Yasser Mohseni Behbahani

This study uses deep learning to design anti-VEGF nanobodies with improved affinity and stability through structure-guided mutational modeling and a sequence-based melting temperature predictor.

DLA estimates binding affinity changes upon mutation by analyzing protein interfaces with 3D convolutions and achieves a Pearson correlation of 0.81 on 2000 mutations.

DLA-mutation predicts mutation-induced changes in protein-protein binding affinity using a deep learning framework trained on protein complex structures.

DLA-Ranker evaluates protein docking conformations using locally oriented cubes to distinguish near-native from incorrect ones, outperforming other deep learning-based scoring functions.

This paper introduces a molecular cross-docking approach to identify protein partners and assesses its performance against sequence-based deep learning methods.

The paper introduces a deep learning method to rank protein docking conformations by analyzing local interface geometry and combines it with physics-based and statistical scoring functions.

LEVELNET is a tool for visualizing, exploring, and comparing protein-protein interaction networks using multilayered graphs and structural data from the Protein Data Bank.