Exploring the Role of Molecular Dynamics Simulations in Most Recent Cancer Research: Insights into Treatment Strategies

Cancer is a complex disease that is characterized by uncontrolled growth and division of cells. It involves a complex interplay between genetic and environmental factors that lead to the initiation and progression of tumors. Recent advances in molecular dynamics simulations have revolutionized our understanding of the molecular mechanisms underlying cancer initiation and progression. Molecular dynamics simulations enable researchers to study the behavior of biomolecules at an atomic level, providing insights into the dynamics and interactions of proteins, nucleic acids, and other molecules involved in cancer development. In this review paper, we provide an overview of the latest advances in molecular dynamics simulations of cancer cells. We will discuss the principles of molecular dynamics simulations and their applications in cancer research. We also explore the role of molecular dynamics simulations in understanding the interactions between cancer cells and their microenvironment, including signaling pathways, proteinprotein interactions, and other molecular processes involved in tumor initiation and progression. In addition, we highlight the current challenges and opportunities in this field and discuss the potential for developing more accurate and personalized simulations. Overall, this review paper aims to provide a comprehensive overview of the current state of molecular dynamics simulations in cancer research, with a focus on the molecular mechanisms underlying cancer initiation and progression.Comment: 49 pages, 2 figure

Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs

Rational drug design of antineoplastic agents using 3D-QSAR, cheminformatic, and virtual screening approaches

Support was kindly provided by the EU COST Action CM1406 and CA15135. KN and JV kindly acknowledge national project number 172033 supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.PostprintPeer reviewe

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification

Human Sirt-1: Molecular Modeling and Structure-Function Relationships of an Unordered Protein

BACKGROUND: Sirt-1 is a NAD+-dependent nuclear deacetylase of 747 residues that in mammals is involved in various important metabolic pathways, such as glucose metabolism and insulin secretion, and often works on many different metabolic substrates as a multifunctional protein. Sirt-1 down-regulates p53 activity, rising lifespan, and cell survival; it also deacetylases peroxisome proliferator-activated receptor-gamma (PPAR-gamma) and its coactivator 1 alpha (PGC-1alpha), promoting lipid mobilization, positively regulating insulin secretion, and increasing mitochondrial dimension and number. Therefore, it has been implicated in diseases such as diabetes and the metabolic syndrome and, also, in the mechanisms of longevity induced by calorie restriction. Its whole structure is not yet experimentally determined and the structural features of its allosteric site are unknown, and no information is known about the structural changes determined by the binding of its allosteric effectors. METHODOLOGY: In this study, we modelled the whole three-dimensional structure of Sirt-1 and that of its endogenous activator, the nuclear protein AROS. Moreover, we modelled the Sirt-1/AROS complex in order to study the structural basis of its activation and regulation. CONCLUSIONS: Amazingly, the structural data show that Sirt-1 is an unordered protein with a globular core and two large unordered structural regions at both termini, which play an important role in the protein-protein interaction. Moreover, we have found on Sirt-1 a conserved pharmacophore pocket of which we have discussed the implication

Investigating the Substrate Selectivity and Regulation of Histone Deacetylases

Lysine acetylation regulates thousands of proteins and nearly every cellular process from cell replication to cell death. Dysregulated acetylation has been implicated in diseases including cancer, neurodegenerative disorders, and infectious diseases. For this reason, the enzymes that regulate acetylation, including the histone deacetylases (HDACs), are targeted for drug development, and understanding their biological function is of the utmost importance. Unfortunately, few HDAC-specific substrates have been identified, and how HDACs recognize and select for their substrates, a key aspect of their biological function, is poorly understood. HDAC8, a unique member of class I, is phosphorylated at S39, which affects HDAC8 substrate selectivity in vitro and may be used by the cell to regulate HDAC8 biological function. Measuring HDAC8 phosphomimetic mutant S39E-catalyzed deacetylation of various peptides demonstrates altered HDAC8 activity and importantly substrate selectivity. Structural analyses indicate this alteration is due to changes in the substrate binding pocket and active-site architecture. Moreover, wild-type HDAC8 substrate selectivity is influenced by both substrate sequence and structure in vitro. Comparing HDAC8-catalyzed deacetylation of histone H3 K9ac, K14ac, and K56ac peptides and proteins reveals that protein structure enhances activity from 40- to over 300-fold, and local sequence determines substrate selectivity, particularly in less structured regions. These data support the use of peptide substrates to determine relative activity and to identify HDAC substrates. To expand on these results, HDAC6-catalyzed deacetylation of a library of peptides was tested to develop a structure-based model of HDAC6 activity. The results reveal HDAC6 distinguishes between sequences, catalyzing deacetylation of peptides with kcat/KM values from 10 to 106 M-1s-1. These data demonstrate the usefulness of a prediction model based on peptides. Together, these investigations reveal that phosphorylation, local sequence, and protein structure affect HDAC substrate selectivity and activity in vitro and likely play key roles in the biological function and dysfunction of HDACs in the cell. Finally, development of structure-based models combined with peptide-based experiments can be used to identify HDAC substrate candidates for study in vivo.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153418/1/krleng_1.pd

Prioritizing Small Molecules for Drug Discovery or Chemical Safety Assessments using Ligand- and Structure-based Cheminformatics Approaches

Recent growth in the experimental data describing the effects of chemicals at the molecular, cellular, and organism level has triggered the development of novel computational approaches for the prediction of a chemical's effect on an organism. The studies described in this dissertation research predict chemical activity at three levels of biological complexity: binding of drugs to a single protein target, selective binding to a family of protein targets, and systemic toxicity. Optimizing cheminformatics methods that examine diverse sources of experimental data can lead to novel insight into the therapeutic use and toxicity of chemicals. In the first study, a combinatorial Quantitative Structure-Activity Relationship (QSAR) modeling workflow was successfully applied to the discovery of novel bioactive compound against one specific protein target: histone deacetylase inhibitors (HDACIs). Four candidate molecules were selected from the virtual screening hits to be tested experimentally, and three of them were confirmed active against HDAC. Next, a receptor-based protocol was established and applied to discover target-selective ligands within a family of proteins. This protocol extended the concept of protein/ligand interaction-guided pose selection by employing a binary classifier to discriminate poses of interest from a calibration set. The resulting virtual screening tools were applied for enriching beta2-adrenergic receptor (β2AR) ligands that are selective against other subtypes in the βAR family (i.e. β1AR and β3AR). Moreover, some computational 3D protein structures used in this study have exhibited comparative or even better performance in virtual screening than X-ray crystal structures of β2AR, and therefore computational tools that use these computational structures could complement tools utilizing experimental structures. Finally, a two-step hierarchical QSAR modeling approach was developed to estimate in vivo toxicity effects of small molecules. Besides the chemical structural descriptors, the developed models utilized additional biological information from in vitro bioassays. The derived models were more accurate than traditional QSAR models utilizing chemical descriptors only. Moreover, retrospective analysis of the developed models helped to identify the most informative bioassays, suggesting potential applicability of this methodology in guiding future toxicity experiments. These studies contribute to the development of computational strategies for comprehensive analysis of small molecules' biological properties, and have the potential to be integrated into existing methods for modern rational drug design and discovery.Doctor of Philosoph

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies

VIRTUAL SCREENING AND DISCOVERY OF LEAD COMPOUNDS AS POTENTIAL DNA METHYLTRANSFERASE 1 INHIBITORS AND ANTICANCER AGENTS

Epigenetic changes consist of DNA methylation, histone modification, micro RNA and genome imprinting. DNA methylation of the CpG islands is one of the main methods of epigenetic inactivation of genes and aberrant methylations at promoter regions of tumor suppressor genes can alter gene expression and play an important role in cancer development. DNA methyltransferase I (Dnmt1) is the enzyme responsible for maintaining methylation patterns during cell division and it is overexpressed in many cancers. Thus, Dnmt1 is a promising therapeutic target for development of novel anticancer agents and epigenetic modulators. We have developed two promising class of lead candidates, compounds 5-hydroxy-2-(4-hydroxyphenethyl)-3-oxo-N-pentyl-4-(4-(trifluoromethyl)phenyl)isoindoline-1-carboxamide 47, 2-(2-(1H-indol-3-yl)ethyl)-5-hydroxy-3-oxo-N-pentyl-4-(4-(trifluoromethyl)phenyl)isoindoline- 1-carboxamide 51 and 1-(4-isopropylphenyl)-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indole 96, as potential leads compounds that can be optimized for pharmaceutical applications.