Binding affinity prediction of nanobody–protein complexes by scoring of molecular dynamics trajectories

2018 ◽  
Vol 20 (5) ◽  
pp. 3438-3444 ◽  
Author(s):  
Miguel A. Soler ◽  
Sara Fortuna ◽  
Ario de Marco ◽  
Alessandro Laio
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Affinity ◽  
Protein Complexes ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Dynamics Simulations

Accurate binding affinity prediction of modelled nanobody–protein complexes by using the assistance of molecular dynamics simulations for achieving stable conformations.

scholarly journals ISLAND: in-silico proteins binding affinity prediction using sequence information

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Wajid Arshad Abbasi ◽  
Adiba Yaseen ◽  
Fahad Ul Hassan ◽  
Saiqa Andleeb ◽  
Fayyaz Ul Amir Afsar Minhas
Keyword(s):  
Machine Learning ◽  
Protein Binding ◽  
Binding Affinity ◽  
State Of The Art ◽  
Protein Complexes ◽  
Protein Structures ◽  
Sequence Information ◽  
Binding Affinity Prediction ◽  
Generalization Performance ◽  
Affinity Prediction

Abstract Background Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. Method We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. Results We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. Conclusion This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.

Recognition of bio-relevant dicarboxylate anions by an azacalix[2]arene[2]triazine derivative decorated with urea moieties

2015 ◽  
Vol 13 (10) ◽  
pp. 3070-3085 ◽  
Author(s):  
Miguel M. Santos ◽  
Igor Marques ◽  
Sílvia Carvalho ◽  
Cristina Moiteiro ◽  
Vítor Félix
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Affinity ◽  
H Nmr ◽  
Triazine Derivative ◽  
Wide Range ◽  
Dynamics Simulations ◽  
Nmr Titrations

The binding affinity of a dichlorocalix[2]arene[2]triazine based bis-urea azamacrocycle was investigated towards a wide range of bio-relevant dicarboxylate anions by a combination of 1H NMR titrations in CDCl3 and molecular dynamics simulations.

scholarly journals Identification of CDK7 Inhibitors from Natural Sources Using Pharmacoinformatics and Molecular Dynamics Simulations

Biomedicines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1197
Author(s):  
Vikas Kumar ◽  
Shraddha Parate ◽  
Gunjan Thakur ◽  
Gihwan Lee ◽  
Hyeon-Su Ro ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Affinity ◽  
Cost Effective ◽  
Binding Mode ◽  
Kinase Domain ◽  
Dynamics Simulation ◽  
Mode Analysis ◽  
Atp Binding Site ◽  
Dynamics Simulations

The cyclin-dependent kinase 7 (CDK7) plays a crucial role in regulating the cell cycle and RNA polymerase-based transcription. Overexpression of this kinase is linked with various cancers in humans due to its dual involvement in cell development. Furthermore, emerging evidence has revealed that inhibiting CDK7 has anti-cancer effects, driving the development of novel and more cost-effective inhibitors with enhanced selectivity for CDK7 over other CDKs. In the present investigation, a pharmacophore-based approach was utilized to identify potential hit compounds against CDK7. The generated pharmacophore models were validated and used as 3D queries to screen 55,578 natural drug-like compounds. The obtained compounds were then subjected to molecular docking and molecular dynamics simulations to predict their binding mode with CDK7. The molecular dynamics simulation trajectories were subsequently used to calculate binding affinity, revealing four hits—ZINC20392430, SN00112175, SN00004718, and SN00262261—having a better binding affinity towards CDK7 than the reference inhibitors (CT7001 and THZ1). The binding mode analysis displayed hydrogen bond interactions with the hinge region residues Met94 and Glu95, DFG motif residue Asp155, ATP-binding site residues Thr96, Asp97, and Gln141, and quintessential residue outside the kinase domain, Cys312 of CDK7. The in silico selectivity of the hits was further checked by docking with CDK2, the close homolog structure of CDK7. Additionally, the detailed pharmacokinetic properties were predicted, revealing that our hits have better properties than established CDK7 inhibitors CT7001 and THZ1. Hence, we argue that proposed hits may be crucial against CDK7-related malignancies.

scholarly journals Elucidation of SARS-Cov-2 Budding Mechanisms through Molecular Dynamics Simulations of M and E Protein Complexes

2021 ◽  
pp. 12249-12255
Author(s):  
Logan Thrasher Collins ◽  
Tamer Elkholy ◽  
Shafat Mubin ◽  
David Hill ◽  
Ricky Williams ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Protein Complexes ◽  
E Protein ◽  
Dynamics Simulations

scholarly journals Peptide-Ligand Binding Modeling of siRNA with Cell-Penetrating Peptides

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Alfonso T. García-Sosa ◽  
Indrek Tulp ◽  
Kent Langel ◽  
Ülo Langel
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Affinity ◽  
Cell Penetrating Peptides ◽  
Peptide Ligand ◽  
Intermolecular Hydrogen Bonds ◽  
Cell Penetrating ◽  
The Stability ◽  
Dynamics Simulations ◽  
Explicit Water

The binding affinity of a series of cell-penetrating peptides (CPP) was modeled through docking and making use of the number of intermolecular hydrogen bonds, lipophilic contacts, and the number of sp3 molecular orbital hybridization carbons. The new ranking of the peptides is consistent with the experimentally determined efficiency in the downregulation of luciferase activity, which includes the peptides’ ability to bind and deliver the siRNA into the cell. The predicted structures of the complexes of peptides to siRNA were stable throughout 10 ns long, explicit water molecular dynamics simulations. The stability and binding affinity of peptide-siRNA complexes was related to the sidechains and modifications of the CPPs, with the stearyl and quinoline groups improving affinity and stability. The reranking of the peptides docked to siRNA, together with explicit water molecular dynamics simulations, appears to be well suited to describe and predict the interaction of CPPs with siRNA.

Protein–carbohydrate complexes: Binding site analysis, prediction, binding affinity and molecular dynamics simulations

2020 ◽  
pp. 299-332 ◽  
Author(s):  
K. Veluraja ◽  
N. R. Siva Shanmugam ◽  
J. Jino Blessy ◽  
R. A. Jeyaram ◽  
B. Lalithamaheswari ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Site ◽  
Binding Affinity ◽  
Site Analysis ◽  
Dynamics Simulations ◽  
Analysis Prediction
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