Large-scale protein structure modeling of the Saccharomyces cerevisiae genome

Modeling of SARS-CoV-2 Virus Proteins: Implications on Its Proteome

COronaVIrus Disease 19 (COVID-19) is a severe acute respiratory syndrome (SARS) caused by a group of beta coronaviruses, SARS-CoV-2. The SARS-CoV-2 virus is similar to previous SARS- and MERS-causing strains and has infected nearly six hundred and fifty million people all over the globe, while the death toll has crossed the six million mark (as of December, 2022). In this chapter, we look at how computational modeling approaches of the viral proteins could help us understand the various processes in the viral life cycle inside the host, an understanding of which might provide key insights in mitigating this and future threats. This understanding helps us identify key targets for the purpose of drug discovery and vaccine development.

Multiscale models quantifying yeast physiology: towards a whole-cell model

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.

Characterization of the substrate binding site of an iron detoxifying membrane transporter from Plasmodium falciparum

Background

Plasmodium species are entirely dependent upon their host as a source of essential iron. Although it is an indispensable micronutrient, oxidation of excess ferrous iron to the ferric state in the cell cytoplasm can produce reactive oxygen species that are cytotoxic. The malaria parasite must therefore carefully regulate the processes involved in iron acquisition and storage. A 273 amino acid membrane transporter that is a member of the vacuolar iron transporter (VIT) family and an orthologue of the yeast Ca²⁺-sensitive cross complementer (CCC1) protein plays a major role in cytosolic iron detoxification of Plasmodium species and functions in transport of ferrous iron ions into the endoplasmic reticulum for storage. While this transporter, termed PfVIT, is not critical for viability of the parasite evidence from studies of mice infected with VIT-deficient Plasmodium suggests it could still provide an efficient target for chemoprophylactic treatment of malaria. Individual amino acid residues that constitute the Fe²⁺ binding site of the protein were identified to better understand the structural basis of substrate recognition and binding by PfVIT.

Methods

Using the crystal structure of a recently published plant VIT as a template, a high-quality homology model of PfVIT was constructed to identify the amino acid composition of the transporter’s substrate binding site and to act as a guide for subsequent mutagenesis studies. To test the effect of mutation of the substrate binding-site residues on PfVIT function a yeast complementation assay assessed the ability of overexpressed, recombinant wild type and mutant PfVIT to rescue an iron-sensitive deletion strain (ccc1∆) of Saccharomyces cerevisiae yeast from the toxic effects of a high concentration of extracellular iron.

Results

The combined in silico and mutagenesis approach identified a methionine residue located within the cytoplasmic metal binding domain of the transporter as essential for PfVIT function and provided insight into the structural basis for the Fe²⁺-selectivity of the protein.

Conclusion

The structural model of the metal binding site of PfVIT opens the door for rational design of therapeutics to interfere with iron homeostasis within the malaria parasite.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03827-7.

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 Role of Oxidoreductase-Like Protein Olp1 in Sexual Reproduction and Virulence of Cryptococcus neoformans

Cryptococcus neoformans is a basidiomycete human fungal pathogen causing lethal meningoencephalitis, mainly in immunocompromised patients. Oxidoreductases are a class of enzymes that catalyze redox, playing a crucial role in biochemical reactions. In this study, we identified one Cryptococcus oxidoreductase-like protein-encoding gene OLP1 and investigated its role in the sexual reproduction and virulence of C. neoformans. Gene expression patterns analysis showed that the OLP1 gene was expressed in each developmental stage of Cryptococcus, and the Olp1 protein was located in the cytoplasm of Cryptococcus cells. Although it produced normal major virulence factors such as melanin and capsule, the olp1Δ mutants showed growth defects on the yeast extract peptone dextrose (YPD) medium supplemented with lithium chloride (LiCl) and 5-fluorocytosine (5-FC). The fungal mating analysis showed that Olp1 is also essential for fungal sexual reproduction, as olp1Δ mutants show significant defects in hyphae growth and basidiospores production during bisexual reproduction. The fungal nuclei imaging showed that during the bilateral mating of olp1Δ mutants, the nuclei failed to undergo meiosis after fusion in the basidia, indicating that Olp1 is crucial for regulating meiosis during mating. Moreover, Olp1 was also found to be required for fungal virulence in C. neoformans, as the olp1Δ mutants showed significant virulence attenuation in a murine inhalation model. In conclusion, our results showed that the oxidoreductase-like protein Olp1 is required for both fungal sexual reproduction and virulence in C. neoformans.

CRiSP: accurate structure prediction of disulfide-rich peptides with cystine-specific sequence alignment and machine learning

MOTIVATION

High-throughput sequencing discovers many naturally occurring disulfide-rich peptides or cystine-rich peptides (CRPs) with diversified bioactivities. However, their structure information, which is very important to peptide drug discovery, is still very limited.

RESULTS

We have developed a CRP-specific structure prediction method called Cystine-Rich peptide Structure Prediction (CRiSP), based on a customized template database with cystine-specific sequence alignment and three machine-learning predictors. The modeling accuracy is significantly better than several popular general-purpose structure modeling methods, and our CRiSP can provide useful model quality estimations.

AVAILABILITY AND IMPLEMENTATION

The CRiSP server is freely available on the website at http://wulab.com.cn/CRISP.

CONTACT

wuyd@pkusz.edu.cn or jiangfan@pku.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Menthol Binding to the Human α4β2 Nicotinic Acetylcholine Receptor Facilitated by Its Strong Partitioning in the Membrane

We utilize various computational methodologies to study menthol's interaction with multiple organic phases, a lipid bilayer, and the human α4β2 nicotinic acetylcholine receptor (nAChR), the most abundant nAChR in the brain. First, force field parameters developed for menthol are validated in alchemical free energy perturbation simulations to calculate solvation free energies of menthol in water, dodecane, and octanol and compare the results against experimental data. Next, umbrella sampling is used to construct the free energy profile of menthol permeation across a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer. The results from a flooding simulation designed to study the water-membrane partitioning of menthol in a POPC lipid bilayer are used to determine the penetration depth and the preferred orientation of menthol in the bilayer. Finally, employing both docking and flooding simulations, menthol is shown to bind to different sites on the human α4β2 nAChR. The most likely binding mode of menthol to a desensitized membrane-embedded α4β2 nAChR is identified to be via a membrane-mediated pathway in which menthol binds to the sites at the lipid-protein interface after partitioning in the membrane. A rare but distinct binding mode in which menthol binds to the extracellular opening of receptor's ion permeation pore is also reported.

ClusPro LigTBM: Automated Template-based Small Molecule Docking

The template-based approach has been essential for achieving high-quality models in the recent rounds of blind protein-protein docking competition CAPRI (Critical Assessment of Predicted Interactions). However, few such automated methods exist for protein-small molecule docking. In this paper, we present an algorithm for template-based docking of small molecules. It searches for known complexes with ligands that have partial coverage of the target ligand, performs conformational sampling and template-guided energy refinement to produce a variety of possible poses, and then scores the refined poses. The algorithm is available as the automated ClusPro LigTBM server. It allows the user to specify the target protein as a PDB file and the ligand as a SMILES string. The server then searches for templates and uses them for docking, presenting the user with top-scoring poses and their confidence scores. The method is tested on the Astex Diverse benchmark, as well as on the targets from the last round of the D3R (Drug Design Data Resource) Grand Challenge. The server is publicly available as part of the ClusPro docking server suite at https://ligtbm.cluspro.org/.

Predicting and designing therapeutics against the Nipah virus

Despite Nipah virus outbreaks having high mortality rates (>70% in Southeast Asia), there are no licensed drugs against it. In this study, we have considered all 9 Nipah proteins as potential therapeutic targets and computationally identified 4 putative peptide inhibitors (against G, F and M proteins) and 146 small molecule inhibitors (against F, G, M, N, and P proteins). The computations include extensive homology/ab initio modeling, peptide design and small molecule docking. An important contribution of this study is the increased structural characterization of Nipah proteins by approximately 90% of what is deposited in the PDB. In addition, we have carried out molecular dynamics simulations on all the designed protein-peptide complexes and on 13 of the top shortlisted small molecule ligands to check for stability and to estimate binding strengths. Details, including atomic coordinates of all the proteins and their ligand bound complexes, can be accessed at http://cospi.iiserpune.ac.in/Nipah. Our strategy was to tackle the development of therapeutics on a proteome wide scale and the lead compounds identified could be attractive starting points for drug development. To counter the threat of drug resistance, we have analysed the sequences of the viral strains from different outbreaks, to check whether they would be sensitive to the binding of the proposed inhibitors.

Wheat wounding-responsive HD-Zip IV transcription factor GL7 is predominantly expressed in grain and activates genes encoding defensins

Several classes of transcription factors are involved in the activation of defensins. A new type of the transcription factor responsible for the regulation of wheat grain specific defensins was characterised in this work. HD-Zip class IV transcription factors constitute a family of multidomain proteins. A full-length cDNA of HD-Zip IV, designated TaGL7 was isolated from the developing grain of bread wheat, using a specific DNA sequence as bait in the Y1H screen. 3D models of TaGL7 HD complexed with DNA cis-elements rationalised differences that underlined accommodations of binding and non-binding DNA, while the START-like domain model predicted binding of lipidic molecules inside a concave hydrophobic cavity. The 3'-untranslated region of TaGL7 was used as a probe to isolate the genomic clone of TdGL7 from a BAC library prepared from durum wheat. The spatial and temporal activity of the TdGL7 promoter was tested in transgenic wheat, barley and rice. TdGL7 was expressed mostly in ovary at fertilisation and its promoter was active in a liquid endosperm during cellularisation and later in the endosperm transfer cells, aleurone, and starchy endosperm. The pattern of TdGL7 expression resembled that of genes that encode grain-specific lipid transfer proteins, particularly defensins. In addition, GL7 expression was upregulated by mechanical wounding, similarly to defensin genes. Co-bombardment of cultured wheat cells with TdGL7 driven by constitutive promoter and seven grain or root specific defensin promoters fused to GUS gene, revealed activation of four promoters. The data confirmed the previously proposed role of HD-Zip IV transcription factors in the regulation of genes that encode lipid transfer proteins involved in lipid transport and defence. The TdGL7 promoter could be used to engineer cereal grains with enhanced resistance to insects and fungal infections.

Structural characterization of a pathogenicity-related superoxide dismutase codified by a probably essential gene in Xanthomonas citri subsp. citri

Citrus canker is a plant disease caused by the bacteria Xanthomonas citri subsp. citri that affects all domestic varieties of citrus. Some annotated genes from the X. citri subsp. citri genome are assigned to an interesting class named "pathogenicity, virulence and adaptation". Amongst these is sodM, which encodes for the gene product XcSOD, one of four superoxide dismutase homologs predicted from the genome. SODs are widespread enzymes that play roles in the oxidative stress response, catalyzing the degradation of the deleterious superoxide radical. In Xanthomonas, SOD has been associated with pathogenesis as a counter measure against the plant defense response. In this work we initially present the 1.8 Å crystal structure of XcSOD, a manganese containing superoxide dismutase from Xanthomonas citri subsp. citri. The structure bears all the hallmarks of a dimeric member of the MnSOD family, including the conserved hydrogen-bonding network residues. Despite the apparent gene redundancy, several attempts to obtain a sodM deletion mutant were unsuccessful, suggesting the encoded protein to be essential for bacterial survival. This intriguing observation led us to extend our structural studies to the remaining three SOD homologs, for which comparative models were built. The models imply that X. citri subsp. citri produces an iron-containing SOD which is unlikely to be catalytically active along with two conventional Cu,ZnSODs. Although the latter are expected to possess catalytic activity, we propose they may not be able to replace XcSOD for reasons such as distinct subcellular compartmentalization or differential gene expression in pathogenicity-inducing conditions.

Menthol Stereoisomers Exhibit Different Effects on α4β2 nAChR Upregulation and Dopamine Neuron Spontaneous Firing

Menthol contributes to poor cessation rates among smokers, in part because menthol enhances nicotine reward and reinforcement. Mentholated tobacco products contain (-)-menthol and (+)-menthol, in varying proportions. We examined these two menthol stereoisomers for their ability to upregulate α4β2 nAChRs and to alter dopamine neuron firing frequency using long-term, low-dose (≤500 nm) exposure that is pharmacologically relevant to smoking. We found that (-)-menthol upregulates α4β2 nAChRs while (+)-menthol does not. We also found that (-)-menthol decreases dopamine neuron baseline firing and dopamine neuron excitability, while (+)-menthol exhibits no effect. We then examined both stereoisomers for their ability to inhibit α4β2 nAChR function at higher concentrations (>10 µm) using the Xenopus oocyte expression system. To probe for the potential binding site of menthol, we conducted flooding simulations and site-directed mutagenesis. We found that menthol likely binds to the 9´ position on the TM2 (transmembrane M2) helix. We found that menthol inhibition is dependent on the end-to-end distance of the side chain at the 9´ residue. Additionally, we have found that (-)-menthol is only modestly (∼25%) more potent than (+)-menthol at inhibiting wild-type α4β2 nAChRs and a series of L9´ mutant nAChRs. These data reveal that menthol exhibits a stereoselective effect on nAChRs and that the stereochemical effect is much greater for long-term, submicromolar exposure in mice than for acute, higher-level exposure. We hypothesize that of the two menthol stereoisomers, only (-)-menthol plays a role in enhancing nicotine reward through nAChRs on dopamine neurons.

Identification, characterization and gene expression analyses of important flowering genes related to photoperiodic pathway in bamboo

Background

Bamboo is an important member of the family Poaceae and has many inflorescence and flowering features rarely observed in other plant groups. It retains an unusual form of perennialism by having a long vegetative phase that can extend up to 120 years, followed by flowering and death of the plants. In contrast to a large number of studies conducted on the annual, reference plants Arabidopsis thaliana and rice, molecular studies to characterize flowering pathways in perennial bamboo are lacking. Since photoperiod plays a crucial role in flower induction in most plants, important genes involved in this pathway have been studied in the field grown Bambusa tulda, which flowers after 40-50 years.

Results

We identified several genes from B. tulda, including four related to the circadian clock [LATE ELONGATED HYPOCOTYL (LHY), TIMING OF CAB EXPRESSION1 (TOC1), ZEITLUPE (ZTL) and GIGANTEA (GI)], two circadian clock response integrators [CONSTANS A (COA), CONSTANS B (COB)] and four floral pathway integrators [FLOWERING LOCUS T1, 2, 3, 4 (FT1, 2, 3, 4)]. These genes were amplified from either gDNA and/or cDNA using degenerate as well as gene specific primers based on homologous sequences obtained from related monocot species. The sequence identity and phylogenetic comparisons revealed their close relationships to homologs identified in the temperate bamboo Phyllostachys edulis. While the four BtFT homologs were highly similar to each other, BtCOA possessed a full-length B-box domain that was truncated in BtCOB. Analysis of the spatial expression of these genes in selected flowering and non-flowering tissue stages indicated their possible involvement in flowering. The diurnal expression patterns of the clock genes were comparable to their homologs in rice, except for BtZTL. Among multiple BtCO and BtFT homologs, the diurnal pattern of only BtCOA and BtFT3, 4 were synchronized in the flower inductive tissue, but not in the non-flowering tissues.

Conclusion

This study elucidates the photoperiodic regulation of bamboo homologs of important flowering genes. The finding also identifies copy number expansion and gene expression divergence of CO and FT in bamboo. Further studies are required to understand their functional role in bamboo flowering.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4571-7) contains supplementary material, which is available to authorized users.

A global genetic interaction network maps a wiring diagram of cellular function

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

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.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Comparative Protein Structure Modeling Using MODELLER

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.

Use of machine learning approaches for novel drug discovery

INTRODUCTION

The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds.

AREAS COVERED

This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening.

EXPERT OPINION

Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

Proton currents constrain structural models of voltage sensor activation

The Hv1 proton channel is evidently unique among voltage sensor domain proteins in mediating an intrinsic 'aqueous' H⁺ conductance (G_AQ). Mutation of a highly conserved 'gating charge' residue in the S4 helix (R1H) confers a resting-state H⁺ 'shuttle' conductance (G_SH) in VGCs and Ci VSP, and we now report that R1H is sufficient to reconstitute G_SH in Hv1 without abrogating G_AQ. Second-site mutations in S3 (D185A/H) and S4 (N4R) experimentally separate G_SH and G_AQ gating, which report thermodynamically distinct initial and final steps, respectively, in the Hv1 activation pathway. The effects of Hv1 mutations on G_SH and G_AQ are used to constrain the positions of key side chains in resting- and activated-state VS model structures, providing new insights into the structural basis of VS activation and H⁺ transfer mechanisms in Hv1.

Comparative Protein Structure Modeling Using MODELLER

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.

In silico Prediction and Docking of Tertiary Structure of LuxI, an Inducer Synthase of Vibrio fischeri

BACKGROUND

LuxI is a component of the quorum sensing signaling pathway in Vibrio fischeriresponsible for the inducer synthesis that is essential for bioluminescence.

METHODS

Homology modeling of LuxI was carried out using Phyre2 and refined with the GalaxyWEB server. Five models were generated and evaluated by ERRAT, ANOLEA, QMEAN6, and Procheck.

RESULTS

Five refined models were generated by the GalaxyWEB server, with Model 4 having the greatest quality based on the QMEAN6 score of 0.732. ERRAT analysis revealed an overall quality of 98.9%, while the overall quality of the initial model was 54%. The mean force potential energy, as analyzed by ANOLEA, were better compared to the initial model. Sterochemical quality estimation by Procheck showed that the refined Model 4 had a reliable structure, and was therefore submitted to the protein model database. Drug Discovery Workbench V.2 was used to screen 2700 experimental compounds from the DrugBank database to identify inhibitors that can bind to the active site between amino acids 24 and 110. Ten compounds with high negative scores were selected as the best in binding.

CONCLUSION

The model produced, and the predicted acteyltransferase binding site, could be useful in modeling homologous sequences from other microorganisms and the design of new antimicrobials.

Change of function of the wheat stress-responsive transcriptional repressor TaRAP2.1L by repressor motif modification

Plants respond to abiotic stresses by changes in gene regulation, including stress-inducible expression of transcriptional activators and repressors. One of the best characterized families of drought-related transcription factors are dehydration-responsive element binding (DREB) proteins, known as C-repeat binding factors (CBF). The wheat DREB/CBF gene TaRAP2.1L was isolated from drought-affected tissues using a dehydration-responsive element (DRE) as bait in a yeast one-hybrid screen. TaRAP2.1L is induced by elevated abscisic acid, drought and cold. A C-terminal ethylene responsive factor-associated amphiphilic repression (EAR) motif, known to be responsible for active repression of target genes, was identified in the TaRAP2.1L protein. It was found that TaRAP2.1L has a unique selectivity of DNA-binding, which differs from that of DREB activators. This binding selectivity remains unchanged in a TaRAP2.1L variant with an inactivated EAR motif (TaRAP2.1Lmut). To study the role of the TaRAP2.1L repressor activity associated with the EAR motif in planta, transgenic wheat overexpressing native or mutated TaRAP2.1L was generated. Overexpression of TaRAP2.1L under constitutive and stress-inducible promoters in transgenic wheat and barley led to dwarfism and decreased frost tolerance. By contrast, constitutive overexpression of the TaRAP2.1Lmut gene had little or no negative influence on wheat development or grain yield. Transgenic lines with the TaRAP2.1Lmut transgene had an enhanced ability to survive frost and drought. The improved stress tolerance is attributed to up-regulation of several stress-related genes known to be downstream genes of DREB/CBF activators.

Developmentally regulated HEART STOPPER, a mitochondrially targeted L18 ribosomal protein gene, is required for cell division, differentiation, and seed development in Arabidopsis

Evidence is presented for the role of a mitochondrial ribosomal (mitoribosomal) L18 protein in cell division, differentiation, and seed development after the characterization of a recessive mutant, heart stopper (hes). The hes mutant produced uncellularized endosperm and embryos arrested at the late globular stage. The mutant embryos differentiated partially on rescue medium with some forming callus. HES (At1g08845) encodes a mitochondrially targeted member of a highly diverged L18 ribosomal protein family. The substitution of a conserved amino residue in the hes mutant potentially perturbs mitoribosomal function via altered binding of 5S rRNA and/or influences the stability of the 50S ribosomal subunit, affecting mRNA binding and translation. Consistent with this, marker genes for mitochondrial dysfunction were up-regulated in the mutant. The slow growth of the endosperm and embryo indicates a defect in cell cycle progression, which is evidenced by the down-regulation of cell cycle genes. The down-regulation of other genes such as EMBRYO DEFECTIVE genes links the mitochondria to the regulation of many aspects of seed development. HES expression is developmentally regulated, being preferentially expressed in tissues with active cell division and differentiation, including developing embryos and the root tips. The divergence of the L18 family, the tissue type restricted expression of HES, and the failure of other L18 members to complement the hes phenotype suggest that the L18 proteins are involved in modulating development. This is likely via heterogeneous mitoribosomes containing different L18 members, which may result in differential mitochondrial functions in response to different physiological situations during development.

Spatial and temporal activity of the foxtail millet (Setaria italica) seed-specific promoter pF128

pF128 drives GUS specifically expressed in transgenic seeds of foxtail millet and Zea mays with higher activity than the constitutive CaMV35S promoter and the maize seed-specific 19Z promoter. Foxtail millet (Setaria italica), a member of the Poaceae family, is an important food and fodder crop in arid regions. Foxtail millet is an excellent C4 crop model owing to its small genome (~490 Mb), self-pollination and availability of a complete genome sequence. F128 was isolated from a cDNA library of foxtail millet immature seeds. Real-time PCR analysis revealed that F128 mRNA was specifically expressed in immature and mature seeds. The highest F128 mRNA level was observed 5 days after pollination and gradually decreased as the seed matured. Sequence analysis suggested that the protein encoded by F128 is likely a protease inhibitor/seed storage protein/lipid-transfer protein. The 1,053 bp 5' flanking sequence of F128 (pF128) was isolated and fused to the GUS reporter gene. The corresponding vector was then transformed into Arabidopsis thaliana, foxtail millet and Zea mays. GUS analysis revealed that pF128 drove GUS expression efficiently and specifically in the seeds of transgenic Arabidopsis, foxtail millet and Zea mays. GUS activity was also detected in Arabidopsis cotyledons. Activity of pF128 was higher than that observed for the constitutive CaMV35S promoter and the maize seed-specific 19 Zein (19Z) promoter. These results indicate that pF128 is a seed-specific promoter. Its application is expected to be of considerable value in plant genetic engineering.

Increasing the structural coverage of tuberculosis drug targets

High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus "homolog-rescue" strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. Of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal structure. By adding 1675 homologs from nine other mycobacterial species to the pipeline, structures representing an additional 52 otherwise intractable targets were solved. To determine whether these homolog structures would be useful surrogates in TB drug design, we compared the active sites of 106 pairs of Mtb and non-TB mycobacterial (NTM) enzyme homologs with experimentally determined structures, using three metrics of active site similarity, including superposition of continuous pharmacophoric property distributions. Pair-wise structural comparisons revealed that 19/22 pairs with >55% overall sequence identity had active site Cα RMSD <1 Å, >85% side chain identity, and ≥80% PSAPF (similarity based on pharmacophoric properties) indicating highly conserved active site shape and chemistry. Applying these results to the 52 NTM structures described above, 41 shared >55% sequence identity with the Mtb target, thus increasing the effective structural coverage of the 179 Mtb targets over three-fold (from 9% to 32%). The utility of these structures in TB drug design can be tested by designing inhibitors using the homolog structure and assaying the cognate Mtb enzyme; a promising test case, Mtb cytidylate kinase, is described. The homolog-rescue strategy evaluated here for TB is also generalizable to drug targets for other diseases.

Comparative Protein Structure Modeling Using MODELLER

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction

Protein sequence alignment is essential for template-based protein structure prediction and function annotation. We collect 20 sequence alignment algorithms, 10 published and 10 newly developed, which cover all representative sequence- and profile-based alignment approaches. These algorithms are benchmarked on 538 non-redundant proteins for protein fold-recognition on a uniform template library. Results demonstrate dominant advantage of profile-profile based methods, which generate models with average TM-score 26.5% higher than sequence-profile methods and 49.8% higher than sequence-sequence alignment methods. There is no obvious difference in results between methods with profiles generated from PSI-BLAST PSSM matrix and hidden Markov models. Accuracy of profile-profile alignments can be further improved by 9.6% or 21.4% when predicted or native structure features are incorporated. Nevertheless, TM-scores from profile-profile methods including experimental structural features are still 37.1% lower than that from TM-align, demonstrating that the fold-recognition problem cannot be solved solely by improving accuracy of structure feature predictions.

Protein modeling: what happened to the "protein structure gap"?

Computational modeling of three-dimensional macromolecular structures and complexes from their sequence has been a long-standing vision in structural biology. Over the last 2 decades, a paradigm shift has occurred: starting from a large "structure knowledge gap" between the huge number of protein sequences and small number of known structures, today, some form of structural information, either experimental or template-based models, is available for the majority of amino acids encoded by common model organism genomes. With the scientific focus of interest moving toward larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows the study of large and complex molecular machines. One of the open challenges for computational modeling and prediction techniques is to convey the underlying assumptions, as well as the expected accuracy and structural variability of a specific model, which is crucial to understanding its limitations.

Protein structure modeling in the proteomics era

Structural proteomics aims to understand the structural basis of protein interactions and functions. A prerequisite for this is the availability of 3D protein structures that mediate the biochemical interactions. The explosion in the number of available gene sequences set the stage for the next step in genome-scale projects -- to obtain 3D structures for each protein. To achieve this ambitious goal, the slow and costly structure determination experiments are supplemented with theoretical approaches. The current state and recent advances in structure modeling approaches are reviewed here, with special emphasis on comparative protein structure modeling techniques.

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.

Comparative Modeling of Drug Target Proteins☆

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.

Structural insights into Saccharomyces cerevisiae Msh4-Msh5 complex function using homology modeling

The Msh4–Msh5 protein complex in eukaryotes is involved in stabilizing Holliday junctions and its progenitors to facilitate crossing over during Meiosis I. These functions of the Msh4–Msh5 complex are essential for proper chromosomal segregation during the first meiotic division. The Msh4/5 proteins are homologous to the bacterial mismatch repair protein MutS and other MutS homologs (Msh2, Msh3, Msh6). Saccharomyces cerevisiae msh4/5 point mutants were identified recently that show two fold reduction in crossing over, compared to wild-type without affecting chromosome segregation. Three distinct classes of msh4/5 point mutations could be sorted based on their meiotic phenotypes. These include msh4/5 mutations that have a) crossover and viability defects similar to msh4/5 null mutants; b) intermediate defects in crossing over and viability and c) defects only in crossing over. The absence of a crystal structure for the Msh4–Msh5 complex has hindered an understanding of the structural aspects of Msh4–Msh5 function as well as molecular explanation for the meiotic defects observed in msh4/5 mutations. To address this problem, we generated a structural model of the S. cerevisiae Msh4–Msh5 complex using homology modeling. Further, structural analysis tailored with evolutionary information is used to predict sites with potentially critical roles in Msh4–Msh5 complex formation, DNA binding and to explain asymmetry within the Msh4–Msh5 complex. We also provide a structural rationale for the meiotic defects observed in the msh4/5 point mutations. The mutations are likely to affect stability of the Msh4/5 proteins and/or interactions with DNA. The Msh4–Msh5 model will facilitate the design and interpretation of new mutational data as well as structural studies of this important complex involved in meiotic chromosome segregation.

Structural insights into a novel interkingdom signaling circuit by cartography of the ligand-binding sites of the homologous quorum sensing LuxR-family

Recent studies have identified a novel interkingdom signaling circuit, via plant signaling molecules, and a bacterial sub-family of LuxR proteins, bridging eukaryotes and prokaryotes. Indeed pivotal plant-bacteria interactions are regulated by the so called Plant Associated Bacteria (PAB) LuxR solo regulators that, although closely related to the quorum sensing (QS) LuxR family, do not bind or respond to canonical quorum sensing N-acyl homoserine lactones (AHLs), but only to specific host plant signal molecules. The large body of structural data available for several members of the QS LuxR family complexed with different classes of ligands (AHLs and other compounds), has been exploited to dissect the cartography of their regulatory domains through structure-based multiple sequence alignments, structural superimposition and a comparative analysis of the contact residues involved in ligand binding. In the absence of experimentally determined structures of members of the PAB LuxR solos subfamily, an homology model of its prototype OryR is presented, aiming to elucidate the architecture of its ligand-binding site. The obtained model, in combination with the cartography of the regulatory domains of the homologous QS LuxRs, provides novel insights into the 3D structure of its ligand-binding site and unveils the probable molecular determinants responsible for differences in selectivity towards specific host plant signal molecules, rather than to canonical QS compounds.

Comprehensive database of Chorismate synthase enzyme from shikimate pathway in pathogenic bacteria

Background

Infectious diseases are major public health problem. It is increasingly affecting more than 50 million people worldwide. Targeting shikimate pathway could be efficiently used for the development of broad spectrum antimicrobial compound against variety of infectious diseases. Chorismate synthase is an enzyme in shikimate pathway that catalyzes Phosphoenol pyruvate to chorismate in most of the prokaryotic bacteria. This step is crucial for its growth, since Chorismate acts as a precursor molecule for the synthesis of aromatic amino acids. Hence, we present a comprehensive database of Chorismate Synthase Database (CSDB) which is a manually curated database. It provides information on the sequence, structure and biological activity of chorismate synthase from shikimate pathway of pathogenic bacteria. Design of suitable inhibitors for this enzyme, hence could be a probable solution to destroy its proteomic machinery and thereby inhibit the bacterial growth.

Description

The aim of this study was to characterise chorismate synthase enzyme belonging to pathogenic bacteria to analysis the functional and structural characterization of chorismate synthase is very important for both structure-based and ligand based drug design.

Conclusions

The broad range of data easy to use user interface makes csdb.in a useful database for researchers in designing drugs.

Structure-based prediction of protein-protein interactions on a genome-wide scale

The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms^1,2. Much of our current knowledge derives from high-throughput techniques such as yeast two hybrid and affinity purification³, as well as from manual curation of experiments on individual systems⁴. A variety of computational approaches based, for example, on sequence homology, gene co-expression, and phylogenetic profiles have also been developed for the genome-wide inference of protein-protein interactions (PPIs)^5,6. Yet, comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages^7–9. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, PrePPI, that combines structural information with other functional clues is comparable in accuracy to high-throughput experiments, yielding over 30,000 high confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of significant biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.

High-Throughput parallel blind Virtual Screening using BINDSURF

Background

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, usually derived from the interpretation of the protein crystal structure. However, it has been demonstrated that in many cases, diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact.

Results

We present BINDSURF, a novel VS methodology that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases.

Conclusions

BINDSURF is an efficient and fast blind methodology for the determination of protein binding sites depending on the ligand, that uses the massively parallel architecture of GPUs for fast pre-screening of large ligand databases. Its results can also guide posterior application of more detailed VS methods in concrete binding sites of proteins, and its utilization can aid in drug discovery, design, repurposing and therefore help considerably in clinical research.

Ribosome biogenesis factor Bms1-like is essential for liver development in zebrafish

Ribosome biogenesis in the nucleolus requires numerous nucleolar proteins and small non-coding RNAs. Among them is ribosome biogenesis factor Bms1, which is highly conserved from yeast to human. In yeast, Bms1 initiates ribosome biogenesis through recruiting Rcl1 to pre-ribosomes. However, little is known about the biological function of Bms1 in vertebrates. Here we report that Bms1 plays an essential role in zebrafish liver development. We identified a zebrafish bms1l(sq163) mutant which carries a T to A mutation in the gene bms1-like (bms1l). This mutation results in L(152) to Q(152) substitution in a GTPase motif in Bms1l. Surprisingly, bms1l(sq163) mutation confers hypoplasia specifically in the liver, exocrine pancreas and intestine after 3 days post-fertilization (dpf). Consistent with the bms1l(sq163) mutant phenotypes, whole-mount in situ hybridization (WISH) on wild type embryos showed that bms1l transcripts are abundant in the entire digestive tract and its accessory organs. Immunostaining for phospho-Histone 3 (P-H3) and TUNEL assay revealed that impairment of hepatoblast proliferation rather than cell apoptosis is one of the consequences of bms1l(sq163) giving rise to an under-developed liver. Therefore, our findings demonstrate that Bms1l is necessary for zebrafish liver development.

Correlation between protein sequence similarity and crystallization reagents in the biological macromolecule crystallization database

The protein structural entries grew far slower than the sequence entries. This is partly due to the bottleneck in obtaining diffraction quality protein crystals for structural determination using X-ray crystallography. The first step to achieve protein crystallization is to find out suitable chemical reagents. However, it is not an easy task. Exhausting trial and error tests of numerous combinations of different reagents mixed with the protein solution are usually necessary to screen out the pursuing crystallization conditions. Therefore, any attempts to help find suitable reagents for protein crystallization are helpful. In this paper, an analysis of the relationship between the protein sequence similarity and the crystallization reagents according to the information from the existing databases is presented. We extracted information of reagents and sequences from the Biological Macromolecule Crystallization Database (BMCD) and the Protein Data Bank (PDB) database, classified the proteins into different clusters according to the sequence similarity, and statistically analyzed the relationship between the sequence similarity and the crystallization reagents. The results showed that there is a pronounced positive correlation between them. Therefore, according to the correlation, prediction of feasible chemical reagents that are suitable to be used in crystallization screens for a specific protein is possible.

Characterization of the wheat gene encoding a grain-specific lipid transfer protein TdPR61, and promoter activity in wheat, barley and rice

The TaPR61 gene from bread wheat encodes a lipid transfer protein (LTP) with a hydrophobic signal peptide, predicted to direct the TaPR61 protein to the apoplast. Modelling of TaPR61 revealed the presence of an internal cavity which can accommodate at least two lipid molecules. The full-length gene, including the promoter sequence of a TaPR61 orthologue, was cloned from a BAC library of Triticum durum. Quantitative RT-PCR analysis revealed the presence of TaPR61 and TdPR61 mainly in grain. A transcriptional TdPR61 promoter-GUS fusion was stably transformed into wheat, barley, and rice. The strongest GUS expression in all three plants was found in the endosperm transfer cells, the embryo surrounding region (ESR), and in the embryo. The promoter is strong and has similar but not identical spatial patterns of activity in wheat, barley, and rice. These results suggest that the TdPR61 promoter will be a useful tool for improving grain quality by manipulating the quality and quantity of nutrient/lipid uptake to the endosperm and embryo. Mapping of regions important for the promoter function using transient expression assays in developing embryos resulted in the identification of two segments important for promoter activation in embryos. The putative cis-elements from the distal segment were used as bait in a yeast 1-hybrid (Y1H) screen of a cDNA library prepared from the liquid part of the wheat multinucleate syncytium. A transcription factor isolated in the screen is similar to BES1/BLZ1 from Arabidopsis, which is known to be a key transcriptional regulator of the brassinosteroid signalling pathway.

FUNCTION PREDICTION OF UNCHARACTERIZED PROTEINS

Function prediction of uncharacterized protein sequences generated by genome projects has emerged as an important focus for computational biology. We have categorized several approaches beyond traditional sequence similarity that utilize the overwhelmingly large amounts of available data for computational function prediction, including structure-, association (genomic context)-, interaction (cellular context)-, process (metabolic context)-, and proteomics-experiment-based methods. Because they incorporate structural and experimental data that is not used in sequence-based methods, they can provide additional accuracy and reliability to protein function prediction. Here, first we review the definition of protein function. Then the recent developments of these methods are introduced with special focus on the type of predictions that can be made. The need for further development of comprehensive systems biology techniques that can utilize the ever-increasing data presented by the genomics and proteomics communities is emphasized. For the readers' convenience, tables of useful online resources in each category are included. The role of computational scientists in the near future of biological research and the interplay between computational and experimental biology are also addressed.

Using structure to explore the sequence alignment space of remote homologs

Protein structure modeling by homology requires an accurate sequence alignment between the query protein and its structural template. However, sequence alignment methods based on dynamic programming (DP) are typically unable to generate accurate alignments for remote sequence homologs, thus limiting the applicability of modeling methods. A central problem is that the alignment that is “optimal” in terms of the DP score does not necessarily correspond to the alignment that produces the most accurate structural model. That is, the correct alignment based on structural superposition will generally have a lower score than the optimal alignment obtained from sequence. Variations of the DP algorithm have been developed that generate alternative alignments that are “suboptimal” in terms of the DP score, but these still encounter difficulties in detecting the correct structural alignment. We present here a new alternative sequence alignment method that relies heavily on the structure of the template. By initially aligning the query sequence to individual fragments in secondary structure elements and combining high-scoring fragments that pass basic tests for “modelability”, we can generate accurate alignments within a small ensemble. Our results suggest that the set of sequences that can currently be modeled by homology can be greatly extended.

It has been suggested that, for nearly every protein sequence, there is already a protein with a similar structure in current protein structure databases. However, with poor or undetectable sequence relationships, it is expected that accurate alignments and models cannot be generated. Here we show that this is not the case, and that whenever structural relationship exists, there are usually local sequence relationships that can be used to generate an accurate alignment, no matter what the global sequence identity. However, this requires an alternative to the traditional dynamic programming algorithm and the consideration of a small ensemble of alignments. We present an algorithm, S4, and demonstrate that it is capable of generating accurate alignments in nearly all cases where a structural relationship exists between two proteins. Our results thus constitute an important advance in the full exploitation of the information in structural databases. That is, the expectation of an accurate alignment suggests that a meaningful model can be generated for nearly every sequence for which a suitable template exists.

Discovery of novel selective serotonin reuptake inhibitors through development of a protein-based pharmacophore

The serotonin transporter (SERT), a member of the neurotransmitter sodium symporter (NSS) family, is responsible for the reuptake of serotonin from the synaptic cleft to maintain neurotransmitter homeostasis. SERT is established as an important target in the treatment of anxiety and depression. Because a high-resolution crystal structure is not available, a computational model of SERT was built based upon the X-ray coordinates of the leucine transporter LeuT, a bacterial NSS homologue. The model was used to develop the first SERT structure-based pharmacophore. Virtual screening (VS) of a small molecule structural library using the generated SERT computational model yielded candidate ligands of diverse scaffolds. Pharmacological analysis of the VS hits identified two SERT-selective compounds, potential lead compounds for further SERT-related medication development.

Interaction of diverse voltage sensor homologs with lipid bilayers revealed by self-assembly simulations

Voltage sensors (VS) domains couple the activation of ion channels/enzymes to changes in membrane voltage. We used molecular dynamics simulations to examine interactions with lipids of several VS homologs. VSs in intact channels in the activated state are exposed to phospholipids, leading to a characteristic local distortion of the lipid bilayer which decreases its thickness by ∼10 Å. This effect is mediated by a conserved hydrophilic stretch in the S4-S5 segment linking the VS and the pore domains, and may favor gating charges crossing the membrane. In cationic lipid bilayers lacking phosphate groups, VSs form fewer contacts with lipid headgroups. The S3-S4 paddle motifs show persistent interactions of individual lipid molecules, influenced by the hairpin loop. In conclusion, our results suggest common interactions with phospholipids for various VS homologs, providing insights into the molecular basis of their stabilization in the membrane and how they are altered by lipid modification.

Structural underpinnings of nitrogen regulation by the prototypical nitrogen-responsive transcriptional factor NrpR

Plants and microorganisms reduce environmental inorganic nitrogen to ammonium, which then enters various metabolic pathways solely via conversion of 2-oxoglutarate (2OG) to glutamate and glutamine. Cellular 2OG concentrations increase during nitrogen starvation. We recently identified a family of 2OG-sensing proteins--the nitrogen regulatory protein NrpR--that bind DNA and repress transcription of nitrogen assimilation genes. We used X-ray crystallography to determine the structure of NrpR regulatory domain. We identified the NrpR 2OG-binding cleft and show that residues predicted to interact directly with 2OG are conserved among diverse classes of 2OG-binding proteins. We show that high levels of 2OG inhibit NrpRs ability to bind DNA. Electron microscopy analyses document that NrpR adopts different quaternary structures in its inhibited 2OG-bound state compared with its active apo state. Our results indicate that upon 2OG release, NrpR repositions its DNA-binding domains correctly for optimal interaction with DNA thereby enabling gene repression.

Systematic assessment of accuracy of comparative model of proteins belonging to different structural fold classes

In the absence of experimental structures, comparative modeling continues to be the chosen method for retrieving structural information on target proteins. However, models lack the accuracy of experimental structures. Alignment error and structural divergence (between target and template) influence model accuracy the most. Here, we examine the potential additional impact of backbone geometry, as our previous studies have suggested that the structural class (all-α, αβ, all-β) of a protein may influence the accuracy of its model. In the twilight zone (sequence identity ≤ 30%) and at a similar level of target-template divergence, the accuracy of protein models does indeed follow the trend all-α > αβ > all-β. This is mainly because the alignment accuracy follows the same trend (all-α > αβ > all-β), with backbone geometry playing only a minor role. Differences in the diversity of sequences belonging to different structural classes leads to the observed accuracy differences, thus enabling the accuracy of alignments/models to be estimated a priori in a class-dependent manner. This study provides a systematic description of and quantifies the structural class-dependent effect in comparative modeling. The study also suggests that datasets for large-scale sequence/structure analyses should have equal representations of different structural classes to avoid class-dependent bias.

Investigating Molecular Mechanisms of Activation and Mutation of the HER2 Receptor Tyrosine Kinase through Computational Modeling and Simulation

Human epidermal growth factor receptor 2 (HER2)/ErbB2 is a receptor tyrosine kinase belonging to the EGFR/ErbB family and is overexpressed in 20-30% of human breast cancers. Since there is a growing effort to develop pharmacological inhibitors of the HER2 kinase for the treatment of breast cancer, it is clinically valuable to rationalize how specific mutations impact the molecular mechanism of receptor activation. Although several crystal structures of the ErbB kinases have been solved, the precise mechanism of HER2 activation remains unknown, and it has been suggested that HER2 is unique in its requirement for phosphorylation of Y877, a key tyrosine residue located in the activation loop (A-loop). In our studies, discussed here, we have investigated the mechanisms that are important in HER2 kinase domain regulation and compared them with the other ErbB family members, namely EGFR and ErbB4, to determine the molecular basis for HER2's unique mode of activation. We apply computational simulation techniques at the atomic level and at the electronic structure (quantum mechanical) level to elucidate details of the mechanisms governing the kinase domains of these ErbB members. Through analysis of our simulation results, we have discovered potential regulatory mechanisms common to EGFR, HER2, and ErbB4, including a tight coupling between the A-loop and catalytic loop that may contribute to alignment of residues required for catalysis in the active kinase. We further postulate an autoinhibitory mechanism whereby the inactive kinase is stabilized through sequestration of catalytic residues. In HER2, we also predict a role for phosphorylated Y877 in bridging a network of hydrogen bonds that fasten the A-loop in its active conformation, suggesting that HER2 may be unique among the ErbB members in requiring A-loop tyrosine phosphorylation for functionality. In EGFR, HER2, and ErbB4, we discuss the possible effects of activating mutations. Delineation of the activation mechanism of HER2 in the context of the other ErbB members is crucial for understanding how the activated kinase might interact with downstream molecules and couple to signaling cascades that promote cancer. Our comparative analysis furthers insight into the mechanics of activation of the HER2 kinase and enables us to predict the effect of an identified insertion mutation on HER2 activation. Further understanding of the mechanism of HER2 kinase activation at the atomic scale and how it couples to downstream signaling at the cellular scale will elucidate predictive molecular phenotypes that may indicate likelihood of response to specific therapies for HER2-mediated cancers.