A team of scientists have found that certain amino acids — the small molecules that constitute proteins in our body — are more important than others when it comes to longevity, although the reason for this difference is not yet clear.
Each amino acid contributes to health and plays a specific role in ensuring that the body functions normally, regulating vital processes such as muscle growth, sleep, hormone production, and digestion. Among the 20 amino acids, some are essential — they must be obtained through the diet — while non-essential amino acids are naturally produced by the body.
“The amino acid sequences of proteins code a secret language that we need to decode to understand important mechanisms in the cell, including aging,” explained Csaba Kerepesi, one of the researchers who conducted the study.
By analyzing the amino acid sequences of proteins, they found that the proportion of aspartic acid, a non-essential amino acid, is higher in proteins associated with the aging process than others.
“However, currently, we do not know exactly why,” Kerepesi explained. “As aging is often described as a damage accumulation, we tried to link the observed difference to some form of damage that is specific to aspartic acid, such as the formation of isoaspartate. This modification can affect protein structure and function and has been linked to Alzheimer’s disease.”
Data mining reveals new patterns
Kerepesi, a mathematician and bioinformatics researcher at the Hungarian Research Network’s Institute for Computer Science and Control in Budapest, and his collaborators used “biodata mining” to uncover these trends.
He believes this simple approach, which only requires a normal computer and the internet, can be instrumental in making important scientific discoveries if “you have a good research idea”.
To start with, Kerepesi and his team downloaded the amino acid sequences of all human and model organisms from an open-access bioinformatics database called UniProt. From another database, GenAge, they downloaded a list of genes that have been linked to aging, longevity, and anti-longevity in humans as well as in yeast, worms, flies, and mice.
Based on the data, they calculated the proportion of each amino acid in the different groups of proteins and performed a statistical analysis to ensure that their results were significant.
Not only was aspartic acid more prevalent in aging-related proteins, it also appeared more frequently in pro-longevity proteins — gene products that reduce lifespan when under produced and/or extend lifespan when overproduced — than anti-longevity proteins, which have the reverse effect on lifespan.
The researchers also found that the proportion of the essential amino acid tryptophan — the precursor to the neurotransmitter serotonin and hormone melatonin — was lower in pro-longevity proteins than anti-longevity proteins, and that leucine, another essential amino acid, was less prevalent in aging-related proteins.
As with aspartic acid, more research is needed to determine why this is the case, but Kerepesi has a theory that could explain why tryptophan is underrepresented in aging-related proteins.
“Interestingly, tryptophan is more susceptible to [single]-point mutations than other amino acids,” he said. “Most amino acids are coded by multiple codons while tryptophan is coded by a single codon.”
This mutation susceptibility means that tryptophan can be transformed into an entirely different amino acid, accounting for its diminished presence.
Amino acid length and longevity
Another important finding made by the research team involves the amino acid length. They discovered that aging-related proteins tend to be longer (contain more amino acids) than non-aging-related proteins, which is in line with the results of other studies, such as one that revealed a gradual imbalance between long- and short-gene expression as we age.
However, the average length of pro-longevity proteins was not significantly different from that of anti-longevity proteins. Kerepesi said that mechanism behind these differences requires further investigation.
“We plan to use machine learning algorithms to find more interesting patterns in aging-related proteins than simple amino acid proportions,” he shared.
Insights into aging made possible by artificial intelligence could help humans achieve a longer healthspan and potentially avoid non-communicable diseases associated with aging, such as Alzheimer’s disease, cancer, type 2 diabetes, and cardiovascular disease.
Reference: Csaba Kerepesi, et al.,Unique patterns in amino acid sequences of aging-related proteins. Advanced Biology (2023). DOI: 10.1002/adbi.202300436
Feature image credit: micheile henderson on Unsplash
