Farzaneh Esmaili

GCP-VQVAE: A Geometry-Complete Language for Protein 3D Structure

This paper introduces GCP-VQVAE, a geometry-complete tokenizer for protein structures, achieving state-of-the-art performance on benchmark datasets and demonstrating robust generalization to unseen proteins.

Predicting Kinase-Specific Phosphorylation Sites with Pretrained Protein Language Models

The paper introduces two sequence-based models to predict kinase-specific phosphorylation sites, showing improved performance and zero-shot capability.

Using Autoregressive-Transformer Model for Protein-Ligand Binding Site Prediction

This paper introduces a unified model combining a protein language model with an autoregressive transformer to predict protein-ligand binding sites, achieving broad ligand coverage and improved performance for underrepresented ligands.

Extending Prot2Token: Aligning Protein Language Models for Unified and Diverse Protein Prediction Tasks

This paper extends Prot2Token to align protein language models for diverse prediction tasks, improving performance across multiple applications through pre-training and integration with ESM-2.

S-PLM: Structure-aware Protein Language Model via Contrastive Learning between Sequence and Structure

S-PLM improves protein language models by integrating 3D structure information through contrastive learning, outperforming sequence-only models in clustering and classification tasks.

Comprehensive Evaluation of AlphaFold-Multimer, AlphaFold3 and ColabFold, and Scoring Functions in Predicting Protein-Peptide Complex Structures

This study evaluates AlphaFold-Multimer, AlphaFold3, and ColabFold for predicting protein-peptide complex structures using Template-Based and Template-Free methods, finding AlphaFold-Multimer excels in Template-Based predictions.

Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling

Prot2Token is a multi-task framework that uses autoregressive language modeling for protein prediction tasks, achieving performance comparable to specialized models and enabling integration with large language models.