Database Of Protein Structures Research Articles

Detection of translational noncrystallographic symmetry in Patterson functions.

Detection of translational noncrystallographic symmetry (TNCS) can be critical for success in crystallographic phasing, particularly when molecular-replacement models are poor or anomalous phasing information is weak. If the correct TNCS is detected then expected intensity factors for each reflection can be refined, so that the maximum-likelihood functions underlying molecular replacement and single-wavelength anomalous dispersion use appropriate structure-factor normalization and variance terms. Here, an analysis of a curated database of protein structures from the Protein Data Bank to investigate how TNCS manifests in the Patterson function is described. These studies informed an algorithm for the detection of TNCS, which includes a method for detecting the number of vectors involved in any commensurate modulation (the TNCS order). The algorithm generates a ranked list of possible TNCS associations in the asymmetric unit for exploration during structure solution.

Solvent Accessibility of Residues Undergoing Pathogenic Variations in Humans: From Protein Structures to Protein Sequences.

Solvent accessibility (SASA) is a key feature of proteins for determining their folding and stability. SASA is computed from protein structures with different algorithms, and from protein sequences with machine-learning based approaches trained on solved structures. Here we ask the question as to which extent solvent exposure of residues can be associated to the pathogenicity of the variation. By this, SASA of the wild-type residue acquires a role in the context of functional annotation of protein single-residue variations (SRVs). By mapping variations on a curated database of human protein structures, we found that residues targeted by disease related SRVs are less accessible to solvent than residues involved in polymorphisms. The disease association is not evenly distributed among the different residue types: SRVs targeting glycine, tryptophan, tyrosine, and cysteine are more frequently disease associated than others. For all residues, the proportion of disease related SRVs largely increases when the wild-type residue is buried and decreases when it is exposed. The extent of the increase depends on the residue type. With the aid of an in house developed predictor, based on a deep learning procedure and performing at the state-of-the-art, we are able to confirm the above tendency by analyzing a large data set of residues subjected to variations and occurring in some 12,494 human protein sequences still lacking three-dimensional structure (derived from HUMSAVAR). Our data support the notion that surface accessible area is a distinguished property of residues that undergo variation and that pathogenicity is more frequently associated to the buried property than to the exposed one.

Functional and protective hole hopping in metalloenzymes.

Electrons can tunnel through proteins in microseconds with a modest release of free energy over distances in the 15 to 20 Å range. To span greater distances, or to move faster, multiple charge transfers (hops) are required. When one of the reactants is a strong oxidant, it is convenient to consider the movement of a positively charged “hole” in a direction opposite to that of the electron. Hole hopping along chains of tryptophan (Trp) and tyrosine (Tyr) residues is a critical function in several metalloenzymes that generate high-potential intermediates by reactions with O2 or H2O2, or by activation with visible light. Examination of the protein structural database revealed that Tyr/Trp chains are common protein structural elements, particularly among enzymes that react with O2 and H2O2. In many cases these chains may serve a protective role in metalloenzymes by deactivating high-potential reactive intermediates formed in uncoupled catalytic turnover.

DescribePROT: database of amino acid-level protein structure and function predictions.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Ab-Initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks.

Amphipathic helix (AH)features the segregation of polar and nonpolar residues and plays important roles in many membrane-associated biological processes through interacting with both the lipid and the soluble phases. Although the AH structure has been discovered for a long time, few ab initio machine learning-based prediction models have been reported, due to the limited amount of training data. In this study, we report a new deep learning-based prediction model, which is composed of a residual neural network and the uneven-thresholds decision algorithm. It is constructed on 121 membrane proteins, in total 51640 residue samples, which are curated from an up-to-date membrane protein structure database. Through a rigid 10-fold nested cross-validation experiment, we demonstrate that our model can achieve promising predictions and exceed current state-of-the-art approaches in this field. This presents a new avenue for accurately predicting AHs. Analysis on the contribution of the input residues and some cases further reveals the high interpretability and the generalization of our model.

CoV3D: a database of high resolution coronavirus protein structures.

SARS-CoV-2, the etiologic agent of COVID-19, exemplifies the general threat to global health posed by coronaviruses. The urgent need for effective vaccines and therapies is leading to a rapid rise in the number of high resolution structures of SARS-CoV-2 proteins that collectively reveal a map of virus vulnerabilities. To assist structure-based design of vaccines and therapeutics against SARS-CoV-2 and other coronaviruses, we have developed CoV3D, a database and resource for coronavirus protein structures, which is updated on a weekly basis. CoV3D provides users with comprehensive sets of structures of coronavirus proteins and their complexes with antibodies, receptors, and small molecules. Integrated molecular viewers allow users to visualize structures of the spike glycoprotein, which is the major target of neutralizing antibodies and vaccine design efforts, as well as sets of spike-antibody complexes, spike sequence variability, and known polymorphisms. In order to aid structure-based design and analysis of the spike glycoprotein, CoV3D permits visualization and download of spike structures with modeled N-glycosylation at known glycan sites, and contains structure-based classification of spike conformations, generated by unsupervised clustering. CoV3D can serve the research community as a centralized reference and resource for spike and other coronavirus protein structures, and is available at: https://cov3d.ibbr.umd.edu.

In-silico analysis and homology modelling of antioxidant proteins present in Pisum sativum

Garden Peas are edible seeds that have an essential nutrient required for human diet. Peas contain phytochemicals that shows antioxidant activities. A computational approach was adopted to analyse the characteristic properties and structure of the Peas antioxidant proteins. Superoxidase dismutase (SOD), catalase, phospholipid hydroperoxide g lutathione p eroxidase-like p rotein (PHGPX) and ascorbate peroxidase (APX) are the antioxidant proteins which have been used in this study. Physiochemical properties such as isoelectric point, molecular weight, aliphatic index etc. were analysed by using the Expasy's Prot Param server. Secondary structure of these proteins was analysed to study the functional characteristic of the protein. The crystal structure of the ascorbate peroxidase was available, but the crystal structures of other proteins were not available in any of the protein structure database. Hence the threedimensional structures of these proteins were generated using Swiss Modeller and Geno3D followed by validation through SAVES server. This analysis will provide additional help for the analysis of crystal structure for further experimentation.

ProdaMatch: A fast and accurate active site matching algorithm for de novo enzyme design

Developing enzymes with high catalytic activity solely based on computation is still a great challenge in de novo enzyme design. Based on the complex active site model in which the catalytic residues, in addition to interacting with the transition state, impose geometrical constraints on each other, a fast and accurate active-site matching algorithm ProdaMatch was developed to search the whole protein structure database and create fully preorganized active sites in selected scaffolds. The current version of ProdaMatch runs ten times as fast as previous versions while maintaining sub-angstrom matching accuracy for transition states and 90% of the catalytic residues based on tests on the active site recapitulation of 10 enzyme-transition state complex structures. The ProdaMatch method was validated computationally using parallel computation to identify scaffolds from a structure database for Kemp elimination and create fully connected active sites in the scaffolds. The catalytic residues were anchored in geometrically compatible orientations in the scaffolds, and the transition states were positioned in catalytically competent conformations. ProdaMatch can help create fully preorganized enzymes for the de novo design of enzymes with high starting catalytic activities.

GalaxySagittarius: Structure- and Similarity-Based Prediction of Protein Targets for Druglike Compounds

Computational techniques for predicting interactions of proteins and druglike molecules have often been used to search for compounds that bind a given protein with high affinity. More recently, such tools have also been applied to the reverse procedure of searching protein targets for a given compound. Among methods for predicting protein-ligand interactions, ligand-based methods relying on similarity to ligands of known interactions are effective only when similar protein-ligand interactions are known. Receptor-based methods predicting protein-ligand interactions by molecular docking are effective only when high-accuracy receptor structures and binding sites are available. Moreover, the computational cost of molecular docking tends to be too high to be applied to the entire protein structure database. In this paper, an effective target prediction method, which combines ligand similarity-based and receptor structure-based approaches, is introduced. In this method, protein-ligand docking is performed after efficient structure- and similarity-based screening. The enriched protein target database by predicted binding ligands and sites allows detection of protein targets with previously unknown ligand interactions. The method, called GalaxySagittarius, is freely available as a web server at http://galaxy.seoklab.org/sagittarius.

Analyses of protein cores reveal fundamental differences between solution and crystal structures.

There have been several studies suggesting that protein structures solved by NMR spectroscopy and X-ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high-quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures-in the root-mean-square deviations of the C α atomic positions, identities of core amino acids, backbone, and side-chain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modeling protein cores as jammed packings of amino acid-shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by X-ray crystallography. In contrast, highly thermalized packing-generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and X-ray crystallography.

Bacterial Origin and Reductive Evolution of the CPR Group.

The candidate phyla radiation (CPR) is a proposed subdivision within the bacterial domain comprising several candidate phyla. CPR organisms are united by small genome and physical sizes, lack several metabolic enzymes, and populate deep branches within the bacterial subtree of life. These features raise intriguing questions regarding their origin and mode of evolution. In this study, we performed a comparative and phylogenomic analysis to investigate CPR origin and evolution. Unlike previous gene/protein sequence-based reports of CPR evolution, we used protein domain superfamilies classified by protein structure databases to resolve the evolutionary relationships of CPR with non-CPR bacteria, Archaea, Eukarya, and viruses. Across all supergroups, CPR shared maximum superfamilies with non-CPR bacteria and were placed as deep branching bacteria in most phylogenomic trees. CPR contributed 1.22% of new superfamilies to bacteria including the ribosomal protein L19e and encoded four core superfamilies that are likely involved in cell-to-cell interaction and establishing episymbiotic lifestyles. Although CPR and non-CPR bacterial proteomes gained common superfamilies over the course of evolution, CPR and Archaea had more common losses. These losses mostly involved metabolic superfamilies. In fact, phylogenies built from only metabolic protein superfamilies separated CPR and non-CPR bacteria. These findings indicate that CPR are bacterial organisms that have probably evolved in an Archaea-like manner via the early loss of metabolic functions. We also discovered that phylogenies built from metabolic and informational superfamilies gave contrasting views of the groupings among Archaea, Bacteria, and Eukarya, which add to the current debate on the evolutionary relationships among superkingdoms.

Directed Evolution of Structural Proteins using a High Throughput Approach

Design of novel de novo structural proteins from their primary amino acid sequence is an unsolved scientific problem. Moreover, most techniques that are used for predicting the protein structure rely on globular protein-structure databases and are rarely applicable to structural-protein assemblies. Experimental characterization for this class of proteins often employs spectroscopic techniques including but not limited to Fourier transform infrared spectroscopy (FTIR), X-ray crystallography, Raman spectroscopy, or light scattering. Furthermore, existing high-throughput fluorescence and affinity readouts are structure-specific and may miss materials with novel molecular morphologies. We developed a network model based on entropic elasticity to predict structure-property relationship for proteins, and studied the structure-property relationship of these engineered proteins using ultrafast laser-probing spectroscopy. We performed time domain transient transmissivity experiments using purified recombinant structural proteins, which clearly showed “proof of concept” quantification of protein properties in real time. This technique will help us to identify structural proteins that have the ability to assemble and form cross-linked biomaterials.

De novo protein design, a retrospective.

Proteins are molecular machines whose function depends on their ability to achieve complex folds with precisely defined structural and dynamic properties. The rational design of proteins from first-principles, or de novo, was once considered to be impossible, but today proteins with a variety of folds and functions have been realized. We review the evolution of the field from its earliest days, placing particular emphasis on how this endeavor has illuminated our understanding of the principles underlying the folding and function of natural proteins, and is informing the design of macromolecules with unprecedented structures and properties. An initial set of milestones in de novo protein design focused on the construction of sequences that folded in water and membranes to adopt folded conformations. The first proteins were designed from first-principles using very simple physical models. As computers became more powerful, the use of the rotamer approximation allowed one to discover amino acid sequences that stabilize the desired fold. As the crystallographic database of protein structures expanded in subsequent years, it became possible to construct proteins by assembling short backbone fragments that frequently recur in Nature. The second set of milestones in de novo design involves the discovery of complex functions. Proteins have been designed to bind a variety of metals, porphyrins, and other cofactors. The design of proteins that catalyze hydrolysis and oxygen-dependent reactions has progressed significantly. However, de novo design of catalysts for energetically demanding reactions, or even proteins that bind with high affinity and specificity to highly functionalized complex polar molecules remains an importnant challenge that is now being achieved. Finally, the protein design contributed significantly to our understanding of membrane protein folding and transport of ions across membranes. The area of membrane protein design, or more generally of biomimetic polymers that function in mixed or non-aqueous environments, is now becoming increasingly possible.

Facilities that make the PDB data collection more powerful.

We describe a series of databases and tools that directly or indirectly support biomedical research on macromolecules, with focus on their applicability in protein structure bioinformatics research. DSSP, that determines secondary structures of proteins, has been updated to work well with extremely large structures in multiple formats. The PDBREPORT database that lists anomalies in protein structures has been remade to remove many small problems. These reports are now available as PDF‐formatted files with a computer‐readable summary. The VASE software has been added to analyze and visualize HSSP multiple sequence alignments for protein structures. The Lists collection of databases has been extended with a series of databases, most noticeably with a database that gives each protein structure a grade for usefulness in protein structure bioinformatics projects. The PDB‐REDO collection of reanalyzed and re‐refined protein structures that were solved by X‐ray crystallography has been improved by dealing better with sugar residues and with hydrogen bonds, and adding many missing surface loops. All academic software underlying these protein structure bioinformatics applications and databases are now publicly accessible, either directly from the authors or from the GitHub software repository.

Adding Function to Protein Scaffolds.

Biological systems often outperform artificial ones in ordering, assembly, and diversity of structure at the nanoscale. Proteins are particularly remarkable in this context. Through oligomerization, protein monomers assemble on multiple length scales, into larger and more complex structures such as viral capsids, filaments, and regulatory complexes. It is this structural diversity that makes proteins attractive candidates for use as functionalizable scaffolds. Well-established protein structure databases such as the protein data bank (PDB) allow researchers to search for a structure that fits their requirements, allowing them access to shapes and assembly mechanisms that would otherwise be difficult to achieve. Then, by employing functionalization techniques to conjugate artificial or biological molecules to their protein scaffold of choice, researchers can access chemistries beyond the limits of the 20 commonly occurring natural amino acids. Additionally, proteins, with a few exceptions, operate at physiological pH and temperature, making them ideal for medical applications and/or low-cost manufacture. Additionally, proteins sourced from extremophiles such as Thermus aquaticus (a bacterial species sourced from hot springs) display stability across a wide range of temperatures, expanding the scope for scaffold selection. This chapter will cover some of the common methods of protein functionalization as well as some specific examples of popular functionalization methods reported in the literature. It will then present three case studies showing examples of how functionalization and imaging of proteins and protein-based structures can be achieved.

Advances in protein structure prediction and design.

The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.

Using Bioinformatics to Understand Genetic Diseases

This Practical Guide in the Bringing Bioinformatics into the Classroom series outlines a number of basic bioinformatics approaches that can be used to understand the molecular basis of genetic diseases. A rare variation in the insulin gene is discussed, and the impact of the variation on the gene product, and how this results in disease, is explored. Specifically, this Guide introduces a range of commonly used bioinformatics tools and databases with which to analyse both DNA and protein sequences, and protein structures. On reading the Guide and completing the exercises, users will be able to: investigate the locations of genes on the human genome using a genome browser; compare DNA sequences using an alignment tool; translate a DNA sequence into its protein product; search a protein structure database, and visualise the 3D structure of insulin; and infer whether insulin is specific to humans by using protein sequence database search tools.

Discovering Putative Protein Targets of Small Molecules: A Study of the p53 Activator Nutlin.

Small molecule drugs bind to a pocket in disease causing target proteins based on complementarity in shape and physicochemical properties. There is a likelihood that other proteins could have binding sites that are structurally similar to the target protein. Binding to these other proteins could alter their activities leading to off target effects of the drug. One such small molecule drug Nutlin binds the protein MDM2, which is upregulated in several types of cancer and is a negative regulator of the tumor suppressor protein p53. To investigate the off target effects of Nutlin, we present here a shape-based data mining effort. We extracted the binding pocket of Nutlin from the crystal structure of Nutlin bound MDM2. We next mined the protein structural database (PDB) for putative binding pockets in other human protein structures that were similar in shape to the Nutlin pocket in MDM2 using our topology-independent structural superimposition tool CLICK. We detected 49 proteins which have binding pockets that were structurally similar to the Nutlin binding site of MDM2. All of the potential complexes were evaluated using molecular mechanics and AutoDock Vina docking scores. Further, molecular dynamics simulations were carried out on four of the predicted Nutlin-protein complexes. The binding of Nutlin to one of these proteins, gamma glutamyl hydrolase, was also experimentally validated by a thermal shift assay. These findings provide a platform for identifying potential off-target effects of existing/new drugs and also opens the possibilities for repurposing drugs/ligands.

Emerging Diversity in Lipid-Protein Interactions.

Membrane lipids interact with proteins in a variety of ways, ranging from providing a stable membrane environment for proteins to being embedded in to detailed roles in complicated and well-regulated protein functions. Experimental and computational advances are converging in a rapidly expanding research area of lipid–protein interactions. Experimentally, the database of high-resolution membrane protein structures is growing, as are capabilities to identify the complex lipid composition of different membranes, to probe the challenging time and length scales of lipid–protein interactions, and to link lipid–protein interactions to protein function in a variety of proteins. Computationally, more accurate membrane models and more powerful computers now enable a detailed look at lipid–protein interactions and increasing overlap with experimental observations for validation and joint interpretation of simulation and experiment. Here we review papers that use computational approaches to study detailed lipid–protein interactions, together with brief experimental and physiological contexts, aiming at comprehensive coverage of simulation papers in the last five years. Overall, a complex picture of lipid–protein interactions emerges, through a range of mechanisms including modulation of the physical properties of the lipid environment, detailed chemical interactions between lipids and proteins, and key functional roles of very specific lipids binding to well-defined binding sites on proteins. Computationally, despite important limitations, molecular dynamics simulations with current computer power and theoretical models are now in an excellent position to answer detailed questions about lipid–protein interactions.

A fragment activity assay reveals the key residues of TBC1D15 GTPase-activating protein (GAP) in Chiloscyllium plagiosum

BackgroundGTPase-activating proteins (GAPs) with a TBC (Tre-2/Bub2/Cdc16) domain architecture serve as negative regulators of Rab GTPases. The related crystal structure has been studied and reported by other members of our research group in 2017 (Chen et al. in Protein Sci 26(4):834–846, 2017). The protein crystal structure and sequencing data accession numbers in Protein structure database (PDB) are 5TUB (Shark TBC1D15 GAP) and 5TUC (Sus TBC1D15 GAP), respectively. In this paper, we analyzed the Rab-GAP specificity of TBC1D15 in the evolution and influence of key amino acid residue mutations on Rab-GAP activity.ResultsSequence alignment showed that five arginine residues of the TBC1D15-GAP domain are conserved among the species Sus/Mus/Homo but have been replaced by glycine or lysine in Shark. A fragment activity assay was conducted by altering the five residues of Shark TBC1D15-GAP to arginine, and the corresponding arginine in TBC1D15 GAP domains from Sus and Homo species were mutated to resemble Shark TBC1D15-GAP. Our data revealed that the residues of G28, K45, K119, K122 and K221 in the Shark TBC1D15-GAP domain had a key role in determining the specificity for Rab7 and Rab11. Mutation of the five residues significantly altered the Shark TBC1D15-GAP activity.ConclusionsThese results revealed that the substrate specificity of TBC1D15 has had different mechanisms across the evolution of species from lower-cartilaginous fish to higher mammals. Collectively, the data support a different mechanism of Shark TBC1D15-GAP in substrate selection, which provides a new idea for the development of Marine drugs.