Description of Ab Initio Protein Folding Methodology

In what follows, we describe a newly developed method for structure prediction, MONSSTER, which attempts to address the aforementioned problems. As depicted in Figure 4.1, prediction of protein structure can be conceptually divided into four stages: (1) restraint derivation; (2) structure assembly; and (3) selection of the native conformation. In addition, for those sequences whose structures are known either before or after the prediction is made, following the structure selection process, (4) objective, rigorous validation criteria are applied to judge the success of the prediction.

Figure 4.1

Schematic overview of the procedure for tertiary structure prediction.

For (1), restraint derivation, a multiple sequence alignment with the sequence of interest is generated (Sander and Schneider, 1991). Then, predicted secondary structure restraints are obtained from a standard secondary structure prediction scheme (Rost and Sander, 1993; Rost, Schneider et al., 1993) supplemented by our LINKER algorithm (Kolinski, Skolnick et al., 1997)—a quite accurate technique for predicting where the chain reverses global direction. We term such regions "U-turns" (Kolinski, Skolnick et al., 1997). The predicted secondary structural elements between these U-turns define the predicted core regions of the molecule. Tertiary contacts (restraints), termed "seeds," between these core elements are then predicted from multiple sequence alignments. Multiple sequence information is used to derive such seed side-chain contacts based on patterns of residue conservation (Aszodi, Gradwell et al., 1995; Mumenthaler and Braun, 1995) or residue covariation in a set of homologous sequences (Göbel, Sander et al., 1994; Thomas, Cesari et al., 1996; Olmea and Valencia, 1997). Both might be combined for increased sensitivity (Olmea and Valencia, 1997). Here, for the sake of simplicity, we slightly modify the approach of Göbel and coworkers (Göbel, Sander et al., 1994) and calculate the covariation between all residues predicted to be in the putative core of the molecule (Olmea and Valencia, 1997; Ortiz, Kolinski et al., 1998a,b). Unfortunately, there are too few of these seed contacts to assemble a protein from the unfolded state using MONSSTER. Thus, these seed contacts between predicted topological elements (i.e., α-helices and β-strands between U-turns) are enriched by an inverse folding approach that typically produces about N/4 contacts—the number required for successful topology assembly (Olmea and Valencia, 1997;Skolnick, Kolinski et al., 1997; Ortiz, Kolinski et al., 1998a,b).

In (2), the structure assembly step, the set of predicted restraints is used in the MONSSTER method (Skolnick, Kolinski et al., 1997) to drive the conformational search. This uses a reduced-protein-lattice model to assemble the global fold. First, a series of up to 1,000 independent, simulated annealing structure-assembly runs are performed, and the resulting structures are clustered on the basis of their pairwise coordinate root mean square deviation (cRMSD). If the resulting structures do not cluster into several topologies, then no structural prediction is made. If at least a subset of the structures cluster, then we proceed to the structure selection step.

The native structure selection stage (3) consists of long isothermal runs from which the putative native topology is chosen on the basis that it has the lowest average energy. If the differing topologies cannot be selected on this basis, then the prediction consists of several lowest-average-energy representatives of the various generated topologies. In all cases, we report the average cRMSD values corresponding to the lowest-average-energy structures and not the best cRMSD values because in a blind prediction, we would have no means of selecting such structures.

Once the native conformation of the protein of interest is known, we judge the success of the prediction (4) as follows: First, we calculate the global C-α cRMSD between the predicted (lowest average energy) and experimental structures. Since our approach often results in structures whose C-α cRMSD is in the range of 6 Å, there may be substantial topological errors between the native and predicted structure; therefore, a more rigorous assessment of success is necessary. Thus, the predicted fold is subjected to a structural similarity search over a representative database using the DALI (Holm and Sander, 1997) structural superimposition program. We note that a very similar approach has been used to assess the quality of structures predicted by threading techniques, where a sequence is matched to a fold in a library of known structures (Wodak and Rooman, 1993). When a known homologue of the native structure is chosen (or the native structure itself), then the tertiary structural prediction protocol is considered to be successful. If two or more topologies are isoenergetic, both would be subjected to this protocol; if one matches the native topology, we consider this to be a partial success. If the next lowest average energy topology (as predicted by MONSSTER) matches the native fold rather than the lowest average energy structure, this is also considered to be a partial success. Otherwise, by this rigorous criterion, the prediction is unsuccessful.

Validation on Proteins of Known Structure

The above protocol was applied to the set of 19 proteins listed in Table 4.1. On average, for the set of proteins whose native conformation was known in advance, the predicted secondary structure is 69 percent correct; this is slightly less than the reported average for this technique, which is 72 ± 9 percent (Rost and Sander, 1993; Rost, Schneider et al., 1993). Such a large test set is necessary to demonstrate that the current approach can handle a wide variety of folds and different secondary structure types. All are outside the set of proteins employed in the derivation of the empirical potentials. It is very important to emphasize that all predictions use the identical parameter set and folding protocol. Table 4.2 shows the accuracy of the predicted secondary structure and tertiary contacts, as well as the results from the folding simulations. Only about 78 percent of the native contacts are correct within ±2 residues; these are typical of results seen on an even larger class of proteins. Often, there are also a number of grossly incorrect restraints that can lead to non-native topologies. Using this information, in about 10 to 30 percent of the assembly runs, native-like topologies, as subsequently assessed by their global cRMSD and DALI (Holm and Sander, 1997), are recovered for all classes of proteins. But on average, helical proteins are predicted better than alpha/beta proteins, which are predicted better than beta proteins.

TABLE 4.1

List of Proteins of Known Structure That Constitute the Validation Set.

TABLE 4.2

Summary of Prediction Results.

In 14 of 19 cases, success or partial success was obtained, with the lowest average C-α cRMSD values ranging from 3.5 to 6.7 Å. For one partial success whose lowest average energy structure has a higher cRMSD from native life, the lowest-energy fold basically adopts the native topology despite its unsatisfactory cRMSD. Here, a strand region is found at the back of the protein rather than at the edge of the fold. The topology with the next higher energy, i.e., the first excited state, is the native one. For the five unsuccessful cases—3cti, 1tfi, 6pti, 1lea, and 1poh—DALI fails to find any structure that is significantly related to the lattice model; thus the prediction is labeled as being unsuccessful. This is true in spite of the fact that the topology of 6pti is native, and for 1poh, a slightly misfolded state is recovered, but by the DALI selection criterion, this simulation is unsuccessful. Furthermore, for mainly helical proteins such as lpou, the alternative low-energy fold is the topological mirror image (where the helices are right-handed, but the chirality of the turns is reversed from the native conformation). In some situations, e.g., for life and 1poh, the alternative topology differs in the placement of one or two topological elements. In other cases, the alternative and native topology do not have much in common.

Blind Predictions

We next present a representative prediction of the tertiary structure of the 81-residue KIX domain of the CREB binding protein, which is involved in gene expression as mediated by AMPc (Brindle and Montminy, 1992; Radhakrishnan, Perez-Alvarado et al., 1997).

As shown in Figure 4.2, the secondary structure prediction scheme suggests that KIX should adopt a three-helix bundle fold. Correlated mutation analysis provides four seed contacts (22-35, 22-73, 35-73, 17-72) that yielded 38 predicted tertiary contacts when enriched; this is a rather large number as compared to other entries in Table 4.2. A series of 10 independent fold assembly simulations were done; all yielded either a left- or right-handed three-helix bundle. As indicated in Table 4.2, on the basis of their average energies, the two topologies are essentially isoenergetic. Decomposing the energy into its constituent contributions (Ortiz, Kolinski et al., 1998c), the pair interactions, secondary-structure preferences, and hydrogen-bond terms favor the right-handed bundle, whereas the burial energy and terms designed to generate protein-like densities favor the left-handed bundle. The difficulty in distinguishing topological mirror images is a problem that this method often experiences with helical proteins, and indicates that improvements in the empirical potential are necessary. When subsequent predictions were done using the subset of restraints that satisfy each of the two topologies, then the native topology was found to be substantially lower in energy than the incorrect alternative.

Figure 4.2

For KIX, the primary sequence and a comparison of the predicted and observed secondary structure. Here, H denotes a helix, U a U-turn; PRDSEC (OBSEC) is the predicted (observed) secondary structure from PHD and LINKER.

Limitations of Sequence-based Methods

As residue identity falls into the twilight zone, standard sequence-alignment algorithms will pick up false positive sequences as well as miss false negative sequences. Similarly, as sequence diversity increases, the local sequence signatures found in the Prosite (Bairoch, 1990; Bairoch, Bucher et al., 1995), Blocks (Henikoff and Henikoff, 1991), and Prints (Attwood and Beck, 1994; Attwood, Beck et al., 1997) databases will no longer be strong enough to recognize protein sequences as belonging to a functional family, even though the specific active site residues might be strictly conserved. (See Table 4.3.) To illustrate this inability to recognize local sequence signatures as the sequences diverge, we performed an analysis of the Prosite database (Release 13.0, November 1995). Of 1,152 patterns in this release of Prosite, 908 (79 percent) of the patterns were absolutely specific for their sequences (using the set of true and false positives and negatives as identified by the Prosite developers). However, as the number of instances of a local pattern increases, the number of false positives also tends to increase. For 10.5 percent of the patterns, 90 to 99 percent of the selected sequences were true positives, while for the remaining 10.5 percent of the patterns, fewer than 90 percent of the selected sequences were true positives. To overcome this deficiency, the developers of the Prosite database have begun to use weight matrices or profiles for detection of domains. Unlike the typical Prosite, Blocks, and Prints methods, they create profiles of sequence information such as residue type and solvent accessibility (Gribskov, McLachlan et al., 1987) based on the complete protein sequence, not just a small segment. As with domain-matching methods, problems inherent in matching highly divergent parts of the sequence, as well as the highly conserved functional regions, still reassert themselves.

TABLE 4.3

Data for Classification of Possible Thioredoxin Sequences by the Prosite, Prints, and Blocks Algorithms.

Similarity of Global Tertiary Structure Does Not Always Imply Similarity of Function

In principle, additional information might be provided by comparing the complete tertiary structures of proteins; however, comparison of overall structure is also not enough to classify protein function unequivocally. The structural databases such as SCOP (Murzin, Brenner et al., 1995), CATH, and DALI (Holm and Sander, 1997) show significant redundancy in domain structures. Proteins such as the barrels and the sandwiches can exhibit very similar structures even though they have very different functions. Valuable information can be obtained from overall tertiary structure comparison (Murzin, 1996), but two proteins with the same global tertiary structure do not necessarily have the same function.

Proteins with Similar Function Conserve the Local Structure Around the Active Site, Even If the Global Fold Is Dissimilar

As the families become more diverse, the sequence similarity among many proteins in the family falls into and below the twilight zone. Then, standard sequence alignments have difficulty establishing a significant relationship between sequences even though one might exist. For example, the mammalian and bacterial serine proteases demonstrate that proteins with very similar functions can have very different three-dimensional structures (Branden and Tooze, 1991). The geometry of the active site would not be recognized by local sequence signatures or by overall comparison of global tertiary structures, but only from an analysis of the structure of the functional residues around the active site.

Development of a Three-dimensional Library of Functional Motifs

What these examples suggest is that one might be able to excise the local structure around the active site and use this local conformational signature to identify function. In fact, proteins function because of the arrangement of specific residues in three-dimensional space, The residues involved in protein function, particularly those at enzyme active sites, will be highly conserved throughout evolution. This statement seems obvious and it was clearly demonstrated experimentally by the serine protease presented above. The problem with recognizing these residues by sequence alignment is that they are likely to be distant along the sequence, even if they are close together in three-dimensional space. This makes recognition by multiple-sequence-alignment methods problematic. If protein function relies on the specific tertiary placement of residues, then one should use that geometric information to describe functional families. We term these geometric (e.g., distances and angles) and conformational (e.g., a residue must be in a helix) descriptors "fuzzy functional forms" (FFFs). These methods do not rely on evolutionary conservation of local sequence as do the local sequence signature methods, but instead involve the construction of three-dimensional descriptors of protein function.

There are several distinct advantages to using geometric and conformational descriptors rather than local sequence signatures to describe protein function. It permits classification of proteins into families, even if there is little or no sequence identity to other proteins in the database. Thus, proteins that fall below the twilight zone of sequence identity will still be amenable to analysis. Nor does it rely on matching of the overall protein structure. Thus, proteins with similar structures but different functions will be classified differently by this method. Note that the term "function," as used here, is defined very narrowly; what is meant is the biochemical activity of the protein of interest.

The one major disadvantage of this method is that the structure of the protein must be known. However, as described below, FFFs are specific and unique enough that the structure does not have to be known to high resolution. Low- to moderate-resolution structures are sufficient for functional recognition, and current state-of-the-art prediction algorithms can often predict protein structure at sufficient resolution to allow identification of function using the FFFs. Finally, these prediction algorithms can be scaled up to analyze complete genomes.

Representative Case: The Glutaredoxin/Thioredoxin Family

Screening of Entire Genomes

This sequence-to-structure-to-function concept has been applied to the analysis of the complete E. coli genome; i.e., all E. coli open reading frames (ORFs) are screened for the thiol-disulfide oxidoreductase activity of the glutaredoxin/thioredoxin protein family. The method can identify the active site residues in 10 sequences that are known to or proposed to exhibit this activity. Furthermore, oxidoreductase activity is predicted in two other sequences that have not been previously identified. These results are summarized in Table 4.4. The method distinguishes protein pairs with similar active sites from protein pairs that are just topological cousins, i.e., those having similar global folds, but not necessarily similar active sites.

TABLE 4.4

Glutaredoxins and Thioredoxins Identified in E. coli Strain K-12.

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