Bisubstrate Inhibitors for the Enzyme Catechol O-Methyltransferase (COMT): Dramatic Effects of Ribose Modifications on Binding Affinity and Binding Mode

2003 ◽  
Vol 86 (4) ◽  
pp. 1045-1062 ◽  
Author(s):  
Christian Lerner ◽  
Romain Siegrist ◽  
Eliane Schweizer ◽  
François Diederich ◽  
Volker Gramlich ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Binding Mode ◽  
Bisubstrate Inhibitors

scholarly journals Nucleic binding affinity of bacteriophage T4 gene 32 protein in the cooperative binding mode.

1982 ◽  
Vol 257 (11) ◽  
pp. 6184-6193
Author(s):  
A M Bobst ◽  
P W Langemeier ◽  
P E Warwick-Koochaki ◽  
E V Bobst ◽  
J C Ireland
Keyword(s):  
Binding Affinity ◽  
Bacteriophage T4 ◽  
Binding Mode ◽  
Cooperative Binding ◽  
Gene 32 ◽  
T4 Gene 32 Protein

scholarly journals Structural basis of subunit selectivity for competitive NMDA receptor antagonists with preference for GluN2A over GluN2B subunits

2017 ◽  
Vol 114 (33) ◽  
pp. E6942-E6951 ◽  
Author(s):  
Genevieve E. Lind ◽  
Tung-Chung Mou ◽  
Lucia Tamborini ◽  
Martin G. Pomper ◽  
Carlo De Micheli ◽  
...  
Keyword(s):  
Nmda Receptor ◽  
Binding Affinity ◽  
Binding Mode ◽  
Structural Basis ◽  
Nmda Receptor Antagonists ◽  
Receptor Antagonists ◽  
Glutamate Binding ◽  
Subunit Interface ◽  
The Central Nervous System ◽  
Contact Residues

NMDA-type glutamate receptors are ligand-gated ion channels that contribute to excitatory neurotransmission in the central nervous system (CNS). Most NMDA receptors comprise two glycine-binding GluN1 and two glutamate-binding GluN2 subunits (GluN2A–D). We describe highly potent (S)-5-[(R)-2-amino-2-carboxyethyl]-4,5-dihydro-1H-pyrazole-3-carboxylic acid (ACEPC) competitive GluN2 antagonists, of which ST3 has a binding affinity of 52 nM at GluN1/2A and 782 nM at GluN1/2B receptors. This 15-fold preference of ST3 for GluN1/2A over GluN1/2B is improved compared with NVP-AAM077, a widely used GluN2A-selective antagonist, which we show has 11-fold preference for GluN1/2A over GluN1/2B. Crystal structures of the GluN1/2A agonist binding domain (ABD) heterodimer with bound ACEPC antagonists reveal a binding mode in which the ligands occupy a cavity that extends toward the subunit interface between GluN1 and GluN2A ABDs. Mutational analyses show that the GluN2A preference of ST3 is primarily mediated by four nonconserved residues that are not directly contacting the ligand, but positioned within 12 Å of the glutamate binding site. Two of these residues influence the cavity occupied by ST3 in a manner that results in favorable binding to GluN2A, but occludes binding to GluN2B. Thus, we reveal opportunities for the design of subunit-selective competitive NMDA receptor antagonists by identifying a cavity for ligand binding in which variations exist between GluN2A and GluN2B subunits. This structural insight suggests that subunit selectivity of glutamate-site antagonists can be mediated by mechanisms in addition to direct contributions of contact residues to binding affinity.

scholarly journals Design of potential bisubstrate inhibitors against Mycobacterium tuberculosis (Mtb) 1-deoxy-d-xylulose 5-phosphate reductoisomerase (Dxr)—evidence of a novel binding mode

MedChemComm ◽  
2013 ◽  
Vol 4 (7) ◽  
pp. 1099 ◽  
Author(s):  
Géraldine San Jose ◽  
Emily R. Jackson ◽  
Eugene Uh ◽  
Chinchu Johny ◽  
Amanda Haymond ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Binding Mode ◽  
Bisubstrate Inhibitors

CSCORE: A SIMPLE YET EFFECTIVE SCORING FUNCTION FOR PROTEIN–LIGAND BINDING AFFINITY PREDICTION USING MODIFIED CMAC LEARNING ARCHITECTURE

2011 ◽  
Vol 09 (supp01) ◽  
pp. 1-14 ◽  
Author(s):  
XUCHANG OUYANG ◽  
STEPHANUS DANIEL HANDOKO ◽  
CHEE KEONG KWOH
Keyword(s):  
Binding Affinity ◽  
Scoring Function ◽  
Binding Mode ◽  
Computational Method ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Ligand Docking ◽  
Scoring Functions ◽  
Binding Affinity Prediction ◽  
Affinity Prediction

Protein–ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performance of CScore in terms of correlation between predicted and experimental binding affinities is benchmarked under different validation approaches. CScore achieves a prediction with R = 0.7668 and RMSE = 1.4540 when tested on an independent dataset. To the best of our knowledge, this result outperforms other scoring functions tested on the same dataset. The performance of CScore varies on different clusters under the leave-cluster-out validation approach, but still achieves competitive result. Lastly, the target-specified CScore achieves an even better result with R = 0.8237 and RMSE = 1.0872, trained on a much smaller but more relevant dataset for each target. The large dataset of protein–ligand complexes structural information and advances of machine learning techniques enable the data-driven approach in binding affinity prediction. CScore is capable of accurate binding affinity prediction. It is also shown that CScore will perform better if sufficient and relevant data is presented. As there is growth of publicly available structural data, further improvement of this scoring scheme can be expected.

scholarly journals DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7362 ◽  
Author(s):  
Haiping Zhang ◽  
Linbu Liao ◽  
Konda Mani Saravanan ◽  
Peng Yin ◽  
Yanjie Wei
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Mean Squared Error ◽  
Scoring Function ◽  
Binding Mode ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Cellular Functions ◽  
Ligand Complex

Proteins interact with small molecules to modulate several important cellular functions. Many acute diseases were cured by small molecule binding in the active site of protein either by inhibition or activation. Currently, there are several docking programs to estimate the binding position and the binding orientation of protein–ligand complex. Many scoring functions were developed to estimate the binding strength and predict the effective protein–ligand binding. While the accuracy of current scoring function is limited by several aspects, the solvent effect, entropy effect, and multibody effect are largely ignored in traditional machine learning methods. In this paper, we proposed a new deep neural network-based model named DeepBindRG to predict the binding affinity of protein–ligand complex, which learns all the effects, binding mode, and specificity implicitly by learning protein–ligand interface contact information from a large protein–ligand dataset. During the initial data processing step, the critical interface information was preserved to make sure the input is suitable for the proposed deep learning model. While validating our model on three independent datasets, DeepBindRG achieves root mean squared error (RMSE) value of pKa (−logKd or −logKi) about 1.6–1.8 and R value around 0.5–0.6, which is better than the autodock vina whose RMSE value is about 2.2–2.4 and R value is 0.42–0.57. We also explored the detailed reasons for the performance of DeepBindRG, especially for several failed cases by vina. Furthermore, DeepBindRG performed better for four challenging datasets from DUD.E database with no experimental protein–ligand complexes. The better performance of DeepBindRG than autodock vina in predicting protein–ligand binding affinity indicates that deep learning approach can greatly help with the drug discovery process. We also compare the performance of DeepBindRG with a 4D based deep learning method “pafnucy”, the advantage and limitation of both methods have provided clues for improving the deep learning based protein–ligand prediction model in the future.

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.

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