2024/10/11 by Joongwon Chae, Chae, Joongwon, Zhenyu Wang +9 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Biomolecules (q-bio.BM) #Computer science #Electrical engineering #Engineering #Environmental science #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genetics, Bioinformatics, and Biomedical Research #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Transformer #Voltage #cs.AI #cs.LG #q-bio.BM
paper · pdf · doi:10.48550/arxiv.2410.21283
openalex publication_date 2024/10/11 · arxiv published 2024/10/11 · arxiv updated 2025/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Recent advancements in protein structure prediction, particularly AlphaFold2, have revolutionized structural biology by achieving near-experimental accuracy (average RMSD < 1.5\textÅ). However, the computational demands of these models (approximately 30 minutes per protein on an RTX 4090) significantly limit their application in high-throughput protein screening. While large language models like ESM (Evolutionary Scale Modeling) have shown promise in extracting structural information directly from protein sequences, rapid assessment of protein structure quality for large-scale analyses remains a major challenge. We introduce pLDDT-Predictor, a high-speed protein screening tool that achieves a 250,000× speedup compared to AlphaFold2 by leveraging pre-trained ESM2 protein embeddings and a Transformer architecture. Our model predicts AlphaFold2's pLDDT (predicted Local Distance Difference Test) scores with a Pearson correlation of 0.7891 and processes proteins in just 0.007 seconds on average. Using a comprehensive dataset of 1.5 million diverse protein sequences (ranging from 50 to 2048 amino acids), we demonstrate that pLDDT-Predictor accurately classifies high-confidence structures (pLDDT > 70) with 91.2% accuracy and achieves an MSE of 84.8142 compared to AlphaFold2's predictions. The source code and pre-trained models are freely available at https://github.com/jw-chae/pLDDTPredictor, enabling the research community to perform rapid, large-scale protein structure quality assessments.