Rare penetrant mutations confer severe risk of common diseases.

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ~10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared with common-variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.

Rare penetrant mutations confer severe risk of common diseases.

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ∼10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared to common variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction. One sentence summary: Rare variant polygenic risk scores identify individuals with outlier phenotypes in common human diseases and complex traits.

Fully automated antibody structure prediction using BIOVIA tools: Validation study.

We describe the methodology and results from our validation study of the fully automated antibody structure prediction tool available in the BIOVIA (formerly Accelrys) protein modeling suite. Extending our previous study, we have validated the automated approach using a larger and more diverse data set (157 unique antibody Fv domains versus 11 in the previous study). In the current study, we explore the effect of varying several parameter settings in order to better understand their influence on the resulting model quality. Specifically, we investigated the dependence on different methods of framework model construction, antibody numbering schemes (Chothia, IMGT, Honegger and Kabat), the influence of compatibility of loop templates using canonical type filtering, wider exploration of model solution space, and others. Our results show that our recently introduced Top5 framework modeling method results in a small but significant improvement in model quality whereas the effect of other parameters is not significant. Our analysis provides improved guidelines of best practices for using our protocol to build antibody structures. We also identify some limitations of the current computational model which will enhance proper evaluation of model quality by users and suggests possible future enhancements.

Automated antibody structure prediction using Accelrys tools: results and best practices.

We describe the methodology and results from our participation in the second Antibody Modeling Assessment experiment. During the experiment we predicted the structure of eleven unpublished antibody Fv fragments. Our prediction methods centered on template-based modeling; potential templates were selected from an antibody database based on their sequence similarity to the target in the framework regions. Depending on the quality of the templates, we constructed models of the antibody framework regions either using a single, chimeric or multiple template approach. The hypervariable loop regions in the initial models were rebuilt by grafting the corresponding regions from suitable templates onto the model. For the H3 loop region, we further refined models using ab initio methods. The final models were subjected to constrained energy minimization to resolve severe local structural problems. The analysis of the models submitted show that Accelrys tools allow for the construction of quite accurate models for the framework and the canonical CDR regions, with RMSDs to the X-ray structure on average below 1 Å for most of these regions. The results show that accurate prediction of the H3 hypervariable loops remains a challenge. Furthermore, model quality assessment of the submitted models show that the models are of quite high quality, with local geometry assessment scores similar to that of the target X-ray structures.

Loop modeling: Sampling, filtering, and scoring.

We describe a fast and accurate protocol, LoopBuilder, for the prediction of loop conformations in proteins. The procedure includes extensive sampling of backbone conformations, side chain addition, the use of a statistical potential to select a subset of these conformations, and, finally, an energy minimization and ranking with an all-atom force field. We find that the Direct Tweak algorithm used in the previously developed LOOPY program is successful in generating an ensemble of conformations that on average are closer to the native conformation than those generated by other methods. An important feature of Direct Tweak is that it checks for interactions between the loop and the rest of the protein during the loop closure process. DFIRE is found to be a particularly effective statistical potential that can bias conformation space toward conformations that are close to the native structure. Its application as a filter prior to a full molecular mechanics energy minimization both improves prediction accuracy and offers a significant savings in computer time. Final scoring is based on the OPLS/SBG-NP force field implemented in the PLOP program. The approach is also shown to be quite successful in predicting loop conformations for cases where the native side chain conformations are assumed to be unknown, suggesting that it will prove effective in real homology modeling applications.

Local quality assessment in homology models using statistical potentials and support vector machines.

In this study, we address the problem of local quality assessment in homology models. As a prerequisite for the evaluation of methods for predicting local model quality, we first examine the problem of measuring local structural similarities between a model and the corresponding native structure. Several local geometric similarity measures are evaluated. Two methods based on structural superposition are found to best reproduce local model quality assessments by human experts. We then examine the performance of state-of-the-art statistical potentials in predicting local model quality on three qualitatively distinct data sets. The best statistical potential, DFIRE, is shown to perform on par with the best current structure-based method in the literature, ProQres. A combination of different statistical potentials and structural features using support vector machines is shown to provide somewhat improved performance over published methods.

Comparative structural analysis of a novel glutathioneS-transferase (ATU5508) from Agrobacterium tumefaciens at 2.0 A resolution.

Glutathione S-transferases (GSTs) comprise a diverse superfamily of enzymes found in organisms from all kingdoms of life. GSTs are involved in diverse processes, notably small-molecule biosynthesis or detoxification, and are frequently also used in protein engineering studies or as biotechnology tools. Here, we report the high-resolution X-ray structure of Atu5508 from the pathogenic soil bacterium Agrobacterium tumefaciens (atGST1). Through use of comparative sequence and structural analysis of the GST superfamily, we identified local sequence and structural signatures, which allowed us to distinguish between different GST classes. This approach enables GST classification based on structure, without requiring additional biochemical or immunological data. Consequently, analysis of the atGST1 crystal structure suggests a new GST class, distinct from previously characterized GSTs, which would make it an attractive target for further biochemical studies.

An unusual Zn-finger/FH2 domain protein controls a left/right asymmetric neuronal fate decision in C. elegans.

Gene regulatory networks that control the terminally differentiated state of a cell are, by and large, only superficially understood. In a mutant screen aimed at identifying regulators of gene batteries that define the differentiated state of two left/right asymmetric C. elegans gustatory neurons, ASEL and ASER, we have isolated a mutant, fozi-1, with a novel mixed-fate phenotype, characterized by de-repression of ASEL fate in ASER. fozi-1 codes for a protein that functions in the nucleus of ASER to inhibit the expression of the LIM homeobox gene lim-6, neuropeptide-encoding genes and putative chemoreceptors of the GCY gene family. The FOZI-1 protein displays a highly unusual domain architecture, that combines two functionally essential C2H2 zinc-finger domains, which are probably involved in transcriptional regulation, with a formin homology 2 (FH2) domain, normally found only in cytosolic regulators of the actin cytoskeleton. We demonstrate that the FH2 domain of FOZI-1 has lost its actin polymerization function but maintains its phylogenetically ancient ability to homodimerize. fozi-1 genetically interacts with several transcription factors and micro RNAs in the context of specific regulatory network motifs. These network motifs endow the system with properties that provide insights into how cells adopt their stable terminally differentiated states.

Adaptive integration method for Monte Carlo simulations.

We present an adaptive sampling method for computing free energies, radial distribution functions, and potentials of mean force. The method is characterized by simplicity and accuracy, with the added advantage that the data are obtained in terms of quasicontinuous functions. The method is illustrated and tested with simulations on a high density fluid, including a stringent consistency test involving an unusual thermodynamic cycle that highlights its advantages.