Potential of mean force of the hepatitis C virus core protein–monoclonal 19D9D6 antibody interaction

Wang, Yeng‐Tseng; Su, Zhiyuan; Chen, Cheng-Lung

doi:10.1016/j.bpc.2009.09.004

Cited by 6 publications

(3 citation statements)

References 30 publications

(32 reference statements)

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“…A more recent study also used the KFC method in the study of the interaction of the antibody, called 19D9D6, and the hepatitis C virus (HCV). Comparison of MD simulation results matched three residues that were predicted as hot spots in KFC that are important for the interaction between the virus and antibody [101]. The KFC method is now available via an interactive, public web server, where users can submit complexes (via uploaded file, or providing the PDB code) and view results for each job, as well as upload scores from Robetta’s alanine scanning, ConSurf sequence conservation, or experimental data [99] (Figure 4).…”

Section: Machine Learning-based Methodsmentioning

confidence: 90%

Computational Prediction of Protein Hot Spot Residues

Morrow¹,

Zhang²

2012

CPD

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Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

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Section: Machine Learning-based Methodsmentioning

confidence: 90%

Computational Prediction of Protein Hot Spot Residues

Morrow¹,

Zhang²

2012

CPD

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“…With regard to biological systems such as antigen-antibody systems [15][16][17], the PMF can be used to analyse the intermolecular interactions and thermodynamic properties. The PMF for stretching the complex structure for equilibrium MD simulations was calculated using umbrella sampling techniques and the WHAM.…”

Section: Introductionmentioning

confidence: 99%

“…The stretching distance ranged from 3.6 to 5.6 nm. The CCDAs method is useful to predict important residues in the biomolecule dissociation systems [15][16][17]. In the biomolecule systems, backbone rotations are provided with higher energy barriers, [19][20] and backbone dihedral angles are representative of backbone rotations.…”

Section: Introductionmentioning

confidence: 99%

Binding in Human Antibody-DNA Interface: A Computer-Simulation Study

Wang

2012

2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications

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The stretching, that is, the distance between the centre of mass of the ssDNA and the antibody, has been studied using the potential of mean force calculations based on molecular dynamics and the explicit water model. Our simulations indicate that the 6 residues Gln43, Gly44, Arg98, Tyr100, Val136 and Thr137, pair interactions of antibodyssDNA, and the 5 interprotein molecular hydrogen bonds might play important roles in the antibody-ssDNA interaction, and this finding may be useful for protein engineering of this antibody-ssDNA structure. In addition, the 6 residues Gln43, Gly44, Arg98, Tyr100, Val136 and Thr137 might be more important in the development of bioactive antibody analogues.

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Binding hot-spots in an antibody–ssDNA interface: a molecular dynamics study

Wang

Lee

2012

Mol. BioSyst.

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Simulating antigen-antibody interactions is essential for elucidating antigen-antibody mechanics. Proteins interactions are vital for elucidating antibody-ssDNA associations in immunology. Therefore, this study investigated the dissociation of the human systemic lupus erythematosus antibody-ssDNA complex structure. Dissociation (i.e. the distance between the center of mass of the ssDNA and the antibody) is also studied using the potential of mean force calculations based on molecular dynamics and the explicit water model. The MM-PBSA method is also used to prove our dissociation simulations. With 605 nanosecond molecular dynamics simulations, the results indicate that the 8 residues (i.e. Gly44 (HCDR2), Asn54 (HCDR2), Arg98 (HCDR3), Tyr100 (HCDR3), Asp101 (HCDR3), Tyr32 (LCDR1), Tyr49 (LCDR2) and Asn50 (LCDR2)), and the five inter-protein molecular hydrogen bonds may profoundly impact the antibody-ssDNA interaction, a finding which may be useful for protein engineering of this antibody-ssDNA structure. Experimental binding affinity of this antibody-ssDNA complex equals 7.00 kcal mol(-1). Our dissociation binding affinity is 7.96 ± 0.33 kcal mol(-1) and MM-PBSA binding affinity is 9.12 ± 1.65 kcal mol(-1), which is close to the experimental value. Additionally, the 8 residues Gly44 (HCDR2), Asn54 (HCDR2), Arg98 (HCDR3), Tyr100 (HCDR3), Asp101 (HCDR3), Tyr32 (LCDR1), Tyr49 (LCDR2) and Asn50 (LCDR2) may play a more significant role in developing bioactive antibody analogues.

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Potential of mean force of the hepatitis C virus core protein–monoclonal 19D9D6 antibody interaction

Cited by 6 publications

References 30 publications

Computational Prediction of Protein Hot Spot Residues

Computational Prediction of Protein Hot Spot Residues

Binding in Human Antibody-DNA Interface: A Computer-Simulation Study

Binding hot-spots in an antibody–ssDNA interface: a molecular dynamics study

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