QSAR studies applied to the prediction of antigen-antibody interaction kinetics as measured by BIACORE

The objective of this work was to investigate the potential of the quantitative structure-activity relationships (QSAR) approach for predictive modulation of molecular interaction kinetics. A multivariate QSAR approach involving modifications in peptide sequence and buffer composition was recently used in an attempt to predict the kinetics of peptide-antibody interactions as measured by BIACORE. Quantitative buffer-kinetics relationships (QBKR) and quantitative sequence-kinetics relationships (QSKR) models were developed. Their predictive capacity was investigated in this study by comparing predicted and observed kinetic dissociation parameters (k(d)) for new antigenic peptides, or in new buffers. The range of experimentally measured k(d) variations was small (300-fold), limiting the practical value of the approach for this particular interaction. However, the models were validated from a statistical point of view. In QSKR, the leave-one-out cross validation gave Q(2) = 0.71 for 24 peptides (all but one outlier), compared to 0.81 for 17 training peptides. A more precise model (Q(2) = 0.92) could be developed when removing sets of peptides sharing distinctive structural features, suggesting that different peptides use slightly different binding modes. All models share the most important factor and are informative for structure-kinetics relationships. In QBKR, the measured effect on k(d) of individual additives in the buffers was consistent with the effect predicted from multivariate buffers. Our results open new perspectives for the predictive optimization of interaction kinetics, with important implications in pharmacology and biotechnology.     

Chemical basis for the affinity maturation of a camel single domain antibody

Affinity maturation of classic antibodies supposedly proceeds through the pre-organization of the reactive germ line conformational isomer. It is less evident to foresee how this can be accomplished by camelid heavy-chain antibodies lacking light chains. Although these antibodies are subjected to somatic hypermutation, their antigen-binding fragment consists of a single domain with restricted flexibility in favor of binding energy. An antigen-binding domain derived from a dromedary heavy-chain antibody, cAb-Lys3, accumulated five amino acid substitutions in CDR1 and CDR2 upon maturation against lysozyme. Three of these residues have hydrophobic side chains, replacing serines, and participate in the hydrophobic core of the CDR1 in the mature antibody, suggesting that conformational rearrangements might occur in this loop during maturation. However, transition state analysis of the binding kinetics of mature cAb-Lys3 and germ line variants show that the maturation of this antibody relies on events late in the reaction pathway. This is reflected by a limited perturbation of k(a) and a significantly decreased k(d) upon maturation. In addition, binding reactions and the maturation event are predominantly enthalpically driven. Therefore, maturation proceeds through the increase of favorable binding interactions, or by the reduction of the enthalpic penalty for desolvation, as opposed to large entropic penalties associated with conformational changes and structural plasticity. Furthermore, the crystal structure of the mutant with a restored germ line CDR2 sequence illustrates that the matured hydrophobic core of CDR1 in cAb-Lys3 might be compensated in the germ line precursor by burying solvent molecules engaged in a stable hydrogen-bonding network with CDR1 and CDR2.     

Prediction of nicotinamide adenine dinucleotide interacting sites based on ensemble support vector machine

Nicotinamide adenine dinucleotide (NAD) plays an important role in cellular metabolism and acts as hydrideaccepting and hydride-donating coenzymes in energy production. Identification of NAD protein interacting sites can significantly aid in understanding the NAD dependent metabolism and pathways, and it could further contribute useful information for drug development. In this study, a computational method is proposed to predict NAD-protein interacting sites using the sequence information and structure-based information. All models developed in this work are evaluated using the 7-fold cross validation technique. Results show that using the position specific scoring matrix (PSSM) as an input feature is quite encouraging for predicting NAD interacting sites. After considering the unbalance dataset, the ensemble support vector machine (SVM), which is an assembly of many individual SVM classifiers, is developed to predict the NAD interacting sites. It was observed that the overall accuracy (Acc) thus obtained was 87.31% with Matthew's correlation coefficient (MCC) equal to 0.56. In contrast, the corresponding rate by the single SVM approach was only 80.86% with MCC of 0.38. These results indicated that the prediction accuracy could be remarkably improved via the ensemble SVM classifier approach.     

Generation of a functional monomolecular protein lattice consisting of an s-layer fusion protein comprising the variable domain of a camel heavy chain antibody

Crystalline bacterial cell surface layer (S-layer) proteins are composed of a single protein or glycoprotein species. Isolated S-layer subunits frequently recrystallize into monomolecular protein lattices on various types of solid supports. For generating a functional protein lattice, a chimeric protein was constructed, which comprised the secondary cell wall polymer-binding region and the self-assembly domain of the S-layer protein SbpA from Bacillus sphaericus CCM 2177, and a single variable region of a heavy chain camel antibody (cAb-Lys3) recognizing lysozyme as antigen. For construction of the S-layer fusion protein, the 3'-end of the sequence encoding the C-terminally truncated form rSbpA(31)(-)(1068) was fused via a short linker to the 5'-end of the sequence encoding cAb-Lys3. The functionality of the fused cAb-Lys3 in the S-layer fusion protein was proved by surface plasmon resonance measurements. Dot blot assays revealed that the accessibility of the fused functional sequence for the antigen was independent of the use of soluble or assembled S-layer fusion protein. Recrystallization of the S-layer fusion protein into the square lattice structure was observed on peptidoglycan-containing sacculi of B. sphaericus CCM 2177, on polystyrene or on gold chips precoated with thiolated secondary cell wall polymer, which is the natural anchoring molecule for the S-layer protein in the bacterial cell wall. Thereby, the fused cAb-Lys3 remained located on the outer S-layer surface and accessible for lysozyme binding. Together with solid supports precoated with secondary cell wall polymers, S-layer fusion proteins comprising rSbpA(31)(-)(1068) and cAbs directed against various antigens shall be exploited for building up monomolecular functional protein lattices as required for applications in nanobiotechnology.     

Perfusion characteristics of the human hepatic microcirculation based on three-dimensional reconstructions and computational fluid dynamic analysis

The perfusion of the liver microcirculation is often analyzed in terms of idealized functional units (hexagonal liver lobules) based on a porous medium approach. More elaborate research is essential to assess the validity of this approach and to provide a more adequate and quantitative characterization of the liver microcirculation. To this end, we modeled the perfusion of the liver microcirculation using an image-based three-dimensional (3D) reconstruction of human liver sinusoids and computational fluid dynamics techniques. After vascular corrosion casting, a microvascular sample (±0.134 mm(3)) representing three liver lobules, was dissected from a human liver vascular replica and scanned using a high resolution (2.6 μm) micro-CT scanner. Following image processing, a cube (0.15?×?0.15?×?0.15 mm(3)) representing a sample of intertwined and interconnected sinusoids, was isolated from the 3D reconstructed dataset to define the fluid domain. Three models were studied to simulate flow along three orthogonal directions (i.e., parallel to the central vein and in the radial and circumferential directions of the lobule). Inflow and outflow guidances were added to facilitate solution convergence, and good quality volume meshes were obtained using approximately 9?×?10(6) tetrahedral cells. Subsequently, three computational fluid dynamics models were generated and solved assuming Newtonian liquid properties (viscosity 3.5 mPa s). Post-processing allowed to visualize and quantify the microvascular flow characteristics, to calculate the permeability tensor and corresponding principal permeability axes, as well as the 3D porosity. The computational fluid dynamics simulations provided data on pressure differences, preferential flow pathways and wall shear stresses. Notably, the pressure difference resulting from the flow simulation parallel to the central vein (0-100 Pa) was clearly smaller than the difference from the radial (0-170 Pa) and circumferential (0-180 Pa) flow directions. This resulted in a higher permeability along the central vein direction (k(d,33)?=?3.64?×?10(-14) m(2)) in comparison with the radial (k(d,11)?=?1.56?×?10(-14) m(2)) and circumferential (k(d,22)?=?1.75?×?10(-14) m(2)) permeabilities which were approximately equal. The mean 3D porosity was 14.3. Our data indicate that the human hepatic microcirculation is characterized by a higher permeability along the central vein direction, and an about two times lower permeability along the radial and circumferential directions of a lobule. Since the permeability coefficients depend on the flow direction, (porous medium) liver microcirculation models should take into account sinusoidal anisotropy.     

INTERACTING PHENOTYPES AND THE EVOLUTIONARY PROCESS: I. DIRECT AND INDIRECT GENETIC EFFECTS OF SOCIAL INTERACTIONS

Interacting phenotypes are traits whose expression is affected by interactions with conspecifics. Commonly-studied interacting phenotypes include aggression, courtship, and communication. More extreme examples of interacting phenotypes—traits that exist exclusively as a product of interactions—include social dominance, intraspecific competitive ability, and mating systems. We adopt a quantitative genetic approach to assess genetic influences on interacting phenotypes. We partition genetic and environmental effects so that traits in conspecifics that influence the expression of interacting phenotypes are a component of the environment. When the trait having the effect is heritable, the environmental influence arising from the interaction has a genetic basis and can be incorporated as an indirect genetic effect. However, because it has a genetic basis, this environmental component can evolve. Therefore, to consider the evolution of interacting phenotypes we simultaneously consider changes in the direct genetic contributions to a trait (as a standard quantitative genetic approach would evaluate) as well as changes in the environmental (indirect genetic) contribution to the phenotype. We then explore the ramifications of this model of inheritance on the evolution of interacting phenotypes. The relative rate of evolution in interacting phenotypes can be quite different from that predicted by a standard quantitative genetic analysis. Phenotypic evolution is greatly enhanced or inhibited depending on the nature of the direct and indirect genetic effects. Further, unlike most models of phenotypic evolution, a lack of variation in direct genetic effects does not preclude evolution if there is genetic variance in the indirect genetic contributions. The available empirical evidence regarding the evolution of behavior expressed in interactions, although limited, supports the predictions of our model.     

A computational model for predicting protein interactions based on multidomain collaboration

Recently, several domain-based computational models for predicting protein-protein interactions (PPIs) have been proposed. The conventional methods usually infer domain or domain combination (DC) interactions from already known interacting sets of proteins, and then predict PPIs using the information. However, the majority of these models often have limitations in providing detailed information on which domain pair (single domain interaction) or DC pair (multidomain interaction) will actually interact for the predicted protein interaction. Therefore, a more comprehensive and concrete computational model for the prediction of PPIs is needed. We developed a computational model to predict PPIs using the information of intraprotein domain cohesion and interprotein DC coupling interaction. A method of identifying the primary interacting DC pair was also incorporated into the model in order to infer actual participants in a predicted interaction. Our method made an apparent improvement in the PPI prediction accuracy, and the primary interacting DC pair identification was valid specifically in predicting multidomain protein interactions. In this paper, we demonstrate that 1) the intraprotein domain cohesion is meaningful in improving the accuracy of domain-based PPI prediction, 2) a prediction model incorporating the intradomain cohesion enables us to identify the primary interacting DC pair, and 3) a hybrid approach using the intra/interdomain interaction information can lead to a more accurate prediction.     

Exploring the Link between Genetic Relatedness r and Social Contact Structure k in Animal Social Networks

Our understanding of how cooperation can arise in a population of selfish individuals has been greatly advanced by theory. More than one approach has been used to explore the effect of population structure. Inclusive fitness theory uses genetic relatedness r to express the role of population structure. Evolutionary graph theory models the evolution of cooperation on network structures and focuses on the number of interacting partners k as a quantity of interest. Here we use empirical data from a hierarchically structured animal contact network to examine the interplay between independent, measurable proxies for these key parameters. We find strong inverse correlations between estimates of r and k over three levels of social organization, suggesting that genetic relatedness and social contact structure capture similar structural information in a real population.     

A single-domain antibody fragment in complex with RNase A: non-canonical loop structures and nanomolar affinity using two CDR loops

BACKGROUND: Camelid serum contains a large fraction of functional heavy-chain antibodies - homodimers of heavy chains without light chains. The variable domains of these heavy-chain antibodies (VHH) have a long complementarity determining region 3 (CDR3) loop that compensates for the absence of the antigen-binding loops of the variable light chains (VL). In the case of the VHH fragment cAb-Lys3, part of the 24 amino acid long CDR3 loop protrudes from the antigen-binding surface and inserts into the active-site cleft of its antigen, rendering cAb-Lys3 a competitive enzyme inhibitor. RESULTS: A dromedary VHH with specificity for bovine RNase A, cAb-RN05, has a short CDR3 loop of 12 amino acids and is not a competitive enzyme inhibitor. The structure of the cAb-RN05-RNase A complex has been solved at 2.8 A. The VHH scaffold architecture is close to that of a human VH (variable heavy chain). The structure of the antigen-binding hypervariable 1 loop (H1) of both cAb-RN05 and cAb-Lys3 differ from the known canonical structures; in addition these H1 loops resemble each other. The CDR3 provides an antigen-binding surface and shields the face of the domain that interacts with VL in conventional antibodies. CONCLUSIONS: VHHs adopt the common immunoglobulin fold of variable domains, but the antigen-binding loops deviate from the predicted canonical structure. We define a new canonical structure for the H1 loop of immunoglobulins, with cAb-RN05 and cAb-Lys3 as reference structures. This new loop structure might also occur in human or mouse VH domains. Surprisingly, only two loops are involved in antigen recognition; the CDR2 does not participate. Nevertheless, the antigen binding occurs with nanomolar affinities because of a preferential usage of mainchain atoms for antigen interaction.     

A comparison of substrate dynamics in human CYP2E1 and CYP2A6

Considering the dynamic nature of CYPs, methods that reveal information about substrate and enzyme dynamics are necessary to generate predictive models. To compare substrate dynamics in CYP2E1 and CYP2A6, intramolecular isotope effect experiments were conducted, using deuterium labeled substrates: o-xylene, m-xylene, p-xylene, 2,6-dimethylnaphthalene, and 4,4'-dimethylbiphenyl. Competitive intermolecular experiments were also conducted using d(0)- and d(6)-labeled p-xylene. Both CYP2E1 and CYP2A6 displayed full isotope effect expression for o-xylene oxidation and almost complete suppression for dimethylbiphenyl. Interestingly, (k(H)/k(D))(obs) for d(3)-p-xylene oxidation ((k(H)/k(D))(obs)=6.04 and (k(H)/k(D))(obs)=5.53 for CYP2E1 and CYP2A6, respectively) was only slightly higher than (k(H)/k(D))(obs) for d(3)-dimethylnaphthalene ((k(H)/k(D))(obs)=5.50 and (k(H)/k(D))(obs)=4.96, respectively). One explanation is that in some instances (k(H)/k(D))(obs) values are generated by the presence of two substrates-bound simultaneously to the CYP. Speculatively, if this explanation is valid, then intramolecular isotope effect experiments should be useful in the mechanistic investigation of P450 cooperativity.     

A statistical framework for quantitative trait mapping

We describe a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses. Our main result is based on the observation that, by conditioning on the unobserved QTL genotypes, the problem can be split into two statistically independent and manageable parts. The first part involves only the relationship between the QTL and the phenotype. The second part involves only the location of the QTL in the genome. We developed a simple Monte Carlo algorithm to implement Bayesian QTL analysis. This algorithm simulates multiple versions of complete genotype information on a genomewide grid of locations using information in the marker genotype data. Weights are assigned to the simulated genotypes to capture information in the phenotype data. The weighted complete genotypes are used to approximate quantities needed for statistical inference of QTL locations and effect sizes. One advantage of this approach is that only the weights are recomputed as the analyst considers different candidate models. This device allows the analyst to focus on modeling and model comparisons. The proposed framework can accommodate multiple interacting QTL, nonnormal and multivariate phenotypes, covariates, missing genotype data, and genotyping errors in any type of inbred line cross. A software tool implementing this procedure is available. We demonstrate our approach to QTL analysis using data from a mouse backcross population that is segregating multiple interacting QTL associated with salt-induced hypertension.     

NAGbinder: An approach for identifying N‐acetylglucosamine interacting residues of a protein from its primary sequence

N‐acetylglucosamine (NAG) belongs to the eight essential saccharides that are required to maintain the optimal health and precise functioning of systems ranging from bacteria to human. In the present study, we have developed a method, NAGbinder, which predicts the NAG‐interacting residues in a protein from its primary sequence information. We extracted 231 NAG‐interacting nonredundant protein chains from Protein Data Bank, where no two sequences share more than 40% sequence identity. All prediction models were trained, validated, and evaluated on these 231 protein chains. At first, prediction models were developed on balanced data consisting of 1,335 NAG‐interacting and noninteracting residues, using various window size. The model developed by implementing Random Forest using binary profiles as the main principle for identifying NAG‐interacting residue with window size 9, performed best among other models. It achieved highest Matthews Correlation Coefficient (MCC) of 0.31 and 0.25, and Area Under Receiver Operating Curve (AUROC) of 0.73 and 0.70 on training and validation data set, respectively. We also developed prediction models on realistic data set (1,335 NAG‐interacting and 47,198 noninteracting residues) using the same principle, where the model achieved MCC of 0.26 and 0.27, and AUROC of 0.70 and 0.71, on training and validation data set, respectively. The success of our method can be appraised by the fact that, if a sequence of 1,000 amino acids is analyzed with our approach, 10 residues will be predicted as NAG‐interacting, out of which five are correct. Best models were incorporated in the standalone version and in the webserver available at https://webs.iiitd.edu.in/raghava/nagbinder/     

Simultaneous fine mapping of closely linked epistatic quantitative trait loci using combined linkage disequilibrium and linkage with a general pedigree

Causal mutations and their intra- and inter-locus interactions play a critical role in complex trait variation. It is often not easy to detect epistatic quantitative trait loci (QTL) due to complicated population structure requirements for detecting epistatic effects in linkage analysis studies and due to main effects often being hidden by interaction effects. Mapping their positions is even harder when they are closely linked. The data structure requirement may be overcome when information on linkage disequilibrium is used. We present an approach using a mixed linear model nested in an empirical Bayesian approach, which simultaneously takes into account additive, dominance and epistatic effects due to multiple QTL. The covariance structure used in the mixed linear model is based on combined linkage disequilibrium and linkage information. In a simulation study where there are complex epistatic interactions between QTL, it is possible to simultaneously map interacting QTL into a small region using the proposed approach. The estimated variance components are accurate and less biased with the proposed approach compared with traditional models.     

Partner-aware prediction of interacting residues in protein-protein complexes from sequence data

Computational prediction of residues that participate in protein-protein interactions is a difficult task, and state of the art methods have shown only limited success in this arena. One possible problem with these methods is that they try to predict interacting residues without incorporating information about the partner protein, although it is unclear how much partner information could enhance prediction performance. To address this issue, the two following comparisons are of crucial significance: (a) comparison between the predictability of inter-protein residue pairs, i.e., predicting exactly which residue pairs interact with each other given two protein sequences; this can be achieved by either combining conventional single-protein predictions or making predictions using a new model trained directly on the residue pairs, and the performance of these two approaches may be compared: (b) comparison between the predictability of the interacting residues in a single protein (irrespective of the partner residue or protein) from conventional methods and predictions converted from the pair-wise trained model. Using these two streams of training and validation procedures and employing similar two-stage neural networks, we showed that the models trained on pair-wise contacts outperformed the partner-unaware models in predicting both interacting pairs and interacting single-protein residues. Prediction performance decreased with the size of the conformational change upon complex formation; this trend is similar to docking, even though no structural information was used in our prediction. An example application that predicts two partner-specific interfaces of a protein was shown to be effective, highlighting the potential of the proposed approach. Finally, a preliminary attempt was made to score docking decoy poses using prediction of interacting residue pairs; this analysis produced an encouraging result.     

The Fractal Model: a new model to describe the species accumulation process and relative abundance distribution (RAD)

Quantifying a sampled collection of taxa is a widespread problem in ecology. A number of diversity indices have been proposed and used in numerous works in spite of a lack of statistical characteristics and tests of comparison. These Relative Abundance Distributions (RAD) can also be described using graphic representations exhibiting both the number of taxa and the quantitative distribution of the individuals which constitute these taxa. Fitting these RADs to deterministic or stochastic models remains problematic for different reasons like scale dependence or complexity of the numerical procedures. In our study, we introduce a new model to describe RAD and ecological succession. This model is a Fractal Model based on the assumption that during an ecological succession/accumulation process, at each step of the succession, K new species appear which are k times more abundant with K = k ^d (where d is a fractal dimension). We use a Monte-Carlo procedure with a Kolmogorov-Smirnov distance to fit this model and to estimate parameters. Through the study of entomological data from a Mediterranean Man And Biosphere reserve, we demonstrate the qualities of the new Fractal Model. Thus, the Fractal Model was rejected for none of the 13 RADs tested. In addition, the parameters ( K , k and d ) are independent of the Hill family diversity indices and of three evenness indices and are able to provide additional information concerning both the diversity of the sampled ecosystem and development of this biodiversity during the species accumulation process in this ecosystem.     

Trophic links between functional groups of arable plants and beetles are stable at a national scale

1. There is an urgent need to accurately model how environmental change affects the wide-scale functioning of ecosystems, but advances are hindered by a lack of knowledge of how trophic levels are linked across space. It is unclear which theoretical approach to take to improve modelling of such interactions, but evidence is gathering that linking species responses to their functional traits can increase understanding of ecosystem dynamics. Currently, there are no quantitative studies testing how this approach might improve models of multiple, trophically interacting species, at wide spatial scales. 2. Arable weeds play a foundational role in linking food webs, providing resources for many taxa, including carabid beetles that feed on their seeds and weed-associated invertebrate prey. Here, we model associations between weeds and carabids across farmland in Great Britain (GB), to test the hypothesis that wide-scale trophic links between these groups are structured by their species functional traits. 3. A network of c. 250 arable fields, covering four crops and most lowland areas of GB, was sampled for weed, carabid and invertebrate taxa over 3 years. Data sets of these groups were closely matched in time and space, and each contained numerous species with a range of eco-physiological traits. The consistency of trophic linkages between multiple taxa sharing functional traits was tested within multivariate and log-linear models. 4. Robust links were established between the functional traits of taxa and their trophic interactions. Autumn-germinating, small-seeded weeds were associated with smaller, spring-breeding carabids, more specialised in seed feeding, whereas spring-germinating, large-seeded weeds were associated with a range of larger, autumn-breeding omnivorous carabids. These relationships were strong and dynamic, being independent of changes in invertebrate food resources and consistent across sample dates, crops and regions of GB. 5. We conclude that, in at least one system of interacting taxa, functional traits can be used to predict consistent, wide-scale trophic links. This conceptual approach is useful for assessing how perturbations affecting lower trophic levels are ramified throughout ecosystems and could be used to assess how environmental change affects a wider range of secondary consumers.     

Creating porcine biomedical models through recombineering

Recent advances in genomics provide genetic information from humans and other mammals (mouse, rat, dog and primates) traditionally used as models as well as new candidates (pigs and cattle). In addition, linked enabling technologies, such as transgenesis and animal cloning, provide innovative ways to design and perform experiments to dissect complex biological systems. Exploitation of genomic information overcomes the traditional need to choose naturally occurring models. Thus, investigators can utilize emerging genomic knowledge and tools to create relevant animal models. This approach is referred to as reverse genetics. In contrast to 'forward genetics', in which gene(s) responsible for a particular phenotype are identified by positional cloning (phenotype to genotype), the 'reverse genetics' approach determines the function of a gene and predicts the phenotype of a cell, tissue, or organism (genotype to phenotype). The convergence of classical and reverse genetics, along with genomics, provides a working definition of a 'genetic model' organism (3). The recent construction of phenotypic maps defining quantitative trait loci (QTL) in various domesticated species provides insights into how allelic variations contribute to phenotypic diversity. Targeted chromosomal regions are characterized by the construction of bacterial artificial chromosome (BAC) contigs to isolate and characterize genes contributing towards phenotypic variation. Recombineering provides a powerful methodology to harvest genetic information responsible for phenotype. Linking recombineering with gene-targeted homologous recombination, coupled with nuclear transfer (NT) technology can provide 'clones' of genetically modified animals.