DeePSLiM: A Deep Learning Approach to Identify Predictive Short-linear Motifs for Protein Sequence Classification
Bibliographic record
Abstract
SLiMs (Short Linear Motifs) are patterns of three to 20 amino acids within proteins that are sufficient to fulfill certain functions. SLiMs play a critical role in many biological processes. Hence, with the increasing quantity of biological data, it is important to develop algorithms that can quickly find patterns in large databases of DNA, RNA and protein sequences. Previous research has been very successful at applying deep learning methods to the problems of motif detection as well as classification of biological sequences. There are, however, limitations to these approaches. Most are limited to finding motifs of a single length. In addition, most research has focused on DNA and RNA, both of which use a four-letter alphabet. A few of these have attempted to apply deep learning methods on the larger, twenty letter, alphabet of proteins. We present an enhanced deep learning model, called DeePSLiM, capable of detecting predictive SLiMs in protein sequences. The model is a shallow network that can be trained quickly on large amounts of data. The SLiMs are predictive because they can be used to classify the sequences into their respective families. In this study, first, we propose a new deep learning approach for finding predictive SLiMs in protein sequences. Then, we use these predictive SLiMs for the classification task of protein sequences to evaluate our proposed method. The model was able to reach scores of 94.5% on accuracy, precision, recall, F1-Score and Matthews-correlation coefficient, as well as 99.9% area under the receiver operator characteristic curve (AUROC). Availability: The source code, sample data, and supplementary material are available via a Github project at https://github.com/sshaghayeghs/DeePSLiM.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".