Successful protein fold recognition by optimal sequence threading validated by rigorous blind testing

Analysis of the results of the recent protein structure prediction experiment for our method shows that we achieved a high level of success, Of the 18 available prediction targets of known structure, the assessors have identified 11 chains which either entirely match a previously known fold, or which partially match a substantial region of a known fold. Of these 11 chains, we made predictions for 9, and correctly assigned the folds in 5 cases. We have also identified a further 2 chains which also partially match known folds, and both of these were correctly predicted. The success rate for our method under blind testing is therefore 7 out of 11 chains. A further 2 folds could have easily been recognized but failed due to either overzealous filtering of potential matches, or to simple human error on our part. One of the two targets for which we did not submit a prediction, prosubtilisin, would not have been recognized by our usual criteria, but even in this case, it is possible that a correct prediction could have been made by considerin a combination of pairwise energy and solvation energy Z-scores. Inspection of the threading alignments for the (αβ)₈ barrels provides clues as to how fold recognition by threading works, in that these folds are recognized by parts rather than as a whole. The prospects for developing sequence threading technology further is discussed. © 1995 Wiley-Liss, Inc.     

Assessment of a protein fold recognition method that takes into account four physicochemical properties: Side-chain packing,solvation, hydrogen-bonding,and local conformation

A protein fold recognition method was tested by the blind prediction of the structures of a set of proteins. The method evaluates the compatibility of an amino acid sequence with a three-dimensional structure using the four evaluation functions: side-chain packing, solvation, hydrogen-bonding, and local conformation functions. The structures of 14 proteins containing 19 sequences were predicted. The predictions were compared with the experimental structures. The experimental results showed that 9 of the 19 target sequences have known folds or portions of known folds. Among them, the folds of Klebsiella aerogenes urease β subunit (KAUB) and pyruvate phosphate dikinase domain 4 (PPDK4) were successfully recognized; our method predicted that KAUB and PPDK4 would adopt the folds of macromomycin (Ig-fold) and phosphoribosylanthra-nilate isomerase:indoleglycerol-phosphate synthase (TIM barrel), respectively, and the experimental structure revealed that they actually adopt the predicted folds. The predictions for the other targets were not successful, but they often gave secondary structural patterns similar to those of the experimental structures. © 1995 Wiley-Liss, Inc.     

Protein fold recognition using sequence-derived predictions.

In protein fold recognition, one assigns a probe amino acid sequence of unknown structure to one of a library of target 3D structures. Correct assignment depends on effective scoring of the probe sequence for its compatibility with each of the target structures. Here we show that, in addition to the amino acid sequence of the probe, sequence-derived properties of the probe sequence (such as the predicted secondary structure) are useful in fold assignment. The additional measure of compatibility between probe and target is the level of agreement between the predicted secondary structure of the probe and the known secondary structure of the target fold. That is, we recommend a sequence-structure compatibility function that combines previously developed compatibility functions (such as the 3D-1D scores of Bowie et al. [1991] or sequence-sequence replacement tables) with the predicted secondary structure of the probe sequence. The effect on fold assignment of adding predicted secondary structure is evaluated here by using a benchmark set of proteins (Fischer et al., 1996a). The 3D structures of the probe sequences of the benchmark are actually known, but are ignored by our method. The results show that the inclusion of the predicted secondary structure improves fold assignment by about 25%. The results also show that, if the true secondary structure of the probe were known, correct fold assignment would increase by an additional 8-32%. We conclude that incorporating sequence-derived predictions significantly improves assignment of sequences to known 3D folds. Finally, we apply the new method to assign folds to sequences in the SWISSPROT database; six fold assignments are given that are not detectable by standard sequence-sequence comparison methods; for two of these, the fold is known from X-ray crystallography and the fold assignment is correct.     

Protein structure prediction by threading methods: Evaluation of current techniques

This paper evaluates the results of a protein structure prediction contest. The predictions were made using threading procedures, which employ techniques for aligning sequences with 3D structures to select the correct fold of a given sequence from a set of alternatives. Nine different teams submitted 86 predictions, on a total of 21 target proteins with little or no sequence homology to proteins of known structure. The 3D structures of these proteins were newly determined by experimental methods, but not yet published or otherwise available to the predictors. The predictions, made from the amino acid sequence alone, thus represent a genuine test of the current performance of threading methods. Only a subset of all the predictions is evaluated here. It corresponds to the 44 predictions submitted for the 11 target proteins seen to adopt known folds. The predictions for the remaining 10 proteins were not analyzed, although weak similarities with known folds may also exist in these proteins. We find that threading methods are capable of identifying the correct fold in many cases, but not reliably enough as yet. Every team predicts correctly a different set of targets, with virtually all targets predicted correctly by at least one team. Also, common folds such as TIM barrels are recognized more readily than folds with only a few known examples. However, quite surprisingly, the quality of the sequence-structure alignments, corresponding to correctly recognized folds, is generally very poor, as judged by comparison with the corresponding 3D structure alignments. Thus, threading can presently not be relied upon to derive a detailed 3D model from the amino acid sequence. This raises a very intriguing question: how is fold recognition achieved? Our analysis suggests that it may be achieved because threading procedures maximize hydrophobic interactions in the protein core, and are reasonably good at recognizing local secondary structure. © 1995 Wiley-Liss, Inc.     

Helix-bundle membrane protein fold templates

In the fold recognition approach to structure prediction, a sequence is tested for compatibility with an already known fold. For membrane proteins, however, few folds have been determined experimentally. Here the feasibility of computing the vast majority of likely membrane protein folds is tested. The results indicate that conformation space can be effectively sampled for small numbers of helices. The vast majority of potential monomeric membrane protein structures can be represented by about 30-folds for three helices, but increases exponentially to about 1,500,000 folds for seven helices. The generated folds could serve as templates for fold recognition or as starting points for conformational searches that are well distributed throughout conformation space.     

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.     

Representing and comparing protein folds and fold families using three‐dimensional shape‐density representations

The question of how best to compare and classify the (three‐dimensional) structures of proteins is one of the most important unsolved problems in computational biology. To help tackle this problem, we have developed a novel shape‐density superposition algorithm called 3D‐Blast which represents and superposes the shapes of protein backbone folds using the spherical polar Fourier correlation technique originally developed by us for protein docking. The utility of this approach is compared with several well‐known protein structure alignment algorithms using receiver‐operator‐characteristic plots of queries against the “gold standard” CATH database. Despite being completely independent of protein sequences and using no information about the internal geometry of proteins, our results from searching the CATH database show that 3D‐Blast is highly competitive compared to current state‐of‐the‐art protein structure alignment algorithms. A novel and potentially very useful feature of our approach is that it allows an average or “consensus” fold to be calculated easily for a given group of protein structures. We find that using consensus shapes to represent entire fold families also gives very good database query performance. We propose that using the notion of consensus fold shapes could provide a powerful new way to index existing protein structure databases, and that it offers an objective way to cluster and classify all of the currently known folds in the protein universe. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

Predictable fold switching by the SARS‐CoV‐2 protein ORF9b

Extant fold‐switching proteins remodel their secondary structures and change their functions in response to environmental stimuli. These shapeshifting proteins regulate biological processes and are associated with a number of diseases, including tuberculosis, cancer, Alzheimer''s, and autoimmune disorders. Thus, predictive methods are needed to identify more fold‐switching proteins, especially since all naturally occurring instances have been discovered by chance. In response to this need, two high‐throughput predictive methods have recently been developed. Here we test them on ORF9b, a newly discovered fold switcher and potential therapeutic target from the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2). Promisingly, both methods correctly indicate that ORF9b switches folds. We then tested the same two methods on ORF9b1, the ORF9b homolog from SARS‐CoV‐1. Again, both methods predict that ORF9b1 switches folds, a finding consistent with experimental binding studies. Together, these results (a) demonstrate that protein fold switching can be predicted using high‐throughput computational approaches and (b) suggest that fold switching might be a general characteristic of ORF9b homologs.     

A novel member of the YchN-like fold: solution structure of the hypothetical protein Tm0979 from Thermotoga maritima

We report herein the NMR structure of Tm0979, a structural proteomics target from Thermotoga maritima. The Tm0979 fold consists of four beta/alpha units, which form a central parallel beta-sheet with strand order 1234. The first three helices pack toward one face of the sheet and the fourth helix packs against the other face. The protein forms a dimer by adjacent parallel packing of the fourth helices sandwiched between the two beta-sheets. This fold is very interesting from several points of view. First, it represents the first structure determination for the DsrH family of conserved hypothetical proteins, which are involved in oxidation of intracellular sulfur but have no defined molecular function. Based on structure and sequence analysis, possible functions are discussed. Second, the fold of Tm0979 most closely resembles YchN-like folds; however the proteins that adopt these folds differ in secondary structural elements and quaternary structure. Comparison of these proteins provides insight into possible mechanisms of evolution of quaternary structure through a simple mechanism of hydrophobicity-changing mutations of one or two residues. Third, the Tm0979 fold is found to be similar to flavodoxin-like folds and beta/alpha barrel proteins, and may provide a link between these very abundant folds and putative ancestral half-barrel proteins.     

Monte Carlo estimation of the number of possible protein folds: effects of sampling bias and folds distributions

The estimation of the number of protein folds in nature is a matter of considerable interest. In this study, a Monte Carlo method employing the broken stick model is used to assign a given number of proteins into a given number of folds. Subsequently, random, integer, non-repeating numbers are generated in order to simulate the process of fold discovery. With this conceptual framework at hand, the effects of two factors upon the fold identification process were investigated: (1) the nature of folds distributions and (2) preferential sampling bias of previously identified folds. Depending on the type of distribution, dividing 100,000 proteins into 1,000 folds resulted in 10-30% of the folds having 10 proteins or less per fold, approximately 10% of the folds having 10-20 proteins per fold, 31-45% having 20-100 proteins per fold, and >30% of the folds having more than 100 proteins per fold. After randomly sampling one tenth of the proteins, 68-96% of the folds were identified. These percentages depend both on folds distribution and biased/non-biased sampling. Only upon increasing the sampling bias for previously identified folds to 1,000, did the model result in a reduction of the number of proteins identified by an order of magnitude (approximately 9%). Thus, assuming the structures of one tenth of the population of proteins in nature have been solved, the results of the Monte Carlo simulation are more consistent with recent lower estimates of the number of folds, 相似文献   

Assessing the reliability of sequence similarities detected through hydrophobic cluster analysis

Hydrophobic cluster analysis (HCA) has long been used as a tool to detect distant homologies between protein sequences, and to classify them into different folds. However, it relies on expert human intervention, and is sensitive to subjective interpretations of pattern similarities. In this study, we describe a novel algorithm to assess the similarity of hydrophobic amino acid distributions between two sequences. Our algorithm correctly identifies as misattributions several HCA-based proposals of structural similarity between unrelated proteins present in the literature. We have also used this method to identify the proper fold of a large variety of sequences, and to automatically select the most appropriate structure for homology modeling of several proteins with low sequence identity to any other member of the protein data bank. Automatic modeling of the target proteins based on these templates yielded structures with TM-scores (vs. experimental structures) above 0.60, even without further refinement. Besides enabling a reliable identification of the correct fold of an unknown sequence and the choice of suitable templates, our algorithm also shows that whereas most structural classes of proteins are very homogeneous in hydrophobic cluster composition, a tenth of the described families are compatible with a large variety of hydrophobic patterns. We have built a browsable database of every major representative hydrophobic cluster pattern present in each structural class of proteins, freely available at http://www2.ufp.pt/ pedros/HCA_db/index.htm.     

ProVal: a protein-scoring function for the selection of native and near-native folds

A low-resolution scoring function for the selection of native and near-native structures from a set of predicted structures for a given protein sequence has been developed. The scoring function, ProVal (Protein Validate), used several variables that describe an aspect of protein structure for which the proximity to the native structure can be assessed quantitatively. Among the parameters included are a packing estimate, surface areas, and the contact order. A partial least squares for latent variables (PLS) model was built for each candidate set of the 28 decoy sets of structures generated for 22 different proteins using the described parameters as independent variables. The C(alpha) RMS of the candidate structures versus the experimental structure was used as the dependent variable. The final generalized scoring function was an average of all models derived, ensuring that the function was not optimized for specific fold classes or method of structure generation of the candidate folds. The results show that the crystal structure was scored best in 64% of the 28 test sets and was clearly separated from the decoys in many examples. In all the other cases in which the crystal structure did not rank first, it ranked within the top 10%. Thus, although ProVal could not distinguish between predicted structures that were similar overall in fold quality due to its inherently low resolution, it can clearly be used as a primary filter to eliminate approximately 90% of fold candidates generated by current prediction methods from all-atom modeling and further evaluation. The correlation between the predicted and actual C(alpha) RMS values varies considerably between the candidate fold sets.     

Hidden Markov models that use predicted secondary structures for fold recognition.

There are many proteins that share the same fold but have no clear sequence similarity. To predict the structure of these proteins, so called "protein fold recognition methods" have been developed. During the last few years, improvements of protein fold recognition methods have been achieved through the use of predicted secondary structures (Rice and Eisenberg, J Mol Biol 1997;267:1026-1038), as well as by using multiple sequence alignments in the form of hidden Markov models (HMM) (Karplus et al., Proteins Suppl 1997;1:134-139). To test the performance of different fold recognition methods, we have developed a rigorous benchmark where representatives for all proteins of known structure are matched against each other. Using this benchmark, we have compared the performance of automatically-created hidden Markov models with standard-sequence-search methods. Further, we combine the use of predicted secondary structures and multiple sequence alignments into a combined method that performs better than methods that do not use this combination of information. Using only single sequences, the correct fold of a protein was detected for 10% of the test cases in our benchmark. Including multiple sequence information increased this number to 16%, and when predicted secondary structure information was included as well, the fold was correctly identified in 20% of the cases. Moreover, if the correct secondary structure was used, 27% of the proteins could be correctly matched to a fold. For comparison, blast2, fasta, and ssearch identifies the fold correctly in 13-17% of the cases. Thus, standard pairwise sequence search methods perform almost as well as hidden Markov models in our benchmark. This is probably because the automatically-created multiple sequence alignments used in this study do not contain enough diversity and because the current generation of hidden Markov models do not perform very well when built from a few sequences.     

Design and discovery of metamorphic proteins

Metamorphic proteins are single amino acid sequences that reversibly interconvert between multiple, dramatically different native structures, often with distinct functions. Since the discovery of the first metamorphic proteins in the early 2000s, several additional metamorphic proteins have been identified, and it was suggested that up to 4% of proteins in the PDB may switch folds. Metamorphic proteins have been found to share common features such as marginal thermostability and inconsistencies in predicted secondary structures. Outstanding challenges in the field include the search for more metamorphic proteins and the design of new proteins that switch folds. Identification of novel metamorphic proteins in nature will improve therapeutic targeting of fold-switching proteins involved in human pathology and will enhance the design of protein-based therapies. Designed fold switching proteins have applications as biosensors, molecular switches, molecular machines, and self-assembling systems.     

Spruce budworm antifreeze protein: changes in structure and dynamics at low temperature

Antifreeze proteins (AFPs) prevent the growth of ice, and are used by some organisms that live in sub-zero environments for protection against freezing. All AFPs are thought to function by an adsorption inhibition process. In order to elucidate the ice-binding mechanism, the structures of several AFPs have been determined, and have been shown to consist of different folds. Recently, the first structures of the highly active insect AFPs have been characterized. These proteins have a beta-helix structure, which adds yet another fold to the AFP family. The 90-residue spruce budworm (Choristoneura fumiferana) AFP consists of a beta-helix with 15 residues per coil. The structure contains two ranks of aligned threonine residues (known as the TXT motif), which were shown by mutagenesis experiments to be located in the ice-binding face. In our previous NMR study of this AFP at 30 degrees C, we found that the TXT face was not optimally defined because of the broadening of NMR resonances potentially due to weak oligomerization. We present here a structure of spruce budworm AFP determined at 5 degrees C, where this broadening is reduced. In addition, the 1H-15N NMR dynamics of the protein were examined at 30 degrees C and 5 degrees C. The results show that the spruce budworm AFP is more structured at 5 degrees C, and support the general observation that AFPs become more rigid as the temperature is lowered.     

Recognition of errors in three-dimensional structures of proteins

A major problem in the determination of the three-dimensional structure of proteins concerns the quality of the structural models obtained from the interpretation of experimental data. New developments in X-ray crystallography and nuclear magnetic resonance spectroscopy have acceleratedd the process of structure determination and the biological community is confronted with a steadily increasing number of experimentally determined protein folds. However, in the recent past several experimentally determined protein structures have been proven to contain major errors, indicating that in some cases the interpretation of experimental data is difficult and may yield incorrect models. Such problems can be avoided when computational methods are employed which complement experimental structure determinations. A prerequisite of such computational tools is that they are independent of the parameters obtained from a particular experiment. In addition such techniques are able to support and accelerate experimental structure determinations. Here we present techniques based on knowledge based mean fields which can be used to judge the quality of protein folds. The methods can be used to identify misfolded structures as well as faulty parts of structural models. The techniques are even applicable in cases where only the C_α trace of a protein conformation is available. The capabilities of the technique are demonstrated using correct and incorrect protein folds. © 1993 Wiley-Liss, Inc.     

Evaluation of protein fold comparison servers

When a new protein structure has been determined, comparison with the database of known structures enables classification of its fold as new or belonging to a known class of proteins. This in turn may provide clues about the function of the protein. A large number of fold comparison programs have been developed, but they have never been subjected to a comprehensive and critical comparative analysis. Here we describe an evaluation of 11 publicly available, Web-based servers for automatic fold comparison. Both their functionality (e.g., user interface, presentation, and annotation of results) and their performance (i.e., how well established structural similarities are recognized) were assessed. The servers were subjected to a battery of performance tests covering a broad spectrum of folds as well as special cases, such as multidomain proteins, Calpha-only models, new folds, and NMR-based models. The CATH structural classification system was used as a reference. These tests revealed the strong and weak sides of each server. On the whole, CE, DALI, MATRAS, and VAST showed the best performance, but none of the servers achieved a 100% success rate. Where no structurally similar proteins are found by any individual server, it is recommended to try one or two other servers before any conclusions concerning the novelty of a fold are put on paper.     

Large scale ab initio modeling of structurally uncharacterized antimicrobial peptides reveals known and novel folds

Antimicrobial resistance within a wide range of infectious agents is a severe and growing public health threat. Antimicrobial peptides (AMPs) are among the leading alternatives to current antibiotics, exhibiting broad spectrum activity. Their activity is determined by numerous properties such as cationic charge, amphipathicity, size, and amino acid composition. Currently, only around 10% of known AMP sequences have experimentally solved structures. To improve our understanding of the AMP structural universe we have carried out large scale ab initio 3D modeling of structurally uncharacterized AMPs that revealed similarities between predicted folds of the modeled sequences and structures of characterized AMPs. Two of the peptides whose models matched known folds are Lebocin Peptide 1A (LP1A) and Odorranain M, predicted to form β-hairpins but, interestingly, to lack the intramolecular disulfide bonds, cation-π or aromatic interactions that generally stabilize such AMP structures. Other examples include Ponericin Q42, Latarcin 4a, Kassinatuerin 1, Ceratotoxin D, and CPF-B1 peptide, which have α-helical folds, as well as mixed αβ folds of human Histatin 2 peptide and Garvicin A which are, to the best of our knowledge, the first linear αββ fold AMPs lacking intramolecular disulfide bonds. In addition to fold matches to experimentally derived structures, unique folds were also obtained, namely for Microcin M and Ipomicin. These results help in understanding the range of protein scaffolds that naturally bear antimicrobial activity and may facilitate protein design efforts towards better AMPs.     

Defining the fold space of membrane proteins: the CAMPS database

Recent progress in structure determination techniques has led to a significant growth in the number of known membrane protein structures, and the first structural genomics projects focusing on membrane proteins have been initiated, warranting an investigation of appropriate bioinformatics strategies for optimal structural target selection for these molecules. What determines a membrane protein fold? How many membrane structures need to be solved to provide sufficient structural coverage of the membrane protein sequence space? We present the CAMPS database (Computational Analysis of the Membrane Protein Space) containing almost 45,000 proteins with three or more predicted transmembrane helices (TMH) from 120 bacterial species. This large set of membrane proteins was subjected to single‐linkage clustering using only sequence alignments covering at least 40% of the TMH present in a given family. This process yielded 266 sequence clusters with at least 15 members, roughly corresponding to membrane structural folds, sufficiently structurally homogeneous in terms of the variation of TMH number between individual sequences. These clusters were further subdivided into functionally homogeneous subclusters according to the COG (Clusters of Orthologous Groups) system as well as more stringently defined families sharing at least 30% identity. The CAMPS sequence clusters are thus designed to reflect three main levels of interest for structural genomics: fold, function, and modeling distance. We present a library of Hidden Markov Models (HMM) derived from sequence alignments of TMH at these three levels of sequence similarity. Given that 24 out of 266 clusters corresponding to membrane folds already have associated known structures, we estimate that 242 additional new structures, one for each remaining cluster, would provide structural coverage at the fold level of roughly 70% of prokaryotic membrane proteins belonging to the currently most populated families. Proteins 2006. © 2006 Wiley‐Liss, Inc.     

Novel protein folds and their nonsequential structural analogs

Newly determined protein structures are classified to belong to a new fold, if the structures are sufficiently dissimilar from all other so far known protein structures. To analyze structural similarities of proteins, structure alignment tools are used. We demonstrate that the usage of nonsequential structure alignment tools, which neglect the polypeptide chain connectivity, can yield structure alignments with significant similarities between proteins of known three-dimensional structure and newly determined protein structures that possess a new fold. The recently introduced protein structure alignment tool, GANGSTA, is specialized to perform nonsequential alignments with proper assignment of the secondary structure types by focusing on helices and strands only. In the new version, GANGSTA+, the underlying algorithms were completely redesigned, yielding enhanced quality of structure alignments, offering alignment against a larger database of protein structures, and being more efficient. We applied DaliLite, TM-align, and GANGSTA+ on three protein crystal structures considered to be novel folds. Applying GANGSTA+ to these novel folds, we find proteins in the ASTRAL40 database, which possess significant structural similarities, albeit the alignments are nonsequential and in some cases involve secondary structure elements aligned in reverse orientation. A web server is available at http://agknapp.chemie.fu-berlin.de/gplus for pairwise alignment, visualization, and database comparison.