A Definitive Pharmacophore Modelling Study on CDK2 ATP Pocket Binders: Tracing the Path of New Virtual High-Throughput Screenings

Cyclin Dependent Kinases-2 (CDK2) are members of serine/threonine protein kinases family. They play an important role in the regulation events of the eukaryotic cell division cycle, especially during the G1 to S phase transition. Experimental evidence indicate that excessive expression of CDK2s should cause abnormal cell cycle regulation. Therefore, since a long time, CDK2s have been considered potential therapeutic targets for cancer therapy. In this work, onehundred and forty-nine complexes of inhibitors bound in the CDK2-ATP pocket were submitted to short MD simulations (10ns) and free energy calculation. Comparison with experimental data (Ki, Kd and pIC50) revealed that short simulations are exhaustive to examine the crucial ligand-protein interactions within the complexes. Information collected on MD simulations of protein-ligand complexes has been used to perform a molecular modelling approach that incorporates flexibility into structure-based pharmacophore modelling (Common Hits Approach, CHA). The high number of pharmacophore models resulting from the MD simulation was thus reduced to a few representative groups of pharmacophore models. The performance of the models has been assessed by using the ROC curves analysis. This definitive set of validated pharmacophore models could be used to screen in-house and/or commercial datasets for detection of new CDK-2 inhibitors. We provide the models to all the researchers involved in this field.

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Biological Role of CDK 11 and 12 in Cell Cycle and its Function in Tumorigenesis

Pakistan Journal of Medicine and Dentistry ◽

10.36283/pjmd9-3/020 ◽

2020 ◽

Author(s):

Shamim Mushtaq

Keyword(s):

Breast Cancer ◽

Cell Cycle ◽

Web Of Science ◽

The Body ◽

Main Role ◽

Hallmarks Of Cancer ◽

Cyclin Dependent Kinases ◽

Tumor Studies ◽

Cycle Regulation

Uninhibited proliferation and abnormal cell cycle regulation are the hallmarks of cancer. The main role of cyclin dependent kinases is to regulate the cell cycle and cell proliferation. These protein kinases are frequently down regulated or up regulated in various cancers. Two CDK family members, CDK 11 and 12, have contradicting views about their roles in different cancers. For example, one study suggests that the CDK 11 isoforms, p58, inhibits growth of breast cancer whereas, the CDK 11 isoform, p110, is highly expressed in breast tumor. Studies regarding CDK 12 show variation of opinion towards different parts of the body, however there is a consensus that upregulation of cdk12 increases the risk of breast cancer. Hence, CDK 11 and CDK 12 need to be analyzed to confirm their mechanism and their role regarding therapeutics, prognostic value, and ethnicity in cancer. This article gives an outline on both CDKs of information known up to date from Medline, PubMed, Google Scholar and Web of Science search engines, which were explored and thirty relevant researches were finalized.

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

Current Pharmaceutical Design ◽

10.2174/1381612825666190304123545 ◽

2019 ◽

Vol 25 (7) ◽

pp. 750-773 ◽

Cited By ~ 11

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.

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Decoding Protein-protein Interactions: An Overview

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200226105312 ◽

2020 ◽

Vol 20 (10) ◽

pp. 855-882

Author(s):

Olivia Slater ◽

Bethany Miller ◽

Maria Kontoyianni

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Drug Repurposing ◽

Protein Docking ◽

Target Space ◽

Protein Protein Interactions ◽

X Ray Crystallography ◽

Protein Protein Interaction ◽

Interaction Sites ◽

Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis

Medicinal Chemistry ◽

10.2174/1573406415666190222125333 ◽

2019 ◽

Vol 15 (6) ◽

pp. 588-601 ◽

Cited By ~ 1

Author(s):

Mahmoud A. Al-Sha'er ◽

Rua'a A. Al-Aqtash ◽

Mutasem O. Taha

Keyword(s):

Virtual Screening ◽

Inflammatory Diseases ◽

Qsar Model ◽

Qsar Analysis ◽

Pharmacophore Modelling ◽

Physicochemical Descriptors ◽

Qsar Modelling ◽

Ic50 Values ◽

Phosphoinositide 3 Kinase ◽

Pharmacophore Models

Background: PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding. Methods: Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). Results: The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. Conclusion: Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.

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The Emerging Role of Cyclin-Dependent Kinases (CDKs) in Pancreatic Ductal Adenocarcinoma

International Journal of Molecular Sciences ◽

10.3390/ijms19103219 ◽

2018 ◽

Vol 19 (10) ◽

pp. 3219 ◽

Cited By ~ 11

Author(s):

Balbina García-Reyes ◽

Anna-Laura Kretz ◽

Jan-Philipp Ruff ◽

Silvia von Karstedt ◽

Andreas Hillenbrand ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Therapeutic Potential ◽

Current Knowledge ◽

Ductal Adenocarcinoma ◽

Transcriptional Elongation ◽

Cyclin Dependent Kinases ◽

Dismal Prognosis ◽

The Family ◽

Cycle Regulation

The family of cyclin-dependent kinases (CDKs) has critical functions in cell cycle regulation and controlling of transcriptional elongation. Moreover, dysregulated CDKs have been linked to cancer initiation and progression. Pharmacological CDK inhibition has recently emerged as a novel and promising approach in cancer therapy. This idea is of particular interest to combat pancreatic ductal adenocarcinoma (PDAC), a cancer entity with a dismal prognosis which is owed mainly to PDAC’s resistance to conventional therapies. Here, we review the current knowledge of CDK biology, its role in cancer and the therapeutic potential to target CDKs as a novel treatment strategy for PDAC.

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Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces

Molecules ◽

10.3390/molecules23081959 ◽

2018 ◽

Vol 23 (8) ◽

pp. 1959 ◽

Cited By ~ 14

Author(s):

Jérémie Mortier ◽

Pratik Dhakal ◽

Andrea Volkamer

Keyword(s):

Protein Interactions ◽

Pharmacophore Modeling ◽

Macromolecular Structure ◽

Target Structure ◽

Protein Protein Interactions ◽

Energy Functions ◽

Chemical Structures ◽

Molecular Interaction Fields ◽

Protein Classes ◽

Pharmacophore Models

Pharmacophore models are an accurate and minimal tridimensional abstraction of intermolecular interactions between chemical structures, usually derived from a group of molecules or from a ligand-target complex. Only a limited amount of solutions exists to model comprehensive pharmacophores using the information of a particular target structure without knowledge of any binding ligand. In this work, an automated and customable tool for truly target-focused (T²F) pharmacophore modeling is introduced. Key molecular interaction fields of a macromolecular structure are calculated using the AutoGRID energy functions. The most relevant points are selected by a newly developed filtering cascade and clustered to pharmacophore features with a density-based algorithm. Using five different protein classes, the ability of this method to identify essential pharmacophore features was compared to structure-based pharmacophores derived from ligand-target interactions. This method represents an extremely valuable instrument for drug design in a situation of scarce ligand information available, but also in the case of underexplored therapeutic targets, as well as to investigate protein allosteric pockets and protein-protein interactions.

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An integrative approach unveils a distal encounter site for rPTPε and phospho-Src complex formation

10.1101/2021.01.13.426511 ◽

2021 ◽

Author(s):

Nadendla EswarKumar ◽

Cheng-Han Yang ◽

Sunilkumar Tewary ◽

Yi-Qi Yeh ◽

Hsiao-Ching Yang ◽

...

Keyword(s):

Protein Interactions ◽

Protein Complex ◽

Tyrosine Phosphatase ◽

Complex Structure ◽

Protein Crystallography ◽

Md Simulations ◽

Integrative Approach ◽

Single Mutation ◽

Protein Protein Interactions ◽

Transient Nature

AbstractProtein tyrosine phosphatase: phospho-protein complex structure determination, which requires to understand how specificity is achieved at the protein level remains a significant challenge for protein crystallography and cryoEM due to the transient nature of binding interactions. Using rPTPεD1 and phospho-SrcKD as a model system, we established an integrative workflow involving protein crystallography, SAXS and pTyr-tailored MD simulations to reveal the complex formed between rPTPεD1 and phospho-SrcKD, revealing transient protein–protein interactions distal to the active site. To support our finding, we determined the associate rate between rPTPεD1 and phospho-SrcKD and showed that a single mutation on rPTPεD1 disrupts this transient interaction, resulting in the reduction of association rate and activity. Our simulations suggest that rPTPεD1 employs a binding mechanism involving conformational change prior to the engagement of cSrcKD. This integrative approach is applicable to other PTP: phospho-protein complex determination and is a general approach for elucidating transient protein surface interactions.

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FrustratometeR: an R-package to compute Local frustration in protein structures, point mutants and MD simulations

10.1101/2020.11.26.400432 ◽

2020 ◽

Author(s):

Atilio O. Rausch ◽

Maria I. Freiberger ◽

Cesar O. Leonetti ◽

Diego M. Luna ◽

Leandro G. Radusky ◽

...

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Protein Structures ◽

Md Simulations ◽

R Package ◽

Protein Protein Interactions ◽

Large Scale Analysis ◽

Functional Aspects ◽

Catalytic Sites ◽

Polypeptide Chains

Once folded natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i.e. they have highly frustrated regions. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein-protein interactions, small ligand recognition, catalytic sites and allostery. Here we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and MD trajectories, allowing straightforward integration of local frustration analysis in to pipelines for protein structural analysis.Availability and implementation: https://github.com/proteinphysiologylab/frustratometeR

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Development of potential inhibitors of cell division protein kinase 2 by ligand based drug design

Main Group Chemistry ◽

10.3233/mgc-210013 ◽

2021 ◽

pp. 1-10

Author(s):

Vildan Enisoğlu Atalay ◽

Büşra Savaş

Keyword(s):

Hydrogen Bond ◽

Cell Division ◽

Drug Design ◽

Discovery Studio ◽

Cyclin Dependent Kinases ◽

Hydrogen Bond Interactions ◽

Damage Repair ◽

Cancer Types ◽

Potential Inhibitors ◽

Cycle Regulation

Cyclin-dependent kinases (CDKs) are commonly known by their role in cell cycle regulation which affects cancer mechanism. In many cancer types, CDKs show extreme activity or CDK inhibiting proteins are dysfunctional. Specifically, CDK2 plays an indispensable role in cell division especially in the G1/S phase and DNA damage repair. Therefore, it is important to find new potential CDK2 inhibitors. In this study, ligand-based drug design is used to design new potential CDK2 inhibitors. Y8 L ligand is obtained from the X-ray crystal structure of human CDK2 (PDB ID: 2XNB) (www.pdb.org) and used as a structure model. By adding hydrophilic and hydrophobic groups to the structure, a training set of 36 molecules is generated. Each molecule examined with Spartan’14 and optimized structures are used for docking to CDK2 structure by AutoDock and AutoDock Vina programs. Ligand-amino acid interactions are analysed with Discovery Studio Visualizer. Van der Waals, Pi-Pi T-shaped, alkyl, pi-alkyl, conventional hydrogen bond and carbon-hydrogen bond interactions are observed. By docking results and viewed interactions, some molecules are identified and discussed as potential CDK2 inhibitors. Additionally, 8 different QSAR descriptors obtained from Spartan’14, Preadmet and ALOGPS 2.1 programs are investigated with multiple linear regulation (MLR) analysis with SPSS program for their impact on affinity value.

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