Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments

Preclinical development of three novel CARs targeting CD79b for the treatment of non-Hodgkin's lymphoma and characterization of the loss of the target antigen

BACKGROUND

Infusion of T cells modified with a chimeric antigen receptor (CAR) targeting CD19 has achieved exceptional responses in patients with non-Hodgkin's lymphoma (NHL), which led to the approval of CAR targeting CD19 (CART19) (Axi-cel and Liso-cel) as second line of treatment for adult patients with relapsed/refractory NHL. Unfortunately, 60% of patients still relapse after CART19 due to either a loss of expression of the target antigen (CD19) in the tumor cell, observed in 27% of relapsed patients, a limited CAR-T persistence, and additional mechanisms, including the suppression of the tumor microenvironment. Clinic strategies to prevent target antigen loss include sequential treatment with CARs directed at CD20 or CD22, which have caused loss of the second antigen, suggesting targeting other antigens less prone to disappear. CD79b, expressed in NHL, is a target in patients treated with antibody-drug conjugates (ADC). However, the limited efficacy of ADC suggests that a CAR therapy targeting CD79b might improve results.

METHODS

We designed three new CARs against CD79b termed CAR for Lymphoma (CARLY)1, 2 and 3. We compared their efficacy, phenotype, and inflammatory profiles with CART19 (ARI0001) and CARTBCMA (ARI0002h), which can treat NHL. We also analyzed the target antigen's expression loss (CD79b, CD19, and B-cell maturation antigen(BCMA)).

RESULTS

We found that CARLY2 and CARLY3 had high affinity and specificity towards CD79b on B cells. In vitro, all CAR-T cells had similar anti-NHL efficacy, which was retained in an NHL model of CD19- relapse. In vivo, CARLY3 showed the highest efficacy. Analysis of the loss of the target antigen demonstrated that CARLY cells induced CD79b and CD19 downregulation on NHL cells with concomitant trogocytosis of these antigens to T cells, being most notorious in CARLY2, which had the highest affinity towards CD79b and CD19, and supporting the selection of CARLY3 to design a new treatment for patients with NHL. Finally, we created a CAR treatment based on dual targeting of CD79b and BCMA to avoid losing the target antigen. This treatment showed the highest efficacy and did not cause loss of the target antigen.

CONCLUSIONS

Based on specificity, efficacy, and loss of the target antigen, CARLY3 represents a potential novel CAR treatment for NHL.

Prediction of interactions between cell surface proteins by machine learning

Cells detect changes in their external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and, thus, challenging to detect using traditional experimental techniques. Here, we tackle this challenge using a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in the immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells or between proteins on the same cell surface. In practice, we collected all structural data on Ig domain interactions and transformed them into an interface fragment pair library. A high-dimensional profile can then be constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile so that the probability of interaction between the query proteins could be predicted. We tested our models on an experimentally derived dataset that contains 564 cell surface proteins in humans. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in Caenorhabditis elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literature. In conclusion, our computational platform serves as a useful tool to help identify potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study the interactions of proteins in other domain superfamilies.

High-Performance Genetically Encoded Green Fluorescent Biosensors for Intracellular l-Lactate

l-Lactate is a monocarboxylate produced during the process of cellular glycolysis and has long generally been considered a waste product. However, studies in recent decades have provided new perspectives on the physiological roles of l-lactate as a major energy substrate and a signaling molecule. To enable further investigations of the physiological roles of l-lactate, we have developed a series of high-performance (ΔF/F = 15 to 30 in vitro), intensiometric, genetically encoded green fluorescent protein (GFP)-based intracellular l-lactate biosensors with a range of affinities. We evaluated these biosensors in cultured cells and demonstrated their application in an ex vivo preparation of Drosophila brain tissue. Using these biosensors, we were able to detect glycolytic oscillations, which we analyzed and mathematically modeled.

Biochemical characterisation of a PL24 ulvan lyase from seaweed-associated Vibrio sp. FNV38

Ulvan is a green macroalgal cell wall polysaccharide that has tremendous potential for valorisation due to its unique composition of sulphated rhamnose, glucuronic acid, iduronic acid and xylose. Several potential applications such as production of biofuels, bioplastics and other value-added products necessitate the breakdown of the polysaccharide to oligomers or monomers. Research on ulvan saccharifying enzymes has been continually increasing over the last decade, with the increasing focus on valorisation of seaweed biomass for a biobased economy. Lyases are the first of several enzymes that are involved in saccharifying the polysaccharide and several ulvan lyases have been structurally and biochemically characterised to enable their effective use in the valorisation processes. This study investigates the whole genome of Vibrio sp. FNV38, an ulvan metabolising organism and biochemical characteristics of a PL24 ulvan lyase that it possesses. The genome of Vibrio sp. FNV38 has a diverse CAZy profile with several genes involved in the metabolism of ulvan, cellulose, agar, and alginate. The enzyme exhibits optimal activity at pH 8.5 in 100 mM Tris-HCl buffer and 30 °C. However, its thermal stability is poor with significant loss of activity after 2 h of incubation at temperatures above 25 °C. Breakdown product analysis reveals that the enzyme depolymerised the polysaccharide predominantly to disaccharides and tetrasaccharides.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10811-023-03136-3.

Prediction of Interactions between Cell Surface Proteins by Machine Learning

Cells detect changes of external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and thus challenging to detect using traditional experimental techniques. Here we tackle this challenge by a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells, or between proteins on the same cell surface. In practice, we collected all structural data of Ig domain interactions and transformed them into an interface fragment pair library. A high dimensional profile can be then constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile, so that the probability of interaction between the query proteins can be predicted. We tested our models to an experimentally derived dataset which contains 564 cell surface proteins in human. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in C elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literatures. In conclusion, our computational platform serves a useful tool to help identifying potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study interactions of proteins in other domain superfamilies.

Self-association features of NS1 proteins from different flaviviruses

Flaviviruses comprise a large group of arboviral species that are distributed in several countries of the tropics, neotropics, and some temperate zones. Since they can produce neurological pathologies or vascular damage, there has been intense research seeking better diagnosis and treatments for their infections in the last decades. The flavivirus NS1 protein is a relevant clinical target because it is involved in viral replication, immune evasion, and virulence. Being a key factor in endothelial and tissue-specific modulation, NS1 has been largely studied to understand the molecular mechanisms exploited by the virus to reprogram host cells. A central part of the viral maturation processes is the NS1 oligomerization because many stages rely on these protein-protein assemblies. In the present study, the self-associations of NS1 proteins from Zika, Dengue, and West Nile viruses are examined through constant-pH coarse-grained biophysical simulations. Free energies of interactions were estimated for different oligomeric states and pH conditions. Our results show that these proteins can form both dimers and tetramers under conditions near physiological pH even without the presence of lipids. Moreover, pH plays an important role mainly controlling the regimes where van der Waals interactions govern their association. Finally, despite the similarity at the sequence level, we found that each flavivirus has a well-characteristic protein-protein interaction profile. These specific features can provide new hints for the development of binders both for better diagnostic tools and the formulation of new therapeutic drugs.

Identification of novel inhibitors targeting TIRAP interactions with BTK and PKCδ in inflammation through an in silico approach

Advanced computational tools focusing on protein-protein interaction (PPI) based drug development is a powerful platform to accelerate the therapeutic development of small lead molecules and repurposed drugs. Toll/interleukin-1 receptor (TIR) domain-containing adapter protein (TIRAP) and its interactions with other proteins in macrophages signalling are crucial components of severe or persistent inflammation. TIRAP activation through Bruton's tyrosine kinase (BTK) and Protein Kinase C delta (PKCδ) is essential for downstream inflammatory signalling. We created homology-based structural models of BTK and PKCδ in MODELLER 9.24. TIRAP interactions with BTK and PKCδ in its non-phosphorylated and phosphorylated states were determined by multiple docking tools including HADDOCK 2.4, pyDockWEB and ClusPro 2.0. Food and Drug Administration (FDA)-approved drugs were virtually screened through Discovery Studio LibDock and Autodock Vina tools to target the common TIR domain residues of TIRAP, which interact with both BTK and PKC at the identified interfacial sites of the complexes. Four FDA-approved drugs were identified and found to have stable interactions over a range of 100 ns MD simulation timescales. These drugs block the interactions of both kinases with TIRAP in silico. Hence, these drugs have the potential to dampen downstream inflammatory signalling and inflammation-mediated disease.

Functional Characterization of an In-Frame Deletion in the Basic Domain of the Retinal Transcription Factor ATOH7

Basic helix–loop–helix (bHLH) transcription factors are evolutionarily conserved and structurally similar proteins important in development. The temporospatial expression of atonal bHLH transcription factor 7 (ATOH7) directs the differentiation of retinal ganglion cells and mutations in the human gene lead to vitreoretinal and/or optic nerve abnormalities. Characterization of pathogenic ATOH7 mutations is needed to understand the functions of the conserved bHLH motif. The published ATOH7 in-frame deletion p.(Arg41_Arg48del) removes eight highly conserved amino acids in the basic domain. We functionally characterized the mutant protein by expressing V5-tagged ATOH7 constructs in human embryonic kidney 293T (HEK293T) cells for subsequent protein analyses, including Western blot, cycloheximide chase assays, Förster resonance energy transfer fluorescence lifetime imaging, enzyme-linked immunosorbent assays and dual-luciferase assays. Our results indicate that the in-frame deletion in the basic domain causes mislocalization of the protein, which can be rescued by a putative dimerization partner transcription factor 3 isoform E47 (E47), suggesting synergistic nuclear import. Furthermore, we observed (i) increased proteasomal degradation of the mutant protein, (ii) reduced protein heterodimerization, (iii) decreased DNA-binding and transcriptional activation of a reporter gene, as well as (iv) inhibited E47 activity. Altogether our observations suggest that the DNA-binding basic domain of ATOH7 has additional roles in regulating the nuclear import, dimerization, and protein stability.

Proteomic Tools for the Analysis of Cytoskeleton Proteins

Proteomic analyses have become an essential part of the toolkit of the molecular biologist, given the widespread availability of genomic data and open source or freely accessible bioinformatics software. Tools are available for detecting homologous sequences, recognizing functional domains, and modeling the three-dimensional structure for any given protein sequence, as well as for predicting interactions with other proteins or macromolecules. Although a wealth of structural and functional information is available for many cytoskeletal proteins, with representatives spanning all of the major subfamilies, the majority of cytoskeletal proteins remain partially or totally uncharacterized. Moreover, bioinformatics tools provide a means for studying the effects of synthetic mutations or naturally occurring variants of these cytoskeletal proteins. This chapter discusses various freely available proteomic analysis tools, with a focus on in silico prediction of protein structure and function. The selected tools are notable for providing an easily accessible interface for the novice while retaining advanced functionality for more experienced computational biologists.

Efficient elimination of primary B-ALL cells in vitro and in vivo using a novel 4-1BB-based CAR targeting a membrane-distal CD22 epitope

Background

There are few therapeutic options available for patients with B-cell acute lymphoblastic leukemia (B-ALL) relapsing as CD19^– either after chemotherapy or CD19-targeted immunotherapies. CD22-chimeric antigen receptor (CAR) T cells represent an attractive addition to CD19-CAR T cell therapy because they will target both CD22⁺CD19^– B-ALL relapses and CD19^– preleukemic cells. However, the immune escape mechanisms from CD22-CAR T cells, and the potential contribution of the epitope binding of the anti-CD22 single-chain variable fragment (scFv) remain understudied.

Methods

Here, we have developed and comprehensively characterized a novel CD22-CAR (clone hCD22.7) targeting a membrane-distal CD22 epitope and tested its cytotoxic effects against B-ALL cells both in in vitro and in vivo assays.

Results

Conformational epitope mapping, cross-blocking, and molecular docking assays revealed that the hCD22.7 scFv is a high-affinity binding antibody which specifically binds to the ESTKDGKVP sequence, located in the Ig-like V-type domain, the most distal domain of CD22. We observed efficient killing of B-ALL cells in vitro, although the kinetics were dependent on the level of CD22 expression. Importantly, we show an efficient in vivo control of patients with B-ALL derived xenografts with diverse aggressiveness, coupled to long-term hCD22.7-CAR T cell persistence. Remaining leukemic cells at sacrifice maintained full expression of CD22, ruling out CAR pressure-mediated antigen loss. Finally, the immunogenicity capacity of this hCD22.7-scFv was very similar to that of other CD22 scFv previously used in adoptive T cell therapy.

Conclusions

We report a novel, high-affinity hCD22.7 scFv which targets a membrane-distal epitope of CD22. 4-1BB-based hCD22.7-CAR T cells efficiently eliminate clinically relevant B- CD22^high and CD22^low ALL primary samples in vitro and in vivo. Our study supports the clinical translation of this hCD22.7-CAR as either single or tandem CD22–CD19-CAR for both naive and anti-CD19-resistant patients with B-ALL.

Green fluorescent protein-based lactate and pyruvate indicators suitable for biochemical assays and live cell imaging

Glycolysis is the metabolic pathway that converts glucose into pyruvate, whereas fermentation can then produce lactate from pyruvate. Here, we developed single fluorescent protein (FP)-based lactate and pyruvate indicators with low EC₅₀ for trace detection of metabolic molecules and live cell imaging and named them “Green Lindoblum” and “Green Pegassos,” respectively. Green Lindoblum (EC₅₀ of 30 µM for lactate) and Green Pegassos (EC₅₀ of 70 µM for pyruvate) produced a 5.2- and 3.3-fold change in fluorescence intensity in response to lactate and pyruvate, respectively. Green Lindoblum measured lactate levels in mouse plasma, and Green Pegassos in combination with D-serine dehydratase successfully estimated D-serine levels released from mouse primary cultured neurons and astrocytes by measuring pyruvate level. Furthermore, live cell imaging analysis revealed their utility for dual-colour imaging, and the interplay between lactate, pyruvate, and Ca²⁺ in human induced pluripotent stem cell-derived cardiomyocytes. Therefore, Green Lindoblum and Green Pegassos will be useful tools that detect specific molecules in clinical use and monitor the interplay of metabolites and other related molecules in diverse cell types.

Galaxy InteractoMIX: An Integrated Computational Platform for the Study of Protein-Protein Interaction Data

Protein interactions play a crucial role among the different functions of a cell and are central to our understanding of cellular processes both in health and disease. Here we present Galaxy InteractoMIX (http://galaxy.interactomix.com), a platform composed of 13 different computational tools each addressing specific aspects of the study of protein-protein interactions, ranging from large-scale cross-species protein-wide interactomes to atomic resolution level of protein complexes. Galaxy InteractoMIX provides an intuitive interface where users can retrieve consolidated interactomics data distributed across several databases or uncover links between diseases and genes by analyzing the interactomes underlying these diseases. The platform makes possible large-scale prediction and curation protein interactions using the conservation of motifs, interology, or presence or absence of key sequence signatures. The range of structure-based tools includes modeling and analysis of protein complexes, delineation of interfaces and the modeling of peptides acting as inhibitors of protein-protein interactions. Galaxy InteractoMIX includes a range of ready-to-use workflows to run complex analyses requiring minimal intervention by users. The potential range of applications of the platform covers different aspects of life science, biomedicine, biotechnology and drug discovery where protein associations are studied.

A Functional Kinase Is Necessary for Cyclin-Dependent Kinase G1 (CDKG1) to Maintain Fertility at High Ambient Temperature in Arabidopsis

Maintaining fertility in a fluctuating environment is key to the reproductive success of flowering plants. Meiosis and pollen formation are particularly sensitive to changes in growing conditions, especially temperature. We have previously identified cyclin-dependent kinase G1 (CDKG1) as a master regulator of temperature-dependent meiosis and this may involve the regulation of alternative splicing (AS), including of its own transcript. CDKG1 mRNA can undergo several AS events, potentially producing two protein variants: CDKG1L and CDKG1S, differing in their N-terminal domain which may be involved in co-factor interaction. In leaves, both isoforms have distinct temperature-dependent functions on target mRNA processing, but their role in pollen development is unknown. In the present study, we characterize the role of CDKG1L and CDKG1S in maintaining Arabidopsis fertility. We show that the long (L) form is necessary and sufficient to rescue the fertility defects of the cdkg1-1 mutant, while the short (S) form is unable to rescue fertility. On the other hand, an extra copy of CDKG1L reduces fertility. In addition, mutation of the ATP binding pocket of the kinase indicates that kinase activity is necessary for the function of CDKG1. Kinase mutants of CDKG1L and CDKG1S correctly localize to the cell nucleus and nucleus and cytoplasm, respectively, but are unable to rescue either the fertility or the splicing defects of the cdkg1-1 mutant. Furthermore, we show that there is partial functional overlap between CDKG1 and its paralog CDKG2 that could in part be explained by overlapping gene expression.

TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes

The interaction between the class I major histocompatibility complex (MHC), the peptide presented by the MHC and the T-cell receptor (TCR) is a key determinant of the cellular immune response. Here, we present TCRpMHCmodels, a method for accurate structural modelling of the TCR-peptide-MHC (TCR-pMHC) complex. This TCR-pMHC modelling pipeline takes as input the amino acid sequence and generates models of the TCR-pMHC complex, with a median Cα RMSD of 2.31 Å. TCRpMHCmodels significantly outperforms TCRFlexDock, a specialised method for docking pMHC and TCR structures. TCRpMHCmodels is simple to use and the modelling pipeline takes, on average, only two minutes. Thanks to its ease of use and high modelling accuracy, we expect TCRpMHCmodels to provide insights into the underlying mechanisms of TCR and pMHC interactions and aid in the development of advanced T-cell-based immunotherapies and rational design of vaccines. The TCRpMHCmodels tool is available at http://www.cbs.dtu.dk/services/TCRpMHCmodels/.

Dissecting RAF Inhibitor Resistance by Structure-based Modeling Reveals Ways to Overcome Oncogenic RAS Signaling

Clinically used RAF inhibitors are ineffective in RAS mutant tumors because they enhance homo- and heterodimerization of RAF kinases, leading to paradoxical activation of ERK signaling. Overcoming enhanced RAF dimerization and the resulting resistance is a challenge for drug design. Combining multiple inhibitors could be more effective, but it is unclear how the best combinations can be chosen. We built a next-generation mechanistic dynamic model to analyze combinations of structurally different RAF inhibitors, which can efficiently suppress MEK/ERK signaling. This rule-based model of the RAS/ERK pathway integrates thermodynamics and kinetics of drug-protein interactions, structural elements, posttranslational modifications, and cell mutational status as model rules to predict RAF inhibitor combinations for inhibiting ERK activity in oncogenic RAS and/or BRAFV600E backgrounds. Predicted synergistic inhibition of ERK signaling was corroborated by experiments in mutant NRAS, HRAS, and BRAFV600E cells, and inhibition of oncogenic RAS signaling was associated with reduced cell proliferation and colony formation.

Methyl-CpG-binding (SmMBD2/3) and chromobox (SmCBX) proteins are required for neoblast proliferation and oviposition in the parasitic blood fluke Schistosoma mansoni

While schistosomiasis remains a significant health problem in low to middle income countries, it also represents a recently recognised threat to more economically-developed regions. Until a vaccine is developed, this neglected infectious disease is primarily controlled by praziquantel, a drug with a currently unknown mechanism of action. By further elucidating how Schistosoma molecular components cooperate to regulate parasite developmental processes, next generation targets will be identified. Here, we continue our studies on schistosome epigenetic participants and characterise the function of a DNA methylation reader, the Schistosoma mansoni methyl-CpG-binding domain protein (SmMBD2/3). Firstly, we demonstrate that SmMBD2/3 contains amino acid features essential for 5-methyl cytosine (5mC) binding and illustrate that adult schistosome nuclear extracts (females > males) contain this activity. We subsequently show that SmMBD2/3 translocates into nuclear compartments of transfected murine NIH-3T3 fibroblasts and recombinant SmMBD2/3 exhibits 5mC binding activity. Secondly, using a yeast-two hybrid (Y2H) screen, we show that SmMBD2/3 interacts with the chromo shadow domain (CSD) of an epigenetic adaptor, S. mansoni chromobox protein (SmCBX). Moreover, fluorescent in situ hybridisation (FISH) mediated co-localisation of Smmbd2/3 and Smcbx to mesenchymal cells as well as somatic- and reproductive- stem cells confirms the Y2H results and demonstrates that these interacting partners are ubiquitously expressed and found within both differentiated as well as proliferating cells. Finally, using RNA interference, we reveal that depletion of Smmbd2/3 or Smcbx in adult females leads to significant reductions (46-58%) in the number of proliferating somatic stem cells (PSCs or neoblasts) as well as in the quantity of in vitro laid eggs. Collectively, these results further expand upon the schistosome components involved in epigenetic processes and suggest that pharmacological inhibition of SmMBD2/3 and/or SmCBX biology could prove useful in the development of future schistosomiasis control strategies.

Mycobacterium tuberculosis universal stress protein Rv2623 interacts with the putative ATP binding cassette (ABC) transporter Rv1747 to regulate mycobacterial growth

We have previously shown that the Mycobacterium tuberculosis universal stress protein Rv2623 regulates mycobacterial growth and may be required for the establishment of tuberculous persistence. Here, yeast two-hybrid and affinity chromatography experiments have demonstrated that Rv2623 interacts with one of the two forkhead-associated domains (FHA I) of Rv1747, a putative ATP-binding cassette transporter annotated to export lipooligosaccharides. FHA domains are signaling protein modules that mediate protein-protein interactions to modulate a wide variety of biological processes via binding to conserved phosphorylated threonine (pT)-containing oligopeptides of the interactors. Biochemical, immunochemical and mass spectrometric studies have shown that Rv2623 harbors pT and specifically identified threonine 237 as a phosphorylated residue. Relative to wild-type Rv2623 (Rv2623WT), a mutant protein in which T237 has been replaced with a non-phosphorylatable alanine (Rv2623T237A) exhibits decreased interaction with the Rv1747 FHA I domain and diminished growth-regulatory capacity. Interestingly, compared to WT bacilli, an M. tuberculosis Rv2623 null mutant (ΔRv2623) displays enhanced expression of phosphatidyl-myo-inositol mannosides (PIMs), while the ΔRv1747 mutant expresses decreased levels of PIMs. Animal studies have previously shown that ΔRv2623 is hypervirulent, while ΔRv1747 is growth-attenuated. Collectively, these data have provided evidence that Rv2623 interacts with Rv1747 to regulate mycobacterial growth; and this interaction is mediated via the recognition of the conserved Rv2623 pT237-containing FHA-binding motif by the Rv1747 FHA I domain. The divergent aberrant PIM profiles and the opposing in vivo growth phenotypes of ΔRv2623 and ΔRv1747, together with the annotated lipooligosaccharide exporter function of Rv1747, suggest that Rv2623 interacts with Rv1747 to modulate mycobacterial growth by negatively regulating the activity of Rv1747; and that Rv1747 might function as a transporter of PIMs. Because these glycolipids are major mycobacterial cell envelope components that can impact on the immune response, our findings raise the possibility that Rv2623 may regulate bacterial growth, virulence, and entry into persistence, at least in part, by modulating the levels of bacillary PIM expression, perhaps through negatively regulating the Rv1747-dependent export of the immunomodulatory PIMs to alter host-pathogen interaction, thereby influencing the fate of M. tuberculosis in vivo.

Iterative Sequencing and Variant Screening (ISVS) as a novel pathogenic mutations search strategy - application for TMPRSS3 mutations screen

Autosomal recessive diseases (ARD) are typically caused by a limited number of mutations whose identification is challenged by their low prevalence. Our purpose was to develop a novel approach allowing an efficient search for mutations causing ARD and evaluation of their pathogenicity without a control group. We developed Iterative Sequencing and Variant Screening (ISVS) approach based on iterative cycles of gene sequencing and mutation screening, and ISVS Simulator software ( http://zsibio.ii.pw.edu.pl/shiny/isvs/ ) for assessment of detected variants' significance. As shown by simulations, ISVS efficiently identifies and correctly classifies pathogenic mutations except for cases where the gene of interest has extremely high number of low frequency nonpathogenic variants. By applying ISVS, we found 4 known and 9 novel (p.C73Y, p.S124L, p.C194Mfs*17, c.782 + 2 T > A, c.953-5 A > G, p.L325Q, p.D334Mfs*24, p.R436G, p.M448T) TMPRSS3 variants among deaf patients. For 3 known and 5 novel variants the disease association was supported by ISVS Simulator odds >90:1. Pathogenicity of 6 novel mutations has been supported by in-silico predictions of variants' deleteriousness. By directly comparing variant prevalence in patients and controls, disease association was demonstrated only for two variants and it was relatively weak (P < 0.05). Summarizing, ISVS strategy and ISVS Simulator are useful for detection of genetic variants causing AR diseases.

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.

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.

Epitope mapping of Brugia malayi ALT-2 and the development of a multi-epitope vaccine for lymphatic filariasis

Human lymphatic filariasis is a neglected tropical disease, causing permanent and long-term disability with severe immunopathology. Abundant larval transcript (ALT) plays a crucial role in parasite establishment in the host, due to its multi-faceted ability in host immune regulation. Although ALT protein is a key filarial target, its exact function is yet to be explored. Here, we report epitope mapping and a structural model of Brugia malayi ALT-2, leading to development of a multi-epitope vaccine. Structural analysis revealed that ALT represents unique parasitic defence proteins belonging to a toxin family that carries a 'knottin' fold. ALT-2 has been a favourite vaccine antigen and was protective in filarial models. Due to the immunological significance of ALT-2, we mapped B-cell epitopes systematically and identified two epitope clusters, 1-30 and 89-128. To explore the prophylactic potential of epitope clusters, a recombinant multi-epitopic gene comprising the epitopic domains was engineered and the protective efficacy of recombinant ALT epitope protein (AEP) was tested in the permissive model, Mastomys coucha. AEP elicited potent antibody responses with predominant IgG1 isotype and conferred significantly high protection (74.59%) compared to ALT-2 (61.95%). This proved that these epitopic domains are responsible for the protective efficacy of ALT-2 and engineering protective epitopes as a multi-epitope protein may be a novel vaccine strategy for complex parasitic infections.

Identification of an Extracellular Gate for the Proton-coupled Folate Transporter (PCFT-SLC46A1) by Cysteine Cross-linking

The proton-coupled folate transporter (PCFT, SLC46A1) is required for intestinal folate absorption and folate homeostasis in humans. A homology model of PCFT, based upon theEscherichia coliglycerol 3-phosphate transporter structure, predicted that PCFT transmembrane domains (TMDs) 1, 2, 7, and 11 form an extracellular gate in the inward-open conformation. To assess this model, five residues (Gln(45)-TMD1, Asn(90)-TMD2, Leu(290)-TMD7, Ser(407)-TMD11 and Asn(411)-TMD11) in the predicted gate were substituted with Cys to generate single and nine double mutants. Transport function of the mutants was assayed in transient transfectants by measurement of [(3)H]substrate influx as was accessibility of the Cys residues to biotinylation. Pairs of Cys residues were assessed for spontaneous formation of a disulfide bond, induction of a disulfide bond by oxidization with dichloro(1,10-phenanthroline)copper (II) (CuPh), or the formation of a Cd(2+)complex. The data were consistent with the formation of a spontaneous disulfide bond between the N90C/S407C pair and a CuPh- and Cd(2+)-induced disulfide bond and complex, respectively, for the Q45C/L290C and L290C/N411C pairs. The decrease in activity induced by cross-linkage of the Cys residue pairs was due to a decrease in the influxVmaxconsistent with restriction in the mobility of the transporter. The presence of folate substrate decreased the CuPh-induced inhibition of transport. Hence, the data support the glycerol 3-phosphate transporter-based homology model of PCFT and the presence of an extracellular gate formed by TMDs 1, 2, 7, and 11.

In silico study of subtilisin-like protease 1 (SUB1) from different Plasmodium species in complex with peptidyl-difluorostatones and characterization of potent pan-SUB1 inhibitors

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Homology models of four SUB1 orthologues from P. falciparum species were produced.

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We analyzed the binding mode of our previous difluorostatone inhibitors to six SUB1.

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In vitro activity of our difluorostatone-based inhibitors was correctly predicted.

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We derived a structure-based pan-SUB1 pharmacophore, and validated it in silico.

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We confirmed that development of pan-SUB1 inhibitors is a feasible task.

Plasmodium falciparum subtilisin-like protease 1 (SUB1) is a novel target for the development of innovative antimalarials. We recently described the first potent difluorostatone-based inhibitors of the enzyme ((4S)-(N-((N-acetyl-l-lysyl)-l-isoleucyl-l-threonyl-l-alanyl)-2,2-difluoro-3-oxo-4-aminopentanoyl)glycine (1) and (4S)-(N-((N-acetyl-l-isoleucyl)-l-threonyl-l-alanylamino)-2,2-difluoro-3-oxo-4-aminopentanoyl)glycine (2)). As a continuation of our efforts towards the definition of the molecular determinants of enzyme-inhibitor interaction, we herein propose the first comprehensive computational investigation of the SUB1 catalytic core from six different Plasmodium species, using homology modeling and molecular docking approaches. Investigation of the differences in the binding sites as well as the interactions of our inhibitors 1,2 with all SUB1 orthologues, allowed us to highlight the structurally relevant regions of the enzyme that could be targeted for developing pan-SUB1 inhibitors. According to our in silico predictions, compounds 1,2 have been demonstrated to be potent inhibitors of SUB1 from all three major clinically relevant Plasmodium species (P. falciparum, P. vivax, and P. knowlesi). We next derived multiple structure-based pharmacophore models that were combined in an inclusive pan-SUB1 pharmacophore (SUB1-PHA). This latter was validated by applying in silico methods, showing that it may be useful for the future development of potent antimalarial agents.

Increased efficiency of Campylobacter jejuni N-oligosaccharyltransferase PglB by structure-guided engineering

Conjugate vaccines belong to the most efficient preventive measures against life-threatening bacterial infections. Functional expression of N-oligosaccharyltransferase (N-OST) PglB of Campylobacter jejuni in Escherichia coli enables a simplified production of glycoconjugate vaccines in prokaryotic cells. Polysaccharide antigens of pathogenic bacteria can be covalently coupled to immunogenic acceptor proteins bearing engineered glycosylation sites. Transfer efficiency of PglB_Cj is low for certain heterologous polysaccharide substrates. In this study, we increased glycosylation rates for Salmonella enterica sv. Typhimurium LT2 O antigen (which lacks N-acetyl sugars) and Staphylococcus aureus CP5 polysaccharides by structure-guided engineering of PglB. A three-dimensional homology model of membrane-associated PglB_Cj, docked to the natural C. jejuni N-glycan attached to the acceptor peptide, was used to identify potential sugar-interacting residues as targets for mutagenesis. Saturation mutagenesis of an active site residue yielded the enhancing mutation N311V, which facilitated fivefold to 11-fold increased in vivo glycosylation rates as determined by glycoprotein-specific ELISA. Further rounds of in vitro evolution led to a triple mutant S80R-Q287P-N311V enabling a yield improvement of S. enterica LT2 glycoconjugates by a factor of 16. Our results demonstrate that bacterial N-OST can be tailored to specific polysaccharide substrates by structure-guided protein engineering.

Proteomic Tools for the Analysis of Cytoskeleton Proteins

Proteomic analyses have become an essential part of the toolkit of the molecular biologist, given the widespread availability of genomic data and open source or freely accessible bioinformatics software. Tools are available for detecting homologous sequences, recognizing functional domains, and modeling the three-dimensional structure for any given protein sequence. Although a wealth of structural and functional information is available for a large number of cytoskeletal proteins, with representatives spanning all of the major subfamilies, the majority of cytoskeletal proteins remain partially or totally uncharacterized. Moreover, bioinformatics tools provide a means for studying the effects of synthetic mutations or naturally occurring variants of these cytoskeletal proteins. This chapter discusses various freely available proteomic analysis tools, with a focus on in silico prediction of protein structure and function. The selected tools are notable for providing an easily accessible interface for the novice, while retaining advanced functionality for more experienced computational biologists.

Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling

Homology modeling predicts the 3D structure of a query protein based on the sequence alignment with one or more template proteins of known structure. Its great importance for biological research is owed to its speed, simplicity, reliability and wide applicability, covering more than half of the residues in protein sequence space. Although multiple templates have been shown to generally increase model quality over single templates, the information from multiple templates has so far been combined using empirically motivated, heuristic approaches.

We present here a rigorous statistical framework for multi-template homology modeling. First, we find that the query proteins’ atomic distance restraints can be accurately described by two-component Gaussian mixtures. This insight allowed us to apply the standard laws of probability theory to combine restraints from multiple templates. Second, we derive theoretically optimal weights to correct for the redundancy among related templates. Third, a heuristic template selection strategy is proposed.

We improve the average GDT-ha model quality score by 11% over single template modeling and by 6.5% over a conventional multi-template approach on a set of 1000 query proteins. Robustness with respect to wrong constraints is likewise improved. We have integrated our multi-template modeling approach with the popular MODELLER homology modeling software in our free HHpred server http://toolkit.tuebingen.mpg.de/hhpred and also offer open source software for running MODELLER with the new restraints at https://bitbucket.org/soedinglab/hh-suite.

Since a protein’s function is largely determined by its structure, predicting a protein’s structure from its amino acid sequence can be very useful to understand its molecular functions and its role in biological pathways. By far the most widely used computational approach for protein structure prediction relies on detecting a homologous relationship with a protein of known structure and using this protein as a template to model the structure of the query protein on it. The basic concepts of this homology modelling approach have not changed during the last 20 years. In this study we extend the probabilistic formulation of homology modelling to the consistent treatment of multiple templates. Our new theoretical approach allowed us to improve the quality of homology models by 11% over a baseline single-template approach and by 6.5% over a multi-template approach.

Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor

BACKGROUND

Two component systems (TCS) are signalling complexes manifested by a histidine kinase (receptor) and a response regulator (effector). They are the most abundant signalling pathways in prokaryotes and control a wide range of biological processes. The pairing of these two components is highly specific, often requiring costly and time-consuming experimental characterisation. Therefore, there is considerable interest in developing accurate prediction tools to lessen the burden of experimental work and cope with the ever-increasing amount of genomic information.

RESULTS

We present a novel meta-predictor, MetaPred2CS, which is based on a support vector machine. MetaPred2CS integrates six sequence-based prediction methods: in-silico two-hybrid, mirror-tree, gene fusion, phylogenetic profiling, gene neighbourhood, and gene operon. To benchmark MetaPred2CS, we also compiled a novel high-quality training dataset of experimentally deduced TCS protein pairs for k-fold cross validation, to act as a gold standard for TCS partnership predictions. Combining individual predictions using MetaPred2CS improved performance when compared to the individual methods and in comparison with a current state-of-the-art meta-predictor.

CONCLUSION

We have developed MetaPred2CS, a support vector machine-based metapredictor for prokaryotic TCS protein pairings. Central to the success of MetaPred2CS is a strategy of integrating individual predictors that improves the overall prediction accuracy, with the in-silico two-hybrid method contributing most to performance. MetaPred2CS outperformed other available systems in our benchmark tests, and is available online at http://metapred2cs.ibers.aber.ac.uk, along with our gold standard dataset of TCS interaction pairs.

Identification of Tyr residues that enhance folate substrate binding and constrain oscillation of the proton-coupled folate transporter (PCFT-SLC46A1)

The proton-coupled folate transporter (PCFT) mediates intestinal folate absorption and transport of folates across the choroid plexus. This study focuses on the role of Tyr residues in PCFT function. The substituted Cys-accessibility method identified four Tyr residues (Y291, Y362, Y315, and Y414) that are accessible to the extracellular compartment; three of these (Y291, Y362, and Y315) are located within or near the folate binding pocket. When the Tyr residues were replaced with Cys or Ala, these mutants showed similar (up to 6-fold) increases in influx Vmax and Kt/Ki for [(3)H]methotrexate and [(3)H]pemetrexed. When the Tyr residues were replaced with Phe, these changes were moderated or absent. When Y315A PCFT was used as representative of the mutants and [(3)H]pemetrexed as the transport substrate, this substitution did not increase the efflux rate constant. Furthermore, neither influx nor efflux mediated by Y315A PCFT was transstimulated by the presence of substrate in the opposite compartment; however, substantial bidirectional transstimulation of transport was mediated by wild-type PCFT. This resulted in a threefold greater efflux rate constant for cells that express wild-type PCFT than for cells that express Y315 PCFT under exchange conditions. These data suggest that these Tyr residues, possibly through their rigid side chains, secure the carrier in a high-affinity state for its folate substrates. However, this may be achieved at the expense of constraining the carrier's mobility, thereby decreasing the rate at which the protein oscillates between its conformational states. The Vmax generated by these Tyr mutants may be so rapid that further augmentation during transstimulation may not be possible.

Discovery of GPCR ligands for probing signal transduction pathways

G protein-coupled receptors (GPCRs) are seven integral transmembrane proteins that are the primary targets of almost 30% of approved drugs and continue to represent a major focus of pharmaceutical research. All of GPCR targeted medicines were discovered by classical medicinal chemistry approaches. After the first GPCR crystal structures were determined, the docking screens using these structures lead to discovery of more novel and potent ligands. There are over 360 pharmaceutically relevant GPCRs in the human genome and to date about only 30 of structures have been determined. For these reasons, computational techniques such as homology modeling and molecular dynamics simulations have proven their usefulness to explore the structure and function of GPCRs. Furthermore, structure-based drug design and in silico screening (High Throughput Docking) are still the most common computational procedures in GPCRs drug discovery. Moreover, ligand-based methods such as three-dimensional quantitative structure–selectivity relationships, are the ideal molecular modeling approaches to rationalize the activity of tested GPCR ligands and identify novel GPCR ligands. In this review, we discuss the most recent advances for the computational approaches to effectively guide selectivity and affinity of ligands. We also describe novel approaches in medicinal chemistry, such as the development of biased agonists, allosteric modulators, and bivalent ligands for class A GPCRs. Furthermore, we highlight some knockout mice models in discovering biased signaling selectivity.

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.

Structural elucidation of SrtA enzyme in Enterococcus faecalis: an emphasis on screening of potential inhibitors against the biofilm formation

Enterococcus faecalis is a pathogenic Gram-positive bacterium, which mainly infects humans through urinary tract infections. SrtA is an essential enzyme for survival of E. faecalis, and inhibition of this particular enzyme will reduce the virulence of biofilm formation. It is proved to be associated with the microbial surface protein embedded signal transduction mechanism and promising as a suitable anti-microbial drug target for E. faecalis. The present work gives an inclusive description of SrtA isolated from E. faecalis through computational and experimental methodologies. For exploring the mechanism of SrtA and to screen potential leads against E. faecalis, we have generated three-dimensional models through homology modeling. The 3D model showed conformational stability over time, confirming the quality of the starting 3D model. Large scale 100 ns molecular dynamics simulations show the intramolecular changes occurring in SrtA, and multiple conformations of structure based screening elucidate potential leads against this pathogen. Experimental results showed that the screened compounds are active showing anti-microbial and anti-biofilm activity, as SrtA is known to play an important role in E. faecalis biofilm formation. Experimental results also suggest that SrtA specific screened compounds have better anti-biofilm activity than the available inhibitors. Therefore, we believe that development of these compounds would be an impetus to design the novel chief SrtA inhibitors against E. faecalis.

Adenylyl cyclase-associated protein 1 is a receptor for human resistin and mediates inflammatory actions of human monocytes

Human resistin is a cytokine that induces low-grade inflammation by stimulating monocytes. Resistin-mediated chronic inflammation can lead to obesity, atherosclerosis, and other cardiometabolic diseases. Nevertheless, the receptor for human resistin has not been clarified. Here, we identified adenylyl cyclase-associated protein 1 (CAP1) as a functional receptor for human resistin and clarified its intracellular signaling pathway to modulate inflammatory action of monocytes. We found that human resistin directly binds to CAP1 in monocytes and upregulates cyclic AMP (cAMP) concentration, protein kinase A (PKA) activity, and NF-κB-related transcription of inflammatory cytokines. Overexpression of CAP1 in monocytes enhanced the resistin-induced increased activity of the cAMP-dependent signaling. Moreover, CAP1-overexpressed monocytes aggravated adipose tissue inflammation in transgenic mice that express human resistin from their monocytes. In contrast, suppression of CAP1 expression abrogated the resistin-mediated inflammatory activity both in vitro and in vivo. Therefore, CAP1 is the bona fide receptor for resistin leading to inflammation in humans.

Trends in structural coverage of the protein universe and the impact of the Protein Structure Initiative

The exponential growth of protein sequence data provides an ever-expanding body of unannotated and misannotated proteins. The National Institutes of Health-supported Protein Structure Initiative and related worldwide structural genomics efforts facilitate functional annotation of proteins through structural characterization. Recently there have been profound changes in the taxonomic composition of sequence databases, which are effectively redefining the scope and contribution of these large-scale structure-based efforts. The faster-growing bacterial genomic entries have overtaken the eukaryotic entries over the last 5 y, but also have become more redundant. Despite the enormous increase in the number of sequences, the overall structural coverage of proteins--including proteins for which reliable homology models can be generated--on the residue level has increased from 30% to 40% over the last 10 y. Structural genomics efforts contributed ∼50% of this new structural coverage, despite determining only ∼10% of all new structures. Based on current trends, it is expected that ∼55% structural coverage (the level required for significant functional insight) will be achieved within 15 y, whereas without structural genomics efforts, realizing this goal will take approximately twice as long.

Conformational flexibility of the oncogenic protein LMO2 primes the formation of the multi-protein transcription complex

LMO2 was discovered via chromosomal translocations in T-cell leukaemia and shown normally to be essential for haematopoiesis. LMO2 is made up of two LIM only domains (thus it is a LIM-only protein) and forms a bridge in a multi-protein complex. We have studied the mechanism of formation of this complex using a single domain antibody fragment that inhibits LMO2 by sequestering it in a non-functional form. The crystal structure of LMO2 with this antibody fragment has been solved revealing a conformational difference in the positioning and angle between the two LIM domains compared with its normal binding. This contortion occurs by bending at a central helical region of LMO2. This is a unique mechanism for inhibiting an intracellular protein function and the structural contusion implies a model in which newly synthesized, intrinsically disordered LMO2 binds to a partner protein nucleating further interactions and suggests approaches for therapeutic targeting of LMO2.

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.

Functional characterization of genetic enzyme variations in human lipoxygenases

Mammalian lipoxygenases play a role in normal cell development and differentiation but they have also been implicated in the pathogenesis of cardiovascular, hyperproliferative and neurodegenerative diseases. As lipid peroxidizing enzymes they are involved in the regulation of cellular redox homeostasis since they produce lipid hydroperoxides, which serve as an efficient source for free radicals. There are various epidemiological correlation studies relating naturally occurring variations in the six human lipoxygenase genes (SNPs or rare mutations) to the frequency for various diseases in these individuals, but for most of the described variations no functional data are available. Employing a combined bioinformatical and enzymological strategy, which included structural modeling and experimental site-directed mutagenesis, we systematically explored the structural and functional consequences of non-synonymous genetic variations in four different human lipoxygenase genes (ALOX5, ALOX12, ALOX15, and ALOX15B) that have been identified in the human 1000 genome project. Due to a lack of a functional expression system we resigned to analyze the functionality of genetic variations in the hALOX12B and hALOXE3 gene. We found that most of the frequent non-synonymous coding SNPs are located at the enzyme surface and hardly alter the enzyme functionality. In contrast, genetic variations which affect functional important amino acid residues or lead to truncated enzyme variations (nonsense mutations) are usually rare with a global allele frequency<0.1%. This data suggest that there appears to be an evolutionary pressure on the coding regions of the lipoxygenase genes preventing the accumulation of loss-of-function variations in the human population.

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Non-synonymous coding variations in human lipoxygenases are mostly rare with a global allele frequency <1%.

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Common ALOX SNPs are mainly localized on the enzyme surface and hardly effect the enzyme functionality.

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hALOX15B Ala416Asp is a newly discovered loss-of-function mutation in the hALOX gene family while inactivity seems to be caused by severe structural alterations.

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Our data indicate that there is evolutionary pressure on these redox enzymes preventing the accumulation of loss-of-function variations in the human population.

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1000 Genome database is a useful tool to analyze the distribution and functionality of variations in genes of interest.

Constitutive nuclear localization of an alternatively spliced sirtuin-2 isoform

Sirtuin-2 (SIRT2), the cytoplasmic member of the sirtuin family, has been implicated in the deacetylation of nuclear proteins. Although the enzyme has been reported to be located to the nucleus during G2/M phase, its spectrum of targets suggests functions in the nucleus throughout the cell cycle. While a nucleocytoplasmic shuttling mechanism has been proposed for SIRT2, recent studies have indicated the presence of a constitutively nuclear isoform. Here we report the identification of a novel splice variant (isoform 5) of SIRT2 that lacks a nuclear export signal and encodes a predominantly nuclear isoform. This novel isoform 5 fails to show deacetylase activity using several assays, both in vitro and in vivo, and we are led to conclude that this isoform is catalytically inactive. Nevertheless, it retains the ability to interact with p300, a known interaction partner. Moreover, changes in intrinsic tryptophan fluorescence upon denaturation indicate that the protein is properly folded. These data, together with computational analyses, confirm the structural integrity of the catalytic domain. Our results suggest an activity-independent nuclear function of the novel isoform.

Protein structural statistics with PSS

Characterizing the variability within an ensemble of protein structures is a common requirement in structural biology and bioinformatics. With the increasing number of protein structures becoming available, there is a need for new tools capable of automating the structural comparison of large ensemble of structures. We present Protein Structural Statistics (PSS), a command-line program written in Perl for Unix-like environments, dedicated to the calculation of structural statistics for a set of proteins. PSS can perform multiple sequence alignments, structure superpositions, calculate Cartesian and dihedral coordinate statistics, and execute cluster analyses. An HTML report that contains a convenient summary of results with figures, tables, and hyperlinks can also be produced. PSS is a new tool providing an automated way to compare multiple structures. It integrates various types of structural analyses through an user-friendly and flexible interface, facilitating the access to powerful but more specialized programs. PSS is easy to modify and extend and is distributed under a free and open source license. The relevance of PSS is illustrated by examples of application to pertinent biological problems.

Discovery of the first histone deacetylase 6/8 dual inhibitors

We disclose the first small molecule histone deacetylase (HDAC) inhibitor (3, BRD73954) capable of potently and selectively inhibiting both HDAC6 and HDAC8 despite the fact that these isoforms belong to distinct phylogenetic classes within the HDAC family of enzymes. Our data demonstrate that meta substituents of phenyl hydroxamic acids are readily accommodated upon binding to HDAC6 and, furthermore, are necessary for the potent inhibition of HDAC8.