The Protein Data Bank archive as an open data resource

Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.

Ten recommendations for organising bioimaging data for archival

Organised data is easy to use but the rapid developments in the field of bioimaging, with improvements in instrumentation, detectors, software and experimental techniques, have resulted in an explosion of the volumes of data being generated, making well-organised data an elusive goal. This guide offers a handful of recommendations for bioimage depositors, analysts and microscope and software developers, whose implementation would contribute towards better organised data in preparation for archival. Based on our experience archiving large image datasets in EMPIAR, the BioImage Archive and BioStudies, we propose a number of strategies that we believe would improve the usability (clarity, orderliness, learnability, navigability, self-documentation, coherence and consistency of identifiers, accessibility, succinctness) of future data depositions more useful to the bioimaging community (data authors and analysts, researchers, clinicians, funders, collaborators, industry partners, hardware/software producers, journals, archive developers as well as interested but non-specialist users of bioimaging data). The recommendations that may also find use in other data-intensive disciplines. To facilitate the process of analysing data organisation, we present bandbox, a Python package that provides users with an assessment of their data by flagging potential issues, such as redundant directories or invalid characters in file or folder names, that should be addressed before archival. We offer these recommendations as a starting point and hope to engender more substantial conversations across and between the various data-rich communities.

lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation

Public proteomics data often lack essential metadata, limiting its potential. To address this, we present lesSDRF, a tool to simplify the process of metadata annotation, thereby ensuring that data leave a lasting, impactful legacy well beyond its initial publication.

Standardizing macromolecular structure files: further efforts are needed

Investigating large datasets of biological information by automatic procedures may offer chances of progress in knowledge. Recently, tremendous improvements in structural biology have allowed the number of structures in the Protein Data Bank (PDB) archive to increase rapidly, in particular those for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated proteins. However, their automatic analysis can be hampered by the nonuniform descriptors used by authors in some records of the PDB and PDBx/mmCIF files. In this opinion article we highlight the difficulties encountered in automating the analysis of hundreds of structures, suggesting that further standardization of the description of these molecular entities and of their attributes, generalized to the macromolecular structures contained in the PDB, might generate files more suitable for automatized analyses of a large number of structures.

Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps

Cryo-electron microscopy (cryo-EM) allows a macromolecular structure such as protein-DNA/RNA complexes to be reconstructed in a three-dimensional coulomb potential map. The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial-intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fitting residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https://deeptracer.uw.edu/.

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/ .

Computational investigation of functional water molecules in GPCRs bound to G protein or arrestin

G protein-coupled receptors (GPCRs) are membrane proteins constituting the largest family of drug targets. The activated GPCR binds either the heterotrimeric G proteins or arrestin through its activation cycle. Water molecules have been reported to play a role in GPCR activation. Nevertheless, reported studies are focused on the hydrophobic helical bundle region. How water molecules function in GPCR bound either G protein or arrestin is rarely studied. To address this issue, we carried out computational studies on water molecules in both GPCR/G protein complexes and GPCR/arrestin complexes. Using inhomogeneous fluid theory (IFT), we locate all possible hydration sites in GPCRs binding either to G protein or arrestin. We observe that the number of water molecules on the interaction surface between GPCRs and signal proteins are correlated with the insertion depths of the α5-helix from G-protein or "finger loop" from arrestin in GPCRs. In three out of the four simulation pairs, the interfaces of Rhodopsin, M₂R and NTSR1 in the G protein-associated systems show more water-mediated hydrogen-bond networks when compared to these in arrestin-associated systems. This reflects that more functionally relevant water molecules may probably be attracted in G protein-associated structures than that in arrestin-associated structures. Moreover, we find the water-mediated interaction networks throughout the NPxxY region and the orthosteric pocket, which may be a key for GPCR activation. Reported studies show that non-biased agonist, which can trigger both GPCR-G protein and GPCR-arrestin activation signal, can result in pharmacologically toxicities. Our comprehensive studies of the hydration sites in GPCR/G protein complexes and GPCR/arrestin complexes may provide important insights in the design of G-protein biased agonists.

Improved compound-protein interaction site and binding affinity prediction using self-supervised protein embeddings

BACKGROUND

Compound-protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound-protein interaction. For protein inputs, how to make use of protein primary sequence and tertiary structure information has impact on prediction results.

RESULTS

In this study, we propose a deep learning model based on a multi-objective neural network, which involves a multi-objective neural network for compound-protein interaction site and binding affinity prediction. We used several kinds of self-supervised protein embeddings to enrich our protein inputs and used convolutional neural networks to extract features from them. Our results demonstrate that our model had improvements in terms of interaction site prediction and affinity prediction compared to previous models. In a case study, our model could better predict binding sites, which also showed its effectiveness.

CONCLUSION

These results suggest that our model could be a helpful tool for compound-protein related predictions.

PDBx/mmCIF Ecosystem: Foundational Semantic Tools for Structural Biology

PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.

Molecular Dynamics and MM-PBSA Analysis of the SARS-CoV-2 Gamma Variant in Complex with the hACE-2 Receptor

The SARS-CoV-2 virus, since its appearance in 2019, has caused millions of cases and deaths. To date, there is no effective treatment or a vaccine that is fully protective. Despite the efforts made by governments and health institutions around the globe to control its propagation, the evolution of the virus has accelerated, diverging into hundreds of variants. However, not all of them are variants of concern (VoC’s). VoC’s have appeared in different regions and throughout the two years of the pandemic they have spread around the world. Specifically, in South America, the gamma variant (previously known as P.1) appeared in early 2021, bringing with it a second wave of infections. This variant contains the N501Y, E484K and K417T mutations in the receptor binding domain (RBD) of the spike protein. Although these mutations have been described experimentally, there is still no clarity regarding their role in the stabilization of the complex with the human angiotensin converting enzyme 2 (hACE-2) receptor. In this article we dissect the influence of mutations on the interaction with the hACE-2 receptor using molecular dynamics and estimations of binding affinity through a screened version of the molecular mechanics Poisson Boltzmann surface area (MM-PBSA) and interaction entropy. Our results indicate that mutations E484K and K417T compensate each other in terms of binding affinity, while the mutation N501Y promotes a more convoluted effect. This effect consists in the adoption of a cis configuration in the backbone of residue Y495 within the RBD, which in turn promotes polar interactions with the hACE-2 receptor. These results not only correlate with experimental observations and complement previous knowledge, but also expose new features associated with the specific contribution of concerned mutations. Additionally, we propose a recipe to assess the residue-specific contribution to the interaction entropy.

PDBeCIF: an open-source mmCIF/CIF parsing and processing package

Background

Biomacromolecular structural data outgrew the legacy Protein Data Bank (PDB) format which the scientific community relied on for decades, yet the use of its successor PDBx/Macromolecular Crystallographic Information File format (PDBx/mmCIF) is still not widespread. Perhaps one of the reasons is the availability of easy to use tools that only support the legacy format, but also the inherent difficulties of processing mmCIF files correctly, given the number of edge cases that make efficient parsing problematic. Nevertheless, to fully exploit macromolecular structure data and their associated annotations such as multiscale structures from integrative/hybrid methods or large macromolecular complexes determined using traditional methods, it is necessary to fully adopt the new format as soon as possible.

Results

To this end, we developed PDBeCIF, an open-source Python project for manipulating mmCIF and CIF files. It is part of the official list of mmCIF parsers recorded by the wwPDB and is heavily employed in the processes of the Protein Data Bank in Europe. The package is freely available both from the PyPI repository (http://pypi.org/project/pdbecif) and from GitHub (https://github.com/pdbeurope/pdbecif) along with rich documentation and many ready-to-use examples.

Conclusions

PDBeCIF is an efficient and lightweight Python 2.6+/3+ package with no external dependencies. It can be readily integrated with 3rd party libraries as well as adopted for broad scientific analyses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04271-9.

Interaction energy between neuronal cell receptors and peptoid ligands

Peptoids as an extracellular matrix (ECM) material is gaining importance in in vitro neuronal cell culture studies due to their biocompatibility, self-assembling structure, and stability. Mechanotransduction between a neuronal cell and an ECM is mediated by neuronal cell receptors such as integrin and neural cellular adhesion molecule. In this study, using molecular dynamics, we investigate the interaction energies between peptoid and neuronal cell receptors, and also study the effect of peptoid bundle size. We investigate the interaction surface between peptoid bundles and neuronal cell receptors, integrin and neural cellular adhesion molecule, using the solvent accessible surface area method to find the influence of hydrophobic and hydrophilic residues of the peptoid chain. We find the free energy landscape using the umbrella sampling method and then evaluate the potential mean force (PMF) and unbinding force during the dissociation between peptoid bundles and neuronal cell receptors. We find that the peptoid bundles have a higher affinity for the neuronal cell receptors, however increasing the size of peptoid bundles increases the affinity for integrin and neural cell adhesion molecule. PMF data for peptoid and neuronal cell receptor dissociation indicates that binding force increases as the size of the peptoid bundle increases. The higher binding strength during peptoid and neuronal cell receptors are due to the hydrophobic residue cluster area in the binding region. These findings will provide a better insight into using peptoid as an ECM.

ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis

The ATSAS software suite encompasses a number of programs for the processing, visualization, analysis and modelling of small-angle scattering data, with a focus on the data measured from biological macromolecules. Here, new developments in the ATSAS 3.0 package are described. They include IMSIM, for simulating isotropic 2D scattering patterns; IMOP, to perform operations on 2D images and masks; DATRESAMPLE, a method for variance estimation of structural invariants through parametric resampling; DATFT, which computes the pair distance distribution function by a direct Fourier transform of the scattering data; PDDFFIT, to compute the scattering data from a pair distance distribution function, allowing comparison with the experimental data; a new module in DATMW for Bayesian consensus-based concentration-independent molecular weight estimation; DATMIF, an ab initio shape analysis method that optimizes the search model directly against the scattering data; DAMEMB, an application to set up the initial search volume for multiphase modelling of membrane proteins; ELLLIP, to perform quasi-atomistic modelling of liposomes with elliptical shapes; NMATOR, which models conformational changes in nucleic acid structures through normal mode analysis in torsion angle space; DAMMIX, which reconstructs the shape of an unknown intermediate in an evolving system; and LIPMIX and BILMIX, for modelling multilamellar and asymmetric lipid vesicles, respectively. In addition, technical updates were deployed to facilitate maintainability of the package, which include porting the PRIMUS graphical interface to Qt5, updating SASpy - a PyMOL plugin to run a subset of ATSAS tools - to be both Python 2 and 3 compatible, and adding utilities to facilitate mmCIF compatibility in future ATSAS releases. All these features are implemented in ATSAS 3.0, freely available for academic users at https://www.embl-hamburg.de/biosaxs/software.html.

Targeting a conserved pocket (n-octyl-β-D-glucoside) on the dengue virus envelope protein by small bioactive molecule inhibitors

Dengue virus enters the cell by receptor-mediated endocytosis followed by a viral envelope (DENVE) protein-mediated membrane fusion. A small detergent molecule n-octyl-β-D-glucoside (βOG) occupies the hydrophobic pocket which is located in the hinge region plays a major role in the rearrangement. It has been reported that mutations occurred in this binding pocket lead to the alterations of pH threshold for fusion. In addition to this event, the protonation of histidine residues present in the hydrophobic pocket would also impart the conformational change of the E protein evidence this pocket as a promising target. The present study identified novel cinnamic acid analogs as significant blockers of the hydrophobic pocket through molecular modeling studies against DENVE. A library of seventy-two analogs of cinnamic acid was undertaken for the discovery process of DENV inhibitors. A Molecular docking study was used to analyze the binding mechanism between these compounds and DENV followed by ADMET prediction. Binding energies were predicted by the MMGBSA study. The Molecular dynamic simulation was utilized to confirm the stability of potential compound binding. The compounds CA and SCA derivatives have been tested against HSV-1 & 2 viruses. From the computational results, the compounds CA1, CA2, SCA 60, SCA 57, SCA 37, SCA 58, and SCA 14 exhibited favorable interaction energy. The compounds have in-vitro antiviral activity; the results clearly indicate that the compounds showed the activity against both the viruses (HSV-1 & HSV-2). Our study provides valuable information on the discovery of small molecules DENVE inhibitors.Communicated by Ramaswamy H. Sarma.

An Educational Bioinformatics Project to Improve Genome Annotation

Scientific advancement is hindered without proper genome annotation because biologists lack a complete understanding of cellular protein functions. In bacterial cells, hypothetical proteins (HPs) are open reading frames with unknown functions. HPs result from either an outdated database or insufficient experimental evidence (i.e., indeterminate annotation). While automated annotation reviews help keep genome annotation up to date, often manual reviews are needed to verify proper annotation. Students can provide the manual review necessary to improve genome annotation. This paper outlines an innovative classroom project that determines if HPs have outdated or indeterminate annotation. The Hypothetical Protein Characterization Project uses multiple well-documented, freely available, web-based, bioinformatics resources that analyze an amino acid sequence to (1) detect sequence similarities to other proteins, (2) identify domains, (3) predict tertiary structure including active site characterization and potential binding ligands, and (4) determine cellular location. Enough evidence can be generated from these analyses to support re-annotation of HPs or prioritize HPs for experimental examinations such as structural determination via X-ray crystallography. Additionally, this paper details several approaches for selecting HPs to characterize using the Hypothetical Protein Characterization Project. These approaches include student- and instructor-directed random selection, selection using differential gene expression from mRNA expression data, and selection based on phylogenetic relations. This paper also provides additional resources to support instructional use of the Hypothetical Protein Characterization Project, such as example assignment instructions with grading rubrics, links to training videos in YouTube, and several step-by-step example projects to demonstrate and interpret the range of achievable results that students might encounter. Educational use of the Hypothetical Protein Characterization Project provides students with an opportunity to learn and apply knowledge of bioinformatic programs to address scientific questions. The project is highly customizable in that HP selection and analysis can be specifically formulated based on the scope and purpose of each student's investigations. Programs used for HP analysis can be easily adapted to course learning objectives. The project can be used in both online and in-seat instruction for a wide variety of undergraduate and graduate classes as well as undergraduate capstone, honor's, and experiential learning projects.

BinaryCIF and CIFTools-Lightweight, efficient and extensible macromolecular data management

3D macromolecular structural data is growing ever more complex and plentiful in the wake of substantive advances in experimental and computational structure determination methods including macromolecular crystallography, cryo-electron microscopy, and integrative methods. Efficient means of working with 3D macromolecular structural data for archiving, analyses, and visualization are central to facilitating interoperability and reusability in compliance with the FAIR Principles. We address two challenges posed by growth in data size and complexity. First, data size is reduced by bespoke compression techniques. Second, complexity is managed through improved software tooling and fully leveraging available data dictionary schemas. To this end, we introduce BinaryCIF, a serialization of Crystallographic Information File (CIF) format files that maintains full compatibility to related data schemas, such as PDBx/mmCIF, while reducing file sizes by more than a factor of two versus gzip compressed CIF files. Moreover, for the largest structures, BinaryCIF provides even better compression—factor ten and four versus CIF files and gzipped CIF files, respectively. Herein, we describe CIFTools, a set of libraries in Java and TypeScript for generic and typed handling of CIF and BinaryCIF files. Together, BinaryCIF and CIFTools enable lightweight, efficient, and extensible handling of 3D macromolecular structural data.

Conformational analysis of a cyclic AKH neuropeptide analog that elicits selective activity on locust versus honeybee receptor

Neuropeptides belonging to the adipokinetic hormone (AKH) family elicit metabolic effects as their main function in insects, by mobilizing trehalose, diacylgycerol, or proline, which are released from the fat body into the hemolymph as energy sources for muscle contraction required for energy-intensive processes, such as locomotion. One of the AKHs produced in locusts is a decapeptide, Locmi-AKH-I (pELNFTPNWGT-NH₂). A head-to-tail cyclic, octapeptide analog of Locmi-AKH-I, cycloAKH (cyclo[LNFTPNWG]) was synthesized to severely restrict the conformational freedom of the AKH structure. In vitro, cycloAKH selectively retains full efficacy on a pest insect (desert locust) AKH receptor, while showing little or no activation of the AKH receptor of a beneficial insect (honeybee). Molecular dynamic analysis incorporating NMR data indicate that cycloAKH preferentially adopts a type II β-turn under micelle conditions, whereas its linear counterpart and natural AKH adopts a type VI β-turn under similar conditions. CycloAKH, linear LNFTPNWG-NH₂, and Locmi-AKH-I feature the same binding site during docking simulations with the desert locust AKH receptor (Schgr-AKHR), but differ in the details of the ligand/receptor interactions. However, cycloAKH failed to enter the binding pocket of the honeybee receptor 3D model during docking simulations. Since the locust AKH receptor has a greater tolerance than the honeybee receptor for the cyclic conformational constraint in vitro receptor assays, it could suggest a greater tolerance for a shift in the direction of the type II β turn exhibited by cycloAKH from the type VI β turn of the linear octapeptide and the native locust decapeptide AKH. Selectivity in biostable mimetic analogs could potentially be enhanced by incorporating conformational constraints that emphasize this shift. Biostable mimetic analogs of AKH offer the potential of selectively disrupting AKH-regulated processes, leading to novel, environmentally benign control strategies for pest insect populations.

Expansin Engineering Database: A navigation and classification tool for expansins and homologues

Expansins have the remarkable ability to loosen plant cell walls and cellulose material without showing catalytic activity and therefore have potential applications in biomass degradation. To support the study of sequence-structure-function relationships and the search for novel expansins, the Expansin Engineering Database (ExED, https://exed.biocatnet.de) collected sequence and structure data on expansins from Bacteria, Fungi, and Viridiplantae, and expansin-like homologues such as carbohydrate binding modules, glycoside hydrolases, loosenins, swollenins, cerato-platanins, and EXPNs. Based on global sequence alignment and protein sequence network analysis, the sequences are highly diverse. However, many similarities were found between the expansin domains. Newly created profile hidden Markov models of the two expansin domains enable standard numbering schemes, comprehensive conservation analyses, and genome annotation. Conserved key amino acids in the expansin domains were identified, a refined classification of expansins and carbohydrate binding modules was proposed, and new sequence motifs facilitate the search of novel candidate genes and the engineering of expansins.

An integrated exploration of pharmacological potencies of Bischofia javanica (Blume) leaves through experimental and computational modeling

Bischofia javanica (Blume), an edible wild plant, has both prospective nutraceutical and therapeutic properties. Here, we intended to explore the pharmacological potentials of the methanol extract of B. javanica (MEBJ) through integrated approaches. Phytochemical screening revealed the presence of important phytoconstituents which were found to be safe during cytotoxicity analysis. The sedative potential of MEBJ (200 and 400 mg/kg) was determined by employing open field, hole cross, and thiopental sodium-induced sleeping time tests, where a significant reduction of the locomotor performance and an enhancement in the duration of sleeping have been observed, respectively. In addition, mice treated with MEBJ exhibited superior exploration during both elevated plus maze and hole board tests. In parallel, anti-diabetic potency was investigated via alpha-amylase inhibitory assay, where a dose-response increase in the percentage of inhibition has been marked. A similar response, such as an increased percentage of clot lysis, was observed during the thrombolytic test. Furthermore, molecular docking was performed with the identified compounds, demonstrated strong binding affinities to the target receptors of the experiments as mentioned above. Also, ADME/T and toxicological parameters verified the drug-like properties of the identified compounds. Collectively, these results indicate bioactivity of Bischofia javanica, which can be a potential candidate in the food and pharmaceutical industries.

A Multiscale Model to Predict Neuronal Cell Deformation with Varying Extracellular Matrix Stiffness and Topography

INTRODUCTION

Neuronal cells are sensitive to mechanical properties of extracellular matrix (ECM) such as stiffness and topography. Cells contract and exert a force on ECM to detect the microenvironment, which activates the signaling pathway to influence the cell functions such as differentiation, migration, and proliferation. There are numerous transmembrane proteins that transmit signals; however, integrin and neural cellular adhesion molecules (NCAM) play an important role in sensing the ECM mechanical properties. Mechanotransduction of cell-ECM is the key to understand the influence of ECM stiffness and topography; therefore, in this study, we develop a multiscale computational model to investigate these properties.

METHODS

This model couples the molecular behavior of integrin and NCAM to microscale interactions of neuronal cell and the ECM. We analyze the atomistic/molecular behavior of integrin and NCAM due to mechanical stimuli using steered molecular dynamics. The microscale properties of the neuronal cell and the ECM are simulated using non-linear finite element analysis by applying cell contractility.

RESULTS

We predict that by increasing the ECM stiffness, a neuronal cell exerts greater stress on the ECM. However, this stress reaches a saturation value for a threshold stiffness of ECM, and the saturation value is affected by the ECM thickness, topography, and clustering of integrin and NCAMs. Further, the ECM topography leads to asymmetric stress and deformation in the neuronal cell. Predicted stress distribution in neuronal cell and ECM are consistent with experimental results from the literature.

CONCLUSION

The multiscale computational model will guide in selecting the optimal ECM stiffness and topography range for in vitro studies.

Understanding and Controlling Food Protein Structure and Function in Foods: Perspectives from Experiments and Computer Simulations

The structure and interactions of proteins play a critical role in determining the quality attributes of many foods, beverages, and pharmaceutical products. Incorporating a multiscale understanding of the structure-function relationships of proteins can provide greater insight into, and control of, the relevant processes at play. Combining data from experimental measurements, human sensory panels, and computer simulations through machine learning allows the construction of statistical models relating nanoscale properties of proteins to the physicochemical properties, physiological outcomes, and tastes of foods. This review highlights several examples of advanced computer simulations at molecular, mesoscale, and multiscale levels that shed light on the mechanisms at play in foods, thereby facilitating their control. It includes a practical simulation toolbox for those new to in silico modeling.

Phote-HrTH (Phormia terraenovae Hypertrehalosaemic Hormone), the Metabolic Hormone of the Fruit Fly: Solution Structure and Receptor Binding Model

Fruit flies are a widely distributed pest insect that pose a significant threat to food security. Flight is essential for the dispersal of the adult flies to find new food sources and ideal breeding spots. The supply of metabolic fuel to power the flight muscles of insects is regulated by adipokinetic hormones (AKHs). The fruit fly, Drosophila melanogaster, has the same AKH that is present in the blowfly, Phormia terraenovae; this AKH has the code-name Phote-HrTH. Binding of the AKH to the extra-cellular binding site of a G protein-coupled receptor causes its activation. In this paper, the structure of Phote-HrTH in sodium dodecyl sulfate (SDS) micelle solution was determined using NMR restrained molecular dynamics. The peptide was found to bind to the micelle and be fairly rigid, with an S2 order parameter of 0.96. The translated protein sequence of the AKH receptor from the fruit fly, D. melanogaster, Drome-AKHR, was used to construct two models of the receptor. It is proposed that these two models represent the active and inactive state of the receptor. The model based on the crystal structure of the β-2 adrenergic receptor was found to bind Phote-HrTH with a binding constant of −102kJmol−1, while the other model, based on the crystal structure of rhodopsin, did not bind the peptide. Under molecular dynamic simulation, in a palmitoyloleoylphosphatidylcholine (POPC) membrane, the receptor complex changed from an inactive to an active state. The identification and characterisation of the ligand binding site of Drome-AKHR provide novel information of ligand–receptor interaction, which could lead to the development of species-specific control substances to use discriminately against the fruit fly.

A Novel Network Pharmacology Strategy to Decode Metabolic Biomarkers and Targets Interactions for Depression

Depression is one of the most prevalent and serious mental disorders with a worldwide significant health burden. Metabolic abnormalities and disorders in patients with depression have attracted great research attention. Thirty-six metabolic biomarkers of clinical plasma metabolomics were detected by platform technologies, including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and proton nuclear magnetic resonance (¹H-NMR), combined with multivariate data analysis techniques in previous work. The principal objective of this study was to provide valuable information for the pathogenesis of depression by comprehensive analysis of 36 metabolic biomarkers in the plasma of depressed patients. The relationship between biomarkers and enzymes were collected from the HMDB database. Then the metabolic biomarkers-enzymes interactions (MEI) network was performed and analyzed to identify hub metabolic biomarkers and enzymes. In addition, the docking score-weighted multiple pharmacology index (DSWMP) was used to assess the important pathways of hub metabolic biomarkers involved. Finally, validated these pathways by published literature. The results show that stearic acid, phytosphingosine, glycine, glutamine and phospholipids were important metabolic biomarkers. Hydrolase, transferase and acyltransferase involve the largest number of metabolic biomarkers. Nine metabolite targets (TP53, IL1B, TNF, PTEN, HLA-DRB1, MTOR, HRAS, INS and PIK3CA) of potential drug proteins for treating depression are widely involved in the nervous system, immune system and endocrine system. Seven important pathways, such as PI3K-Akt signaling pathway and mTOR signaling pathway, are closely related to the pathology mechanisms of depression. The application of important biomarkers and pathways in clinical practice may help to improve the diagnosis of depression and the evaluation of antidepressant effect, which provides important clues for the study of metabolic characteristics of depression.

The Integrated Resource for Reproducibility in Macromolecular Crystallography: Experiences of the first four years

It has been increasingly recognized that preservation and public accessibility of primary experimental data are cornerstones necessary for the reproducibility of empirical sciences. In the field of molecular crystallography, many journals now recommend that authors of manuscripts presenting a new crystal structure should deposit their primary experimental data (X-ray diffraction images) to one of the dedicated resources created in recent years. Here, we describe our experiences developing the Integrated Resource for Reproducibility in Molecular Crystallography (IRRMC) and describe several examples of a crucial role that diffraction data can play in improving previously determined protein structures. In its first four years, several hundred crystallographers have deposited data from over 5200 diffraction experiments performed at over 60 different synchrotron beamlines or home sources all over the world. In addition to improving the resource and curating submitted data, we have been building a pipeline for extraction or, in some cases, reconstruction of the metadata necessary for seamless automated processing. Preliminary analysis indicates that about 95% of the archived data can be automatically reprocessed. A high rate of reprocessing success shows the feasibility of using the automated metadata extraction and automated processing as a validation step for the deposition of raw diffraction images. The IRRMC is guided by the Findable, Accessible, Interoperable, and Reusable data management principles.

MRPC (Missing Regions in Polypeptide Chains): a knowledgebase

Missing regions in protein crystal structures are those regions that cannot be resolved, mainly owing to poor electron density (if the three-dimensional structure was solved using X-ray crystallography). These missing regions are known to have high B factors and could represent loops with a possibility of being part of an active site of the protein molecule. Thus, they are likely to provide valuable information and play a crucial role in the design of inhibitors and drugs and in protein structure analysis. In view of this, an online database, Missing Regions in Polypeptide Chains (MRPC), has been developed which provides information about the missing regions in protein structures available in the Protein Data Bank. In addition, the new database has an option for users to obtain the above data for non-homologous protein structures (25 and 90%). A user-friendly graphical interface with various options has been incorporated, with a provision to view the three-dimensional structure of the protein along with the missing regions using JSmol. The MRPC database is updated regularly (currently once every three months) and can be accessed freely at the URL http://cluster.physics.iisc.ac.in/mrpc.

Identification and Analysis of Long Repeats of Proteins at the Domain Level

Amino acid repeats play an important role in the structure and function of proteins. Analysis of long repeats in protein sequences enables one to understand their abundance, structure and function in the protein universe. In the present study, amino acid repeats of length >50 (long repeats) were identified in a non-redundant set of UniProt sequences using the RADAR program. The underlying structures and functions of these long repeats were carried out using the Gene3D for structural domains, Pfam for functional domains and enzyme and non-enzyme functional classification for catalytic and binding of the proteins. From a structural perspective, these long repeats seem to predominantly occur in certain architectures such as sandwich, bundle, barrel, and roll and within these architectures abundant in the superfolds. The lengths of the repeats within each fold are not uniform exhibiting different structures for different functions. We also observed that long repeats are in the domain regions of the family and are involved in the function of the proteins. After grouping based on enzyme and non-enzyme classes, we observed the abundant occurrence of long repeats in specific catalytic and binding of the proteins. In this study, we have analyzed the occurrence of long repeats in the protein sequence universe apart from well-characterized short tandem repeats in sequences and their structures and functions of the proteins at the domain level. The present study suggests that long repeats may play an important role in the structure and function of domains of the proteins.

Super Secondary Structure Consisting of a Polyproline II Helix and a β-Turn in Leucine Rich Repeats in Bacterial Type III Secretion System Effectors

Leucine rich repeats (LRRs) are present in over 100,000 proteins from viruses to eukaryotes. The LRRs are 20–30 residues long and occur in tandem. LRRs form parallel stacks of short β-strands and then assume a super helical arrangement called a solenoid structure. Individual LRRs are separated into highly conserved segment (HCS) with the consensus of LxxLxLxxNxL and variable segment (VS). Eight classes have been recognized. Bacterial LRRs are short and characterized by two prolines in the VS; the consensus is xxLPxLPxx with Nine residues (N-subtype) and xxLPxxLPxx with Ten residues (T-subtype). Bacterial LRRs are contained in type III secretion system effectors such as YopM, IpaH3/9.8, SspH1/2, and SlrP from bacteria. Some LRRs in decorin, fribromodulin, TLR8/9, and FLRT2/3 from vertebrate also contain the motifs. In order to understand structural features of bacterial LRRs, we performed both secondary structures assignments using four programs—DSSP-PPII, PROSS, SEGNO, and XTLSSTR—and HELFIT analyses (calculating helix axis, pitch, radius, residues per turn, and handedness), based on the atomic coordinates of their crystal structures. The N-subtype VS adopts a left handed polyproline II helix (PPII) with four, five or six residues and a type I β-turn at the C-terminal side. Thus, the N-subtype is characterized by a super secondary structure consisting of a PPII and a β-turn. In contrast, the T-subtype VS prefers two separate PPIIs with two or three and two residues. The HELFIT analysis indicates that the type I β-turn is a right handed helix. The HELFIT analysis determines three unit vectors of the helix axes of PPII (P), β-turn (B), and LRR domain (A). Three structural parameters using these three helix axes are suggested to characterize the super secondary structure and the LRR domain.

Human Albumin Impairs Amyloid β-peptide Fibrillation Through its C-terminus: From docking Modeling to Protection Against Neurotoxicity in Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative process characterized by the accumulation of extracellular deposits of amyloid β-peptide (Aβ), which induces neuronal death. Monomeric Aβ is not toxic but tends to aggregate into β-sheets that are neurotoxic. Therefore to prevent or delay AD onset and progression one of the main therapeutic approaches would be to impair Aβ assembly into oligomers and fibrils and to promote disaggregation of the preformed aggregate. Albumin is the most abundant protein in the cerebrospinal fluid and it was reported to bind Aβ impeding its aggregation. In a previous work we identified a 35-residue sequence of clusterin, a well-known protein that binds Aβ, that is highly similar to the C-terminus (CTerm) of albumin. In this work, the docking experiments show that the average binding free energy of the CTerm-Aβ1-42 simulations was significantly lower than that of the clusterin-Aβ1-42 binding, highlighting the possibility that the CTerm retains albumin's binding properties. To validate this observation, we performed in vitro structural analysis of soluble and aggregated 1 μM Aβ1-42 incubated with 5 μM CTerm, equimolar to the albumin concentration in the CSF. Reversed-phase chromatography and electron microscopy analysis demonstrated a reduction of Aβ1-42 aggregates when the CTerm was present. Furthermore, we treated a human neuroblastoma cell line with soluble and aggregated Aβ1-42 incubated with CTerm obtaining a significant protection against Aβ-induced neurotoxicity. These in silico and in vitro data suggest that the albumin CTerm is able to impair Aβ aggregation and to promote disassemble of Aβ aggregates protecting neurons.

Alternative interaction sites in the influenza A virus nucleoprotein mediate viral escape from the importin-α7 mediated nuclear import pathway

Influenza A viruses are able to adapt to restrictive conditions due to their high mutation rates. Importin-α7 is a component of the nuclear import machinery required for avian-mammalian adaptation and replicative fitness in human cells. Here, we elucidate the mechanisms by which influenza A viruses may escape replicative restriction in the absence of importin-α7. To address this question, we assessed viral evolution in mice lacking the importin-α7 gene. We show that three mutations in particular occur with high frequency in the viral nucleoprotein (NP) protein (G102R, M105K and D375N) in a specific structural area upon in vivo adaptation. Moreover, our findings suggest that the adaptive NP mutations mediate viral escape from importin-α7 requirement likely due to the utilization of alternative interaction sites in NP beyond the classical nuclear localization signal. However, viral escape from importin-α7 by mutations in NP is, at least in part, associated with reduced viral replication highlighting the crucial contribution of importin-α7 to replicative fitness in human cells.

Structural and Computational Characterization of Disease-Related Mutations Involved in Protein-Protein Interfaces

One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are located at protein-protein interfaces, which are composed of two main regions, core and rim, with different evolutionary conservation and physicochemical properties. Here we have performed a structural, energetics and computational analysis of interactions between proteins hosting mutations related to diseases detected in newborn screening. Interface residues were classified as core or rim, showing that the core residues contribute the most to the binding free energy of the PPI. Disease-causing variants are more likely to occur at the interface core region rather than at the interface rim (p < 0.0001). In contrast, neutral variants are more often found at the interface rim or at the non-interacting surface rather than at the interface core region. We also found that arginine, tryptophan, and tyrosine are over-represented among mutated residues leading to disease. These results can enhance our understanding of disease at molecular level and thus contribute towards personalized medicine by helping clinicians to provide adequate diagnosis and treatments.

Brazilian malaria molecular targets (BraMMT): selected receptors for virtual high-throughput screening experiments

BACKGROUND

Owing to increased spending on pharmaceuticals since 2010, discussions about rising costs for the development of new medical technologies have been focused on the pharmaceutical industry. Computational techniques have been developed to reduce costs associated with new drug development. Among these techniques, virtual high-throughput screening (vHTS) can contribute to the drug discovery process by providing tools to search for new drugs with the ability to bind a specific molecular target.

OBJECTIVES

In this context, Brazilian malaria molecular targets (BraMMT) was generated to execute vHTS experiments on selected molecular targets of Plasmodium falciparum.

METHODS

In this study, 35 molecular targets of P. falciparum were built and evaluated against known antimalarial compounds.

FINDINGS

As a result, it could predict the correct molecular target of market drugs, such as artemisinin. In addition, our findings suggested a new pharmacological mechanism for quinine, which includes inhibition of falcipain-II and a potential new antimalarial candidate, clioquinol.

MAIN CONCLUSIONS

The BraMMT is available to perform vHTS experiments using OCTOPUS or Raccoon software to improve the search for new antimalarial compounds. It can be retrieved from www.drugdiscovery.com.br or download of Supplementary data.

D+: software for high-resolution hierarchical modeling of solution X-ray scattering from complex structures

This paper presents the computer program D+ (https://scholars.huji.ac.il/uriraviv/book/d-0), where the reciprocal-grid (RG) algorithm is implemented. D+ efficiently computes, at high-resolution, the X-ray scattering curves from complex structures that are isotropically distributed in random orientations in solution. Structures are defined in hierarchical trees in which subunits can be represented by geometric or atomic models. Repeating subunits can be docked into their assembly symmetries, describing their locations and orientations in space. The scattering amplitude of the entire structure can be calculated by computing the amplitudes of the basic subunits on 3D reciprocal-space grids, moving up in the hierarchy, calculating the RGs of the larger structures, and repeating this process for all the leaves and nodes of the tree. For very large structures (containing over 100 protein subunits), a hybrid method can be used to avoid numerical artifacts. In the hybrid method, only grids of smaller subunits are summed and used as subunits in a direct computation of the scattering amplitude. D+ can accurately analyze both small- and wide-angle solution X-ray scattering data. This article describes how D+ applies the RG algorithm, accounts for rotations and translations of subunits, processes atomic models, accounts for the contribution of the solvent as well as the solvation layer of complex structures in a scalable manner, writes and accesses RGs, interpolates between grid points, computes numerical integrals, enables the use of scripts to define complicated structures, applies fitting algorithms, accounts for several coexisting uncorrelated populations, and accelerates computations using GPUs. D+ may also account for different X-ray energies to analyze anomalous solution X-ray scattering data. An accessory tool that can identify repeating subunits in a Protein Data Bank file of a complex structure is provided. The tool can compute the orientation and translation of repeating subunits needed for exploiting the advantages of the RG algorithm in D+. A Python wrapper (https://scholars.huji.ac.il/uriraviv/book/python-api) is also available, enabling more advanced computations and integration of D+ with other computational tools. Finally, a large number of tests are presented. The results of D+ are compared with those of other programs when possible, and the use of D+ to analyze solution scattering data from dynamic microtubule structures with different protofilament number is demonstrated. D+ and its source code are freely available for academic users and developers (https://bitbucket.org/uriraviv/public-dplus/src/master/).

D+: software for high-resolution hierarchical modeling of solution X-ray scattering from complex structures

This paper presents the computer program D+ (https://scholars.huji.ac.il/uriraviv/book/d-0), where the reciprocal-grid (RG) algorithm is implemented. D+ efficiently computes, at high-resolution, the X-ray scattering curves from complex structures that are isotropically distributed in random orientations in solution. Structures are defined in hierarchical trees in which subunits can be represented by geometric or atomic models. Repeating subunits can be docked into their assembly symmetries, describing their locations and orientations in space. The scattering amplitude of the entire structure can be calculated by computing the amplitudes of the basic subunits on 3D reciprocal-space grids, moving up in the hierarchy, calculating the RGs of the larger structures, and repeating this process for all the leaves and nodes of the tree. For very large structures (containing over 100 protein subunits), a hybrid method can be used to avoid numerical artifacts. In the hybrid method, only grids of smaller subunits are summed and used as subunits in a direct computation of the scattering amplitude. D+ can accurately analyze both small- and wide-angle solution X-ray scattering data. This article describes how D+ applies the RG algorithm, accounts for rotations and translations of subunits, processes atomic models, accounts for the contribution of the solvent as well as the solvation layer of complex structures in a scalable manner, writes and accesses RGs, interpolates between grid points, computes numerical integrals, enables the use of scripts to define complicated structures, applies fitting algorithms, accounts for several coexisting uncorrelated populations, and accelerates computations using GPUs. D+ may also account for different X-ray energies to analyze anomalous solution X-ray scattering data. An accessory tool that can identify repeating subunits in a Protein Data Bank file of a complex structure is provided. The tool can compute the orientation and translation of repeating subunits needed for exploiting the advantages of the RG algorithm in D+. A Python wrapper (https://scholars.huji.ac.il/uriraviv/book/python-api) is also available, enabling more advanced computations and integration of D+ with other computational tools. Finally, a large number of tests are presented. The results of D+ are compared with those of other programs when possible, and the use of D+ to analyze solution scattering data from dynamic microtubule structures with different protofilament number is demonstrated. D+ and its source code are freely available for academic users and developers (https://bitbucket.org/uriraviv/public-dplus/src/master/).

Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers

Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.

A Network Pharmacology-Based Approach to Investigate the Novel TCM Formula against Huntington's Disease and Validated by Support Vector Machine Model

Several pathways are crucial in Huntington's disease (HD). Based on the concept of multitargets, network pharmacology-based analysis was employed to find out related proteins in disease network. The network target method aims to find out related mechanism of efficacy substances in rational design way. Traditional Chinese medicine prescriptions would be used for research and development against HD. Virtual screening was performed to obtain drug molecules with high binding capacity from traditional Chinese medicine (TCM) database@Taiwan. Quantitative structure-activity relationship (QSAR) models were conducted by MLR, SVM, CoMFA, and CoMSIA, constructed to predict the bioactivities of candidates. The compounds with high-dock score were further analyzed compared with control. Traditional Chinese medicine reported in the literature could be the training set provided for constructing novel formula by SVM model. We tried to find a novel formula that can bind well with these targets at the same time, which indicates our design could be highly related to the HD. Additionally, the candidates would validate by a long-term molecular dynamics (MD) simulation, 5 microseconds. Thus, we suggested the herbs Brucea javanica, Holarrhena antidysenterica, Dichroa febrifuga, Erythrophleum guineense, etc. which contained active compounds might be a novel medicine formula toward Huntington's disease.

Synchrotron Big Data Science

The rapid development of synchrotrons has massively increased the speed at which experiments can be performed, while new techniques have increased the amount of raw data collected during each experiment. While this has created enormous new opportunities, it has also created tremendous challenges for national facilities and users. With the huge increase in data volume, the manual analysis of data is no longer possible. As a result, only a fraction of the data collected during the time- and money-expensive synchrotron beam-time is analyzed and used to deliver new science. Additionally, the lack of an appropriate data analysis environment limits the realization of experiments that generate a large amount of data in a very short period of time. The current lack of automated data analysis pipelines prevents the fine-tuning of beam-time experiments, further reducing their potential usage. These effects, collectively known as the "data deluge," affect synchrotrons in several different ways including fast data collection, available local storage, data management systems, and curation of the data. This review highlights the Big Data strategies adopted nowadays at synchrotrons, documenting this novel and promising hybridization between science and technology, which promise a dramatic increase in the number of scientific discoveries.

Marine Bacterial Compounds Evaluated by In Silico Studies as Antipsychotic Drugs Against Schizophrenia

Schizophrenia (SCZ) is one of the brain disorders which affects the thinking and behavioral skills of patients. This disorder comes along with an overproduction of kynurenic acid in the cerebrospinal fluid and the prefrontal cortex of SCZ patients. In this study, marine bacterial compounds were screened for their suitability as antagonists against human kynurenine aminotransferase (hKAT-1) which causes the synthesis of kynurenic acid downstream which ultimately causes the SCZ disorder according to the kynurenic hypothesis of SCZ. The marine actinobacterial compound bonactin shows more promising results than other tested marine compounds such as the histamine H2 blocker famotidine and indole-3-acetic acid (IAC) from docking and in silico toxicological studies carried out here. The obtained results of the Grid-based Ligand Docking with Energetics (Glide) scores of extra-precision (XP) Glide against the target protein hKAT-1 on IAC, famotidine, and bonactin were - 6.581, - 6.500 and - 7.730 kcal/mol where Glide energies were - 29.84, - 28.391, and - 47.565 kcal/mol, respectively. Bonactin is known as an antibacterial and antifungal compound being extracted from a marine Streptomyces sp. Comparing tested compounds against the drug target hKAT-1, bonactin alone showed the best Glide score and Glide energy on the target protein hKAT-1.

Homology-based loop modeling yields more complete crystallographic protein structures

Inherent protein flexibility, poor or low-resolution diffraction data or poorly defined electron-density maps often inhibit the building of complete structural models during X-ray structure determination. However, recent advances in crystallographic refinement and model building often allow completion of previously missing parts. This paper presents algorithms that identify regions missing in a certain model but present in homologous structures in the Protein Data Bank (PDB), and 'graft' these regions of interest. These new regions are refined and validated in a fully automated procedure. Including these developments in the PDB-REDO pipeline has enabled the building of 24 962 missing loops in the PDB. The models and the automated procedures are publicly available through the PDB-REDO databank and webserver. More complete protein structure models enable a higher quality public archive but also a better understanding of protein function, better comparison between homologous structures and more complete data mining in structural bioinformatics projects.

Identification of Adjacent NNRTI Binding Pocket in Multi-mutated HIV1- RT Enzyme Model: An in silico Study

Introduction:

A possible strategy to combat mutant strains is to have a thorough structural evaluation before and after mutations to identify the diversity in the non-nucleoside inhibitor binding pocket and their effects on enzyme-ligand interactions to generate novel NNRTI’s accordingly.

Objective:

The primary objective of this study was to find effects of multiple point mutations on NNRTI binding pocket. This study included the contribution of each individual mutation in NNIBP that propose an adjacent binding pocket which can be used to discover novel NNRTI derivatives.

Methods:

An in Silico model of HIV-1 RT enzyme with multiple mutations K103N, Y181C and Y188L was developed and evaluated. Two designed NNRTI pyridinone derivatives were selected as ligands for docking studies with the homology model through alignment based docking and residue based docking approaches. Binding pockets of wild type HIV-1 RT and multi-mutated homology model were compared thoroughly.

Result and Discussion:

K103N mutation narrowed the entrance of NNRTI binding pocket and forbade electrostatic interaction with α amino group of LYS103. Mutations Y181C and Y188L prevented NNRTI binding by eliminating aromatic π interactions offered by tyrosine rings. Docking study against new homology model suggested an adjacent binding pocket with combination of residues in palm and connection domains. This pocket is approximately 14.46Å away from conventional NNRTI binding site.

Conclusion:

Increased rigidity, steric hindrance and losses of important interactions cumulatively prompt ligands to adapt adjacent NNRTI binding pocket. The proposed new and adjacent binding pocket is identified by this study which can further be evaluated to generate novel derivatives.

All-atom molecular dynamics comparison of disease-associated zinc fingers

An important regulatory domain of NF-[Formula: see text]B Essential Modulator (NEMO) is a ubiquitin-binding zinc finger, with a tetrahedral CYS3HIS1 zinc-coordinating binding site. Two variations of NEMO's zinc finger are implicated in various disease states including ectodermal dysplasia and adult-onset glaucoma. To discern structural and dynamical differences between these disease states, we present results of 48-[Formula: see text]s of molecular dynamics simulations for three zinc finger systems each in two states, with and without zinc-bound and correspondingly appropriate cysteine thiol/thiolate configurations. The wild-type protein, often studied for its role in cancer, maintains the most rigid and conformationally stable zinc-bound configuration compared with the diseased counterparts. The glaucoma-related protein has persistent loss of secondary structure except within the dominant conformation. Conformational overlap between wild-type and glaucoma isoforms indicate a competitive binding mechanism may be substantial in the malfunctioning configuration, while the alpha-helical disruption of the ectodermal dysplasia suggests a loss of binding selectivity is responsible for aberrant function.

Biophysical, Biochemical, and Cell Based Approaches Used to Decipher the Role of Carbonic Anhydrases in Cancer and to Evaluate the Potency of Targeted Inhibitors

Carbonic anhydrases (CAs) are thought to be important for regulating pH in the tumor microenvironment. A few of the CA isoforms are upregulated in cancer cells, with only limited expression in normal cells. For these reasons, there is interest in developing inhibitors that target these tumor-associated CA isoforms, with increased efficacy but limited nonspecific cytotoxicity. Here we present some of the biophysical, biochemical, and cell based techniques and approaches that can be used to evaluate the potency of CA targeted inhibitors and decipher the role of CAs in tumorigenesis, cancer progression, and metastatic processes. These techniques include esterase activity assays, stop flow kinetics, and mass inlet mass spectroscopy (MIMS), all of which measure enzymatic activity of purified protein, in the presence or absence of inhibitors. Also discussed is the application of X-ray crystallography and Cryo-EM as well as other structure-based techniques and thermal shift assays to the studies of CA structure and function. Further, large-scale genomic and proteomic analytical methods, as well as cell based techniques like those that measure cell growth, apoptosis, clonogenicity, and cell migration and invasion, are discussed. We conclude by reviewing approaches that test the metastatic potential of CAs and how the aforementioned techniques have contributed to the field of CA cancer research.

CryoEM-based hybrid modeling approaches for structure determination

Recent advances in cryo-electron microscopy (cryoEM) have dramatically improved the resolutions at which vitrified biological specimens can be studied, revealing new structural and mechanistic insights over a broad range of spatial scales. Bolstered by these advances, much effort has been directed toward the development of hybrid modeling methodologies for the construction and refinement of high-fidelity atomistic models from cryoEM data. In this brief review, we will survey the key elements of cryoEM-based hybrid modeling, providing an overview of available computational tools and strategies as well as several recent applications.

Structural biology data archiving - where we are and what lies ahead

For almost 50 years, structural biology has endeavoured to conserve and share its experimental data and their interpretations (usually, atomistic models) through global public archives such as the Protein Data Bank, Electron Microscopy Data Bank and Biological Magnetic Resonance Data Bank (BMRB). These archives are treasure troves of freely accessible data that document our quest for molecular or atomic understanding of biological function and processes in health and disease. They have prepared the field to tackle new archiving challenges as more and more (combinations of) techniques are being utilized to elucidate structure at ever increasing length scales. Furthermore, the field has made substantial efforts to develop validation methods that help users to assess the reliability of structures and to identify the most appropriate data for their needs. In this Review, we present an overview of public data archives in structural biology and discuss the importance of validation for users and producers of structural data. Finally, we sketch our efforts to integrate structural data with bioimaging data and with other sources of biological data. This will make relevant structural information available and more easily discoverable for a wide range of scientists.

Using SMILES strings for the description of chemical connectivity in the Crystallography Open Database

Computer descriptions of chemical molecular connectivity are necessary for searching chemical databases and for predicting chemical properties from molecular structure. In this article, the ongoing work to describe the chemical connectivity of entries contained in the Crystallography Open Database (COD) in SMILES format is reported. This collection of SMILES is publicly available for chemical (substructure) search or for any other purpose on an open-access basis, as is the COD itself. The conventions that have been followed for the representation of compounds that do not fit into the valence bond theory are outlined for the most frequently found cases. The procedure for getting the SMILES out of the CIF files starts with checking whether the atoms in the asymmetric unit are a chemically acceptable image of the compound. When they are not (molecule in a symmetry element, disorder, polymeric species,etc.), the previously published cif_molecule program is used to get such image in many cases. The program package Open Babel is then applied to get SMILES strings from the CIF files (either those directly taken from the COD or those produced by cif_molecule when applicable). The results are then checked and/or fixed by a human editor, in a computer-aided task that at present still consumes a great deal of human time. Even if the procedure still needs to be improved to make it more automatic (and hence faster), it has already yielded more than 160,000 curated chemical structures and the purpose of this article is to announce the existence of this work to the chemical community as well as to spread the use of its results.

Development of a Prototype System for Archiving Integrative/Hybrid Structure Models of Biological Macromolecules

Essential processes in biology are carried out by large macromolecular assemblies, whose structures are often difficult to determine by traditional methods. Increasingly, researchers combine measured data and computed information from several complementary methods to obtain "hybrid" or "integrative" structural models of macromolecules and their assemblies. These integrative/hybrid (I/H) models are not archived in the PDB because of the absence of standard data representations and processing mechanisms. Here we present the development of data standards and a prototype system for archiving I/H models. The data standards provide the definitions required for representing I/H models that span multiple spatiotemporal scales and conformational states, as well as spatial restraints derived from different experimental techniques. Based on these data definitions, we have built a prototype system called PDB-Dev, which provides the infrastructure necessary to archive I/H structural models. PDB-Dev is now accepting structures and is open to the community for new submissions.

A recombinant Fasciola gigantica 14-3-3 epsilon protein (rFg14-3-3e) modulates various functions of goat peripheral blood mononuclear cells

Background

The molecular structure of Fasciola gigantica 14-3-3 protein has been characterized. However, the involvement of this protein in parasite pathogenesis remains elusive and its effect on the functions of innate immune cells is unknown. We report on the cloning and expression of a recombinant F. gigantica 14-3-3 epsilon protein (rFg14-3-3e), and testing its effects on specific functions of goat peripheral blood mononuclear cells (PBMCs).

Methods

rFg14-3-3e protein was expressed in Pichia pastoris. Western blot and immunofluorescence assay (IFA) were used to examine the reactivity of rFg14-3-3e protein to anti-F. gigantica and anti-rFg14-3-3e antibodies, respectively. Various assays were used to investigate the stimulatory effects of the purified rFg14-3-3e protein on specific functions of goat PBMCs, including cytokine secretion, proliferation, migration, nitric oxide (NO) production, phagocytosis, and apoptotic capabilities. Potential protein interactors of rFg14-3-3e were identified by querying the databases Intact, String, BioPlex and BioGrid. A Total Energy analysis of each of the identified interaction was performed. Gene Ontology (GO) enrichment analysis was conducted using Funcassociate 3.0.

Results

Sequence analysis revealed that rFg14-3-3e protein had 100% identity to 14-3-3 protein from Fasciola hepatica. Western blot analysis showed that rFg14-3-3e protein is recognized by sera from goats experimentally infected with F. gigantica and immunofluorescence staining using rat anti-rFg14-3-3e antibodies demonstrated the specific binding of rFg14-3-3e protein to the surface of goat PBMCs. rFg14-3-3e protein stimulated goat PBMCs to produce interleukin-10 (IL-10) and transforming growth factor beta (TGF-β), corresponding with low levels of IL-4 and interferon gamma (IFN-γ). Also, this recombinant protein promoted the release of NO and cell apoptosis, and inhibited the proliferation and migration of goat PBMCs and suppressed monocyte phagocytosis. Homology modelling revealed 65% identity between rFg14-3-3e and human 14-3-3 protein YWHAE. GO enrichment analysis of the interacting proteins identified terms related to apoptosis, protein binding, locomotion, hippo signalling and leukocyte and lymphocyte differentiation, supporting the experimental findings.

Conclusions

Our data suggest that rFg14-3-3e protein can influence various cellular and immunological functions of goat PBMCs in vitro and may be involved in mediating F. gigantica pathogenesis. Because of its involvement in F. gigantica recognition by innate immune cells, rFg14-3-3e protein may have applications for development of diagnostics and therapeutic interventions.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2745-4) contains supplementary material, which is available to authorized users.