FragKB: structural and literature annotation resource of conserved peptide fragments and residues

Switching an active site helix in dihydrofolate reductase reveals limits to subdomain modularity

To what degree are individual structural elements within proteins modular such that similar structures from unrelated proteins can be interchanged? We study subdomain modularity by creating 20 chimeras of an enzyme, Escherichia coli dihydrofolate reductase (DHFR), in which a catalytically important, 10-residue α-helical sequence is replaced by α-helical sequences from a diverse set of proteins. The chimeras stably fold but have a range of diminished thermal stabilities and catalytic activities. Evolutionary coupling analysis indicates that the residues of this α-helix are under selection pressure to maintain catalytic activity in DHFR. Reversion to phenylalanine at key position 31 was found to partially restore catalytic activity, which could be explained by evolutionary coupling values. We performed molecular dynamics simulations using replica exchange with solute tempering. Chimeras with low catalytic activity exhibit nonhelical conformations that block the binding site and disrupt the positioning of the catalytically essential residue D27. Simulation observables and in vitro measurements of thermal stability and substrate-binding affinity are strongly correlated. Several E. coli strains with chromosomally integrated chimeric DHFRs can grow, with growth rates that follow predictions from a kinetic flux model that depends on the intracellular abundance and catalytic activity of DHFR. Our findings show that although α-helices are not universally substitutable, the molecular and fitness effects of modular segments can be predicted by the biophysical compatibility of the replacement segment.

Molecular Dynamics Information Improves cis-Peptide-Based Function Annotation of Proteins

cis-Peptide bonds, whose occurrence in proteins is rare but evolutionarily conserved, are implicated to play an important role in protein function. This has led to their previous use in a homology-independent, fragment-match-based protein function annotation method. However, proteins are not static molecules; dynamics is integral to their activity. This is nicely epitomized by the geometric isomerization of cis-peptide to trans form for molecular activity. Hence we have incorporated both static (cis-peptide) and dynamics information to improve the prediction of protein molecular function. Our results show that cis-peptide information alone cannot detect functional matches in cases where cis-trans isomerization exists but 3D coordinates have been obtained for only the trans isomer or when the cis-peptide bond is incorrectly assigned as trans. On the contrary, use of dynamics information alone includes false-positive matches for cases where fragments with similar secondary structure show similar dynamics, but the proteins do not share a common function. Combining the two methods reduces errors while detecting the true matches, thereby enhancing the utility of our method in function annotation. A combined approach, therefore, opens up new avenues of improving existing automated function annotation methodologies.

Identifying functionally important cis-peptide containing segments in proteins and their utility in molecular function annotation

Cis-peptide embedded segments are rare in proteins but often highlight their important role in molecular function when they do occur. The high evolutionary conservation of these segments illustrates this observation almost universally, although no attempt has been made to systematically use this information for the purpose of function annotation. In the present study, we demonstrate how geometric clustering and level-specific Gene Ontology molecular-function terms (also known as annotations) can be used in a statistically significant manner to identify cis-embedded segments in a protein linked to its molecular function. The present study identifies novel cis-peptide fragments, which are subsequently used for fragment-based function annotation. Annotation recall benchmarks interpreted using the receiver-operator characteristic plot returned an area-under-curve > 0.9, corroborating the utility of the annotation method. In addition, we identified cis-peptide fragments occurring in conjunction with functionally important trans-peptide fragments, providing additional insights into molecular function. We further illustrate the applicability of our method in function annotation where homology-based annotation transfer is not possible. The findings of the present study add to the repertoire of function annotation approaches and also facilitate engineering, design and allied studies around the cis-peptide neighborhood of proteins.

Detecting protein candidate fragments using a structural alphabet profile comparison approach

Predicting accurate fragments from sequence has recently become a critical step for protein structure modeling, as protein fragment assembly techniques are presently among the most efficient approaches for de novo prediction. A key step in these approaches is, given the sequence of a protein to model, the identification of relevant fragments - candidate fragments - from a collection of the available 3D structures. These fragments can then be assembled to produce a model of the complete structure of the protein of interest. The search for candidate fragments is classically achieved by considering local sequence similarity using profile comparison, or threading approaches. In the present study, we introduce a new profile comparison approach that, instead of using amino acid profiles, is based on the use of predicted structural alphabet profiles, where structural alphabet profiles contain information related to the 3D local shapes associated with the sequences. We show that structural alphabet profile-profile comparison can be used efficiently to retrieve accurate structural fragments, and we introduce a fully new protocol for the detection of candidate fragments. It identifies fragments specific of each position of the sequence and of size varying between 6 and 27 amino-acids. We find it outperforms present state of the art approaches in terms (i) of the accuracy of the fragments identified, (ii) the rate of true positives identified, while having a high coverage score. We illustrate the relevance of the approach on complete target sets of the two previous Critical Assessment of Techniques for Protein Structure Prediction (CASP) rounds 9 and 10. A web server for the approach is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/SAFrag.

Fast protein fragment similarity scoring using a Binet-Cauchy kernel

MOTIVATION

Meaningful scores to assess protein structure similarity are essential to decipher protein structure and sequence evolution. The mining of the increasing number of protein structures requires fast and accurate similarity measures with statistical significance. Whereas numerous approaches have been proposed for protein domains as a whole, the focus is progressively moving to a more local level of structure analysis for which similarity measurement still remains without any satisfactory answer.

RESULTS

We introduce a new score based on Binet-Cauchy kernel. It is normalized and bounded between 1-maximal similarity that implies exactly the same conformations for protein fragments-and -1-mirror image conformations, the unrelated conformations having a null mean score. This allows for the search of both similar and mirror conformations. In addition, such score addresses two major issue of the widely used root mean square deviation (RMSD). First, it achieves length independent statistics even for short fragments. Second, it shows better performance in the discrimination of medium range RMSD values. Being simpler and faster to compute than the RMSD, it also provides the means for large-scale mining of protein structures.

AVAILABILITY AND IMPLEMENTATION

The computer software implementing the score is available at http://bioserv.rpbs.univ-paris-diderot.fr/BCscore/

CONTACT

frederic.guyon@univ-paris-diderot.fr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein design with fragment databases

Structure-based computational methods are popular tools for designing proteins and interactions between proteins because they provide the necessary insight and details required for rational engineering. Here, we first argue that large-scale databases of fragments contain a discrete but complete set of building blocks that can be used to design structures. We show that these structural alphabets can be saturated to provide conformational ensembles that sample the native structure space around energetic minima. Second, we show that catalogs of interaction patterns hold the key to overcome the lack of scaffolds when computationally designing protein interactions. Finally, we illustrate the power of database-driven computational protein design methods by recent successful applications and discuss what challenges remain to push this field forward.