Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations.

Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis. Current data-driven and bioinformatics approaches are, however, limited by the large number of new variations found in each newly sequenced genome, and often do not provide direct mechanistic insight. Here we demonstrate, for the first time, that saturation mutagenesis, biophysical modeling and co-variation analysis, performed in silico, can predict the abundance, metabolic stability, and function of proteins inside living cells. As a model system, we selected the human mismatch repair protein, MSH2, where missense variants are known to cause the hereditary cancer predisposition disease, known as Lynch syndrome. We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function, and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors. Thus, in conclusion, in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome, and perhaps other hereditary diseases.

Structure of the Bacterial Cytoskeleton Protein Bactofilin by NMR Chemical Shifts and Sequence Variation.

Bactofilins constitute a recently discovered class of bacterial proteins that form cytoskeletal filaments. They share a highly conserved domain (DUF583) of which the structure remains unknown, in part due to the large size and noncrystalline nature of the filaments. Here, we describe the atomic structure of a bactofilin domain from Caulobacter crescentus. To determine the structure, we developed an approach that combines a biophysical model for proteins with recently obtained solid-state NMR spectroscopy data and amino acid contacts predicted from a detailed analysis of the evolutionary history of bactofilins. Our structure reveals a triangular ß-helical (solenoid) conformation with conserved residues forming the tightly packed core and polar residues lining the surface. The repetitive structure explains the presence of internal repeats as well as strongly conserved positions, and is reminiscent of other fibrillar proteins. Our work provides a structural basis for future studies of bactofilin biology and for designing molecules that target them, as well as a starting point for determining the organization of the entire bactofilin filament. Finally, our approach presents new avenues for determining structures that are difficult to obtain by traditional means.

Rapid protein stability prediction using deep learning representations.

Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 230 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available-including via a Web interface-and enables large-scale analyses of stability in experimental and predicted protein structures.

A dual-reporter system for investigating and optimizing protein translation and folding in E. coli.

Strategies for investigating and optimizing the expression and folding of proteins for biotechnological and pharmaceutical purposes are in high demand. Here, we describe a dual-reporter biosensor system that simultaneously assesses in vivo protein translation and protein folding, thereby enabling rapid screening of mutant libraries. We have validated the dual-reporter system on five different proteins and find an excellent correlation between reporter signals and the levels of protein expression and solubility of the proteins. We further demonstrate the applicability of the dual-reporter system as a screening assay for deep mutational scanning experiments. The system enables high throughput selection of protein variants with high expression levels and altered protein stability. Next generation sequencing analysis of the resulting libraries of protein variants show a good correlation between computationally predicted and experimentally determined protein stabilities. We furthermore show that the mutational experimental data obtained using this system may be useful for protein structure calculations.

Yeast recombinant production of intact human membrane proteins with long intrinsically disordered intracellular regions for structural studies.

Membrane proteins exist in lipid bilayers and mediate solute transport, signal transduction, cell-cell communication and energy conversion. Their activities are fundamental for life, which make them prominent subjects of study, but access to only a limited number of high-resolution structures complicates their mechanistic understanding. The absence of such structures relates mainly to difficulties in expressing and purifying high quality membrane protein samples in large quantities. An additional layer of complexity stems from the presence of intra- and/or extra-cellular domains constituted by unstructured intrinsically disordered regions (IDR), which can be hundreds of residues long. Although IDRs form key interaction hubs that facilitate biological processes, these are regularly removed to enable structural studies. To advance mechanistic insight into intact intrinsically disordered membrane proteins, we have developed a protocol for their purification. Using engineered yeast cells for optimized expression and purification, we have purified to homogeneity two very different human membrane proteins each with >300 residues long IDRs; the sodium proton exchanger 1 and the growth hormone receptor. Subsequent to their purification we have further explored their incorporation into membrane scaffolding protein nanodiscs, which will enable future structural studies.

Enhancing coevolution-based contact prediction by imposing structural self-consistency of the contacts.

Based on the development of new algorithms and growth of sequence databases, it has recently become possible to build robust higher-order sequence models based on sets of aligned protein sequences. Such models have proven useful in de novo structure prediction, where the sequence models are used to find pairs of residues that co-vary during evolution, and hence are likely to be in spatial proximity in the native protein. The accuracy of these algorithms, however, drop dramatically when the number of sequences in the alignment is small. We have developed a method that we termed CE-YAPP (CoEvolution-YAPP), that is based on YAPP (Yet Another Peak Processor), which has been shown to solve a similar problem in NMR spectroscopy. By simultaneously performing structure prediction and contact assignment, CE-YAPP uses structural self-consistency as a filter to remove false positive contacts. Furthermore, CE-YAPP solves another problem, namely how many contacts to choose from the ordered list of covarying amino acid pairs. We show that CE-YAPP consistently improves contact prediction from multiple sequence alignments, in particular for proteins that are difficult targets. We further show that the structures determined from CE-YAPP are also in better agreement with those determined using traditional methods in structural biology.