AcidProNet: Acidophilic Protein Classification via DCGAN-GP-Based Data Augmentation and Parameter-Shared Mixture-of-Experts Transformer
Bibliographic record
Abstract
With the continued exploration of biological resources in extreme environments, functional proteins such as acidophilic proteins have attracted increasing attention. These proteins can maintain structural stability and biological functionality under highly acidic conditions (pH < 3), demonstrating significant application potential. However, the current identification of acidophilic proteins still relies on labor-intensive, time-consuming, and costly wet-lab experiments. Existing shallow machine learning methods (e.g., SVM, RF) are limited by their constrained model capacity─characterized by fewer parameters and shallow architectures─which restricts their ability to capture complex sequence-function relationships in acidophilic proteins. To address this issue, we propose an integrated computational framework, AcidProNet, which incorporates three key components: a CNN-based generative adversarial module, DCGAN-GP, for data augmentation; a sparsely and discretely activated, parameter-sharing Mixture-of-Experts Transformer, PS-MoE, designed to effectively optimize encoded features; and the utilization of a pretrained protein language model, ESM C, for extracting biologically meaningful protein embeddings. On an independent test set, our method outperforms existing models. Further expert-grouped and ablation experiments confirm its advantages in model stability and representational power. This study is the first to integrate embedding generation, data augmentation, and expert modeling within a unified framework, providing an efficient and scalable approach for functional protein prediction and laying a methodological foundation for advances in protein engineering applications. Additionally, we have developed a Web site for direct identification of acidophilic proteins, which can be accessed at http://www.bioai-lab.com/AcidNetPro.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".