Large-Scale Computational Modeling of H5 Influenza Variants Against HA1-Neutralizing Antibodies DOI Creative Commons
Colby T. Ford, Shirish Yasa,

Khaled Obeid

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июль 17, 2024

Abstract The United States Department of Agriculture has recently released reports that show samples from 2022-2024 highly pathogenic avian influenza (H5N1) have been detected in mammals and birds (1). To date, the Centers for Disease Control there 27 humans infected with H5N1 2024 (2). broader potential impact on human health remains unclear. In this study, we computationally model 1,804 protein complexes consisting various H5 isolates 1959 to against 11 hemagglutinin domain 1 (HA1)-neutralizing antibodies. This study shows a trend weakening binding affinity existing antibodies over time, indicating virus is evolving immune escape our medical defenses. We also found based wide variety host species geographic locations which was observed transmitted mammals, not single central reservoir or location associated H5N1’s spread. These results indicate move epidemic pandemic status near future. illustrates value high-performance computing rapidly protein-protein interactions viral genomic sequence data at-scale functional insights into preparedness. Research Context Evidence before Previous studies shown cases transmissions are increasing frequency, concern health. Since 1997, nearly thousand reported 52% fatality rate. analyses indicated specific mutations allow “host jumping” between (3). There evidence recent strains reduced neutralization sera (4). Added provides comprehensive look at mutational space presents computational predictions HA1-neutralizing derived vaccinated patients humanized mice representative HA1 proteins. confirm other enable zoonosis affect affinities tested. Furthermore, through phylogenetic analyses, quantify avian-to-mammalian persistent circulation North America Europe. Taken together, continuous transmission increase immuno-evasive HA sampled time suggest antigenic drift source spillover risk. Implications all available Our findings worsening antibody binding, along risks public Through previous can now monitor interest, quantified by their evasion, inform monitoring circulating beyond. addition, these may help guide future vaccine therapeutic development fight infections humans.

Язык: Английский

Chemokines simultaneously bind SARS-CoV-2 nucleocapsid protein RNA-binding and dimerization domains DOI Creative Commons
Alberto Domingo López-Muñoz, Jonathan W. Yewdell

Virology Journal, Год журнала: 2025, Номер 22(1)

Опубликована: Март 17, 2025

Abstract Viruses express chemokine (CHK)-binding proteins to interfere with the host CHK network and thereby modulate leukocyte migration. SARS-CoV-2 Nucleocapsid (N) protein binds a subset of human CHKs high affinity, inhibiting their chemoattractant properties. Here, we report that both N’s RNA-binding dimerization domains participate individually in binding. typically possess independent sites for binding glycosaminoglycans (GAG) receptor proteins. We show interaction N occurs through GAG-binding site, pointing way developing compounds block this potential anti-coronavirus therapeutics.

Язык: Английский

Процитировано

0
Large-scale computational modelling of H5 influenza variants against HA1-neutralising antibodies DOI
Colby T. Ford, Shirish Yasa,

Khaled Obeid

и другие.

EBioMedicine, Год журнала: 2025, Номер 114, С. 105632 - 105632

Опубликована: Март 17, 2025

Язык: Английский

Процитировано

0
Predicting antibody and ACE2 affinity for SARS-CoV-2 BA.2.86 and JN.1 with in silico protein modeling and docking DOI Creative Commons
Shirish Yasa, Sayal Guirales-Medrano, Denis Jacob Machado

и другие.

Frontiers in Virology, Год журнала: 2024, Номер 4

Опубликована: Июль 19, 2024

The emergence of SARS-CoV-2 lineages derived from Omicron, including BA.2.86 (nicknamed “Pirola”) and its relative, JN.1, has raised concerns about their potential impact on public personal health due to numerous novel mutations. Despite this, predicting implications based solely mutation counts proves challenging. Empirical evidence JN.1’s increased immune evasion capacity in relation previous variants is mixed. To improve predictions beyond what possible counts, we conducted extensive silico analyses the binding affinity between RBD different (Wuhan-Hu-1, BA.1/B.1.1.529, BA.2, XBB.1.5, BA.2.86, JN.1) neutralizing antibodies vaccinated or infected individuals, as well human angiotensin-converting enzyme 2 (ACE2) receptor. We observed no statistically significant difference JN.1 other variants. Therefore, conclude that new have pronounced escape infection compared However, minor reductions for both ACE2 were noted JN.1. Future research this area will benefit structural memory B-cell should emphasize importance choosing appropriate samples studies assess protection provided by vaccination infection. Moreover, fitness benefits genomic variation outside need be investigated. This contributes understanding variants’ health.

Язык: Английский

Процитировано

2
PD-1 Targeted Antibody Discovery Using AI Protein Diffusion DOI Creative Commons
Colby T. Ford

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Янв. 23, 2024

Abstract The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint inhibits T lymphocytes from attacking other cells the body and thus blocking it improves clearance of tumor by immune system. While there are already multiple FDA-approved anti-PD-1 antibodies, including nivolumab ( Opdivo ® Bristol-Myers Squibb) pembrolizumab Keytruda Merck), ongoing efforts to discover new improved inhibitor therapeutics. In this study, we present antibody fragments that were derived computationally using diffusion evaluated through our scalable, silico pipeline. Here nine synthetic Fv structures suitable for further empirical testing their activity due desirable predicted binding performance.

Язык: Английский

Процитировано

1
Cell surface RNA virus nucleocapsid proteins: a viral strategy for immunosuppression? DOI Creative Commons
Alberto Domingo López-Muñoz, Jonathan W. Yewdell

npj Viruses, Год журнала: 2024, Номер 2(1)

Опубликована: Сен. 2, 2024

Abstract Nucleocapsid protein (N), or nucleoprotein (NP) coats the genome of most RNA viruses, protecting and shielding from cytosolic RNAases innate immune sensors, plays a key role in virion biogenesis viral transcription. Often one highly expressed gene products, N induces strong antibody (Ab) T cell responses. different viruses is present on infected surface copy numbers ranging tens thousands to millions per cell, it can be released bind uninfected cells. Surface targeted by Abs, which contribute clearance via Fc-mediated cellular cytotoxicity. modulate host immunity sequestering chemokines (CHKs), extending prior findings that interferes with adaptive immunity. In this review, we consider aspects biology immunology describe its potential as target for anti-viral intervention.

Язык: Английский

Процитировано

1
PD-1 Targeted Antibody Discovery Using AI Protein Diffusion DOI Creative Commons
Colby T. Ford

Technology in Cancer Research & Treatment, Год журнала: 2024, Номер 23

Опубликована: Янв. 1, 2024

The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint inhibits T lymphocytes from attacking other cells the body and thus blocking it improves clearance of tumor by immune system. While there are already multiple FDA-approved anti-PD-1 antibodies, including nivolumab ( Opdivo ® Bristol-Myers Squibb) pembrolizumab Keytruda Merck), ongoing efforts to discover new improved inhibitor therapeutics. In this study, we present antibody fragments that were derived computationally using diffusion evaluated through our scalable, silico pipeline. Here nine synthetic Fv structures suitable for further empirical testing their activity due desirable predicted binding performance.

Язык: Английский

Процитировано

1
EuDockScore: Euclidean graph neural networks for scoring protein-protein interfaces DOI Creative Commons
Matthew McFee, Jisun Kim, Philip M. Kim

и другие.

Bioinformatics, Год журнала: 2024, Номер 40(11)

Опубликована: Окт. 21, 2024

Abstract Motivation Protein–protein interactions are essential for a variety of biological phenomena including mediating biochemical reactions, cell signaling, and the immune response. Proteins seek to form interfaces which reduce overall system energy. Although determination single polypeptide chain protein structures has been revolutionized by deep learning techniques, complex prediction still not perfected. Additionally, experimentally determining is incredibly resource time expensive. An alternative technique computational docking, takes solved individual proteins produce candidate (decoys). Decoys then scored using mathematical function that assess quality system, known as scoring functions. Beyond functions critical component assessing produced many generative models. Scoring models also used final filtering in those generate antibody binders, perform docking. Results In this work, we present improved protein–protein utilizes cutting-edge Euclidean graph neural network architectures, interfaces. These docking score EuDockScore, EuDockScore-Ab with latter being antibody–antigen dock specific. Finally, provided EuDockScore-AFM model trained on outputs from AlphaFold-Multimer (AFM) proves useful reranking large numbers AFM outputs. Availability implementation The code these available at https://gitlab.com/mcfeemat/eudockscore.

Язык: Английский

Процитировано

1
EuDockScore: euclidean graph neural networks for scoring protein-protein interfaces DOI Creative Commons
Matthew McFee, Jisun Kim, Philip M. Kim

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июнь 6, 2024

Abstract Protein-protein interactions are essential for a variety of biological phenomena including mediating bio-chemical reactions, cell signaling, and the immune response. Proteins seek to form interfaces which reduce overall system energy. Although determination single polypeptide chain protein structures has been revolutionized by deep learning techniques, complex prediction still not perfected. Additionally, experimentally determining is incredibly resource time expensive. An alternative technique computational docking, takes solved individual proteins produce candidate (decoys). Decoys then scored using mathematical function that predicts energy system, know as scoring functions. Beyond functions critical component assessing produced many generative models. Scoring models also used final filtering in those generate antibody binders, perform docking. In this work we present improved protein-protein utilizes cutting-edge euclidean graph neural network architectures, assess interfaces. These docking score known EuDockScore, EuDockScore-Ab with latter being antibody-antigen dock specific. Finally, provided EuDockScore-AFM model trained on outputs from AlphaFold-Multimer proves useful re-ranking large numbers outputs. The code these available at https://gitlab.com/mcfeemat/eudockscore .

Язык: Английский

Процитировано

0
Large-Scale Computational Modeling of H5 Influenza Variants Against HA1-Neutralizing Antibodies DOI Creative Commons
Colby T. Ford, Shirish Yasa,

Khaled Obeid

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июль 17, 2024

Abstract The United States Department of Agriculture has recently released reports that show samples from 2022-2024 highly pathogenic avian influenza (H5N1) have been detected in mammals and birds (1). To date, the Centers for Disease Control there 27 humans infected with H5N1 2024 (2). broader potential impact on human health remains unclear. In this study, we computationally model 1,804 protein complexes consisting various H5 isolates 1959 to against 11 hemagglutinin domain 1 (HA1)-neutralizing antibodies. This study shows a trend weakening binding affinity existing antibodies over time, indicating virus is evolving immune escape our medical defenses. We also found based wide variety host species geographic locations which was observed transmitted mammals, not single central reservoir or location associated H5N1’s spread. These results indicate move epidemic pandemic status near future. illustrates value high-performance computing rapidly protein-protein interactions viral genomic sequence data at-scale functional insights into preparedness. Research Context Evidence before Previous studies shown cases transmissions are increasing frequency, concern health. Since 1997, nearly thousand reported 52% fatality rate. analyses indicated specific mutations allow “host jumping” between (3). There evidence recent strains reduced neutralization sera (4). Added provides comprehensive look at mutational space presents computational predictions HA1-neutralizing derived vaccinated patients humanized mice representative HA1 proteins. confirm other enable zoonosis affect affinities tested. Furthermore, through phylogenetic analyses, quantify avian-to-mammalian persistent circulation North America Europe. Taken together, continuous transmission increase immuno-evasive HA sampled time suggest antigenic drift source spillover risk. Implications all available Our findings worsening antibody binding, along risks public Through previous can now monitor interest, quantified by their evasion, inform monitoring circulating beyond. addition, these may help guide future vaccine therapeutic development fight infections humans.

Язык: Английский

Процитировано

0