PCE: web tools to compute protein continuum electrostatics

Targeting ERK-MYD88 interaction leads to ERK dysregulation and immunogenic cancer cell death

The quest for targeted therapies is critical in the battle against cancer. The RAS/MAP kinase pathway is frequently implicated in neoplasia, with ERK playing a crucial role as the most distal kinase in the RAS signaling cascade. Our previous research demonstrated that the interaction between ERK and MYD88, an adaptor protein in innate immunity, is crucial for RAS-dependent transformation and cancer cell survival. In this study, we examine the biological consequences of disrupting the ERK-MYD88 interaction through the ERK D-recruitment site (DRS), while preserving ERK's kinase activity. Our results indicate that EI-52, a small-molecule benzimidazole targeting ERK-MYD88 interaction induces an HRI-mediated integrated stress response (ISR), resulting in immunogenic apoptosis specific to cancer cells. Additionally, EI-52 exhibits anti-tumor efficacy in patient-derived tumors and induces an anti-tumor T cell response in mice in vivo. These findings suggest that inhibiting the ERK-MYD88 interaction may be a promising therapeutic approach in cancer treatment.

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.

Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages

In December 2019, a new coronavirus was identified in the Hubei province of central china and named SARS-CoV-2. This new virus induces COVID-19, a severe respiratory disease with high death rate. A putative target to interfere with the virus is the host transmembrane serine protease family member II (TMPRSS2). This enzyme is critical for the entry of coronaviruses into human cells by cleaving and activating the spike protein (S) of SARS-CoV-2. Repositioning approved, investigational and experimental drugs on the serine protease domain of TMPRSS2 could thus be valuable. There is no experimental structure for TMPRSS2 but it is possible to develop quality structural models for the serine protease domain using comparative modeling strategies as such domains are highly structurally conserved. Beside the TMPRSS2 catalytic site, we predicted on our structural models a main exosite that could be important for the binding of protein partners and/or substrates. To block the catalytic site or the exosite of TMPRSS2 we used structure-based virtual screening computations and two different collections of approved, investigational and experimental drugs. We propose a list of 156 molecules that could bind to the catalytic site and 100 compounds that may interact with the exosite. These small molecules should now be tested in vitro to gain novel insights over the roles of TMPRSS2 or as starting point for the development of second generation analogs.

ELA/APELA precursor cleaved by furin displays tumor suppressor function in renal cell carcinoma through mTORC1 activation

Apelin is a well-established mediator of survival and mitogenic signaling through the apelin receptor (Aplnr) and has been implicated in various cancers; however, little is known regarding Elabela (ELA/APELA) signaling, also mediated by Aplnr, and its role and the role of the conversion of its precursor proELA into mature ELA in cancer are unknown. Here, we identified a function of mTORC1 signaling as an essential mediator of ELA that repressed kidney tumor cell growth, migration, and survival. Moreover, sunitinib and ELA showed a synergistic effect in repressing tumor growth and angiogenesis in mice. The use of site-directed mutagenesis and pharmacological experiments provided evidence that the alteration of the cleavage site of proELA by furin induced improved ELA antitumorigenic activity. Finally, a cohort of tumors and public data sets revealed that ELA was only repressed in the main human kidney cancer subtypes, namely clear cell, papillary, and chromophobe renal cell carcinoma. Aplnr was expressed by various kidney cells, whereas ELA was generally expressed by epithelial cells. Collectively, these results showed the tumor-suppressive role of mTORC1 signaling mediated by ELA and established the potential use of ELA or derivatives in kidney cancer treatment.

Expression profiling and in silico homology modeling of Inositol pentakisphosphate 2-kinase, a potential candidate gene for low phytate trait in soybean

Low phytate soybeans are desirable both from a nutritional and economic standpoint. Inositol 1, 3, 4, 5, 6-pentakisphosphate 2-kinase (IPK1), optimizes the metabolic flux of phytate generation in soybean and thus shows much promise as a likely candidate for pathway regulation. In the present study, the differential spatial and temporal expression profiling of GmIpk1 and its two homologs Glyma06g03310 and Glyma04g03310 were carried out in Glycine max L. var Pusa 9712 revealing the early stages of seed development to be the potential target for gene manipulation. NCBI databank was screened using BLASTp to retrieve 32 plant IPK1 sequences showing high homology to GmIPK1 and its homologs. Bio-computational tools were employed to predict the protein's properties, conserved domains, and secondary structures. Using state-of-the-art in silico physicochemical approach, the three-dimensional (3D) GmIPK1 protein model (PMD ID-PM0079931), was developed based on Arabidopsis thaliana (PDB ID: 4AQK). Superimposition of 4AQK and best model of GmIPK1 revealed that the GmIPK1 aligned well and shows a sequence identity score of 54.32% with 4AQK and a low RMSD of 0.163 nm and almost similar structural features. The modeled structure was further refined considering the stereochemical geometry, energy and packing environment between the model and the template along with validation of its intrinsic dynamics. Molecular dynamics simulation studies of GmIPK1 were carried out to obtain structural insights and to understand the interactive behavior of this enzyme with ligands ADP and IP₆. The results of this study provide some fundamental knowledge on the distinct mechanistic step performed by the key residues to elucidate the structure-function relationship of GmIPK1, as an initiative towards engineering "low phytate soybean".

Using Fluorescence Quenching Titration to Determine the Orientation of a Model Transmembrane Protein in Mimic Membranes

After synthesis of transmembrane proteins (TMPs), they are transferred and inserted into plasma membranes to play biological functions. Crucially, orientation of TMPs in membranes determines whether they have biological activities. In cellular environments, a number of cofactors, such as translocon, can assist TMPs to be inserted into membranes in defined orientations. During in vitro reconstitution of TMPs with mimic membranes, both insertion and orientation of TMPs are primarily determined by interactions with the membrane. Yet the knowledge is limited, hindering the in vitro applications of TMPs. Here, we take Bacteriorhodopsin (bR) as a model TMP, using fluorescence quenching titration experiment to identify orientation of bR in mimic membranes, examining effects of a number of factors, including lipid composition, pH value, ionic strength and membrane curvature. The most effective determinant is the lipid type, which modulates insertion and orientation of bR in membranes by changing the membrane surface charge and the membrane fluidity. Both the pH value and the ionic strength play secondary roles by tuning the nature of the electrostatic interaction. The membrane curvature was found to have a minor effect on orientation of bR in membranes. By comparing orientations of bR in folded and unfolded states, no obvious change was observed, informing that nascent proteins could be inserted into membranes in defined orientations before folding into the native state inside the membrane.

Insights into molecular mechanisms of drug metabolism dysfunction of human CYP2C9*30

Cytochrome P450 2C9 (CYP2C9) metabolizes about 15% of clinically administrated drugs. The allelic variant CYP2C9*30 (A477T) is associated to diminished response to the antihypertensive effects of the prodrug losartan and affected metabolism of other drugs. Here, we investigated molecular mechanisms involved in the functional consequences of this amino-acid substitution. Molecular dynamics (MD) simulations performed for the active species of the enzyme (heme in the Compound I state), in the apo or substrate-bound state, and binding energy analyses gave insights into altered protein structure and dynamics involved in the defective drug metabolism of human CYP2C9.30. Our data revealed an increased rigidity of the key Substrate Recognition Sites SRS1 and SRS5 and shifting of the β turn 4 of SRS6 toward the helix F in CYP2C9.30. Channel and binding substrate dynamics analyses showed altered substrate channel access and active site accommodation. These conformational and dynamic changes are believed to be involved in the governing mechanism of the reduced catalytic activity. An ensemble of representative conformations of the WT and A477T mutant properly accommodating drug substrates were identified, those structures can be used for prediction of new CYP2C9 and CYP2C9.30 substrates and drug-drug interactions.

Computational Resources for Predicting Protein-Protein Interactions

Proteins are the essential building blocks and functional components of a cell. They account for the vital functions of an organism. Proteins interact with each other and form protein interaction networks. These protein interactions play a major role in all the biological processes and pathways. The previous methods of predicting protein interactions were experimental which focused on a small set of proteins or a particular protein. However, these experimental approaches are low-throughput as they are time-consuming and require a significant amount of human effort. This led to the development of computational techniques that uses high-throughput experimental data for analyzing protein-protein interactions. The main purpose of this review is to provide an overview on the computational advancements and tools for the prediction of protein interactions. The major databases for the deposition of these interactions are also described. The advantages, as well as the specific limitations of these tools, are highlighted which will shed light on the computational aspects that can help the biologist and researchers in their research.

Determinants of Macromolecular Specificity from Proteomics-Derived Peptide Substrate Data

BACKGROUND

Recent advances in proteomics methodologies allow for high throughput profiling of proteolytic cleavage events. The resulting substrate peptide distributions provide deep insights in the underlying macromolecular recognition events, as determinants of biomolecular specificity identified by proteomics approaches may be compared to structure-based analysis of corresponding protein-protein interfaces.

METHOD

Here, we present an overview of experimental and computational methodologies and tools applied in the area and provide an outlook beyond the protein class of proteases.

RESULTS AND CONCLUSION

We discuss here future potential, synergies and needs of the emerging overlap disciplines of proteomics and structure-based modelling.

Computer simulations of the interaction of human immunodeficiency virus (HIV) aspartic protease with spherical gold nanoparticles: implications in acquired immunodeficiency syndrome (AIDS)

The interaction of gold nanoparticles (AuNP) with human immune-deficiency virus aspartic protease (HIVPR) is modelled using a regime of molecular dynamics simulations. The simulations of the 'docking', first as a rigid-body complex, and eventually through flexible-fit analysis, creates 36 different complexes from four initial orientations of the nanoparticle strategically positioned around the surface of the enzyme. The structural deviations of the enzymes from the initial x-ray crystal structure during each docking simulation are assessed by comparative analysis of secondary structural elements, root mean square deviations, B-factors, interactive bonding energies, dihedral angles, radius of gyration (R g), circular dichroism (CD), volume occupied by C α , electrostatic potentials, solvation energies and hydrophobicities. Normalisation of the data narrows the selection from the initial 36 to one 'final' probable structure. It is concluded that, after computer simulations on each of the 36 initial complexes incorporating the 12 different biophysical techniques, the top five complexes are the same no matter which technique is explored. The significance of the present work is an expansion of an earlier study on the molecular dynamic simulation for the interaction of HIVPR with silver nanoparticles. This work is supported by experimental evidence since the initial 'orientation' of the AgNP with the enzyme is the same as the 'final' AuNP-HIVPR complex generated in the present study. The findings will provide insight into the forces of the binding of the HIVPR to AuNP. It is anticipated that the protocol developed in this study will act as a standard process for the interaction of any nanoparticle with any biomedical target.

Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6

MOTIVATION

Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery.

RESULTS

We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

Theileria parasites secrete a prolyl isomerase to maintain host leukocyte transformation

Infectious agents develop intricate mechanisms to interact with host cell pathways and hijack the genetic and epigenetic machinery to change phenotypic states. Amongst the Apicomplexa phylum of obligate intracellular parasites which cause veterinary and human diseases, Theileria is the only genus which transforms its mammalian host cells¹. Theileria infection of bovine leukocytes induces proliferative and invasive phenotypes associated with activated signalling pathways, notably JNK and AP-1². The transformed phenotypes are reversed by treatment with the theilericidal drug Buparvaquone³. We used comparative genomics to identify a homologue of the Peptidyl Prolyl Isomerase Pin1 (designated TaPin1) in T. annulata which is secreted into the host cell and modulates oncogenic signalling pathways. Here we show that TaPin1 is a bona fide prolyl isomerase and that it interacts with the host ubiquitin ligase FBW7 leading to its degradation and subsequent stabilization of c-Jun which promotes transformation. We performed in vitro analysis and in vivo zebrafish xenograft experiments to demonstrate that TaPin1 is directly inhibited by the anti-parasite drug Buparvaquone (and other known Pin1 inhibitors) and is mutated in a drug-resistant strain. Prolyl isomerisation is thus a conserved mechanism which is important in cancer and is used by Theileria parasites to manipulate host oncogenic signaling.

In silico mechanistic profiling to probe small molecule binding to sulfotransferases

Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug–drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands’ binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.

Modeling, docking and dynamics simulations of a non-specific lipid transfer protein from Peganum harmala L

Non-specific lipid transfer proteins (ns-LTPs), ubiquitously found in various types of plants, have been well-known to transfer amphiphilic lipids and promote the lipid exchange between mitochondria and microbody. In this study, an in silico analysis was proposed to study ns-LTP in Peganum harmala L., which may belong to ns-LTP1 family, aiming at constructing its three-dimensional structure. Moreover, we adopted MEGA to analyze ns-LTPs and other species phylogenetically, which brought out an initial sequence alignment of ns-LTPs. In addition, we used molecular docking and molecular dynamics simulations to further investigate the affinities and stabilities of ns-LTP with several ligands complexes. Taken together, our results about ns-LTPs and their ligand-binding activities can provide a better understanding of the lipid-protein interactions, indicating some future applications of ns-LTP-mediated transport.

DelPhi Web Server: A comprehensive online suite for electrostatic calculations of biological macromolecules and their complexes

Here we report a web server, the DelPhi web server, which utilizes DelPhi program to calculate electrostatic energies and the corresponding electrostatic potential and ionic distributions, and dielectric map. The server provides extra services to fix structural defects, as missing atoms in the structural file and allows for generation of missing hydrogen atoms. The hydrogen placement and the corresponding DelPhi calculations can be done with user selected force field parameters being either Charmm22, Amber98 or OPLS. Upon completion of the calculations, the user is given option to download fixed and protonated structural file, together with the parameter and Delphi output files for further analysis. Utilizing Jmol viewer, the user can see the corresponding structural file, to manipulate it and to change the presentation. In addition, if the potential map is requested to be calculated, the potential can be mapped onto the molecule surface. The DelPhi web server is available from http://compbio.clemson.edu/delphi_webserver.

Targeting the proangiogenic VEGF-VEGFR protein-protein interface with drug-like compounds by in silico and in vitro screening

Protein-protein interactions play a central role in medicine, and their modulation with small organic compounds remains an enormous challenge. Because it has been noted that the macromolecular complexes modulated to date have a relatively pronounced binding cavity at the interface, we decided to perform screening experiments over the vascular endothelial growth factor receptor (VEGFR), a validated target for antiangiogenic treatments with a very flat interface. We focused the study on the VEGFR-1 D2 domain, and 20 active compounds were identified. These small compounds contained a (3-carboxy-2-ureido)thiophen unit and had IC(50) values in the low micromolar range. The most potent compound inhibited the VEGF-induced VEGFR-1 transduction pathways. Our findings suggest that our best hit may be a promising scaffold to probe this macromolecular complex and for the development of treatments of VEGFR-1-dependent diseases.

Structural stability as a probe for molecular evolution of homologous albumins studied by spectroscopy and bioinformatics

Equilibrium unfolding by guanidinium hydrochloride (GuHCl) and urea as well as evolutionary trends of two homologous albumins, pig serum albumin (PSA) and rabbit serum albumin (RSA), has been studied with circular dichroism, tryptophanyl fluorescence and bioinformatics. GuHCl cannot distinguish the contribution of electrostatic interactions to the proteins which were otherwise effectively monitored by urea. Higher differences in free energy changes due to urea than GuHCl show electrostatic interactions among charged amino acids are possibly responsible for higher structural stability of RSA in comparison to PSA. From the sequence of HSA and RSA, deletion of arginine at position 117 and the presence of one extra tryptophan at position 135 may possess some clue for lesser stability of PSA. Here, for comparison, chemical unfolding data of HSA and BSA had been taken into consideration. We found that thermodynamically RSA and PSA are closer to HSA and BSA, respectively, in accordance with their sequence homologies. Taxonomically, rabbit belongs to lagomorph which is closer to hominids than ungulates. Hence, on the basis of these thermodynamic data of protein denaturation of different species we can use this new approach to analyze the phylogenetic relationship among the major clades of eutherian mammals to obtain their evolutionary trends.

Exploring NMR ensembles of calcium binding proteins: perspectives to design inhibitors of protein-protein interactions

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.

Analysis of binding sites on complement factor I that are required for its activity

The central complement inhibitor factor I (FI) degrades activated complement factors C4b and C3b in the presence of cofactors such as C4b-binding protein, factor H, complement receptor 1, and membrane cofactor protein. FI is a serine protease composed of two chains. The light chain comprises the serine protease domain, whereas the heavy chain contains several domains; that is, the FI and membrane attack complex domain (FIMAC), CD5, low density lipoprotein receptor 1 (LDLr1) and LDLr2 domains. To understand better how FI acts as a complement inhibitor, we used homology-based models of FI domains to predict potential binding sites. Specific amino acids were then mutated to yield 16 well expressed mutants, which were then purified from media of eukaryotic cells for functional analyses. The Michaelis constant (K(m)) of all FI mutants toward a small substrate was not altered, whereas some mutants showed increased maximum initial velocity (V(max)). All the mutations in the FIMAC domain affected the ability of FI to degrade C4b and C3b irrespective of the cofactor used, whereas only some mutations in the CD5 and LDLr1/2 domains had a similar effect. These same mutants also showed impaired binding to C3met. In conclusion, the FIMAC domain appears to harbor the main binding sites important for the ability of FI to degrade C4b and C3b.

Physical properties of intact proteins may predict allergenicity or lack thereof

Background

Predicting the allergenicity of proteins is challenging. We considered the possibility that the properties of the intact protein that may alter the likelihood of being taken up by antigen presenting cells, may be useful adjuncts in predicting allergens and non-allergens in silico. It has been shown that negatively charged acidic proteins are preferentially processed by dendritic cells.

Methodology

Datasets (aeroallergen, food-allergen and non-allergen) for in-silico study were obtained from public databases. Isoelectric point (pI), net charge, and electrostatic potential (EP) were calculated from the protein sequence (for pI and net charge) or predicted structure (for EP).

Result

Allergens and non allergens differed significantly in pI, net charge, and EP (p<0.0001). Cluster analysis based on these parameters resulted in well defined clusters. Non-allergens were characterized by neutral to basic pI (mean±SE, 7.6±0.16) and positive charge. In contrast allergens were acidic (5.7±0.15) and negatively charged. Surface electrostatic potentials calculated from predicted structures were mostly negative for allergens and mostly positive for non-allergens. The classification accuracy for non-allergens was superior to that for allergens. Thus neutral to basic pI, positive charge, and positive electrostatic potentials characterize non-allergens, and seem rare in allergens (p<0.0001). It may be possible to predict reduced likelihood of allergenicity in such proteins, but this needs to be prospectively validated.

Modelling substrate specificity and enantioselectivity for lipases and esterases by substrate-imprinted docking

Background

Previously, ways to adapt docking programs that were developed for modelling inhibitor-receptor interaction have been explored. Two main issues were discussed. First, when trying to model catalysis a reaction intermediate of the substrate is expected to provide more valid information than the ground state of the substrate. Second, the incorporation of protein flexibility is essential for reliable predictions.

Results

Here we present a predictive and robust method to model substrate specificity and enantioselectivity of lipases and esterases that uses reaction intermediates and incorporates protein flexibility. Substrate-imprinted docking starts with covalent docking of reaction intermediates, followed by geometry optimisation of the resulting enzyme-substrate complex. After a second round of docking the same substrate into the geometry-optimised structures, productive poses are identified by geometric filter criteria and ranked by their docking scores. Substrate-imprinted docking was applied in order to model (i) enantioselectivity of Candida antarctica lipase B and a W104A mutant, (ii) enantioselectivity and substrate specificity of Candida rugosa lipase and Burkholderia cepacia lipase, and (iii) substrate specificity of an acetyl- and a butyrylcholine esterase toward the substrates acetyl- and butyrylcholine.

Conclusion

The experimentally observed differences in selectivity and specificity of the enzymes were reproduced with an accuracy of 81%. The method was robust toward small differences in initial structures (different crystallisation conditions or a co-crystallised ligand), although large displacements of catalytic residues often resulted in substrate poses that did not pass the geometric filter criteria.

Protein structure analysis online

Computational biology/chemistry tools are used in most areas of life/health science research. These methods are continually being developed and their use can present difficulties for both experienced and novice investigators. To facilitate the use of these applications, many packages have been implemented online during these last 5 years. This unit focuses on online computational methods with a special emphasis on structural refinement/atomic simulations, protein electrostatic calculations, searches for functional sites, searches for druggable pockets, protein docking and small molecule docking, and prediction of potential impact of amino acid variations on the structure and function of the protein molecules.

PBEQ-Solver for online visualization of electrostatic potential of biomolecules

PBEQ-Solver provides a web-based graphical user interface to read biomolecular structures, solve the Poisson-Boltzmann (PB) equations and interactively visualize the electrostatic potential. PBEQ-Solver calculates (i) electrostatic potential and solvation free energy, (ii) protein–protein (DNA or RNA) electrostatic interaction energy and (iii) pKa of a selected titratable residue. All the calculations can be performed in both aqueous solvent and membrane environments (with a cylindrical pore in the case of membrane). PBEQ-Solver uses the PBEQ module in the biomolecular simulation program CHARMM to solve the finite-difference PB equation of molecules specified by users. Users can interactively inspect the calculated electrostatic potential on the solvent-accessible surface as well as iso-electrostatic potential contours using a novel online visualization tool based on MarvinSpace molecular visualization software, a Java applet integrated within CHARMM-GUI (http://www.charmm-gui.org). To reduce the computational time on the server, and to increase the efficiency in visualization, all the PB calculations are performed with coarse grid spacing (1.5 Å before and 1 Å after focusing). PBEQ-Solver suggests various physical parameters for PB calculations and users can modify them if necessary. PBEQ-Solver is available at http://www.charmm-gui.org/input/pbeqsolver.

webPIPSA: a web server for the comparison of protein interaction properties

Protein molecular interaction fields are key determinants of protein functionality. PIPSA (Protein Interaction Property Similarity Analysis) is a procedure to compare and analyze protein molecular interaction fields, such as the electrostatic potential. PIPSA may assist in protein functional assignment, classification of proteins, the comparison of binding properties and the estimation of enzyme kinetic parameters. webPIPSA is a web server that enables the use of PIPSA to compare and analyze protein electrostatic potentials. While PIPSA can be run with downloadable software (see http://projects.eml.org/mcm/software/pipsa), webPIPSA extends and simplifies a PIPSA run. This allows non-expert users to perform PIPSA for their protein datasets. With input protein coordinates, the superposition of protein structures, as well as the computation and analysis of electrostatic potentials, is automated. The results are provided as electrostatic similarity matrices from an all-pairwise comparison of the proteins which can be subjected to clustering and visualized as epograms (tree-like diagrams showing electrostatic potential differences) or heat maps. webPIPSA is freely available at: http://pipsa.eml.org.

Prediction of reversibly oxidized protein cysteine thiols using protein structure properties

Protein cysteine thiols can be divided into four groups based on their reactivities: those that form permanent structural disulfide bonds, those that coordinate with metals, those that remain in the reduced state, and those that are susceptible to reversible oxidation. Physicochemical parameters of oxidation-susceptible protein thiols were organized into a database named the Balanced Oxidation Susceptible Cysteine Thiol Database (BALOSCTdb). BALOSCTdb contains 161 cysteine thiols that undergo reversible oxidation and 161 cysteine thiols that are not susceptible to oxidation. Each cysteine was represented by a set of 12 parameters, one of which was a label (1/0) to indicate whether its thiol moiety is susceptible to oxidation. A computer program (the C4.5 decision tree classifier re-implemented as the J48 classifier) segregated cysteines into oxidation-susceptible and oxidation-non-susceptible classes. The classifier selected three parameters critical for prediction of thiol oxidation susceptibility: (1) distance to the nearest cysteine sulfur atom, (2) solvent accessibility, and (3) pKa. The classifier was optimized to correctly predict 136 of the 161 cysteine thiols susceptible to oxidation. Leave-one-out cross-validation analysis showed that the percent of correctly classified cysteines was 80.1% and that 16.1% of the oxidation-susceptible cysteine thiols were incorrectly classified. The algorithm developed from these parameters, named the Cysteine Oxidation Prediction Algorithm (COPA), is presented here. COPA prediction of oxidation-susceptible sites can be utilized to locate protein cysteines susceptible to redox-mediated regulation and identify possible enzyme catalytic sites with reactive cysteine thiols.

Modeling structure and flexibility of Candida antarctica lipase B in organic solvents

Background

The structure and flexibility of Candida antarctica lipase B in water and five different organic solvent models was investigated using multiple molecular dynamics simulations to describe the effect of solvents on structure and dynamics. Interactions of the solvents with the protein and the distribution of water molecules at the protein surface were examined.

Results

The simulated structure was independent of the solvent, and had a low deviation from the crystal structure. However, the hydrophilic surface of CALB in non-polar solvents decreased by 10% in comparison to water, while the hydrophobic surface is slightly increased by 1%. There is a large influence on the flexibility depending on the dielectric constant of the solvent, with a high flexibility in water and a low flexibility in organic solvents. With decreasing dielectric constant, the number of surface bound water molecules significantly increased and a spanning water network with an increasing size was formed.

Conclusion

The reduced flexibility of Candida antarctica lipase B in organic solvents is caused by a spanning water network resulting from less mobile and slowly exchanging water molecules at the protein-surface. The reduced flexibility of Candida antarctica lipase B in organic solvent is not only caused by the interactions between solvent-protein, but mainly by the formation of a spanning water network.

Possible Mechanism of Proton Transfer through Peptide Groups in the H-Pathway of the Bovine CytochromecOxidase

The peptide group connecting Tyr440 and Ser441 of the bovine cytochrome c oxidase is involved in a recently proposed proton-transfer path (H-path) where, at variance with other pathways (D- and K-paths), a usual hydrogen-bond network is interrupted, thus making this proton propagation rather unconventional. Our density-functional based molecular dynamics simulations show that, despite this anomaly and provided that a proton can reach a nearby water, a multistep proton-transfer pathway can become a viable pathway for such a reaction: a proton is initially transferred to the carbonyl oxygen of a keto form of the Tyr440-Ser441 peptide group [-CO-NH-], producing an imidic acid [-C(OH)-NH-] as a metastable state; the amide proton of the imidic acid is then transferred, spontaneously to the deprotonated carboxyl group of the Asp51 side chain, leading to the formation of an enol form [-C(OH)=N-] of the Tyr440-Ser441 peptide group. Then a subsequent enol-to-keto tautomerization occurs via a double proton-transfer path realized in the two adjacent Tyr440-Ser441 and Ser441-Asp442 peptide groups. An analysis of this multistep proton-transfer pathway shows that each elementary process occurs through the shortest distance, no permanent conformational changes are induced, thus preserving the X-ray crystal structure, and the reaction path is characterized by a reasonable activation barrier.

Design of protein membrane interaction inhibitors by virtual ligand screening, proof of concept with the C2 domain of factor V

Most orally bioavailable drugs on the market are competitive inhibitors of catalytic sites, but a significant number of targets remain undrugged, because their molecular functions are believed to be inaccessible to drug-like molecules. This observation specifically applies to the development of small-molecule inhibitors of macromolecular interactions such as protein-membrane interactions that have been essentially neglected thus far. Nonetheless, many proteins containing a membrane-targeting domain play a crucial role in health and disease, and the inhibition of such interactions therefore represents a very promising therapeutic strategy. In this study, we demonstrate the use of combined in silico structure-based virtual ligand screening and surface plasmon resonance experiments to identify compounds that specifically disrupt protein-membrane interactions. Computational analysis of several membrane-binding domains revealed they all contain a druggable pocket within their membrane-binding region. We applied our screening protocol to the second discoidin domain of coagulation factor V and screened >300,000 drug-like compounds in silico against two known crystal structure forms. For each C2 domain structure, the top 500 molecules predicted as likely factor V-membrane inhibitors were evaluated in vitro. Seven drug-like hits were identified, indicating that therapeutic targets that bind transiently to the membrane surface can be investigated cost-effectively, and that inhibitors of protein-membrane interactions can be designed.

A novel approach to characterize the binding orientation of lysozyme on ion-exchange resins

Much work has been done to qualify and quantify chromatographic adsorption and transportation mechanisms in different adsorber materials. An important aspect in all studies is the understanding of the binding mechanism between protein and resin on a molecular level in order to optimize processes on the level of adsorber design. We established a method to determine the binding orientation of lysozyme for different materials under various experimental conditions enabling us to observe changes in the mode of adsorption. We varied the protein load of two different adsorber types, Source 15S, a conventional cation exchange resin and EMD Fractogel SO(3), a tentacle-type cation exchanger. We found similar preferential binding sites for the interaction between lysozyme and the surface of these adsorbers at low surface coverage, however, the tentacle adsorber exhibited multi-point binding whereas the binding on Source was limited to one binding site only. With increasing protein density on the surface, lysozyme rotates from a space-consuming side-on to a space-saving end-on orientation on Fractogel, explaining a higher maximum binding capacity for Fractogel. This re-orientation could not be observed for Source.

A network-based analysis of polyanion-binding proteins utilizing human protein arrays

The existence of interactions between many cellular proteins and various polyanionic surfaces within a cell is now well established. The functional role of such interactions, however, remains to be clearly defined. The existence of protein arrays, with a large selection of different kinds of proteins, provides a way to better address a number of aspects of this question. We have therefore investigated the interaction between five cellular polyanions (actin, tubulin, heparin, heparan sulfate, and DNA) and approximately 5,000 human proteins using protein microarrays in an attempt to better understand the functional nature of such interaction(s). We demonstrate that a large number of polyanion-binding proteins exist that contain multiple positively charged regions, are often disordered, are involved in phosphorylation processes, and appear to play a role in protein-protein interaction networks. Considering the crowded nature of cellular interiors, we propose that polyanion-binding proteins interact with a wide variety of polyanionic surfaces in cells in a functionally significant manner.

A Network-based Analysis of Polyanion-binding Proteins Utilizing Yeast Protein Arrays

The high affinity of certain cellular polyanions for many proteins (polyanion-binding proteins (PABPs)) has been demonstrated previously. It has been hypothesized that such polyanions may be involved in protein structure stabilization, stimulation of folding through chaperone-like activity, and intra- and extracellular protein transport as well as intracellular organization. The purpose of the proteomics studies reported here was to seek evidence for the idea that the nonspecific but high affinity interactions of PABPs with polyanions have a functional role in intracellular processes. Utilizing yeast protein arrays and five biotinylated cellular polyanion probes (actin, tubulin, heparin, heparan sulfate, and DNA), we identified proteins that interact with these probes and analyzed their structural and amino acid sequence requirements as well as their predicted functions in the yeast proteome. We also provide evidence for the existence of a network-like system for PABPs and their potential roles as critical hubs in intracellular behavior. This investigation takes a first step toward achieving a better understanding of the nature of polyanion-protein interactions within cells and introduces an alternative way of thinking about intracellular organization.

PHEPS: web-based pH-dependent Protein Electrostatics Server

PHEPS (pH-dependent Protein Electrostatics Server) is a web service for fast prediction and experiment planning support, as well as for correlation and analysis of experimentally obtained results, reflecting charge-dependent phenomena in globular proteins. Its implementation is based on long-term experience (PHEI package) and the need to explain measured physicochemical characteristics at the level of protein atomic structure. The approach is semi-empirical and based on a mean field scheme for description and evaluation of global and local pH-dependent electrostatic properties: protein proton binding; ionic sites proton population; free energy electrostatic term; ionic groups proton affinities (pK_a,i) and their Coulomb interaction with whole charge multipole; electrostatic potential of whole molecule at fixed pH and pH-dependent local electrostatic potentials at user-defined set of points. The speed of calculation is based on fast determination of distance-dependent pair charge-charge interactions as empirical three exponential function that covers charge–charge, charge–dipole and dipole–dipole contributions. After atomic coordinates input, all standard parameters are used as defaults to facilitate non-experienced users. Special attention was given to interactive addition of non-polypeptide charges, extra ionizable groups with intrinsic pK_as or fixed ions. The output information is given as plain-text, readable by ‘RasMol’, ‘Origin’ and the like. The PHEPS server is accessible at .

PDB_Hydro: incorporating dipolar solvents with variable density in the Poisson-Boltzmann treatment of macromolecule electrostatics

We describe a new way to calculate the electrostatic properties of macromolecules which eliminates the assumption of a constant dielectric value in the solvent region, resulting in a Generalized Poisson–Boltzmann–Langevin equation (GPBLE). We have implemented a web server () that both numerically solves this equation and uses the resulting water density profiles to place water molecules at preferred sites of hydration. Surface atoms with high or low hydration preference can be easily displayed using a simple PyMol script, allowing for the tentative prediction of the dimerization interface in homodimeric proteins, or lipid binding regions in membrane proteins. The web site includes options that permit mutations in the sequence as well as reconstruction of missing side chain and/or main chain atoms. These tools are accessible independently from the electrostatics calculation, and can be used for other modeling purposes. We expect this web server to be useful to structural biologists, as the knowledge of solvent density should prove useful to get better fits at low resolution for X-ray diffraction data and to computational biologists, for whom these profiles could improve the calculation of interaction energies in water between ligands and receptors in docking simulations.

Electrostatics calculations: latest methodological advances

Electrostatics plays a major role in the stabilization and function of biomolecules; as such, it remains a major focus of theoretical and computational studies of macromolecules. Electrostatic interactions are long range, and strongly dependent on the solvent and ions surrounding the biomolecule under study. During the past year, progress has been reported in the treatment of electrostatics in explicit and implicit solvent models. Interesting new developments of explicit solvent models include more efficient Ewald summation methods, as well as alternative approaches based on reaction field theory, periodic images and Euler summations. Implicit solvent models remain divided into those that solve the Poisson-Boltzmann equation numerically and those based on the generalized Born formalism. Both approaches are now included in molecular dynamics simulations and their accuracies may be assessed by direct comparison against experimental data. It is worth mentioning the recent development of web interfaces that facilitate access to and usage of existing tools for computing electrostatic interactions.