Antibody modeling assessment

A blinded study to assess the state of the art in three‐dimensional structure modeling of the variable region (Fv) of antibodies was conducted. Nine unpublished high‐resolution x‐ray Fab crystal structures covering a wide range of antigen‐binding site conformations were used as benchmark to compare Fv models generated by four structure prediction methodologies. The methodologies included two homology modeling strategies independently developed by CCG (Chemical Computer Group) and Accerlys Inc, and two fully automated antibody modeling servers: PIGS (Prediction of ImmunoGlobulin Structure), based on the canonical structure model, and Rosetta Antibody Modeling, based on homology modeling and Rosetta structure prediction methodology. The benchmark structure sequences were submitted to Accelrys and CCG and a set of models for each of the nine antibody structures were generated. PIGS and Rosetta models were obtained using the default parameters of the servers. In most cases, we found good agreement between the models and x‐ray structures. The average rmsd (root mean square deviation) values calculated over the backbone atoms between the models and structures were fairly consistent, around 1.2 Å. Average rmsd values of the framework and hypervariable loops with canonical structures (L1, L2, L3, H1, and H2) were close to 1.0 Å. H3 prediction yielded rmsd values around 3.0 Å for most of the models. Quality assessment of the models and the relative strengths and weaknesses of the methods are discussed. We hope this initiative will serve as a model of scientific partnership and look forward to future antibody modeling assessments. Proteins 2011; © 2011 Wiley‐Liss, Inc.     

Selection of a representative set of structures from Brookhaven Protein Data Bank.

Reliable structural and statistical analyses of three dimensional protein structures should be based on unbiased data. The Protein Data Bank is highly redundant, containing several entries for identical or very similar sequences. A technique was developed for clustering the known structures based on their sequences and contents of alpha- and beta-structures. First, sequences were aligned pairwise. A representative sample of sequences was then obtained by grouping similar sequences together, and selecting a typical representative from each group. The similarity significance threshold needed in the clustering method was found by analyzing similarities of random sequences. Because three dimensional structures for proteins of same structural class are generally more conserved than their sequences, the proteins were clustered also according to their contents of secondary structural elements. The results of these clusterings indicate conservation of alpha- and beta-structures even when sequence similarity is relatively low. An unbiased sample of 103 high resolution structures, representing a wide variety of proteins, was chosen based on the suggestions made by the clustering algorithm. The proteins were divided into structural classes according to their contents and ratios of secondary structural elements. Previous classifications have suffered from subjective view of secondary structures, whereas here the classification was based on backbone geometry. The concise view lead to reclassification of some structures. The representative set of structures facilitates unbiased analyses of relationships between protein sequence, function, and structure as well as of structural characteristics.     

Fold change in evolution of protein structures

Typically, protein spatial structures are more conserved in evolution than amino acid sequences. However, the recent explosion of sequence and structure information accompanied by the development of powerful computational methods led to the accumulation of examples of homologous proteins with globally distinct structures. Significant sequence conservation, local structural resemblance, and functional similarity strongly indicate evolutionary relationships between these proteins despite pronounced structural differences at the fold level. Several mechanisms such as insertions/deletions/substitutions, circular permutations, and rearrangements in beta-sheet topologies account for the majority of detected structural irregularities. The existence of evolutionarily related proteins that possess different folds brings new challenges to the homology modeling techniques and the structure classification strategies and offers new opportunities for protein design in experimental studies.     

Nonlinear methods in the analysis of protein sequences: a case study in rubredoxins

Two computational methods widely used in time series analysis were applied to protein sequences, and their ability to derive structural information not directly accessible through classical sequence comparisons methods was assessed. The primary structures of 19 rubredoxins of both mesophilic and thermophilic bacteria, coded with hydrophobicity values of amino acid residues, were considered as time series and were analyzed by 1) recurrence quantification analysis and 2) spectral analysis of the sequence major eigenfunctions. The results of the two methods agreed to a large extent and generated a classification consistent with known 3D structural characteristics of the studied proteins. This classification separated in a clearcut manner a thermophilic protein from mesophilic proteins. The classification of primary structures given by the two dynamical methods was demonstrated to be basically different from classification stemming from classical sequence homology metrics. Moreover, on a more detailed scale, the method was able to discriminate between thermophilic and mesophilic proteins from a set of chimeric sequences generated from the mixing of a mesophilic (Rubr Clopa) and a thermophilic (Rubr Pyrfu) protein. Overall, our results point to a new way of looking at protein sequence comparisons.     

Database of homology-derived protein structures and the structural meaning of sequence alignment

The database of known protein three-dimensional structures can be significantly increased by the use of sequence homology, based on the following observations. (1) The database of known sequences, currently at more than 12,000 proteins, is two orders of magnitude larger than the database of known structures. (2) The currently most powerful method of predicting protein structures is model building by homology. (3) Structural homology can be inferred from the level of sequence similarity. (4) The threshold of sequence similarity sufficient for structural homology depends strongly on the length of the alignment. Here, we first quantify the relation between sequence similarity, structure similarity, and alignment length by an exhaustive survey of alignments between proteins of known structure and report a homology threshold curve as a function of alignment length. We then produce a database of homology-derived secondary structure of proteins (HSSP) by aligning to each protein of known structure all sequences deemed homologous on the basis of the threshold curve. For each known protein structure, the derived database contains the aligned sequences, secondary structure, sequence variability, and sequence profile. Tertiary structures of the aligned sequences are implied, but not modeled explicitly. The database effectively increases the number of known protein structures by a factor of five to more than 1800. The results may be useful in assessing the structural significance of matches in sequence database searches, in deriving preferences and patterns for structure prediction, in elucidating the structural role of conserved residues, and in modeling three-dimensional detail by homology.     

Remote homolog detection using local sequence-structure correlations

Remote homology detection refers to the detection of structural homology in proteins when there is little or no sequence similarity. In this article, we present a remote homolog detection method called SVM-HMMSTR that overcomes the reliance on detectable sequence similarity by transforming the sequences into strings of hidden Markov states that represent local folding motif patterns. These state strings are transformed into fixed-dimension feature vectors for input to a support vector machine. Two sets of features are defined: an order-independent feature set that captures the amino acid and local structure composition; and an order-dependent feature set that captures the sequential ordering of the local structures. Tests using the Structural Classification of Proteins (SCOP) 1.53 data set show that the SVM-HMMSTR gives a significant improvement over several current methods.     

Never born proteins as a test case for ab initio protein structures prediction

The number of natural proteins although large is significantly smaller than the theoretical number of proteins that can be obtained combining the 20 natural amino acids, the so-called “never born proteins” (NBPs). The study of the structure and properties of these proteins allows to investigate the sources of the natural proteins being of unique characteristics or special properties. However the structural study of NPBs can also been intended as an ideal test for evaluating the efficiency of software packages for the ab initio protein structure prediction. In this research, 10.000 three-dimensional structures of proteins of completely random sequence generated according to ROSETTA and FOD model were compared. The results show the limits of these software packages, but at the same time indicate that in many cases there is a significant agreement between the prediction obtained.     

Contingency Table Browser ? prediction of early stage protein structure

The Early Stage (ES) intermediate represents the starting structure in protein folding simulations based on the Fuzzy Oil Drop (FOD) model. The accuracy of FOD predictions is greatly dependent on the accuracy of the chosen intermediate. A suitable intermediate can be constructed using the sequence-structure relationship information contained in the so-called contingency table − this table expresses the likelihood of encountering various structural motifs for each tetrapeptide fragment in the amino acid sequence. The limited accuracy with which such structures could previously be predicted provided the motivation for a more indepth study of the contingency table itself. The Contingency Table Browser is a tool which can visualize, search and analyze the table. Our work presents possible applications of Contingency Table Browser, among them − analysis of specific protein sequences from the point of view of their structural ambiguity.     

Nonlinearities in protein space limit the utility of informatics in protein biophysics

We examine the utility of informatic‐based methods in computational protein biophysics. To do so, we use newly developed metric functions to define completely independent sequence and structure spaces for a large database of proteins. By investigating the relationship between these spaces, we demonstrate quantitatively the limits of knowledge‐based correlation between the sequences and structures of proteins. It is shown that there are well‐defined, nonlinear regions of protein space in which dissimilar structures map onto similar sequences (the conformational switch), and dissimilar sequences map onto similar structures (remote homology). These nonlinearities are shown to be quite common—almost half the proteins in our database fall into one or the other of these two regions. They are not anomalies, but rather intrinsic properties of structural encoding in amino acid sequences. It follows that extreme care must be exercised in using bioinformatic data as a basis for computational structure prediction. The implications of these results for protein evolution are examined. Proteins 2015; 83:1923–1928. © 2015 Wiley Periodicals, Inc.     

Structure- and sequence-based function prediction for non-homologous proteins

The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.     

Molecular Characterization of Extrachromosomal DNA Accompanying Primula Red Phytoplasma

The complete nucleotide sequence of an extrachromosomal element found in primula red isolate of ‘Candidatus Phytoplasma asteris’ (16SrI‐B subgroup) was determined. The plasmid, named pPrR, is 4378 bp in length and has 75% A+T content that is similar to that of the phytoplasma genome. It encodes six putative open reading frames (ORF) longer than 100 amino acids and two smaller ones. The structural organization of the rep gene is similar to that found in plasmids which replicate via rolling circle mechanism. Furthermore, it has homology to both the plasmid pLS1 family and helicase domains of replication‐associated proteins (Rap) of eukaryotic viruses and geminiviruses. The ORF arrangement and genes sequences are most similar to the pPARG1 plasmid from ‘Rehmannia glutinosa’ phytoplasma.     

用距离几何算法和同源建模法对水稻类金属硫蛋白（rgMT）的三维结构建模

水稻类金属硫蛋白(rgMT)的两端是高度保守的半胱氨酸富含区的结构域(CR区),中间是不含半胱氨酸的间隔区,呈典型的三段式结构。本研究分别采用距离几何算法和同源建模相结合的方法对水稻类金属硫蛋白进行三级结构建模。在排列出CR区的所有可能的半胱氨酸-金属硫络合的组合方式,并对每一种组合方式给出一定的限制条件后各生成20个随机构象。根据生成的随机构象足否能形成金属硫络合结构,从900个随机构象中最终选出6个构象(N端4种,C端2种组合)作为可能的结构模型。另一方面,采用GOR方法对间隔区进行了二级结构预测,随后用同源建模法对其建模。将上述建成的三部分模型连接起来后形成rgMT的整体三维构象。结果表明rgMT能像哺乳动物MT蛋白一样,可形成两个独立的、在结构和能量上均没有障碍的金属-硫络合结构。介于所有植物类金属硫蛋白都具有典型的三段式结构,其中的一部分还具有与rgMT相同的半胱氨酸排列方式,所以rgMT三维结构模型的建立对于其他植物类金属硫蛋白的结构研究具有重要的参考价值。     

Homology modeling of human Toll‐like receptors TLR7, 8, and 9 ligand‐binding domains

Toll‐like receptors (TLRs) play a key role in the innate immune system. The TLR7, 8, and 9 compose a family of intracellularly localized TLRs that signal in response to pathogen‐derived nucleic acids. So far, there are no crystallographic structures for TLR7, 8, and 9. For this reason, their ligand‐binding mechanisms are poorly understood. To enable first predictions of the receptor–ligand interaction sites, we developed three‐dimensional structures for the leucine‐rich repeat ectodomains of human TLR7, 8, and 9 based on homology modeling. To achieve a high sequence similarity between targets and templates, structural segments from all known TLR ectodomain structures (human TLR1/2/3/4 and mouse TLR3/4) were used as candidate templates for the modeling. The resulting models support previously reported essential ligand‐binding residues. They also provide a basis to identify three potential receptor dimerization mechanisms. Additionally, potential ligand‐binding residues are identified using combined procedures. We suggest further investigations of these residues through mutation experiments. Our modeling approach can be extended to other members of the TLR family or other repetitive proteins.     

用距离几何算法和同源建模法对水稻类金属硫蛋白(rgMT)的三维结构建模

水稻类金属硫蛋白(rgMT)的两端是高度保守的半胱氨酸富含区的结构域(CR区),中间是不含半胱氨酸的间隔区,呈典型的三段式结构.本研究分别采用距离几何算法和同源建模相结合的方法对水稻类金属硫蛋白进行三级结构建模.在排列出CR区的所有可能的半胱氨酸-金属硫络合的组合方式,并对每一种组合方式给出一定的限制条件后各生成20个随机构象.根据生成的随机构象是否能形成金属硫络合结构,从900个随机构象中最终选出6个构象(N端4种,C端2种组合)作为可能的结构模型.另一方面,采用GOR方法对间隔区进行了二级结构预测,随后用同源建模法对其建模.将上述建成的三部分模型连接起来后形成rgMT的整体三维构象.结果表明rgMT能像哺乳动物MT蛋白一样,可形成两个独立的、在结构和能量上均没有障碍的金属-硫络合结构.介于所有植物类金属硫蛋白都具有典型的三段式结构,其中的一部分还具有与rgMT相同的半胱氨酸排列方式,所以rgMT三维结构模型的建立对于其他植物类金属硫蛋白的结构研究具有重要的参考价值.     

Efficient identification of near‐native conformations in ab initio protein structure prediction using structural profiles

One of the major bottlenecks in many ab initio protein structure prediction methods is currently the selection of a small number of candidate structures for high‐resolution refinement from large sets of low‐resolution decoys. This step often includes a scoring by low‐resolution energy functions and a clustering of conformations by their pairwise root mean square deviations (RMSDs). As an efficient selection is crucial to reduce the overall computational cost of the predictions, any improvement in this direction can increase the overall performance of the predictions and the range of protein structures that can be predicted. We show here that the use of structural profiles, which can be predicted with good accuracy from the amino acid sequences of proteins, provides an efficient means to identify good candidate structures. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Heterogeneity and a Coiled Coil Prediction of Trypanosomatid-Like Flagellar Rod Proteins in Euglena

The emergent flagellum of euglenoids and trypanosomatids contained in addition to microtubules a prominent filamentous structure—the flagellar rod (paraflageliar/paraxonemal rod). Immunoblots and immunofluorescence localization using three antibodies generated against gel-isolated proteins confirmed previous studies that the Euglena flagellar rod consisted of polypeptides migrating at 66-, 69-, and 75-kD. Immunoblotting after two dimensional gel electrophoresis identified ten or more isoforms of these polypeptides. Differences in migration in acrylamide gels under nonreducing and reducing conditions suggested that the rod proteins contain intramolecular disulfide linkages. Comparative peptide mapping showed that the 66-. 69-, and 75-kD polypeptides were distinct, but related proteins, and also identified a fourth related protein migrating at 64-kD. Using antibodies against rod proteins, two overlapping cDNAs were isolated and from their sequences the cDNAs were predicted to encode 334 amino acids of the 66-kD protein: the amino acid sequence had >65% identity to the carboxyl-terminus of the trypanosomatid flagellar rod proteins. Secondary structural prediction suggested that flagellar rod proteins contain an extended segmented coiled coil stalk and two nonhelical heads. Coiled coil appeared to be an important structural motif in the construction of flagellar rod filaments.     

Targeting novel folds for structural genomics

The ultimate goal of structural genomics is to obtain the structure of each protein coded by each gene within a genome to determine gene function. Because of cost and time limitations, it remains impractical to solve the structure for every gene product experimentally. Up to a point, reasonably accurate three‐dimensional structures can be deduced for proteins with homologous sequences by using comparative modeling. Beyond this, fold recognition or threading methods can be used for proteins showing little homology to any known fold, although this is relatively time‐consuming and limited by the library of template folds currently available. Therefore, it is appropriate to develop methods that can increase our knowledge base, expanding our fold libraries by earmarking potentially “novel” folds for experimental structure determination. How can we sift through proteomic data rapidly and yet reliably identify novel folds as targets for structural genomics? We have analyzed a number of simple methods that discriminate between “novel” and “known” folds. We propose that simple alignments of secondary structure elements using predicted secondary structure could potentially be a more selective method than both a simple fold recognition method (GenTHREADER) and standard sequence alignment at finding novel folds when sequences show no detectable homology to proteins with known structures. Proteins 2002;48:44–52. © 2002 Wiley‐Liss, Inc.     

Fold homology detection using sequence fragment composition profiles of proteins

The effectiveness of sequence alignment in detecting structural homology among protein sequences decreases markedly when pairwise sequence identity is low (the so‐called “twilight zone” problem of sequence alignment). Alternative sequence comparison strategies able to detect structural kinship among highly divergent sequences are necessary to address this need. Among them are alignment‐free methods, which use global sequence properties (such as amino acid composition) to identify structural homology in a rapid and straightforward way. We explore the viability of using tetramer sequence fragment composition profiles in finding structural relationships that lie undetected by traditional alignment. We establish a strategy to recast any given protein sequence into a tetramer sequence fragment composition profile, using a series of amino acid clustering steps that have been optimized for mutual information. Our method has the effect of compressing the set of 160,000 unique tetramers (if using the 20‐letter amino acid alphabet) into a more tractable number of reduced tetramers (～15–30), so that a meaningful tetramer composition profile can be constructed. We test remote homology detection at the topology and fold superfamily levels using a comprehensive set of fold homologs, culled from the CATH database that share low pairwise sequence similarity. Using the receiver‐operating characteristic measure, we demonstrate potentially significant improvement in using information‐optimized reduced tetramer composition, over methods relying only on the raw amino acid composition or on traditional sequence alignment, in homology detection at or below the “twilight zone”. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

人白介素15及突变体的分子设计

以人白介素－２晶体结构为模板同源模型建人白介素１５及其两株突变体（Ｎ端缺失４个氨基酸、Ｃ端缺失３个氨基酸）的空间构象。在ＣＶＦＦ力场下,经过分子力学优化、常温分子动力学模拟获得稳定立体结构模型。借助空间构象残基的亲疏水性分析,利用Ｄｅｌｐｈｉ一性分析蛋白表达静电分布,进行从理论上预测人白介素１５及两株突变体生物学功能的性、差异性。结合分子生物实验,在ＰＢＶ２２０载体中克隆表达获得人白介素１５及两载     

Protein structural similarities predicted by a sequence-structure compatibility method.

A method for protein structure prediction has been developed, which evaluates the compatibility of an amino acid sequence with known 3-dimensional structures and identifies the most likely structure. The method was applied to a large number of sequences in a database, and the structures of the following proteins were predicted: (1) shikimate kinase (SKase), (2) the hydrophilic subunit of mannose permease (IIABMan), (3) rat tyrosine aminotransferase (Tyr AT), and (4) threonine dehydratase (TDH). The functional and evolutionary implications of the predictions are discussed. (1) The structural similarity between SKase and adenylate kinase was predicted. Alignment of their sequences reveals that the ATP-binding type A sequence motif and 2 ATP-binding arginine residues are conserved. The prediction suggests a similarity in their functional mechanisms as well as an evolutionary relationship. (2) The structural similarity between IIABMan and galactose/glucose-binding protein (GGBP) was predicted. The IIA and IIB domains are aligned with the N- and C-terminal domains of GGBP, respectively. The 2 phosphorylated residues, His 10 and His 175, of IIABMan are threaded onto loops located in the substrate-binding cleft of GGBP. The prediction accounts for the phosphoryl transfer from His 10 to His 175, and to the sugar substrate. (3) The structural similarity between rat Tyr AT and Escherichia coli aspartate AT was predicted, as well as (4) the structural similarity between TDH and the tryptophan synthase beta subunit. Predictions (3) and (4) support the previous predictions based on observations of the functional similarities between the proteins.