Design of new molecular dynamics global minimum search protocols for mapping energy landscapes and conformations of folded polypeptides and mini-proteins.

First, a new MD search strategy called DIvergent Path (DIP) search simulation is developed in which the simulations start with several independent polypeptides having the same initial coordinates and temperatures but different velocity directions, which evolve into different trajectories. The DIP simulations reveal three primary limitations of conventional MD simulations: potential energy traps, free energy traps, and kinetic traps. Among them, kinetic traps are the most limiting factor for MD simulations intended to sample varied conformational space at room temperature. This trap is caused by mechanical equilibrium (when both kinetic and potential energies have reached equilibrium) and can be easily overcome by intervening to reassign atomic velocities and thus randomize simulation trajectories. By combining this trajectory randomization strategy at one temperature with cycles of heating and cooling, the DIsrupted VElocity (DIVE) search simulation is further developed. The DIVE simulations can explore wide ranges of the rugged potential energy surfaces of peptides, sample myriad potential energy minima, and explore diverse conformations even in a very limited simulation time. Finally, a combined procedure is also built in which the global potential energy minimum and myriad local potential energy minima are explored by using DIVE simulations followed by DIP simulations to search for the global free energy minimum near in vivo temperatures.Molecular dynamics (MD) simulations are widely used for global conformational searches in protein folding. However, conventional canonical ensemble simulations (constant NVT) usually cannot explore biologically active natural structures of proteins because such simulations have extreme difficulty sampling conformational space sufficiently for global energy minimum searches. The work described here delineates the crucial limitations restricting conventional NVT simulations from covering a wide variety of conformations and develops several new MD simulation protocols for efficiently sampling diverse regions of conformational space to search for the global minimum energy structure of polypeptides and mini-proteins.We performed the new MD simulations for mapping energy landscapes and conformations of a model 13 residues polyalanine peptide Ala13, an amphiphilic octadecapeptide, peptide F, and a 20-residue mini-protein, Trp-cage, using the AMBER force field either in vacuo or in a generalized Born/solvent-accessible surface area (GB/SA) implicit solvent for water. The simulation results are also compared with those from several other simulation algorithms including conventional NVT simulations, the replica exchange method (REM), and locally enhanced sampling (LES) molecular dynamics. Our newly developed MD simulation protocols sample the most diverse region of conformational space and complement existing global geometry optimization techniques for predicting 3D protein structures from only primary sequence data

Design of Mixing Station for Alternative Gaseous Fuels

Úkolem diplomové práce je navrhnout potrubní rozvod plynů do směšovací stanice a následně i navrhnout samotnou směšovací stanici. V textové části je nejprve vytvořena rešerše týkající se využívání technických a topných plynů v praxi. Na tuto část postupně navazují specifikace plynů, které se budou dopravovat do směšovací stanice. Ve výpočtové části se práce prostřednictvím výpočtových vztahů zabývá složením směsného plynu, fyzikálními vlastnostmi jednotlivých plynů i samotného plynu směsného, přičemž jsou vypočteny a navrženy potrubní tratě jednotlivých plynů směřujících do směšovací stanice. Konstrukční část práce se zabývá návrhem směšovací stanice a směšovací komory, ve které se jednotlivé plyny mísí. Grafická část práce obsahuje schémata jednotlivých potrubních tratí a směšovací stanice, 3D model směšovací komory a výkres směšovací komory.The task of the diploma thesis is to design the pipeline distribution of gases to the mixing station and then design the mixing station itself. In the text part is first created a search regarding the use of technical and heating gases in practice. This part is followed by the gas specifications that will be transported to the mixing station. In the computational part, the work with the help of calculation relations deals with the composition of the mixed gas, the physical properties of the individual gases and the mixing gas itself, and the pipelines of the individual gases leading to the mixing station are calculated and designed. The construction part deals with the design of the mixing station and the mixing chamber, in which the individual gases are mixed. The graphical part of the thesis contains diagrams of individual pipeline lines and mixing stations, a 3D model of the mixing chamber and a drawing of the mixing chamber.361 - Katedra energetikyvýborn

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives

Inexpensive Method for Selecting Receptor Structures for Virtual Screening

This article introduces a screening performance index (SPI) to help select from a number of experimental structures one or a few that are more likely to identify more actives among its top hits from virtual screening of a compound library. It achieved this by docking only known actives to the experimental structures without considering a large number of decoys to reduce computational costs. The SPI is calculated by using the docking energies of the actives to all the receptor structures. We evaluated the performance of the SPI by applying it to study eight protein systems: fatty acid binding protein adipocyte FABP4, serine/threonine-protein kinase BRAF, beta-1 adrenergic receptor ADRB1, TGF-beta receptor type I TGFR1, adenosylhomocysteinase SAHH, thyroid hormone receptor beta-1 THB, phospholipase A2 group IIA PA2GA, and cytochrome P450 3a4 CP3A4. We found that the SPI agreed with the results from other popular performance metrics such as Boltzmann-Enhanced Discrimination Receiver Operator Characteristics (BEDROC), Robust Initial Enhancement (RIE), Area Under Accumulation Curve (AUAC), and Enrichment Factor (EF) but is less expensive to calculate. SPI also performed better than the best docking energy, the molecular volume of the bound ligand, and the resolution of crystal structure in selecting good receptor structures for virtual screening. The implications of these findings were further discussed in the context of ensemble docking, in situations when no experimental structure for the targeted protein was available, or under circumstances when quick choices of receptor structures need to be made before quantitative indexes such as the SPI and BEDROC can be calculated

Exploring the Characteristics of Monkeypox-Related Genes in Pan-Cancer

Monkeypox, an infectious virus that is a member of the Poxviridae family, has raised great threats to humans. Compared to the known oncoviruses, the relationship between monkeypox and cancer still remains obscure. Hence, in this study, we analyzed the multi-omics data from the Cancer Genome Atlas (TCGA) database by using genomic and transcriptomic approaches to comprehensively assess the monkeypox-related genes (MRGs) in tumor samples from 33 types of cancers. Based on the results, the expression of MRGs was highly correlated with the immune infiltration and could be further utilized to predict survival in cancer patients. Furthermore, it was shown that tumorigenesis and patient survival were frequently associated with the genomic alterations of MRGs. Moreover, pathway analysis showed that MRGs participated in the regulation of apoptosis, cell cycle, Epithelial to Mesenchymal Transition (EMT), DNA damage, and hormone androgen receptor (AR), as well as RAS/MAPK and RTK signaling pathways. Besides, we also developed the prognostic features and consensus clustering clusters of MRGs in cancers. Lastly, by mining the cancer drug sensitivity genomics database, we further identified a series of candidate drugs that may target MRGs. Collectively, this study revealed genomic alterations and clinical features of MRGs, which may provide new hints to explore the potential molecular mechanisms between viruses and cancers as well as to provide new clinical guidance of cancer patients who also face the threats during the monkeypox epidemic

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction

Molecular Dynamics Simulation of Drug Solubilization Behavior in Surfactant and Cosolvent Injections

Surfactants and cosolvents are often combined to solubilize insoluble drugs in commercially available intravenous formulations to achieve better solubilization. In this study, six marketed parenteral formulations with surfactants and cosolvents were investigated on the aggregation processes of micelles, the structural characterization of micelles, and the properties of solvent using molecular dynamics simulations. The addition of cosolvents resulted in better hydration of the core and palisade regions of micelles and an increase in both radius of gyration (Rg) and the solvent accessible surface area (SASA), causing a rise in critical micelle concentration (CMC), which hindered the phase separation of micelles. At the same time, the presence of cosolvents disrupted the hydrogen bonding structure of water in solution, increasing the solubility of insoluble medicines. Therefore, the solubilization mechanism of the cosolvent and surfactant mixtures was successfully analyzed by molecular dynamics simulation, which will benefit future formulation development for drug delivery

Correction: Haque et al. Marine Natural Products in Clinical Use. Mar. Drugs 2022, 20, 528

The authors would like to make corrections to a recently published paper [...

Identification of Hypoxia-Related Prognostic Signature and Competing Endogenous RNA Regulatory Axes in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common type of liver cancer and one of the highly lethal diseases worldwide. Hypoxia plays an important role in the development and prognosis of HCC. This study aimed to construct a new hypoxia-related prognosis signature and investigate its potential ceRNA axes in HCC. RNA profiles and hypoxia genes were downloaded, respectively, from the Cancer Genome Atlas hepatocellular carcinoma database and Gene Set Enrichment Analysis website. Cox regression analyses were performed to select the prognostic genes and construct the risk model. The ENCORI database was applied to build the lncRNA-miRNA–mRNA prognosis-related network. The TIMER and CellMiner databases were employed to analyze the association of gene expression in ceRNA with immune infiltration and drug sensitivity, respectively. Finally, the co-expression analysis was carried out to construct the potential lncRNA/miRNA/mRNA regulatory axes. We obtained a prognostic signature including eight hypoxia genes (ENO2, KDELR3, PFKP, SLC2A1, PGF, PPFIA4, SAP30, and TKTL1) and further established a hypoxia-related prognostic ceRNA network including 17 lncRNAs, six miRNAs, and seven mRNAs for hepatocellular carcinoma. Then, the analysis of immune infiltration and drug sensitivity showed that gene expression in the ceRNA network was significantly correlated with the infiltration abundance of multiple immune cells, the expression level of immune checkpoints, and drug sensitivity. Finally, we identified three ceRNA regulatory axes (SNHG1/miR-101-3p/PPFIA4, SNHG1/miR-101-3p/SAP30, and SNHG1/miR-101-3p/TKTL1) associated with the progression of HCC under hypoxia. Here, we constructed a prognosis gene signature and a ceRNA network related to hypoxia for hepatocellular carcinoma. Among the ceRNA network, six highly expressed lncRNAs (AC005540.1, AC012146.1, AC073529.1, AC090772.3, AC138150.2, AL390728.6) and one highly expressed mRNA (PPFIA4) were the potential biomarkers of hepatocellular carcinoma which we firstly reported. The three predicted hypoxia-related regulatory axes may play a vital role in the progression of hepatocellular carcinoma