Andrea Pagnani

The paper introduces CLAPP, a method to determine paralog interactions by class, showing it can accurately assign interaction partners using weak or noisy scores when class labels are reliable.

The paper models protein sequence evolution using an Ornstein-Uhlenbeck process in a latent space to improve contact prediction by integrating directed evolution data with natural homologs.

FeatureDCA improves protein sequence generation by conditioning DCA on principal components, achieving accurate structural and functional results across multiple families.

This study compares three simulation schemes for modeling protein evolution, finding that population genetics dynamics best capture phylogenetic correlations and selective sweeps without requiring additional data.

The paper introduces a continuous-time generative model for protein evolution that captures epistatic effects and reveals how epistasis influences evolutionary dynamics and distance estimation.

This paper introduces a method using autoregressive generative models to reconstruct ancestral protein sequences while accounting for epistasis, outperforming existing methods in simulations and experimental data.

This paper introduces an attention-based DCA model for protein structure prediction and demonstrates its effectiveness across multiple protein families.

The paper develops a method to generate protein domain sequences that interact with another domain, outperforming existing approaches and exploring fine-tuning and structure prediction.

DCAlign v1.0 improves sequence alignment by incorporating co-evolution models and informative priors to reduce computational demands.

The paper introduces simple autoregressive models for efficient generative modeling of protein sequences, showing comparable performance to complex models at lower computational cost.

The paper introduces DCAlign, a method for sequence alignment that incorporates coevolution and second-order interactions, improving upon profile models for proteins and RNA.

The paper introduces a computational method to infer protein fitness landscapes from deep mutational scans, showing accurate predictions and structural insights.

This paper introduces a method to infer protein-protein interaction networks from sequence co-evolution, achieving high true-positive rates in test cases.