Limits...
Amino acid selective unlabeling for sequence specific resonance assignments in proteins.

Krishnarjuna B, Jaipuria G, Thakur A, D'Silva P, Atreya HS - J. Biomol. NMR (2010)

Bottom Line: The traditional approach to selective labeling yields only the chemical shifts of the particular amino acid being selected and does not help in establishing a link between adjacent residues along the polypeptide chain, which is important for sequential assignments.A detailed survey involving unlabeling of different amino acid types individually or in pairs reveals that the proposed approach is also robust to misincorporation of (14)N at undesired sites.Taken together, this study represents the first application of selective unlabeling for sequence specific resonance assignments and opens up new avenues to using this methodology in protein structural studies.

View Article: PubMed Central - PubMed

Affiliation: NMR Research Centre, Indian Institute of Science, Bangalore 560012, India.

ABSTRACT
Sequence specific resonance assignment constitutes an important step towards high-resolution structure determination of proteins by NMR and is aided by selective identification and assignment of amino acid types. The traditional approach to selective labeling yields only the chemical shifts of the particular amino acid being selected and does not help in establishing a link between adjacent residues along the polypeptide chain, which is important for sequential assignments. An alternative approach is the method of amino acid selective 'unlabeling' or reverse labeling, which involves selective unlabeling of specific amino acid types against a uniformly (13)C/(15)N labeled background. Based on this method, we present a novel approach for sequential assignments in proteins. The method involves a new NMR experiment named, {(12)CO( i )-(15)N( i+1)}-filtered HSQC, which aids in linking the (1)H(N)/(15)N resonances of the selectively unlabeled residue, i, and its C-terminal neighbor, i + 1, in HN-detected double and triple resonance spectra. This leads to the assignment of a tri-peptide segment from the knowledge of the amino acid types of residues: i - 1, i and i + 1, thereby speeding up the sequential assignment process. The method has the advantage of being relatively inexpensive, applicable to (2)H labeled protein and can be coupled with cell-free synthesis and/or automated assignment approaches. A detailed survey involving unlabeling of different amino acid types individually or in pairs reveals that the proposed approach is also robust to misincorporation of (14)N at undesired sites. Taken together, this study represents the first application of selective unlabeling for sequence specific resonance assignments and opens up new avenues to using this methodology in protein structural studies.

Show MeSH
Statistical analysis of the uniqueness of tri-peptide segments in proteins rendered with selective unlabeling. The numbers/percentages corresponding to the selective unlabeling approach are shown in pink colour, whereas those corresponding to the non-selective method are indicated in blue. a The number of proteins in the database of 186 non-homologues proteins which have unique tri-peptide sequences containing a given amino acid residue in the central position. In such tri-peptide segments, the residue preceding and following the central residue is given a number code according to its amino acid type (Fig. 1) and the central residue is given a unique code (see Figure S3 of Supporting Information). The entire polypeptide chain (also converted to a sequence of codes) is then searched for multiple occurrences of such tri-peptide segments. b In proteins which had more than one occurrences of a given tri-peptide segment, the fraction of tri-peptide segments (expressed as percentage) that were unique using the criterion described in (a). c The ratio of number of tri-peptide segments that had multiple occurrences without the knowledge of the central amino acid residue as against multiple occurrences with selective identification
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3020294&req=5

Fig9: Statistical analysis of the uniqueness of tri-peptide segments in proteins rendered with selective unlabeling. The numbers/percentages corresponding to the selective unlabeling approach are shown in pink colour, whereas those corresponding to the non-selective method are indicated in blue. a The number of proteins in the database of 186 non-homologues proteins which have unique tri-peptide sequences containing a given amino acid residue in the central position. In such tri-peptide segments, the residue preceding and following the central residue is given a number code according to its amino acid type (Fig. 1) and the central residue is given a unique code (see Figure S3 of Supporting Information). The entire polypeptide chain (also converted to a sequence of codes) is then searched for multiple occurrences of such tri-peptide segments. b In proteins which had more than one occurrences of a given tri-peptide segment, the fraction of tri-peptide segments (expressed as percentage) that were unique using the criterion described in (a). c The ratio of number of tri-peptide segments that had multiple occurrences without the knowledge of the central amino acid residue as against multiple occurrences with selective identification

Mentions: We have carried out a statistical analysis of 186 non-homologues primary sequences from the TALOS database (Cornilescu et al. 1999) to estimate the extent to which such tri-peptide segments can be placed uniquely in a protein primary sequence. The analysis was carried out by assigning a unique number code to each of the 9 categories shown in Fig. 1 except for the residue being selectively unlabeled, which was assigned a separate code (given that its type is known exactly). Thus, both the tri-peptide segment and the polypeptide chain were converted into a new sequence of codes, which was then searched for multiple occurrences of the tri-peptide segment (this is further illustrated in Figure S3 of Supporting Information for a 99 amino acid residue protein HIVprotease). This approach was carried out for 15 amino acids (excluding Ala, Gly, Ser, Thr and Pro) in the central position of the tri-peptide segment and repeated for each protein in the database. The following two comparisons were made to judge the advantage of selective unlabeling: First, the number of proteins in the database was taken wherein all tri-peptide segments (comprising a given amino acid type in the centre) were rendered unique with and without selective unlabeling. This number brings out the extent of uniqueness obtained from the knowledge of the central and its C-terminal residue of the tri-peptide segment by selective unlabeling as compared to the conventional sequential assignment process without selective identification of amino acid types. The second calculation involved the remaining proteins in the database in which some of the tri-peptide segments had multiple occurrences. In such cases, the increase in the percentage of unique tri-peptide segments due to knowledge of the central amino acid residue was evaluated. The results are depicted in Fig. 9.Fig. 9


Amino acid selective unlabeling for sequence specific resonance assignments in proteins.

Krishnarjuna B, Jaipuria G, Thakur A, D'Silva P, Atreya HS - J. Biomol. NMR (2010)

Statistical analysis of the uniqueness of tri-peptide segments in proteins rendered with selective unlabeling. The numbers/percentages corresponding to the selective unlabeling approach are shown in pink colour, whereas those corresponding to the non-selective method are indicated in blue. a The number of proteins in the database of 186 non-homologues proteins which have unique tri-peptide sequences containing a given amino acid residue in the central position. In such tri-peptide segments, the residue preceding and following the central residue is given a number code according to its amino acid type (Fig. 1) and the central residue is given a unique code (see Figure S3 of Supporting Information). The entire polypeptide chain (also converted to a sequence of codes) is then searched for multiple occurrences of such tri-peptide segments. b In proteins which had more than one occurrences of a given tri-peptide segment, the fraction of tri-peptide segments (expressed as percentage) that were unique using the criterion described in (a). c The ratio of number of tri-peptide segments that had multiple occurrences without the knowledge of the central amino acid residue as against multiple occurrences with selective identification
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3020294&req=5

Fig9: Statistical analysis of the uniqueness of tri-peptide segments in proteins rendered with selective unlabeling. The numbers/percentages corresponding to the selective unlabeling approach are shown in pink colour, whereas those corresponding to the non-selective method are indicated in blue. a The number of proteins in the database of 186 non-homologues proteins which have unique tri-peptide sequences containing a given amino acid residue in the central position. In such tri-peptide segments, the residue preceding and following the central residue is given a number code according to its amino acid type (Fig. 1) and the central residue is given a unique code (see Figure S3 of Supporting Information). The entire polypeptide chain (also converted to a sequence of codes) is then searched for multiple occurrences of such tri-peptide segments. b In proteins which had more than one occurrences of a given tri-peptide segment, the fraction of tri-peptide segments (expressed as percentage) that were unique using the criterion described in (a). c The ratio of number of tri-peptide segments that had multiple occurrences without the knowledge of the central amino acid residue as against multiple occurrences with selective identification
Mentions: We have carried out a statistical analysis of 186 non-homologues primary sequences from the TALOS database (Cornilescu et al. 1999) to estimate the extent to which such tri-peptide segments can be placed uniquely in a protein primary sequence. The analysis was carried out by assigning a unique number code to each of the 9 categories shown in Fig. 1 except for the residue being selectively unlabeled, which was assigned a separate code (given that its type is known exactly). Thus, both the tri-peptide segment and the polypeptide chain were converted into a new sequence of codes, which was then searched for multiple occurrences of the tri-peptide segment (this is further illustrated in Figure S3 of Supporting Information for a 99 amino acid residue protein HIVprotease). This approach was carried out for 15 amino acids (excluding Ala, Gly, Ser, Thr and Pro) in the central position of the tri-peptide segment and repeated for each protein in the database. The following two comparisons were made to judge the advantage of selective unlabeling: First, the number of proteins in the database was taken wherein all tri-peptide segments (comprising a given amino acid type in the centre) were rendered unique with and without selective unlabeling. This number brings out the extent of uniqueness obtained from the knowledge of the central and its C-terminal residue of the tri-peptide segment by selective unlabeling as compared to the conventional sequential assignment process without selective identification of amino acid types. The second calculation involved the remaining proteins in the database in which some of the tri-peptide segments had multiple occurrences. In such cases, the increase in the percentage of unique tri-peptide segments due to knowledge of the central amino acid residue was evaluated. The results are depicted in Fig. 9.Fig. 9

Bottom Line: The traditional approach to selective labeling yields only the chemical shifts of the particular amino acid being selected and does not help in establishing a link between adjacent residues along the polypeptide chain, which is important for sequential assignments.A detailed survey involving unlabeling of different amino acid types individually or in pairs reveals that the proposed approach is also robust to misincorporation of (14)N at undesired sites.Taken together, this study represents the first application of selective unlabeling for sequence specific resonance assignments and opens up new avenues to using this methodology in protein structural studies.

View Article: PubMed Central - PubMed

Affiliation: NMR Research Centre, Indian Institute of Science, Bangalore 560012, India.

ABSTRACT
Sequence specific resonance assignment constitutes an important step towards high-resolution structure determination of proteins by NMR and is aided by selective identification and assignment of amino acid types. The traditional approach to selective labeling yields only the chemical shifts of the particular amino acid being selected and does not help in establishing a link between adjacent residues along the polypeptide chain, which is important for sequential assignments. An alternative approach is the method of amino acid selective 'unlabeling' or reverse labeling, which involves selective unlabeling of specific amino acid types against a uniformly (13)C/(15)N labeled background. Based on this method, we present a novel approach for sequential assignments in proteins. The method involves a new NMR experiment named, {(12)CO( i )-(15)N( i+1)}-filtered HSQC, which aids in linking the (1)H(N)/(15)N resonances of the selectively unlabeled residue, i, and its C-terminal neighbor, i + 1, in HN-detected double and triple resonance spectra. This leads to the assignment of a tri-peptide segment from the knowledge of the amino acid types of residues: i - 1, i and i + 1, thereby speeding up the sequential assignment process. The method has the advantage of being relatively inexpensive, applicable to (2)H labeled protein and can be coupled with cell-free synthesis and/or automated assignment approaches. A detailed survey involving unlabeling of different amino acid types individually or in pairs reveals that the proposed approach is also robust to misincorporation of (14)N at undesired sites. Taken together, this study represents the first application of selective unlabeling for sequence specific resonance assignments and opens up new avenues to using this methodology in protein structural studies.

Show MeSH