Biology subjects

Alfaro, J. A.

Publications and source records attributed to Alfaro, J. A..

11 recordsLinked to original sources

bioRxiv2026

SpeciefAI: Multi-species mRNA-level Antibody Framework Generation using Transformers

MotivationEncoding antibodies (Abs) and nanobodies (Nbs) as mRNA enables in vivo production of therapeutic proteins. However, this approach requires meeting two species-dependent requirements: the mRNA encoding must support efficient expression in the host species, and the encoded protein sequence must resemble the natural Ab repertoire of the recipient species to minimize immunogenicity. These requirements motivate species-conditioned generative models for joint mRNA and protein design. ResultsWe propose SpeciefAI a transformer-based model for multi-species Ab and Nb species sequence-harmonisation by generation of novel Framework Regions (FRs) tailored to input Complementarity-Determining Regions (CDRs). Our model works directly in the mRNA space and learns the correspondence between FRs and CDRs in six species. The model is capable of generating sequences with a highly similar distribution to natural sequences and a mean absolute difference in codon adaptation index (CAI) of 0.013 and 0.033 for humans and dogs respectively. We show that the generated human sequences are highly human (0.95 T20 score) and canine sequences highly canine (0.95 cT20 score). We furthermore demonstrate that we can generate diverse candidate sequences using our method. Availability and ImplementationSource code is available on https://github.com/Dominko/SpeciefAI. OAS and COGNANO data are publicly available on https://opig.stats.ox.ac.uk/webapps/oas/ and https://cognanous.com/datasets/vhh-corpus (preprocessed versions available upon request). Canine data is available on https://zenodo.org/records/18301526.

bioinformatics↗

bioRxiv2025

FAK inhibitor treatment systemically regulates the glioblastoma immune environment via blocking monocyte trafficking

Glioblastoma (GBM) is the most common primary malignant brain tumour in adults with dismal survival rates, and current therapies, including most immunotherapies, are not efficacious due to the highly immunosuppressive microenvironment. Studies in other solid cancers report that impairment of the integrin effector pathway involving focal adhesion kinase (FAK) can promote anti-tumour immune responses. Therefore, we set out to address whether, and if so how, suppressing FAK function may influence GBM by using both tumour cell-specific FAK gene deletion and systemic delivery of a clinically relevant FAK kinase inhibitor (FAKi) VS-4718 in an orthotopic murine stem cell model of GBM. We found that treatment with the FAKi, but not tumour cell-specific FAK gene deletion, resulted in GBM clearance and improved survival. This was dependent on adaptive immunity, and tumour-infiltrating T cells in FAKi-treated tumours displayed increased cytotoxic potential and reduced exhaustion. We also found a significant reduction in immuno-suppressive peripherally-derived macrophages and FAKi treatment caused sequestration of inflammatory monocytes within the bone marrow, resulting in impaired monocyte trafficking to tumours as judged by adoptive transfer. This is due to suppression of key adhesion and migration signalling through 4{beta}1 integrin and CX3CR1 in peripheral monocytes. Our work here describes a previously unidentified role for FAK in trafficking of peripheral suppressive macrophages to GBM tumours, reducing T cell exhaustion and promoting anti-tumour immunity. This highlights a new way in which systemic FAK inhibitors can be used to provide a beneficial immune modulatory strategy for the treatment of GBM.

immunology↗

bioRxiv2025

DoggifAI: a transformer based approach for antibodycaninisation

Antibody translation across species offers a compelling strategy to extend the vast and expensive investments in human therapeutic antibodies to veterinary oncology, with applications in both veterinary medicine and comparative oncology. While precise, low-immunogenic treatments are essential for canine cancer care, traditional species conversion methods rely on ad hoc bioinformatics modifications. These methods often implicitly decouple the framework (FR) and complementarity-determining regions (CDRs), ignoring how structural changes in FRs can affect the conformation and function of CDRs. This can compromise binding specificity and require costly high-throughput in vitro screening. To address this, we present DoggifAI, a transformer model that translates non-canine antibody sequences into canine ones by generating species-appropriate framework regions (FRs) based on desired CDRs. This allows the model to better preserve structural compatibility between FRs and CDRs. The model is pretrained in a T5-style text-to-text denoising task on a large multispecies antibody dataset, which allows further finetuning on a much smaller species-specific dataset. DoggifAI generates highly canine-like antibodies and shows promising results in preserving binding specificity. To support further progress in this field, we also release a curated dataset of over 430,000 unique canine antibody chain sequences, significantly expanding the public sequence repertoire. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/656573v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@13c10adorg.highwire.dtl.DTLVardef@6afca6org.highwire.dtl.DTLVardef@1f11961org.highwire.dtl.DTLVardef@1b9563a_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG HighlightsO_LIWe show that transformer models are capable of generating plausible antibody framework regions based on CDRs C_LIO_LIWe show that resulting framework regions are highly recognisable as coming from the desired species C_LIO_LIWe show promising results for the retention of binding specificity when translating antibody sequences in this way C_LIO_LIWe release a large, high-quality dataset of canine antibody sequences to support future research C_LI

bioinformatics↗

bioRxiv2025

Expanding the definition of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers with CARMEN

Promiscuity in T-cell antigen landscapes refers to the dual flexibility of peptides binding multiple MHC alleles and MHC alleles presenting diverse arrays of peptides. By understanding how neoantigens are shared across varied HLA backgrounds, promiscuity analysis can inform the selection of cancer-vaccine targets that reach a wider segment of the population and help refine patient stratification for diverse immunotherapies. We expand the concept of promiscuity to encompass peptides, MHC alleles, individuals, populations, and genomic regions. Our CARMEN database release harmonizes data from 72 publications (2,323 samples) across tissue types, with a focus on cancer. Using Gibbs clustering and dimensionality reduction (UMAP), we systematically map promiscuity and immunological versatility across these biological levels. Gene and mutation analysis reveals recurrent cancer mutations in highly promiscuous genomic regions, highly mutated cancer genes that avoid presented regions, and sheds light on genomic regions important to response to immunotherapy.

bioinformatics↗

bioRxiv2025

A novel decoding strategy for ProteinMPNN to design with less MHC Class I immune-visibility

Due to their versatility and diverse production methods, proteins have attracted a lot of interest for industrial as well as therapeutic applications. Designing new therapeutics requires careful consideration of immune responses, particularly the cytotoxic T-lymphocyte (CTL) reaction to intra-cellular proteins. In this study, we introduce CAPE-Beam, a novel decoding strategy for the established ProteinMPNN protein design model. Our approach minimizes CTL immunogenicity risk by limiting designs to only consist of kmers that are either predicted not to be presented to CTLs or are subject to central tolerance. We compare CAPE-Beam to greedily sampling from ProteinMPNN and CAPE-MPNN. We find that our novel decoding strategy can produce structurally similar proteins while incorporating more human like kmers. This significantly lowers CTL immunogenicity risk in precision medicine, and represents a key step towards reducing this risk in protein therapeutics targeting a wider patient population.

bioinformatics↗

bioRxiv2024

Barking Up the Right Tree: Immune Checkpoint Signatures of Human and Dog Cancers

In the quest for improved therapeutics targeting immune checkpoints (ICs), we turn to spontaneously developing dog (canine) cancers, which are unique models that genetically and clinically mirror human equivalents. Despite its potential, canine cancer immunology remains largely unexplored. Here, we examine the RNA-seq-based expression of 44 ICs across 14 canine cancer types and an extensive human dataset. We unveil diverse canine IC expression patterns and unique human IC signatures that reflect the histological type and primary site of cancer. We uncover a striking similarity between canine brain cancers, osteosarcoma, and their human counterparts, identifying them as prospective immunotherapy models. Four ICs--CD160, A2AR, NKG2A, and OX40--are key to the differences observed between species. Moreover, individual patient IC signatures exhibit varying alignment with their respective cancer types, a finding with profound implications for personalized human therapy. This exploration illuminates new aspects of canine and human cancer immunology, setting the stage for discoveries at their crossroads.

cancer biology↗

bioRxiv2024

Integrating MHC Class I visibility targets into the ProteinMPNN protein design process

ProteinMPNN is crucial in many protein design pipelines, identifying amino acid (AA) sequences that fold into given 3D protein backbone structures. We explore ProteinMPNN in the context of designing therapeutic proteins that need to avoid triggering unwanted immune reactions. More specifically, we focus on intra-cellular proteins that face the challenge of evading detection by Cytotoxic T-lymphocytes (CTLs) that detect their presence via the MHC Class I (MHC-I) pathway. To reduce visibility of the designed proteins to this immune-system component, we develop a framework that uses the large language model (LLM) tuning method, Direct Preference Optimization (DPO), to guide ProteinMPNN in minimizing the number of predicted MHC-I epitopes in its designs. Our goal is to design proteins with low MHC-I immune-visibility while preserving the original structure and function. For our assessment, we first use AlphaFold to predict the 3D structures of designed protein sequences. We then use TM-score, that measures the structural alignment between the predicted design and original protein, to evaluate fidelity to the original protein structure. We find our LLM-based tuning method for constraining MHC-I visibility is able to effectively reduce visibility without compromising structural similarity to the original protein.

bioinformatics↗

bioRxiv2024

3D culture of pancreatic cancer cells in vitro recapitulates an aberrant mitochondrial oxidative phosphorylation genotype observed in vivo

The treatment of many aggressive cancers remains a significant unmet medical need. An aberrant dependence of tumors on mitochondrial oxidative phosphorylation pathways has been well characterized in mediating the progression of pancreatic cancers, as well as some other malignancies. However, the discovery and development of new therapeutic strategies targeting or manipulating this pathway in pancreatic tumors has been relatively slow when compared with other cancers, limiting the current options for treating patients. One key technical challenge in discovering new therapies has been the limited ability of cancer cell lines when grown in 2D conditions to recapitulate the mitochondrial oxidative phosphorylation pathways in vitro that have been observed in vivo. In part, this is because 2D cultures fail to mimic the extracellular 3D tumoral environment. More generally, the translation of data based on 2D pancreatic cancer cell culture systems have also been constraining options for discovering new therapies. Conversely, 3D cultures can provide an improved technology platform in which pathologically relevant pathways in tumors can be recapitulated and analyzed in vitro. In this study, we have demonstrated in 3D cultures the reconstruction of the mitochondrial oxidative phosphorylation genotype in vitro, which more closely resembles that observed in vivo. Our study highlights the value of using transcriptomic readouts as a method to demonstrate the relevance and utility of 3D preclinical models in vitro, when grown in a more physiological extracellular environment. This key technological advance can now better enable the discovery and subsequent development of new therapeutic strategies targeting disease-relevant pathways found in pancreatic and other tumors.

cancer biology↗

bioRxiv2023

Microenvironment of metastatic site reveals key predictors of PD-1 blockade response in renal cell carcinoma

Immune checkpoint blockade (ICB) therapies have improved the overall survival (OS) of many patients with advanced cancers. However, the response rate to ICB varies widely among patients, exposing non-responders to potentially severe immune-related adverse events. The discovery of new biomarkers to identify patients responding to ICB is now a critical need in the clinic. We therefore investigated the tumor microenvironment (TME) of advanced clear cell renal cell carcinoma (ccRCC) samples from primary and metastatic sites to identify molecular and cellular markers of response to ICB. We revealed a significant discrepancy in treatment response between subgroups based on cell fractions inferred from metastatic sites. One of the subgroups was enriched in non-responders and harbored a lower fraction of CD8+ T cells and plasma cells, as well as a decreased expression of immunoglobulin genes. In addition, we developed the Tumor-Immunity Differential (TID) score which combines features from tumor cells and the TME to accurately predict response to anti-PD-1 immunotherapy (AUC-ROC=0.88, log-rank tests for PFS P < 0.0001, OS P = 0.01). Finally, we also defined TID-related genes (YWHAE, CXCR6 and BTF3), among which YWHAE was validated as a robust predictive marker of ICB response in independent cohorts of pre- or on-treatment biopsies of melanoma and lung cancers. Overall, these results provide a rationale to further explore variations in the cell composition of metastatic sites, and underlying gene signatures, to predict patient response to ICB treatments. One Sentence SummaryTumor microenvironment balance of metastasis and associated genes are key predictors of immunotherapy patient response in kidney cancer.

bioinformatics↗

bioRxiv2022

HLA-Glyco: A large-scale interrogation of the glycosylated immunopeptidome

MHC-associated peptides (MAPs) bearing post-translational modifications (PTMs) have raised intriguing questions regarding their attractiveness for targeted therapies. Here, we developed a novel computational glyco-immunopeptidomics workflow that integrates the ultrafast glycopeptide search of MSFragger with a glycopeptide-focused false discovery rate (FDR) control. We performed a harmonized analysis of 8 large-scale publicly available studies and found that glycosylated MAPs are predominantly presented by the MHC class II. We created HLA-Glyco, a resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. Our comprehensive resource reveals high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between classical HLA class II allele groups. To support the nascent field of glyco-immunopeptidomics, we include the optimized workflow in the FragPipe suite and provide HLA-Glyco as a free web resource.

immunology↗

bioRxiv2022

A comprehensive library of canonical and non-canonical MHC class I antigens for cancer vaccine development.

A longstanding disconnect between the growing number of MHC Class I immunopeptidomic studies and genomic medicine hinders cancer vaccine design. We develop COD-dipp to genomically map the full spectrum of detected canonical and non-canonical (non-exonic) MHC Class I antigens from 26 cancer studies. We demonstrate that patient mutations in regions overlapping physically identified antigens better predict immunotherapy response when compared to neoantigen predictions. We suggest a vaccine design approach using 140,966 highly immune-visible regions of the genome annotated by their expression and haplotype frequency in the human population. These regions tend to be highly conserved, mutated in cancer and harbor 7.8 times more immunogenicity. Intersecting pan-cancer mutations with these immune surveilled regions revealed a potential to create off-the-shelf multi-epitope vaccines against public neoantigens. Here we release COD-dipp, a cancer vaccine toolkit as a web-application (https://www.proteogenomics.ca/COD-dipp) and open-source high-throughput resource.

bioinformatics↗