ConoMode, a database for conopeptide binding modes

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Lectins (Ls) play an important role in many diseases such as different types of cancer, parasitic infections and other diseases. Interestingly, the Protein Data Bank (PDB) contains +3000 protein 3D structures with unknown function. Thus, we can in principle, discover new Ls mining non-annotated structures from PDB or other sources. However, there are no general models to predict new biologically relevant Ls based on 3D chemical structures. We used the MARCH-INSIDE software to calculate the Markov-Shannon 3D electrostatic entropy parameters for the complex networks of protein structure of 2200 different protein 3D structures, including 1200 Ls. We have performed a Linear Discriminant Analysis (LDA) using these parameters as inputs in order to seek a new Quantitative Structure-Activity Relationship (QSAR) model, which is able to discriminate 3D structure of Ls from other proteins. We implemented this predictor in the web server named LECTINPred, freely available at http://bio-aims.udc.es/LECTINPred.php. This web server showed the following goodness-of-fit statistics: Sensitivity=96.7 % (for Ls), Specificity=87.6 % (non-active proteins), and Accuracy=92.5 % (for all proteins), considering altogether both the training and external prediction series. In mode 2, users can carry out an automatic retrieval of protein structures from PDB. We illustrated the use of this server, in operation mode 1, performing a data mining of PDB. We predicted Ls scores for +2000 proteins with unknown function and selected the top-scored ones as possible lectins. In operation mode 2, LECTINPred can also upload 3D structural models generated with structure-prediction tools like LOMETS or PHYRE2. The new Ls are expected to be of relevance as cancer biomarkers or useful in parasite vaccine design.

Protein structure prediction aims to model the three‐dimensional (3D) structure of so far structurally uncharacterised proteins from their amino acid sequence. Motivated by the observation that homologous proteins with related amino acid sequences have similar 3D structures, protein homology modelling uses comparative methods to generate models for a target protein based on one or more related proteins with known 3D structure. The coordinates of the model are generated based on alignments between the target's and template's amino acid sequences, which define the correspondence between residues in both proteins. Ultimately, the quality of a computational model determines its usefulness for specific biomedical applications. Therefore, model quality estimation methods are used to identify unreliable or erroneous regions in the resulting models, and to estimate the overall accuracy of a model. Homology modelling (or comparative modelling) is currently the most accurate computational method available to routinely generate models of sufficient quality for various applications in life science research. Comparative protein modelling methods have been completely automated in recent years, and several Internet servers offer protein modelling services which are reliable and easy to use – also for the nonexpert in computational biology. Key Concepts: Protein structure prediction aims to model the three‐dimensional structure of so far structurally uncharacterised proteins (‘target’) based on their amino acid sequence. Homologous proteins with related amino acid sequences have similar three‐dimensional structures. Protein homology modelling uses information from one or more related proteins with known three‐dimensional structure (‘template’) to generate models for the target protein. Sensitive sequence searching methods are applied to identify template proteins with known structures in large databases. An alignment between the target's and template's amino acid sequences describes the correspondence between residues in both proteins. The coordinates of the model are constructed by extracting positional information from the corresponding structural template. Segments of the target protein not covered by template information (e.g. insertions/deletion in the alignment) have to be constructed using de novo modelling methods. Model quality estimation methods are used to identify unreliable or erroneous regions in the resulting models. Ultimately, the quality of a structural model determines its usefulness for specific biomedical applications.

In the previous chapter, we saw that protein dynamics is a major candidate to explain the structure-function relationship. Experimental techniques, such as X-ray crystallography and Nuclear Magnetic Resonance (NMR), have become very powerful nowadays in providing accurate descriptions of the three-dimensional protein structure. Plenty of structures are in fact resolved every year and deposited into public databases, such as the Protein Data Bank (PDB) [21]. As of December 2020, the PDB comprises a total of about 160,000 protein structures, 89% of which obtained from X-ray crystallography, 7% from NMR, and the remaining 4% resolved with other methodologies such as Electron Microscopy (EM) and hybrid methods [22]. The knowledge of the three-dimensional protein structure is obviously an important starting point in order to investigate its mechanism of action. A thorough analysis of its dynamics subsequently allows to predict its functional behavior. Hence, how can we investigate protein dynamics?One of the most used approach is through Molecular Dynamics (MD) simulations [23, 24]. MD is based on solving the Newton’s laws of motion for a system subjected to certain forces. The initial model of the system is usually taken from experimental structures, which can be represented at various levels of detail. Once the system is built, the forces acting on every atom are computed based on the derivation of potential energies. Different forms of potentials can be used for this purpose. Often these potential energies include spring terms for bond stretching and angle distortions, Lennard-Jones terms for long-range interactions, as well as Coulomb’s laws for Van der Waals and electrostatic effects [24]. Obtained the forces acting on the systems, the equations of motion are integrated to obtain the velocities and positions of all the atoms. This is carried out by adopting numerical integration strategies, which require discretization into small time frames of the order of femtoseconds. Although MD simulations can provide detailed and often accurate predictions of the trajectories of the system, the high computational burden and the difficulty to achieve stable solutions at very large time scales often constitute serious obstacles.

RNA molecules with novel functions have revived interest in the accurate prediction of RNA three-dimensional (3D) structure and folding dynamics. However, existing methods are inefficient in automated 3D structure prediction. Here, we report a robust computational approach for rapid folding of RNA molecules. We develop a simplified RNA model for discrete molecular dynamics (DMD) simulations, incorporating base-pairing and base-stacking interactions. We demonstrate correct folding of 150 structurally diverse RNA sequences. The majority of DMD-predicted 3D structures have <4 A deviations from experimental structures. The secondary structures corresponding to the predicted 3D structures consist of 94% native base-pair interactions. Folding thermodynamics and kinetics of tRNA(Phe), pseudoknots, and mRNA fragments in DMD simulations are in agreement with previous experimental findings. Folding of RNA molecules features transient, non-native conformations, suggesting non-hierarchical RNA folding. Our method allows rapid conformational sampling of RNA folding, with computational time increasing linearly with RNA length. We envision this approach as a promising tool for RNA structural and functional analyses.

Modeller: Generation and Refinement of Homology-Based Protein Structure Models

BackgroundStructural information about epitopes, particularly the three-dimensional (3D) structures of antigens in complex with immune receptors, presents a valuable source of data for immunology. This information is available in the Protein Data Bank (PDB) and provided in curated form by the Immune Epitope Database and Analysis Resource (IEDB). With continued growth in these data and the importance in understanding molecular level interactions of immunological interest there is a need for new specialized molecular visualization and analysis tools.ResultsThe EpitopeViewer is a platform-independent Java application for the visualization of the three-dimensional structure and sequence of epitopes and analyses of their interactions with antigen-specific receptors of the immune system (antibodies, T cell receptors and MHC molecules). The viewer renders both 3D views and two-dimensional plots of intermolecular interactions between the antigen and receptor(s) by reading curated data from the IEDB and/or calculated on-the-fly from atom coordinates from the PDB. The 3D views and associated interactions can be saved for future use and publication. The EpitopeViewer can be accessed from the IEDB Web site through the quick link 'Browse Records by 3D Structure.'ConclusionThe EpitopeViewer is designed and been tested for use by immunologists with little or no training in molecular graphics. The EpitopeViewer can be launched from most popular Web browsers without user intervention. A Java Runtime Environment (RJE) 1.4.2 or higher is required.

Monoclonal antibodies have recently started to deliver on their promise as highly specific and active drugs; however, a more effective, knowledge-based approach to the selection, design, and optimization of potential therapeutic antibodies is currently limited by the surprising lack of detailed structural information for complexes formed with target proteins. Here we show that complexes formed with minimal antigen binding single chain variable fragments (scFv) reliably reflect all the features of the binding interface present in larger Fab fragments, which are commonly used as therapeutics, and report the development of a robust, reliable, and relatively rapid approach to the determination of high resolution models for scFv-target protein complexes. This NMR spectroscopy-based approach combines experimental determination of the interaction surfaces and relative orientations of the scFv and target protein, with NMR restraint-driven, semiflexible docking of the proteins to produce a reliable and highly informative model of the complex. Experience with scFvs and Fabs targeted at a number of secreted regulatory proteins suggests that the approach will be applicable to many therapeutic antibodies targeted at proteins, and its application is illustrated for a potential therapeutic antibody targeted at the cytokine IL-1beta. The detailed structural information that can be obtained by this approach has the potential to have a major impact on the rational design and development of an increasingly important class of biological pharmaceuticals.

ObjectivesTo evaluate the drug-target interaction of the antibacterial compounds from the Streptomyces chrestomyceticus ADP4.MethodsStaphylococcus aureus is of the WHO priority pathogen, causative agent for multiple soft tissue and skin infections, gastroenteritis etc. making it a suitable candidate for investigation of the drug target based studies. Computational biology has emerged as promising, sustainable and efficient technique for shortlisting, screening and to predict drug target interaction and possible antibacterial mechanisms involved. The study involves molecular docking studies of compounds with the bacterial antibacterial targets of S. aureus which includes topoisomerase, metabolic enzymes such as malate dehydrogenase, dihydrofolate reductase etc. The protein structures were collected from protein data bank and homology models were constructed using templates with high sequence identity to get reliable 3D structure. The blind docking was performed using the PyRx software using the AutoDock Vina. Molecular dynamic (MD) simulation analysis was carried out using GROMACS tool. The drug-target complexes were screened and selected based on the threshold binding energy from molecular docking. The trajectory obtained from MD simulation were analysed for the time dependent interaction, conformational changes, stability, residue flexibility, compactness, solvent accessible surface area (SASA) of the drug- target complexes.ResultsMolecular Docking analysis showed that the compounds interact and bind with target enzyme with intermediate binding affinity upto-7kcal/mol with decent stability and interaction in MD analysis. It further indicates the compounds as promising antibacterial agents with further scope of lead optimization for better antimicrobial potential.ConclusionsThe study also highlights on the mode of action of the compounds by predicting the inhibition of the key metabolic enzymes and therefore metabolic pathways leading to bactericidal activity. This approach helps fast tracking the drug discovery initiatives time bound and early stage identification of the antimicrobial molecules.

BackgroundAn important mechanism of endocrine activity is chemicals entering target cells via transport proteins and then interacting with hormone receptors such as the estrogen receptor (ER). α-Fetoprotein (AFP) is a major transport protein in rodent serum that can bind and sequester estrogens, thus preventing entry to the target cell and where they could otherwise induce ER-mediated endocrine activity. Recently, we reported rat AFP binding affinities for a large set of structurally diverse chemicals, including 53 binders and 72 non-binders. However, the lack of three-dimensional (3D) structures of rat AFP hinders further understanding of the structural dependence for binding. Therefore, a 3D structure of rat AFP was built using homology modeling in order to elucidate rat AFP-ligand binding modes through docking analyses and molecular dynamics (MD) simulations.MethodsHomology modeling was first applied to build a 3D structure of rat AFP. Molecular docking and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) scoring were then used to examine potential rat AFP ligand binding modes. MD simulations and free energy calculations were performed to refine models of binding modes.ResultsA rat AFP tertiary structure was first obtained using homology modeling and MD simulations. The rat AFP-ligand binding modes of 13 structurally diverse, representative binders were calculated using molecular docking, (MM-GBSA) ranking and MD simulations. The key residues for rat AFP-ligand binding were postulated through analyzing the binding modes.ConclusionThe optimized 3D rat AFP structure and associated ligand binding modes shed light on rat AFP-ligand binding interactions that, in turn, provide a means to estimate binding affinity of unknown chemicals. Our results will assist in the evaluation of the endocrine disruption potential of chemicals.

Rand protease is a serine protease that shared common characteristics with members of the MEROPS S8 subtilisin family. It is thermostable, highly stable in organic solvent and broad in specificity. Many structures of homologous protein solved by X-ray crystallography and NMR have been deposited to Protein Data Bank (PDB) which allowed this study to rely on structure prediction by deep learning to build three-dimensional (3D) structure of full length and mature Rand protease (flRP and mRP). In silico cysteine mutation to 7 predicted high affinity Ca2+ coordinating residues were introduced, and the mutants were subjected to molecular dynamics simulation to study its effect on flRP and mRP. MD simulation showed a marked increase in flexibility of the pro-peptide segment indicating the impact of single cysteine substitution at high affinity Ca2+ coordinating residues to autolysis of flRP. MD simulation for mRP reaffirmed the role of Ca2+ coordinating sites in providing stability to Rand protease. In addition, these residues also affect the autolysis, folding and hydrophobicity of RP. Essential dynamics observed large contribution of the first few eigenvectors of flRP, mRP and their high affinity Ca2+ coordinating residues mutants to the TMSF values which indicates that these values account for a large portion of the overall atomic fluctuations. These results have given a more comprehensive understanding on the role of cysteine substituted Ca2+ coordinating surface loop to the structure of flRP and mRP which are important in contributing to the structural stability of subtilisin. Communicated by Ramaswamy H. Sarma

Narciclasine is an alkaloid belonging to the Amaryllidaceae family which has been reported to have many beneficial properties. Especially its anticancer properties have been widely reported. Here, we have focused on its potential use in suppressing the inflammatory response in sepsis using in silico methods. Lipopolysaccharide (LPS) is an endotoxin which is present in the outer membrane of gram-negative bacteria and is a crucial player in the pathogenesis of gram-negative sepsis. Activation of toll-like receptor 4 (TLR4) signaling by LPS is an important event in the pathogenesis of gram-negative sepsis. This initiates a downstream signaling pathway comprising of several adaptor proteins such as toll/interleukin-1 receptor domain-containing adapter protein (TIRAP), myeloid differentiation primary response protein 88 (MyD88), interleukin-1 receptor-associated kinase (IRAK)-1, IRAK-4, interferon regulatory factor 3 (IRF-3), tumor necrosis factor receptor-associated factor 6 (TRAF-6) leading to nuclear factor kappa B (NF-κβ) activation resulting in elevated production of inflammatory cytokines such as tumor necrosis factor alpha (TNF-α) and interleukin (IL)-6. S100 calcium binding proteins A8/A9 (S100A8/A9) have been found to be an agonist of TLR4, and it amplifies the inflammatory response in sepsis. Molecular docking studies of narciclasine with target proteins associated with the LPS-TLR4 pathway showed that it has good binding affinity and stable interactions with the targets studied. Molecular dynamics (MD) simulation studies over 100 ns showed that most of the ligand-target complexes were stable. The structures of all the targets except TRAF-6 were retrieved from the Protein Data Bank (PDB) database. Homology modeling was done to predict the 3-dimensional structure of TRAF-6. MD simulation of narciclasine-TRAF-6 complex showed that the structure is stable. Metapocket was used for active site prediction in the target proteins. Toxicity analysis by admetSAR revealed that narciclasine was readily biodegradable and exhibited minimum toxicity. These results indicate that narciclasine has effective anti-inflammatory properties which could be useful in suppressing the inflammatory response in sepsis.

RNA molecules have key functions in cellular processes beyond being carriers of protein-coding information. These functions are often dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is difficult, which has prompted the development of computational methods for structure prediction from sequence. Recent progress in 3D structure modeling of RNA and emerging approaches for predicting RNA interactions with ions, ligands and proteins have been stimulated by successes in protein 3D structure modeling.

Visualizing three-dimensional molecular structures is crucial to understanding and predicting their chemical behavior. Existing visualization software, however, can be cumbersome to use, and, for many, hand-drawn skeletal structures remain the preferred method of chemical communication. Although convenient, the static, two-dimensional nature of these drawings can be misleading in conveying the molecule’s 3D structure, not to mention that dynamic movement is completely disregarded. Here, we combine machine learning and augmented reality (AR) to develop MolAR, an immersive mobile application for visualizing molecules in real-world scenes. The application uses deep learning to recognize hand-drawn hydrocarbons structures which it converts into interactive 3D molecules in AR. Users can also “hunt” for chemicals in food and drink to uncover molecules in their real-life environment. A variety of interesting molecules are pre-loaded into the application, and users can visualize molecules in PubChem by providing their name or SMILES string and proteins in the Protein Data Bank by providing their PDB ID. MolAR was designed to be used in both research and education settings, providing an almost barrierless platform to visualize and interact with 3D molecular structures in a uniquely immersive way.

Bovine pancreatic beta-trypsin (PDB ID-code: 1TPO) which is registered in the Brookhaven Protein Data Bank (PDB) consists of four exons. The results of homology searches for each exon in the PDB showed that homologous proteins were tonin (PDB ID-code: 1TON), rat mast cell protease (PDB ID-code: 3RP2__A), kallikrein A (PDB ID-code: 2PKA__B) and kallikrein A (2PKA__B) respectively. Thus, for the three-dimensional structure prediction of 1TPO, a chimera protein was constructed from the three proteins mentioned above and the 3-D structure prediction was performed using this chimera reference protein. The modelled structure of 1TPO was energetically optimized by molecular mechanics and molecular dynamics simulation and was compared with its X-ray crystal structure registered in the PDB. The root mean square deviations (r.m.s.d.) of main chain atoms and the neighbouring active site (5 A sphere from His57, Asp102 and Ser195) between the modelled structure and the X-ray structure were 1.66 and 0.94 A respectively. Porcine pancreatic elastase (PDB ID-code: 3EST) which is registered in the PDB was used as the reference protein and the modelled structure from 3EST was also compared with the X-ray data. The r.m.s.d. of main chain atoms and that of the active site were 2.14 and 1.18 A respectively. These results clearly support the propriety of this method using the chimera reference protein.