A composite score for predicting errors in protein structure models

Insights into the specific feature of the electrostatic recognition binding mechanism between BM2 and BM1: a molecular dynamics simulation study

Matrix protein 2 (M2) and matrix protein 1 (M1) of the influenza B virus are two important proteins, and the interactions between BM2 and BM1 play an important role in the process of virus assembly and replication. However, the interaction details between BM2 and BM1 are still unclear at the atomic level. Here, we constructed the BM2-BM1 complex system using homology modelling and molecular docking methods. Molecular dynamics (MD) simulations were used to illustrate the binding mechanism between BM2 and BM1. The results identify that the eight polar residues (E88B, E89B, H119BM1, E94B, R101BM1, K102BM1, R105BM1, and E104B) play an important role in stabilizing the binding through the formation of hydrogen bond networks and salt-bridge interactions at the binding interface. Furthermore, based on the simulation results and the experimental facts, the mutation experiments were designed to verify the influence of the mutation of residues both within and outside the effector domain. The mutations directly or indirectly disrupt interactions between polar residues, thus affecting viral assembly and replication. The results could help us understand the details of the interactions between BM2 and BM1 and provide useful information for the anti-influenza drug design.

Spike-Independent Infection of Human Coronavirus 229E in Bat Cells

Bats are the reservoir for numerous human pathogens, including coronaviruses. Despite many coronaviruses having descended from bat ancestors, little is known about virus-host interactions and broader evolutionary history involving bats. Studies have largely focused on the zoonotic potential of coronaviruses with few infection experiments conducted in bat cells. To determine genetic changes derived from replication in bat cells and possibly identify potential novel evolutionary pathways for zoonotic virus emergence, we serially passaged six human 229E isolates in a newly established Rhinolophus lepidus (horseshoe bat) kidney cell line. Here, we observed extensive deletions within the spike and open reading frame 4 (ORF4) genes of five 229E viruses after passaging in bat cells. As a result, spike protein expression and infectivity of human cells was lost in 5 of 6 viruses, but the capability to infect bat cells was maintained. Only viruses that expressed the spike protein could be neutralized by 229E spike-specific antibodies in human cells, whereas there was no neutralizing effect on viruses that did not express the spike protein inoculated on bat cells. However, one isolate acquired an early stop codon, abrogating spike expression but maintaining infection in bat cells. After passaging this isolate in human cells, spike expression was restored due to acquisition of nucleotide insertions among virus subpopulations. Spike-independent infection of human coronavirus 229E may provide an alternative mechanism for viral maintenance in bats that does not rely on the compatibility of viral surface proteins and known cellular entry receptors. IMPORTANCE Many viruses, including coronaviruses, originated from bats. Yet, we know little about how these viruses switch between hosts and enter human populations. Coronaviruses have succeeded in establishing in humans at least five times, including endemic coronaviruses and the recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In an approach to identify requirements for host switches, we established a bat cell line and adapted human coronavirus 229E viruses by serial passage. The resulting viruses lost their spike protein but maintained the ability to infect bat cells, but not human cells. Maintenance of 229E viruses in bat cells appears to be independent of a canonical spike receptor match, which in turn might facilitate cross-species transmission in bats.

In Silico Screening of Drugs That Target Different Forms of E Protein for Potential Treatment of COVID-19

Recently the E protein of SARS-CoV-2 has become a very important target in the potential treatment of COVID-19 since it is known to regulate different stages of the viral cycle. There is biochemical evidence that E protein exists in two forms, as monomer and homopentamer. An in silico screening analysis was carried out employing 5852 ligands (from Zinc databases), and performing an ADMET analysis, remaining a set of 2155 compounds. Furthermore, docking analysis was performed on specific sites and different forms of the E protein. From this study we could identify that the following ligands showed the highest binding affinity: nilotinib, dutasteride, irinotecan, saquinavir and alectinib. We carried out some molecular dynamics simulations and free energy MM–PBSA calculations of the protein–ligand complexes (with the mentioned ligands). Of worthy interest is that saquinavir, nilotinib and alectinib are also considered as a promising multitarget ligand because it seems to inhibit three targets, which play an important role in the viral cycle. On the other side, saquinavir was shown to be able to bind to E protein both in its monomeric as well as pentameric forms. Finally, further experimental assays are needed to probe our hypothesis derived from in silico studies.

Uncovering cryptic pockets in the SARS-CoV-2 spike glycoprotein

The COVID-19 pandemic has prompted a rapid response in vaccine and drug development. Herein, we modeled a complete membrane-embedded SARS-CoV-2 spike glycoprotein and used molecular dynamics simulations with benzene probes designed to enhance discovery of cryptic pockets. This approach recapitulated lipid and host metabolite binding sites previously characterized by cryo-electron microscopy, revealing likely ligand entry routes, and uncovered a novel cryptic pocket with promising druggable properties located underneath the 617-628 loop. A full representation of glycan moieties was essential to accurately describe pocket dynamics. A multi-conformational behavior of the 617-628 loop in simulations was validated using hydrogen-deuterium exchange mass spectrometry experiments, supportive of opening and closing dynamics. The pocket is the site of multiple mutations associated with increased transmissibility found in SARS-CoV-2 variants of concern including Omicron. Collectively, this work highlights the utility of the benzene mapping approach in uncovering potential druggable sites on the surface of SARS-CoV-2 targets.

Allosteric perspective on the mutability and druggability of the SARS-CoV-2 Spike protein

Recent developments in the SARS-CoV-2 pandemic point to its inevitable transformation into an endemic disease, urging both refinement of diagnostics for emerging variants of concern (VOCs) and design of variant-specific drugs in addition to vaccine adjustments. Exploring the structure and dynamics of the SARS-CoV-2 Spike protein, we argue that the high-mutability characteristic of RNA viruses coupled with the remarkable flexibility and dynamics of viral proteins result in a substantial involvement of allosteric mechanisms. While allosteric effects of mutations should be considered in predictions and diagnostics of new VOCs, allosteric drugs advantageously avoid escape mutations via non-competitive inhibition originating from alternative distal locations. The exhaustive allosteric signaling and probing maps presented herein provide a comprehensive picture of allostery in the spike protein, making it possible to locate potential mutations that could work as new VOC "drivers" and to determine binding patches that may be targeted by newly developed allosteric drugs.

A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models

Protein structure prediction is an important issue in structural bioinformatics. In this process, model quality assessment (MQA), which estimates the accuracy of the predicted structure, is also practically important. Currently, the most commonly used dataset to evaluate the performance of MQA is the critical assessment of the protein structure prediction (CASP) dataset. However, the CASP dataset does not contain enough targets with high-quality models, and thus cannot sufficiently evaluate the MQA performance in practical use. Additionally, most application studies employ homology modeling because of its reliability. However, the CASP dataset includes models generated by de novo methods, which may lead to the mis-estimation of MQA performance. In this study, we created new benchmark datasets, named a homology models dataset for model quality assessment (HMDM), that contain targets with high-quality models derived using homology modeling. We then benchmarked the performance of the MQA methods using the new datasets and compared their performance to that of the classical selection based on the sequence identity of the template proteins. The results showed that model selection by the latest MQA methods using deep learning is better than selection by template sequence identity and classical statistical potentials. Using HMDM, it is possible to verify the MQA performance for high-accuracy homology models.

Current Approaches in Supersecondary Structures Investigation

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

Site-Specific Steric Control of SARS-CoV-2 Spike Glycosylation

A central tenet in the design of vaccines is the display of native-like antigens in the elicitation of protective immunity. The abundance of N-linked glycans across the SARS-CoV-2 spike protein is a potential source of heterogeneity among the many different vaccine candidates under investigation. Here, we investigate the glycosylation of recombinant SARS-CoV-2 spike proteins from five different laboratories and compare them against S protein from infectious virus, cultured in Vero cells. We find patterns that are conserved across all samples, and this can be associated with site-specific stalling of glycan maturation that acts as a highly sensitive reporter of protein structure. Molecular dynamics simulations of a fully glycosylated spike support a model of steric restrictions that shape enzymatic processing of the glycans. These results suggest that recombinant spike-based SARS-CoV-2 immunogen glycosylation reproducibly recapitulates signatures of viral glycosylation.

Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae

Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

Site-specific steric control of SARS-CoV-2 spike glycosylation

A central tenet in the design of vaccines is the display of native-like antigens in the elicitation of protective immunity. The abundance of N-linked glycans across the SARS-CoV-2 spike protein is a potential source of heterogeneity between the many different vaccine candidates under investigation. Here, we investigate the glycosylation of recombinant SARS-CoV-2 spike proteins from five different laboratories and compare them against infectious virus S protein. We find patterns which are conserved across all samples and this can be associated with site-specific stalling of glycan maturation which act as a highly sensitive reporter of protein structure. Molecular dynamics (MD) simulations of a fully glycosylated spike support s a model of steric restrictions that shape enzymatic processing of the glycans. These results suggest that recombinant spike-based SARS-CoV-2 immunogen glycosylation reproducibly recapitulates signatures of viral glycosylation.

The Interplay of Cholesterol and Ligand Binding in hTSPO from Classical Molecular Dynamics Simulations

The translocator protein (TSPO) is a 18kDa transmembrane protein, ubiquitously present in human mitochondria. It is overexpressed in tumor cells and at the sites of neuroinflammation, thus representing an important biomarker, as well as a promising drug target. In mammalian TSPO, there are cholesterol–binding motifs, as well as a binding cavity able to accommodate different chemical compounds. Given the lack of structural information for the human protein, we built a model of human (h) TSPO in the apo state and in complex with PK11195, a molecule routinely used in positron emission tomography (PET) for imaging of neuroinflammatory sites. To better understand the interactions of PK11195 and cholesterol with this pharmacologically relevant protein, we ran molecular dynamics simulations of the apo and holo proteins embedded in a model membrane. We found that: (i) PK11195 stabilizes hTSPO structural fold; (ii) PK11195 might enter in the binding site through transmembrane helices I and II of hTSPO; (iii) PK11195 reduces the frequency of cholesterol binding to the lower, N–terminal part of hTSPO in the inner membrane leaflet, while this impact is less pronounced for the upper, C–terminal part in the outer membrane leaflet, where the ligand binding site is located; (iv) very interestingly, cholesterol most frequently binds simultaneously to the so-called CRAC and CARC regions in TM V in the free form (residues L150–X–Y152–X(3)–R156 and R135–X(2)–Y138–X(2)–L141, respectively). However, when the protein is in complex with PK11195, cholesterol binds equally frequently to the CRAC–resembling motif that we observed in TM I (residues L17–X(2)–F20–X(3)–R24) and to CRAC in TM V. We expect that the CRAC–like motif in TM I will be of interest in future experimental investigations. Thus, our MD simulations provide insight into the structural features of hTSPO and the previously unknown interplay between PK11195 and cholesterol interactions with this pharmacologically relevant protein.

SARS-CoV-2 S protein:ACE2 interaction reveals novel allosteric targets

The spike (S) protein is the main handle for SARS-CoV-2 to enter host cells via surface angiotensin-converting enzyme 2 (ACE2) receptors. How ACE2 binding activates proteolysis of S protein is unknown. Here, using amide hydrogen–deuterium exchange mass spectrometry and molecular dynamics simulations, we have mapped the S:ACE2 interaction interface and uncovered long-range allosteric propagation of ACE2 binding to sites necessary for host-mediated proteolysis of S protein, critical for viral host entry. Unexpectedly, ACE2 binding enhances dynamics at a distal S1/S2 cleavage site and flanking protease docking site ~27 Å away while dampening dynamics of the stalk hinge (central helix and heptad repeat [HR]) regions ~130 Å away. This highlights that the stalk and proteolysis sites of the S protein are dynamic hotspots in the prefusion state. Our findings provide a dynamics map of the S:ACE2 interface in solution and also offer mechanistic insights into how ACE2 binding is allosterically coupled to distal proteolytic processing sites and viral–host membrane fusion. Thus, protease docking sites flanking the S1/S2 cleavage site represent alternate allosteric hotspot targets for potential therapeutic development.

Molecular modeling, docking and dynamics analysis of lipid droplet associated enzyme Ypr147cp from Saccharomyces cerevisiae

Ypr147cp of Saccharomyces cerevisiae was localized to lipid droplets. The recombinant Ypr147cp showed both triacylglycerol lipase and ester hydrolase activities. Knock out of YPR147C led to accumulation of TAG in ypr147cΔ when compared to wild type (WT). Transmission electron microscopic analysis of ypr147cΔ cells show increased lipid bodies. Moreover, the lipid profiling confirmed the accumulation of fatty acids derived from neutral and phospholipids in ypr147cΔ cells. Sequence analysis of Ypr147cp show the presence of an a/b hydrolase domain with the conserved GXSXG lipase motif. The YPR147c homology model was built and the modeled protein was analysed using RMSD and root mean square fluctuation (RMSF) for a 100 ns simulation trajectory. Docking the acetate, butyrate and palmitate ligands with the model confirmed covalent binding of ligands with the Ser207 of the GXSXG motif. Thus, Ypr147cp is a lipid droplet associated triacylglycerol lipase having short chain ester hydrolyzing capacity.

Protein Structure Modeling with MODELLER

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.

The impact of Gag non-cleavage site mutations on HIV-1 viral fitness from integrative modelling and simulations

The high mutation rate in retroviruses is one of the leading causes of drug resistance. In human immunodeficiency virus type-1 (HIV-1), synergistic mutations in its protease and the protease substrate - the Group-specific antigen (Gag) polyprotein - work together to confer drug resistance against protease inhibitors and compensate the mutations affecting viral fitness. Some Gag mutations can restore Gag-protease binding, yet most Gag-protease correlated mutations occur outside of the Gag cleavage site. To investigate the molecular basis for this, we now report multiscale modelling approaches to investigate various sequentially cleaved Gag products in the context of clinically relevant mutations that occur outside of the cleavage sites, including simulations of the largest Gag proteolytic product in its viral membrane-bound state. We found that some mutations, such as G123E and H219Q, involve direct interaction with cleavage site residues to influence their local environment, while certain mutations in the matrix domain lead to the enrichment of lipids important for Gag targeting and assembly. Collectively, our results reveal why non-cleavage site mutations have far-reaching implications outside of Gag proteolysis, with important consequences for drugging Gag maturation intermediates and tackling protease inhibitor resistance.

Concentration- and pH-Dependent Oligomerization of the Thrombin-Derived C-Terminal Peptide TCP-25

Peptide oligomerization dynamics affects peptide structure, activity, and pharmacodynamic properties. The thrombin C-terminal peptide, TCP-25 (GKYGFYTHVFRLKKWIQKVIDQFGE), is currently in preclinical development for improved wound healing and infection prevention. It exhibits turbidity when formulated at pH 7.4, particularly at concentrations of 0.3 mM or more. We used biochemical and biophysical approaches to explore whether the peptide self-associates and forms oligomers. The peptide showed a dose-dependent increase in turbidity as well as α-helical structure at pH 7.4, a phenomenon not observed at pH 5.0. By analyzing the intrinsic tryptophan fluorescence, we demonstrate that TCP-25 is more stable at high concentrations (0.3 mM) when exposed to high temperatures or a high concentration of denaturant agents, which is compatible with oligomer formation. The denaturation process was reversible above 100 µM of peptide. Dynamic light scattering demonstrated that TCP-25 oligomerization is sensitive to changes in pH, time, and temperature. Computational modeling with an active 18-mer region of TCP-25 showed that the peptide can form pH-dependent higher-order end-to-end oligomers and micelle-like structures, which is in agreement with the experimental data. Thus, TCP-25 exhibits pH- and temperature-dependent dynamic changes involving helical induction and reversible oligomerization, which explains the observed turbidity of the pharmacologically developed formulation.

Homology modeling in the time of collective and artificial intelligence

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

Smooth orientation-dependent scoring function for coarse-grained protein quality assessment

MOTIVATION

Protein quality assessment (QA) is a crucial element of protein structure prediction, a fundamental and yet open problem in structural bioinformatics. QA aims at ranking predicted protein models to select the best candidates. The assessment can be performed based either on a single model or on a consensus derived from an ensemble of models. The latter strategy can yield very high performance but substantially depends on the pool of available candidate models, which limits its applicability. Hence, single-model QA methods remain an important research target, also because they can assist the sampling of candidate models.

RESULTS

We present a novel single-model QA method called SBROD. The SBROD (Smooth Backbone-Reliant Orientation-Dependent) method uses only the backbone protein conformation, and hence it can be applied to scoring coarse-grained protein models. The proposed method deduces its scoring function from a training set of protein models. The SBROD scoring function is composed of four terms related to different structural features: residue-residue orientations, contacts between backbone atoms, hydrogen bonding and solvent-solute interactions. It is smooth with respect to atomic coordinates and thus is potentially applicable to continuous gradient-based optimization of protein conformations. Furthermore, it can also be used for coarse-grained protein modeling and computational protein design. SBROD proved to achieve similar performance to state-of-the-art single-model QA methods on diverse datasets (CASP11, CASP12 and MOULDER).

AVAILABILITY AND IMPLEMENTATION

The standalone application implemented in C++ and Python is freely available at https://gitlab.inria.fr/grudinin/sbrod and supported on Linux, MacOS and Windows.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational approaches for drug discovery against trypanosomatid-caused diseases

During three decades, only about 20 new drugs have been developed for malaria, tuberculosis and all neglected tropical diseases (NTDs). This critical situation was reached because NTDs represent only 10% of health research investments; however, they comprise about 90% of the global disease burden. Computational simulations applied in virtual screening (VS) strategies are very efficient tools to identify pharmacologically active compounds or new indications for drugs already administered for other diseases. One of the advantages of this approach is the low time-consuming and low-budget first stage, which filters for testing experimentally a group of candidate compounds with high chances of binding to the target and present trypanocidal activity. In this work, we review the most common VS strategies that have been used for the identification of new drugs with special emphasis on those applied to trypanosomiasis and leishmaniasis. Computational simulations based on the selected protein targets or their ligands are explained, including the method selection criteria, examples of successful VS campaigns applied to NTDs, a list of validated molecular targets for drug development and repositioned drugs for trypanosomatid-caused diseases. Thereby, here we present the state-of-the-art of VS and drug repurposing to conclude pointing out the future perspectives in the field.

Covalent Complex of DNA and Bacterial Topoisomerase: Implications in Antibacterial Drug Development

A topoisomerase-DNA transient covalent complex can be a druggable target for novel topoisomerase poison inhibitors that represent a new class of antibacterial or anticancer drugs. Herein, we have investigated molecular features of the functionally important Escherichia coli topoisomerase I (EctopoI)-DNA covalent complex (EctopoIcc) for molecular simulations, which is very useful in the development of new antibacterial drugs. To demonstrate the usefulness of our approach, we used a model small molecule (SM), NSC76027, obtained from virtual screening. We examined the direct binding of NSC76027 to EctopoI as well as inhibition of EctopoI relaxation activity of this SM via experimental techniques. We then performed molecular dynamics (MD) simulations to investigate the dynamics and stability of EctopoIcc and EctopoI-NSC76027-DNA ternary complex. Our simulation results show that NSC76027 forms a stable ternary complex with EctopoIcc. EctopoI investigated here also serves as a model system for investigating a complex of topoisomerase and DNA in which DNA is covalently attached to the protein.

Ten quick tips for homology modeling of high-resolution protein 3D structures

The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

Not all therapeutic antibody isotypes are equal: the case of IgM versus IgG in Pertuzumab and Trastuzumab

The therapeutic potential of immunoglobulin M (IgM) is of considerable interest in immunotherapy due to its complement-activating and cell-agglutinating abilities. Pertuzumab and Trastuzumab are monoclonal antibodies used to treat human epidermal growth factor receptor 2 (HER2)-positive breast cancer but exhibit significantly different binding affinities as IgM when compared to its IgG isotype. Using integrative multiscale modelling and simulations of complete antibody assemblies, we show that Pertuzumab IgM is able to utilize all of its V-regions to bind multiple HER2 receptors simultaneously, while similar binding in Trastuzumab IgM is prohibited by steric clashes caused by the large globular domain of HER2. This is subsequently validated by confirming that Pertuzumab IgM inhibits proliferation in HER2 over-expressing live cells more effectively than its IgG counterpart and Trastuzumab IgM. Our study highlights the importance of understanding the molecular details of antibody-antigen interactions for the design and isotype selection of therapeutic antibodies.

Protein-peptide docking using CABS-dock and contact information

CABS-dock is a computational method for protein-peptide molecular docking that does not require predefinition of the binding site. The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein-peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein-peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server (at http://biocomp.chem.uw.edu.pl/CABSdock/).

SHORT ABSTRACT

CABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein-peptide contact(s). Using information on individual protein-peptide contacts allows to improve the accuracy of CABS-dock docking.

Insights into the Interaction Mechanisms of the Proviral Integration Site of Moloney Murine Leukemia Virus (Pim) Kinases with Pan-Pim Inhibitors PIM447 and AZD1208: A Molecular Dynamics Simulation and MM/GBSA Calculation Study

Based on the up-regulation of the proviral integration site of the Moloney murine leukemia virus (Pim) kinase family (Pim1, 2, and 3) observed in several types of leukemias and lymphomas, the development of pan-Pim inhibitors is an attractive therapeutic strategy. While only PIM447 and AZD1208 have entered the clinical stages. To elucidate the interaction mechanisms of three Pim kinases with PIM447 and AZD1208, six Pim/ligand systems were studied by homology modeling, molecular docking, molecular dynamics (MD) simulation and molecular mechanics/generalized Born surface area (MM/GBSA) binding free energy calculation. The residues of the top group (Leu44, Val52, Ala65, Lys67, and Leu120 in Pim1) dominated the pan-Pim inhibitors binding to Pim kinases. The residues of the bottom group (Gln127, Asp128, and Leu174 in Pim1) were crucial for Pims/PIM447 systems, while the contributions of these residues were decreased sharply for Pims/AZD1208 systems. It is likely that the more potent pan-Pim inhibitors should be bound strongly to the top and bottom groups. The residues of the left, right and loop groups were located in the loop regions of the binding pocket, however, the flexibility of these regions triggered the protein interacting with diverse pan-Pim inhibitors efficiently. We hope this work can provide valuable information for the design of novel pan-Pim inhibitors in the future.

All-Atom Knowledge-Based Potential for RNA Structure Discrimination Based on the Distance-Scaled Finite Ideal-Gas Reference State

Noncoding RNAs are increasingly found to play a wide variety of roles in living organisms. Yet, their functional mechanisms are poorly understood because their structures are difficult to determine experimentally. As a result, developing more effective computational techniques to predict RNA structures becomes increasingly an urgent task. One key challenge in RNA structure prediction is the lack of an accurate free energy function to guide RNA folding and discriminate native and near-native structures from decoy conformations. In this study, we developed an all-atom distance-dependent knowledge-based energy function for RNA that is based on a reference state (distance-scaled finite ideal-gas reference state, DFIRE) proven successful for protein structure discrimination. Using four separate benchmarks including RNA puzzles, we found that this DFIRE-based RNA statistical energy function is able to discriminate native and near-native structures against decoys with performance comparable with or better than several existing scoring functions compared. The energy function is expected to be useful for improving the detection of RNA near-native structures.

Granular type I corneal dystrophy in a large consanguineous Tunisian family with homozygous p.R124S mutation in the TGFBI gene

Purpose: We report the clinical features and the mutational analysis in a large Tunisian family with granular corneal dystrophy type I (GCD1). Patients and Methods: Thirty-three members of the Tunisian family underwent a complete ophthalmologic examination. DNA extraction and direct Sanger sequencing of the exons 4 and 12 of transforming growth factor β Induced (TGFBI) gene was performed for 42 members. For the molecular modeling of TGFBI protein, we used pGenTHREADER method to identify templates, 3D-EXPRESSO program to align sequences, MODELLER to get a homology model for the FAS1 (fasciclin-like) domains and finally NOMAD-ref web server for the energy minimization. Results: The diagnosis of GCD1 was clinically and genetically confirmed. Sequencing of exon 4 of TGFBI gene revealed the p.[R124S] mutation at heterozygous and homozygous states in patients with different clinical severities. Visual acuity was severely affected in the homozygous patients leading to a first penetrating keratoplasty. Recurrence occurred rapidly, began in the seat of the corneal stitches and remained superficial up to 40 years after the graft. For heterozygous cases, visual acuity ranged from 6/10 to 10/10. Corneal opacities were deeper and predominating in the stromal center. According to bioinformatic analysis, this mutation likely perturbs the protein physicochemical properties and reduces its solubility without structural modification. Conclusions: Our study describes for the first time phenotype-genotype correlation in a large Tunisian family with GCDI and illustrates for the first time clinical and histopathological presentation of homozygous p.[R124S] mutation. These results help to understand pathophysiology of the disease.

Aminoacyl tRNA synthetases as malarial drug targets: a comparative bioinformatics study

BACKGROUND

Treatment of parasitic diseases has been challenging due to evolution of drug resistant parasites, and thus there is need to identify new class of drugs and drug targets. Protein translation is important for survival of malarial parasite, Plasmodium, and the pathway is present in all of its life cycle stages. Aminoacyl tRNA synthetases are primary enzymes in protein translation as they catalyse amino acid addition to the cognate tRNA. This study sought to understand differences between Plasmodium and human aminoacyl tRNA synthetases through bioinformatics analysis.

METHODS

Plasmodium berghei, Plasmodium falciparum, Plasmodium fragile, Plasmodium knowlesi, Plasmodium malariae, Plasmodium ovale, Plasmodium vivax, Plasmodium yoelii and human aminoacyl tRNA synthetase sequences were retrieved from UniProt database and grouped into 20 families based on amino acid specificity. These families were further divided into two classes. Both families and classes were analysed. Motif discovery was carried out using the MEME software, sequence identity calculation was done using an in-house Python script, multiple sequence alignments were performed using PROMALS3D and TCOFFEE tools, and phylogenetic tree calculations were performed using MEGA vs 7.0 tool. Possible alternative binding sites were predicted using FTMap webserver and SiteMap tool.

RESULTS

Motif discovery revealed Plasmodium-specific motifs while phylogenetic tree calculations showed that Plasmodium proteins have different evolutionary history to the human homologues. Human aaRSs sequences showed low sequence identity (below 40%) compared to Plasmodium sequences. Prediction of alternative binding sites revealed potential druggable sites in PfArgRS, PfMetRS and PfProRS at regions that are weakly conserved when compared to the human homologues. Multiple sequence analysis, motif discovery, pairwise sequence identity calculations and phylogenetic tree analysis showed significant differences between parasite and human aaRSs proteins despite functional and structural conservation. These differences may provide a basis for further exploration of Plasmodium aminoacyl tRNA synthetases as potential drug targets.

CONCLUSION

This study showed that, despite, functional and structural conservation, Plasmodium aaRSs have key differences from the human homologues. These differences in Plasmodium aaRSs can be targeted to develop anti-malarial drugs with less toxicity to the host.

All-atom four-body knowledge-based statistical potential to distinguish native tertiary RNA structures from nonnative folds

Scientific breakthroughs in recent decades have uncovered the capability of RNA molecules to fulfill a wide array of structural, functional, and regulatory roles in living cells, leading to a concomitantly significant increase in both the number and diversity of experimentally determined RNA three-dimensional (3D) structures. Atomic coordinates from a representative training set of solved RNA structures, displaying low sequence and structure similarity, facilitate derivation of knowledge-based energy functions. Here we develop an all-atom four-body statistical potential and evaluate its capacity to distinguish native RNA 3D structures from nonnative folds based on calculated free energy scores. Atomic four-body nearest-neighbors are objectively identified by their occurrence as tetrahedral vertices in the Delaunay tessellations of RNA structures, and rates of atomic quadruplet interactions expected by chance are obtained from a multinomial reference distribution. Our four-body energy function, referred to as RAMP (ribonucleic acids multibody potential), is subsequently derived by applying the inverted Boltzmann principle to the frequency data, yielding an energy score for each type of atomic quadruplet interaction. Several well-known benchmark datasets reveal that RAMP is comparable with, and often outperforms, existing knowledge- and physics-based energy functions. To the best of our knowledge, this is the first study detailing an RNA tertiary structure-based multibody statistical potential and its comparative evaluation.

Structural and functional dissection of differentially expressed tomato WRKY transcripts in host defense response against the vascular wilt pathogen (Fusarium oxysporum f. sp. lycopersici)

The WRKY transcription factors have indispensable role in plant growth, development and defense responses. The differential expression of WRKY genes following the stress conditions has been well demonstrated. We investigated the temporal and tissue-specific (root and leaf tissues) differential expression of plant defense-related WRKY genes, following the infection of Fusarium oxysporum f. sp. lycopersici (Fol) in tomato. The genome-wide computational analysis revealed that during the Fol infection in tomato, 16 different members of WRKY gene superfamily were found to be involved, of which only three WRKYs (SolyWRKY4, SolyWRKY33, and SolyWRKY37) were shown to have clear-cut differential gene expression. The quantitative real time PCR (qRT-PCR) studies revealed different gene expression profile changes in tomato root and leaf tissues. In root tissues, infected with Fol, an increased expression for SolyWRKY33 (2.76 fold) followed by SolyWRKY37 (1.93 fold) gene was found at 24 hrs which further increased at 48 hrs (5.0 fold). In contrast, the leaf tissues, the expression was more pronounced at an earlier stage of infection (24 hrs). However, in both cases, we found repression of SolyWRKY4 gene, which further decreased at an increased time interval. The biochemical defense programming against Fol pathogenesis was characterized by the highest accumulation of H₂O₂ (at 48 hrs) and enhanced lignification. The functional diversity across the characterized WRKYs was explored through motif scanning using MEME suite, and the WRKYs specific gene regulation was assessed through the DNA protein docking studies The functional WRKY domain modeled had β sheets like topology with coil and turns. The DNA-protein interaction results revealed the importance of core residues (Tyr, Arg, and Lys) in a feasible WRKY-W-box DNA interaction. The protein interaction network analysis revealed that the SolyWRKY33 could interact with other proteins, such as mitogen-activated protein kinase 5 (MAPK), sigma factor binding protein1 (SIB1) and with other WRKY members including WRKY70, WRKY1, and WRKY40, to respond various biotic and abiotic stresses. The STRING results were further validated through Predicted Tomato Interactome Resource (PTIR) database. The CELLO2GO web server revealed the functional gene ontology annotation and protein subcellular localization, which predicted that SolyWRKY33 is involved in amelioration of biological stress (39.3%) and other metabolic processes (39.3%). The protein (SolyWRKY33) most probably located inside the nucleus (91.3%) with having transcription factor binding activity. We conclude that the defense response following the Fol challenge was accompanied by differential expression of the SolyWRKY4(↓), SolyWRKY33(↑) and SolyWRKY37(↑) transcripts. The biochemical changes are occupied by elicitation of H₂O₂ generation and accumulation and enhanced lignified tissues.

In silico designing of a new cysteine analogue of hirudin variant 3 for site specific PEGylation

Hirudin is an anticoagulant agent of the salivary glands of the medicinal leech. Recombinant hirudin (r-Hir) displays certain drawbacks including bleeding and immunogenicity. To solve these problems, cysteine-specific PEGylation has been proposed as a successful technique. However, proper selection of the appropriate cysteine residue for substitution is a critical step. This study has, for the first time, used a computational approach aimed at identifying a single potential PEGylation site for replacement by cysteine residue in the hirudin variant 3 (HV3). Homology modeling (HM) was performed using MODELLER. All non-cysteine residues of the HV3 were replaced with the cysteine. The best model was selected based on the results of discrete optimized protein energy score, PROCHECK software, and Verify3D. The receptor binding was investigated using protein-protein docking by ClusPro web tool which was then visualized using LigPlot+ software and PyMOL. Finally, multiple sequence alignment (MSA) using ClustalW software and disulfide bond prediction were performed. According to the results of HM and docking, Q33C, which was located on the surface of the protein, was the best site for PEGylation. Furthermore, MSA showed that Q33 was not a conserved residue and LigPlot+ software showed that it is not involved in the hirudin-thrombin binding pocket. Moreover, prediction softwares established that it is not involved in disulfide bond formation. In this study, for the first time, the utility of the in silico approach for creating a cysteine analogue of HV3 was introduced. Our study demonstrated that the substitution of Q33 by cysteine probably has no effect on the biological activity of the HV3. However, experimental analyses are required to confirm the results.

Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models

Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method (GMQ), explicitly considers the predicted quality of spatially neighboring residues using a graph representation of a query protein structure model. GMQ uses conditional random field as its core of the algorithm, and performs a binary prediction of the quality of each residue in a model, indicating if a residue position is likely to be within an error cutoff or not. The accuracy of GMQ was improved by considering larger graphs to include quality information of more surrounding residues. Moreover, we found that using different edge weights in graphs reflecting different secondary structures further improves the accuracy. GMQ showed competitive performance on a benchmark for quality assessment of structure models from the Critical Assessment of Techniques for Protein Structure Prediction (CASP).

Structural and Functional Insights into WRKY3 and WRKY4 Transcription Factors to Unravel the WRKY-DNA (W-Box) Complex Interaction in Tomato (Solanum lycopersicum L.). A Computational Approach

The WRKY transcription factors (TFs), play crucial role in plant defense response against various abiotic and biotic stresses. The role of WRKY3 and WRKY4 genes in plant defense response against necrotrophic pathogens is well-reported. However, their functional annotation in tomato is largely unknown. In the present work, we have characterized the structural and functional attributes of the two identified tomato WRKY transcription factors, WRKY3 (SlWRKY3), and WRKY4 (SlWRKY4) using computational approaches. Arabidopsis WRKY3 (AtWRKY3: NP_178433) and WRKY4 (AtWRKY4: NP_172849) protein sequences were retrieved from TAIR database and protein BLAST was done for finding their sequential homologs in tomato. Sequence alignment, phylogenetic classification, and motif composition analysis revealed the remarkable sequential variation between, these two WRKYs. The tomato WRKY3 and WRKY4 clusters with Solanum pennellii showing the monophyletic origin and evolution from their wild homolog. The functional domain region responsible for sequence specific DNA-binding occupied in both proteins were modeled [using AtWRKY4 (PDB ID:1WJ2) and AtWRKY1 (PDBID:2AYD) as template protein structures] through homology modeling using Discovery Studio 3.0. The generated models were further evaluated for their accuracy and reliability based on qualitative and quantitative parameters. The modeled proteins were found to satisfy all the crucial energy parameters and showed acceptable Ramachandran statistics when compared to the experimentally resolved NMR solution structures and/or X-Ray diffracted crystal structures (templates). The superimposition of the functional WRKY domains from SlWRKY3 and SlWRKY4 revealed remarkable structural similarity. The sequence specific DNA binding for two WRKYs was explored through DNA-protein interaction using Hex Docking server. The interaction studies found that SlWRKY4 binds with the W-box DNA through WRKYGQK with Tyr⁴⁰⁸, Arg⁴⁰⁹, and Lys⁴¹⁹ with the initial flanking sequences also get involved in binding. In contrast, the SlWRKY3 made interaction with RKYGQK along with the residues from zinc finger motifs. Protein-protein interactions studies were done using STRING version 10.0 to explore all the possible protein partners involved in associative functional interaction networks. The Gene ontology enrichment analysis revealed the functional dimension and characterized the identified WRKYs based on their functional annotation.

Protein Structure Modeling with MODELLER

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.

Intramolecular hydrophobic interactions are critical mediators of STAT5 dimerization

STAT5 is an essential transcription factor in hematopoiesis, which is activated through tyrosine phosphorylation in response to cytokine stimulation. Constitutive activation of STAT5 is a hallmark of myeloid and lymphoblastic leukemia. Using homology modeling and molecular dynamics simulations, a model of the STAT5 phosphotyrosine-SH2 domain interface was generated providing first structural information on the activated STAT5 dimer including a sequence, for which no structural information is available for any of the STAT proteins. We identified a novel intramolecular interaction mediated through F706, adjacent to the phosphotyrosine motif, and a unique hydrophobic interface on the surface of the SH2 domain. Analysis of corresponding STAT5 mutants revealed that this interaction is dispensable for Epo receptor-mediated phosphorylation of STAT5 but essential for dimer formation and subsequent nuclear accumulation. Moreover, the herein presented model clarifies molecular mechanisms of recently discovered leukemic STAT5 mutants and will help to guide future drug development.

Recognizing drug targets using evolutionary information: implications for repurposing FDA-approved drugs against Mycobacterium tuberculosis H37Rv

Drug repurposing to explore target space has been gaining pace over the past decade with the upsurge in the use of systematic approaches for computational drug discovery. Such a cost and time-saving approach gains immense importance for pathogens of special interest, such as Mycobacterium tuberculosis H37Rv. We report a comprehensive approach to repurpose drugs, based on the exploration of evolutionary relationships inferred from the comparative sequence and structural analyses between targets of FDA-approved drugs and the proteins of M. tuberculosis. This approach has facilitated the identification of several polypharmacological drugs that could potentially target unexploited M. tuberculosis proteins. A total of 130 FDA-approved drugs, originally intended against other diseases, could be repurposed against 78 potential targets in M. tuberculosis. Additionally, we have also made an attempt to augment the chemical space by recognizing compounds structurally similar to FDA-approved drugs. For three of the attractive cases we have investigated the probable binding modes of the drugs in their corresponding M. tuberculosis targets by means of structural modelling. Such prospective targets and small molecules could be prioritized for experimental endeavours, and could significantly influence drug-discovery and drug-development programmes for tuberculosis.

Genetic and structural analyses of cytochrome P450 hydroxylases in sex hormone biosynthesis: Sequential origin and subsequent coevolution

Biosynthesis of steroid hormones in vertebrates involves three cytochrome P450 hydroxylases, CYP11A1, CYP17A1 and CYP19A1, which catalyze sequential steps in steroidogenesis. These enzymes are conserved in the vertebrates, but their origin and existence in other chordate subphyla (Tunicata and Cephalochordata) have not been clearly established. In this study, selected protein sequences of CYP11A1, CYP17A1 and CYP19A1 were compiled and analyzed using multiple sequence alignment and phylogenetic analysis. Our analyses show that cephalochordates have sequences orthologous to vertebrate CYP11A1, CYP17A1 or CYP19A1, and that echinoderms and hemichordates possess CYP11-like but not CYP19 genes. While the cephalochordate sequences have low identity with the vertebrate sequences, reflecting evolutionary distance, the data show apparent origin of CYP11 prior to the evolution of CYP19 and possibly CYP17, thus indicating a sequential origin of these functionally related steroidogenic CYPs. Co-occurrence of the three CYPs in early chordates suggests that the three genes may have coevolved thereafter, and that functional conservation should be reflected in functionally important residues in the proteins. CYP19A1 has the largest number of conserved residues while CYP11A1 sequences are less conserved. Structural analyses of human CYP11A1, CYP17A1 and CYP19A1 show that critical substrate binding site residues are highly conserved in each enzyme family. The results emphasize that the steroidogenic pathways producing glucocorticoids and reproductive steroids are several hundred million years old and that the catalytic structural elements of the enzymes have been conserved over the same period of time. Analysis of these elements may help to identify when precursor functions linked to these enzymes first arose.

CHOPIN: a web resource for the structural and functional proteome of Mycobacterium tuberculosis

Tuberculosis kills more than a million people annually and presents increasingly high levels of resistance against current first line drugs. Structural information about Mycobacterium tuberculosis (Mtb) proteins is a valuable asset for the development of novel drugs and for understanding the biology of the bacterium; however, only about 10% of the ∼4000 proteins have had their structures determined experimentally. The CHOPIN database assigns structural domains and generates homology models for 2911 sequences, corresponding to ∼73% of the proteome. A sophisticated pipeline allows multiple models to be created using conformational states characteristic of different oligomeric states and ligand binding, such that the models reflect various functional states of the proteins. Additionally, CHOPIN includes structural analyses of mutations potentially associated with drug resistance. Results are made available at the web interface, which also serves as an automatically updated repository of all published Mtb experimental structures. Its RESTful interface allows direct and flexible access to structures and metadata via intuitive URLs, enabling easy programmatic use of the models.

Database URL: http://structure.bioc.cam.ac.uk/chopin

Ligand-target prediction by structural network biology using nAnnoLyze

Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for “anonymous” compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound–protein interactions and thus may benefit drug development.

Description of the “mode-of-action” of a small chemical compound against a protein target is essential for the drug discovery process. Such description relies on three main steps: i) the identification of the target protein within the thousands of proteins in an organism, ii) the localization of the binding interaction site in the identified target protein, and iii) the molecular characterization of the compound’s binding mode in the binding site of the target protein. Here, we introduce a new computational method, called nAnnoLyze, which uses graph theory principles to relate compounds and target proteins based on comparative principles. nAnnoLyze aims at addressing two of the three previous steps, that is, target identification and binding site localization. Our results suggest that the nAnnoLyze accuracy and proteome-wide applicability enables the large-scale annotation and analysis of compound–protein interaction and thus may benefit drug development.

Evaluation of a possible role of Stigmatella aurantiaca ACE in Aβ peptide degradation: a molecular modeling approach

Amyloid-β (Aβ)-degrading enzymes are known to degrade Aβ peptides, a causative agent of Alzheimer's disease. These enzymes are responsible for maintaining Aβ concentration. However, loss of such enzymes or their Aβ-degrading activity because of certain genetic as well as nongenetic reasons initiates the accumulation of Aβ peptides in the human brain. Considering the limitations of the human enzymes in clearing Aβ peptide, the search for microbial enzymes that could cleave Aβ is necessary. Hence, we built a three-dimensional model of angiotensin-converting enzyme (ACE) from Stigmatella aurantiaca using homology modeling technique. Molecular docking and molecular dynamics simulation techniques were used to outline the possible cleavage mechanism of Aβ peptide. These findings suggest that catalytic residue Glu 434 of the model could play a crucial role to degrade Aβ peptide between Asp 7 and Ser 8. Thus, ACE from S. aurantiaca might cleave Aβ peptides similar to human ACE and could be used to design new therapeutic strategies against Alzheimer's disease.

Environmental contaminants activate human and polar bear (Ursus maritimus) pregnane X receptors (PXR, NR1I2) differently

BACKGROUND

Many persistent organic pollutants (POPs) accumulate readily in polar bears because of their position as apex predators in Arctic food webs. The pregnane X receptor (PXR, formally NR1I2, here proposed to be named promiscuous xenobiotic receptor) is a xenobiotic sensor that is directly involved in metabolizing pathways of a wide range of environmental contaminants.

OBJECTIVES

In the present study, we comparably assess the ability of 51 selected pharmaceuticals, pesticides and emerging contaminants to activate PXRs from polar bears and humans using an in vitro luciferase reporter gene assay.

RESULTS

We found that polar bear PXR is activated by a wide range of our test compounds (68%) but has a slightly more narrow ligand specificity than human PXR that was activated by 86% of the 51 test compounds. The majority of the agonists identified (70%) produces a stronger induction of the reporter gene via human PXR than via polar bear PXR, however with some notable and environmentally relevant exceptions.

CONCLUSIONS

Due to the observed differences in activation of polar bear and human PXRs, exposure of each species to environmental agents is likely to induce biotransformation differently in the two species. Bioinformatics analyses and structural modeling studies suggest that amino acids that are not part of the ligand-binding domain and do not interact with the ligand can modulate receptor activation.

Structure based annotation of Helicobacter pylori strain 26695 proteome

The availability of complete genome sequences of H. pylori 26695 has provided a wealth of information enabling us to carry out in silico studies to identify new molecular targets for pharmaceutical treatment. In order to construe the structural and functional information of complete proteome, use of computational methods are more relevant since these methods are reliable and provide a solution to the time consuming and expensive experimental methods. Out of 1590 predicted protein coding genes in H. pylori, experimentally determined structures are available for only 145 proteins in the PDB. In the absence of experimental structures, computational studies on the three dimensional (3D) structural organization would help in deciphering the protein fold, structure and active site. Functional annotation of each protein was carried out based on structural fold and binding site based ligand association. Most of these proteins are uncharacterized in this proteome and through our annotation pipeline we were able to annotate most of them. We could assign structural folds to 464 uncharacterized proteins from an initial list of 557 sequences. Of the 1195 known structural folds present in the SCOP database, 411 (34% of all known folds) are observed in the whole H. pylori 26695 proteome, with greater inclination for domains belonging to α/β class (36.63%). Top folds include P-loop containing nucleoside triphosphate hydrolases (22.6%), TIM barrel (16.7%), transmembrane helix hairpin (16.05%), alpha-alpha superhelix (11.1%) and S-adenosyl-L-methionine-dependent methyltransferases (10.7%).

Detecting local residue environment similarity for recognizing near-native structure models

We developed a new representation of local amino acid environments in protein structures called the Side-chain Depth Environment (SDE). An SDE defines a local structural environment of a residue considering the coordinates and the depth of amino acids that locate in the vicinity of the side-chain centroid of the residue. SDEs are general enough that similar SDEs are found in protein structures with globally different folds. Using SDEs, we developed a procedure called PRESCO (Protein Residue Environment SCOre) for selecting native or near-native models from a pool of computational models. The procedure searches similar residue environments observed in a query model against a set of representative native protein structures to quantify how native-like SDEs in the model are. When benchmarked on commonly used computational model datasets, our PRESCO compared favorably with the other existing scoring functions in selecting native and near-native models.

Interaction between Nbp35 and Cfd1 proteins of cytosolic Fe-S cluster assembly reveals a stable complex formation in Entamoeba histolytica

Iron-Sulfur (Fe-S) proteins are involved in many biological functions such as electron transport, photosynthesis, regulation of gene expression and enzymatic activities. Biosynthesis and transfer of Fe-S clusters depend on Fe-S clusters assembly processes such as ISC, SUF, NIF, and CIA systems. Unlike other eukaryotes which possess ISC and CIA systems, amitochondriate Entamoeba histolytica has retained NIF & CIA systems for Fe-S cluster assembly in the cytosol. In the present study, we have elucidated interaction between two proteins of E. histolytica CIA system, Cytosolic Fe-S cluster deficient 1 (Cfd1) protein and Nucleotide binding protein 35 (Nbp35). In-silico analysis showed that structural regions ranging from amino acid residues (P33-K35, G131-V135 and I147-E151) of Nbp35 and (G5-V6, M34-D39 and G46-A52) of Cfd1 are involved in the formation of protein-protein complex. Furthermore, Molecular dynamic (MD) simulations study suggested that hydrophobic forces surpass over hydrophilic forces between Nbp35 and Cfd1 and Van-der-Waal interaction plays crucial role in the formation of stable complex. Both proteins were separately cloned, expressed as recombinant fusion proteins in E. coli and purified to homogeneity by affinity column chromatography. Physical interaction between Nbp35 and Cfd1 proteins was confirmed in vitro by co-purification of recombinant Nbp35 with thrombin digested Cfd1 and in vivo by pull down assay and immunoprecipitation. The insilico, in vitro as well as in vivo results prove a stable interaction between these two proteins, supporting the possibility of its involvement in Fe-S cluster transfer to target apo-proteins through CIA machinery in E. histolytica. Our study indicates that initial synthesis of a Fe-S precursor in mitochondria is not necessary for the formation of Cfd1-Nbp35 complex. Thus, Cfd1 and Nbp35 with the help of cytosolic NifS and NifU proteins can participate in the maturation of non-mitosomal Fe-S proteins without any apparent assistance of mitosomes.

Molecular modeling and ligand docking for solute carrier (SLC) transporters

Solute Carrier (SLC) transporters are membrane proteins that transport solutes, such as ions, metabolites, peptides, and drugs, across biological membranes, using diverse energy coupling mechanisms. In human, there are 386 SLC transporters, many of which contribute to the absorption, distribution, metabolism, and excretion of drugs and/or can be targeted directly by therapeutics. Recent atomic structures of SLC transporters determined by X-ray crystallography and NMR spectroscopy have significantly expanded the applicability of structure-based prediction of SLC transporter ligands, by enabling both comparative modeling of additional SLC transporters and virtual screening of small molecules libraries against experimental structures as well as comparative models. In this review, we begin by describing computational tools, including sequence analysis, comparative modeling, and virtual screening, that are used to predict the structures and functions of membrane proteins such as SLC transporters. We then illustrate the applications of these tools to predicting ligand specificities of select SLC transporters, followed by experimental validation using uptake kinetic measurements and other assays. We conclude by discussing future directions in the discovery of the SLC transporter ligands.

Physicochemical properties of the modeled structure of astacin metalloprotease moulting enzyme NAS-36 and mapping the druggable allosteric space of Heamonchus contortus, Brugia malayi and Ceanorhabditis elegans via molecular dynamics simulation

Nematodes represent the second largest phylum in the animal kingdom. It is the most abundant species (500,000) in the planet. It causes chronic, debilitating infections worldwide such as ascariasis, trichuriasis, hookworm, enterobiasis, strongyloidiasis, filariasis and trichinosis, among others. Molecular modeling tools can play an important role in the identification and structural investigation of molecular targets that can act as a vital candidate against filariasis. In this study, sequence analysis of NAS-36 from H. contortus (Heamonchus contortus), B. malayi (Brugia malayi) and C. elegans (Ceanorhabditis elegans) has been performed, in order to identify the conserved residues. Tertiary structure was developed for an insight into the molecular structure of the enzyme. Molecular Dynamics Simulation (MDS) studies have been carried out to analyze the stability and the physical properties of the proposed enzyme models in the H. contortus, B. malayi and C. elegans. Moreover, the drug binding sites have been mapped for inhibiting the function of NAS-36 enzyme. The molecular identity of this protease could eventually demonstrate how ex-sheathment is regulated, as well as provide a potential target of anthelmintics for the prevention of nematode infections.

Protein structure modeling with MODELLER

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In this chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.

Identification, modeling and ligand affinity of early deuterostome CYP51s, and functional characterization of recombinant zebrafish sterol 14α-demethylase

BACKGROUND

Sterol 14α-demethylase (cytochrome P450 51, CYP51, P45014DM) is a microsomal enzyme that in eukaryotes catalyzes formation of sterols essential for cell membrane function and as precursors in biosynthesis of steroid hormones. Functional properties of CYP51s are unknown in non-mammalian deuterostomes.

METHODS

PCR-cloning and sequencing and computational analyses (homology modeling and docking) addressed CYP51 in zebrafish Danio rerio, the reef fish sergeant major Abudefduf saxatilis, and the sea urchin Strongylocentrotus purpuratus. Following N-terminal amino acid modification, zebrafish CYP51 was expressed in Escherichia coli, and lanosterol 14α-demethylase activity and azole inhibition of CYP51 activity were characterized using GC-MS.

RESULTS

Molecular phylogeny positioned S. purpuratus CYP51 at the base of the deuterostome clade. In zebrafish, CYP51 is expressed in all organs examined, most strongly in intestine. The recombinant protein bound lanosterol and catalyzed 14α-demethylase activity, at 3.2nmol/min/nmol CYP51. The binding of azoles to zebrafish CYP51 gave KS (dissociation constant) values of 0.26μM for ketoconazole and 0.64μM for propiconazole. Displacement of carbon monoxide also indicated zebrafish CYP51 has greater affinity for ketoconazole. Docking to homology models showed that lanosterol docks in fish and sea urchin CYP51s with an orientation essentially the same as in mammalian CYP51s. Docking of ketoconazole indicates it would inhibit fish and sea urchin CYP51s.

CONCLUSIONS

Biochemical and computational analyses are consistent with lanosterol being a substrate for early deuterostome CYP51s.

GENERAL SIGNIFICANCE

The results expand the phylogenetic view of animal CYP51, with evolutionary, environmental and therapeutic implications.