A5051850545- Monoclonal and Polyclonal Antibodies Research
- Protein purification and stability
- Protein Structure and Dynamics
- vaccines and immunoinformatics approaches
- SARS-CoV-2 and COVID-19 Research
- Machine Learning in Bioinformatics
- Computational Drug Discovery Methods
- Radiomics and Machine Learning in Medical Imaging
- Advanced Biosensing Techniques and Applications
- Viral Infectious Diseases and Gene Expression in Insects
- Cancer Immunotherapy and Biomarkers
- Glycosylation and Glycoproteins Research
- Endoplasmic Reticulum Stress and Disease
- Biosimilars and Bioanalytical Methods
- HER2/EGFR in Cancer Research
- Advanced Proteomics Techniques and Applications
- Alzheimer's disease research and treatments
- Chronic Lymphocytic Leukemia Research
- Viral gastroenteritis research and epidemiology
- Genetics, Bioinformatics, and Biomedical Research
- Immune responses and vaccinations
- CRISPR and Genetic Engineering
- COVID-19 diagnosis using AI
- Influenza Virus Research Studies
- Lymphoma Diagnosis and Treatment
Oslo University Hospital
2021-2025
Indian Institute of Technology Madras
2018-2025
University of Oslo
2021-2025
Amity University
2025
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...
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....
Abstract Supervised machine learning models rely on training datasets with positive (target class) and negative examples. Therefore, the composition of dataset has a direct influence model performance. Specifically, sample selection bias, concerning samples not representing target class, presents challenges across range domains such as text classification protein-protein interaction prediction. Machine-learning-based immunotherapeutics design is an increasingly important area research,...
Therapeutic antibodies have gained prominence in recent years due to their precision targeting specific diseases. As these molecules become increasingly essential modern medicine, comprehensive data tracking and analysis are critical for advancing research ensuring successful clinical outcomes. YAbS, The Antibody Society's Therapeutics Database, serves as a vital resource monitoring the development progress of therapeutic antibodies. database catalogues detailed information on over 2,900...
Therapeutic antibodies have gained prominence in recent years due to their precision targeting specific diseases. As these molecules become increasingly essential modern medicine, comprehensive data tracking and analysis are critical for advancing research ensuring successful clinical outcomes. YAbS, The Antibody Society's Therapeutics Database, serves as a vital resource monitoring the development progress of therapeutic antibodies. database catalogs detailed information on over 2,900...
ABSTRACT Light chain amyloidosis is a medical condition characterized by the aggregation of misfolded antibody light chains into insoluble amyloid fibrils in target organs, causing organ dysfunction, failure, and death. Despite extensive research to understand factors contributing amyloidogenesis, accurately predicting whether given protein will form amyloids under specific conditions remains formidable challenge. In this study, we have conducted comprehensive analysis amyloidogenic...
The Curated Protein Aggregation Database (CPAD) is a manually curated and open-access database dedicated to providing comprehensive information related mechanistic, kinetic structural aspects of protein peptide aggregation. has been updated CPAD 2.0 by significantly expanding datasets improving the user-interface. Key features are (i) 83,098 data points on aggregation kinetics experiments, (ii) 565 structures aggregation, which classified into proteins, fibrils, protein-ligand complexes,...
Coronaviruses are responsible for several epidemics, including the 2002 SARS, 2012 MERS, and COVID-19. The emergence of recent COVID-19 pandemic due to SARS-CoV-2 virus in December 2019 has resulted considerable research efforts design antiviral drugs other therapeutics against coronaviruses. In this context, it is crucial understand biophysical structural features major proteins that involved virus-host interactions. current study, we have compared spike from three strains coronaviruses...
Abstract The light chain (AL) amyloidosis is caused by the aggregation of antibodies into amyloid fibrils. There are plenty computational resources available for prediction short aggregation-prone regions within proteins. However, it still a challenging task to predict amyloidogenic nature whole protein using sequence/structure information. In case antibody chains, common architecture and known binding sites can provide vital information amyloidogenicity at physiological conditions. Here, in...
The coronavirus disease 2019 (COVID-19) has affected the lives of millions people around world. In an effort to develop therapeutic interventions and control pandemic, scientists have isolated several neutralizing antibodies against SARS-CoV-2 from vaccinated convalescent individuals. These can be explored further understand specific antigen-antibody interactions biophysical parameters related binding affinity, which utilized engineer more potent for current emerging variants. present study,...
Mitigating the devastating effect of COVID-19 is necessary to control infectivity and mortality rates. Hence, several strategies such as quarantine exposed infected individuals restricting movement through lockdown geographical regions have been implemented in most countries. On other hand, standard SEIR based mathematical models developed understand disease dynamics COVID-19, proper inclusion these restrictions rate-limiting step for success models. In this work, we a hybrid...
Abstract Motivation Protein aggregation is a major unsolved problem in biochemistry with implications for several human diseases, biotechnology and biomaterial sciences. A majority of sequence-structural properties known their mechanistic roles protein do not correlate well the kinetics. This limits practical utility predictive algorithms. Results We analyzed experimental data on 183 unique single point mutations that lead to change rates 23 polypeptides proteins. Our initial mathematical...
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 The urgent need for a treatment of COVID-19 has left researchers with limited choice either developing an effective vaccine or identifying approved/investigational drugs developed other medical conditions potential repurposing, thus bypassing long clinical trials. In this work, we compared the sequences experimentally verified SARS-CoV-2 neutralizing antibodies and sequentially/structurally similar commercialized therapeutic monoclonal antibodies. We have identified three...
Several prediction algorithms and tools have been developed in the last two decades to predict protein peptide aggregation. These silico aid aggregation propensity amyloidogenicity as well identification of aggregation-prone regions. Despite immense interest field, it is prime importance systematically compare these for their performance. In this review, we provided a rigorous performance analysis nine using variety assessments. The assessments were carried out on several non-redundant...