Accurate and Efficient Calculation of Protein–Protein Binding Free Energy-Interaction Entropy with Residue Type-Specific Dielectric Constants

AbstractMycobacterium tuberculosis, the leading bacterial killer disease worldwide, causes Human tuberculosis (TB). Due to the growing problem of drug resistant Mycobacterium tuberculosis strains, new anti-TB drugs are urgently needed. Natural sources such as plant extracts have long played an important role in tuberculosis management and can be used as a template to design new drugs. A wide screening of natural sources is time consuming but the process can be significantly sped up using molecular docking. In this study, we used a molecular docking approach to investigate the interactions between selected natural constituents and three proteins MtPanK, MtDprE1 and MtKasA involved in the physiological functions of Mycobacterium tuberculosis which are necessary for the bacteria to survive and cause disease. The molecular docking score, a score that accounts for the binding affinity between a ligand and a target protein, for each protein was calculated against 150 chemical constituents of different classes to estimate the binding free energy. The docking scores represent the binding free energy. The best docking scores indicates the highest ligand protein binding which is indicated by the lowest energy value. Among the natural constituents, Shermilamine B showed a docking score of - 8.5kcal/mol, Brachystamide B showed a docking score of −8.6 kcal/mol with MtPanK, Monoamphilectine A showed a score of −9.8kcal/mol with MtDprE1.These three compounds showed docking scores which were superior to the control inhibitors and represent the opportunity of in vitro biological evaluation and anti-TB drug design. Consequently, all these compounds belonged to the alkaloid class. Specific interactions were studied to further understand the nature of intermolecular bonds between the most active ligands and the protein binding site residues which proved that among the constituents monoamphilectine A and Shermilamine B show more promise as Anti-TB drugs. Furthermore, the ADMET properties of these compounds or ligands showed that they have no corrosive or carcinogenic parameters.Graphical Abstract

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SAMBE: The New Webserver for Predicting the Effect of nssNP on the Protein-Protein Binding Free Energy

Biophysical Journal ◽

10.1016/j.bpj.2014.11.867 ◽

2015 ◽

Vol 108 (2) ◽

pp. 157a-158a

Author(s):

Marharyta Petukh ◽

Jacon Morrison ◽

Minghui Li ◽

Anna Panchenko ◽

Emil Alexov

Keyword(s):

Free Energy ◽

Protein Binding ◽

Binding Free Energy

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Insights into Protein–Protein Binding by Binding Free Energy Calculation and Free Energy Decomposition for the Ras–Raf and Ras–RalGDS Complexes

Journal of Molecular Biology ◽

10.1016/s0022-2836(03)00610-7 ◽

2003 ◽

Vol 330 (4) ◽

pp. 891-913 ◽

Cited By ~ 748

Author(s):

Holger Gohlke ◽

Christina Kiel ◽

David A. Case

Keyword(s):

Free Energy ◽

Protein Binding ◽

Binding Free Energy ◽

Free Energy Calculation ◽

Energy Calculation ◽

Energy Decomposition ◽

Binding Free Energy Calculation ◽

Free Energy Decomposition

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Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis

Molecules ◽

10.3390/molecules26185696 ◽

2021 ◽

Vol 26 (18) ◽

pp. 5696

Author(s):

Wei Lim Chong ◽

Koollawat Chupradit ◽

Sek Peng Chin ◽

Mai Mai Khoo ◽

Sook Mei Khor ◽

...

Keyword(s):

Molecular Dynamics ◽

Free Energy ◽

Protein Binding ◽

Protein Interactions ◽

Binding Free Energy ◽

Single Point ◽

Md Simulations ◽

Nanoparticle Tracking Analysis ◽

Buffer Solution ◽

Dynamics Simulations

Protein-protein interaction plays an essential role in almost all cellular processes and biological functions. Coupling molecular dynamics (MD) simulations and nanoparticle tracking analysis (NTA) assay offered a simple, rapid, and direct approach in monitoring the protein-protein binding process and predicting the binding affinity. Our case study of designed ankyrin repeats proteins (DARPins)—AnkGAG1D4 and the single point mutated AnkGAG1D4-Y56A for HIV-1 capsid protein (CA) were investigated. As reported, AnkGAG1D4 bound with CA for inhibitory activity; however, it lost its inhibitory strength when tyrosine at residue 56 AnkGAG1D4, the most key residue was replaced by alanine (AnkGAG1D4-Y56A). Through NTA, the binding of DARPins and CA was measured by monitoring the increment of the hydrodynamic radius of the AnkGAG1D4-gold conjugated nanoparticles (AnkGAG1D4-GNP) and AnkGAG1D4-Y56A-GNP upon interaction with CA in buffer solution. The size of the AnkGAG1D4-GNP increased when it interacted with CA but not AnkGAG1D4-Y56A-GNP. In addition, a much higher binding free energy (∆GB) of AnkGAG1D4-Y56A (−31 kcal/mol) obtained from MD further suggested affinity for CA completely reduced compared to AnkGAG1D4 (−60 kcal/mol). The possible mechanism of the protein-protein binding was explored in detail by decomposing the binding free energy for crucial residues identification and hydrogen bond analysis.

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Molecular simulation of SARS-CoV-2 spike protein binding to pangolin ACE2 or human ACE2 natural variants reveals altered susceptibility to infection

Journal of General Virology ◽

10.1099/jgv.0.001452 ◽

2020 ◽

Vol 101 (9) ◽

pp. 921-924 ◽

Cited By ~ 2

Author(s):

Jingfang Wang ◽

Xintian Xu ◽

Xinbo Zhou ◽

Ping Chen ◽

Huiying Liang ◽

...

Keyword(s):

Free Energy ◽

Protein Binding ◽

Binding Affinity ◽

Binding Free Energy ◽

Virus Transmission ◽

Dynamics Simulation ◽

Spike Protein ◽

Susceptibility To Infection ◽

Natural Variants ◽

Complex Models

We constructed complex models of SARS-CoV-2 spike protein binding to pangolin or human ACE2, the receptor for virus transmission, and estimated the binding free energy changes using molecular dynamics simulation. SARS-CoV-2 can bind to both pangolin and human ACE2, but has a significantly lower binding affinity for pangolin ACE2 due to the increased binding free energy (9.5 kcal mol−1). Human ACE2 is among the most polymorphous genes, for which we identified 317 missense single-nucleotide variations (SNVs) from the dbSNP database. Three SNVs, E329G (rs143936283), M82I (rs267606406) and K26R (rs4646116), had a significant reduction in binding free energy, which indicated higher binding affinity than wild-type ACE2 and greater susceptibility to SARS-CoV-2 infection for people with them. Three other SNVs, D355N (rs961360700), E37K (rs146676783) and I21T (rs1244687367), had a significant increase in binding free energy, which indicated lower binding affinity and reduced susceptibility to SARS-CoV-2 infection.

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Navigating ligand–protein binding free energy landscapes: universality and diversity of protein folding and molecular recognition mechanisms

Chemical Physics Letters ◽

10.1016/s0009-2614(01)00161-0 ◽

2001 ◽

Vol 336 (5-6) ◽

pp. 495-503 ◽

Cited By ~ 9

Author(s):

Gennady M. Verkhivker ◽

Paul A. Rejto ◽

Djamal Bouzida ◽

Sandra Arthurs ◽

Anthony B. Colson ◽

...

Keyword(s):

Free Energy ◽

Protein Folding ◽

Molecular Recognition ◽

Protein Binding ◽

Binding Free Energy ◽

Energy Landscapes ◽

Free Energy Landscapes

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SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein–protein binding affinity

Bioinformatics ◽

10.1093/bioinformatics/btaa761 ◽

2020 ◽

Author(s):

Gen Li ◽

Swagata Pahari ◽

Adithya Krishna Murthy ◽

Siqi Liang ◽

Robert Fragoza ◽

...

Keyword(s):

Free Energy ◽

Protein Binding ◽

Binding Affinity ◽

Protein Interactions ◽

Structural Information ◽

Binding Free Energy ◽

Supplementary Information ◽

Sequence Information ◽

Protein Protein Interactions ◽

Genome Scale

Abstract Motivation Vast majority of human genetic disorders are associated with mutations that affect protein–protein interactions by altering wild-type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein–protein binding free energy to assist the development of therapeutic solutions. Currently, the most popular approaches use structural information to deliver the predictions, which precludes them to be applicable on genome-scale investigations. Indeed, with the progress of genomic sequencing, researchers are frequently dealing with assessing effect of mutations for which there is no structure available. Results Here, we report a Gradient Boosting Decision Tree machine learning algorithm, the SAAMBE-SEQ, which is completely sequence-based and does not require structural information at all. SAAMBE-SEQ utilizes 80 features representing evolutionary information, sequence-based features and change of physical properties upon mutation at the mutation site. The approach is shown to achieve Pearson correlation coefficient (PCC) of 0.83 in 5-fold cross validation in a benchmarking test against experimentally determined binding free energy change (ΔΔG). Further, a blind test (no-STRUC) is compiled collecting experimental ΔΔG upon mutation for protein complexes for which structure is not available and used to benchmark SAAMBE-SEQ resulting in PCC in the range of 0.37–0.46. The accuracy of SAAMBE-SEQ method is found to be either better or comparable to most advanced structure-based methods. SAAMBE-SEQ is very fast, available as webserver and stand-alone code, and indeed utilizes only sequence information, and thus it is applicable for genome-scale investigations to study the effect of mutations on protein–protein interactions. Availability and implementation SAAMBE-SEQ is available at http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started. Supplementary information Supplementary data are available at Bioinformatics online.

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