Potential for assessing quality of protein structure based on contact number prediction

We developed a novel knowledge-based residue environment potential for assessing the quality of protein structures in protein structure prediction. The potential uses the contact number of residues in a protein structure and the absolute contact number of residues predicted from its amino acid sequence using a new prediction method based on a support vector regression (SVR). The contact number of an amino acid residue in a protein structure is defined by the number of residues around a given residue. First, the contact number of each residue is predicted using SVR from an amino acid sequence of a target protein. Then, the potential of the protein structure is calculated from the probability distribution of the native contact numbers corresponding to the predicted ones. The performance of this potential is compared with other score functions using decoy structures to identify both native structure from other structures and near-native structures from nonnative structures. This potential improves not only the ability to identify native structures from other structures but also the ability to discriminate near-native structures from nonnative structures.     

Three-dimensional cluster analysis identifies interfaces and functional residue clusters in proteins

Three-dimensional cluster analysis offers a method for the prediction of functional residue clusters in proteins. This method requires a representative structure and a multiple sequence alignment as input data. Individual residues are represented in terms of regional alignments that reflect both their structural environment and their evolutionary variation, as defined by the alignment of homologous sequences. From the overall (global) and the residue-specific (regional) alignments, we calculate the global and regional similarity matrices, containing scores for all pairwise sequence comparisons in the respective alignments. Comparing the matrices yields two scores for each residue. The regional conservation score (C(R)(x)) defines the conservation of each residue x and its neighbors in 3D space relative to the protein as a whole. The similarity deviation score (S(x)) detects residue clusters with sequence similarities that deviate from the similarities suggested by the full-length sequences. We evaluated 3D cluster analysis on a set of 35 families of proteins with available cocrystal structures, showing small ligand interfaces, nucleic acid interfaces and two types of protein-protein interfaces (transient and stable). We present two examples in detail: fructose-1,6-bisphosphate aldolase and the mitogen-activated protein kinase ERK2. We found that the regional conservation score (C(R)(x)) identifies functional residue clusters better than a scoring scheme that does not take 3D information into account. C(R)(x) is particularly useful for the prediction of poorly conserved, transient protein-protein interfaces. Many of the proteins studied contained residue clusters with elevated similarity deviation scores. These residue clusters correlate with specificity-conferring regions: 3D cluster analysis therefore represents an easily applied method for the prediction of functionally relevant spatial clusters of residues in proteins.     

Conformational preference functions for predicting helices in membrane proteins

A suite of FORTRAN programs, PREF, is described for calculating preference functions from the data base of known protein structures and for comparing smoothed profiles of sequence-dependent preferences in proteins of unknown structure. Amino acid preferences for a secondary structure are considered as functions of a sequence environment. Sequence environment of amino acid residue in a protein is defined as an average over some physical, chemical, or statistical property of its primary structure neighbors. The frequency distribution of sequence environments in the data base of soluble protein structures is approximately normal for each amino acid type of known secondary conformation. An analytical expression for the dependence of preferences on sequence environment is obtained after each frequency distribution is replaced by corresponding Gaussian function. The preference for the α-helical conformation increases for each amino acid type with the increase of sequence environment of buried solvent-accessible surface areas. We show that a set of preference functions based on buried surface area is useful for predicting folding motifs in α-class proteins and in integral membrane proteins. The prediction accuracy for helical residues is 79% for 5 integral membrane proteins and 74% for 11 α-class soluble proteins. Most residues found in transmembrane segments of membrane proteins with known α-helical structure are predicted to be indeed in the helical conformation because of very high middle helix preferences. Both extramembrane and transmembrane helices in the photosynthetic reaction center M and L subunits are correctly predicted. We point out in the discussion that our method of conformational preference functions can identify what physical properties of the amino acids are important in the formation of particular secondary structure elements. © 1993 John Wiley & Sons, Inc.     

A rapid method of protein structure alignment

A reduction in the time required to compare two protein structures has been achieved for a previously developed structure alignment method, by reducing the number of residue pair comparisons which must be performed between the two structures. Subsets of residue pairs are selected by an iterative procedure. Initially, selection is based on similarities in solvent accessible surface areas or torsional angles or a combination of both properties, giving subsets containing approximately 2% of the total number of residue pairs. Using these subsets, a rough comparison of the two structures is generated by the structural alignment program. The information returned from this can be used to identify more accurately topologically equivalent residues in the two proteins, thus enabling a new and much smaller subset (less than 0.2% of the total number of residue pairs) to be selected. The process of iterative refinement of the residue pair subsets is repeated once more, when in 95% of the structure comparisons tested, the correct alignment of the proteins was obtained. Times required to compare the structures using the refined subsets are insignificant compared to the initial comparison, so that considerable increases in speed are possible. The method was tested on two groups of proteins, a set of remotely related alpha/beta nucleotide proteins and the variable and constant domains of the immunoglobulins. Increases in speed ranging from 50-fold to greater than 150-fold were obtained depending on the degree of similarity of the two structures. In some comparisons the alignment was improved due to the reduction in noise obtained by comparing mainly equivalent residues.     

Fast structure alignment for protein databank searching.

A fast method is described for searching and analyzing the protein structure databank. It uses secondary structure followed by residue matching to compare protein structures and is developed from a previous structural alignment method based on dynamic programming. Linear representations of secondary structures are derived and their features compared to identify equivalent elements in two proteins. The secondary structure alignment then constrains the residue alignment, which compares only residues within aligned secondary structures and with similar buried areas and torsional angles. The initial secondary structure alignment improves accuracy and provides a means of filtering out unrelated proteins before the slower residue alignment stage. It is possible to search or sort the protein structure databank very quickly using just secondary structure comparisons. A search through 720 structures with a probe protein of 10 secondary structures required 1.7 CPU hours on a Sun 4/280. Alternatively, combined secondary structure and residue alignments, with a cutoff on the secondary structure score to remove pairs of unrelated proteins from further analysis, took 10.1 CPU hours. The method was applied in searches on different classes of proteins and to cluster a subset of the databank into structurally related groups. Relationships were consistent with known families of protein structure.     

ENVIRON: a software package to compare protein three-dimensional structures with homologous sequences using local structural motifs

This work presents a method to compare local clusters of interactingresidues as observed in a known three-dimensional protein structurewith corresponding clusters inferred from homologous proteinsequences, assuming conserved protein folding. For this purposethe local environment of a selected residue in a known proteinstructure is defined as the ensemble of amino acids in contactwith it in the folded state. Using a multiple sequence alignmentto identify corresponding residues in homologous proteins, adetailed comparison can be performed between the local environmentof a selected amino acid in the template protein structure andthe expected local environments at the sets of equivalent residues,derived from the aligned protein sequences. The comparison makesit possible to detect conserved local features such as hydrogenbonding or complementarity in residue substitution. A globalmeasure of environmental similarity is also defined, to searchfor conserved amino acid clusters subject to functional or structural constraints. The proposed approach is useful for investigatingprotein function as well as for site-directed mutagenesis experiments,where appropriate amino acid substitutions can be suggestedby observing naturally occurring protein variants.     

MINRMS: an efficient algorithm for determining protein structure similarity using root-mean-squared-distance

MOTIVATION: Existing algorithms for automated protein structure alignment generate contradictory results and are difficult to interpret. An algorithm which can provide a context for interpreting the alignment and uses a simple method to characterize protein structure similarity is needed. RESULTS: We describe a heuristic for limiting the search space for structure alignment comparisons between two proteins, and an algorithm for finding minimal root-mean-squared-distance (RMSD) alignments as a function of the number of matching residue pairs within this limited search space. Our alignment algorithm uses coordinates of alpha-carbon atoms to represent each amino acid residue and requires a total computation time of O(m(3) n(2)), where m and n denote the lengths of the protein sequences. This makes our method fast enough for comparisons of moderate-size proteins (fewer than approximately 800 residues) on current workstation-class computers and therefore addresses the need for a systematic analysis of multiple plausible shape similarities between two proteins using a widely accepted comparison metric.     

Sequence and structure conservation in a protein core

In order to study structural aspects of sequence conservation in families of homologous proteins, we have analyzed structurally aligned sequences of 585 proteins grouped into 128 homologous families. The conservation of a residue in a family is defined as the average residue similarity in a given position of aligned sequences. The residue similarities were expressed in the form of log-odd substitution tables that take into account the environments of amino acids in three-dimensional structures. The protein core is defined as those residues that have less then 7% solvent accessibility. The density of a protein core is described in terms of atom packing, which is investigated as a criterion for residue substitution and conservation. Although there is no significant correlation between sequence conservation and average atom packing around nonpolar residues such as leucine, valine and isoleucine, a significant correlation is observed for polar residues in the protein core. This may be explained by the hydrogen bonds in which polar residues are involved; the better their protection from water access the more stable should be the structure in that position. Proteins 33:358–366, 1998. © 1998 Wiley-Liss, Inc.     

Vorolign--fast structural alignment using Voronoi contacts

Vorolign, a fast and flexible structural alignment method for two or more protein structures is introduced. The method aligns protein structures using double dynamic programming and measures the similarity of two residues based on the evolutionary conservation of their corresponding Voronoi-contacts in the protein structure. This similarity function allows aligning protein structures even in cases where structural flexibilities exist. Multiple structural alignments are generated from a set of pairwise alignments using a consistency-based, progressive multiple alignment strategy. RESULTS: The performance of Vorolign is evaluated for different applications of protein structure comparison, including automatic family detection as well as pairwise and multiple structure alignment. Vorolign accurately detects the correct family, superfamily or fold of a protein with respect to the SCOP classification on a set of difficult target structures. A scan against a database of >4000 proteins takes on average 1 min per target. The performance of Vorolign in calculating pairwise and multiple alignments is found to be comparable with other pairwise and multiple protein structure alignment methods. AVAILABILITY: Vorolign is freely available for academic users as a web server at http://www.bio.ifi.lmu.de/Vorolign     

The 1.7 A crystal structure of BPI: a study of how two dissimilar amino acid sequences can adopt the same fold

We have extended the resolution of the crystal structure of human bactericidal/permeability-increasing protein (BPI) to 1.7 A. BPI has two domains with the same fold, but with little sequence similarity. To understand the similarity in structure of the two domains, we compare the corresponding residue positions in the two domains by the method of 3D-1D profiles. A 3D-1D profile is a string formed by assigning each position in the 3D structure to one of 18 environment classes. The environment classes are defined by the local secondary structure, the area of the residue which is buried from solvent, and the fraction of the area buried by polar atoms. A structural alignment between the two BPI domains was used to compare the 3D-1D environments of structurally equivalent positions. Greater than 31% of the aligned positions have conserved 3D-1D environments, but only 13% have conserved residue identities. Analysis of the 3D-1D environmentally conserved positions helps to identify pairs of residues likely to be important in conserving the fold, regardless of the residue similarity. We find examples of 3D-1D environmentally conserved positions with dissimilar residues which nevertheless play similar structural roles. To generalize our findings, we analyzed four other proteins with similar structures yet dissimilar sequences. Together, these examples show that aligned pairs of dissimilar residues often share similar structural roles, stabilizing dissimilar sequences in the same fold.     

距离约束的HMC采样算法在蛋白质结构预测中的运用

蛋白质结构预测中,采样是指在构象空间中生成具有最小自由能的状态。传统的采样方法是对自由度直接赋值。这种方法在处理较少的残基时能取得好的效果。但是对于包含100个残基以上的蛋白质结构,由于构象空间的急剧增长,难以得到理想的结构。本文引入深度学习中的HMC(Hybrid Monte Carlo)采样方法,以概率分布为依据对蛋白质的自由度进行采样,能够对包含100、200甚至更多个残基的蛋白质结构进行采样。并且,在采样的过程中加入残基间的距离约束,使得一个结构中,相对于Rosetta的ab initio最多有75%(平均40%)的残基对得到优化,满足距离约束。     

Noncoded residues as building blocks in the design of specific secondary structures: Symmetrically disubstituted glycines and β-alanine

Structural changes can be induced in a peptide by selective substitution of coded α-amino acid residues by noncoded α-amino acid residues and the consequent production of analogues with modified structure and conformational preferences. In this review article we summarize the solid state structural results and the conformational preferences of two classes of “building blocks”: (a) the linear and cyclic symmetrically α, α-disubstituted glycines in which either two identical n-alkyl groups replace the hydrogen atoms of the glycine residue or a cyclic aliphatic side-chain system is formed by linking the two α-carbon side chains, respectively; and (b) the β-alanine residue. Examples, whenever possible, of the use of these residues for the elucidation of the bioactive conformation in the appropriate biological systems will be given. © 1993 John Wiley & Sons, Inc.     

Fast detection of common geometric substructure in proteins.

We consider the problem of identifying common three-dimensional substructures between proteins. Our method is based on comparing the shape of the alpha-carbon backbone structures of the proteins in order to find three-dimensional (3D) rigid motions that bring portions of the geometric structures into correspondence. We propose a geometric representation of protein backbone chains that is compact yet allows for similarity measures that are robust against noise and outliers. This representation encodes the structure of the backbone as a sequence of unit vectors, defined by each adjacent pair of alpha-carbons. We then define a measure of the similarity of two protein structures based on the root mean squared (RMS) distance between corresponding orientation vectors of the two proteins. Our measure has several advantages over measures that are commonly used for comparing protein shapes, such as the minimum RMS distance between the 3D positions of corresponding atoms in two proteins. A key advantage is that this new measure behaves well for identifying common substructures, in contrast with position-based measures where the nonmatching portions of the structure dominate the measure. At the same time, it avoids the quadratic space and computational difficulties associated with methods based on distance matrices and contact maps. We show applications of our approach to detecting common contiguous substructures in pairs of proteins, as well as the more difficult problem of identifying common protein domains (i.e., larger substructures that are not necessarily contiguous along the protein chain).     

A new size-independent score for pairwise protein structure alignment and its application to structure classification and nucleic-acid binding prediction

A structure alignment program aligns two structures by optimizing a scoring function that measures structural similarity. It is highly desirable that such scoring function is independent of the sizes of proteins in comparison so that the significance of alignment across different sizes of the protein regions aligned is comparable. Here, we developed a new score called SP‐score that fixes the cutoff distance at 4 Å and removed the size dependence using a normalization prefactor. We further built a program called SPalign that optimizes SP‐score for structure alignment. SPalign was applied to recognize proteins within the same structure fold and having the same function of DNA or RNA binding. For fold discrimination, SPalign improves sensitivity over TMalign for the chain‐level comparison by 12% and over DALI for the domain‐level comparison by 13% at the same specificity of 99.6%. The difference between TMalign and SPalign at the chain level is due to the inability of TMalign to detect single domain similarity between multidomain proteins. For recognizing nucleic acid binding proteins, SPalign consistently improves over TMalign by 12% and DALI by 31% in average value of Mathews correlation coefficients for four datasets. SPalign with default setting is 14% faster than TMalign. SPalign is expected to be useful for function prediction and comparing structures with or without domains defined. The source code for SPalign and the server are available at http://sparks.informatics.iupui.edu . Proteins 2012;. © 2012 Wiley Periodicals, Inc.     

Stereochemical studies on cyclic peptides. IX. Conformational studies on cyclic tetrapeptides containing alternating cis and trans peptide units

Conformational analyses of cyclic tetrapeptides consisting of alternating cis and trans peptide units have been made using contact criteria and energy calculations. This study has been restricted to those structures having a symmetry element in the backbone ring, such as a twofold axis (d) or a center of inversion (i). There are five main results. (1) There are two distinct types of conformations, which are stereochemically favorable corresponding to each of twofold and inversion-symmetrical structures, designated as d₁, d₂ (for twofold symmetrical) and i₁, i₂ (for inversion-symmetrical). Among these, the i₁ type has the lowest energy when glycyl residues occur at all four α-carbon atoms. (2) With the glycyl residue at all four α-carbon atoms, methyl substitution at the cis peptide nitrogen atoms is possible in all the four types, whereas the substitution at trans peptide nitrogen atoms is possible only for the i₁ type. Thus only in the i₁ type can all the nitrogen atoms be methylated simultaneously. The conformation of the molecule in the crystal structure of cyclotetrasarcosyl belongs to the i₁ type. (3) When alanyl residues occur at all four α-carbon atoms, the possible symmetrical type is dependent on the enantiomorphic form and the actual sequence of the alanyl residues. (4) The methyl substitution at peptide nitrogen atoms for cyclic tetrapeptides having alanyl residues causes more stereochemical restriction in the allowed conformations than with glycyl residues. (5) The prolyl residue can be incorporated favorably at the cis-trans junction of both d and i types of structures. The results of the present study are compared with the data on cyclic tetrapeptides available from the crystal structure and nmr studies. The results show an overall agreement both regarding the type of symmetry and the conformational parameters.     

Structural Alphabets for Protein Structure Classification: A Comparison Study

Finding structural similarities between proteins often helps reveal shared functionality, which otherwise might not be detected by native sequence information alone. Such similarity is usually detected and quantified by protein structure alignment. Determining the optimal alignment between two protein structures, however, remains a hard problem. An alternative approach is to approximate each three-dimensional protein structure using a sequence of motifs derived from a structural alphabet. Using this approach, structure comparison is performed by comparing the corresponding motif sequences or structural sequences. In this article, we measure the performance of such alphabets in the context of the protein structure classification problem. We consider both local and global structural sequences. Each letter of a local structural sequence corresponds to the best matching fragment to the corresponding local segment of the protein structure. The global structural sequence is designed to generate the best possible complete chain that matches the full protein structure. We use an alphabet of 20 letters, corresponding to a library of 20 motifs or protein fragments having four residues. We show that the global structural sequences approximate well the native structures of proteins, with an average coordinate root mean square of 0.69 Å over 2225 test proteins. The approximation is best for all α-proteins, while relatively poorer for all β-proteins. We then test the performance of four different sequence representations of proteins (their native sequence, the sequence of their secondary-structure elements, and the local and global structural sequences based on our fragment library) with different classifiers in their ability to classify proteins that belong to five distinct folds of CATH. Without surprise, the primary sequence alone performs poorly as a structure classifier. We show that addition of either secondary-structure information or local information from the structural sequence considerably improves the classification accuracy. The two fragment-based sequences perform better than the secondary-structure sequence but not well enough at this stage to be a viable alternative to more computationally intensive methods based on protein structure alignment.     

Crystal structures of mutant ribosomal proteins L1

Nine mutant ribosomal proteins L1 from the bacterium Thermus thermophilus and archaeon Methanococcus jannaschii were obtained and their crystal structures were determined and analyzed. The structure of the S179C TthL1 mutant, determined earlier, was also analyzed. In half of the proteins studied, point mutations of the amino acid residues exposed on the protein surface essentially changed the spatial structure of the protein. This proves that a correct study of biological processes with the help of site-directed mutagenesis requires a preliminary determination or, at least, modeling of the structures of mutant proteins. A detailed comparison of the structures of the L1 mutants and the corresponding wild-type L1 proteins demonstrated that the side chain of a mutated amino acid residue tends to adopt a location similar to that of the side chain of the corresponding residue in the wild-type protein. This observation assists in modeling the structure of mutant proteins.     

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.     

A simple topological representation of protein structure: implications for new, fast, and robust structural classification

A topological representation of proteins is developed that makes use of two metrics: the Euclidean metric for identifying natural nearest neighboring residues via the Delaunay tessellation in Cartesian space and the distance between residues in sequence space. Using this representation, we introduce a quantitative and computationally inexpensive method for the comparison of protein structural topology. The method ultimately results in a numerical score quantifying the distance between proteins in a heuristically defined topological space. The properties of this scoring scheme are investigated and correlated with the standard Calpha distance root-mean-square deviation measure of protein similarity calculated by rigid body structural alignment. The topological comparison method is shown to have a characteristic dependence on protein conformational differences and secondary structure. This distinctive behavior is also observed in the comparison of proteins within families of structural relatives. The ability of the comparison method to successfully classify proteins into classes, superfamilies, folds, and families that are consistent with standard classification methods, both automated and human-driven, is demonstrated. Furthermore, it is shown that the scoring method allows for a fine-grained classification on the family, protein, and species level that agrees very well with currently established phylogenetic hierarchies. This fine classification is achieved without requiring visual inspection of proteins, sequence analysis, or the use of structural superimposition methods. Implications of the method for a fast, automated, topological hierarchical classification of proteins are discussed.     

Identification of local conformational similarity in structurally variable regions of homologous proteins using protein blocks

Structure comparison tools can be used to align related protein structures to identify structurally conserved and variable regions and to infer functional and evolutionary relationships. While the conserved regions often superimpose well, the variable regions appear non superimposable. Differences in homologous protein structures are thought to be due to evolutionary plasticity to accommodate diverged sequences during evolution. One of the kinds of differences between 3-D structures of homologous proteins is rigid body displacement. A glaring example is not well superimposed equivalent regions of homologous proteins corresponding to α-helical conformation with different spatial orientations. In a rigid body superimposition, these regions would appear variable although they may contain local similarity. Also, due to high spatial deviation in the variable region, one-to-one correspondence at the residue level cannot be determined accurately. Another kind of difference is conformational variability and the most common example is topologically equivalent loops of two homologues but with different conformations. In the current study, we present a refined view of the "structurally variable" regions which may contain local similarity obscured in global alignment of homologous protein structures. As structural alphabet is able to describe local structures of proteins precisely through Protein Blocks approach, conformational similarity has been identified in a substantial number of 'variable' regions in a large data set of protein structural alignments; optimal residue-residue equivalences could be achieved on the basis of Protein Blocks which led to improved local alignments. Also, through an example, we have demonstrated how the additional information on local backbone structures through protein blocks can aid in comparative modeling of a loop region. In addition, understanding on sequence-structure relationships can be enhanced through our approach. This has been illustrated through examples where the equivalent regions in homologous protein structures share sequence similarity to varied extent but do not preserve local structure.