SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks
Bibliographic record
Abstract
The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple experimental steps, which requires a lot of manpower and material resources. In addition, studies have shown that not all proteins can form crystals under experimental conditions, and the success rate of the final crystallization of proteins is only <10%. Although some protein crystallization predictors have been developed, not many tools capable of predicting multi-stage protein crystallization propensity are available and the accuracy of these tools is not satisfactory. In this paper, we propose a novel deep learning framework, named SADeepcry, for predicting protein crystallization propensity. The framework can be used to estimate the three steps (protein material production, purification and crystallization) in protein crystallization experiments and the success rate of the final protein crystallization. SADeepcry uses the optimized self-attention and auto-encoder modules to extract sequence, structure and physicochemical features from the proteins. Compared with other state-of-the-art protein crystallization propensity prediction models, SADeepcry can obtain more complex global spatial long-distance dependence of protein sequence information. Our computational results show that SADeepcry has increased Matthews correlation coefficient and area under the curve, by 100.3% and 13.4%, respectively, over the DCFCrystal method on the benchmark dataset. The codes of SADeepcry are available at https://github.com/zhc940702/SADeepcry.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".