Biological activity differences between TGF-β1 and TGF-β3 correlate with differences in the rigidity and arrangement of their component monomers

[Image: see text] TGF-β1, -β2, and -β3 are small, secreted signaling proteins. They share 71–80% sequence identity and signal through the same receptors, yet the isoform-specific null mice have distinctive phenotypes and are inviable. The replacement of the coding sequence of TGF-β1 with TGF-β3 and TGF-β3 with TGF-β1 led to only partial rescue of the mutant phenotypes, suggesting that intrinsic differences between them contribute to the requirement of each in vivo. Here, we investigated whether the previously reported differences in the flexibility of the interfacial helix and arrangement of monomers was responsible for the differences in activity by generating two chimeric proteins in which residues 54–75 in the homodimer interface were swapped. Structural analysis of these using NMR and functional analysis using a dermal fibroblast migration assay showed that swapping the interfacial region swapped both the conformational preferences and activity. Conformational and activity differences were also observed between TGF-β3 and a variant with four helix-stabilizing residues from TGF-β1, suggesting that the observed changes were due to increased helical stability and the altered conformation, as proposed. Surface plasmon resonance analysis showed that TGF-β1, TGF-β3, and variants bound the type II signaling receptor, TβRII, nearly identically, but had small differences in the dissociation rate constant for recruitment of the type I signaling receptor, TβRI. However, the latter did not correlate with conformational preference or activity. Hence, the difference in activity arises from differences in their conformations, not their manner of receptor binding, suggesting that a matrix protein that differentially binds them might determine their distinct activities

Detection of the TCDD binding-fingerprint within the Ah receptor ligand binding domain by structurally driven mutagenesis and functional analysis

The aryl hydrocarbon receptor (AhR) is a ligand-dependent, basic helix-loop-helix Per-Arnt-Sim (PAS)-containing transcription factor that can bind and be activated by structurally diverse chemicals, including the toxic environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Our previous three-dimensional homology model of the mouse AhR (mAhR) PAS B ligand binding domain allowed identification of the binding site and its experimental validation. We have extended this analysis by conducting comparative structural modeling studies of the ligand binding domains of six additional highaffinity mammalian AhRs. These results, coupled with site-directed mutagenesis and AhR functional analysis, have allowed detection of the "TCDD binding-fingerprint" of conserved residues within the ligand binding cavity necessary for high-affinity TCDD binding and TCDD-dependent AhR transformation DNA binding. The essential role of selected residues was further evaluated using molecular docking simulations of TCDD with both wild-type and mutant mAhRs. Taken together, our results dramatically improve our understanding of the molecular determinants of TCDD binding and provide a basis for future studies directed toward rationalizing the observed species differences in AhR sensitivity to TCDD and understanding the mechanistic basis for the dramatic diversity in AhR ligand structure. © 2009 American Chemical Society

A comprehensive evaluation of the activity and selectivity profile of ligands for RGD-binding integrins

Integrins, a diverse class of heterodimeric cell surface receptors, are key regulators of cell structure and behaviour, affecting cell morphology, proliferation, survival and differentiation. Consequently, mutations in specific integrins, or their deregulated expression, are associated with a variety of diseases. In the last decades, many integrin-specific ligands have been developed and used for modulation of integrin function in medical as well as biophysical studies. The IC50-values reported for these ligands strongly vary and are measured using different cell-based and cell-free systems. A systematic comparison of these values is of high importance for selecting the optimal ligands for given applications. In this study, we evaluate a wide range of ligands for their binding affinity towards the RGD-binding integrins avß3, avß5, avß6, avß8, a5ß1, aIIbß3, using homogenous ELISA-like solid phase binding assay.Postprint (published version

Associations between the K232A polymorphism in the diacylglycerol-O-transferase 1 (DGAT1) gene and performance in Irish Holstein-Friesian dairy cattle

peer-reviewedSelection based on genetic polymorphisms requires accurate quantification of the effect or association of the polymorphisms with all traits of economic importance. The objective of this study was to estimate, using progeny performance data on 848 Holstein-Friesian bulls, the association between a non-conservative alanine to lysine amino acid change (K232A) in exon 8 of the diacylglycerol-O-transferase 1 (DGAT1) gene and milk production and functionality in the Irish Holstein-Friesian population. The DGAT1 gene encodes the diacylglycerol-O-transferase microsomal enzyme necessary to catalyze the final step in triglyceride synthesis. Weighted mixed model methodology, accounting for the additive genetic relationships among animals, was used to evaluate the association between performance and the K232A polymorphism. The minor allele frequency (K allele) was 0.32. One copy of the K allele was associated (P < 0.001) with 77 kg less milk yield, 4.22 kg more fat yield, 0.99 kg less protein yield, and 1.30 and 0.28 g/kg greater milk fat and protein concentration, respectively; all traits were based on predicted 305-day production across the first five lactations. The K232A polymorphism explained 4.8%, 10.3% and 1.0% of the genetic variance in milk yield, fat yield and protein yield, respectively. There was no association between the K232A polymorphism and fertility, functional survival, calving performance, carcass traits, or any conformation trait with the exception of rump width and carcass conformation. Using the current economic values for the milk production traits in the Irish total merit index, one copy of the K allele is worth €5.43 in expected profitability of progeny. Results from this study will be useful in quantifying the cost-benefit of including the K232A polymorphism in the Irish national breeding programme

Selective Constraints on Amino Acids Estimated by a Mechanistic Codon Substitution Model with Multiple Nucleotide Changes

Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution. We develop a codon-based model, in which mutational tendencies of codon, a genetic code, and the strength of selective constraints against amino acid replacements can be tailored to a given gene. First, selective constraints averaged over proteins are estimated by maximizing the likelihood of each 1-PAM matrix of empirical amino acid (JTT, WAG, and LG) and codon (KHG) substitution matrices. Then, selective constraints specific to given proteins are approximated as a linear function of those estimated from the empirical substitution matrices. Akaike information criterion (AIC) values indicate that a model allowing multiple nucleotide changes fits the empirical substitution matrices significantly better. Also, the ML estimates of transition-transversion bias obtained from these empirical matrices are not so large as previously estimated. The selective constraints are characteristic of proteins rather than species. However, their relative strengths among amino acid pairs can be approximated not to depend very much on protein families but amino acid pairs, because the present model, in which selective constraints are approximated to be a linear function of those estimated from the JTT/WAG/LG/KHG matrices, can provide a good fit to other empirical substitution matrices including cpREV for chloroplast proteins and mtREV for vertebrate mitochondrial proteins. The present codon-based model with the ML estimates of selective constraints and with adjustable mutation rates of nucleotide would be useful as a simple substitution model in ML and Bayesian inferences of molecular phylogenetic trees, and enables us to obtain biologically meaningful information at both nucleotide and amino acid levels from codon and protein sequences.Comment: Table 9 in this article includes corrections for errata in the Table 9 published in 10.1371/journal.pone.0017244. Supporting information is attached at the end of the article, and a computer-readable dataset of the ML estimates of selective constraints is available from 10.1371/journal.pone.001724

Nucleotide sequence and genomic organization of an ophiovirus associated with lettuce big-vein disease

The complete nucleotide sequence of an ophiovirus associated with lettuce big-vein disease has been elucidated. The genome consisted of four RNA molecules of approximately 7ò8, 1ò7, 1ò5 and 1ò4 kb. Virus particles were shown to contain nearly equimolar amounts of RNA molecules of both polarities. The 5'- and 3'-terminal ends of the RNA molecules are largely, but not perfectly, complementary to each other. The virus genome contains seven open reading frames. Database searches with the putative viral products revealed homologies with the RNA-dependent RNA polymerases of rhabdoviruses and Ranunculus white mottle virus, and the capsid protein of Citrus psorosis virus. The gene encoding the viral polymerase appears to be located on the RNA segment 1, while the nucleocapsid protein is encoded by the RNA3. No significant sequence similarities were observed with other viral proteins. In spite of the morphological resemblance with species in the genus Tenuivirus, the ophioviruses appear not to be evolutionary closely related to this genus nor any other viral genus

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/