Visualization Of Protein Structures Research Articles

A Tool to Teach Evolution of Protein Sequences and Structures

Computer modeling and protein structure visualization tools are effective and engaging ways of presenting various molecular biology concepts to high school and college students. Here, we describe a series of activities and exercises that use online bioinformatics databases and programs to search for and obtain protein sequence and structure data and use it to build homology models of proteins. Exercises in homology modeling can serve the pedagogical purpose of introducing and illustrating the concept of homology within gene and protein families, which results in conservation of the 3D structures of proteins and allows us to predict structures when experimental data are not available.

In Silico Analysis of Single Nucleotide Polymorphisms Related to Susceptibility and Severity in COVID-19 Patients

Coronavirus disease 2019 (COVID-19) is a respiratory tract symptoms caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which exhibits a wide range of symptoms, from mild to severe. Genetic factors play a significant role in determining severity of the symptoms. Specifically, certain variants within genes can predispose individuals to experience severe COVID-19 symptoms. Therefore, this study aims to analyze these variants, also known as single nucleotide polymorphisms (SNP) within the ABO, TMPRSS2, ACE2, PAI-1, and IFNAR2 genes. The in silico method was used based on internet sites and software to identify the level of SNP pathogenicity, stability, visualization of 3-dimensional protein structures, and figures of amino acid changes as well as to analyze the pathomechanism of variants that lead to susceptibility or severe symptoms of COVID-19 patients. The results showed that the presence of SNP influences changes in the structure or stability of proteins, and the 3-dimensional structure of all proteins affected by SNP was successfully visualized. Based on pathomechanism analysis and amino acid structure of F216I and P74S protein ABO residues, V160M protein TMPRSS2, I468V, and K26R protein ACE2 are associated with susceptibility of the patient to SARS-CoV-2 infection. In addition, the results of the in silico analysis showed that A15T residues of PAI-1 protein, F10V, and F8S IFNAR2 protein were included with pathomechanisms leading to severe symptoms of COVID-19.

In silico analysis of bioactive peptide of goat milk based on beta-casein protein

The composition, interactions, and properties of bioactive peptides found in the most abundant milk protein, CSN2, are the focus of goat milk peptidomics. The peptidome of goat milk is regarded as a valuable source of a large number of biologically active peptides as well as health-promoting activities. Through data mining for bioactive peptide selection associated with current bioactive peptide data algorithms resulting in bioactive peptide profiles and visualization of three-dimensional protein structure, in silico analysis on bioinformatic approaches has led to enhancements of global knowledge regarding the health benefits of dairy products. Biological potentials for cardiovascular and nervous diseases, as well as metabolic and immune treatments, are identified using these techniques, in order to increase the commercialization of goat milk bioactive peptides as a functional food and promising natural source treatments.

NGS Analysis to Detect Mutation in Brain Tumor Diagnostic

Abstract: This study presents an integrated computational approach for analyzing protein sequences and their 3D structures. By leveraging the MMDB Macromolecular database, homologs of a protein sequence of interest are identified, and interactive visualization of their structural properties is provided. The computational alignment method, using BLASTP, allows for efficient determination of sequence similarities and identification of conserved regions among multiple protein sequences. COBALT is employed to refine sequence alignments and facilitate graphical analysis of sequence relationships. RASMOL, a computational analysis program, generates 2-D representations of protein-ligand complexes, enabling visual exploration of their interactions. ORF finder is used to identify coding regions in mRNA sequences, aiding in the prediction of protein-coding regions. The approach is applied to brain tumor diagnostics using human biological samples, exploring the structural properties of brain tumor-related proteins with the help of the 2RHU protein structure and PYMOL visualization software. Overall, this integrated computational framework offers a comprehensive toolkit for protein sequence analysis, structure visualization, and homology modeling, with potential applications in drug discovery, molecular biology, and medical diagnostics

Progress toward Proteome-Wide Photo-Cross-Linking to Enable Residue-Level Visualization of Protein Structures and Networks In Vivo.

Cross-linking mass spectrometry (XL-MS) is emerging as a method at the crossroads of structural and cellular biology, uniquely capable of identifying protein-protein interactions with residue-level resolution and on the proteome-wide scale. With the development of cross-linkers that can form linkages inside cells and easily cleave during fragmentation on the mass spectrometer (MS-cleavable cross-links), it has become increasingly facile to identify contacts between any two proteins in complex samples, including in live cells or tissues. Photo-cross-linkers possess the advantages of high temporal resolution and high reactivity, thereby engaging all residue-types (rather than just lysine); nevertheless, photo-cross-linkers have not enjoyed widespread use and are yet to be employed for proteome-wide studies because their products are challenging to identify. Here, we demonstrate the synthesis and application of two heterobifunctional photo-cross-linkers that feature diazirines and N-hydroxy-succinimidyl carbamate groups, the latter of which unveil doubly fissile MS-cleavable linkages upon acyl transfer to protein targets. Moreover, these cross-linkers demonstrate high water-solubility and cell-permeability. Using these compounds, we demonstrate the feasibility of proteome-wide photo-cross-linking in cellulo. These studies elucidate a small portion of Escherichia coli's interaction network, albeit with residue-level resolution. With further optimization, these methods will enable the detection of protein quinary interaction networks in their native environment at residue-level resolution, and we expect that they will prove useful toward the effort to explore the molecular sociology of the cell.

Molecular Docking: Bioactive Compounds in Indramayu Mango (Mangifera indica L.) Peel Waste as NS5B Hepatitis C Virus (HCV) Inhibitor

Background: Hepatitis C is caused by hepatitis C virus (HCV) infection. HCV infection is one of the biggest causes of chronic liver disease. About 60-80% of patients with acute hepatitis C will develop chronic hepatitis C. Objective: This study aimed to analyze the potential of mango peel compounds as HCV NS5B inhibitors. Methods: The methods in this study are ligand preparation, physicochemical and pharmacokinetic predictions, protein structure preparation, molecular docking, data analysis, and visualization. Results: The results showed that the test ligands had binding free energies close to the reference ligands, namely Mangiferin -7.862 kcal/mol and respectively D-(+)-Maltose -6.453 kcal/mol, Dibutyl – phthalate -6.326 kcal/mol, bis-β-D-fructofuranose 1,2':2,3'-dianhydride -6.249 kcal/mol, 16-Heptadecyne-1,2,4-triol -5.476 kcal/mol, 3,4,5-trihydroxycyclohex-1-ene-1-carboxylic acid -5,360 kcal/mol, Trigonelline -4.905 kcal/mol, Hexitol -4.552 kcal/mol, α-Glucoheptitol -4.403 kcal/mol. All the test ligands bind the NS5B active site with hydrogen bonds. Furthermore, the ligand-receptor complex has a dissociation constant value and hydrogen bond length. Conclusion: The results showed that Mangiferin was the most potential ligand in inhibiting NS5B HCV of all the test ligands used.

Identification of Uncharacterized Plasmodium falciparum Proteins via In-silico Analysis

Background: Numerous types of research on malaria were done over a long period of time but there are still some unknowns. However, it is globally known that malaria is caused by the Plasmodium parasite, mainly and most lethally by Plasmodium falciparum. The purpose of this research is to understand the structure and function of three uncharacterized P. falciparum proteins (PF3D7_1468000, PF3D7_1147400, PF3D7_1351100) using bioinformatic methods in hopes to learn more about malaria. Methods: The three uncharacterized P. falciparum proteins were inserted into Phyre2 for knowing the protein homology, InterPro, and SUPERFAMILY hidden Markov models for understanding the domain annotation, scanprosite for knowing the post-translational modification, Ramachandran plot for protein validation, and Yasara for visualizing the protein. Results: According to the Phyre2 results, the third protein showed the highest confidence and coverage level of 100%, followed by the second protein, and the lowest was the first protein. Interpro and SUPERFAMILY results identified the first protein as WD40 repeat superfamily, the second protein as Cytochrome C subunit II-like, and the third protein as CXXC motif. Scanprosite revealed all sequences possessing protein domains in which the first protein has three protein domains, the second protein has one protein domain, and the third protein has two protein domains. According to the Ramachandran plot, the first and second protein generally has an α-helix structure while the third protein has an overall β-sheet structure, which differs to some extent from the protein structure visualization. The three protein visualizations exhibited secondary structures and more than 50 amino acid residues for each protein. Conclusion: This research concluded that the second and third uncharacterized proteins (PF3D7_1147400, PF3D7_1351100) could be promising antimalarial drug targets leading to the P. falciparum parasite death.

PRRGNVis: Multi-Level Visual Analysis of Comparison for Predicted Results of Recurrent Geometric Network

The structure of a protein determines its function, and the advancement of machine learning has led to the rapid development of protein structure prediction. Protein structure comparison is crucial for inferring the evolutionary relationship of proteins, drug discovery, and protein design. In this paper, we propose a multi-level visual analysis method to improve the protein structure comparison between predicted and actual structures. Our method takes the predicted results of the Recurrent Geometric Network (RGN) as the main research object and is mainly designed following three levels of protein structure visualization on RGN. Firstly, at the prediction accuracy level of the RGN, we use the Global Distance Test—Total Score (GDT_TS) as the evaluation standard, then compare it with distance-based root mean square deviation (dRMSD) and Template Modeling Score (TM-Score) to analyze the prediction characteristics of the RGN. Secondly, the distance deviation, torsion angle, and other attributes are used to analyze the difference between the predicted structure and the actual structure at the structural similarity level. Next, at the structural stability level, the Ramachandran Plot and PictorialBar combine to be improved to detect the quality of the predicted structure and analyze whether the amino acid residues conform to the theoretical configuration. Finally, we interactively analyze the characteristics of the RGN with the above visualization effects and give reasons and reasonable suggestions. By case studies, we demonstrate that our method is effective and can also be used to analyze other predictive network results.

RaacFold: a webserver for 3D visualization and analysis of protein structure by using reduced amino acid alphabets.

Protein structure exhibits greater complexity and diversity than DNA structure, and usually affects the interpretation of the function, interactions and biological annotations. Reduced amino acid alphabets (Raaa) exhibit a powerful ability to decrease protein complexity and identify functional conserved regions, which motivated us to create RaacFold. The RaacFold provides 687 reduced amino acid clusters (Raac) based on 58 reduction methods and offers three analysis tools: Protein Analysis, Align Analysis, and Multi Analysis. The Protein Analysis and Align Analysis provide reduced representations of sequence-structure according to physicochemical similarities and computational biology strategies. With the simplified representations, the protein structure can be viewed more concise and clearer to capture biological insight than the unreduced structure. Thus, the design of artificial protein will be more convenient, and redundant interference is avoided. In addition, Multi Analysis allows users to explore biophysical variation and conservation in the evolution of protein structure and function. This supplies important information for the identification and exploration of the nonhomologous functions of paralogs. Simultaneously, RaacFold provides powerful 2D and 3D rendering performance with advanced parameters for sequences, structures, and related annotations. RaacFold is freely available at http://bioinfor.imu.edu.cn/raacfold.

Using 3D Printing to Make Models for Visualization of Protein Structure

Using 3D Printing to Make Models for Visualization of Protein Structure

Discovering novel mechanisms of taxane resistance in human breast cancer by whole-exome sequencing.

Taxanes are important drugs used in the treatment of breast cancer; however, some cancer types are taxane-resistant. The aim of the present study was to investigate the underlying mechanisms of taxane resistance using whole-exome sequencing (WES). Six patients with breast cancer whose tumors responded well to anthracycline treatment but grew rapidly during neoadjuvant taxane-based chemotherapy, were included in the present study. WES of samples from these patients was carried out to identify somatic mutations of candidate genes thought to affect taxane resistance, and the candidate proteins were structurally modeled. The mRNA and protein expression levels of these candidate genes in other breast cancers treated with taxanes were also examined. Nine variants common to all six patients were identified and two of these [R552P in V-type proton ATPase catalytic subunit A (ATP6V1A) and T114P in apolipoprotein B MRNA editing enzyme catalytic subunit 3F (APOBEC3F)] were selected. The results also showed that, protein-structure visualization suggested that these mutations may cause structural changes. The Kaplan-Meier analyses revealed that higher APT6V1A and APOBEC3F expression levels were significantly associated with poorer disease-free survival (DFS) and overall survival. Moreover, multivariate analysis identified high ATP6V1A mRNA expression as an independent risk factor for poor DFS. Two specific mutations that might affect taxane resistance were identified. Thus, these results suggest that breast cancer patients receiving taxanes who have high ATP6V1A or APOBEC3F expression levels may have shorter survival.

RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB.

MotivationMembrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task.ResultsWe present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D.Availability and implementationAnnotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar).Supplementary information Supplementary data are available at Bioinformatics online.

Identification of intact peptides by top-down peptidomics reveals cleavage spots in thermolabile wine proteins

Prevention of haze formation in wines is challenging for winemakers. Thermolabile proteins in wines, notably thaumatin-like proteins (TLPs) and chitinases (CHIs), undergo structural changes under varying physicochemical conditions, resulting in protein aggregation and visible haze in bottled products. Peptidases are an alternative fining method, although an effective proteolysis under typical winemaking conditions (acidic pH and low temperature) is difficult to achieve. In this study, tryptic peptides from TLPs and CHIs were identified by MS-based peptidomics (top-down proteomics) after exposure of scissile bonds on the protein surface. As proposed by the theory of limited proteolysis, protein conformational changes following temperature and pH variations allowed the detection of enzyme-accessible regions. Protein structure visualization and molecular dynamics simulations were used to highlight cleavage spots and provide the scientific basis for haze formation mechanisms. The described method offers a tool to the search for ideal enzymes to prevent wine haze.

CheSPI: chemical shift secondary structure population inference.

NMR chemical shifts (CSs) are delicate reporters of local protein structure, and recent advances in random coil CS (RCCS) prediction and interpretation now offer the compelling prospect of inferring small populations of structure from small deviations from RCCSs. Here, we present CheSPI, a simple and efficient method that provides unbiased and sensitive aggregate measures of local structure and disorder. It is demonstrated that CheSPI can predict even very small amounts of residual structure and robustly delineate subtle differences into four structural classes for intrinsically disordered proteins. For structured regions and proteins, CheSPI provides predictions for up to eight structural classes, which coincide with the well-known DSSP classification. The program is freely available, and can either be invoked from URL www.protein-nmr.org as a web implementation, or run locally from command line as a python program. CheSPI generates comprehensive numeric and graphical output for intuitive annotation and visualization of protein structures. A number of examples are provided.

Missense RHD single nucleotide variants induce weakened D antigen expression by altering splicing and/or protein expression.

Although D variant phenotype is known to be due to genetic defects, including rare missense single nucleotide variants (SNVs), within the RHD gene, few studies have addressed the molecular and cellular mechanisms driving this altered expression. We and others showed previously that splicing is commonly disrupted by SNVs in constitutive splice sites and their vicinity. We thus sought to investigate whether rare missense SNVs located in "deep" exonic regions could also impair this mechanism. Forty-six missense SNVs reported within exons 6 and 7 were first selected from the Human RhesusBase. Their respective effect on splicing was assessed by using an in vitro assay. An RhD-negative cell model was further generated by using the CRISPR-Cas9 approach. RhD-mutated proteins were overexpressed in the newly created model, and cell membrane expression of the D antigen was measured by flow cytometry. Minigene splicing assay showed that 14 of 46 (30.4%) missense SNVs alter splicing. Very interestingly, further investigation of two missense SNVs, which both affect codon 338 and confer a weak D phenotype, showed various mechanisms: c.1012C>G (p.Leu338Val) disrupts splicing only, while c.1013T>C (p.Leu338Pro) alters only the protein structure, in agreement with in silico prediction tools and 3D protein structure visualization. Our functional data set suggests that missense SNVs damage quantitatively D antigen expression by, at least, two different mechanisms (splicing alteration and protein destabilization) that may act independently. These data thereby contribute to extend the current knowledge of the molecular mechanisms governing weakened D expression.

Crosslinking mass spectrometry: A link between structural biology and systems biology.

Protein structure underpins functional roles in all biological processes; therefore, improved understanding of protein structures is of fundamental importance in nearly all biological and biomedical research areas. Traditional techniques such as X-ray crystallography and more recently, cryo-EM, can reveal structural features on isolated proteins/protein complexes at atomic resolution level and have become indispensable tools for structural biology. Crosslinking mass spectrometry (XL-MS), on the other hand, is an emerging technique capable of capturing transient and dynamic information on protein interactions and assemblies in their native environment. The combination of XL-MS with traditional techniques holds potential for bridging the gap between structural biology and systems biology approaches. Such a combination will enable visualization of protein structures and interactions within the crowded macromolecular environment in living systems that can dramatically increase understanding of biological functions. In this review, we first discuss general strategies of XL-MS and then survey recent examples to show how qualitative and quantitative XL-MS studies can be integrated with available protein structural data to better understand biological function at systems level.

SWOTein: a structure-based approach to predict stability Strengths and Weaknesses of prOTEINs.

Although structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes. We developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale analysis of the different types of strengths and weaknesses demonstrated their complementarity and the enhancement of the information they provide. Moreover, a good average correlation was observed between predicted and experimental strengths and weaknesses obtained from native hydrogen exchange data. SWOTein application to three test cases further showed its suitability to predict and interpret strong and weak residues in the context of folding, conformational changes and protein-protein binding. In summary, SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes. The SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein/.

TactViz: A VMD Plugin for Tactile Visualization of Protein Structures

Scientific disciplines spanning biology, biochemistry, and biophysics involve the study of proteins and their functions. Visualization of protein structures represents a barrier to education and research in these disciplines for students who are blind or visually impaired. Here, we present a software plugin for readily producing variable-height tactile graphics of proteins using the free biomolecular visualization software Visual Molecular Dynamics (VMD) and protein structure data that is publicly available through the Protein Data Bank. Our method also supports interactive tactile visualization of proteins with VMD on electronic refreshable tactile display devices. Employing our method in an academic laboratory has enabled an undergraduate student who is blind to carry out research alongside her sighted peers. By making the study of protein structures accessible to students who are blind or visually impaired, we aim to promote diversity and inclusion in STEM education and research.

Visualization and Analysis of Protein Structures with LiteMol Suite.

LiteMol suite is an innovative solution that enables near-instant delivery of model and experimental biomacromolecular structural data, providing users with an interactive and responsive experience in all modern web browsers and mobile devices. LiteMol suite is a combination of data delivery services (CoordinateServer and DensityServer), compression format (BinaryCIF), and a molecular viewer (LiteMol Viewer). The LiteMol suite is integrated into Protein Data Bank in Europe (PDBe) and other life science web applications (e.g., UniProt, Ensemble, SIB, and CNRS services), it is freely available at https://litemol.org , and its source code is available via GitHub. LiteMol suite provides advanced functionality (annotations and their visualization, powerful selection features), and this chapter will describe their use for visual inspection of protein structures.

CASTp 3.0: computed atlas of surface topography of proteins.

Geometric and topological properties of protein structures, including surface pockets, interior cavities and cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins (CASTp) is a web server that provides online services for locating, delineating and measuring these geometric and topological properties of protein structures. It has been widely used since its inception in 2003. In this article, we present the latest version of the web server, CASTp 3.0. CASTp 3.0 continues to provide reliable and comprehensive identifications and quantifications of protein topography. In addition, it now provides: (i) imprints of the negative volumes of pockets, cavities and channels, (ii) topographic features of biological assemblies in the Protein Data Bank, (iii) improved visualization of protein structures and pockets, and (iv) more intuitive structural and annotated information, including information of secondary structure, functional sites, variant sites and other annotations of protein residues. The CASTp 3.0 web server is freely accessible at http://sts.bioe.uic.edu/castp/.