Identification of DNA-binding protein target sequences by physical effective energy functions. Free energy analysis of lambda repressor-DNA complexes

Specific binding of proteins to DNA is one of the most common ways in which gene expression is controlled. Although general rules for the DNA-protein recognition can be derived, the ambiguous and complex nature of this mechanism precludes a simple recognition code, therefore the prediction of DNA target sequences is not straightforward. DNA-protein interactions can be studied using computational methods which can complement the current experimental methods and offer some advantages. In the present work we use physical effective potentials to evaluate the DNA-protein binding affinities for the lambda repressor-DNA complex for which structural and thermodynamic experimental data are available. The effect of conformational sampling by Molecular Dynamics simulations on the computed binding energy is assessed; results show that this effect is in general negative and the reproducibility of the experimental values decreases with the increase of simulation time considered. The free energy of binding for non-specific complexes agrees with earlier theoretical suggestions. Moreover, as a results of these analyses, we propose a protocol for the prediction of DNA-binding target sequences. The possibility of searching regulatory elements within the bacteriophage-lambda genome using this protocol is explored. Our analysis shows good prediction capabilities, even in the absence of any thermodynamic data and information on the naturally recognized sequence. This study supports the conclusion that physics-based methods can offer a completely complementary methodology to sequence-based methods for the identification of DNA-binding protein target sequences.Comment: 35 pages,8 figure

Insights into a Protein-Nanoparticle System by Paramagnetic Perturbation NMR Spectroscopy

BACKGROUND: The interaction between proteins and nanoparticles is a very relevant subject because of the potential applications in medicine and material science in general. Further interest derives from the amyloidogenic character of the considered protein, \u3b22-microglobulin (\u3b22m), which may be regarded as a paradigmatic system for possible therapeutic strategies. Previous evidence showed in fact that gold nanoparticles (AuNPs) are able to inhibit \u3b22m fibril formation in vitro. METHODS: NMR (Nuclear Magnetic Resonance) and ESR (Electron Spin Resonance) spectroscopy are employed to characterize the paramagnetic perturbation of the extrinsic nitroxide probe Tempol on \u3b22m in the absence and presence of AuNPs to determine the surface accessibility properties and the occurrence of chemical or conformational exchange, based on measurements conducted under magnetization equilibrium and non-equilibrium conditions. RESULTS: The nitroxide perturbation analysis successfully identifies the protein regions where protein-protein or protein-AuNPs interactions hinder accessibility or/and establish exchange contacts. These information give interesting clues to recognize the fibrillation interface of \u3b22m and hypothesize a mechanism for AuNPs fibrillogenesis inhibition. CONCLUSIONS: The presented approach can be advantageously applied to the characterization of the interface in protein-protein and protein-nanoparticles interactions

A novel de novo HDAC8 missense mutation causing Cornelia de Lange syndrome

Background: Cornelia de Lange syndrome (CdLS) is a rare and clinically variable syndrome characterized by growth impairment, multi-organ anomalies, and a typical set of facial dysmorphisms. Here we describe a 2-year-old female child harboring a novel de novo missense variant in HDAC8, whose phenotypical score, according to the recent consensus on CdLS clinical diagnostic criteria, allowed the diagnosis of a non-classic CdLS. Methods: Clinical exome sequencing was performed on the trio, identifying a de novo heterozygous variant in HDAC8 (NM_018486; c. 356C>G p.Thr119Arg). Molecular modeling was performed to evaluate putative functional consequence of the HDAC8 protein. Results: The variant HDAC8 c.356C>G is classified as pathogenic following the ACMG (American College of Medical Genetics and Genomics)/AMP (Association for Molecular Pathology) guidelines. By molecular modeling, we confirmed the deleterious effect of this variant, since the amino acid change compromises the conformational flexibility of the HDAC8 loop required for optimal catalytic function. Conclusion: We described a novel Thr119Arg mutation in HDAC8 in a patient displaying the major phenotypic traits of the CdLS. Our results suggest that a modest change outside an active site is capable of triggering global structural changes that propagate through the protein scaffold to the catalytic site, creating de facto haploinsufficiency

Topologically non-trivial metal-organic assemblies inhibit \u3b22-microglobulin amyloidogenesis

Inhibiting amyloid aggregation through high-turnover dynamic interactions could be an efficient strategy that is already used by small heat-shock proteins in different biological contexts. We report the interactions of three topologically non-trivial, zinc-templated metal-organic assemblies, a [2]catenane, a trefoil knot (TK), and Borromean rings, with two \u3b22-microglobulin (\u3b22m) variants responsible for amyloidotic pathologies. Fast exchange and similar patterns of preferred contact surface are observed by NMR, consistent with molecular dynamics simulations. In vitro fibrillation is inhibited by each complex, whereas the zinc-free TK induces protein aggregation and does not inhibit fibrillogenesis. The metal coordination imposes structural rigidity that determines the contact area on the \u3b22m surface depending on the complex dimensions, ensuring in vitro prevention of fibrillogenesis. Administration of TK, the best protein-contacting species, to a disease-model organism, namely a Caenorhabditis elegans mutant expressing the D76N \u3b22m variant, confirms the bioactivity potential of the knot topology and suggests new developments

Adaptability and stability of wheat cultivars in the Northern region of Rio Grande do Sul

Abstract: The experiment evaluated the performance of 12 wheat cultivars indicated to be grown in the northern of Rio Grande do Sul. According to Scott-Knott?s mean comparison test, they obtained higher grain yields of Ametista, Marfím, ORS Vintecinco, TBIO Mestre, TBIO Sintonia and TBIO Sinuelo, and regarding grain quality, Ametista, Jadeíte 11, ORS Vintecinco and Topázio. According to the method by Eberhart and Russell, Ametista, BRS Marcante, TBIO Iguaçu and TBIO Sinuelo were stable in relation to productivity and hectolitre weight. Using the Lin and Binns analysis, the Ametista showed higher average yield and greater stability in the evaluated cultivation conditions, and the TBIO Sintonia is indicated for favorable environments. In reference to the AMMI method, Ametista and TBIO Sinuelo were the most stable regarding productivity, and the 2014 and 2016 harvests were stable. In relation to hectolitre weight, Jadeíte 11 and TBIO Mestre and the year 2016 showed more stability. Resumo: O experimento avaliou o desempenho de 12 cultivares de trigo indicadas para cultivo no norte do estado do Rio Grande do Sul. De acordo com o teste de comparação da média de Scott-Knott, obtiveram maiores rendimentos de grãos as cultivares Ametista, Marfím, ORS Vintecinco, TBIO Mestre, TBIO Sintonia e TBIO Sinuelo, e em relação à qualidade dos grãos, Ametista, Jadeíte 11, ORS Vintecinco e Topázio. De acordo com o método de Eberhart e Russell, Ametista, BRS Marcante, TBIO Iguaçu e TBIO Sinuelo foram cultivares mais estáveis em relação à produtividade e peso do hectolitro. Utilizando a análise de Lin e Binns, a cultivar Ametista mostrou maior rendimento médio e maior estabilidade nas condições de cultivo avaliadas, e o TBIO Sintonia é o mais indicado para ambientes favoráveis. Em referência ao método AMMI, Ametista e TBIO Sinuelo foram as cultivares mais estáveis em relação à produtividade, e os cultivos de 2014 e 2016 foram estáveis. Em relação ao peso hectolitro, Jadeíte 11 e TBIO Mestre e o ano de 2016 demostraram mais estabilidade

Bioinformatics in Italy: BITS2011, the Eighth Annual Meeting of the Italian Society of Bioinformatics

The BITS2011 meeting, held in Pisa on June 20-22, 2011, brought together more than 120 Italian researchers working in the field of Bioinformatics, as well as students in Bioinformatics, Computational Biology, Biology, Computer Sciences, and Engineering, representing a landscape of Italian bioinformatics research

The osmotic pressure of charged colloidal suspensions: A unified approach to linearized Poisson-Boltzmann theory

We study theoretically the osmotic pressure of a suspension of charged objects (e.g., colloids, polyelectrolytes, clay platelets, etc.) dialyzed against an electrolyte solution using the cell model and linear Poisson-Boltzmann (PB) theory. From the volume derivative of the grand potential functional of linear theory we obtain two novel expressions for the osmotic pressure in terms of the potential- or ion-profiles, neither of which coincides with the expression known from nonlinear PB theory, namely, the density of microions at the cell boundary. We show that the range of validity of linearization depends strongly on the linearization point and proof that expansion about the selfconsistently determined average potential is optimal in several respects. For instance, screening inside the suspension is automatically described by the actual ionic strength, resulting in the correct asymptotics at high colloid concentration. Together with the analytical solution of the linear PB equation for cell models of arbitrary dimension and electrolyte composition explicit and very general formulas for the osmotic pressure ensue. A comparison with nonlinear PB theory is provided. Our analysis also shows that whether or not linear theory predicts a phase separation depends crucially on the precise definition of the pressure, showing that an improper choice could predict an artificial phase separation in systems as important as DNA in physiological salt solution.Comment: 16 pages, 5 figures, REVTeX4 styl

A specific nanobody prevents amyloidogenesis of D76N β2-microglobulin in vitro and modifies its tissue distribution in vivo

Systemic amyloidosis is caused by misfolding and aggregation of globular proteins in vivo for which effective treatments are urgently needed. Inhibition of protein self-aggregation represents an attractive therapeutic strategy. Studies on the amyloidogenic variant of β2-microglobulin, D76N, causing hereditary systemic amyloidosis, have become particularly relevant since fibrils are formed in vitro in physiologically relevant conditions. Here we compare the potency of two previously described inhibitors of wild type β2-microglobulin fibrillogenesis, doxycycline and single domain antibodies (nanobodies). The β2-microglobulin -binding nanobody, Nb24, more potently inhibits D76N β2-microglobulin fibrillogenesis than doxycycline with complete abrogation of fibril formation. In β2-microglobulin knock out mice, the D76N β2-microglobulin/ Nb24 pre-formed complex, is cleared from the circulation at the same rate as the uncomplexed protein; however, the analysis of tissue distribution reveals that the interaction with the antibody reduces the concentration of the variant protein in the heart but does not modify the tissue distribution of wild type β2-microglobulin. These findings strongly support the potential therapeutic use of this antibody in the treatment of systemic amyloidosis

Subfamily specific conservation profiles for proteins based on n-gram patterns

<p>Abstract</p> <p>Background</p> <p>A new algorithm has been developed for generating conservation profiles that reflect the evolutionary history of the subfamily associated with a query sequence. It is based on n-gram patterns (NP{<it>n,m</it>}) which are sets of <it>n </it>residues and <it>m </it>wildcards in windows of size <it>n+m</it>. The generation of conservation profiles is treated as a signal-to-noise problem where the signal is the count of n-gram patterns in target sequences that are similar to the query sequence and the noise is the count over all target sequences. The signal is differentiated from the noise by applying singular value decomposition to sets of target sequences rank ordered by similarity with respect to the query.</p> <p>Results</p> <p>The new algorithm was used to construct 4,248 profiles from 120 randomly selected Pfam-A families. These were compared to profiles generated from multiple alignments using the consensus approach. The two profiles were similar whenever the subfamily associated with the query sequence was well represented in the multiple alignment. It was possible to construct subfamily specific conservation profiles using the new algorithm for subfamilies with as few as five members. The speed of the new algorithm was comparable to the multiple alignment approach.</p> <p>Conclusion</p> <p>Subfamily specific conservation profiles can be generated by the new algorithm without aprioi knowledge of family relationships or domain architecture. This is useful when the subfamily contains multiple domains with different levels of representation in protein databases. It may also be applicable when the subfamily sample size is too small for the multiple alignment approach.</p

Amino acid "little Big Bang": Representing amino acid substitution matrices as dot products of Euclidian vectors

<p>Abstract</p> <p>Background</p> <p>Sequence comparisons make use of a one-letter representation for amino acids, the necessary quantitative information being supplied by the substitution matrices. This paper deals with the problem of finding a representation that provides a comprehensive description of amino acid intrinsic properties consistent with the substitution matrices.</p> <p>Results</p> <p>We present a Euclidian vector representation of the amino acids, obtained by the singular value decomposition of the substitution matrices. The substitution matrix entries correspond to the dot product of amino acid vectors. We apply this vector encoding to the study of the relative importance of various amino acid physicochemical properties upon the substitution matrices. We also characterize and compare the PAM and BLOSUM series substitution matrices.</p> <p>Conclusions</p> <p>This vector encoding introduces a Euclidian metric in the amino acid space, consistent with substitution matrices. Such a numerical description of the amino acid is useful when intrinsic properties of amino acids are necessary, for instance, building sequence profiles or finding consensus sequences, using machine learning algorithms such as Support Vector Machine and Neural Networks algorithms.</p