Bingding Huang

NanoLAS 2.0: A Comprehensive Update on a Nanobody-Focused Platform with Advanced Visualization and Docking Simulation Features

NanoLAS 2.0 updates a nanobody database with improved search, 3D visualization, docking simulation, and epitope prediction tools.

Iterative In Silico Screening for Optimizing Stable Conformation of Anti-SARS-CoV-2 Nanobodies

The paper introduces an in silico screening strategy to optimize stable conformation of anti-SARS-CoV-2 nanobodies through iterative docking and analysis.

NanoLAS: a Comprehensive Nanobody Database with Data Integration, Consolidation, and Application

NanoLAS integrates and consolidates nanobody data to provide a comprehensive database with improved search and display features, enhancing research and application potential.

NanoBERTa-ASP: Predicting Nanobody Binding Epitopes Based on a Pretrained RoBERTa Model

NanoBERTa-ASP predicts nanobody-antigen binding sites using a BERT-based model trained specifically on nanobody sequences, achieving high precision and AUC.

Nanotope: A Graph Neural Network-Based Model for Nanobody paratope prediction

Nanotope predicts nanobody paratopes using graph neural networks and sequence/structural features, achieving higher accuracy than traditional methods without requiring antigen data.

A benchmark study of protein folding algorithms on nanobodies

The paper benchmarks five deep-learning-based protein folding algorithms on nanobody sequences to compare their accuracy against experimental structures.