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.     

Three-dimensional structures of membrane proteins from genomic sequencing

We show that amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families, demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization, functional sites, and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing, more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation, greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method.     

Structural similarity of DNA-binding domains of bacteriophage repressors and the globin core

BACKGROUND: In recent years, the determination of large numbers of protein structures has created a need for automatic and objective methods for the comparison of structures or conformations. Many protein structures show similarities of conformation that are undetectable by comparing their sequences. Comparison of structures can reveal similarities between proteins thought to be unrelated, providing new insight into the interrelationships of sequence, structure and function. RESULTS: Using a new tool that we have developed to perform rapid structural alignment, we present the highlights of an exhaustive comparison of all pairs of protein structures in the Brookhaven protein database. Notably, we find that the DNA-binding domain of the bacteriophage repressor family is almost completely embedded in the larger eight-helix fold of the globin family of proteins. The significant match of specific residues is correlated with functional, structural and evolutionary information. CONCLUSION: Our method can help to identify structurally similar folds rapidly and with high-sensitivity, providing a powerful tool for analyzing the ever-increasing number of protein structures being elucidated.     

Recurring structural motifs in proteins with different functions

BACKGROUND: Structures that have diverged from a common ancestor often retain functional and sequence similarity, although the latter may be very reduced. Even so, the overall fold of the structure is generally highly conserved. Now however, several have been identified of proteins that have been identified that have different functions but which have converged to a similar fold. These proteins will also have low sequence identities. RESULTS: By comparing the complete structure databank against itself, using sequence and structure alignment techniques, we have been able to identify six new examples of structurally related folds that have no apparent sequence or functional similarity. These related proteins include a family of crambin-like folds and a family of ferredoxin II folds. We found that all the similarities between structures are present in small proteins and occur as motifs within the core of a larger protein. CONCLUSION: The low sequence similarity and the lack of any obvious functional relationship between proteins with similar structures suggest that the proteins have diverged from independent ancestors. The similarities may therefore be of interest for understanding the various stereochemical and physical criteria that operate to generate a favourable fold.     

Backbone solution structures of proteins using residual dipolar couplings: application to a novel structural genomics target

Structural genomics (or proteomics) activities are critically dependent on the availability of high-throughput structure determination methodology. Development of such methodology has been a particular challenge for NMR based structure determination because of the demands for isotopic labeling of proteins and the requirements for very long data acquisition times. We present here a methodology that gains efficiency from a focus on determination of backbone structures of proteins as opposed to full structures with all sidechains in place. This focus is appropriate given the presumption that many protein structures in the future will be built using computational methods that start from representative fold family structures and replace as many as 70% of the sidechains in the course of structure determination. The methodology we present is based primarily on residual dipolar couplings (RDCs), readily accessible NMR observables that constrain the orientation of backbone fragments irrespective of separation in space. A new software tool is described for the assembly of backbone fragments under RDC constraints and an application to a structural genomics target is presented. The target is an 8.7 kDa protein from Pyrococcus furiosus, PF1061, that was previously not well annotated, and had a nearest structurally characterized neighbor with only 33% sequence identity. The structure produced shows structural similarity to this sequence homologue, but also shows similarity to other proteins, which suggests a functional role in sulfur transfer. Given the backbone structure and a possible functional link this should be an ideal target for development of modeling methods.     

Universally conserved positions in protein folds: reading evolutionary signals about stability, folding kinetics and function.

Here, we provide an analysis of molecular evolution of five of the most populated protein folds: immunoglobulin fold, oligonucleotide-binding fold, Rossman fold, alpha/beta plait, and TIM barrels. In order to distinguish between "historic", functional and structural reasons for amino acid conservations, we consider proteins that acquire the same fold and have no evident sequence homology. For each fold we identify positions that are conserved within each individual family and coincide when non-homologous proteins are structurally superimposed. As a baseline for statistical assessment we use the conservatism expected based on the solvent accessibility. The analysis is based on a new concept of "conservatism-of-conservatism". This approach allows us to identify the structural features that are stabilized in all proteins having a given fold, despite the fact that actual interactions that provide such stabilization may vary from protein to protein. Comparison with experimental data on thermodynamics, folding kinetics and function of the proteins reveals that such universally conserved clusters correspond to either: (i) super-sites (common location of active site in proteins having common tertiary structures but not function) or (ii) folding nuclei whose stability is an important determinant of folding rate, or both (in the case of Rossman fold). The analysis also helps to clarify the relation between folding and function that is apparent for some folds.     

Backbone solution structures of proteins using residual dipolar couplings: Application to a novel structural genomics target

Structural genomics (or proteomics) activities are critically dependent on the availability of high-throughput structure determination methodology. Development of such methodology has been a particular challenge for NMR based structure determination because of the demands for isotopic labeling of proteins and the requirements for very long data acquisition times. We present here a methodology that gains efficiency from a focus on determination of backbone structures of proteins as opposed to full structures with all sidechains in place. This focus is appropriate given the presumption that many protein structures in the future will be built using computational methods that start from representative fold family structures and replace as many as 70% of the sidechains in the course of structure determination. The methodology we present is based primarily on residual dipolar couplings (RDCs), readily accessible NMR observables that constrain the orientation of backbone fragments irrespective of separation in space. A new software tool is described for the assembly of backbone fragments under RDC constraints and an application to a structural genomics target is presented. The target is an 8.7 kDa protein from Pyrococcus furiosus, PF1061, that was previously not well annotated, and had a nearest structurally characterized neighbor with only 33% sequence identity. The structure produced shows structural similarity to this sequence homologue, but also shows similarity to other proteins, which suggests a functional role in sulfur transfer. Given the backbone structure and a possible functional link this should be an ideal target for development of modeling methods. This revised version was published online in March 2005 with corrections to the references.     

Three-dimensional structure of the AAH26994.1 protein from Mus musculus, a putative eukaryotic Urm1

We have used NMR spectroscopy to determine the solution structure of protein AAH26994.1 from Mus musculus and propose that it represents the first three-dimensional structure of a ubiquitin-related modifier 1 (Urm1) protein. Amino acid sequence comparisons indicate that AAH26994.1 belongs to the Urm1 family of ubiquitin-like modifier proteins. The best characterized member of this family has been shown to be involved in nutrient sensing, invasive growth, and budding in yeast. Proteins in this family have only a weak sequence similarity to ubiquitin, and the structure of AAH26994.1 showed a much closer resemblance to MoaD subunits of molybdopterin synthases (known structures are of three bacterial MoaD proteins with 14%-26% sequence identity to AAH26994.1). The structures of AAH26994.1 and the MoaD proteins each contain the signature ubiquitin secondary structure fold, but all differ from ubiquitin largely in regions outside of this fold. This structural similarity bolsters the hypothesis that ubiquitin and ubiquitin-related proteins evolved from a protein-based sulfide donor system of the molybdopterin synthase type.     

Inferring functional relationships of proteins from local sequence and spatial surface patterns

We describe a novel approach for inferring functional relationship of proteins by detecting sequence and spatial patterns of protein surfaces. Well-formed concave surface regions in the form of pockets and voids are examined to identify similarity relationship that might be directly related to protein function. We first exhaustively identify and measure analytically all 910,379 surface pockets and interior voids on 12,177 protein structures from the Protein Data Bank. The similarity of patterns of residues forming pockets and voids are then assessed in sequence, in spatial arrangement, and in orientational arrangement. Statistical significance in the form of E and p-values is then estimated for each of the three types of similarity measurements. Our method is fully automated without human intervention and can be used without input of query patterns. It does not assume any prior knowledge of functional residues of a protein, and can detect similarity based on surface patterns small and large. It also tolerates, to some extent, conformational flexibility of functional sites. We show with examples that this method can detect functional relationship with specificity for members of the same protein family and superfamily, as well as remotely related functional surfaces from proteins of different fold structures. We envision that this method can be used for discovering novel functional relationship of protein surfaces, for functional annotation of protein structures with unknown biological roles, and for further inquiries on evolutionary origins of structural elements important for protein function.     

Sequence-structure analysis of FAD-containing proteins

We have analyzed structure-sequence relationships in 32 families of flavin adenine dinucleotide (FAD)-binding proteins, to prepare for genomic-scale analyses of this family. Four different FAD-family folds were identified, each containing at least two or more protein families. Three of these families, exemplified by glutathione reductase (GR), ferredoxin reductase (FR), and p-cresol methylhydroxylase (PCMH) were previously defined, and a family represented by pyruvate oxidase (PO) is newly defined. For each of the families, several conserved sequence motifs have been characterized. Several newly recognized sequence motifs are reported here for the PO, GR, and PCMH families. Each FAD fold can be uniquely identified by the presence of distinctive conserved sequence motifs. We also analyzed cofactor properties, some of which are conserved within a family fold while others display variability. Among the conserved properties is cofactor directionality: in some FAD-structural families, the adenine ring of the FAD points toward the FAD-binding domain, whereas in others the isoalloxazine ring points toward this domain. In contrast, the FAD conformation and orientation are conserved in some families while in others it displays some variability. Nevertheless, there are clear correlations among the FAD-family fold, the shape of the pocket, and the FAD conformation. Our general findings are as follows: (a) no single protein 'pharmacophore' exists for binding FAD; (b) in every FAD-binding family, the pyrophosphate moiety binds to the most strongly conserved sequence motif, suggesting that pyrophosphate binding is a significant component of molecular recognition; and (c) sequence motifs can identify proteins that bind phosphate-containing ligands.     

A comparison of sequence and structure protein domain families as a basis for structural genomics.

MOTIVATION: Protein families can be defined based on structure or sequence similarity. We wanted to compare two protein family databases, one based on structural and one on sequence similarity, to investigate to what extent they overlap, the similarity in definition of corresponding families, and to create a list of large protein families with unknown structure as a resource for structural genomics. We also wanted to increase the sensitivity of fold assignment by exploiting protein family HMMs. RESULTS: We compared Pfam, a protein family database based on sequence similarity, to Scop, which is based on structural similarity. We found that 70% of the Scop families exist in Pfam while 57% of the Pfam families exist in Scop. Most families that occur in both databases correspond well to each other, but in some cases they are different. Such cases highlight situations in which structure and sequence approaches differ significantly. The comparison enabled us to compile a list of the largest families that do not occur in Scop; these are suitable targets for structure prediction and determination, and may be useful to guide projects in structural genomics. It can be noted that 13 out of the 20 largest protein families without a known structure are likely transmembrane proteins. We also exploited Pfam to increase the sensitivity of detecting homologs of proteins with known structure, by comparing query sequences to Pfam HMMs that correspond to Scop families. For SWISSPROT+TREMBL, this yielded an increase in fold assignment from 31% to 42% compared to using FASTA only. This method assigned a structure to 22% of the proteins in Saccharomyces cerevisiae, 24% in Escherichia coli, and 16% in Methanococcus jannaschii.     

A comparison of genotype-phenotype maps for RNA and proteins

The relationship between the genotype (sequence) and the phenotype (structure) of macromolecules affects their ability to evolve new structures and functions. We here compare the genotype space organization of proteins and RNA molecules to identify differences that may affect this ability. To this end, we computationally study the genotype-phenotype relationship for short RNA and lattice proteins of a reduced monomer alphabet size, to make exhaustive analysis and direct comparison of their genotype spaces feasible. We find that many fewer protein molecules than RNA molecules fold, but they fold into many more structures than RNA. In consequence, protein phenotypes have smaller genotype networks whose member genotypes tend to be more similar than for RNA phenotypes. Neighborhoods in sequence space of a given radius around an RNA molecule contain more novel structures than for protein molecules. We compare this property to evidence from natural RNA and protein molecules, and conclude that RNA genotype space may be more conducive to the evolution of new structure phenotypes.     

Identification of lipases with activity towards monoacylglycerol by criterion of conserved cap architectures

Monoacylglycerol lipases (MGL) are a subclass of lipases that predominantly hydrolyze monoacylglycerol (MG) into glycerol and fatty acid. MGLs are ubiquitous enzymes across species and play a role in lipid metabolism, affecting energy homeostasis and signaling processes. Structurally, MGLs belong to the α/β hydrolase fold family with a cap covering the substrate binding pocket. Analysis of the known 3D structures of human, yeast and bacterial MGLs revealed striking similarity of the cap architecture. Since MGLs from different organisms share very low sequence similarity, it is difficult to identify MGLs based on the amino acid sequence alone. Here, we investigated whether the cap architecture could be a characteristic feature of this subclass of lipases with activity towards MG and whether it is possible to identify MGLs based on the cap shape. Through database searches, we identified the structures of five different candidate α/β hydrolase fold proteins with unknown or reported esterase activity. These proteins exhibit cap architecture similarities to known human, yeast and bacterial MGL structures. Out of these candidates we confirmed MGL activity for the protein LipS, which displayed the highest structural similarity to known MGLs. Two further enzymes, Avi_0199 and VC1974, displayed low level MGL activities. These findings corroborate our hypothesis that this conserved cap architecture can be used as criterion to identify lipases with activity towards MGs.     

Identification of related proteins on family, superfamily and fold level

Proteins might have considerable structural similarities even when no evolutionary relationship of their sequences can be detected. This property is often referred to as the proteins sharing only a "fold". Of course, there are also sequences of common origin in each fold, called a "superfamily", and in them groups of sequences with clear similarities, designated "family". Developing algorithms to reliably identify proteins related at any level is one of the most important challenges in the fast growing field of bioinformatics today. However, it is not at all certain that a method proficient at finding sequence similarities performs well at the other levels, or vice versa.Here, we have compared the performance of various search methods on these different levels of similarity. As expected, we show that it becomes much harder to detect proteins as their sequences diverge. For family related sequences the best method gets 75% of the top hits correct. When the sequences differ but the proteins belong to the same superfamily this drops to 29%, and in the case of proteins with only fold similarity it is as low as 15%. We have made a more complete analysis of the performance of different algorithms than earlier studies, also including threading methods in the comparison. Using this method a more detailed picture emerges, showing multiple sequence information to improve detection on the two closer levels of relationship. We have also compared the different methods of including this information in prediction algorithms.For lower specificities, the best scheme to use is a linking method connecting proteins through an intermediate hit. For higher specificities, better performance is obtained by PSI-BLAST and some procedures using hidden Markov models. We also show that a threading method, THREADER, performs significantly better than any other method at fold recognition.     

Old fold in a new X‐ray diffraction dataset? Low‐resolution molecular replacement using representative structural templates can provide phase information

The advent of structural genomics has led to a dramatic increase in the number of structures deposited in the Protein Data Bank. The number of new folds, however, still remains a very small fraction of the total number of deposited structures. Recent data on the progress of the structural genomics initiative reveals that more than 85% of target proteins that progress to the stage of data collection and structure determination have a known fold. Enzymes, which tend to exploit reaction space while adopting a common stable scaffold, contribute significantly to this observation. Herein, we evaluate a method to examine the "old fold in a new dataset" scenario likely to be encountered in the structural genomics pipeline. We demonstrate that a fold detection strategy based on secondary structure signatures followed by molecular replacement using a minimalist model can be effectively used to solve the phase problem in X-ray crystallography without further recourse to heavy atom derivatives or multiple anomalous dispersion techniques. Three common folds-the triosephosphate isomerase (TIM), adenine nucleotide alpha hydrolase-like (HUP), and RNA recognition motif (RRM)-were examined using this approach. The results presented herein also provide an estimate of the extent of phase information that can be derived from a single domain in a large multidomain structure.     

Structure determination of a new protein from backbone-centered NMR data and NMR-assisted structure prediction

Targeting of proteins for structure determination in structural genomic programs often includes the use of threading and fold recognition methods to exclude proteins belonging to well-populated fold families, but such methods can still fail to recognize preexisting folds. The authors illustrate here a method in which limited amounts of structural data are used to improve an initial homology search and the data are subsequently used to produce a structure by data-constrained refinement of an identified structural template. The data used are primarily NMR-based residual dipolar couplings, but they also include additional chemical shift and backbone-nuclear Overhauser effect data. Using this methodology, a backbone structure was efficiently produced for a 10 kDa protein (PF1455) from Pyrococcus furiosus. Its relationship to existing structures and its probable function are discussed.     

Similarities and differences among 105 members of the Int family of site-specific recombinases.

Alignments of 105 site-specific recombinases belonging to the Int family of proteins identified extended areas of similarity and three types of structural differences. In addition to the previously recognized conservation of the tetrad R-H-R-Y, located in boxes I and II, several newly identified sequence patches include charged amino acids that are highly conserved and a specific pattern of buried residues contributing to the overall protein fold. With some notable exceptions, unconserved regions correspond to loops in the crystal structures of the catalytic domains of lambda Int (Int c170) and HP1 Int (HPC) and of the recombinases XerD and Cre. Two structured regions also harbor some pronounced differences. The first comprises beta-sheets 4 and 5, alpha-helix D and the adjacent loop connecting it to alpha-helix E: two Ints of phages infecting thermophilic bacteria are missing this region altogether; the crystal structures of HPC, XerD and Cre reveal a lack of beta-sheets 4 and 5; Cre displays two additional beta-sheets following alpha-helix D; five recombinases carry large insertions. The second involves the catalytic tyrosine and is seen in a comparison of the four crystal structures. The yeast recombinases can theoretically be fitted to the Int fold, but the overall differences, involving changes in spacing as well as in motif structure, are more substantial than seen in most other proteins. The phenotypes of mutations compiled from several proteins are correlated with the available structural information and structure-function relationships are discussed. In addition, a few prokaryotic and eukaryotic enzymes with partial homology with the Int family of recombinases may be distantly related, either through divergent or convergent evolution. These include a restriction enzyme and a subgroup of eukaryotic RNA helicases (D-E-A-D proteins).     

Evolutionary Dynamics on Protein Bi-stability Landscapes can Potentially Resolve Adaptive Conflicts

Experimental studies have shown that some proteins exist in two alternative native-state conformations. It has been proposed that such bi-stable proteins can potentially function as evolutionary bridges at the interface between two neutral networks of protein sequences that fold uniquely into the two different native conformations. Under adaptive conflict scenarios, bi-stable proteins may be of particular advantage if they simultaneously provide two beneficial biological functions. However, computational models that simulate protein structure evolution do not yet recognize the importance of bi-stability. Here we use a biophysical model to analyze sequence space to identify bi-stable or multi-stable proteins with two or more equally stable native-state structures. The inclusion of such proteins enhances phenotype connectivity between neutral networks in sequence space. Consideration of the sequence space neighborhood of bridge proteins revealed that bi-stability decreases gradually with each mutation that takes the sequence further away from an exactly bi-stable protein. With relaxed selection pressures, we found that bi-stable proteins in our model are highly successful under simulated adaptive conflict. Inspired by these model predictions, we developed a method to identify real proteins in the PDB with bridge-like properties, and have verified a clear bi-stability gradient for a series of mutants studied by Alexander et al. (Proc Nat Acad Sci USA 2009, 106:21149–21154) that connect two sequences that fold uniquely into two different native structures via a bridge-like intermediate mutant sequence. Based on these findings, new testable predictions for future studies on protein bi-stability and evolution are discussed.     

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.