Recognition of related proteins by iterative template refinement (ITR).

Predicting the structural fold of a protein is an important and challenging problem. Available computer programs for determining whether a protein sequence is compatible with a known 3-dimensional structure fall into 2 categories: (1) structure-based methods, in which structural features such as local conformation and solvent accessibility are encoded in a template, and (2) sequence-based methods, in which aligned sequences of a set of related proteins are encoded in a template. In both cases, the programs use a static template based on a predetermined set of proteins. Here, we describe a computer-based method, called iterative template refinement (ITR), that uses templates combining structure-based and sequence-based information and employs an iterative search procedure to detect related proteins and sequentially add them to the templates. Starting from a single protein of known structure, ITR performs sequential cycles of database search to construct an expanding tree of templates with the aim of identifying subtle relationships among proteins. Evaluating the performance of ITR on 6 proteins, we found that the method automatically identified a variety of subtle structural similarities to other proteins. For example, the method identified structural similarity between arabinose-binding protein and phosphofructokinase, a relationship that has not been widely recognized.     

Theoretical model of restriction endonuclease HpaI in complex with DNA, predicted by fold recognition and validated by site-directed mutagenesis

Type II restriction enzymes are commercially important deoxyribonucleases and very attractive targets for protein engineering of new specificities. At the same time they are a very challenging test bed for protein structure prediction methods. Typically, enzymes that recognize different sequences show little or no amino acid sequence similarity to each other and to other proteins. Based on crystallographic analyses that revealed the same PD-(D/E)XK fold for more than a dozen case studies, they were nevertheless considered to be related until the combination of bioinformatics and mutational analyses has demonstrated that some of these proteins belong to other, unrelated folds PLD, HNH, and GIY-YIG. As a part of a large-scale project aiming at identification of a three-dimensional fold for all type II REases with known sequences (currently approximately 1000 proteins), we carried out preliminary structure prediction and selected candidates for experimental validation. Here, we present the analysis of HpaI REase, an ORFan with no detectable homologs, for which we detected a structural template by protein fold recognition, constructed a model using the FRankenstein monster approach and identified a number of residues important for the DNA binding and catalysis. These predictions were confirmed by site-directed mutagenesis and in vitro analysis of the mutant proteins. The experimentally validated model of HpaI will serve as a low-resolution structural platform for evolutionary considerations in the subgroup of blunt-cutting REases with different specificities. The research protocol developed in the course of this work represents a streamlined version of the previously used techniques and can be used in a high-throughput fashion to build and validate models for other enzymes, especially ORFans that exhibit no sequence similarity to any other protein in the database.     

FORESST: fold recognition from secondary structure predictions of proteins

MOTIVATION: A method for recognizing the three-dimensional fold from the protein amino acid sequence based on a combination of hidden Markov models (HMMs) and secondary structure prediction was recently developed for proteins in the Mainly-Alpha structural class. Here, this methodology is extended to Mainly-Beta and Alpha-Beta class proteins. Compared to other fold recognition methods based on HMMs, this approach is novel in that only secondary structure information is used. Each HMM is trained from known secondary structure sequences of proteins having a similar fold. Secondary structure prediction is performed for the amino acid sequence of a query protein. The predicted fold of a query protein is the fold described by the model fitting the predicted sequence the best. RESULTS: After model cross-validation, the success rate on 44 test proteins covering the three structural classes was found to be 59%. On seven fold predictions performed prior to the publication of experimental structure, the success rate was 71%. In conclusion, this approach manages to capture important information about the fold of a protein embedded in the length and arrangement of the predicted helices, strands and coils along the polypeptide chain. When a more extensive library of HMMs representing the universe of known structural families is available (work in progress), the program will allow rapid screening of genomic databases and sequence annotation when fold similarity is not detectable from the amino acid sequence. AVAILABILITY: FORESST web server at http://absalpha.dcrt.nih.gov:8008/ for the library of HMMs of structural families used in this paper. FORESST web server at http://www.tigr.org/ for a more extensive library of HMMs (work in progress). CONTACT: valedf@tigr.org; munson@helix.nih.gov; garnier@helix.nih.gov     

Aromatic claw: A new fold with high aromatic content that evades structural prediction

We determined the NMR structure of a highly aromatic (13%) protein of unknown function, Aq1974 from Aquifex aeolicus (PDB ID: 5SYQ). The unusual sequence of this protein has a tryptophan content five times the normal (six tryptophan residues of 114 or 5.2% while the average tryptophan content is 1.0%) with the tryptophans occurring in a WXW motif. It has no detectable sequence homology with known protein structures. Although its NMR spectrum suggested that the protein was rich in β‐sheet, upon resonance assignment and solution structure determination, the protein was found to be primarily α‐helical with a small two‐stranded β‐sheet with a novel fold that we have termed an Aromatic Claw. As this fold was previously unknown and the sequence unique, we submitted the sequence to CASP10 as a target for blind structural prediction. At the end of the competition, the sequence was classified a hard template based model; the structural relationship between the template and the experimental structure was small and the predictions all failed to predict the structure. CSRosetta was found to predict the secondary structure and its packing; however, it was found that there was little correlation between CSRosetta score and the RMSD between the CSRosetta structure and the NMR determined one. This work demonstrates that even in relatively small proteins, we do not yet have the capacity to accurately predict the fold for all primary sequences. The experimental discovery of new folds helps guide the improvement of structural prediction methods.     

Fold recognition and accurate sequence-structure alignment of sequences directing beta-sheet proteins

The ability to predict structure from sequence is particularly important for toxins, virulence factors, allergens, cytokines, and other proteins of public health importance. Many such functions are represented in the parallel beta-helix and beta-trefoil families. A method using pairwise beta-strand interaction probabilities coupled with evolutionary information represented by sequence profiles is developed to tackle these problems for the beta-helix and beta-trefoil folds. The algorithm BetaWrapPro employs a "wrapping" component that may capture folding processes with an initiation stage followed by processive interaction of the sequence with the already-formed motifs. BetaWrapPro outperforms all previous motif recognition programs for these folds, recognizing the beta-helix with 100% sensitivity and 99.7% specificity and the beta-trefoil with 100% sensitivity and 92.5% specificity, in crossvalidation on a database of all nonredundant known positive and negative examples of these fold classes in the PDB. It additionally aligns 88% of residues for the beta-helices and 86% for the beta-trefoils accurately (within four residues of the exact position) to the structural template, which is then used with the side-chain packing program SCWRL to produce 3D structure predictions. One striking result has been the prediction of an unexpected parallel beta-helix structure for a pollen allergen, and its recent confirmation through solution of its structure. A Web server running BetaWrapPro is available and outputs putative PDB-style coordinates for sequences predicted to form the target folds.     

A distant evolutionary relationship between bacterial sphingomyelinase and mammalian DNase I.

The three-dimensional structure of bacterial sphingomyelinase (SMase) was predicted using a protein fold recognition method; the search of a library of known structures showed that the SMase sequence is highly compatible with the mammalian DNase I structure, which suggested that SMase adopts a structure similar to that of DNase I. The amino acid sequence alignment based on the prediction revealed that, despite the lack of overall sequence similarity (less than 10% identity), those residues of DNase I that are involved in the hydrolysis of the phosphodiester bond, including two histidine residues (His 134 and His 252) of the active center, are conserved in SMase. In addition, a conserved pentapeptide sequence motif was found, which includes two catalytically critical residues, Asp 251 and His 252. A sequence database search showed that the motif is highly specific to mammalian DNase I and bacterial SMase. The functional roles of SMase residues identified by the sequence comparison were consistent with the results from mutant studies. Two Bacillus cereus SMase mutants (H134A and H252A) were constructed by site-directed mutagenesis. They completely abolished their catalytic activity. A model for the SMase-sphingomyelin complex structure was built to investigate how the SMase specifically recognizes its substrate. The model suggested that a set of residues conserved among bacterial SMases, including Trp 28 and Phe 55, might be important in the substrate recognition. The predicted structural similarity and the conservation of the functionally important residues strongly suggest a distant evolutionary relationship between bacterial SMase and mammalian DNase I. These two phosphodiesterases must have acquired the specificity for different substrates in the course of evolution.     

Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment‐based local and energy‐based nonlocal profiles

Locating sequences compatible with a protein structural fold is the well‐known inverse protein‐folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy‐optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment‐derived sequence profiles and structure‐derived energy profiles. SPIN improves over the fragment‐derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild‐type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single‐body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks‐lab.org . Proteins 2014; 82:2565–2573. © 2014 Wiley Periodicals, Inc.     

Immunoglobulin fold and tandem repeat structures in proteoglycan N-terminal domains and link protein

Detailed primary sequence and secondary structure analyses are reported for the hyaluronate binding region (G1 domain) and link protein of proteoglycan aggregates. These are based on six full or partial sequences from the chicken, pig, human, rat and bovine proteins. Determinations of a full pig and a partial human link protein sequence are reported in the Appendix. Five sequences at the N terminus in both proteins were compared with the structures of 11 variable immunoglobulin (Ig) fold domains for which crystal structures are available. Despite only modest sequence homology, a clear alignment could be proposed. Analysis of this shows that the equivalents of the first and second hypervariable segments are now significantly longer, and both proteins have N-terminal extensions that are up to 23 residues in length. Secondary structure predictions showed that these sequences could be identified with available crystal structures for the variable Ig fold. However the hydrophobic residues involved in interactions between the light and heavy chains in Igs are replaced by hydrophilic charged groups in both proteins. These results imply that both proteins are members of the Ig superfamily, but exhibit structural differences distinct from other members of this superfamily for which crystal structures are known. The proteoglycan tandem repeat (PTR) is a repeat of 99 residues that is found twice in the amino acid sequence of link protein and the proteoglycan G1 domain adjacent to the Ig fold, and also twice in the proteoglycan G2 domain. A total of 16 PTRs was available for analysis. Compositional analyses show that these are positively charged if these originate from link protein, and negatively charged if from the G1 or G2 domains. The 16 Robson secondary structure predictions for the PTRs were averaged to improve the statistics of the prediction, and checked by comparison with Chou-Fasman calculations. A strong alpha-helix prediction was found at residues 13 to 25, and several beta-strands were predicted. The overall content is 18% alpha-helix and 28% beta-sheet, with 44% of the remaining sequence being predicted as turns. These analyses show that both the proteoglycan G1 domain and link protein are constructed from two distinct globular components, which may provide the two functional roles of these proteins in proteoglycan aggregation.     

Analysis of the sequence and structural features of the left-handed beta-helical fold

The left-handed parallel beta-helix (LbetaH) is a structurally repetitive, highly regular, and symmetrical fold formed by coiling of elongated beta-sheets into helical "rungs." This canonical fold has recently received interest as a possible solution to the fibril structure of amyloid and as a building block of self-assembled nanotubular structures. In light of this interest, we aimed to understand the structural requirements of the LbetaH fold. We first sought to determine the sequence characteristics of the repeats by analyzing known structures to identify positional preferences of specific residues types. We then used molecular dynamics simulations to demonstrate the stabilizing effect of successive rungs and the hydrophobic core of the LbetaH. We show that a two-rung structure is the minimally stable LbetaH structure. In addition, we defined the structure-based sequence preference of the LbetaH and undertook a genome-wide sequence search to determine the prevalence of this unique protein fold. This profile-based LbetaH search algorithm predicted a large fraction of LbetaH proteins from microbial origins. However, the relative number of predicted LbetaH proteins per specie was approximately equal across the genomes from prokaryotes to eukaryotes.     

Rampant Exchange of the Structure and Function of Extramembrane Domains between Membrane and Water Soluble Proteins

Of the membrane proteins of known structure, we found that a remarkable 67% of the water soluble domains are structurally similar to water soluble proteins of known structure. Moreover, 41% of known water soluble protein structures share a domain with an already known membrane protein structure. We also found that functional residues are frequently conserved between extramembrane domains of membrane and soluble proteins that share structural similarity. These results suggest membrane and soluble proteins readily exchange domains and their attendant functionalities. The exchanges between membrane and soluble proteins are particularly frequent in eukaryotes, indicating that this is an important mechanism for increasing functional complexity. The high level of structural overlap between the two classes of proteins provides an opportunity to employ the extensive information on soluble proteins to illuminate membrane protein structure and function, for which much less is known. To this end, we employed structure guided sequence alignment to elucidate the functions of membrane proteins in the human genome. Our results bridge the gap of fold space between membrane and water soluble proteins and provide a resource for the prediction of membrane protein function. A database of predicted structural and functional relationships for proteins in the human genome is provided at sbi.postech.ac.kr/emdmp.     

Structural and functional characterization of the human DNA repair helicase XPD by comparative molecular modeling and site-directed mutagenesis of the bacterial repair protein UvrB

A molecular model for the human nucleotide excision repair protein, XPD, was developed based on the structural and functional relationship of the protein with a bacterial nucleotide excision repair (NER) protein, UvrB. Whereas XPD does not share significant sequence identity with UvrB, the proteins share seven highly conserved helicase motifs that define a common protein structural template. They also have similar functional roles in their ATPase activity and the ability to unwind DNA and verify damaged strands in the process of NER. The validity of using the crystal structure of UvrB as a template for the development of an XPD model was tested by mimicking human disease-causing mutations (XPD: R112H, D234N, R601L) in UvrB (E110R, D338N, R506A) and by mutating two highly conserved residues (XPD, His-237 and Asp-609; UvrB, H341A and D510A). The XPD structural model can be employed in understanding the molecular mechanism of XPD human disease causing mutations. The value of this XPD model demonstrates the generalized approach for the prediction of the structure of a mammalian protein based on the crystal structure of a structurally and functionally related bacterial protein sharing extremely low sequence identity (<15%).     

DPANN: improved sequence to structure alignments following fold recognition

In fold recognition (FR) a protein sequence of unknown structure is assigned to the closest known three-dimensional (3D) fold. Although FR programs can often identify among all possible folds the one a sequence adopts, they frequently fail to align the sequence to the equivalent residue positions in that fold. Such failures frustrate the next step in structure prediction, protein model building. Hence it is desirable to improve the quality of the alignments between the sequence and the identified structure. We have used artificial neural networks (ANN) to derive a substitution matrix to create alignments between a protein sequence and a protein structure through dynamic programming (DPANN: Dynamic Programming meets Artificial Neural Networks). The matrix is based on the amino acid type and the secondary structure state of each residue. In a database of protein pairs that have the same fold but lack sequences-similarity, DPANN aligns over 30% of all sequences to the paired structure, resembling closely the structural superposition of the pair. In over half of these cases the DPANN alignment is close to the structural superposition, although the initial alignment from the step of fold recognition is not close. Conversely, the alignment created during fold recognition outperforms DPANN in only 10% of all cases. Thus application of DPANN after fold recognition leads to substantial improvements in alignment accuracy, which in turn provides more useful templates for the modeling of protein structures. In the artificial case of using actual instead of predicted secondary structures for the probe protein, over 50% of the alignments are successful.     

DNABind: A hybrid algorithm for structure‐based prediction of DNA‐binding residues by combining machine learning‐ and template‐based approaches

Accurate prediction of DNA‐binding residues has become a problem of increasing importance in structural bioinformatics. Here, we presented DNABind, a novel hybrid algorithm for identifying these crucial residues by exploiting the complementarity between machine learning‐ and template‐based methods. Our machine learning‐based method was based on the probabilistic combination of a structure‐based and a sequence‐based predictor, both of which were implemented using support vector machines algorithms. The former included our well‐designed structural features, such as solvent accessibility, local geometry, topological features, and relative positions, which can effectively quantify the difference between DNA‐binding and nonbinding residues. The latter combined evolutionary conservation features with three other sequence attributes. Our template‐based method depended on structural alignment and utilized the template structure from known protein–DNA complexes to infer DNA‐binding residues. We showed that the template method had excellent performance when reliable templates were found for the query proteins but tended to be strongly influenced by the template quality as well as the conformational changes upon DNA binding. In contrast, the machine learning approach yielded better performance when high‐quality templates were not available (about 1/3 cases in our dataset) or the query protein was subject to intensive transformation changes upon DNA binding. Our extensive experiments indicated that the hybrid approach can distinctly improve the performance of the individual methods for both bound and unbound structures. DNABind also significantly outperformed the state‐of‐art algorithms by around 10% in terms of Matthews's correlation coefficient. The proposed methodology could also have wide application in various protein functional site annotations. DNABind is freely available at http://mleg.cse.sc.edu/DNABind/ . Proteins 2013; 81:1885–1899. © 2013 Wiley Periodicals, Inc.     

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.     

Structure of the adenovirus E4 Orf6 protein predicted by fold recognition and comparative protein modeling

To facilitate investigation of the molecular and biochemical functions of the adenovirus E4 Orf6 protein, we sought to derive three-dimensional structural information using computational methods, particularly threading and comparative protein modeling. The amino acid sequence of the protein was used for secondary structure and hidden Markov model (HMM) analyses, and for fold recognition by the ProCeryon program. Six alternative models were generated from the top-scoring folds identified by threading. These models were examined by 3D-1D analysis and evaluated in the light of available experimental evidence. The final model of the E4 protein derived from these and additional threading calculations was a chimera, with the tertiary structure of its C-terminal 226 residues derived from a TIM barrel template and a mainly alpha-nonbundle topology for its poorly conserved N-terminal 68 residues. To assess the accuracy of this model, additional threading calculations were performed with E4 Orf6 sequences altered as in previous experimental studies. The proposed structural model is consistent with the reported secondary structure of a functionally important C-terminal sequence and can account for the properties of proteins carrying alterations in functionally important sequences or of those that disrupt an unusual zinc-coordination motif.     

Inference of relationships in the 'twilight zone' of homology using a combination of bioinformatics and site-directed mutagenesis: a case study of restriction endonucleases Bsp6I and PvuII

Thus far, identification of functionally important residues in Type II restriction endonucleases (REases) has been difficult using conventional methods. Even though known REase structures share a fold and marginally recognizable active site, the overall sequence similarities are statistically insignificant, unless compared among proteins that recognize identical or very similar sequences. Bsp6I is a Type II REase, which recognizes the palindromic DNA sequence 5′GCNGC and cleaves between the cytosine and the unspecified nucleotide in both strands, generating a double-strand break with 5′-protruding single nucleotides. There are no solved structures of REases that recognize similar DNA targets or generate cleavage products with similar characteristics. In straightforward comparisons, the Bsp6I sequence shows no significant similarity to REases with known structures. However, using a fold-recognition approach, we have identified a remote relationship between Bsp6I and the structure of PvuII. Starting from the sequence–structure alignment between Bsp6I and PvuII, we constructed a homology model of Bsp6I and used it to predict functionally significant regions in Bsp6I. The homology model was supported by site-directed mutagenesis of residues predicted to be important for dimerization, DNA binding and catalysis. Completing the picture of sequence–structure–function relationships in protein superfamilies becomes an essential task in the age of structural genomics and our study may serve as a paradigm for future analyses of superfamilies comprising strongly diverged members with little or no sequence similarity.