The future of the Protein Data Bank

Discovery of potential RIPK1 inhibitors by machine learning and molecular dynamics simulations

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) plays a crucial role in inflammation and cell death, so it is a promising candidate for the treatment of autoimmune, inflammatory, neurodegenerative, and ischemic diseases. So far, there are no approved RIPK1 inhibitors available. In this study, four machine learning algorithms were employed (random forest, extra trees, extreme gradient boosting and light gradient boosting machine) to predict small molecule inhibitors of RIPK1. The statistical metrics revealed similar performance and demonstrated outstanding predictive capabilities in all four models. Molecular docking and clustering analysis were employed to confirm six compounds that are structurally distinct from existing RIPK1 inhibitors. Subsequent molecular dynamics simulations were performed to evaluate the binding ability of these compounds. Utilizing the Shapley additive explanation (SHAP) method, the 1855 bit has been identified as the most significant molecular fingerprint fragment. The findings propose that these six small molecules exhibit promising potential for targeting RIPK1 in associated diseases. Notably, the identification of Cpd-1 small molecule (ZINC000085897746) from the Musa acuminate highlights its natural product origin, warranting further attention and investigation.

Virtual Screening of Benzimidazole Derivatives as Potential Triose Phosphate Isomerase Inhibitors with Biological Activity against Leishmania mexicana

Leishmania mexicana (L. mexicana) is a causal agent of cutaneous leishmaniasis (CL), a “Neglected disease”, for which the search for new drugs is a priority. Benzimidazole is a scaffold used to develop antiparasitic drugs; therefore, it is interesting molecule against L. mexicana. In this work, a ligand-based virtual screening (LBVS) of the ZINC15 database was performed. Subsequently, molecular docking was used to predict the compounds with potential binding at the dimer interface of triosephosphate isomerase (TIM) of L. mexicana (LmTIM). Compounds were selected on binding patterns, cost, and commercial availability for in vitro assays against L. mexicana blood promastigotes. The compounds were analyzed by molecular dynamics simulation on LmTIM and its homologous human TIM. Finally, the physicochemical and pharmacokinetic properties were determined in silico. A total of 175 molecules with docking scores between −10.8 and −9.0 Kcal/mol were obtained. Compound E2 showed the best leishmanicidal activity (IC50 = 4.04 µM) with a value similar to the reference drug pentamidine (IC50 = 2.23 µM). Molecular dynamics analysis predicted low affinity for human TIM. Furthermore, the pharmacokinetic and toxicological properties of the compounds were suitable for developing new leishmanicidal agents.

Identification of residues involved in allosteric signal transmission from amino acid binding site of pyruvate kinase muscle isoform 2

PKM2 is a rate-limiting enzyme in the glycolytic process and is involved in regulating tumor proliferation. Several amino acids (AAs) such as Asn, Asp, Val, and Cys have been shown to bind to the AA binding pocket of PKM2 and modulate its oligomeric state, substrate binding affinity, and activity. Although previous studies have attributed that the main chain and side chain of bound AAs are responsible for initiating signal to regulate PKM2, the signal transduction pathway remains elusive. To identify the residues involved in signal transfer process, N70 and N75 located at two ends of a β strand connecting the active site and AA binding pocket were altered. Biochemical studies of these variants with various AA ligands (Asn, Asp, Val, and Cys), illustrate that N70 and N75, along with β1 connecting these residues are part of the signal transduction pathway between the AA binding pocket and the active site. The results demonstrate that mutation of N70 to D prevents the transfer of the inhibitory signal mediated by Val and Cys, whereas N75 to L alteration blocks the activating signal initiated by Asn and Asp. Taken together, this study confirms that N70 is one of the residues responsible for transmitting the inhibitory signal and N75 is involved in the activation signal flow.

Crystallographic investigation of the ubiquinone binding site of respiratory Complex II and its inhibitors

The quinone binding site (Q-site) of Mitochondrial Complex II (succinate-ubiquinone oxidoreductase) is the target for a number of inhibitors useful for elucidating the mechanism of the enzyme. Some of these have been developed as fungicides or pesticides, and species-specific Q-site inhibitors may be useful against human pathogens. We report structures of chicken Complex II with six different Q-site inhibitors bound, at resolutions 2.0-2.4 Å. These structures show the common interactions between the inhibitors and their binding site. In every case a carbonyl or hydroxyl oxygen of the inhibitor is H-bonded to Tyr58 in subunit SdhD and Trp173 in subunit SdhB. Two of the inhibitors H-bond Ser39 in subunit SdhC directly, while two others do so via a water molecule. There is a distinct cavity that accepts the 2-substituent of the carboxylate ring in flutolanil and related inhibitors. A hydrophobic "tail pocket" opens to receive a side-chain of intermediate-length inhibitors. Shorter inhibitors fit entirely within the main binding cleft, while the long hydrophobic side chains of ferulenol and atpenin A5 protrude out of the cleft into the bulk lipid region, as presumably does that of ubiquinone. Comparison of mitochondrial and Escherichia coli Complex II shows a rotation of the membrane-anchor subunits by 7° relative to the iron‑sulfur protein. This rotation alters the geometry of the Q-site and the H-bonding pattern of SdhB:His216 and SdhD:Asp57. This conformational difference, rather than any active-site mutation, may be responsible for the different inhibitor sensitivity of the bacterial enzyme.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

RCSB Protein Data Bank: Architectural Advances Towards Integrated Searching and Efficient Access to Macromolecular Structure Data from the PDB Archive

The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) serves many millions of unique users worldwide by delivering experimentally-determined 3D structures of biomolecules integrated with >40 external data resources via RCSB.org, application programming interfaces (APIs), and FTP downloads. Herein, we present the architectural redesign of RCSB PDB data delivery services that build on existing PDBx/mmCIF data schemas. New data access APIs (data.rcsb.org) enable efficient delivery of all PDB archive data. A novel GraphQL-based API provides flexible, declarative data retrieval along with a simple-to-use REST API. A powerful new search system (search.rcsb.org) seamlessly integrates heterogeneous types of searches across the PDB archive. Searches may combine text attributes, protein or nucleic acid sequences, small-molecule chemical descriptors, 3D macromolecular shapes, and sequence motifs. The new RCSB.org architecture adheres to the FAIR Principles, empowering users to address a wide array of research problems in fundamental biology, biomedicine, biotechnology, bioengineering, and bioenergy.

Analysis of host protein interactions in plant viruses: an in silico study using Sesbania mosaic virus

The dynamics of interactions of viral proteins with their host are pivotal in establishing a successful infection and ensuring systemic spread. To uncover these, an in silico analysis of the interactions between the coat protein (CP) of Sesbania mosaic virus (SeMV), a group IV virus with single-stranded positive-sense RNA genome was carried out with the known crystal structures of proteins belonging to the Fabaceae family, which is its natural host. SeMV is an isometric plant virus which infects Sesbania grandiflora, a member of Fabaceae, and causes mosaic symptoms. Earlier results have indicated that the assembly and disassembly events of SeMV favor the formation of CP dimers. Hence, the ability and strength of interactions of CP dimer with the host proteins were assessed using in silico protein-protein docking approaches. A set of 61 unique crystal structures of native proteins belonging to Fabaceae were downloaded from the Protein Data Bank (PDB) and docked with the CP dimer of SeMV. From the docking scores and interaction analysis, the host proteins were ranked according to their strength and significance of interactions with the CP dimers. The leads that were identified present themselves as strong candidates for developing antivirals against not only SeMV but also other related viruses that infect Fabaceae. The study is a prototype to understand host protein interactions in viruses and hosts.

Molecular dynamics simulation of the heart type fatty acid binding protein in a crystal environment

Crystallographic data comes from a space-time average over all the unit cells within the crystal, so dynamic phenomena do not contribute significantly to the diffraction data. Many efforts have been made to reconstitute the movement of the macromolecules and explore the microstates that the confined proteins can adopt in the crystalline network. We explored different strategies to simulate a heart fatty acid binding protein (H-FABP) crystal by means of Molecular Dynamics (MD) simulations. We evaluate the effect of introducing restraints according to experimental isotropic B-factors and we analyzed the H-FABP motions in the crystal using Principal Component Analysis (PCA), isotropic and anisotropic B-factors. We compared the behavior of the protein simulated in the crystal confinement versus in solution, and we observed the effect of that confinement in the mobility of the protein residues. Restraining one-third of Cα atoms based on experimental B-factors produce lower B-factors than simulations without restraints, showing that the position restraint of the atoms with the lowest experimental B-factor is a good strategy to maintain the geometry of the crystal with an obvious decrease in the degrees of motion of the protein. PCA shows that, as position restraint reduces the conformational space explored by the system, the motion of the crystal is better recovered, for an essential subspace of the same size, in the simulations without restraints. Restraining only one Cα seems to be a good balance between giving flexibility to the system and preserving its structure. Communicated by Ramaswamy H. Sarma.

Cirsimaritin, a lung squamous carcinoma cells (NCIH-520) proliferation inhibitor

Cirsimaritin is a dimethoxy flavone, which is present in Ocimum sanctum, Microtea debilis, Artemisia judaica, Cirsium japonicum, and Lithocarpus dealbatus. Its antiproliferative potential has been explored in breast and gall bladder cancer cell lines. However, no reports are available on skin and squamous lung carcinoma. Also, the complete mode of action is unknown. Therefore, in the present study, the anticancer potential of cirsimaritin is explored in organ-specific cell lines by using MTT assay. Further, the inhibitory potential and binding interaction with the selected targets were analyzed through in vitro and in-silico analysis. Cirsimaritin showed selective anticancer activity against NCIH-520 cell-line (IC₅₀ 23.29 µM), also inhibited the proliferation of other cell-lines up to 48% at 100 µM. In NCIH-520 cell-line, cirsimaritin significantly increased the apoptosis of the cells at both the tested concentrations (10 and 100 µM), which was confirmed by Annexin-V signifying the induction of late apoptosis. Besides, an increase in the ROS levels of 1.6 fold (10 µM) and 1.8 fold (100 µM), circimaritin also inhibits the activity of ODC and CATD with the IC₅₀ 57.30 and 68.22 µM respectively. It exhibited a good binding score with the selected targets, follow Lipinski's rule of five and non-mutagenic. Hence, cirsimaritin is a potent molecule, which inhibits the proliferation of lung squamous cell lines by inducing apoptosis. It also inhibited the activity of ODC and CATD responsible for the progression phase in the cancer cells. Communicated by Ramaswamy H. Sarma.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

In silico T-cell and B-cell Epitope Based Vaccine Design Against Alphavirus Strain of Chikungunya

BACKGROUND

Chikungunya an arbovirus, is transmitted to humans by the bite of Aedes mosquito. The virus occurrences have been reported in Southeast Asian countries including Pakistan. Its symptoms include typical febrile illness and arthralgic syndrome. The virus has not decisively proved to be life-threatening.

METHODS

The attempt was to design T-cell and B-cell epitope-based vaccine for Chikungunya. The proteome of chikungunya was retrieved, antigenic proteins were identified and T-cell epitopes and B-cell epitopes were predicted. Interacting HLA alleles were also identified. The final analysis was done to confirm that predicted T-cell epitopes and B-cell epitopes can be used as a vaccine.

RESULTS

About 32 T-cell epitopes and a 10mer B-cell epitope were identified. Both T-cell and Bcell epitopes demonstrated strong interactions with HLA alleles. The predicted T-cell and B-cell epitopes were docked with respective HLA alleles. The docking analysis showed that the predicted respective epitopes best fit into the binding pockets of the alleles.

CONCLUSION

On the basis of this computational analysis, it is suggested that these predicted epitopes can be used as a remedy against Alphavirus strain of chikungunya. Further laboratory experiments can be conducted to determine the efficacy and stability of this work.

ECOD: identification of distant homology among multidomain and transmembrane domain proteins

The manual classification of protein domains is approaching its 20th anniversary. ECOD is our mixed manual-automatic domain classification. Over time, the types of proteins which require manual curation has changed. Depositions with complex multidomain and multichain arrangements are commonplace. Transmembrane domains are regularly classified. Repeatedly, domains which are initially believed to be novel are found to have homologous links to existing classified domains. Here we present a brief summary of recent manual curation efforts in ECOD generally combined with specific case studies of transmembrane and multidomain proteins wherein manual curation was useful for discovering new homologous relationships. We present a new taxonomy for the classification of ABC transporter transmembrane domains. We examine alternate topologies of the leucine-specific (LS) domain of Leucine tRNA-synthetase. Finally, we elaborate on a distant homologous links between two helical dimerization domains.

Riboswitch distribution, structure, and function in bacteria

Riboswitches are gene control elements that directly bind to specific ligands to regulate gene expression without the need for proteins. They are found in all three domains of life, including Bacteria, Archaea, and Eukaryota. Riboswitches are mostly spread in bacteria and archaea. In this paper, we discuss the general distribution, structure, and function of 28 different riboswitch classes as we focus our attention on riboswitches in bacteria. Bacterial riboswitches regulate gene expression by four distinct mechanisms. They regulate the expression of a limited number of genes. However, most of these genes are responsible for the synthesis of essential metabolites without which the cell cannot function. Therefore, riboswitch distribution is also important for antibacterial drug development.

Prediction of secondary and tertiary structures of human BC200 RNA (BCYRN1) based on experimental and bioinformatic cross-validation

Based on experimental and bioinformatic approaches, we present the first empirically established complete secondary structure of human BC200 RNA. BC200 RNA is a brain-specific non-messenger RNA with a confirmed regulatory role in dendritic translation in neurons. Although the involvement of human BC200 RNA in various types of tumour and Alzheimer's disease has been repeatedly confirmed, the exact secondary structure remains not fully elucidated. To determine the secondary structure of BC200 RNA in vitro, we performed partial hydrolysis with sequence-specific nucleases and lead-induced cleavage. We also examined the availabilities of putative single-stranded regions and base-pairing interactions via specific DNAzymes and RNase H assay. To determine the complete spatial folding of BC200 RNA, we used experimental data as constraints in structure prediction programs and performed a comparison of results obtained by several algorithms using different criteria. Based on the experimental-derived secondary structure of BC200 RNA, we also predicted the tertiary structure of BC200 RNA. The presented combination of experimental and bioinformatic approaches not only enabled the determination of the most reliable secondary and tertiary structures of human BC200 RNA (largely in agreement with the previous phylogenetic model), but also verified the compatibility and potential disadvantages of utilizing in silico structure prediction programs.

Overview of the structure-based non-genomic effects of the nuclear receptor RXRα

The nuclear receptor RXRα (retinoid X receptor-α) is a transcription factor that regulates the expression of multiple genes. Its non-genomic function is largely related to its structure, polymeric forms and modification. Previous research revealed that some non-genomic activity of RXRα occurs via formation of heterodimers with Nur77. RXRα-Nur77 heterodimers translocate from the nucleus to the mitochondria in response to certain apoptotic stimuli and this activity correlates with cell apoptosis. More recent studies revealed a significant role for truncated RXRα (tRXRα), which interacts with the p85α subunit of the PI3K/AKT signaling pathway, leading to enhanced activation of AKT and promoting cell growth in vitro and in animals. We recently reported on a series of NSAID sulindac analogs that can bind to tRXRα through a unique binding mechanism. We also identified one analog, K-80003, which can inhibit cancer cell growth by inducing tRXRα to form a tetramer, thus disrupting p85α-tRXRα interaction. This review analyzes the non-genomic effects of RXRα in normal and tumor cells, and discusses the functional differences based on RXRα protein structure (structure source: the RCSB Protein Data Bank).

The MIntAct Project and Molecular Interaction Databases

Molecular interaction databases collect, organize, and enable the analysis of the increasing amounts of molecular interaction data being produced and published as we move towards a more complete understanding of the interactomes of key model organisms. The organization of these data in a structured format supports analyses such as the modeling of pairwise relationships between interactors into interaction networks and is a powerful tool for understanding the complex molecular machinery of the cell. This chapter gives an overview of the principal molecular interaction databases, in particular the IMEx databases, and their curation policies, use of standardized data formats and quality control rules. Special attention is given to the MIntAct project, in which IntAct and MINT joined forces to create a single resource to improve curation and software development efforts. This is exemplified as a model for the future of molecular interaction data collation and dissemination.

OneDep: Unified wwPDB System for Deposition, Biocuration, and Validation of Macromolecular Structures in the PDB Archive

OneDep, a unified system for deposition, biocuration, and validation of experimentally determined structures of biological macromolecules to the PDB archive, has been developed as a global collaboration by the worldwide PDB (wwPDB) partners. This new system was designed to ensure that the wwPDB could meet the evolving archiving requirements of the scientific community over the coming decades. OneDep unifies deposition, biocuration, and validation pipelines across all wwPDB, EMDB, and BMRB deposition sites with improved focus on data quality and completeness in these archives, while supporting growth in the number of depositions and increases in their average size and complexity. In this paper, we describe the design, functional operation, and supporting infrastructure of the OneDep system, and provide initial performance assessments.

Octopus: a platform for the virtual high-throughput screening of a pool of compounds against a set of molecular targets

Octopus is an automated workflow management tool that is scalable for virtual high-throughput screening (vHTS). It integrates MOPAC2016, MGLTools, PyMOL, and AutoDock Vina. In contrast to other platforms, Octopus can perform docking simulations of an unlimited number of compounds into a set of molecular targets. After generating the ligands in a drawing package in the Protein Data Bank (PDB) format, Octopus can carry out geometry refinement using the semi-empirical method PM7 implemented in MOPAC2016. Docking simulations can be performed using AutoDock Vina and can utilize the Our Own Molecular Targets (OOMT) databank. Finally, the proposed software compiles the best binding energies into a standard table. Here, we describe two successful case studies that were verified by biological assay. In the first case study, the vHTS process was carried out for 22 (phenylamino)urea derivatives. The vHTS process identified a metalloprotease with the PDB code 1GKC as a molecular target for derivative LE&007. In a biological assay, compound LE&007 was found to inhibit 80% of the activity of this enzyme. In the second case study, compound Tx001 was submitted to the Octopus routine, and the results suggested that Plasmodium falciparum ATP6 (PfATP6) as a molecular target for this compound. Following an antimalarial assay, Tx001 was found to have an inhibitory concentration (IC₅₀) of 8.2 μM against PfATP6. These successful examples illustrate the utility of this software for finding appropriate molecular targets for compounds. Hits can then be identified and optimized as new antineoplastic and antimalarial drugs. Finally, Octopus has a friendly Linux-based user interface, and is available at www.drugdiscovery.com.br . Graphical Abstract Octopus: A platform for inverse virtual screening (IVS) to search new molecular targets for drugs.

Bioinformatics Analysis of Functional Associations of PTMs

Post-translational modifications (PTMs) are an important source of protein regulation; they fine-tune the function, localization, and interaction with other molecules of the majority of proteins and are partially responsible for their multifunctionality. Usually, proteins have several potential modification sites, and their patterns of occupancy are associated with certain functional states. These patterns imply cross talk among PTMs within and between proteins, the majority of which are still to be discovered. Several methods detect associations between PTMs; these have recently combined into a global resource, the PTMcode database, which contains already known and predicted functional associations between pairs of PTMs from more than 45,000 proteins in 19 eukaryotic species.

Bioinformatics Tools for the Discovery of New Nonribosomal Peptides

This chapter helps in the use of bioinformatics tools relevant to the discovery of new nonribosomal peptides (NRPs) produced by microorganisms. The strategy described can be applied to draft or fully assembled genome sequences. It relies on the identification of the synthetase genes and the deciphering of the domain architecture of the nonribosomal peptide synthetases (NRPSs). In the next step, candidate peptides synthesized by these NRPSs are predicted in silico, considering the specificity of incorporated monomers together with their isomery. To assess their novelty, the two-dimensional structure of the peptides can be compared with the structural patterns of all known NRPs. The presented workflow leads to an efficient and rapid screening of genomic data generated by high throughput technologies. The exploration of such sequenced genomes may lead to the discovery of new drugs (i.e., antibiotics against multi-resistant pathogens or anti-tumors).

Docking, QM/MM, and molecular dynamics simulations of the hexose transporter from Plasmodium falciparum (PfHT)

Malaria is the most prevalent parasitic disease in the world. Currently, an effective vaccine for malaria does not exist, and chemotherapy must be used to treat the disease. Because of increasing resistance to current antimalarial drugs, new treatments must be developed. Among the many potential molecular targets, the hexose transporter of Plasmodium falciparum (PfHT) is particularly promising because it plays a vital role in glucose transport for the parasite. Thus, this study aims to determine the three-dimensional structure of PfHT and to describe the intermolecular interactions between active glycoside derivatives and PfHT. Such information should aid in the development of new antimalarial drugs. The receptor PfHT was constructed from primary sequences deposited in the SWISS MODEL database. Next, molecular docking simulations between O-(undec-10-en)-l-D-glucose and the constructed active site models were performed using Autodock Vina. The glycoside derivative-PfHT complexes were then refined using the hybrid QM/MM (PM3/ff03) method within the AMBER package. The models were then evaluated using Ramachandran plots, which indicated that 93.2% of the residues in the refined PfHT models (P5) were present in favorable regions. Furthermore, graphical plots using ANOLEA showed that the potential energies of interaction for atoms unbonded to P5 were negative. Finally, the O-(undec-10-en)-l-D-glucose-PfHT complex was evaluated using 20-ns Molecular Dynamics simulations with an ff03 force field. Docking and QM/MM studies revealed the amino acids essential for molecular recognition of and activity on glycosides. Inhibition of glucose transporters may prevent the development and metabolism of P. falciparum, so a description of the receptor's structure is a critical step towards rational drug design.

2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions

2P2Idb is a hand-curated structural database dedicated to protein–protein interactions with known small molecule orthosteric modulators. It compiles the structural information related to orthosteric inhibitors and their target [i.e. related 3D structures available in the RCSB Protein Data Bank (PDB)] and provides links to other useful databases. 2P2Idb includes all interactions for which both the protein–protein and protein–inhibitor complexes have been structurally characterized. Since its first release in 2010, the database has grown constantly and the current version contains 27 protein–protein complexes and 274 protein–inhibitor complexes corresponding to 242 unique small molecule inhibitors which represent almost a 5-fold increase compared to the previous version. A number of new data have been added, including new protein–protein complexes, binding affinities, molecular descriptors, precalculated interface parameters and links to other webservers. A new query tool has been implemented to search for inhibitors within the database using standard molecular descriptors. A novel version of the 2P2I-inspector tool has been implemented to calculate a series of physical and chemical parameters of the protein interfaces. Several geometrical parameters including planarity, eccentricity and circularity have been added as well as customizable distance cutoffs. This tool has also been extended to protein–ligand interfaces. The 2P2I database thus represents a wealth of structural source of information for scientists interested in the properties of protein–protein interactions and the design of protein–protein interaction modulators.

Database URL:http://2p2idb.cnrs-mrs.fr

Protein Structure Databases

Web-based protein structure databases come in a wide variety of types and levels of information content. Those having the most general interest are the various atlases that describe each experimentally determined protein structure and provide useful links, analyses, and schematic diagrams relating to its 3D structure and biological function. Also of great interest are the databases that classify 3D structures by their folds as these can reveal evolutionary relationships which may be hard to detect from sequence comparison alone. Related to these are the numerous servers that compare folds-particularly useful for newly solved structures, and especially those of unknown function. Beyond these are a vast number of databases for the more specialized user, dealing with specific families, diseases, structural features, and so on.

Data Mining of Macromolecular Structures

The use of macromolecular structures is widespread for a variety of applications, from teaching protein structure principles all the way to ligand optimization in drug development. Applying data mining techniques on these experimentally determined structures requires a highly uniform, standardized structural data source. The Protein Data Bank (PDB) has evolved over the years toward becoming the standard resource for macromolecular structures. However, the process selecting the data most suitable for specific applications is still very much based on personal preferences and understanding of the experimental techniques used to obtain these models. In this chapter, we will first explain the challenges with data standardization, annotation, and uniformity in the PDB entries determined by X-ray crystallography. We then discuss the specific effect that crystallographic data quality and model optimization methods have on structural models and how validation tools can be used to make informed choices. We also discuss specific advantages of using the PDB_REDO databank as a resource for structural data. Finally, we will provide guidelines on how to select the most suitable protein structure models for detailed analysis and how to select a set of structure models suitable for data mining.

Structure-based drug design studies of the interactions of ent-kaurane diterpenes derived from Wedelia paludosa with the Plasmodium falciparum sarco/endoplasmic reticulum Ca²⁺-ATPase PfATP6

Malaria is responsible for more deaths around the world than any other parasitic disease. Due to the emergence of strains that are resistant to the current chemotherapeutic antimalarial arsenal, the search for new antimalarial drugs remains urgent though hampered by a lack of knowledge regarding the molecular mechanisms of artemisinin resistance. Semisynthetic compounds derived from diterpenes from the medicinal plant Wedelia paludosa were tested in silico against the Plasmodium falciparum Ca²⁺-ATPase, PfATP6. This protein was constructed by comparative modelling using the three-dimensional structure of a homologous protein, 1IWO, as a scaffold. Compound 21 showed the best docking scores, indicating a better interaction with PfATP6 than that of thapsigargin, the natural inhibitor. Inhibition of PfATP6 by diterpene compounds could promote a change in calcium homeostasis, leading to parasite death. These data suggest PfATP6 as a potential target for the antimalarial ent-kaurane diterpenes.

EMDataBank unified data resource for 3DEM

Three-dimensional Electron Microscopy (3DEM) has become a key experimental method in structural biology for a broad spectrum of biological specimens from molecules to cells. The EMDataBank project provides a unified portal for deposition, retrieval and analysis of 3DEM density maps, atomic models and associated metadata (emdatabank.org). We provide here an overview of the rapidly growing 3DEM structural data archives, which include maps in EM Data Bank and map-derived models in the Protein Data Bank. In addition, we describe progress and approaches toward development of validation protocols and methods, working with the scientific community, in order to create a validation pipeline for 3DEM data.

X-ray refinement significantly underestimates the level of microscopic heterogeneity in biomolecular crystals

Biomolecular X-ray structures typically provide a static, time- and ensemble-averaged view of molecular ensembles in crystals. In the absence of rigid-body motions and lattice defects, B-factors are thought to accurately reflect the structural heterogeneity of such ensembles. In order to study the effects of averaging on B-factors, we employ molecular dynamics simulations to controllably manipulate microscopic heterogeneity of a crystal containing 216 copies of villin headpiece. Using average structure factors derived from simulation, we analyse how well this heterogeneity is captured by high-resolution molecular-replacement-based model refinement. We find that both isotropic and anisotropic refined B-factors often significantly deviate from their actual values known from simulation: even at high 1.0 Å resolution and Rfree of 5.9%, B-factors of some well-resolved atoms underestimate their actual values even sixfold. Our results suggest that conformational averaging and inadequate treatment of correlated motion considerably influence estimation of microscopic heterogeneity via B-factors, and invite caution in their interpretation.

Shared resources, shared costs--leveraging biocuration resources

The manual curation of the information in biomedical resources is an expensive task. This article argues the value of this approach in comparison with other apparently less costly options, such as automated annotation or text-mining, then discusses ways in which databases can make cost savings by sharing infrastructure and tool development. Sharing curation effort is a model already being adopted by several data resources. Approaches taken by two of these, the Gene Ontology annotation effort and the IntAct molecular interaction database, are reviewed in more detail. These models help to ensure long-term persistence of curated data and minimizes redundant development of resources by multiple disparate groups.

Database URL:http://www.ebi.ac.uk/intact and http://www.ebi.ac.uk/GOA/

An algorithm to enumerate all possible protein conformations verifying a set of distance constraints

BACKGROUND

The determination of protein structures satisfying distance constraints is an important problem in structural biology. Whereas the most common method currently employed is simulated annealing, there have been other methods previously proposed in the literature. Most of them, however, are designed to find one solution only.

RESULTS

In order to explore exhaustively the feasible conformational space, we propose here an interval Branch-and-Prune algorithm (iBP) to solve the Distance Geometry Problem (DGP) associated to protein structure determination. This algorithm is based on a discretization of the problem obtained by recursively constructing a search space having the structure of a tree, and by verifying whether the generated atomic positions are feasible or not by making use of pruning devices. The pruning devices used here are directly related to features of protein conformations.

CONCLUSIONS

We described the new algorithm iBP to generate protein conformations satisfying distance constraints, that would potentially allows a systematic exploration of the conformational space. The algorithm iBP has been applied on three α-helical peptides.

Prioritization of active antimalarials using structural interaction profile of Plasmodium falciparum enoyl-acyl carrier protein reductase (PfENR)-triclosan derivatives

An empirical relationship between the experimental inhibitory activities of triclosan derivatives and its computationally predicted Plasmodium falciparum enoyl-acyl carrier protein (ACP) reductase (PfENR) dock poses was developed to model activities of known antimalarials. A statistical model was developed using 57 triclosan derivatives with significant measures (r = 0.849, q(2) = 0.619, s = 0.481) and applied on structurally related and structurally diverse external datasets. A substructure-based search on ChEMBL malaria dataset (280 compounds) yielded only two molecules with significant docking energy, whereas eight active antimalarials (EC(50) < 100 nM, tested on 3D7 strain) with better predicted activities (pIC(50) ~ 7) from Open Access Malaria Box (400 compounds) were prioritized. Further, calculations on the structurally diverse rhodanine molecules (known PfENR inhibitors) distinguished actives (experimental IC(50) = 0.035 μM; predicted pIC(50) = 6.568) and inactives (experimental IC(50) = 50 μM; predicted pIC50 = -4.078), which showed that antimalarials possessing dock poses similar to experimental interaction profiles can be used as leads to test experimentally on enzyme assays.

Bioinformatics Approaches for Predicting Disordered Protein Motifs

Short, linear motifs (SLiMs) in proteins are functional microdomains consisting of contiguous residue segments along the protein sequence, typically not more than 10 consecutive amino acids in length with less than 5 defined positions. Many positions are 'degenerate' thus offering flexibility in terms of the amino acid types allowed at those positions. Their short length and degenerate nature confers evolutionary plasticity meaning that SLiMs often evolve convergently. Further, SLiMs have a propensity to occur within intrinsically unstructured protein segments and this confers versatile functionality to unstructured regions of the proteome. SLiMs mediate multiple types of protein interactions based on domain-peptide recognition and guide functions including posttranslational modifications, subcellular localization of proteins, and ligand binding. SLiMs thus behave as modular interaction units that confer versatility to protein function and SLiM-mediated interactions are increasingly being recognized as therapeutic targets. In this chapter we start with a brief description about the properties of SLiMs and their interactions and then move on to discuss algorithms and tools including several web-based methods that enable the discovery of novel SLiMs (de novo motif discovery) as well as the prediction of novel occurrences of known SLiMs. Both individual amino acid sequences as well as sets of protein sequences can be scanned using these methods to obtain statistically overrepresented sequence patterns. Lists of putatively functional SLiMs are then assembled based on parameters such as evolutionary sequence conservation, disorder scores, structural data, gene ontology terms and other contextual information that helps to assess the functional credibility or significance of these motifs. These bioinformatics methods should certainly guide experiments aimed at motif discovery.

Expansion and Function of Repeat Domain Proteins During Stress and Development in Plants

The recurrent repeats having conserved stretches of amino acids exists across all domains of life. Subsequent repetition of single sequence motif and the number and length of the minimal repeating motifs are essential characteristics innate to these proteins. The proteins with tandem peptide repeats are essential for providing surface to mediate protein-protein interactions for fundamental biological functions. Plants are enriched in tandem repeat containing proteins typically distributed into various families. This has been assumed that the occurrence of multigene repeats families in plants enable them to cope up with adverse environmental conditions and allow them to rapidly acclimatize to these conditions. The evolution, structure, and function of repeat proteins have been studied in all kingdoms of life. The presence of repeat proteins is particularly profuse in multicellular organisms in comparison to prokaryotes. The precipitous expansion of repeat proteins in plants is presumed to be through internal tandem duplications. Several repeat protein gene families have been identified in plants. Such as Armadillo (ARM), Ankyrin (ANK), HEAT, Kelch-like repeats, Tetratricopeptide (TPR), Leucine rich repeats (LRR), WD40, and Pentatricopeptide repeats (PPR). The structure and functions of these repeat proteins have been extensively studied in plants suggesting a critical role of these repeating peptides in plant cell physiology, stress and development. In this review, we illustrate the structural, functional, and evolutionary prospects of prolific repeat proteins in plants.

PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins

The post-translational regulation of proteins is mainly driven by two molecular events, their modification by several types of moieties and their interaction with other proteins. These two processes are interdependent and together are responsible for the function of the protein in a particular cell state. Several databases focus on the prediction and compilation of protein–protein interactions (PPIs) and no less on the collection and analysis of protein post-translational modifications (PTMs), however, there are no resources that concentrate on describing the regulatory role of PTMs in PPIs. We developed several methods based on residue co-evolution and proximity to predict the functional associations of pairs of PTMs that we apply to modifications in the same protein and between two interacting proteins. In order to make data available for understudied organisms, PTMcode v2 (http://ptmcode.embl.de) includes a new strategy to propagate PTMs from validated modified sites through orthologous proteins. The second release of PTMcode covers 19 eukaryotic species from which we collected more than 300 000 experimentally verified PTMs (>1 300 000 propagated) of 69 types extracting the post-translational regulation of >100 000 proteins and >100 000 interactions. In total, we report 8 million associations of PTMs regulating single proteins and over 9.4 million interplays tuning PPIs.

The Protein Data Bank archive as an open data resource

The Protein Data Bank archive was established in 1971, and recently celebrated its 40th anniversary (Berman et al. in Structure 20:391, 2012). An analysis of interrelationships of the science, technology and community leads to further insights into how this resource evolved into one of the oldest and most widely used open-access data resources in biology.

Redundancy-weighting for better inference of protein structural features

MOTIVATION

Structural knowledge, extracted from the Protein Data Bank (PDB), underlies numerous potential functions and prediction methods. The PDB, however, is highly biased: many proteins have more than one entry, while entire protein families are represented by a single structure, or even not at all. The standard solution to this problem is to limit the studies to non-redundant subsets of the PDB. While alleviating biases, this solution hides the many-to-many relations between sequences and structures. That is, non-redundant datasets conceal the diversity of sequences that share the same fold and the existence of multiple conformations for the same protein. A particularly disturbing aspect of non-redundant subsets is that they hardly benefit from the rapid pace of protein structure determination, as most newly solved structures fall within existing families.

RESULTS

In this study we explore the concept of redundancy-weighted datasets, originally suggested by Miyazawa and Jernigan. Redundancy-weighted datasets include all available structures and associate them (or features thereof) with weights that are inversely proportional to the number of their homologs. Here, we provide the first systematic comparison of redundancy-weighted datasets with non-redundant ones. We test three weighting schemes and show that the distributions of structural features that they produce are smoother (having higher entropy) compared with the distributions inferred from non-redundant datasets. We further show that these smoothed distributions are both more robust and more correct than their non-redundant counterparts. We suggest that the better distributions, inferred using redundancy-weighting, may improve the accuracy of knowledge-based potentials and increase the power of protein structure prediction methods. Consequently, they may enhance model-driven molecular biology.

Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine

Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.

The evolution of enzyme function in the isomerases

The advent of computational approaches to measure functional similarity between enzymes adds a new dimension to existing evolutionary studies based on sequence and structure. This paper reviews research efforts aiming to understand the evolution of enzyme function in superfamilies, presenting a novel strategy to provide an overview of the evolution of enzymes belonging to an individual EC class, using the isomerases as an exemplar.

Modelling three-dimensional protein structures for applications in drug design

A structural perspective of drug target and anti-target proteins, and their molecular interactions with biologically active molecules, largely advances many areas of drug discovery, including target validation, hit and lead finding and lead optimisation. In the absence of experimental 3D structures, protein structure prediction often offers a suitable alternative to facilitate structure-based studies. This review outlines recent methodical advances in homology modelling, with a focus on those techniques that necessitate consideration of ligand binding. In this context, model quality estimation deserves special attention because the accuracy and reliability of different structure prediction techniques vary considerably, and the quality of a model ultimately determines its usefulness for structure-based drug discovery. Examples of G-protein-coupled receptors (GPCRs) and ADMET-related proteins were selected to illustrate recent progress and current limitations of protein structure prediction. Basic guidelines for good modelling practice are also provided.

A novel approach to represent and compare RNA secondary structures

Structural information is crucial in ribonucleic acid (RNA) analysis and functional annotation; nevertheless, how to include such structural data is still a debated problem. Dot-bracket notation is the most common and simple representation for RNA secondary structures but its simplicity leads also to ambiguity requiring further processing steps to dissolve. Here we present BEAR (Brand nEw Alphabet for RNA), a new context-aware structural encoding represented by a string of characters. Each character in BEAR encodes for a specific secondary structure element (loop, stem, bulge and internal loop) with specific length. Furthermore, exploiting this informative and yet simple encoding in multiple alignments of related RNAs, we captured how much structural variation is tolerated in RNA families and convert it into transition rates among secondary structure elements. This allowed us to compute a substitution matrix for secondary structure elements called MBR (Matrix of BEAR-encoded RNA secondary structures), of which we tested the ability in aligning RNA secondary structures. We propose BEAR and the MBR as powerful resources for the RNA secondary structure analysis, comparison and classification, motif finding and phylogeny.

Focused chemical libraries--design and enrichment: an example of protein-protein interaction chemical space

One of the many obstacles in the development of new drugs lies in the limited number of therapeutic targets and in the quality of screening collections of compounds. In this review, we present general strategies for building target-focused chemical libraries with a particular emphasis on protein-protein interactions (PPIs). We describe the chemical spaces spanned by nine commercially available PPI-focused libraries and compare them to our 2P2I3D academic library, dedicated to orthosteric PPI modulators. We show that although PPI-focused libraries have been designed using different strategies, they share common subspaces. PPI inhibitors are larger and more hydrophobic than standard drugs; however, an effort has been made to improve the drug-likeness of focused chemical libraries dedicated to this challenging class of targets.

Dependence of protein crystal stability on residue charge states and ion content of crystal solvent

Protein crystallization is frequently induced by the addition of various precipitants, which directly affect protein solubility. In addition to organic cosolvents and long-chain polymers, salts belong to the most widely used precipitants in protein crystallography. However, despite such widespread usage, their mode of action at the atomistic level is still largely unknown. Here, we perform extensive molecular dynamics simulations of the villin headpiece crystal unit cell to examine its stability at different concentrations of sodium sulfate. We show that the inclusion of ions in crystal solvent at high concentration can prevent large rearrangements of the asymmetric units and a loss of symmetry of the unit cell without significantly affecting protein dynamics. Of importance, a similar result can be achieved by neutralizing several specific charged residues suggesting that they may play an active role in crystal destabilization due to unfavorable electrostatic interactions. Our results provide a microscopic picture behind salt-induced stabilization of a protein crystal and further suggest that adequate modeling of realistic crystallization conditions may be necessary for successful molecular dynamics simulations of protein crystals.

The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases

IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org).

RepeatsDB: a database of tandem repeat protein structures

RepeatsDB (http://repeatsdb.bio.unipd.it/) is a database of annotated tandem repeat protein structures. Tandem repeats pose a difficult problem for the analysis of protein structures, as the underlying sequence can be highly degenerate. Several repeat types haven been studied over the years, but their annotation was done in a case-by-case basis, thus making large-scale analysis difficult. We developed RepeatsDB to fill this gap. Using state-of-the-art repeat detection methods and manual curation, we systematically annotated the Protein Data Bank, predicting 10 745 repeat structures. In all, 2797 structures were classified according to a recently proposed classification schema, which was expanded to accommodate new findings. In addition, detailed annotations were performed in a subset of 321 proteins. These annotations feature information on start and end positions for the repeat regions and units. RepeatsDB is an ongoing effort to systematically classify and annotate structural protein repeats in a consistent way. It provides users with the possibility to access and download high-quality datasets either interactively or programmatically through web services.