scholarly journals Estudo QM/MM da proteína tirosina Bcr-Abl mutada T315I

2020 ◽  
Author(s):  
Washington A. Pereira ◽  
Érica C. M. Nascimento ◽  
João B. L. Martins
Keyword(s):  
Active Site ◽  
Myeloid Leukemia ◽  
Empirical Method ◽  
Rational Drug Design ◽  
Signaling Cascade ◽  
Amber Force Field ◽  
Abl Tyrosine Kinase ◽  
Rational Drug ◽  
High Layer ◽  
Semi Empirical

Bcr-Abl tyrosine kinase protein activates the substrate starting kinase signaling cascade that results in cell division. When in excess this activation can lead to chronic myeloid leukemia. Currently, the treatment of this disease is achieved through drugs developed by rational drug design, e.g., imatinib. In this work we study descriptors of the following molecules: imatinib, nilotinib and ponatinib, together with a mutated protein. To characterize interactions between the residues of the active site against those inhibitors, a QM/MM study was carried out, using the hybrid method ONIOM, through the AMBER Force field (low layer) and the semi-empirical method PM6 (high layer).

Characterization of the Trypanosoma brucei Pteridine Reductase Active- Site using Computational Docking and Virtual Screening Techniques

2020 ◽  
Vol 16 (5) ◽  
pp. 583-598 ◽  
Author(s):  
Hina Shamshad ◽  
Abdul Hafiz ◽  
Ismail I. Althagafi ◽  
Maria Saeed ◽  
Agha Zeeshan Mirza
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Trypanosoma Brucei ◽  
Active Site ◽  
Critical Role ◽  
Rational Drug Design ◽  
Small Scale ◽  
Chain Flexibility ◽  
Rational Drug

Background: Human African trypanosomiasis is a fatal disease prevalent in approximately 36 sub-Saharan countries. Emerging reports of drug resistance in Trypanosoma brucei are a serious cause of concern as only limited drugs are available for the treatment of the disease. Pteridine reductase is an enzyme of Trypanosoma brucei. Methods: It plays a critical role in the pterin metabolic pathway that is absolutely essential for its survival in the human host. The success of finding a potent inhibitor in structure-based drug design lies within the ability of computational tools to efficiently and accurately dock a ligand into the binding cavity of the target protein. Here we report the computational characterization of Trypanosoma brucei pteridine reductase (Tb-PR) active-site using twenty-four high-resolution co-crystal structures with various drugs. Structurally, the Tb-PR active site can be grouped in two clusters; one with high Root Mean Square Deviation (RMSD) of atomic positions and another with low RMSD of atomic positions. These clusters provide fresh insight for rational drug design against Tb-PR. Henceforth, the effect of several factors on docking accuracy, including ligand and protein flexibility were analyzed using Fred. Results: The online server was used to analyze the side chain flexibility and four proteins were selected on the basis of results. The proteins were subjected to small-scale virtual screening using 85 compounds, and statistics were calculated using Bedroc and roc curves. The enrichment factor was also calculated for the proteins and scoring functions. The best scoring function was used to understand the ligand protein interactions with top common compounds of four proteins. In addition, we made a 3D structural comparison between the active site of Tb-PR and Leishmania major pteridine reductase (Lm- PR). We described key structural differences between Tb-PR and Lm-PR that can be exploited for rational drug design against these two human parasites. Conclusion: The results indicated that relying just on re-docking and cross-docking experiments for virtual screening of libraries isn’t enough and results might be misleading. Hence it has been suggested that small scale virtual screening should be performed prior to large scale screening.

Inhibitor Binding Modulates Protonation States in the Active Site of SARS-CoV-2 Main Protease

2021 ◽  
Author(s):  
Daniel W. Kneller ◽  
Gwyndalyn Phillips ◽  
Kevin L. Weiss ◽  
Qiu Zhang ◽  
Leighton Coates ◽  
...  
Keyword(s):  
Active Site ◽  
Rational Drug Design ◽  
Inhibitor Binding ◽  
Histidine Residues ◽  
Main Protease ◽  
Rational Drug ◽  
Ligand Free ◽  
Essential Enzyme ◽  
Neutron Protein Crystallography ◽  
Active Site Histidine

ABSTRACTThe main protease (3CL Mpro) from SARS-CoV-2, the virus that causes COVID-19, is an essential enzyme for viral replication with no human counterpart, making it an attractive drug target. Although drugs have been developed to inhibit the proteases from HIV, hepatitis C and other viruses, no such therapeutic is available to inhibit the main protease of SARS-CoV-2. To directly observe the protonation states in SARS-CoV-2 Mpro and to elucidate their importance in inhibitor binding, we determined the structure of the enzyme in complex with the α-ketoamide inhibitor telaprevir using neutron protein crystallography at near-physiological temperature. We compared protonation states in the inhibitor complex with those determined for a ligand-free neutron structure of Mpro. This comparison revealed that three active-site histidine residues (His41, His163 and His164) adapt to ligand binding, altering their protonation states to accommodate binding of telaprevir. We suggest that binding of other α-ketoamide inhibitors can lead to the same protonation state changes of the active site histidine residues. Thus, by studying the role of active site protonation changes induced by inhibitors we provide crucial insights to help guide rational drug design, allowing precise tailoring of inhibitors to manipulate the electrostatic environment of SARS-CoV-2 Mpro.

Modeling the Effects of O-sulfonation on the CID of Serine

2020 ◽  
Author(s):  
Kenneth Lucas ◽  
George Barnes
Keyword(s):  
Sulfo Group ◽  
High Energy ◽  
Empirical Method ◽  
Side Chain ◽  
Energy Structure ◽  
Direct Dynamics ◽  
Theoretical Mass ◽  
Intermolecular Proton Transfer ◽  
Dynamics Simulations ◽  
Semi Empirical

We present the results of direct dynamics simulations and DFT calculations aimed at elucidating the effect of \textit{O}-sulfonation on the collision induced dissociation for serine. Towards this end, direct dynamics simulations of both serine and sulfoserine were performed at multiple collision energies and theoretical mass spectra obtained. Comparisons to experimental results are favorable for both systems. Peaks related to the sulfo group are identified and the reaction dynamics explored. In particular, three significant peaks (m\z 106, 88, and 81) seen in the theoretical mass spectrum directly related to the sulfo group are analyzed as well as major peaks shared by both systems. Our analysis shows that the m\z 106 peaks result from intramolecular rearrangements, intermolecular proton transfer among complexes composed of initial fragmentation products, and at high energy side-chain fragmentation. The \mz 88 peak was found to contain multiple constitutional isomers, including a previously unconsidered, low energy structure. It was also seen that the RM1 semi empirical method was not able to obtain all of the major peaks seen in experiment for sulfoserine. In contrast, PM6 did obtain all major experimental peaks.

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

Theoretical investigation of Ramachandran plot of N-formyl-L-alanine-amide

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Genetic Algorithm ◽  
Theoretical Investigation ◽  
Energy Analysis ◽  
Empirical Method ◽  
Conformational Space ◽  
Ramachandran Plot ◽  
Global Minima ◽  
Reasonable Time ◽  
Semi Empirical

The full conformational space of N-formyl-L-alanine-amide was explored by the semi-empirical method AM1 coupled to the Multi Niche Crowding (MNC) genetic algorithm implemented in a package of programs developed in our laboratory. The structural and energy analysis of the resulting conformational space E(,ψ) exhibits 5 regions or minima ɣL, ɣD, ɛL, D and αD. The technique provides better detection of local and global minima within a reasonable time.

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