Growth of PDZNet by the acquisition of binding motifs.

<p>(A) Two evolutionary models describe the expansion of PDZ domain-ligand interactions. In the gene duplication model, a new PDZ domain-ligand interaction is added by duplication of an existing PDZ ligand. In the sequence mutation model, a new interaction is added by mutations of the C-terminal sequence of the non-PDZ ligand. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins (<i>left</i>) and PDZ proteins that share the same PDZ ligands (<i>right</i>). (C) An example of a PDZ domain-ligand interaction created by sequence mutations. Phylogenetic profiles of NOS1AP and NOS1 are presented. ‘−’ indicates that no ortholog was found in the corresponding species. Four C-terminal residues of NOS1AP orthologs are placed on the right side of the protein. (D) The PWM of NOS1. Four C-terminal residues of vertebrate NOS1AP orthologs (EIAV) are presented in red. “Others” indicates amino acids that were not preferred in the binding pockets.</p

Correlation between binding score and binding affinity.

<p>(A and C) PWMs of the PDZ domains of SNA1 (human) and ERBIN (human). Black bars represent the affinity contribution of the binding scores to the corresponding amino acids. Clusters of amino acids with no preference are labeled “others.” (B and D) Scatter plots showing the correlation between binding score and binding affinity.</p

Validation of PWMs on <i>in vivo</i> partners.

<p>(A) The PWM of the PSD-95_1 domain. (B) Known interacting partners of the PSD-95_1 domain from three species are shown. (C) Fraction of known PDZ domain-ligand interactions are examined by percentile rank of binding scores.</p

Fractions of the proteins in PDZNet whose mutations are associated with specific disease classes.

<p>Disease classes with a <i>p</i> value less than 0.05 are shown.</p

Construction of PDZNet.

<p>(A) Building position weight matrices (PWMs) of human PDZ domains. Experimental data of the PDZ domain and peptide interactions were used to generate PWMs of PDZ domains. (B) Construction of the PDZ domain-ligand interaction network. Human protein interactions were collected by integrating existing PPI databases. (C) Integration of binding strengths into PDZNet.</p

Construction of a quantitative model of PDZ domain-ligand interactions and generation of PWMs of human PDZ domains.

<p>(<i>Left</i>) Training step. The procedure to build selectivity spaces for the four pockets of the PDZ domain. From multiple sequence alignments of PDZ domain sequences, pocket residues were extracted and converted into feature vectors. The feature vectors were alternatively assembled into 20 training sets, each comprising a specific amino acid-preferring group (positive set) and the remainder (negative set). Groups are represented by circles, and the preferred amino acids are shown within the circles. FLD analysis was performed on these training sets to generate 20 selectivity axes that assemble into a pocket selectivity space. Pockets are shown as spheres in the selectivity space. This procedure was repeated for each pocket, resulting in four selectivity spaces that correspond to each ligand position. We note that the selectivity “dots” have 20 dimensions but are represented by three-dimensional cubes for convenience. (<i>Right</i>) Prediction step. The procedure to build a PWM of the query domain. Pocket residues were extracted from the query domain and converted into a feature vector. This feature vector was projected on the selectivity space. The nearest 40 pockets from the query were collected, and their amino acid preferences were averaged. The averaged preferences were then converted into affinity contribution profiles. This procedure was repeated for each pocket, producing a PWM.</p

Network analyses of human PDZ protein-ligand interactions.

<p>(A) Network representation of PDZNet. Orange and blue circles correspond to PDZ proteins and ligands, respectively. The size of the node is proportional to the number of interacting partners. (B) Construction of the PDZ Protein Network (PPN) and the PDZ Ligand Network (PLN). (<i>Center</i>) A subset of PDZ protein-ligand interactions with experimental evidence of physical association. PDZ-binding motifs are presented on the right side of the ligands. (<i>Left</i>) The PPN projection of PDZNet in which two PDZ proteins are connected if they interact with the same ligand. (<i>Right</i>) The PLN projection in which two ligands are connected if they interact with the same PDZ protein.</p

Strong binders were enriched by directed evolution of the 1OZJ and 1RK9 scaffolds.

<p>(A) The reactivity of the protein binders (wild-type and mutant clones) against EGFR (black) and BSA (gray) as assessed by phage-ELISA. Error bars represent the standard deviation of triplicate measures. (B) The sequence of the mutant clones generated from 1OZJ. (C) The sequence of the mutant clone generated from 1RK9.</p

Design scheme of target-specific scaffolds.

<p>(A) Synthetic antibodies can achieve extremely diverse structures through sequence randomization of the complementarity determining region (CDR). Among diverse structures, only antibodies with complementary shapes are able to recognize and bind to a particular epitope. (B) By imitating synthetic antibody generation, we devised a strategy to select target-specific scaffolds from the human proteome with shapes that are complementary to the target surface patch. (C) The flow chart shows a two-step strategy to obtain target-specific scaffolds (middle). In the first step, a virtual screening of a human protein scaffold library is conducted to determine a framework specific to the surface patch of interest. Target specific-scaffolds with shapes complementary to the surface patch of interest are selected from the scaffold library through protein docking simulations (upper right). The scaffold–target docking structures with the most favorable complex formation energies are further evaluated (left). In the second step, the scaffold interface in the selected scaffold–target model is optimized by sequence randomization and phage display using directed evolution (lower right).</p

Binders generated from the 1OZJ and 1RK9 scaffolds bind to EGFR fragments I–IV and I–II.

<p>The reactivity of the protein binders (wild-type and mutant clones) against EGFR domain I–IV (dark blue) and EGFR domain I–II (light blue) as assessed by phage-ELISA. Error bars represent the standard deviation of triplicate phage-ELISA experiments.</p