ArticleMolecular biology and evolution2007
A likelihood-based index of protein protein binding affinities with application to influenza HA escape from antibodies.
Article in Molecular biology and evolution, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Trace Reconstruction Problems in Computational Biology.IEEE transactions on information theory · 2021Article
- Viral evolution and transmission effectiveness.World journal of virology · 2012Article
- Structural considerations in the fitness landscape of a virus.Molecular biology and evolution · 2010Article
- Therapeutic activity of an anti-idiotypic antibody-derived killer peptide against influenza A virus experimental infection.Antimicrobial agents and chemotherapy · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In many biological systems, proteins interact with other organic molecules to produce indispensable functions, in which molecular recognition phenomena are essential. Proteins have kept or gained their functions during molecular evolution. Their functions seem to be flexible, and a few amino acid substitutions sometimes cause drastic changes in function. In order to monitor and predict such drastic changes in the early stages in target populations, we need to identify patterns of structural changes during molecular evolution causing decreases or increases in the binding affinity of protein complexes. In previous work, we developed a likelihood-based index to quantify the degree to which a sequence fits a given structure. This index was named the sequence-structure fitness (SSF) and is calculated empirically based on amino acid preferences and pairwise interactions in the structural environment present in template structures. In the present work, we used the SSF to develop an index to measure the binding affinity of protein-protein complexes defined as the log likelihood ratio, contrasting the fitness of the sequences to the structure of the complex and that of the uncomplexed proteins. We applied the developed index to the complexes formed between influenza A hemagglutinin (HA) and four antibodies. The antibody-antigen binding region of HA is under strong selection pressure by the host immune system. Hence, examination of the long-term adaptation of HA to the four antibodies could reveal the strategy of the molecular evolution of HA. Two antibodies cover the HA receptor-binding region, while the other two bind away from the receptor-binding region. By focusing on branches with a significant decline in binding ability, we could detect key amino acid replacements and investigate the mechanism via conditional probabilities. The contrast between the adaptations to the two types of antibodies suggests that the virus adapts to the immune system at the cost of structural change.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.