Protein Data Bank Files Research Articles

A dataset of alternately located segments in protein crystal structures

Protein Data Bank (PDB) files list the relative spatial location of atoms in a protein structure as the final output of the process of fitting and refining to experimentally determined electron density measurements. Where experimental evidence exists for multiple conformations, atoms are modelled in alternate locations. Programs reading PDB files commonly ignore these alternate conformations by default leaving users oblivious to the presence of alternate conformations in the structures they analyze. This has led to underappreciation of their prevalence, under characterisation of their features and limited the accessibility to this high-resolution data representing structural ensembles. We have trawled PDB files to extract structural features of residues with alternately located atoms. The output includes the distance between alternate conformations and identifies the location of these segments within the protein chain and in proximity of all other atoms within a defined radius. This dataset should be of use in efforts to predict multiple structures from a single sequence and support studies investigating protein flexibility and the association with protein function.

From PDB files to protein features: a comparative analysis of PDB bind and STCRDAB datasets.

Understanding protein structures is crucial for various bioinformatics research, including drug discovery, disease diagnosis, and evolutionary studies. Protein structure classification is a critical aspect of structural biology, where supervised machine learning algorithms classify structures based on data from databases such as Protein Data Bank (PDB). However, the challenge lies in designing numerical embeddings for protein structures without losing essential information. Although some effort has been made in the literature, researchers have not effectively and rigorously combined the structural and sequence-based features for efficient protein classification to the best of our knowledge. To this end, we propose numerical embeddings that extract relevant features for protein sequences fetched from PDB structures from popular datasets such as PDB Bind and STCRDAB. The features are physicochemical properties such as aromaticity, instability index, flexibility, Grand Average of Hydropathy (GRAVY), isoelectric point, charge at pH, secondary structure fracture, molar extinction coefficient, and molecular weight. We also incorporate scaling features for the sliding windows (e.g., k-mers), which include Kyte and Doolittle (KD) hydropathy scale, Eisenberg hydrophobicity scale, Hydrophilicity scale, Flexibility of the amino acids, and Hydropathy scale. Multiple-feature selection aims to improve the accuracy of protein classification models. The results showed that the selected features significantly improved the predictive performance of existing embeddings.

Parameterization of General Organic Polymers within the Open Force Field Framework.

Polymer and chemically modified biopolymer systems present unique challenges to traditional molecular simulation preparation workflows. First, typical polymer and biomolecular input formats, such as Protein Data Bank (PDB) files, lack adequate chemical information needed for the parameterization of new chemistries. Second, polymers are typically too large for accurate partial charge generation methods. In this work, we employ direct chemical perception through the Open Force Field toolkit to create a flexible polymer simulation workflow for organic polymers, encompassing everything from biopolymers to soft materials. We propose and test a new input specification for monomer information that can, along with a 3D conformational geometry, parametrize and simulate most soft-material systems within the same workflow used for smaller ligands. The monomer format encompasses a subset of the SMIRKS substructure query language to uniquely identify chemical information and repeating charges in underspecified systems through matching atomic connectivity. This workflow is combined with several different approaches for automatic partial-charge generation for larger systems. As an initial proof of concept, a variety of diverse polymeric systems were parametrized with the Open Force Field toolkit, including functionalized proteins, DNA, homopolymers, cross-linked systems, and sugars. Additionally, shape properties and radial distribution functions were computed from molecular dynamics simulations of poly(ethylene glycol), polyacrylamide, and poly(N-isopropylacrylamide) homopolymers in aqueous solution and compared to previous simulation results in order to demonstrate a start-to-finish workflow for simulation and property prediction. We expect that these tools will greatly expedite the day-to-day computational research of soft-matter simulations and create a robust atomic-scale polymer specification in conjunction with existing polymer structural notations.

In Silico Compounds Analysis in Ginger (Zingiber Officinale) as Candidate for Acute Coronary Syndrome Medication

Based on the World Health Organization and other authorized health organizations, heart disease is one of the diseases with the largest contributor to death in the world, including Acute Coronary Syndrome with an estimated number of patients of 126 million in 2020. This research entails phytoconstituents of Zingiber officinale as possible Acute Coronary Syndrome medication with in silico methods by altering the activity of HMG-CoA Reductase, PPAR-α, PPAR-γ, NPC1L1, β1-AR, ACE and P2Y12R. This research used Molegro Virtual Docker 6.0 as tool to analyze the compounds with docking computation approach and also utilize Protein Data Bank (PDB) files : 1HWK, 2ZNN, 4EMA, 4AMJ, 2YDM, 4PXZ and 7DFZ. Chemdraw 3D was used in order to minimize the ligand’s energy. Furthermore, to analyze the pharmacokinetics aspects, this study involved pkCSM and SwissADME to predict a few parameters in each aspect, and finalized the research with Dynamic Molecular approach with YASARA.

Reverse vaccinology approaches to introduce promising immunogenic and drug targets against antibiotic-resistant Neisseria gonorrhoeae: Thinking outside the box in current prevention and treatment.

Gonorrhea is an urgent antimicrobial resistance threat and its therapeutic options are continuously getting restricted. Moreover, no vaccine has been approved against it so far. Hence, the present study aimed to introduce novel immunogenic and drug targets against antibiotic-resistant Neisseria gonorrhoeae strains. In the first step, the core proteins of 79 complete genomes of N. gonorrhoeae were retrieved. Next, the surface-exposed proteins were evaluated from different aspects such as antigenicity, allergenicity, conservancy, and B-cell and T-cell epitopes to introduce promising immunogenic candidates. Then, the interactions with human Toll-like receptors (TLR-1, 2, and 4), and immunoreactivity to elicit humoral and cellular immune responses were simulated. On the other hand, to identify novel broad-spectrum drug targets, the cytoplasmic and essential proteins were detected. Then, the N. gonorrhoeae metabolome-specific proteins were compared to the drug targets of the DrugBank, and novel drug targets were retrieved. Finally, the protein data bank (PDB) file availability and prevalence among the ESKAPE group and common sexually transmitted infection (STI) agents were assessed. Our analyses resulted in the recognition of ten novel and putative immunogenic targets including murein transglycosylase A, PBP1A, Opa, NlpD, Azurin, MtrE, RmpM, LptD, NspA, and TamA. Moreover, four potential and broad-spectrum drug targets were identified including UMP kinase, GlyQ, HU family DNA-binding protein, and IF-1. Some of the shortlisted immunogenic and drug targets have confirmed roles in adhesion, immune evasion, and antibiotic resistance that can induce bactericidal antibodies. Other immunogenic and drug targets might be associated with the virulence of N. gonorrhoeae as well. Thus, further experimental studies and site-directed mutations are recommended to investigate the role of potential vaccine and drug targets in the pathogenesis of N. gonorrhoeae. It seems that the efforts for proposing novel vaccines and drug targets appear to be pavingthe way for a prevention-treatment strategy against this bacterium. Additionally, a combination of bactericidal monoclonal antibodies and antibiotics is a promising approach to curing N. gonorrhoeae.

BeEM: fast and faithful conversion of mmCIF format structure files to PDB format

BackgroundAlthough mmCIF is the current official format for deposition of protein and nucleic acid structures to the protein data bank (PDB) database, the legacy PDB format is still the primary supported format for many structural bioinformatics tools. Therefore, reliable software to convert mmCIF structure files to PDB files is needed. Unfortunately, existing conversion programs fail to correctly convert many mmCIF files, especially those with many atoms and/or long chain identifies.ResultsThis study proposed BeEM, which converts any mmCIF format structure files to PDB format. BeEM conversion faithfully retains all atomic and chain information, including chain IDs with more than 2 characters, which are not supported by any existing mmCIF to PDB converters. The conversion speed of BeEM is at least ten times faster than existing converters such as MAXIT and Phenix. Part of the reason for the speed improvement is the avoidance of conversion between numerical values and text strings.ConclusionBeEM is a fast and accurate tool for mmCIF-to-PDB format conversion, which is a common procedure in structural biology. The source code is available under the BSD licence at https://github.com/kad-ecoli/BeEM/.

PASSer: fast and accurate prediction of protein allosteric sites.

Allostery refers to the biological process by which an effector modulator binds to a protein at a site distant from the active site, known as allosteric site. Identifying allosteric sites is essential for discovering allosteric process and is considered a critical factor in allosteric drug development. To facilitate related research, we developed PASSer (Protein Allosteric Sites Server) at https://passer.smu.edu, a web application for fast and accurate allosteric site prediction and visualization. The website hosts three trained and published machine learning models: (i) an ensemble learning model with extreme gradient boosting and graph convolutional neural network, (ii) an automated machine learning model with AutoGluonand (iii) a learning-to-rank model with LambdaMART. PASSer accepts protein entries directly from the Protein Data Bank (PDB) or user-uploaded PDB files, and can conduct predictions within seconds. The results are presented in an interactive window that displays protein and pockets' structures, as well as a table that summarizes predictions of the top three pockets with the highest probabilities/scores. To date, PASSer has been visited over 49000times in over 70 countries and has executed over 6200 jobs.

Abstract 3132: CLUMPS-PTM: Spatial clustering of post-translational modifications across cancer types

Abstract Post-translational modifications (PTMs) play key roles in regulating cell signaling in health and disease processes. Recent advances in mass spectrometry enable comprehensive investigation of PTMs. However, identifying functionally relevant PTMs and protein domains implicated in disease remains a key challenge. We present CLUMPS-PTM, an algorithm for spatial clustering of PTMs to propose functional domains. We apply CLUMPS-PTM to the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset (one of the largest proteogenomic pan-cancer PTM datasets) comprising 1110 samples spanning 11 tumor types. First, we map over 14K phospho-sites and 13K acetyl-sites detected in the CPTAC dataset to protein data bank (PDB) files. We expand this to the predicted AlphaFold proteome and recover additional 12K phospho-sites and 10K acetyl-sites exceeding model confidence (pLDDT) of 70%. Consistent with previous research, phospho-sites are found on residues with lower AlphaFold prediction confidence than acetyl-sites due to their abundance in unstructured domains (p-value < 1e-4). To identify functionally relevant protein domains in the pan-cancer context, we characterize the 1110 samples by applying SignatureAnalyzer, a Bayesian non-negative matrix factorization method, to discover multi-omic patterns. This analysis identifies strong signatures associated with DNA damage response (DDR) and stratifies the CPTAC cohort into DDR-high (n=442) and DDR-low (n=668) groups. Next, we applied CLUMPS-PTM to differentially modified phospho/acetyl-sites between these groups. We identified 22 proteins (including GMPS, NCBP1, and ANPEP (CD13)) with significant clusters of differentially phosphorylated sites (q-value < 0.1) in DDR-high samples. ANPEP is a zinc-dependent aminopeptidase with a significant cluster (q-value = 0.0129) of differentially phosphorylated sites in the ERAP1-C domain that faces peptidase active sites known to have proliferative activity via the MAPK pathway. CLUMPS-PTM also identified a significant cluster of acetyl-sites on ARID1A (a SWI/SNF chromatin remodeler known to be mutated in cancer) in DDR-high samples (q-value = 0.085). The ARID1A cluster is on the C-terminal tail of the protein within the glucocorticoid receptor (GR) domain, an upstream regulator of proteins that increase catabolism, reduce inflammation, and increase cell survival. Increased acetylation may block a ubiquitination site at the C-terminus of the protein, resulting in increased protein levels, likely due to reduced ARID1A degradation, and increased GR signaling. Overall, CLUMPS-PTM is an open-source tool that allows near proteome-wide spatial analysis with the growing availability of PTM data. We anticipate that it will be useful to the broader proteomic community for the discovery of novel targets and generation of insights into functional mechanisms. Citation Format: Shankara K. Anand, Yifat Geffen, Yo Akiyama, Francois Aguet, Gad Getz. CLUMPS-PTM: Spatial clustering of post-translational modifications across cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3132.

PDC: a highly compact file format to store protein 3D coordinates.

Recent improvements in computational and experimental techniques for obtaining protein structures have resulted in an explosion of 3D coordinate data. To cope with the ever-increasing sizes of structure databases, this work proposes the Protein Data Compression (PDC) format, which compresses coordinates and temperature factors of full-atomic and Cα-only protein structures. Without loss of precision, PDC results in 69% to 78% smaller file sizes than Protein Data Bank (PDB) and macromolecular Crystallographic Information File (mmCIF) files with standard GZIP compression. It uses ∼60% less space than existing compression algorithms specific to macromolecular structures. PDC optionally performs lossy compression with minimal sacrifice of precision, which allows reduction of file sizes by another 79%. Conversion between PDC, mmCIF and PDB formats is typically achieved within 0.02 s. The compactness and fast reading/writing speed of PDC make it valuable for storage and analysis of large quantity of tertiary structural data. Database URL https://github.com/kad-ecoli/pdc.

SmoothT-a server constructing low-energy pathways from conformational ensembles for interactive visualization and enhanced sampling.

The SmoothT software and webservice offers the construction of pathways from an ensemble of conformations. The user provides an archive of molecule conformations in Protein Databank (PDB) format, from which a starting and a final conformation need to be selected. The individual PDB files need to contain an energy value or score, estimating the quality of the respective confirmation. Additionally, the user has to provide a root-mean-square deviation (RMSD) cut-off, below which conformations are considered neighboring. From this, SmoothT constructs a graph that connects similar conformations. SmoothT returns the energetically most favorable pathway within in this graph. This pathway is directly displayed as interactive animation using the NGL viewer. Simultaneously, the energy along the pathway is plotted, highlighting the conformation that is currently displayed in the 3D window. SmoothT is available as webservice at: http://proteinformatics.org/smoothT. Examples, a tutorial, and FAQs can be found there. Ensembles up to 2 GB (compressed) can be uploaded. Results will be stored for 5 days. The server is completely free and requires no registration. The C++ source code is available at: https://github.com/starbeachlab/smoothT.

Identification of Putative Drug Targets in Highly Resistant Gram-Negative Bacteria; and Drug Discovery Against Glycyl-tRNA Synthetase as a New Target.

Gram-negative bacterial infections are on the rise due to the high prevalence of multidrug-resistant bacteria, and efforts must be made to identify novel drug targets and then new antibiotics. In the upstream part, we retrieved the genome sequences of 4 highly resistant Gram-negative bacteria (e.g., Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter cloacae). The core proteins were assessed to find common, cytoplasmic, and essential proteins with no similarity to the human proteome. Novel drug targets were identified using DrugBank, and their sequence conservancy was evaluated. Protein Data Bank files and STRING interaction networks were assessed. Finally, the aminoacylation cavity of glycyl-tRNA synthetase (GlyQ) was virtually screened to identify novel inhibitors using AutoDock Vina and the StreptomeDB library. Ligands with high binding affinity were clustered, and then the pharmacokinetics properties of therapeutic agents were investigated. A total of 6 common proteins (e.g., RP-L28, RP-L30, RP-S20, RP-S21, Rnt, and GlyQ) were selected as novel and widespread drug targets against highly resistant Gram-negative superbugs based on different criteria. In the downstream analysis, virtual screening revealed that Rimocidin, Flavofungin, Chaxamycin, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin, and Platensimycin were promising hit compounds against GlyQ protein. Finally, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin was identified as the best potential inhibitor of GlyQ protein. This compound showed high absorption capacity in the human intestine. The results of this study provide 6 common putative new drug targets against 4 highly resistant and Gram-negative bacteria. Moreover, we presented 5 different hit compounds against GlyQ protein as a novel therapeutic target. However, further in vitro and in vivo studies are needed to explore the bactericidal effects of proposed hit compounds against these superbugs.

Toward the Discovery of SARS-CoV-2 Main Protease Inhibitors: Exploring Therapeutic Potentials of Evodiamine and Its Derivatives, Virtual Screening, Molecular Docking, and Molecular Dynamic Studies

Continuous scientific research is necessary to help in the discovery of new promising remedies for the treatment of COVID-19, caused by the SARS-CoV-2 virus. This current research was aimed at identifying potential novel inhibitors of the SARS-CoV-2 main protease, which represents one of the most important targets in the viral life cycle. Protein data bank file ID: 7JQ2 was used containing the co-crystallized inhibitor MPI5 with the Main protease. A virtual screening process for natural evodiamine compounds was performed through absorption, distribution, metabolism, elimination, and toxicity studies, and the promising hits were docked into the binding site of the enzyme. 13-(4-Chlorobenzoyl)-10-hydroxy-14-methyl-8,13,13 b,14-tetrahydroindolo[2′,3′:3,4]pyrido[2,1- b]-quinazolin-5(7 H)-one (29) interacted favorably with the enzyme; it showed high similarity to MPI5. Molecular dynamic simulations for 29 proved the stability of its binding to SARS-CoV-2 protease over 100 ns; subsequent MMGBSA analysis also supported this principle. Furthermore, 29 elucidated higher limiting action on enzymatic behavior throughout the whole process when compared to MPI5. This provides sufficient evidence for the potential of evodiamine compounds in modern antiviral research, especially compound 29, against the modern COVID-19 pandemic.

PDBsum1: A standalone program for generating PDBsum analyses.

PDBsum1 is a standalone set of programs to perform the same structural analyses as provided by the PDBsum web server (https://www.ebi.ac.uk/pdbsum). The server has pages for every entry in the Protein Data Bank (PDB) and can also process user-uploaded PDB files, returning a password-protected set of pages that are retained for around 3months. The standalone version described here allows for in-house processing and indefinite retention of the results. All data files and images are pre-generated, rather than on-the-fly as in the web version, so can be easily accessed. The program runs on Linux, Windows, and mac operating systems and is freely available for academic use at https://www.ebi.ac.uk/thornton-srv/software/PDBsum1.

PyDockDNA: A new web server for energy-based protein-DNA docking and scoring.

Proteins and nucleic acids are essential biological macromolecules for cell life. Indeed, interactions between proteins and DNA regulate many biological processes such as protein synthesis, signal transduction, DNA storage, or DNA replication and repair. Despite their importance, less than 4% of total structures deposited in the Protein Data Bank (PDB) correspond to protein-DNA complexes, and very few computational methods are available to model their structure. We present here the pyDockDNA web server, which can successfully model a protein-DNA complex with a reasonable predictive success rate (as benchmarked on a standard dataset of protein-DNA complex structures, where DNA is in B-DNA conformation). The server implements the pyDockDNA program, as a module of pyDock suite, thus including third-party programs, modules, and previously developed tools, as well as new modules and parameters to handle the DNA properly. The user is asked to enter Protein Data Bank files for protein and DNA input structures (or suitable models) and select the chains to be docked. The server calculations are mainly divided into three steps: sampling by FTDOCK, scoring with new energy-based parameters and the possibility of applying external restraints. The user can select different options for these steps. The final output screen shows a 3D representation of the top 10 models and a table sorting the model according to the scoring function selected previously. All these output files can be downloaded, including the top 100 models predicted by pyDockDNA. The server can be freely accessed for academic use (https://model3dbio.csic.es/pydockdna).

PDBms-An online tool for PDB file splitting and interactive molecular visualization.

The rapid growth of biological databases has resulted in the vast development of many state-of-the-art molecular analysis tools for accurate disease diagnosis and drug discovery. Protein Data Bank (PDB) is a leading molecular database consisting of three-dimensional (3D) experimental structures of macromolecules and small molecules. The most significant role of PDB in Bioinformatics includes molecular modelling and computer-aided drug design (CADD). PDBms is a web tool for splitting PDB file and interactive visualization of molecules. It parses coordinate section records in the PDB file and categorizes them into a group of molecules. Moreover, it supports 3D graphic visualization in the various model for polymers and target-ligand interactions of complex structures. The web interface of PDBms is designed using NGL Viewer/WebGL JavaScript package and programming languages such as PHP, HTML, CSS, JavaScript, AJAX, and jQuery. PDBms is freely accessible at https://www.biogem.org/tool/pdbms/ .

Protein Repair and Analysis Server: A Web Server to Repair PDB Structures, Add Missing Heavy Atoms and Hydrogen Atoms, and Assign Secondary Structures by Amide Interactions.

The Protein Data Bank (PDB) file format developed at Brookhaven National Laboratory is the most popular file format to store biological data and is widely supported by many software programs for the editing and visualization of macromolecular structures. Unfortunately, many of these structures have variety of issues ranging from missing side chains and/or atoms to alternate locations (rotamers). To fix these flaws, one has to learn a new program, compile modules, and install libraries. To overcome these challenges, we present Protein Repair and Analysis Server (PRAS), an easy-to-use web server to repair protein structures and add missing atoms. The PRAS program flow has two main sections. In the first section, several subroutines are called to deal with sequence microheterogeneity, rotamers, missing side chains, heavy atoms, or hydrogen atoms. A log file is generated detailing irregularities and their fixes as carried out by the program. In the later section, the program uses the error-corrected protein structure to assign secondary structure elements based on amide-amide interactions of the backbone. Plots of four Ramachandran types (general, glycine, proline, and pre-proline) and percentage of secondary structure elements; a log file and the error-corrected PDB file can be downloaded by clicking on the download link generated by the server. The server is freely available and is accessed through its web address at www.protein-science.com. Alternatively, users can download the source code from the server or install the program on their local machines following the instructions at https://pypi.org/project/Pras-Server/ or https://github.com/osita-sunday-nnyigide/Pras_Server. While the present work focuses on the repair of structural data in the PDB format, the server is capable of fixing similar errors in the extensible and newly adopted mmCIF format.

IN SILICO PREDICTION OF ESSENTIALS OIL FROM CYMBOPOGON NARDUS AS IMMUNOMODULATOR IN RHEUMATOID ARTHRITIS

Objective: Rheumatoid arthritis (RA) is an autoimmune disease involving the synovial lining of the major joints. Our study involves bioactive compounds of Cymbopogon Nardus as possible Rheumatoid arthritis drugs in silico targeted TNF-α, JAK1/2, JAK3, PAD4, and DHFR.
 Methods: Using computational docking and receptors from the Protein Data Bank (PDB) files 2AZ5, 3EYG, 3LXY, 1DLS, and 1WDA. Molegro Virtual Docker 6.0 was utilized to undertake an in silico anti-arthritis drugs study. ChemDraw 3D was utilized to minimize the ligand's energy before docking, and the structures Native Ligand were employed as positive control medications. A pharmacokinetic and toxicological study was performed using SwissADME (ADME) and PK-CMS.
 Results: Using the Moldock SE mechanism calculates the binding (atom) energies of each protein (Enzyme) and each ligand at the least energetic conformation state. Docking results of ten tested bioactive compounds have not displayed the Lowest rerank score scores and best fit within the prominent active site residues.
 Conclusion: The Essentials oils from Cymbopogon nardus have not effectively suppressed the Rheumatoid Arthritis pathway through inhibition of TNF-α, JAK1/2, JAK3, PAD4, and DHFR which can serve as potential lead compounds for the development of new drugs for the treatment of Rheumatoid Arthritis.

Modeling the role of cyclin C in connecting stress‐induced mitochondrial fission to apoptosis

For normal cell function, exogenous signals must be correctly interpreted, and the proper response executed. The mitochondria are key regulatory nodes of cellular fate. For example, mitochondria undergo fission and fusion cycles depending on the energetic needs of the cell. Additionally, regulated cell death pathways also function at the mitochondria. Cyclin C is a transcriptional regulator of stress response and growth control genes. Following stress, a portion of cyclin C translocates to the cytoplasm, where it interacts with both the mitochondrial fission and apoptotic machinery. Based on these findings, we hypothesize that cyclin C represents a key mediator linking transcription to mitochondrial fission and intrinsic regulated cell death. Members of the cyclin protein family typically contain two conserved cyclin box domains each composed of five alpha‐helices. The N‐terminal cyclin box domain, CB1, is responsible for binding cyclin‐dependent kinases (Cdks), while the C‐terminal cyclin box, CB2, typically facilitates correct protein folding. For cyclin C, CB1 and CB2 mediate protein‐protein interactions with regards to transcriptional regulation (Cdk8) and mitochondrial fission (Drp1), respectively. Pull down assays show that pro‐apoptotic protein Bax interacts in a direct manner with cyclin C and this interaction is required for Bax activation and ultimately efficient intrinsic regulated cell death. Using the docking simulator ClusPro, cyclin C and Bax yielded consistent protein‐protein interaction sites within amino acids 160‐170 of cyclin C. Important residues in order of decreasing frequency include: Q161, D165, E169, Y162, Q164, and D170. Six sets of docking simulations yielded identical results, using multiple cyclin C and Bax protein data bank file combinations as a means of accounting for potential differences in the starting .pdb files. Inspection of this region in cyclin C shows a high degree of homology to the BH2 domain found in members of the Bcl‐2 protein family, such as Bax, Bak, and Bcl2. These data suggest that cyclin C possesses a BH2 domain directing Bax interaction and intrinsic regulated cell death. Interestingly, CB2 is also responsible for binding the fission protein Drp1. However preliminary modeling data suggest that this interaction is mediated by the latter half of CB2. Prior work also has demonstrated that while the BH2 domain is required for Bax binding, it is not required for interaction with Drp1. Co‐immunoprecipitation assays show Drp1 is required for cyclin C‐Bax interaction, suggesting a role for the interaction of all three proteins. Taken together, these results suggest a model that cyclin C physically bridges the fission and apoptotic machinery allowing the cell to coordinate mitochondrial dynamics with programmed cell death pathways.

Development of PDB cleaning script for charmm GUI error reduction

Protein Data Bank files (PDBs) are the main inputs for molecular dynamics simulations. Because they can come from multiple sources (RCSB, OPM, self-made), there are often errors in the formatting of these files. These mis-formatted PDBs make it impossible for some CHARMM-GUI users to simulate their desired molecules. To alleviate the errors that arise from mis-formatted PDBs, a stand-alone python script is in development. The most common errors occur in the connectivity section (CONECT) and the atom list.

Molecular Docking of 6-shogaol and Curcumin on DNMT1 and LSD1 As Potential Agents for Thalassemia Treatment

<p>Beta-thalassemia therapy is developed by increasing γ-globin production which binds to α-globin to form haemoglobin fetal (HbF). Meanwhile, DNA methyltransferase 1 (DNMT1) and lysine specific demethylase 1 (LSD1) play an important role in silencing the <em>HbF</em> gene by inhibiting the production of HbF and inducing haemoglobin subunit alpha (HbA) expression. 6-Shogaol and curcumin induce HbF by inhibiting signal transducer and activator of transcription 3 (STAT3) expression. Therefore, this study predicts the interaction between 6-shogaol and curcumin on DNMT1 and LSD1. The protein structure of DNMT1 (3SWR) and LSD1 (6KGP) was prepared by removing the water molecules, while the validation step was performed by separating protein from native ligands (sinefungin for 3SWR and flavine-adenine dinucleotide (FAD) for 6KGP) in new protein data bank files. Furthermore, the protein was docked with a native ligand to obtain grid box coordinates, while the root means standard deviation (RMSD) was calculated from the conformation results of the validation process. 6-Shogaol and curcumin were docked with coordinates of the validation results, and the best conformation was visualized with Discovery Studio. The validation step results in the RMSD value of 0.861Å and 1.410Å for DNMT1 and LSD1, respectively. The binding affinity of 6-shogaol and curcumin on DNMT1 was -6.5 kcal/mol and -8.0 kcal/mol, respectively. Furthermore, the binding affinity of 6-shogaol and curcumin on LSD1 was -8.2 kcal/mol and -10.1 kcal/mol, respectively. Amino acid residues found in DNMT1 interaction include Gly1147, Phe1145, Glu1168, Asn1278, Pro1225, Leu1151, Val1580, Ala1579, Asn1578, Trp1170, and Ala1579; meanwhile, Val288, Ser289, Arg310, Gly285, Thr624, Leu659, Lys661, Arg316, Leu625, Tyr761, Trp751, Gly330, and Leu659 were found in LSD1. This study showed that curcumin has the potential to inhibit DNMT1 as well as LSD1 proven by lower bonding energy and stronger bond types compared to sinefungin and FAD native ligands and other DNMT1 and LSD1 inhibitors.</p>