Javier A. Alfaro

The paper develops and benchmarks an in silico spectral library workflow to detect low-abundance bacterial peptides in human-dominated samples, finding it has complementary performance to conventional database searches.

This paper uses AI to model the structure of T. pallidum proteins, annotates their functions, identifies potential pathogenesis-related proteins, and highlights vaccine targets.

SpeciefAI generates species-specific mRNA and protein sequences for antibodies and nanobodies using a transformer model trained on six species.

This study shows that systemic FAK inhibition reduces immunosuppressive macrophages in glioblastoma by blocking monocyte trafficking, enhancing T cell activity and improving survival in a mouse model.

DoggifAI translates non-canine antibody sequences into canine ones using a transformer model, generating species-appropriate framework regions and preserving binding specificity.

The paper compares immune checkpoint expression in canine and human cancers, identifying similarities and differences that suggest potential for immunotherapy models.

The paper expands the concept of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers by analyzing data from 2,323 samples.

This paper introduces CAPE-Beam, a decoding strategy for ProteinMPNN that reduces CTL immunogenicity risk by limiting designs to kmers not presented to CTLs or subject to central tolerance.

The paper integrates MHC Class I visibility targets into the ProteinMPNN protein design process to reduce immune visibility while preserving structure.

The paper develops a machine learning method to modify protein immune-visibility to CTLs, showing it can alter HIV Nef's visibility while preserving sequence diversity.

This study shows that 3D cultures of pancreatic cancer cells better replicate the mitochondrial oxidative phosphorylation genotype seen in vivo compared to 2D cultures.

The paper identifies key predictors of PD-1 blockade response in renal cell carcinoma by analyzing the tumor microenvironment of metastatic sites and developing a predictive TID score.

The paper identifies GPA33 as an EAC-specific biomarker and reveals post-transcriptional regulation of protein expression in EAC through multi-omics integration.

The paper develops COD-dipp to map MHC Class I antigens from cancer studies and identifies immune-relevant genomic regions for vaccine design.