Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) are arguably very popular methods for binding free energy prediction since they are more accurate than most scoring functions of molecular docking and less computationally demanding than alchemical free energy methods. MM/PBSA and MM/GBSA have been widely used in biomolecular studies such as protein folding, protein-ligand binding, protein-protein interaction, etc. In this review, methods to adjust the polar solvation energy and to improve the performance of MM/PBSA and MM/GBSA calculations are reviewed and discussed. The latest applications of MM/GBSA and MM/PBSA in drug design are also presented. This review intends to provide readers with guidance for practically applying MM/PBSA and MM/GBSA in drug design and related research fields.

End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design

The recent outbreak of novel coronavirus disease-19 (COVID-19) calls for and welcomes possible treatment strategies using drugs on the market. It is very efficient to apply computer-aided drug design techniques to quickly identify promising drug repurposing candidates, especially after the detailed 3D structures of key viral proteins are resolved. The virus causing COVID-19 is SARS-CoV-2. Taking advantage of a recently released crystal structure of SARS-CoV-2 main protease in complex with a covalently bonded inhibitor, N3 (Liu et al., 10.2210/pdb6LU7/pdb), I conducted virtual docking screening of approved drugs and drug candidates in clinical trials. For the top docking hits, I then performed molecular dynamics simulations followed by binding free energy calculations using an end point method called MM-PBSA-WSAS (molecular mechanics/Poisson-Boltzmann surface area/weighted solvent-accessible surface area; Wang, Chem. Rev. 2019, 119, 9478; Wang, Curr. Comput.-Aided Drug Des. 2006, 2, 287; Wang; ; Hou J. Chem. Inf. Model., 2012, 52, 1199). Several promising known drugs stand out as potential inhibitors of SARS-CoV-2 main protease, including carfilzomib, eravacycline, valrubicin, lopinavir, and elbasvir. Carfilzomib, an approved anticancer drug acting as a proteasome inhibitor, has the best MM-PBSA-WSAS binding free energy, -13.8 kcal/mol. The second-best repurposing drug candidate, eravacycline, is synthetic halogenated tetracycline class antibiotic. Streptomycin, another antibiotic and a charged molecule, also demonstrates some inhibitory effect, even though the predicted binding free energy of the charged form (-3.8 kcal/mol) is not nearly as low as that of the neutral form (-7.9 kcal/mol). One bioactive, PubChem 23727975, has a binding free energy of -12.9 kcal/mol. Detailed receptor-ligand interactions were analyzed and hot spots for the receptor-ligand binding were identified. I found that one hot spot residue, His41, is a conserved residue across many viruses including SARS-CoV, SARS-CoV-2, MERS-CoV, and hepatitis C virus (HCV). The findings of this study can facilitate rational drug design targeting the SARS-CoV-2 main protease.

/pdf/fast-identification-of-possible-drug-treatment-of-3e69g8s0s8.pdf

Fast Identification of Possible Drug Treatment of Coronavirus Disease -19 (COVID-19) Through Computational Drug Repurposing Study

The intercalation process of binding doxorubicin (DOX) in DNA is studied by extensive MD simulations. Many molecular factors that control the binding affinity of DOX to DNA to form a stable complex are inspected and quantified by employing continuum solvation models for estimating the binding free energy. The modified MM-PB(GB)SA methodology provides a complete energetic profile of ΔGele, ΔGvDW, ΔGpolar, ΔGnon-polar, TΔStotal, ΔGdeform, ΔGcon, and ΔGion. To identify the sequence specificity of DOX, two different DNA sequences, d(CGATCG) or DNA1 and d(CGTACG) or DNA2, with one molecule (1 : 1 complex) or two molecule (2 : 1 complex) configurations of DOX were selected in this study. Our results show that the DNA deformation energy (ΔGdeform), the energy cost from translational and rotational entropic contributions (TΔStran+rot), the total electrostatic interactions (ΔGpolar-PB/GB + ΔGele) of incorporation, the intramolecular electrostatic interactions (ΔGele) and electrostatic polar solvation interactions (ΔGpolar-PB/GB) are all unfavorable to the binding of DOX to DNA. However, they are overcome by at least five favorable interactions: the van der Waals interactions (ΔGvDW), the non-polar solvation interaction (ΔGnon-polar), the vibrational entropic contribution (TΔSvib), and the standard concentration dependent free energies of DOX (ΔGcon) and the ionic solution (ΔGion). Specifically, the van der Waals interaction appears to be the major driving force to form a stable DOX–DNA complex. We also predict that DOX has stronger binding to DNA1 than DNA2. The DNA deformation penalty and entropy cost in the 2 : 1 complex are less than those in the 1 : 1 complex, thus they indicate that the 2 : 1 complex is more stable than the 1 : 1 complex. We have calculated the total binding free energy (BFE) (ΔGt-sim) using both MM-PBSA and MM-GBSA methods, which suggests a more stable DOX–DNA complex at lower ionic concentration. The calculated BFE from the modified MM-GBSA method for DOX–DNA1 and DOX–DNA2 in the 1 : 1 complex is −9.1 and −5.1 kcal mol−1 respectively. The same quantities from the modified MM-PBSA method are −12.74 and −8.35 kcal mol−1 respectively. The value of the total BFE ΔGt-sim in the 1 : 1 complex is in reasonable agreement with the experimental value of −7.7 ± 0.3 kcal mol−1.

Molecular mechanism and binding free energy of doxorubicin intercalation in DNA

Novel Mutant of Escherichia Coli Asparaginase II to Reduction of the Glutaminase Activity in Treatment of Acute Lymphocytic Leukemia by Molecular Dynamics Simulations and QM-MM Studies

The clinical signature of Alzheimer's disease (AD) is the deposition of aggregated Aβ fibrils that are neurotoxic to the brain. It is the major form of dementia affecting older people worldwide, impeding their normal function. Finding and testing various natural compounds to target and disrupt stable Aβ fibrils seems to be a promising and attractive therapeutic approach. Four phenolic compounds from plant sources were taken into consideration for the present work, and were initially screened by docking. Ellagic acid (REF) came out to be the best binder of the Aβ oligomer from docking studies. To test the destabilization effect of REF on the Aβ oligomer, MD simulation was conducted. The simulation outcome obtained clearly indicates a drift of terminal chains from the Aβ oligomer, leading to the disorganization of the characteristically organized cross β structure of the Aβ fibrils. Increased values of RMSD, Rg, RMSF, and SASA are indicative of the destabilization of the Aβ fibril in the presence of REF. The disruption of salt bridges and a notable decline in the number of hydrogen bonds and β-sheet content explain the conformational changes in the Aβ fibril structure, ceasing their neurotoxicity. The MM-PBSA results revealed the binding of REF to chain A of the Aβ oligomer. The destabilization potential of ellagic acid, as explained by the MD simulation study, establishes it as a promising drug for curing AD. The molecular-level details about the destabilization mechanism of ellagic acid encourage the intensive mining of other natural compounds for therapeutic intervention for AD.

Destabilization potential of phenolics on Aβ fibrils: mechanistic insights from molecular dynamics simulation.

Combined virtual screening, MMPBSA, molecular docking and dynamics studies against deadly anthrax: An in silico effort to inhibit Bacillus anthracis nucleoside hydrolase.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chiya Jalali

Author Tools

Chat about Author

Papers

Combined virtual screening, MMPBSA, molecular docking and dynamics studies against deadly anthrax: An in silico effort to inhibit Bacillus anthracis nucleoside hydrolase.