Limits...
Computational protein design: validation and possible relevance as a tool for homology searching and fold recognition.

Schmidt Am Busch M, Sedano A, Simonson T - PLoS ONE (2010)

Bottom Line: The results confirm and generalize our earlier study of SH2 and SH3 domains.For some families, designed sequences can be a useful complement to experimental ones for homologue searching.However, improved tools are needed to extract more information from the designed profiles before the method can be of general use.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de Biochimie (CNRS UMR7654), Department of Biology, Ecole Polytechnique, Palaiseau, France.

ABSTRACT

Background: Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases.

Methodology/principal findings: WE EXPLORE THIS STRATEGY FOR FOUR SCOP FAMILIES: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000-300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed.

Conclusions/significance: For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use.

Show MeSH
Exponentiated entropy, , of natural sequences (black line) and designed sequences (grey line).Results computed using a reduced amino acid alphabet with nine classes (see text). Residues are numbered by increasing experimental entropy. Core positions and five Substrate Binding Positions (in the PDZ case; SBPs) are highlighted.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2864755&req=5

pone-0010410-g002: Exponentiated entropy, , of natural sequences (black line) and designed sequences (grey line).Results computed using a reduced amino acid alphabet with nine classes (see text). Residues are numbered by increasing experimental entropy. Core positions and five Substrate Binding Positions (in the PDZ case; SBPs) are highlighted.

Mentions: We next consider the diversity of the designed sequence ensembles, using a standard sequence entropy [37], [78]. The 8,000 lowest energy sequences were used. Table 3 gives the (exponentiated) entropy, averaged over the entire polypeptide chain, or over the core positions only (except for the SKIs, where the protein core is very small). Entropies are also given for the natural, Pfam ensembles (the small sets). Agreement between the designed and natural entropies is good, similar to the SH2 and SH3 cases studied earlier [42]. The highest discrepancies are for the SKIs and chemokines, with natural/designed entropies of 3.6/3.0 and 3.5/3.0, respectively. The variation of the (exponentiated) entropy along the polypeptide chain is shown in Fig. 2 for the chemokines and the PDZ domains. The behavior of the designed and natural sequences are qualitatively similar, though the details are different.


Computational protein design: validation and possible relevance as a tool for homology searching and fold recognition.

Schmidt Am Busch M, Sedano A, Simonson T - PLoS ONE (2010)

Exponentiated entropy, , of natural sequences (black line) and designed sequences (grey line).Results computed using a reduced amino acid alphabet with nine classes (see text). Residues are numbered by increasing experimental entropy. Core positions and five Substrate Binding Positions (in the PDZ case; SBPs) are highlighted.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2864755&req=5

pone-0010410-g002: Exponentiated entropy, , of natural sequences (black line) and designed sequences (grey line).Results computed using a reduced amino acid alphabet with nine classes (see text). Residues are numbered by increasing experimental entropy. Core positions and five Substrate Binding Positions (in the PDZ case; SBPs) are highlighted.
Mentions: We next consider the diversity of the designed sequence ensembles, using a standard sequence entropy [37], [78]. The 8,000 lowest energy sequences were used. Table 3 gives the (exponentiated) entropy, averaged over the entire polypeptide chain, or over the core positions only (except for the SKIs, where the protein core is very small). Entropies are also given for the natural, Pfam ensembles (the small sets). Agreement between the designed and natural entropies is good, similar to the SH2 and SH3 cases studied earlier [42]. The highest discrepancies are for the SKIs and chemokines, with natural/designed entropies of 3.6/3.0 and 3.5/3.0, respectively. The variation of the (exponentiated) entropy along the polypeptide chain is shown in Fig. 2 for the chemokines and the PDZ domains. The behavior of the designed and natural sequences are qualitatively similar, though the details are different.

Bottom Line: The results confirm and generalize our earlier study of SH2 and SH3 domains.For some families, designed sequences can be a useful complement to experimental ones for homologue searching.However, improved tools are needed to extract more information from the designed profiles before the method can be of general use.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de Biochimie (CNRS UMR7654), Department of Biology, Ecole Polytechnique, Palaiseau, France.

ABSTRACT

Background: Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases.

Methodology/principal findings: WE EXPLORE THIS STRATEGY FOR FOUR SCOP FAMILIES: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000-300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed.

Conclusions/significance: For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use.

Show MeSH