Assessment of variability among morphological and molecular characters in wild populations of mint [Mentha longifolia (L.) L.] germplasm

Mentha longifolia is an important medicinal and aromatic perennial herb that exhibits wide distribution range from sub-tropical to temperate regions. In the present study, agro-morphological traits and genetic differences in 19 different populations of M. longifolia were studied to evaluate the level and extent of its diversity. Analysis of variance (ANOVA) showed that the different phenotypic characters show considerable differences among various populations and was significant at p < 0.05. Molecular diversity analysis performed by using arbitrary amplified eleven ISSR primers generated a total of 121 amplicons that range within the size of 200–2500 base pairs (bp). Each primer on average generated 11 amplicons with percentage polymorphism being 100. The analysis of molecular variance (AMOVA) showed more (64%) among population genetic diversity and less (36%) within the populations. Greater genetic differentiation (Gst = 0.6852) among these populations occurs due to low gene flow (Nm = 0.2297) and greater habitat variability. Geographic and genetic distances were positively correlated according to Mantel’s test. In order to remove any kind of biases, we used R software to perform cluster and redundancy analysis to analyse the extent of relatedness among studied populations. In terms of morphological and molecular aspects, the populations were grouped into four and five clusters respectively based on hierarchical clustering method. The results demonstrated that M. longifolia displays a great degree of morphological and genetic variation and can be utilized in breeding, genetic improvement, and gene bank conservation programmes in future.     

Tertiary motifs as building blocks for the design of protein‐binding peptides

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre‐defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface‐complementing fragments or “seeds.” We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM‐based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide‐protein complexes can be covered by seeds generated from single‐chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide‐covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher‐quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide‐binding site. These results demonstrate that known peptide‐binding structures can be constructed from TERMs in single‐chain structures and suggest that TERM information can be applied to efficiently design novel target‐complementing binders.     

Conformational control in structure-based drug design

In structure-based drug design, the basic goal is to design molecules that fit complementarily to a given binding pocket. Since such computationally modeled molecules may not adopt the intended bound conformation outside the binding pocket, one challenge is to ensure that the designed ligands adopt similar low energy conformations both inside and outside of the binding pocket. Computational chemistry methods and conformational preferences of small molecules from PDB and Cambridge Structural Database (CSD) can be used to predict the bound structures of the designed molecules. Herein, we review applications of conformational control in structure-based drug design using selected examples from the recent medicinal chemistry literature. The main purpose is to highlight some intriguing conformational features that can be applied to other drug discovery programs.     

Designing repeat proteins: modular leucine-rich repeat protein libraries based on the mammalian ribonuclease inhibitor family

We present a novel approach to design repeat proteins of the leucine-rich repeat (LRR) family for the generation of libraries of intracellular binding molecules. From an analysis of naturally occurring LRR proteins, we derived the concept to assemble repeat proteins with randomized surface positions from libraries of consensus repeat modules. As a guiding principle, we used the mammalian ribonuclease inhibitor (RI) family, which comprises cytosolic LRR proteins known for their extraordinary affinities to many RNases. By aligning the amino acid sequences of the internal repeats of human, pig, rat, and mouse RI, we derived a first consensus sequence for the characteristic alternating 28 and 29 amino acid residue A-type and B-type repeats. Structural considerations were used to replace all conserved cysteine residues, to define less conserved positions, and to decide where to introduce randomized amino acid residues. The so devised consensus RI repeat library was generated at the DNA level and assembled by stepwise ligation to give libraries of 2-12 repeats. Terminal capping repeats, known to shield the continuous hydrophobic core of the LRR domain from the surrounding solvent, were adapted from human RI. In this way, designed LRR protein libraries of 4-14 LRRs (equivalent to 130-415 amino acid residues) were obtained. The biophysical analysis of randomly chosen library members showed high levels of soluble expression in the Escherichia coli cytosol, monomeric behavior as characterized by gel-filtration, and alpha-helical CD spectra, confirming the success of our design approach.     

Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.     

De novo design of the hydrophobic core of ubiquitin.

We have previously reported the development and evaluation of a computational program to assist in the design of hydrophobic cores of proteins. In an effort to investigate the role of core packing in protein structure, we have used this program, referred to as Repacking of Cores (ROC), to design several variants of the protein ubiquitin. Nine ubiquitin variants containing from three to eight hydrophobic core mutations were constructed, purified, and characterized in terms of their stability and their ability to adopt a uniquely folded native-like conformation. In general, designed ubiquitin variants are more stable than control variants in which the hydrophobic core was chosen randomly. However, in contrast to previous results with 434 cro, all designs are destabilized relative to the wild-type (WT) protein. This raises the possibility that beta-sheet structures have more stringent packing requirements than alpha-helical proteins. A more striking observation is that all variants, including random controls, adopt fairly well-defined conformations, regardless of their stability. This result supports conclusions from the cro studies that non-core residues contribute significantly to the conformational uniqueness of these proteins while core packing largely affects protein stability and has less impact on the nature or uniqueness of the fold. Concurrent with the above work, we used stability data on the nine ubiquitin variants to evaluate and improve the predictive ability of our core packing algorithm. Additional versions of the program were generated that differ in potential function parameters and sampling of side chain conformers. Reasonable correlations between experimental and predicted stabilities suggest the program will be useful in future studies to design variants with stabilities closer to that of the native protein. Taken together, the present study provides further clarification of the role of specific packing interactions in protein structure and stability, and demonstrates the benefit of using systematic computational methods to predict core packing arrangements for the design of proteins.     

Living with observational data in biological anthropology

The establishment of cause and effect relationships is a fundamental objective of scientific research. Many lines of evidence can be used to make cause–effect inferences. When statistical data are involved, alternative explanations for the statistical relationship need to be ruled out. These include chance (apparent patterns due to random factors), confounding effects (a relationship between two variables because they are each associated with an unmeasured third variable), and sampling bias (effects due to preexisting properties of compared groups). The gold standard for managing these issues is a controlled randomized experiment. In disciplines such as biological anthropology, where controlled experiments are not possible for many research questions, causal inferences are made from observational data. Methods that statisticians recommend for this difficult objective have not been widely adopted in the biological anthropology literature. Issues involved in using statistics to make valid causal inferences from observational data are discussed.     

Identification of ortho-hydroxy anilide as a novel scaffold for lysine demethylase 5 inhibitors

Fe(II)/α-ketoglutarate-dependent lysine demethylases (KDMs) are attractive drug targets for several diseases including cancer. In this study, we designed and screened ortho-substituted anilides that are expected to function as Fe(II) chelators, and identified ortho-hydroxy anilide as a novel scaffold for KDM5A inhibitors. Treatment of human lung cancer A549 cells with a prodrug form of 4-carboxy-2-hydroxy-formanilide (9c) increased trimethylated lysine 4 on histone H3 level, suggesting KDM5 inhibition in the cells.     

New hypothesis elucidates self-incompatibility in the olive tree regarding S-alleles dominance relationships as in the sporophytic model

Most olive varieties are not strictly self-incompatible, nevertheless, they request foreign pollen to enhance fruit yield, and consequently orchards should contain pollinisers to ensure fruit set of the main variety. The best way to choose pollinisers is to experiment numerous crosses in a diallel design. Here, the genetic mode of inheritance of SI in the olive is deciphered and it does not correspond to the GSI type, but to the SSI type. It leaves S-allele dominance relationship expression in the male (pollen and pollen tube), but not in the female (stigma and style). Thus, a pair-wise combination of varieties may be inter-compatible in one direction (male to female, or female to male) and inter-incompatible in the other direction. Dominance relationships also explain different levels of self-pollination observed in varieties. Little efficient pollinisers were found and predicted in varieties; nevertheless, some new efficient pair-wise allele combinations are predicted and could be created. This model enables one to forecast compatibility without waiting for several years of yield records and to choose pollinisers in silico.