Even with recent drastic improvements in prediction of protein structure within the vast structural space of the proteome, there is still much to understand about changes in protein structure and functionality, at the level of missense mutations relevant to protein evolution and disease. Point mutations in amino acid sequence often change protein stability and the ability of a protein to bind its ligand or protein binding partners, with corresponding impact on protein function, including through increased degradation or aggregation. These changes may then trigger downstream cascades and lead to unexpected consequences for the cellular physiology. Increased knowledge of protein structures and biochemical databases enable us to understand why a mutation may have its given function, at the molecular level.
Emphasis will be on protein structural understanding, although combination of structural and sequence-based techniques will also be considered. Special emphasis will be on methods stemming from protein topology and machine learning techniques. While the focus is on computational genomics, we welcome contributions that include a combination of computational and experimental work. We include contributions pertaining to both disease and evolution of new functions. Although our primary focus is rooted in bioinformatics, we welcome reports of molecular simulations in which new insight is gained towards mutation and functionality, as well as Genome-scale Metabolic models which facilitate understanding of why a mutation does or does not contribute to disease.
We would be interested especially in new research submissions about any of the following topics:
1. Rationalization, based on protein structure (and also, optionally, systems biology) of why missense mutations are pathogenic or benign.
2. Convergent evolution towards ligand or DNA binding (e.g., pioneer transcription factors that bind specific DNA motifs, make chromatin accessible, and establish new phenotypes).
3. Domain swapping and aggregation upon mutation.
4. Molecular Dynamics or Monte Carlo simulations of mutants, which help explain experiments or make validated predictions (e.g., mutations that increase protein stability or activity).
5. Predictions and/or rationalizations of the ability to treat disease with pharmacological chaperones, based on protein structure (and, optionally, also sequence).
6. Mutational scanning for altering the activity or stability of a protein, combined with structural rationalization.
7. Underground metabolism and evolution of new or specialized functions.
Even with recent drastic improvements in prediction of protein structure within the vast structural space of the proteome, there is still much to understand about changes in protein structure and functionality, at the level of missense mutations relevant to protein evolution and disease. Point mutations in amino acid sequence often change protein stability and the ability of a protein to bind its ligand or protein binding partners, with corresponding impact on protein function, including through increased degradation or aggregation. These changes may then trigger downstream cascades and lead to unexpected consequences for the cellular physiology. Increased knowledge of protein structures and biochemical databases enable us to understand why a mutation may have its given function, at the molecular level.
Emphasis will be on protein structural understanding, although combination of structural and sequence-based techniques will also be considered. Special emphasis will be on methods stemming from protein topology and machine learning techniques. While the focus is on computational genomics, we welcome contributions that include a combination of computational and experimental work. We include contributions pertaining to both disease and evolution of new functions. Although our primary focus is rooted in bioinformatics, we welcome reports of molecular simulations in which new insight is gained towards mutation and functionality, as well as Genome-scale Metabolic models which facilitate understanding of why a mutation does or does not contribute to disease.
We would be interested especially in new research submissions about any of the following topics:
1. Rationalization, based on protein structure (and also, optionally, systems biology) of why missense mutations are pathogenic or benign.
2. Convergent evolution towards ligand or DNA binding (e.g., pioneer transcription factors that bind specific DNA motifs, make chromatin accessible, and establish new phenotypes).
3. Domain swapping and aggregation upon mutation.
4. Molecular Dynamics or Monte Carlo simulations of mutants, which help explain experiments or make validated predictions (e.g., mutations that increase protein stability or activity).
5. Predictions and/or rationalizations of the ability to treat disease with pharmacological chaperones, based on protein structure (and, optionally, also sequence).
6. Mutational scanning for altering the activity or stability of a protein, combined with structural rationalization.
7. Underground metabolism and evolution of new or specialized functions.