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

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Using RosettaLigand for Small Molecule Docking into Comparative Models

<div><p>Computational small molecule docking into comparative models of proteins is widely used to query protein function and in the development of small molecule therapeutics. We benchmark RosettaLigand docking into comparative models for nine proteins built during CASP8 that contain ligands. We supplement the study with 21 additional protein/ligand complexes to cover a wider space of chemotypes. During a full docking run in 21 of the 30 cases, RosettaLigand successfully found a native-like binding mode among the top ten scoring binding modes. From the benchmark cases we find that careful template selection based on ligand occupancy provides the best chance of success while overall sequence identity between template and target do not appear to improve results. We also find that binding energy normalized by atom number is often less than −0.4 in native-like binding modes.</p> </div

Backbone conformational differences between template and target can preclude successful docking.

<p>a) Super-imposition of 1SQA (target, green) on 1YBW (template, grey). The template has no ligand bound. Part of the template's backbone occludes the binding site. Major backbone conformational changes are needed to open the binding pocket. Selection of template with ligands similar to the target ligand pre-forms the binding site providing conserved binding motifs as seen for b) 1PB9 (target, green) on 2RC7 (template, grey), c) 2B1V (target, green) on 1QKN (template, grey), and d) 2QWE (target, green) on 1INF (template, grey). Note that the ligand analog often makes different contacts as in seen in both c) in which the phenyl group points to a different part of the pocket in the template as opposed to the target and d) in which the guanidinium head group occupies a different pocket in the binding site.</p

Presence of a ligand with a similar chemotype in template is more indicative of docking success than sequence identity.

<p>The figure displays docking success dependent on ligand occupation in template (x-axis) and binding site sequence identity (y-axis) in panels a) and b) and overall sequence identity in panels c) and d). Panels a) and c) classify success by energy difference (delta) between the top scoring native-like and best non-native binding mode: Circle delta <−2, Triangle −22. Panels b) and d) determine success by rank of the best scoring native-like binding mode: Circle rank = 1, Triangle rank < = 20, Dash rank >20.</p

Minimum I-RMSD and L-RMSD of native-like binding modes.

<p>I-RMSD is calculated over all heavy atoms within 5 Å of the small molecule in X-ray crystal structure. L-RMSD is calculated over heavy atoms in the small molecule. Cluster Rank is the rank order of the cluster from lowest binding energy to highest binding energy. Error describes the spatial orientation to the native binding mode if the rank 1 cluster is non-native. I = inverted binding mode, W = wrong conformation of ligand, T = Translation in Å, R = Rotation, C = Cofactors present in native which may influence binding mode. Lines in bold indicate lowest energy binding mode is native-like. Lines in italics indicate binding mode in top 10 lowest energy modes is native like. The chemical structures can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050769#pone.0050769.s001" target="_blank">Figure S1</a>.</p

Protein small molecule interface.

<p>RosettaLigand samples complexes by randomly translating, randomly rotating, and picking a random conformation for ligand inside the green sphere. The magenta color atoms are the protein atoms included in the interface RMSD (I-RMSD) calculation.</p

Comparison of best native and non-native binding modes.

<p>Native binding modes are green with red-dashed hydrogen bonds. Non-native binding modes are cyan with yellow-dashed hydrogen bonds. a) 1NJE Energy for the formation of hydrogen bonds discriminates the native binding mode from the best non-native binding mode. b) 1VFN Formation of hydrogen bonds is not sufficient in all cases to distinguish the native binding mode from non-native modes c) 1FD0 non-native binding modes may have deeper energy wells when forming hydrogen bonds with surface residues.</p

Native binding mode minima in RosettaLigand energy function.

<p>a) Energy function performance degrades in comparative models. The x-axis shows the relative depth of the native binding mode energy minima to the best score non-native binding mode found during docking. The y-axis plots the relative depth of the native binding mode in comparative models to the best scoring non-native model from docking. As expected the depth of the native binding mode energy wells decreases in comparative models. However native binding modes do not appear to score worse than non-native binding mode. b) Atom normalized binding energy and Δ Energy of the two best scoring clusters indicates the quality of docking results. A binding mode with an atom normalized binding energy of <−0.4 and a separation of <−0.5 REU is likely to be native-like. Circle native binding mode rank = 1, Triangle 120.</p