Protein Sectors: Evolutionary Units of Three-Dimensional Structure Cell (2009) Najeeb Halabi, Olivier Rivoire, Stanislas Leibler, and Rama Ranganathan presented by Jianewei Zhu

Summary Proteins display a hierarchy of structural features at primary, secondary, tertiary, and higher-order levels, an organization that guides our current understanding of their biological properties and evolutionary origins. Here, we reveal a structural organization distinct from this traditional hierarchy by statistical analysis of correlated evolution between amino acids. Applied to the S1A serine proteases, the analysis indicates a decomposition of the protein into three quasi- independent groups of correlated amino acids that we term ‘‘protein sectors.’’

Summary Each sector is physically connected in the tertiary structure, has a distinct functional role, and constitutes an independent mode of sequence divergence in the protein family. Functionally relevant sectors are evident in other protein families as well, suggesting that they may be general features of proteins. We propose that sectors represent a structural organization of proteins that reflects their evolutionary histories.

Introduction Data support two main findings: –protein domains have a heterogeneous internal organization of amino acid interactions that can comprise multiple functionally distinct subdivisions (the sectors) –these sectors define a decomposition of proteins that is distinct from the hierarchy of primary, secondary, tertiary, and quaternary structure. We propose that the sectors are features of protein structures that reflect the evolutionary histories of their conserved biological properties.

Results From Amino Acid Sequence to Sectors Statistical Independence Structural Connectivity Biochemical Independence Independent Sequence Divergence Sectors in Other Protein Families

Experimental Procedures Sequence alignment construction, annotation, and sequence analyses(SCA) to get sectors Minimum discriminatory information(MDI) method to analysis statistical independence Interpret sectors’ structural connectivity by others’ previous approach Protein purification and kinetic assays to measure catalytic power of biochemical independence, and thermal denaturation assays to measure stability of biochemical independence PCA of the corresponding similarity matrices to provide independent sequence divergence

From Amino Acid Sequence to Sectors Multiple sequence alignments(MSA) Measures of positional conservation Measures of sequence similarity SCA calculations Spectral cleaning Sector identification “Pseudo sectors” Representation of significant correlations

Multiple sequence alignments(MSA) PS: 3TGI in S1A family is rat trypsin FamilyPDBSequencesPositions S1A 3TGI PDZ 1BE PAS 2V0W SH2 1AYA SH3 2ABL 49252

Measures of positional conservation

Measures of sequence similarity

SCA calculations

Spectral cleaning Eigenvalue spectra for the matrix corresponding to the S1A serine protease family (top panel) and for a hundred trials for randomizing the S1A sequence alignment (bottom panel). The randomization process scrambles the order of amino acids in each alignment column independently; thus amino acid frequencies at positions are never changed. This analysis shows that the bulk of the spectrum (comprising the lowest 218 out of 223 total eigenvalues) can be attributed to limited sampling of sequences.

Spectral cleaning Among the significant modes, the first mode has a distinctive property: it describes a "coherent" correlation of all positions and historical noise is expected to produce coherent correlations between sequence positions SCA matrices with a dominant first mode, the first eigenvector should just report the net contribution of each position to the total correlation. The first mode is irrelevant for decomposing the protein sequence into functional units and is removed.

Sector identification

The Red Sector The Blue Sector The Green Sector

Sector identification

The image and instruction is on page 22 of the supplmental data pdf.

“Pseudo sectors”

Representation of significant correlations (E) SCA matrix after reduction of statistical noise and of global coherent correlations. The 65 positions that remain fall into three groups of positions (red, blue, and green, termed ‘‘sectors’’), each displaying strong intragroup correlations and weak intergroup correlations. In each sector, positions are ordered by descending magnitude of contribution (Figure S3), showing that sector positions comprise a hierarchy of correlation strengths.

Statistical Independence The minimum discriminatory information (MDI) –method aims at generalizing the definition of positional conservation based on relative entropies to include correlations between positions. –Its principles are completely distinct from the SCA method. –If two sectors are independent, then the correlation entropy of two taken together must be the sum of their correlation entropies taken individually.

Statistical Independence

Structural Connectivity

No sector corresponds to any known subdivision of proteins by primary structure segments, secondary structure elements, or subdomain architecture.

Structural Connectivity

Biochemical Independence Protein purification and kinetic assays to measure catalytic power of biochemical independence Thermal denaturation assays to measure stability of biochemical independence

Biochemical Independence

Independent Sequence Divergence PCA of the corresponding similarity matrices to provide independent sequence divergence The image and instruction is on page 7 of the protein sectors pdf.

Sectors in Other Protein Families Two sectors are evident in the PSD95/Dlg1/ZO1 (PDZ) domain family of protein interaction modules (blue and red, Figures 7A and S11) Two sectors are also evident in the Per/Arnt/Sim (PAS) domain. Physically contiguous sectors are also evident in the SH2 and SH3 families of interaction modules (Figures 7C, 7D, S13, and S14). The image and instruction is on page 9 of the protein sectors pdf.

What can we learn?