scholarly journals Implementation of adaptive integration method for free energy calculations in molecular systems

2020 ◽  
Vol 6 ◽  
pp. e264
Author(s):  
Christopher A. Mirabzadeh ◽  
F. Marty Ytreberg
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Integration Method ◽  
Free Energy Calculations ◽  
Model Systems ◽  
Adaptive Integration ◽  
Molecular Systems ◽  
Accuracy And Precision ◽  
Amino Acid Mutations ◽  
Modify Protein

Estimating free energy differences by computer simulation is useful for a wide variety of applications such as virtual screening for drug design and for understanding how amino acid mutations modify protein interactions. However, calculating free energy differences remains challenging and often requires extensive trial and error and very long simulation times in order to achieve converged results. Here, we present an implementation of the adaptive integration method (AIM). We tested our implementation on two molecular systems and compared results from AIM to those from a suite of other methods. The model systems tested here include calculating the solvation free energy of methane, and the free energy of mutating the peptide GAG to GVG. We show that AIM is more efficient than other tested methods for these systems, that is, AIM results converge to a higher level of accuracy and precision for a given simulation time.

scholarly journals Implementation of adaptive integration method for free energy calculations in molecular systems

2019 ◽  
Author(s):  
Christopher A Mirabzadeh ◽  
F. Marty Ytreberg
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Integration Method ◽  
Free Energy Calculations ◽  
Model Systems ◽  
Adaptive Integration ◽  
Standard Methods ◽  
Molecular Systems ◽  
Accuracy And Precision ◽  
Amino Acid Mutations

Estimating free energy differences by computer simulation is useful for a wide variety of applications such as virtual screening for drug design and for understanding how amino acid mutations modify protein interactions. However, calculating free energy differences remains challenging and often requires extensive trial and error and very long simulation times in order to achieve converged results. Here, we present an implementation of the adaptive integration method (AIM). We tested our implementation on two molecular systems and compared results from AIM to those from a suite of standard methods. The model systems tested here include calculating the solvation free energy of methane, and the free energy of mutating the peptide GAG to GVG. We show that AIM is more efficient than standard methods for these test cases, that is, AIM results converge to a higher level of accuracy and precision for a given simulation time.

scholarly journals Implementation of adaptive integration method for free energy calculations in molecular systems

2019 ◽  
Author(s):  
Christopher A Mirabzadeh ◽  
F. Marty Ytreberg
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Integration Method ◽  
Free Energy Calculations ◽  
Model Systems ◽  
Adaptive Integration ◽  
Standard Methods ◽  
Molecular Systems ◽  
Accuracy And Precision ◽  
Amino Acid Mutations

Estimating free energy differences by computer simulation is useful for a wide variety of applications such as virtual screening for drug design and for understanding how amino acid mutations modify protein interactions. However, calculating free energy differences remains challenging and often requires extensive trial and error and very long simulation times in order to achieve converged results. Here, we present an implementation of the adaptive integration method (AIM). We tested our implementation on two molecular systems and compared results from AIM to those from a suite of standard methods. The model systems tested here include calculating the solvation free energy of methane, and the free energy of mutating the peptide GAG to GVG. We show that AIM is more efficient than standard methods for these test cases, that is, AIM results converge to a higher level of accuracy and precision for a given simulation time.

scholarly journals Analyzing Machupo virus-receptor binding by molecular dynamics simulations

2014 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Protein Interactions ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Virus Protein ◽  
Test Case ◽  
Distance Curve ◽  
Machupo Virus ◽  
Dynamics Simulations

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-­protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non­alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-­virus protein-­protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-­versus-­distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-­crystal structure shows two large hydrogen-­bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-­resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein­ protein interactions.

scholarly journals Analyzing Machupo virus-receptor binding by molecular dynamics simulations

2014 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Protein Interactions ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Virus Protein ◽  
Test Case ◽  
Protein Protein Interactions ◽  
Distance Curve ◽  
Machupo Virus

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-­protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non­alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-­virus protein-­protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-­versus-­distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-­crystal structure shows two large hydrogen-­bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-­resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.

The adaptive integration method for calculating general free energy functions

2005 ◽  
Vol 169 (1-3) ◽  
pp. 274-276 ◽  
Author(s):  
Robert H. Swendsen ◽  
Marc Fasnacht ◽  
John M. Rosenberg
Keyword(s):  
Free Energy ◽  
Integration Method ◽  
Energy Functions ◽  
Adaptive Integration

scholarly journals Mechanistic Insights into Specific G Protein Interactions with Adenosine Receptors Revealed by Accelerated Molecular Simulations

2019 ◽  
Author(s):  
Jinan Wang ◽  
Yinglong Miao
Keyword(s):  
Drug Design ◽  
G Protein ◽  
Protein Interactions ◽  
Adenosine Receptors ◽  
Molecular Simulations ◽  
Free Energy Calculations ◽  
Model Systems ◽  
G Protein Coupling ◽  
Protein Coupling ◽  
Experimental Findings

AbstractCoupling between G-protein-coupled receptors (GPCRs) and the G proteins is a key step in cellular signaling. Despite extensive experimental and computational studies, the mechanism of specific GPCR-G protein coupling remains poorly understood. This has greatly hindered effective drug design of GPCRs that are primary targets of ~1/3 of currently marketed drugs. Here, we have employed all-atom molecular simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method to decipher the mechanism of the GPCR-G protein interactions. Adenosine receptors (ARs) were used as model systems based on very recently determined cryo-EM structures of the A1AR and A2AAR coupled with the Gi and Gs proteins, respectively. Changing the Gi protein to the Gs led to increased fluctuations in the A1AR and agonist adenosine (ADO), while agonist 5’-N-ethylcarboxamidoadenosine (NECA) binding in the A2AAR could be still stabilized upon changing the Gs protein to the Gi. Free energy calculations identified one stable low-energy conformation for each of the ADO-A1AR-Gi and NECA-A2AAR-Gs complexes as in the cryo-EM structures, similarly for the NECA-A2AAR-Gi complex. In contrast, the ADO agonist and Gs protein sampled multiple conformations in the ADO-A1AR-Gs system. GaMD simulations thus indicated that the ADO-bound A1AR preferred to couple with the Gi protein to the Gs, while the A2AAR could couple with both the Gs and Gi proteins, being highly consistent with experimental findings of the ARs. More importantly, detailed analysis of the atomic simulations showed that the specific AR-G protein coupling resulted from remarkably complementary residue interactions at the protein interface, involving mainly the receptor transmembrane 6 helix and the Gα α5 helix and α4-β6 loop. In summary, the GaMD simulations have provided unprecedented insights into the dynamic mechanism of specific GPCR-G protein interactions at an atomistic level, which is expected to facilitate future drug design efforts of the GPCRs.

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