Victor Greiff

This study shows that immunological imprinting in mice uncouples serological and B-cell responses, with B-cell repertoires updating independently of serum antibody specificity.

This study benchmarks generative models for adaptive immune receptor repertoires, identifying their strengths and limitations in capturing biological features.

The paper describes a computational workflow for designing NAZ-mAb variants targeting p95HER2, showing improved expression yields but weaker binding affinities compared to the parent antibody.

Amylo-Pipe integrates mechanistic and kinetic prediction tools for protein and peptide aggregation, offering a web interface for assessing aggregation behavior and supporting protein engineering.

PEPE efficiently extracts multimodal protein embeddings from language models, enabling scalable and memory-efficient generation of large embedding datasets for downstream biological tasks.

This paper introduces immuneML, a framework for unsupervised machine learning in adaptive immune receptor repertoires, demonstrating its use in generative modeling, clustering, and dataset analysis.

The paper disentangles protein-interaction and protein-quality effects in antibody-antigen binding using deep mutational scanning and evaluates models for their ability to predict these effects.

The paper develops MS-BCR-DB, a unified BCR repertoire database for MS, identifying disease-associated features and shared clonotypes across patients.

The paper evaluates AI-based antibody-antigen structure prediction methods, finding that confidence scores often fail to distinguish correct from incorrect pairings, even with increased sampling.

The paper develops the ASD database to aggregate antibody-antigen interaction data from multiple sources, providing a standardized resource for computational model development.

This paper introduces NbForge, a nanobody folding model that improves prediction of HCDR3 blueprint and disulphide bonds, achieving accuracy comparable to large predictors with faster speed.

The paper identifies TCR subsequence motifs associated with type 1 diabetes using deep learning and shows their enrichment in T1D individuals based on HLA risk alleles.

This study evaluates active learning strategies for antibody-antigen binding prediction in library-on-library settings, finding three algorithms that significantly improve efficiency and reduce experimental costs.

TCR2HLA infers HLA genotypes from TCR repertoires, enabling identification of immunologically relevant metaclonotypes and discovery of HLA-associated TCRs.

The paper identifies putative antigen targets of a stereotyped intrathecal B cell response in multiple sclerosis by generating monoclonal antibodies and using IPMS/MS to screen against proteins.

The paper quantitatively maps antigen specificity in immune repertoire embedding spaces, showing distinct regions for different antigen-specific sequences.

This study improves nanobody structure prediction by using synthetic data and a classifier to filter structural accuracy, achieving better results than existing methods with lower computational cost.

This study benchmarks mass spectrometry-based antibody sequencing methods, revealing biases related to sequence, concentration, protease, and bioinformatics tools.

This study shows that the composition of training data, especially negative examples, affects machine learning model performance and biological rule discovery in antibody-antigen binding.

YAbS catalogues over 2,900 commercially sponsored antibody therapeutics, from clinical entry since 2000 to approval, with milestone-date filtering for pipeline, trend and phase-length analyses; over 450 are openly accessible.

This paper introduces LIgO, a software suite that simulates adaptive immune receptor repertoires with immune information to aid in developing and benchmarking AIRR machine learning methods.

The paper analyzes the sequence distribution of epitope-specific TCR repertoires and shows how this affects machine learning model performance.

This study evaluates AF2's ability to predict protein structural ensembles by comparing its results with MD simulations and NMR data for four test proteins.

The paper quantifies the plasticity of antibody developability by analyzing sequence and structure-based parameters in natural and human-engineered antibodies.

The paper shows that simple computational methods like BLOSUM can create diverse, binder-enriched antibody libraries better than deep learning approaches.

The paper introduces immuneREF, a method to quantify immune repertoire similarity across health and disease, finding that healthy and diseased repertoires are more similar than previously thought.

CompAIRR rapidly compares adaptive immune receptor repertoires using exact or approximate sequence matching, significantly improving speed and memory efficiency compared to existing methods.

The paper evaluates ML models for adaptive immune receptor repertoire classification, finding that simple methods like L1-penalized logistic regression perform well even with rare immune signals.

immuneML provides an open-source ecosystem for reproducible and interoperable machine learning analysis of adaptive immune receptor repertoires, demonstrating its utility through benchmarking and novel methods.

The paper identifies 48 novel light chain variable gene polymorphisms in a Norwegian cohort and finds partial intron retention in alternative splice variants of low-expressed genes.