Qi Li

Predicting enzyme function from its sequence is still an unsolved problem in the life sciences. Moreover, with the explosion of annotated genome data, we are inundated with potential enzymatic sequences that have not yet been biochemically characterized. While it is not possible to assign a not-yet-existing label to such a sequence, there is high value in placing the sequence as accurately as possible in known function space. Doing so can help provide more accurate falsifiable hypotheses for experimentalists wishing to characterize enzymes from specific functional families. Here we present a contrastive learning algorithm for predicting enzyme function from sequence. Our method, EnzPlacer, predicts the third, second, and first EC numbers for a protein whose fourth EC number is not in the training corpus. This novel prediction mechanism accurately places a protein sequence within a narrowed-down functional context, even if the precise function remains unknown. EnzPlacer and data is available at https://github.com/drxiangma/EnzPlacer under a GPL3 license.

Motivation: Predicting enzyme function from its sequence is still an unsolved problem in the life sciences. Moreover, with the explosion of annotated genome data, we are inundated with potential enzymatic sequences that have not yet been biochemically characterized. While it is not possible to assign a not-yet-existing label to such a sequence, there is high value in placing the sequence as accurately as possible in known function space. Doing so can help provide more accurate falsifiable hypotheses for experimentalists wishing to characterize enzymes from specific functional families. Results: Here we present a contrastive learning algorithm for predicting enzyme function from sequence. Our method, EnzPlacer, predicts the third, second, and first EC numbers for a protein whose fourth EC number is not in the training corpus. This novel prediction mechanism accurately places a protein sequence within a narrowed-down functional context, even if the precise function remains unknown. Availability: EnzPlacer is available from https://github.com/drxiangma/EnzPlacer under a GPL3 license. Contact: qli@iastate.edu

Decoding protein–protein interactions at the residue level is crucial for understanding cellular mechanisms and developing targeted therapeutics. We present Seq2Bind webserver, a computational framework that leverages fine-tuned protein language models (PLMs) to determine binding affinity between proteins and identify critical binding residues directly from sequences, eliminating the structural requirements that limit affinity prediction tools. We fine-tuned four architectures, including ProtBERT, ProtT5, Evolutionary Scale Modeling 2 (ESM2), and Bidirectional Long Short-Term Memory on the SKEMPI 2.0 dataset. Through systematic alanine mutagenesis on each residue for 6063 dimer proteins from Protein Data Bank, we evaluated each model’s ability to identify interface residues. Performance was assessed using N-factor metrics, where N-factor = 3 evaluates whether true residues appear within 3n top predictions for n-interface residues. ESM2 achieved 67.4% and ProtBERT 68.2% interface-residue recovery at N-factor = 3. On an independent panel of 14 human health-relevant protein complexes, Seq2Bind outperformed docking and mutation-based baselines, with ESM2 (37.2%) and ProtBERT (35.1%) exceeding the structural docking HADDOCK3 (32.1%) at N-factor = 2. Our sequence-based approach enables rapid screening, handles disordered proteins, and provides comparable accuracy, making Seq2Bind a valuable prior to steer blind docking protocols to identify putative binding residues from each protein for therapeutic targets. Seq2Bind webserver is freely accessible at https://agrivax.onrender.com/seq2bind/scan under StructF-suite.

Aspect-Based Sentiment Analysis (ABSA) comprises several subtasks: aspect term extraction (ATE), opinion term extraction (OTE), aspect term sentiment extraction (ATSE), aspect-opinion pair extraction (AOPE), and aspect sentiment triplet extraction (ASTE). Existing unified frameworks for ABSA rely heavily on large-scale annotated data, limiting scalability across domains. We propose UAOS, a double-layer unified span extraction framework that performs all five ABSA subtasks under weak supervision. Our approach first extracts aspect-opinion pairs using universal dependency-based rules from unannotated corpora. Sentiment labels for these pairs are generated via a novel zero-shot, domain-agnostic prompt-based method. The resulting weak labels train a unified span extraction architecture equipped with canonical correlation analysis for early stopping and a self-training mechanism to mitigate noise and bias in supervision. Extensive experiments on four ABSA benchmarks demonstrate that UAOS achieves competitive or superior performance compared to fully supervised baselines. It improves upon the state-of-the-art ODAO by +1.54 F1 for ATE, +0.56 for OTE, and +0.82 for AOPE. In ATSE and ASTE, where no weakly supervised baselines exist, UAOS outperforms several supervised models, setting new benchmarks. To assess domain generalizability, we evaluate UAOS on a psychology/education-domain dataset of student reflections spanning four instructional conditions. Without in-domain fine-tuning, it achieves macro F1 scores of 71.05 (ATE), 74.39 (OTE), 68.24 (AOPE), and 60.56 (ASTE). These results highlight the model’s ability to generalize to out-of-distribution, non-commercial text, underscoring its scalability for low-resource ABSA applications.

Information extraction tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), are essential for advancing clinical research and applications. However, these tasks are hindered by the scarcity of labeled clinical documents due to privacy concerns and high annotation costs. This study introduces a novel framework combining Large Language Models (LLMs) for data augmentation with an adapted BERT model for clinical information extraction. The framework encodes entity and relational information within clinical note segments, enabling LLMs to generate diverse and contextually accurate augmentations while preserving structural integrity. Augmented data is used to train a segmentation-based BERT model, overcoming sequence length limitations and integrating global context via BiLSTM. Evaluations on public and proprietary datasets demonstrate significant performance improvements, highlighting the approach’s potential to address data scarcity in clinical information extraction tasks.