Predicting binding modes, binding affinities and ‘hot spots’ for protein-ligand complexes using a knowledge-based scoring function

2005 ◽  
pp. 115-144 ◽  
Author(s):  
Holger Gohlke ◽  
Manfred Hendlich ◽  
Gerhard Klebe
Keyword(s):  
Hot Spots ◽  
Scoring Function ◽  
Binding Affinities ◽  
Binding Modes ◽  
Knowledge Based

scholarly journals Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK

2022 ◽  
Vol 9 ◽  
Author(s):  
Zackary Falls ◽  
Jonathan Fine ◽  
Gaurav Chopra ◽  
Ram Samudrala
Keyword(s):  
Molecular Docking ◽  
Characteristic Curve ◽  
Scoring Function ◽  
Hiv Protease ◽  
Binding Affinities ◽  
Knowledge Based ◽  
Pearson Coefficient ◽  
Docking Protocol ◽  
Immunodeficiency Virus ◽  
Hiv 1

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.

Recent Advances of In-Silico Modeling of Potent Antagonists for the Adenosine Receptors

2019 ◽  
Vol 25 (7) ◽  
pp. 750-773 ◽  
Author(s):  
Pabitra Narayan Samanta ◽  
Supratik Kar ◽  
Jerzy Leszczynski
Keyword(s):  
Drug Targets ◽  
Biological Activities ◽  
Scoring Function ◽  
Md Simulations ◽  
Energy Calculation ◽  
Binding Modes ◽  
Macromolecular Systems ◽  
In Silico Modeling ◽  
Mathematical Algorithms ◽  
The Impact

The rapid advancement of computer architectures and development of mathematical algorithms offer a unique opportunity to leverage the simulation of macromolecular systems at physiologically relevant timescales. Herein, we discuss the impact of diverse structure-based and ligand-based molecular modeling techniques in designing potent and selective antagonists against each adenosine receptor (AR) subtype that constitutes multitude of drug targets. The efficiency and robustness of high-throughput empirical scoring function-based approaches for hit discovery and lead optimization in the AR family are assessed with the help of illustrative examples that have led to nanomolar to sub-micromolar inhibition activities. Recent progress in computer-aided drug discovery through homology modeling, quantitative structure-activity relation, pharmacophore models, and molecular docking coupled with more accurate free energy calculation methods are reported and critically analyzed within the framework of structure-based virtual screening of AR antagonists. Later, the potency and applicability of integrated molecular dynamics (MD) methods are addressed in the context of diligent inspection of intricated AR-antagonist binding processes. MD simulations are exposed to be competent for studying the role of the membrane as well as the receptor flexibility toward the precise evaluation of the biological activities of antagonistbound AR complexes such as ligand binding modes, inhibition affinity, and associated thermodynamic and kinetic parameters.

A knowledge-based halogen bonding scoring function for predicting protein-ligand interactions

2013 ◽  
Vol 19 (11) ◽  
pp. 5015-5030 ◽  
Author(s):  
Yingtao Liu ◽  
Zhijian Xu ◽  
Zhuo Yang ◽  
Kaixian Chen ◽  
Weiliang Zhu
Keyword(s):  
Scoring Function ◽  
Halogen Bonding ◽  
Knowledge Based ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

scholarly journals Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Beihong Ji ◽  
Xibing He ◽  
Yuzhao Zhang ◽  
Jingchen Zhai ◽  
Viet Hoang Man ◽  
...  
Keyword(s):  
Computational Cost ◽  
Scoring Function ◽  
Structural Similarity ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Autodock Vina ◽  
Predictive Index ◽  
Drug Lead ◽  
Screening Performance ◽  
Calibration Algorithm

AbstractIn this study, we developed a novel algorithm to improve the screening performance of an arbitrary docking scoring function by recalibrating the docking score of a query compound based on its structure similarity with a set of training compounds, while the extra computational cost is neglectable. Two popular docking methods, Glide and AutoDock Vina were adopted as the original scoring functions to be processed with our new algorithm and similar improvement performance was achieved. Predicted binding affinities were compared against experimental data from ChEMBL and DUD-E databases. 11 representative drug receptors from diverse drug target categories were applied to evaluate the hybrid scoring function. The effects of four different fingerprints (FP2, FP3, FP4, and MACCS) and the four different compound similarity effect (CSE) functions were explored. Encouragingly, the screening performance was significantly improved for all 11 drug targets especially when CSE = S4 (S is the Tanimoto structural similarity) and FP2 fingerprint were applied. The average predictive index (PI) values increased from 0.34 to 0.66 and 0.39 to 0.71 for the Glide and AutoDock vina scoring functions, respectively. To evaluate the performance of the calibration algorithm in drug lead identification, we also imposed an upper limit on the structural similarity to mimic the real scenario of screening diverse libraries for which query ligands are general-purpose screening compounds and they are not necessarily structurally similar to reference ligands. Encouragingly, we found our hybrid scoring function still outperformed the original docking scoring function. The hybrid scoring function was further evaluated using external datasets for two systems and we found the PI values increased from 0.24 to 0.46 and 0.14 to 0.42 for A2AR and CFX systems, respectively. In a conclusion, our calibration algorithm can significantly improve the virtual screening performance in both drug lead optimization and identification phases with neglectable computational cost.

scholarly journals PB-kPRED: knowledge-based prediction of protein backbone conformation using a structural alphabet

2017 ◽  
Author(s):  
Iyanar Vetrivel ◽  
Swapnil Mahajan ◽  
Manoj Tyagi ◽  
Lionel Hoffmann ◽  
Yves-Henri Sanejouand ◽  
...  
Keyword(s):  
Protein Structures ◽  
Scoring Function ◽  
Evolutionary Information ◽  
Structural Alphabet ◽  
Local Structures ◽  
Knowledge Based ◽  
Scanning Strategies ◽  
Accuracy Of Prediction ◽  
The Impact ◽  
Protein Blocks

AbstractLibraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks (PBs), is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of PBs. Thus, predicting the local structure of a protein in terms of protein blocks is a step towards the objective of predicting its 3-D structure. Here a new approach, kPred, is proposed towards this aim that is independent of the evolutionary information available. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) apply a purely knowledge-based algorithm, not relying on secondary structure predictions or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures.Based on the strategy used for scanning the database, the method was able to achieve efficient mean Q16 accuracies between 40.8% and 66.3% for a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. The impact of these scanning strategies on the prediction was evaluated and is discussed. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

scholarly journals Random Forest Refinement of the KECSA2 Knowledge-based Scoring Function for Protein Decoy Detection

2018 ◽  
Author(s):  
Jun Pei ◽  
Zheng Zheng ◽  
Kenneth M. Merz Jr.
Keyword(s):  
Random Forest ◽  
Scoring Function ◽  
Peak Height ◽  
Data Sets ◽  
Atom Pair ◽  
Probability Functions ◽  
Knowledge Based ◽  
Pair Potentials ◽  
Native Proteins ◽  
Combined Data

In this work, via the use of the ‘comparison’ concept, Random Forest (RF) models were successfully generated using unbalanced data sets that assign different importance factors to atom pair potentials to enhance their ability to identify native proteins from decoy proteins. Individual and combined data sets consisting of twelve decoy sets were used to test the performance of the RF models. We find that RF models increase the recognition of native structures without affecting their ability to identify the best decoy structures. We also created models using scrambled atom types, which create physically unrealistic probability functions, in order to test the ability of the RF algorithm to create useful models based on inputted scrambled probability functions. From this test we find that we are unable to create models that are of similar quality relative to the unscrambled probability functions. Next we created uniform probability functions where the peak positions as the same as the original, but each interaction has the same peak height. Using these uniform potentials we were able to recover models as good as the ones using the full potentials suggesting all that is important in these models are the experimental peak positions.

scholarly journals Synthesis, Spectral Characterization and Molecular Docking Studies of Novel Benzidin-bis-tetrahydroacridine Analogues

2020 ◽  
Vol 71 (5) ◽  
pp. 163-181
Author(s):  
Madalina Marina Hrubaru ◽  
Carmellina Daniela Badiceanu ◽  
Anthony Chinonso Ekennia ◽  
Sunday N. Okafor ◽  
Cristian Enache ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Binding Sites ◽  
Docking Studies ◽  
Binding Affinities ◽  
Binding Modes ◽  
Docking Simulations ◽  
Ft Ir ◽  
Dynamics Simulations ◽  
And Behavior ◽  
Human Butyrylcholinesterase

Alzheimer�s is a progresive neurodegenerative disease that interferes with human cognitive ability, memory and behavior. The inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes are major therapeutic routes for the treatment of Alzheimer disease. In the study, nevel bis-polymethylenquinoline-bis-carboxamides (3a-f) and bis-polymethylenquinoline-bis-carboxylic acids (5a-b) having as precursor benzidine, were obtained in good yields by Pfitzinger condensation reactions of bis-isatines with corresponding cyclanones. The compounds were characterized by elemental analysis, FT-IR, NMR and mass spectrometry. Furthermore, the compounds were subjected to molecular docking dynamics simulations to ascertain their potentials as inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). Molecular docking simulations showed varied binding activities towards the two binding sites of acetylcholinesterase: 4EY7 and 1OCD, and human butyrylcholinesterase: 1P0I. Compounds 3e and 5b demostrated strong binding affinities with 1P0I, 1OCD and 4EY7 biotargets similar to the binding modes of donepezil and tacrine (co-crystallized inhibitors of acetylcholinesterase) and butyrate (co-crystallized inhibitors of butyrylcholinesterase).

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