PAbFold: Linear Antibody Epitope Prediction using AlphaFold2 DOI Open Access

Jacob DeRoo,

James S. Terry, Ning Zhao

et al.

Published: June 11, 2024

Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform molecular functions. However, while determining linear monoclonal can be accomplished utilizing well-established empirical procedures, these approaches are generally labor-and time-intensive costly. To take advantage recent advances in protein structure prediction algorithms available scientific community, we developed a calculation pipeline based on localColabFold implementation AlphaFold2 that predict antibody by predicting complex between heavy light chains target peptide sequences derived from antigens. We found this pipeline, which call PAbFold, was able accurately flag known epitope several well-known targets (HA / Myc) when sequence broken into small overlapping peptides complementarity regions (CDRs) were grafted onto different framework single-chain fragment (scFv) format. determine if identify novel with no structural information publicly available, determined anti-SARS-CoV-2 nucleocapsid targeted using our method then experimentally validated computational results competition ELISA assays. These indicate AlphaFold2-based PAbFold capable identifying short time just sequences. This emergent capability sensitive methodological details such as length, neural network versions, multiple-sequence alignment database. at https://github.com/jbderoo/PAbFold.

Language: Английский

Simulation-based approaches for drug delivery systems: Navigating advancements, opportunities, and challenges DOI Creative Commons

Iman Salahshoori,

Mahdi Golriz,

Marcos A.L. Nobre

et al.

Journal of Molecular Liquids, Journal Year: 2023, Volume and Issue: 395, P. 123888 - 123888

Published: Dec. 27, 2023

Efficient drug delivery systems (DDSs) play a pivotal role in ensuring pharmaceuticals' targeted and effective administration. However, the intricate interplay between formulations poses challenges their design optimization. Simulations have emerged as indispensable tools for comprehending these interactions enhancing DDS performance to address this complexity. This comprehensive review explores latest advancements simulation techniques provides detailed analysis. The encompasses various methodologies, including molecular dynamics (MD), Monte Carlo (MC), finite element analysis (FEA), computational fluid (CFD), density functional theory (DFT), machine learning (ML), dissipative particle (DPD). These are critically examined context of research. article presents illustrative case studies involving liposomal, polymer-based, nano-particulate, implantable DDSs, demonstrating influential simulations optimizing systems. Furthermore, addresses advantages limitations It also identifies future directions research development, such integrating multiple techniques, refining validating models greater accuracy, overcoming limitations, exploring applications personalized medicine innovative DDSs. employing like MD, MC, FEA, CFD, DFT, ML, DPD offer crucial insights into behaviour, aiding Despite advantages, rapid cost-effective screening, require validation addressing limitations. Future should focus on models, enhance outcomes. paper underscores contribution emphasizing providing valuable facilitating development optimization ultimately patient As we continue explore impact advancing discovery improving DDSs is expected be profound.

Language: Английский

Citations

61
Enabling the immune escaped etesevimab fully-armed against SARS-CoV-2 Omicron subvariants including KP.2 DOI Creative Commons
Chao Su,

Juanhua He,

Yufeng Xie

et al.

hLife, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

3
Vaccine Target Discovery DOI
Li Chuin Chong, Asif Manzoor Khan

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Citations

12
Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools DOI Creative Commons
Varun Dewaker, Vivek Kumar Morya, Yeon-Ju Kim

et al.

Biomarker Research, Journal Year: 2025, Volume and Issue: 13(1)

Published: March 29, 2025

Antibodies play a crucial role in defending the human body against diseases, including life-threatening conditions like cancer. They mediate immune responses foreign antigens and, some cases, self-antigens. Over time, antibody-based technologies have evolved from monoclonal antibodies (mAbs) to chimeric antigen receptor T cells (CAR-T cells), significantly impacting biotechnology, diagnostics, and therapeutics. Although these advancements enhanced therapeutic interventions, integration of artificial intelligence (AI) is revolutionizing antibody design optimization. This review explores recent AI advancements, large language models (LLMs), diffusion models, generative AI-based applications, which transformed discovery by accelerating de novo generation, enhancing response precision, optimizing efficacy. Through advanced data analysis, enables prediction sequences, 3D structures, complementarity-determining regions (CDRs), paratopes, epitopes, antigen-antibody interactions. These AI-powered innovations address longstanding challenges development, improving speed, specificity, accuracy design. By integrating computational with biomedical driving next-generation cancer therapies, transforming precision medicine, patient outcomes.

Language: Английский

Citations

2
A new era of antibody discovery: an in-depth review of AI-driven approaches DOI
Jin Cheng,

Tianjian Liang,

Xiang‐Qun Xie

et al.

Drug Discovery Today, Journal Year: 2024, Volume and Issue: 29(6), P. 103984 - 103984

Published: April 18, 2024

Language: Английский

Citations

7
PAbFold: Linear Antibody Epitope Prediction using AlphaFold2 DOI Open Access

Jacob DeRoo,

James S. Terry, Ning Zhao

et al.

Published: Jan. 23, 2025

Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform molecular functions. However, while determining linear monoclonal can be accomplished utilizing well-established empirical procedures, these approaches are generally labor- time-intensive costly. To take advantage recent advances in protein structure prediction algorithms available scientific community, we developed a calculation pipeline based on localColabFold implementation AlphaFold2 that predict antibody by predicting complex between heavy light chains target peptide sequences derived from antigens. We found this pipeline, which call PAbFold, was able accurately flag known epitope several well-known targets (HA / Myc) when sequence broken into small overlapping peptides complementarity regions (CDRs) were grafted onto different framework single-chain fragment (scFv) format. determine if identify novel with no structural information publicly available, determined anti-SARS-CoV-2 nucleocapsid targeted using our method then experimentally validated computational results competition ELISA assays. These indicate AlphaFold2-based PAbFold capable identifying short time just sequences. This emergent capability sensitive methodological details such as length, neural network versions, multiple-sequence alignment database. at https://github.com/jbderoo/PAbFold.

Language: Английский

Citations

1
Advancing vaccine development in genomic era: A paradigm shift in vaccine discovery DOI

Miraj Ud din,

Xiaohui Liu, Hui Jiang

et al.

Progress in Biomedical Engineering, Journal Year: 2025, Volume and Issue: 7(2), P. 022004 - 022004

Published: Feb. 5, 2025

Abstract The issue of antibiotic resistance is increasing with time because the quick rise microbial strains. Overuse antibiotics has led to multidrug-resistant, pan-drug-resistant, and extensively drug-resistant bacterial strains, which have worsened situation. Different techniques been considered applied combat this issue, such as developing new antibiotics, practicing stewardship, improving hygiene levels, controlling overuse. Vaccine development made a substantial contribution overcoming although it underestimated. In recent era, reverse vaccinology contributed different kinds vaccines against pathogens, revolutionizing vaccine process. Reverse helps prioritize better candidates by using various tools filter pathogen’s complete genome. review, we will shed light on computational designing, immunoinformatic tools, genomic proteomic data, challenges success stories designing.

Language: Английский

Citations

1
Biophysics of SARS-CoV-2 spike protein’s receptor-binding domain interaction with ACE2 and neutralizing antibodies: from computation to functional insights DOI
Fernando Luís Barroso da Silva,

Karen Paco,

Aatto Laaksonen

et al.

Biophysical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: March 8, 2025

Language: Английский

Citations

1
Revisiting the dimensions of universal vaccine with special focus on COVID-19: Efficacy versus methods of designing DOI

Puja Jaishwal,

Kisalay Jha,

Satarudra Prakash Singh

et al.

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: 277, P. 134012 - 134012

Published: July 22, 2024

Language: Английский

Citations

4
AttABseq: an attention-based deep learning prediction method for antigen–antibody binding affinity changes based on protein sequences DOI Creative Commons

Ruofan Jin,

Qing Ye, Jike Wang

et al.

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(4)

Published: May 23, 2024

Abstract The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational artificial intelligence-based methods have been actively developed to accelerate improve the development antibodies. this study, we an end-to-end sequence-based deep learning model, termed AttABseq, for predictions antigen–antibody binding affinity changes connected with antibody mutations. AttABseq a highly efficient generic attention-based model by utilizing diverse complex sequences input predict residue assessment on three benchmark datasets illustrates that 120% more accurate than other models in terms Pearson correlation coefficient between predicted experimental changes. Moreover, also either outperforms competes favorably structure-based approaches. Furthermore, consistently demonstrates robust predictive capabilities across array conditions, underscoring its remarkable capacity generalization wide spectrum antigen-antibody complexes. It imposes no constraints quantity altered residues, rendering it particularly applicable scenarios where crystallographic structures remain unavailable. interpretability analysis indicates causal effects point mutations antibody–antigen can be visualized at level, which might assist automated sequence optimization. We believe provides fiercely competitive answer

Language: Английский

Citations

3