A5017929614- Monoclonal and Polyclonal Antibodies Research
- vaccines and immunoinformatics approaches
- SMEs Development and Digital Marketing
- T-cell and B-cell Immunology
- Immune Cell Function and Interaction
- Protein purification and stability
- Agriculture and Agroindustry Studies
- Glycosylation and Glycoproteins Research
- Computational Drug Discovery Methods
- Advanced Biosensing Techniques and Applications
- Hepatitis C virus research
- Chemical Synthesis and Analysis
- RNA and protein synthesis mechanisms
- Protein Structure and Dynamics
- Educational Methods and Media Use
- Systemic Lupus Erythematosus Research
- Management and Optimization Techniques
- Viral Infectious Diseases and Gene Expression in Insects
- Lipid Membrane Structure and Behavior
- Immunotherapy and Immune Responses
- Machine Learning in Bioinformatics
- Parasitic Diseases Research and Treatment
- Cellular transport and secretion
- Blood groups and transfusion
- Single-cell and spatial transcriptomics
Oslo University Hospital
2021-2024
University of Oslo
2019-2024
Novo Nordisk (Denmark)
2024
Universitas Halu Oleo
2021
Saarland University
2016-2020
Airlangga University
2018
Universiti Teknologi MARA
2013
University of Malaya
2011
Antibody-antigen binding relies on the specific interaction of amino acids at paratope-epitope interface. The predictability antibody-antigen is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise existence motifs that are universally shared among structures. In dataset non-redundant structures, we identify structural motifs, which together compose commonly structure-based vocabulary interactions. We show this enables machine learnability level using...
Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts confirm this hypothesis have hindered by infeasibility testing arbitrarily large numbers antibody sequences for their most critical parameters: paratope, epitope, affinity, and developability. To address challenge, we leveraged lattice-based antibody-antigen binding simulation framework, which incorporates wide range...
Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 develop therapeutic antibodies. combinatorial structure sequences makes impossible query binding-affinity oracles exhaustively. Moreover, antibodies expected have high target specificity and developability. Here, we...
Abstract Lathe waste contains 97% iron, which has the potential as a raw material for producing hematite pigment (α-Fe 2 O 3 ). Guava leaf extract tannin, reducing agent in synthesizing hematite. In this study, was synthesized using hydrothermal method at temperatures of 120, 140, and 160°C, then calcined hours temperature 750°C. XRD, Color reader, FTIR, SEM characterized resulting products. The XRD characterization shows that product before after calcination with rhombohedral structure....
The adaptive immune system is a natural diagnostic and therapeutic. It recognizes threats earlier than clinical symptoms manifest neutralizes antigen with exquisite specificity. Recognition specificity broad reactivity enabled via B- T-cell receptors: the receptor repertoire. human system, however, not omnipotent. Our defense sometimes loses battle to parasites microbes even turns against us in case of cancer, autoimmune inflammatory disease. A long-standing dream immunoengineers has been,...
Abstract Summary B- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics therapeutics research. Consequently, there is rapidly growing number bioinformatics tools repertoire analysis. Benchmarking such crucial ensuring reproducible generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows...
Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibody's ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack comprehensive understanding of developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes human-engineered relate...
1 Abstract There is currently considerable interest in the field of de novo antibody design, and deep learning techniques are now regularly applied to optimise properties such as binding affinity. However, robust baselines within this have not kept up with recent developments. In study, we generate a dataset over 524,000 Trastuzumab variants use show that standard computational methods BLOSUM, AbLang, ESM, Protein-MPNN can be used design diverse libraries from just single starting sequence....
The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (auto)antigen recognition. VDJ follows probabilistic rules that can be modeled statistically. So far, it remains unknown whether differ individuals. If these differed, identical (auto)antigen-specific sequences would generated with individual-specific probabilities, signifying the available sequence space is...
B and T cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: quantitative multidimensional measure adaptive (and transcriptome) similarity that allows interpretation variation by relying both features cross-referencing simulated experimental datasets. To quantify landscapes...
Finding the ultimate HIV cure remain a challenging tasks for decades.Various active compounds have been tested against various components of virus in effort to halt development infected host.The idea finding from known pharmacologically natural occurring is intriguing and practical.Ganoderma lucidum (Ling-Zhi or Reishi) one most productive found Asian countries.It has used traditionally many years throughout different cultures.More than decade ago, el-Mekkawy co-workers (1998) several this...
Abstract Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application ML to antibody-specificity and benchmarking thereof: The lack unified formalization immunological antibody specificity unavailability large-scale synthetic datasets real-world relevance. Here, we developed Absolut! software suite that enables parameter-based unconstrained generation lattice-based 3D-antibody-antigen binding structures with...
Abstract Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach AIRR-based diagnostic therapeutic discovery. To date, widespread adoption AIRR ML has been inhibited by a lack reproducibility, transparency, interoperability. immuneML ( immuneml.uio.no ) addresses these concerns implementing each...
During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, NEC Immune Profiler (NIP) to scrutinize and predict regions of immunogenicity (hotspots) from entire SARS-CoV-2 viral proteome. These immunogenic offer potential for development universally protective vaccine candidates. Here, validated characterized responses a set minimal epitopes these AI-identified universal hotspots. Utilizing flow cytometry-based activation-induced marker (AIM) assay, identified 59...
Abstract Generative machine learning (ML) has been postulated to be a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts confirm this hypothesis have hindered by infeasibility testing arbitrarily large numbers antibody sequences for their most critical parameters: paratope, epitope, affinity, and developability. To address challenge, we leveraged lattice-based antibody-antigen binding simulation framework, which incorporates wide range...