Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You

The objective of this review is to enable researchers to use the software package ROSETTA for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with ROSETTA. For each of these six tasks, we provide a tutorial that illustrates a basic ROSETTA protocol. The ROSETTA method was originally developed for de novo protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 A ˚. More impressively, there are several cases in which ROSETTA has been used to predict structures with atomic level accuracy better than 2.5 A ˚. In addition to de novo structure prediction, ROSETTA also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. ROSETTA has been used to accurately design a novel protein structure, predict the structure of protein-protein complexes, design altered specificity protein-protein and protein-DNA interactions, and stabilize proteins and protein complexes. Most recently, ROSETTA has been used to solve the X-ray crystallographic phase problem. ROSETTA is a unified software package for protein structure prediction and functional design. It has been used to predic

LINE-1 Evasion of Epigenetic Repression in Humans

Epigenetic silencing defends against LINE-1 (L1) retrotransposition in mammalian cells. However, the mechanisms that repress young L1 families and how L1 escapes to cause somatic genome mosaicism in the brain remain unclear. Here we report that a conserved Yin Yang 1 (YY1) transcription factor binding site mediates L1 promoter DNA methylation in pluripotent and differentiated cells. By analyzing 24 hippocampal neurons with three distinct single-cell genomic approaches, we characterized and validated a somatic L1 insertion bearing a 3' transduction. The source (donor) L1 for this insertion was slightly 5' truncated, lacked the YY1 binding site, and was highly mobile when tested in\ua0vitro. Locus-specific bisulfite sequencing revealed that the donor L1 and other young L1s with mutated YY1 binding sites were hypomethylated in embryonic stem cells, during neurodifferentiation, and in liver and brain tissue. These results explain how L1 can evade repression and retrotranspose in the human body

RosettaTMH: a method for membrane protein structure elucidation combining EPR distance restraints with assembly of transmembrane helices

Membrane proteins make up approximately one third of all proteins, and they play key roles in a plethora of physiological processes. However, membrane proteins make up less than 2% of experimentally determined structures, despite significant advances in structure determination methods, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy. One potential alternative means of structure elucidation is to combine computational methods with experimental EPR data. In 2011, Hirst and others introduced RosettaEPR and demonstrated that this approach could be successfully applied to fold soluble proteins. Furthermore, few computational methods for de novo folding of integral membrane proteins have been presented. In this work, we present RosettaTMH, a novel algorithm for structure prediction of helical membrane proteins. A benchmark set of 34 proteins, in which the proteins ranged in size from 91 to 565 residues, was used to compare RosettaTMH to Rosetta’s two existing membrane protein folding protocols: the published RosettaMembrane folding protocol (“MembraneAbinitio”) and folding from an extended chain (“ExtendedChain”). When EPR distance restraints are used, RosettaTMH+EPR outperforms ExtendedChain+EPR for 11 proteins, including the largest six proteins tested. RosettaTMH+EPR is capable of achieving native-like folds for 30 of 34 proteins tested, including receptors and transporters. For example, the average RMSD100SSE relative to the crystal structure for rhodopsin was 6.1 ± 0.4 Å and 6.5 ± 0.6 Å for the 449-residue nitric oxide reductase subunit B, where the standard deviation reflects variance in RMSD100SSE values across ten different EPR distance restraint sets. The addition of RosettaTMH and RosettaTMH+EPR to the Rosetta family of de novo folding methods broadens the scope of helical membrane proteins that can be accurately modeled with this software suite

Human Germline Antibody Gene Segments Encode Polyspecific Antibodies

<div><p>Structural flexibility in germline gene-encoded antibodies allows promiscuous binding to diverse antigens. The binding affinity and specificity for a particular epitope typically increase as antibody genes acquire somatic mutations in antigen-stimulated B cells. In this work, we investigated whether germline gene-encoded antibodies are optimal for polyspecificity by determining the basis for recognition of diverse antigens by antibodies encoded by three V_H gene segments. Panels of somatically mutated antibodies encoded by a common V_H gene, but each binding to a different antigen, were computationally redesigned to predict antibodies that could engage multiple antigens at once. The Rosetta multi-state design process predicted antibody sequences for the entire heavy chain variable region, including framework, CDR1, and CDR2 mutations. The predicted sequences matched the germline gene sequences to a remarkable degree, revealing by computational design the residues that are predicted to enable polyspecificity, <i>i.e.</i>, binding of many unrelated antigens with a common sequence. The process thereby reverses antibody maturation <i>in silico</i>. In contrast, when designing antibodies to bind a single antigen, a sequence similar to that of the mature antibody sequence was returned, mimicking natural antibody maturation <i>in silico</i>. We demonstrated that the Rosetta computational design algorithm captures important aspects of antibody/antigen recognition. While the hypervariable region CDR3 often mediates much of the specificity of mature antibodies, we identified key positions in the V_H gene encoding CDR1, CDR2, and the immunoglobulin framework that are critical contributors for polyspecificity in germline antibodies. Computational design of antibodies capable of binding multiple antigens may allow the rational design of antibodies that retain polyspecificity for diverse epitope binding.</p> </div

Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You

The objective of this review is to enable researchers to use the software package Rosetta for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with Rosetta. For each of these six tasks, we provide a tutorial that illustrates a basic Rosetta protocol. The Rosetta method was originally developed for <i>de novo</i> protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 Å. More impressively, there are several cases in which Rosetta has been used to predict structures with atomic level accuracy better than 2.5 Å. In addition to <i>de novo</i> structure prediction, Rosetta also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. Rosetta has been used to accurately design a novel protein structure, predict the structure of protein−protein complexes, design altered specificity protein−protein and protein−DNA interactions, and stabilize proteins and protein complexes. Most recently, Rosetta has been used to solve the X-ray crystallographic phase problem

Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You

The objective of this review is to enable researchers to use the software package Rosetta for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with Rosetta. For each of these six tasks, we provide a tutorial that illustrates a basic Rosetta protocol. The Rosetta method was originally developed for <i>de novo</i> protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 Å. More impressively, there are several cases in which Rosetta has been used to predict structures with atomic level accuracy better than 2.5 Å. In addition to <i>de novo</i> structure prediction, Rosetta also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. Rosetta has been used to accurately design a novel protein structure, predict the structure of protein−protein complexes, design altered specificity protein−protein and protein−DNA interactions, and stabilize proteins and protein complexes. Most recently, Rosetta has been used to solve the X-ray crystallographic phase problem