sheet and an outer coaxial cylinder of eight α helices. The α helices connect adjacent strands of the β sheet. Domain B is made up of a closed anti-parallel β sheet structure. Domain C is a five-stranded β sheet of which the fourth strand is anti-parallel and the rest parallel. These strands are also interconnected by α helices.Domain A can be dissected into eight consecutive β strand—α helix units starting from the N-terminus. The arrangement of these relative to each other can be most simply described by relating them to eight planes, each at 40 ° to the cylinder axis and symmetrically placed around the cylinder. When unit 2 is aligned with one of these planes then units 1, 3, 4, 5 and 8 are also closely aligned with a plane. This analysis is also applied to triosephosphate isomerase and a strikingly similar arrangement is found. A detailed comparison of the two structures is presented. Although the lack of a chemical sequence makes it difficult to identify the amino acid residues of pyruvate kinase, side-chains are clearly visible in the map and this information is correlated with the results of previous 6 Å substrate soaking experiments and with the structure of triosephosphate isomerase. The similarities and differences are discussed in terms of similarities and differences in the reactions catalysed and also of different subunit packing.     

Secondary structure based analysis and classification of biological interfaces: identification of binding motifs in protein-protein interactions

MOTIVATION: The increasing amount of data on protein-protein interaction needs to be rationalized for deriving guidelines for the alteration or design of an interface between two proteins. RESULTS: We present a detaild structural analysis and comparison of homo- versus heterodimeric protein-protein interfaces. Regular secondary structures (helices and strands) are the main components of the former, whereas non-regular structures (turns, loops, etc.) frequently mediate interactions in the latter. Interface helices get longer with increasing interface area, but only in heterocomplexes. On average, the homodimers have longer helical segments and prominent helix-helix pairs. There is a surprising distinction in the relative orientation of interface helices, with a tendency for aligned packing in homodimers and a clear preference for packing at 90 degrees in heterodimers. Arg and the aromatic residues have a higher preference to occur in all secondary structural elements (SSEs) in the interface. Based on the dominant SSE, the interfaces have been grouped into four classes: alpha, beta, alphabeta and non-regular. Identity between protein and interface classes is the maximum for alpha proteins, but rather mediocre for the other protein classes. The interface classes of the two chains forming a heterodimer are often dissimilar. Eleven binding motifs can capture the prominent architectural features of most of the interfaces.     

Sequence and conformational preferences at termini of α‐helices in membrane proteins: Role of the helix environment

α‐helices are amongst the most common secondary structural elements seen in membrane proteins and are packed in the form of helix bundles. These α‐helices encounter varying external environments (hydrophobic, hydrophilic) that may influence the sequence preferences at their N and C‐termini. The role of the external environment in stabilization of the helix termini in membrane proteins is still unknown. Here we analyze α‐helices in a high‐resolution dataset of integral α‐helical membrane proteins and establish that their sequence and conformational preferences differ from those in globular proteins. We specifically examine these preferences at the N and C‐termini in helices initiating/terminating inside the membrane core as well as in linkers connecting these transmembrane helices. We find that the sequence preferences and structural motifs at capping (Ncap and Ccap) and near‐helical (N' and C') positions are influenced by a combination of features including the membrane environment and the innate helix initiation and termination property of residues forming structural motifs. We also find that a large number of helix termini which do not form any particular capping motif are stabilized by formation of hydrogen bonds and hydrophobic interactions contributed from the neighboring helices in the membrane protein. We further validate the sequence preferences obtained from our analysis with data from an ultradeep sequencing study that identifies evolutionarily conserved amino acids in the rat neurotensin receptor. The results from our analysis provide insights for the secondary structure prediction, modeling and design of membrane proteins. Proteins 2014; 82:3420–3436. © 2014 Wiley Periodicals, Inc.     

蛋白质折叠速率与其氨基酸序列极性的关系

以大肠杆菌60个蛋白酶以及几种常见病毒（SARS病毒、艾滋病病毒、丙型肝炎病毒及乙型肝炎病毒）各蛋白质序列中的所有α-螺旋和β-折叠片段为研究对象,计算了各片段的折叠速率和平均极性,分别在各物种的α-螺旋和β-折叠两类二级结构片段中分析了二者的相关性。得到结论：不论是大肠杆菌中的蛋白酶还是病毒蛋白,其中的两类氨基酸片段的平均极性与折叠速率都是极显著相关的：对于所有的α片段,二者呈线性正相关,而对于所有的β片段,二者成线性负相关。结果证实了在蛋白质折叠中,氨基酸的极性起着重要的作用。     

How the folding rates of two‐ and multistate proteins depend on the amino acid properties

Proteins fold by either two‐state or multistate kinetic mechanism. We observe that amino acids play different roles in different mechanism. Many residues that are easy to form regular secondary structures (α helices, β sheets and turns) can promote the two‐state folding reactions of small proteins. Most of hydrophilic residues can speed up the multistate folding reactions of large proteins. Folding rates of large proteins are equally responsive to the flexibility of partial amino acids. Other properties of amino acids (including volume, polarity, accessible surface, exposure degree, isoelectric point, and phase transfer energy) have contributed little to folding kinetics of the proteins. Cysteine is a special residue, it triggers two‐state folding reaction and but inhibits multistate folding reaction. These findings not only provide a new insight into protein structure prediction, but also could be used to direct the point mutations that can change folding rate. Proteins 2014; 82:2375–2382. © 2014 Wiley Periodicals, Inc.