Defining the Specificity Space of the Human Src Homology 2 Domain
Bibliographic record
Abstract
Src homology 2 (SH2) domains are the largest family of interaction modules encoded by the human genome to recognize tyrosine-phosphorylated sequences and thereby play pivotal roles in transducing and controlling cellular signals emanating from protein-tyrosine kinases. Different SH2 domains select for distinct phosphopeptides, and the function of a given SH2 domain is often dictated by the specific motifs that it recognizes. Therefore, deciphering the phosphotyrosyl peptide motif recognized by an SH2 domain is the key to understanding its cellular function. Here we cloned all 120 SH2 domains identified in the human genome and determined the phosphotyrosyl peptide binding properties of 76 SH2 domains by screening an oriented peptide array library. Of these 76, we defined the selectivity for 43 SH2 domains and refined the binding motifs for another 33 SH2 domains. We identified a number of novel binding motifs, which are exemplified by the BRDG1 SH2 domain that selects specifically for a bulky, hydrophobic residue at P + 4 relative to the Tyr(P) residue. Based on the oriented peptide array library data, we developed scoring matrix-assisted ligand identification (or SMALI), a Web-based program for predicting binding partners for SH2-containing proteins. When applied to SH2D1A/SAP (SLAM-associated protein), a protein whose mutation or deletion underlies the X-linked lymphoproliferative syndrome, SMALI not only recapitulated known interactions but also identified a number of novel interacting proteins for this disease-associated protein. SMALI also identified a number of potential interactors for BRDG1, a protein whose function is largely unknown. Peptide in-solution binding analysis demonstrated that a SMALI score correlates well with the binding energy of a peptide to a given SH2 domain. The definition of the specificity space of the human SH2 domain provides both the necessary molecular basis and a platform for future exploration of the functions for SH2-containing proteins in cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".