Biology subjects

Hattoh, G.

Publications and source records attributed to Hattoh, G..

3 recordsLinked to original sources

bioRxiv2025

Moremi Bio Agent: Using Neisseria meningitidis Reference Data For The Double Blinded Validation of A General Purpose Biology-Trained Reasoning Model for Pathogen and Antigen Discovery

Antibodies serve as vital diagnostic and therapeutic agents due to their exceptional specificity toward antigenic targets. Mapping antibody-antigen interactions is essential for understanding immune responses and developing vaccines or biologics. Traditional antigen identification relies on labor-intensive wet-lab techniques such as phage display, peptide microarrays, and ELISA, while computational methods employ sequence alignment, epitope mapping, and structure prediction. Despite progress, to our knowledge, no existing AI framework has demonstrated the ability to blindly inference--predicting an antibodys antigen target solely from its amino acid sequence without prior biological context. This research employed Moremi Bio Nano, a general agentic reasoning large language model (LLM), to infer the antigen and pathogen targets of an anonymized monoclonal antibody sequence from Imperial College London. The model received only the VH and VL chain sequences and autonomously hypothesized, ranked, and validated probable targets. Of ten independent inference tests, four were completed successfully, with three correctly identifying the experimentally validated antigen and pathogen; with SARS-CoV-2 Spike RBD, Neisseria meningitidis fHbp v1.1, and SARS-CoV Spike emerging as the top 3-ranked candidates across both reranking strategies. Validation of the models predictions with experimental wet-lab data confirmed its capacity for correct antigen inference, marking Moremi Bio Nano as a first-of-its-kind AI system demonstrating reasoning-driven antigen discover; complementing experimental immunology and advancing automated biological inference.

molecular biology↗

bioRxiv2025

Moremi Bio Agent: Leveraging Agentic Large Language Model for the Discovery of Broad-Spectrum Antibiotics for Enterobacteriaceae

Antimicrobial resistance (AMR) is a pressing global health crisis, exacerbated by a stagnating antibiotic discovery pipeline and the emergence of multidrug-resistant pathogens such as Klebsiella pneumoniae. We hypothesize that dual-target strategies may offer a more robust means to overcome AMR by reducing the likelihood of resistance development compared to single-target approaches. In response, we leveraged Moremi Bio Agent, an agentic large language model (LLM) for the autonomous design, in silico validation, and prioritization of broad-spectrum antibiotics targeting Enterobacteriaceae. Using our proposed dual-target strategy, we generated and evaluated 1,002 candidate molecules predicted to simultaneously inhibit the FabI enzyme and the AcrAB-TolC efflux pump--two key resistance mechanisms in Gram-negative bacteria. Our fully autonomous pipeline integrated compound generation, molecular docking, pharmacodynamics/pharmacokinetic predictions & toxicity profiling, and molecule-ranking based on ADMET and drug-likeness properties. Out of 1,002 molecules generated, 774 passed preliminary ADMET benchmarks, with majority of the 60 top-performing candidates (score[≥] 0.8) showing favorable drug-likeness, minimal toxicity. 391 of the compounds exhibited moderate binding interaction to both targets. This study demonstrates the feasibility of AI-driven antibiotic discovery and lays the foundation for future experimental validation to address AMR.

biochemistry↗

bioRxiv2025

Moremi Bio Agent: Application of A Foundation Model and End-to-End Automation in the Design and Validation of Monoclonal Antibodies Targeting Plasmodium falciparum Invasion Complex

Malaria remains a significant global health challenge, with Plasmodium falciparum responsible for the majority of severe cases and fatalities. Targeting the parasites invasion mechanisms offers a promising therapeutic strategy. In this study, we leveraged a novel agentic foundational model, Moremi Bio Agent, to design monoclonal antibodies targeting the AMA1-RON2 complex, a critical component in the parasites invasion of human red blood cells. Using advanced structural modeling, we generated 999 antibodies, which were evaluated for binding affinity, structural integrity, and physicochemical properties. Binding affinity analysis using PRODIGY identified 864 antibodies with successful target interactions, exhibiting binding free energies ({Delta}G) ranging from -116.8 kcal/mol to -5.6 kcal/mol. The strongest candidates demonstrated exceptionally tight binding, with dissociation constants (Kd) in the femtomolar to attomolar range, indicative of highly stable interactions. Additionally, structural validation confirmed that the antibodies were thermodynamically stable with robust fold reliability, essential for functional efficacy. Epitope mapping revealed highly conserved regions within the target complex, enhancing the likelihood of cross-strain efficacy. Glycosylation analysis identified key sites that could improve antibody stability and immune recognition, while BLAST comparison with known therapeutic antibodies demonstrated significant homology, underscoring their potential for clinical development. This study highlights the power of generative AI-driven computational pipelines in antibody discovery, providing a scalable and cost-effective framework for therapeutic development. The findings establish a foundation for experimental validation and optimization, with the potential to advance novel interventions against malaria and other infectious diseases.

synthetic biology↗