- Research article
- Open Access
Occurrence of protein structure elements in conserved sequence regions
© Sitbon and Pietrokovski; licensee BioMed Central Ltd. 2007
- Received: 04 September 2006
- Accepted: 09 January 2007
- Published: 09 January 2007
Conserved protein sequence regions are extremely useful for identifying and studying functionally and structurally important regions. By means of an integrated analysis of large-scale protein structure and sequence data, structural features of conserved protein sequence regions were identified.
Helices and turns were found to be underrepresented in conserved regions, while strands were found to be overrepresented. Similar numbers of loops were found in conserved and random regions.
These results can be understood in light of the structural constraints on different secondary structure elements, and their role in protein structural stabilization and topology. Strands can tolerate fewer sequence changes and nonetheless keep their specific shape and function. They thus tend to be more conserved than helices, which can keep their shape and function with more changes. Loop behavior can be explained by the presence of both constrained and freely changing loops in proteins. Our detailed statistical analysis of diverse proteins links protein evolution to the biophysics of protein thermodynamic stability and folding. The basic structural features of conserved sequence regions are also important determinants of protein structure motifs and their function.
- Secondary Structure
- Secondary Structure Element
- Solvent Accessibility
- Alpha Helix
- Neutral Network
What are the structurally distinguishing features of conserved protein sequence regions? The structure of many diverse proteins is currently known. In addition, many more protein sequences have been determined. These data can be used to study the relations between structure and conserved sequence features of proteins, such as protein secondary structure, which is a basic structural attribute that defines structural folds.
Sequence conservation of homologous sequences is rarely homogenous along their length; as sequences diverge, their conservation is localized to specific regions. Typically, evolutionary conserved regions are important both structurally and functionally. To obtain the general structural features of conserved regions of all proteins, it is necessary to decide which scale of protein clustering, conserved regions, and structure features to analyze. Natural choices are generically defined protein families , ungapped protein sequence motifs (blocks) that separate proteins into either conserved or random regions , and the four basic secondary structure elements (SSEs), namely, alpha helices, beta strands, structured turns, and loops . It is also of paramount importance to analyze data from a very large and diverse group of proteins, avoiding conclusions drawn from biased and a limited amount of data and using exact statistics to identify subtle but significant features.
Using blocks as the basic unit of analysis is advantageous over other precomputed multiple sequence alignments since the modular nature of proteins naturally lends itself to description by motifs; that is locally conserved sequence regions that include at least a few invariant positions . While specific families are typically described by groups of motifs, each motif is not necessarily associated with only one single family. A motif can appear in different contexts in separate families and be repeated within one family (for an example see ). The conservation of the known family members defines the number and length of its motifs .
Relations between protein sequence and structure can be analyzed by either determining the sequence features of predefined structures, as reviewed by Bystroff , or by determining structural features of conserved sequence regions. Han and Baker studied local structure features that predominate short sequence motifs, identifying correlations between specific sequence and structure motifs . Secondary structure conservation was previously studied in structural alignments of protein families and SSE substitution matrices were created . The conservation of SSEs was also studied in some specific protein families (e.g. ). Protein loops and their flanking regions were found to be conserved to the same extent in an analysis of a large set of proteins .
Protein structures can be divided into four major structural classes, according to their secondary structure content and arrangement (SCOP ). There are two homogeneous classes and two heterogeneous classes. The homogenous classes consists of structures containing mainly alpha helices (termed all alpha) or containing mainly beta strands (all beta). The two heterogeneous classes comprise both alpha helices and beta strands. The alpha/beta class consists of mainly parallel beta sheets (beta-alpha-beta units), and the alpha+beta class that consists of mainly antiparallel beta sheets (segregated alpha and beta regions) . Each class obviously differs in its secondary structure content. An analysis of SSE occurrences should therefore control for the possible bias created by the different representations of each class.
Proteins with similar sequences adopt similar structure [12, 13]. However, similar structures can have less than 12% sequence identity [11, 14–16]. Most amino acids within a protein can thus be changed without affecting its structure, including the secondary structure . Previous experiments have shown that both helices and strands can undergo numerous mutations and still keep their secondary structure – either beta-strand or alpha helix – and also maintain structural stability [18–20]. The number of point mutation per position that can be tolerated by a protein without loosing structural stability can be represented by a neutral network. Protein neutral network models have shown that stable structures have a large neutral network [21–23]. Neutral networks may also be analyzed empirically, using conserved regions and regions from random locations on proteins. Such an analysis would emphasize the connection between conservation in actual protein families and the stability of protein structures.
Exposure to the solvent is known to be anti-correlated with conservation; that is, core residues, particularly polar core residues, are usually highly conserved [24, 25]. Therefore, differences in conservation of SSEs might be due to differences in accessibility to the solvent. Since conservation, solvent accessibility and secondary structures are interrelated, an analysis of conservation should consider the possible effect of surface accessibility.
Helices and strands have a regular repetitive structure, while turns and loops are not repetitive . This suggests that helices and strands might be more conserved than loops and turns. On the other hand, functional sites were shown to be overrepresented in loop-rich regions , suggesting that loops may tend to be evolutionarily conserved.
The findings of our analysis were somewhat surprising and have not been noted in other studies. Helices and turns were found to be underrepresented in conserved regions, unlike strands, which were found to be overrepresented. Loops were found in similar numbers in both conserved and random regions. The significant differences in the occurrence of SSEs between conserved regions and random regions was found in a large set of protein sequences. This can facilitate a clearer understanding of the relationship between evolution and structure. Our detailed analysis of SSEs, in all protein structure types as well as in separate structural classes, shows how the combination of sequence and structural features indicates functionally and structurally important regions. This is an advantage when designing new proteins, as well as for studying structures that do not have any known homologs.
Conserved regions include more beta strands than expected and fewer alpha helices and turns
Statistics of sequence alignments used in this study
Number of protein families
Number of blocks analyzed
Number of analyzed SSE in blocks
Number of amino acids in all blocks
Average (median) block length
Number of structurally resolved amino acids in sequences that contain blocks.
Average (median) no. of resolved amino acids in sequences that contain blocks.
Coverage of average single block length to resolved residues in a sequence
Coverage of all blocks in resolved residues in all sequences
Secondary structure elements in conserved and random regions
Number in blocks conserved regions (Observed)
Average number in random regions (Expected)
Cell X2 *
Statistics of secondary structure elements in conserved and random regions
Difference (random average minus conserved)
% difference from conserved
Number of prediction intervals *
Hence, in comparing the presence of SSEs between conserved regions and all the protein (represented by random region units), strands are significantly more common in conserved regions, while helices and turns are less common in conserved regions, and loops appear similarly in both types of regions (see example in Figure 1(B)).
SSE conservation in separate structural classes and folds
Helix and strand occurrence in different structural classes
Prediction interval *
Number of prediction intervals**
Alpha + beta
Alpha + beta
Solvent accessibility, conservation and SSEs
To check if differences in conservation of SSEs were due to differences in accessibility to the solvent, the relation between accessibility and SSE distribution was examined for conserved regions in two ways. First, the properties of whole conserved regions were analyzed. In addition, the accessibility of each residue in conserved regions was analyzed and compared to residues from whole proteins.
In order to verify that the small difference in helix versus strand distributions in the low accessibility ranges does not affect the significance of the results, the extreme case in which buried strands occur only in conserved regions was assumed. Therefore, only the number of accessible strands in conserved regions was compared to the number of strands in random regions. There are 6846 strands in conserved regions and 6362 strands in random regions. This difference of 484 strands corresponds to 3.51 confidence intervals (Tables 2 and 3). Not counting the 126 buried strands of conserved regions (with an average accessibility of 30%) reduces the difference to 358 strands, which corresponds to 2.59 confidence intervals. The difference between the number of strands in conserved and random regions is therefore significant even when all the buried conserved regions strands are not considered. In other words, the significant difference in the number of strands between conserved and random regions is not only due to buried conserved regions.
A second analysis checked whether strand residues remain more common in conserved regions even when they are exposed to the solvent, regardless of the average accessibility of the whole region. Unlike the previous analysis, here information on which elements within a conserved region were exposed, and which were buried was retained. The odds that an accessible residue would be a strand in conserved regions and in the whole protein were calculated. The log ratio of these two odd values was calculated as 0.15 ± 0.02, which is slightly, but significantly above zero. This means that strands are slightly, although significantly more common in accessible conserved residues relative to accessible residues in the whole protein.
To further validate our findings, block-sized regions were sampled in random locations in proteins, and their amount of accessible strands compared to that in block regions. Normalizing the number of accessible residues in conserved regions to the total number of accessible residues, 9226.2 accessible strand residues were found in the examined conserved regions and 7983.1 accessible strand residues were found in an average of twenty sets of random regions from the same proteins. The confidence interval for the average random regions sets is 468.5. Thus, conserved regions were found to have significantly more accessible strand regions – by 2.65 more confidence intervals. This analysis confirms that accessible strands are more common in conserved regions than in the whole protein.
The above analyses establish that the higher abundance of strands in conserved regions is independent from accessibility.
The small difference in strand, helix and turn occurrences is significant when all proteins in the database were analyzed. This was also true when the proteins were separated into groups according to size and when accessibility was considered. Except for the all-alpha protein class, the same results were obtained for separate protein classes. This shows a real effect in the preference of secondary structure in conserved regions.
In this study we present a novel approach that links evolutionary conservation to protein secondary structure. This large scale analysis allowed us to draw general conclusions on the relation between biophysical aspects of protein folding and sequence space.
We have identified significant differences in SSE occurrence between conserved and random sequence regions. Strands are overrepresented in conserved regions, while helices and turns are underrepresented. While these are significant differences, they are also not large – around 7% difference for all proteins (Table 3), and up to a 10% difference for individual protein classes (Table 4).
In defining conserved regions we focused on highly conserved blocks. Conservation is dependent on the size and variability of a protein family. Therefore, a measure of conservation, consistent across different families is not available. We chose the all-or-none definition of conservation of the Blocks database, knowingly leaving out weakly conserved regions. Our definition created a set of highly conserved sequence regions and a set of sequence regions with the average protein conservation. One consequence of the unavoidable mixture of conserved and non-conserved protein regions is identification of trends, rather than exact values, for conserved region features. Therefore, the differences between conserved and non-conserved regions might actually be stronger than what we observed when analyzing the most conserved regions.
The measured unit in this study was a SSE, disregarding its length. This unit enabled us to compare SSEs as they appear in the protein topology, and ignore phenomena that affect their length, such as the ambiguous structural nature of SSE ends, and insertions/deletions, that do not affect the topology of the protein.
Differences between conserved and random regions could be the result of heterogeneity in the data, due to factors such as structural classes, accessibility, active site regions and regions important for structural stabilization. Dividing the analyzed data to subsets according to structural classes, accessibility values, and protein sizes, while using various statistical methods, enhanced some of the observed differences, as specified below.
Several ways of neutralizing the effect of solvent accessibility did not change our results significantly. The tendency of strands to be more buried than helices is therefore not the main reason for the overabundance of strands in conserved regions.
Differences in SSE content were found between the four major structural classes, as well as between conserved and random regions of each class. We found a significant difference in the SSE content of the four major structural classes, and each class showed differences in SSE content between its conserved and random regions. Structural classes are defined by the content and order of their SSEs (e.g. all alpha proteins are mainly composed of alpha helices while alpha/beta proteins mainly consist of parallel beta sheets). However, the differences we found between the classes are neither trivial nor expected. First, the alpha/beta class is combined with the alpha+beta class in the CATH structural classification database , although we found these two classes to be clearly distinct, therefore agreeing with the class definitions suggested by Levitt and used in SCOP [29, 11]. Our results show that the division of protein structures by topology and not SSE content alone is more meaningful. Next, in all classes, except the all-Alpha, strands and helices occurred in the same way. This general feature of strands and helices justifies drawing general conclusions about the constraints on their occurrences.
A thermodynamics explanation of strands and helices occurrences in conserved regions
The nature of a protein depends on more than its secondary structure. Specific features of SSEs are necessary to create a specific protein trait. For example, SSEs in a specific active site require unique features, such as flexibility, distinct shape, and charge distribution. SSEs that could adopt these features with a limited number of sequence combinations tend to be conserved.
Two attributes that characterize helices and strands could explain our findings regarding the overrepresentation of strands and the underrepresentation of helices in conserved regions. The amino acid diversity in strands and helices is approximately the same [30, 31]. However, in general, helices have a specific backbone conformation while strands are more diverse in shape. A specific helix can thus be created by many sequence combinations, while a specific strand structure requires a more limited set of sequence combinations. The Ramachandran plot shows that the space of possible backbone conformations of helices is more limited than that of strands . In Addition, the bulky helix backbone reduces available degrees of freedom in helix side chains , and residues in alpha helix conformation were shown to have consistently lower entropies than residues in strand conformations . Therefore, there is an entropic constraint – a reduction of degrees of freedom – for helix protein regions. This rigidity allows a larger variety of sequences to maintain a specific helical structure. On the other hand, strand flexibility requires additional sequence constraints in order to maintain a specific strand structure. An alpha helix region can therefore retain its specific tertiary structure with a higher variety of amino acid types than strands.
The above reasoning explains why strands are more likely to be conserved, and helices are less likely to be conserved. Our analysis examined what seems to be a similar question but is in fact not identical, namely, how likely conserved regions are to include strands or helices. Therefore, our findings regarding SSE occurrences in conserved regions could be explained by the conservation likelihood of SSEs.
That an alpha helix is less likely to be conserved can be formally defined by:
where p(c) is the probability that a region is conserved and p(c|a) is the probability that a region is conserved, given that it is an alpha helix.
We observed that the probability for a region to be an alpha helix, given that the region is conserved, is lower than the probability for a region to be an alpha helix in both conserved and random regions. This is formally defined by:
where p(a) is the probability of a region to be an alpha helix.
These two equations are consistent with Bayes' Theorem of conditional probability:
Our findings that helices are less common in conserved regions than in random regions can therefore be explained by the fact that alpha helices can keep their tertiary structure without being highly conserved.
The same reasoning applies to our findings in beta strands. We find beta strands more likely to be conserved, and conserved regions are more likely to be beta strands given the overall occurrence of beta strands. This could be explained by the fact that strands tend to be conserved in order to keep their tertiary structure. It is generally accepted that beta-sheet formation, unlike that of alpha-helix, is to a large extent determined by tertiary context and not by the intrinsic properties of the residues in the strand . Studies confirming this notion [18–20] have not shown that beta strands are generally more resistant to mutations than alpha helices. Therefore, the tertiary structure shown to be important for beta sheet stabilization may well be highly dependent on sequence.
It has been previously suggested by Bornberg-Bauer and Chan that proteins that have a large sequence neutral network – i.e. a large variety of sequences that can keep the initial structure – also tend to have a smooth folding funnel [36, 23]. A smooth folding funnel ensures correct folding, without local minima that may lead to non-native conformations. By using simple exact lattice models, Bornberg-Bauer and Chan have shown that the size of the available sequence space correlates with folding energetics. In our study, we have shown a connection between actual sequence conservation and the number of available conformations (equivalent to density of states in the folding energy descriptions) as derived from secondary structure conformation constraints.
Protein folding paths are usually studied in light of structural constraints on single proteins. The native folding path is the one with "minimal frustration". Conservation emphasizes that proteins are the result of selective evolution. Mutations in a protein are usually fixed only if the protein function is not disturbed. Since a protein that cannot fold properly will not function, the propensity to fold correctly is an evolutionary constraint. If there are structural regions that can tolerate mutations, this increases the robustness of the protein in terms of structural stability and of function. Bornberg-Bauer and Chan suggested the term "superfunnels" for the sequence space that allows protein folding . We suggest that the evolutionary selection for a specific secondary structure is affected by a reduction in structural degrees of freedom, which is inversely correlated with the "sequence degrees of freedom". This is apparent in our analysis of structure features in conserved regions, and by modeling the available protein space as shown by Bornberg-Bauer and Chan.
In other words, when a protein folds, there is an energetic price for losing entropy, namely, a loss of degrees of freedom. We claim that sequence constraints are a kind of entropic loss. Since strands lose a smaller number degrees of freedom just by being strands, they can afford to lose more degrees of freedom by sequence changes.
Loops and turns – connectors, functional sites and structural stabilization
Loops were found to be common to a similar extent in conserved and random regions. This could be explained by the occurrence of loops in different contexts. Some loops are important for structure or function, and are therefore highly conserved within families as well as within superfamilies as previously shown by Liu et al. . The conservation of loops should be especially high due to their inherent flexibility, which is higher than that of strands. Strand flexibility explains strand conservation as described above. Other loops might merely be connectors, so that their exact conformation is not important for protein function or structural stabilization. Therefore they are not conserved. If this is so, the occurrences of these two types of loops may cancel each other out when a large set of data is analyzed.
Turns are even more heterogeneous in context than loops, appearing in various types of structural positions such as the middle of loops, or adjacent to helices and strands. This diversity makes it difficult to identify the reasons for turns being less common in conserved regions.
Sequence conservation indicates the structure and function of the whole protein. Secondary structure is strongly affected by local interactions and residue composition. Nonetheless, we have shown an association between secondary structure and conservation. The significant but small preference for strands, and avoidance of helices and turns in conserved regions implies that secondary structure is part of the interplay between sequence conservation and structural stabilization. Protein secondary structure represents a set of structural constraints that affect amino acid sequence constraints. This provides a better understanding of principles of protein folding, and evolutionary conservation of structure. Secondary structure constraints are also important determinants for combinations of SSEs in conserved sequence regions. These combinations can be identified as protein structure motifs that have specific functions [ES and SP, submitted].
Data collection and integration
The Blocks+ Database, versions 13.0 and 14.0, were used as a definition of conserved regions. The Blocks+ database is a database consisting of short ungapped sequence alignments automatically built using predefined protein families [2, 27]. Protein families are predefined by the InterPro database , which consists of information from several sources. The block finding method used by Blocks is conservative, in order to avoid wrong block definitions, at the cost of possible loss of some correct blocks . For each protein family a set of blocks is determined automatically, blocks range in length from 5 to 55 amino acids and include from 5 to 3100 sequences (most blocks are between 10 and 40 residues in length, and include between 5 and 90 sequences).
Kim Hendrick's SwissProt to PDB mapping, from EBI, was used to assign each conserved region's location on the structure as it appears in the PDB, and to identify resolved region . Class, fold, and super-families of the domains relevant to the block were added from the SCOP database version 1.67 .
Secondary structure definitions are not always identical for homologous sequence, as well as for different structure solutions of the same protein (E. Sitbon, unpublished results). In order to assign a single secondary structure sequence to each conserved region, we first used the secondary structure assigned to each protein in the conserved region by the SwissProt database . SwissProt uses an algorithm that defines a single secondary structure assignment from the best available structures based on DSSP  (Dr. Isabelle Phan (SP team), personal communication), assigning either strand, helix or turn to regions in the sequence. Helices include, 4-helices (alpha), 310 helices and 5 helices (π). Strands include isolated beta bridges and extended strands, and turns include hydrogen bonded turns (Hbond(i,i+n), n = 3,4,5). Structurally resolved regions that do not fit into one of these categories were defined as loops. SSE lengths were ignored, that is, one element was assigned to a stretch of residues with an identical secondary structure definition.
Examples for dealing with SSE variability in a block. Ambiguous regions are highlighted in bold and italics:
SSEs in some blocks
Identical flanks, ambiguous region contains flank SSE.
Different flanks, ambiguous region contains only flank SSEs.
Different flanks, strand is not one of the flank SSEs.
HLLHSS L LLL
Three consecutive ambiguous positions.
Identical flanks, ambiguous region does not contain flank SSE.
Accessibility, like secondary structures, varies between different crystallization experiments and between homologous sequences. The following steps were used to define an average accessibility value for each conserved region. For each region in the database, an average accessibility value was obtained by Naccess . Naccess assigns a relative accessibility value to each amino acid. If this accessibility was above a threshold of 5%, the residue was considered accessible, the analysis was repeated with a threshold of 15% with similar results (data not shown). The average accessibility of the region was defined as the number of its accessible residues divided by the total number of residues in the region. Each protein may have more than one determined structure. The range of accessibilities from different crystal structures for each region of each protein was calculated (max accessibility – min accessibility). If the range was smaller than a threshold of 0.05, the region in that protein was assigned the average of all the accessibility values of the structures of the region of this protein. If the range was higher than the cutoff, no specific accessibility value was assigned for the region of the protein. In the next step, only conserved regions where all the available proteins with structure have an assigned accessibility value were examined. If the range of the different protein accessibility values was below the threshold of 0.01 the average of the average protein accessibilities was assigned to the conserved region. Similarly to SSE assignment, losing conserved regions information was preferred to excess noise when a large variation in accessibility was found between structures or proteins.
When analyzing each residue separately, a crystal structure (with the best resolution if several were available) was used for each block. Each residue was defined as accessible if its relative accessibility (from Naccess ) was over 5%. The secondary structure was defined by the single structure used, not by a consensus as in all other analyses. Unlike the analyses described above, here we counted the number of accessible residues in each SSE. Therefore, the number of residues was normalized by the total number of accessible residues.
Creating background sets
Background sets of regions for SSE and accessibility calculations were generated from random protein regions. For each block, a random region was selected from the representative structure. Random regions were defined as regions with the same lengths as the conserved regions in the analysis. They were chosen in random locations on the protein sequence. The secondary structures of the randomly chosen segments were characterized in the same way as for the set of conserved regions (see above). The only difference was that the secondary structure was taken from one protein, without creating a SSE consensus. This random sampling and analysis was repeated twenty-two times (a large enough number arbitrarily chosen), which created a distribution of each SSE over all the sampled sets. For each subset of the blocks, such as a specific structural class, a corresponding random regions set was created.
Several statistical tests were conducted to examine the significance of the analysis results. Distributions of secondary structure elements in conserved regions were compared to distributions in random regions using Chi square test for independence. This was done in order to test if the distribution between the four categories of secondary structures depends on whether or not the fragments are conserved.
In addition, a prediction interval was created in order to test the following null hypothesis: The number of each SSE type (helix, strand, turn or loop) in conserved regions belongs to the distribution of this SSE type in the sets of random regions with a 5% level of significance. The prediction interval was created using the t-value for alpha = 0.125(0.05/4) and correcting for multiple testing, and 21 degrees of freedom (corresponding to the twenty two samples minus one), multiplied by the standard deviation.
We would like to thank Joel Sussman, Gideon Schreiber, Miriam Eisenstein, Eitan Domany, Dana Reichmann, Gil Amitai, Bareket Dassa, Ilan Samish, Ilya Venger, and Ora Schuler-Furman for helpful discussions and comments on the manuscript, Edna Schechtman for statistical advice, and Michal Kirshner for proofreading the manuscript. This work was supported by the Israel Science Foundation and by the Leo and Julia Forchheimer Center for Molecular Genetics.
- Henikoff S, Greene EA, Pietrokovski S, Bork P, Attwood TK, Hood L: Gene families: the taxonomy of protein paralogs and chimeras. Science 1997, 278: 609–614. 10.1126/science.278.5338.609View ArticlePubMedGoogle Scholar
- Henikoff JG, Greene EA, Pietrokovski S, Henikoff S: Increased coverage of protein families with the blocks database servers. Nucleic Acids Res 2000, 28: 228–230. 10.1093/nar/28.1.228PubMed CentralView ArticlePubMedGoogle Scholar
- Kabsch W, Sander C: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22: 2577–2637. 10.1002/bip.360221211View ArticlePubMedGoogle Scholar
- Henikoff S, Henikoff JG, Alford WJ, Pietrokovski S: Automated construction and graphical presentation of protein blocks from unaligned sequences. Gene 1995, 163: GC17–26. 10.1016/0378-1119(95)00486-PView ArticlePubMedGoogle Scholar
- Sitbon E, Pietrokovski S: New types of conserved sequence domains in DNA-binding regions of homing endonucleases. Trends Biochem Sci 2003, 28: 473–477. 10.1016/S0968-0004(03)00170-1View ArticlePubMedGoogle Scholar
- Bystroff C, Simons KT, Han KF, Baker D: Local sequence-structure correlations in proteins. Curr Opin Biotechnol 1996, 7: 417–421. 10.1016/S0958-1669(96)80117-0View ArticlePubMedGoogle Scholar
- Han KF, Baker D: Global properties of the mapping between local amino acid sequence and local structure in proteins. Proc Natl Acad Sci U S A 1996, 93: 5814–5818. 10.1073/pnas.93.12.5814PubMed CentralView ArticlePubMedGoogle Scholar
- Mizuguchi K, Blundell T: Analysis of conservation and substitutions of secondary structure elements within protein superfamilies. Bioinformatics 2000, 16: 1111–1119. 10.1093/bioinformatics/16.12.1111View ArticlePubMedGoogle Scholar
- Cygler M, Schrag JD, Sussman JL, Harel M, Silman I, Gentry MK, Doctor BP: Relationship between sequence conservation and three-dimensional structure in a large family of esterases, lipases, and related proteins. Protein Sci 1993, 2: 366–382.PubMed CentralView ArticlePubMedGoogle Scholar
- Liu J, Tan H, Rost B: Loopy proteins appear conserved in evolution. J Mol Biol 2002, 322: 53–64. 10.1016/S0022-2836(02)00736-2View ArticlePubMedGoogle Scholar
- Hubbard TJ, Murzin AG, Brenner SE, Chothia C: SCOP: a structural classification of proteins database. Nucleic Acids Res 1997, 25: 236–239. 10.1093/nar/25.1.236PubMed CentralView ArticlePubMedGoogle Scholar
- Chothia C, Lesk AM: The relation between the divergence of sequence and structure in proteins. Embo J 1986, 5: 823–826.PubMed CentralPubMedGoogle Scholar
- Doolittle RF: Similar amino acid sequences: chance or common ancestry? Science 1981, 214: 149–159. 10.1126/science.7280687View ArticlePubMedGoogle Scholar
- Holm L, Sander C: The FSSP database: fold classification based on structure-structure alignment of proteins. Nucleic Acids Res 1996, 24: 206–209. 10.1093/nar/24.1.206PubMed CentralView ArticlePubMedGoogle Scholar
- Brenner SE, Chothia C, Hubbard TJ, Murzin AG: Understanding protein structure: using scop for fold interpretation. Methods Enzymol 1996, 266: 635–643.View ArticlePubMedGoogle Scholar
- Rost B: Protein structures sustain evolutionary drift. Fold Des 1997, 2: S19–24. 10.1016/S1359-0278(97)00059-XView ArticlePubMedGoogle Scholar
- Rost B: Twilight zone of protein sequence alignments. Protein Eng 1999, 12: 85–94. 10.1093/protein/12.2.85View ArticlePubMedGoogle Scholar
- He MM, Wood ZA, Baase WA, Xiao H, Matthews BW: Alanine-scanning mutagenesis of the beta-sheet region of phage T4 lysozyme suggests that tertiary context has a dominant effect on beta-sheet formation. Protein Sci 2004, 13: 2716–2724. 10.1110/ps.04875504PubMed CentralView ArticlePubMedGoogle Scholar
- Heinz DW, Baase WA, Zhang XJ, Blaber M, Dahlquist FW, Matthews BW: Accommodation of amino acid insertions in an alpha-helix of T4 lysozyme. Structural and thermodynamic analysis. J Mol Biol 1994, 236: 869–886. 10.1006/jmbi.1994.1195View ArticlePubMedGoogle Scholar
- Blaber M, Baase WA, Gassner N, Matthews BW: Alanine scanning mutagenesis of the alpha-helix 115–123 of phage T4 lysozyme: effects on structure, stability and the binding of solvent. J Mol Biol 1995, 246: 317–330. 10.1006/jmbi.1994.0087View ArticlePubMedGoogle Scholar
- Bastolla U, Porto M, Eduardo Roman MH, Vendruscolo MH: Connectivity of neutral networks, overdispersion, and structural conservation in protein evolution. J Mol Evol 2003, 56: 243–254. 10.1007/s00239-002-2350-0View ArticlePubMedGoogle Scholar
- Porto M, Roman HE, Vendruscolo M, Bastolla U: Prediction of site-specific amino acid distributions and limits of divergent evolutionary changes in protein sequences. Mol Biol Evol 2005, 22: 630–638. 10.1093/molbev/msi048View ArticlePubMedGoogle Scholar
- Bornberg-Bauer E, Chan HS: Modeling evolutionary landscapes: mutational stability, topology, and superfunnels in sequence space. Proc Natl Acad Sci U S A 1999, 96: 10689–10694. 10.1073/pnas.96.19.10689PubMed CentralView ArticlePubMedGoogle Scholar
- Mirny L, Shakhnovich E: Evolutionary conservation of the folding nucleus. J Mol Biol 2001, 308: 123–129. 10.1006/jmbi.2001.4602View ArticlePubMedGoogle Scholar
- Rodionov MA, Blundell TL: Sequence and structure conservation in a protein core. Proteins: Structure, Function, and Genetics 1998, 33: 358–366. Publisher Full Text 10.1002/(SICI)1097-0134(19981115)33:3<358::AID-PROT5>3.0.CO;2-0View ArticleGoogle Scholar
- Rose GD: Secondary Structure in Protein Analysis. In Encyclopedia of Biological Chemistry. New York, Elsevier Inc.; 2004:1–6.View ArticleGoogle Scholar
- Henikoff S, Henikoff JG, Pietrokovski S: Blocks+: a non-redundant database of protein alignment blocks derived from multiple compilations. Bioinformatics 1999, 15: 471–479. 10.1093/bioinformatics/15.6.471View ArticlePubMedGoogle Scholar
- Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM: CATH--a hierarchic classification of protein domain structures. Structure 1997, 5: 1093–1108. 10.1016/S0969-2126(97)00260-8View ArticlePubMedGoogle Scholar
- Levitt M, Chothia C: Structural patterns in globular proteins. Nature 1976, 261: 552–558. 10.1038/261552a0View ArticlePubMedGoogle Scholar
- Andersen CAF, Rost B: Secondary structure assignment. Methods of Biochemical Analysis. In Structural bioinformatics. Volume 44. Edited by: Bourne FE and Weissig H. Edited by: Suelter CH. Hoboken, NJ, Wiley-Liss; 2003:649.Google Scholar
- Levitt M: Conformational preferences of amino acids in globular proteins. Biochemistry 1978, 17: 4277–4285. 10.1021/bi00613a026View ArticlePubMedGoogle Scholar
- Ramachandran GN, Sasisekharan V: Conformation of polypeptides and proteins. Adv Protein Chem 1968, 23: 283–438.View ArticlePubMedGoogle Scholar
- Creamer TP, Rose GD: Side-chain entropy opposes alpha-helix formation but rationalizes experimentally determined helix-forming propensities. Proc Natl Acad Sci U S A 1992, 89: 5937–5941. 10.1073/pnas.89.13.5937PubMed CentralView ArticlePubMedGoogle Scholar
- Chellgren BW, Creamer TP: Side-chain entropy effects on protein secondary structure formation. Proteins 2006, 62: 411–420. 10.1002/prot.20766View ArticlePubMedGoogle Scholar
- Minor DL Jr., Kim PS: Context is a major determinant of beta-sheet propensity. Nature 1994, 371: 264–267. 10.1038/371264a0View ArticlePubMedGoogle Scholar
- Leopold PE, Montal M, Onuchic JN: Protein folding funnels: a kinetic approach to the sequence-structure relationship. Proc Natl Acad Sci U S A 1992, 89: 8721–8725. 10.1073/pnas.89.18.8721PubMed CentralView ArticlePubMedGoogle Scholar
- Apweiler R, Attwood TK, Bairoch A, Bateman A, Birney E, Biswas M, Bucher P, Cerutti L, Corpet F, Croning MD, Durbin R, Falquet L, Fleischmann W, Gouzy J, Hermjakob H, Hulo N, Jonassen I, Kahn D, Kanapin A, Karavidopoulou Y, Lopez R, Marx B, Mulder NJ, Oinn TM, Pagni M, Servant F: The InterPro database, an integrated documentation resource for protein families, domains and functional sites. Nucleic Acids Res 2001, 29: 37–40. 10.1093/nar/29.1.37PubMed CentralView ArticlePubMedGoogle Scholar
- Hendrick K: SwissProt to PDB mapping.[ftp://ftp.ebi.ac.uk/pub/contrib/mcneil/pdb_sws_mapping.lst.gz]
- Bairoch A, Apweiler R, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O'Donovan C, Redaschi N, Yeh LS: The Universal Protein Resource (UniProt). Nucleic Acids Res 2005, 33: D154–9. 10.1093/nar/gki070PubMed CentralView ArticlePubMedGoogle Scholar
- Henikoff S, Henikoff JG: Position-based sequence weights. J Mol Biol 1994, 243: 574–578. 10.1016/0022-2836(94)90032-9View ArticlePubMedGoogle Scholar
- Hubbard SJ, Thornton JM: 'NACCESS', Computer Program. , Department of Biochemistry and Molecular Biology, University College London; 1993.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.