Improving MM-GB/SA Scoring through the Application of the Variable Dielectric Model

Ravindranathan, Krishna Pratap; Tirado‐Rives, Julian; Jorgensen, William L.; Guimarães, Cristiano Ruch Werneck

doi:10.1021/ct200565u

Cited by 40 publications

(59 citation statements)

References 44 publications

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“…As the BRC-peptide binding cavity of RAD51 is relatively solvated, we chose to use a protein dielectric ε of 13; values of 4–20 are routinely used, often using mixed values (38,39). ΔG bind values obtained from Endpoint Methods, such as MM-PBSA or BEM, should be viewed as enhanced scoring functions, which have enhanced rank-ordering value, rather than as metrics of accurate absolute binding free energy (40).…”

Section: Methodsmentioning

confidence: 99%

Contributions of the RAD51 N-terminal domain to BRCA2-RAD51 interaction

Subramanyam¹,

Jones²,

Spies³

2013

Nucleic Acids Research

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RAD51 DNA strand exchange protein catalyzes the central step in homologous recombination, a cellular process fundamentally important for accurate repair of damaged chromosomes, preservation of the genetic integrity, restart of collapsed replication forks and telomere maintenance. BRCA2 protein, a product of the breast cancer susceptibility gene, is a key recombination mediator that interacts with RAD51 and facilitates RAD51 nucleoprotein filament formation on single-stranded DNA generated at the sites of DNA damage. An accurate atomistic level description of this interaction, however, is limited to a partial crystal structure of the RAD51 core fused to BRC4 peptide. Here, by integrating homology modeling and molecular dynamics, we generated a structure of the full-length RAD51 in complex with BRC4 peptide. Our model predicted previously unknown hydrogen bonding patterns involving the N-terminal domain (NTD) of RAD51. These interactions guide positioning of the BRC4 peptide within a cavity between the core and the NTDs; the peptide binding separates the two domains and restricts internal dynamics of RAD51 protomers. The model’s depiction of the RAD51-BRC4 complex was validated by free energy calculations and in vitro functional analysis of rationally designed mutants. All generated mutants, RAD51E42A, RAD51E59A, RAD51E237A, RAD51E59A/E237A and RAD51E42A/E59A/E237A maintained basic biochemical activities of the wild-type RAD51, but displayed reduced affinities for the BRC4 peptide. Strong correlation between the calculated and experimental binding energies confirmed the predicted structure of the RAD51-BRC4 complex and highlighted the importance of RAD51 NTD in RAD51-BRCA2 interaction.

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Section: Methodsmentioning

confidence: 99%

Contributions of the RAD51 N-terminal domain to BRCA2-RAD51 interaction

Subramanyam¹,

Jones²,

Spies³

2013

Nucleic Acids Research

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“…Recently, Schneider et al have validated the HYDE scoring function through large-scale docking experiments, which resulted to successful prediction of the correct binding mode in 93% of complexes when checked with the Astex diverse set [ 98 ]. Alternatively, Ravindranathan et al have defined a physics based scoring function and more precisely, a variable dielectric model based on residue types for better description of protein–ligand electrostatics in MM-GBSA scoring, which results in a higher correlation with affinity data [ 99 ]. Certainly, while much progress has been made in delivering more accurate scoring functions, further improvements in this direction are desirable.…”

Section: Virtual Screening In Structure-based Drug Discoverymentioning

confidence: 99%

Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

Lionta¹,

Spyrou

Vassilatis³

et al. 2014

CTMC

816

547

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Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.

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“…ε in( ij ) =max{ ε in( i ) , ε in( j ) }. Detailed descriptions regarding this point can be found in references [30, 31]. The values of the internal dielectric constants listed in Table 2 were obtained from reference [32].…”

Section: Structures and Methodsmentioning

confidence: 99%

Adding energy minimization strategy to peptide‐design algorithm enables better search for RNA‐binding peptides: Redesigned λ N peptide binds boxB RNA

et al. 2016

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Our previously-developed peptide-design algorithm was improved by adding an energy minimization strategy which allows the amino acid sidechains to move in a broad configuration space during sequence evolution. In this work, the new algorithm was used to generate a library of 21-mer peptides which could substitute for λ N peptide in binding to boxB RNA. Six potential peptides were obtained from the algorithm, all of which exhibited good binding capability with boxB RNA. Atomistic molecular dynamics simulations were then conducted to examine the ability of the λ N peptide and three best evolved peptides, viz. Pept01, Pept26 and Pept28, to bind to boxB RNA. Simulation results demonstrated that our evolved peptides are better at binding to boxB RNA than the λ N peptide. Sequence searches using the old (without energy minimization strategy) and new (with energy minimization strategy) algorithms confirm that the new algorithm is more effective at finding good RNA-binding peptides than the old algorithm.

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Improving MM-GB/SA Scoring through the Application of the Variable Dielectric Model

Cited by 40 publications

References 44 publications

Contributions of the RAD51 N-terminal domain to BRCA2-RAD51 interaction

Contributions of the RAD51 N-terminal domain to BRCA2-RAD51 interaction

Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

Adding energy minimization strategy to peptide‐design algorithm enables better search for RNA‐binding peptides: Redesigned λ N peptide binds boxB RNA

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