Limits...
"The molecule's the thing:" the promise of molecular modeling and dynamic simulations in aiding the prioritization and interpretation of genomic testing results.

Oliver GR, Zimmermann MT, Klee EW, Urrutia RA - F1000Res (2016)

Bottom Line: Herein we describe the current scenario in genomic testing and in particular the burden of variants of uncertain significance (VUSs).We highlight a role for molecular modeling and molecular dynamic simulations as tools that can significantly increase the yield of information to aid in the evaluation of pathogenicity.Though the application of these methodologies to the interpretation of variants identified by genomic testing is not yet widespread, we predict that an increase in their use will significantly benefit the mission of clinical genomics for individualized medicine.

View Article: PubMed Central - PubMed

Affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA.

ABSTRACT
Clinical genomics is now a reality and lies at the heart of individualized medicine efforts. The success of these approaches is evidenced by the increasing volume of publications that report causal links between genomic variants and disease. In spite of early success, clinical genomics currently faces significant challenges in establishing the relevance of the majority of variants identified by next generation sequencing tests. Indeed, the majority of mutations identified are harbored by proteins whose functions remain elusive. Herein we describe the current scenario in genomic testing and in particular the burden of variants of uncertain significance (VUSs). We highlight a role for molecular modeling and molecular dynamic simulations as tools that can significantly increase the yield of information to aid in the evaluation of pathogenicity. Though the application of these methodologies to the interpretation of variants identified by genomic testing is not yet widespread, we predict that an increase in their use will significantly benefit the mission of clinical genomics for individualized medicine.

No MeSH data available.


Related in: MedlinePlus

The HIV-1 protease as a generic model system for computational biophysics14–16.HIV-1 protease has become a model system because of its disease relevance, the availability of mutational and drug binding data, and for its tractable size and molecular stability. The protein’s function is to cleave HIV peptides into the functional proteins of the infectious HIV virion. This example illustrates generalized concepts of the applicability of protein modeling methods in clinical investigationsA)Ligand binding residues are spatially separated. The functional protein dimer is shown with a pharmacologic inhibitor bound to the active site. Residues that are within 3.5Å of the inhibitor are highlighted in tan. The primary sequence is colored identically to the three-dimensional structure to indicate relative positioning of residues. It is apparent that the ligand binding portion of the protease consists of residues that are non-adjacent within the primary sequence, which illustrates an advantage of modelling over linear sequence analysis.B)Drug resistance mutations tend to occur in residues within the active site. Many mutations have been characterized that are associated with resistance to inhibitor drugs. While the sites of these mutations are also disjoint in sequence, nearly all of them fall into the same set of drug binding residues, indicating how modeling can enable prediction of a mutation’s effect.C)Structural effects of mutations beyond the active site. Drug-resistance mutations in non-ligand-binding residues have been shown, using computational experiments, to impact the flexibility of the protein and therefore alter drug binding. Computational modeling has characterized the flexibility of the protein in multiple mutated states, illustrating the potential to predict the functional effect of mutations beyond an active site.D)Dynamics of ligand-free protein. Computational studies have the advantage of being able to simulate conditions that are difficult to assay experimentally, such as the dynamics of ligand-free forms in atomic detail.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4920209&req=5

f1: The HIV-1 protease as a generic model system for computational biophysics14–16.HIV-1 protease has become a model system because of its disease relevance, the availability of mutational and drug binding data, and for its tractable size and molecular stability. The protein’s function is to cleave HIV peptides into the functional proteins of the infectious HIV virion. This example illustrates generalized concepts of the applicability of protein modeling methods in clinical investigationsA)Ligand binding residues are spatially separated. The functional protein dimer is shown with a pharmacologic inhibitor bound to the active site. Residues that are within 3.5Å of the inhibitor are highlighted in tan. The primary sequence is colored identically to the three-dimensional structure to indicate relative positioning of residues. It is apparent that the ligand binding portion of the protease consists of residues that are non-adjacent within the primary sequence, which illustrates an advantage of modelling over linear sequence analysis.B)Drug resistance mutations tend to occur in residues within the active site. Many mutations have been characterized that are associated with resistance to inhibitor drugs. While the sites of these mutations are also disjoint in sequence, nearly all of them fall into the same set of drug binding residues, indicating how modeling can enable prediction of a mutation’s effect.C)Structural effects of mutations beyond the active site. Drug-resistance mutations in non-ligand-binding residues have been shown, using computational experiments, to impact the flexibility of the protein and therefore alter drug binding. Computational modeling has characterized the flexibility of the protein in multiple mutated states, illustrating the potential to predict the functional effect of mutations beyond an active site.D)Dynamics of ligand-free protein. Computational studies have the advantage of being able to simulate conditions that are difficult to assay experimentally, such as the dynamics of ligand-free forms in atomic detail.

Mentions: The aforementioned computational tools largely leverage genome-centric information. While there is much proven value to these data, the biological reality comprises many additional layers of complexity. Mutations affect atomic-level biophysical changes that may alter the structure and biochemical function of the genome's protein products. While we detect variants in DNA, we typically interpret their effect by inferring or confirming their deleterious effect on encoded proteins (Figure 1). Even the effects of regulatory variants are typically interpreted as altering the probability of a protein to be expressed or spliced into a given functional form. Thus ideally, any method used to assess pathogenicity of genomic variants should possess the ability to look beyond genomic annotation to a functional context, at an atomic resolution. This increased resolution is likely to yield information that can add context to mutations, better identify the mechanism of pathogenicity, and combine with existing knowledge to facilitate in clinical decision making.


"The molecule's the thing:" the promise of molecular modeling and dynamic simulations in aiding the prioritization and interpretation of genomic testing results.

Oliver GR, Zimmermann MT, Klee EW, Urrutia RA - F1000Res (2016)

The HIV-1 protease as a generic model system for computational biophysics14–16.HIV-1 protease has become a model system because of its disease relevance, the availability of mutational and drug binding data, and for its tractable size and molecular stability. The protein’s function is to cleave HIV peptides into the functional proteins of the infectious HIV virion. This example illustrates generalized concepts of the applicability of protein modeling methods in clinical investigationsA)Ligand binding residues are spatially separated. The functional protein dimer is shown with a pharmacologic inhibitor bound to the active site. Residues that are within 3.5Å of the inhibitor are highlighted in tan. The primary sequence is colored identically to the three-dimensional structure to indicate relative positioning of residues. It is apparent that the ligand binding portion of the protease consists of residues that are non-adjacent within the primary sequence, which illustrates an advantage of modelling over linear sequence analysis.B)Drug resistance mutations tend to occur in residues within the active site. Many mutations have been characterized that are associated with resistance to inhibitor drugs. While the sites of these mutations are also disjoint in sequence, nearly all of them fall into the same set of drug binding residues, indicating how modeling can enable prediction of a mutation’s effect.C)Structural effects of mutations beyond the active site. Drug-resistance mutations in non-ligand-binding residues have been shown, using computational experiments, to impact the flexibility of the protein and therefore alter drug binding. Computational modeling has characterized the flexibility of the protein in multiple mutated states, illustrating the potential to predict the functional effect of mutations beyond an active site.D)Dynamics of ligand-free protein. Computational studies have the advantage of being able to simulate conditions that are difficult to assay experimentally, such as the dynamics of ligand-free forms in atomic detail.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920209&req=5

f1: The HIV-1 protease as a generic model system for computational biophysics14–16.HIV-1 protease has become a model system because of its disease relevance, the availability of mutational and drug binding data, and for its tractable size and molecular stability. The protein’s function is to cleave HIV peptides into the functional proteins of the infectious HIV virion. This example illustrates generalized concepts of the applicability of protein modeling methods in clinical investigationsA)Ligand binding residues are spatially separated. The functional protein dimer is shown with a pharmacologic inhibitor bound to the active site. Residues that are within 3.5Å of the inhibitor are highlighted in tan. The primary sequence is colored identically to the three-dimensional structure to indicate relative positioning of residues. It is apparent that the ligand binding portion of the protease consists of residues that are non-adjacent within the primary sequence, which illustrates an advantage of modelling over linear sequence analysis.B)Drug resistance mutations tend to occur in residues within the active site. Many mutations have been characterized that are associated with resistance to inhibitor drugs. While the sites of these mutations are also disjoint in sequence, nearly all of them fall into the same set of drug binding residues, indicating how modeling can enable prediction of a mutation’s effect.C)Structural effects of mutations beyond the active site. Drug-resistance mutations in non-ligand-binding residues have been shown, using computational experiments, to impact the flexibility of the protein and therefore alter drug binding. Computational modeling has characterized the flexibility of the protein in multiple mutated states, illustrating the potential to predict the functional effect of mutations beyond an active site.D)Dynamics of ligand-free protein. Computational studies have the advantage of being able to simulate conditions that are difficult to assay experimentally, such as the dynamics of ligand-free forms in atomic detail.
Mentions: The aforementioned computational tools largely leverage genome-centric information. While there is much proven value to these data, the biological reality comprises many additional layers of complexity. Mutations affect atomic-level biophysical changes that may alter the structure and biochemical function of the genome's protein products. While we detect variants in DNA, we typically interpret their effect by inferring or confirming their deleterious effect on encoded proteins (Figure 1). Even the effects of regulatory variants are typically interpreted as altering the probability of a protein to be expressed or spliced into a given functional form. Thus ideally, any method used to assess pathogenicity of genomic variants should possess the ability to look beyond genomic annotation to a functional context, at an atomic resolution. This increased resolution is likely to yield information that can add context to mutations, better identify the mechanism of pathogenicity, and combine with existing knowledge to facilitate in clinical decision making.

Bottom Line: Herein we describe the current scenario in genomic testing and in particular the burden of variants of uncertain significance (VUSs).We highlight a role for molecular modeling and molecular dynamic simulations as tools that can significantly increase the yield of information to aid in the evaluation of pathogenicity.Though the application of these methodologies to the interpretation of variants identified by genomic testing is not yet widespread, we predict that an increase in their use will significantly benefit the mission of clinical genomics for individualized medicine.

View Article: PubMed Central - PubMed

Affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA.

ABSTRACT
Clinical genomics is now a reality and lies at the heart of individualized medicine efforts. The success of these approaches is evidenced by the increasing volume of publications that report causal links between genomic variants and disease. In spite of early success, clinical genomics currently faces significant challenges in establishing the relevance of the majority of variants identified by next generation sequencing tests. Indeed, the majority of mutations identified are harbored by proteins whose functions remain elusive. Herein we describe the current scenario in genomic testing and in particular the burden of variants of uncertain significance (VUSs). We highlight a role for molecular modeling and molecular dynamic simulations as tools that can significantly increase the yield of information to aid in the evaluation of pathogenicity. Though the application of these methodologies to the interpretation of variants identified by genomic testing is not yet widespread, we predict that an increase in their use will significantly benefit the mission of clinical genomics for individualized medicine.

No MeSH data available.


Related in: MedlinePlus