Lars Malmström

Structural probing of a protein phosphatase 2A network by chemical cross-linking and mass spectrometry.

Dynamic Assembly Structural characterization of protein complexes has yielded significant insight into biological function; however, most structural techniques require stable, homogenous samples. This presents a challenge in characterizing transient signaling complexes. Herzog et al. (p. 1348) used chemical cross-linking and mass spectroscopy (XL-MS) to characterize the modular and dynamic interaction network involving phosphatase 2A (PP2A), which interacts with tens of regulatory and adaptor proteins in diverse signaling pathways. They found 176 interprotein and 569 intraprotein distance restraints that delineated the topology of the network. The study establishes the importance of XL-MS in the suite of structural methods used to characterize dynamic assemblies. Spatial restraints revealed by chemical cross-linking and mass spectrometry elucidate the topology of a dynamic signaling network. The identification of proximate amino acids by chemical cross-linking and mass spectrometry (XL-MS) facilitates the structural analysis of homogeneous protein complexes. We gained distance restraints on a modular interaction network of protein complexes affinity-purified from human cells by applying an adapted XL-MS protocol. Systematic analysis of human protein phosphatase 2A (PP2A) complexes identified 176 interprotein and 570 intraprotein cross-links that link specific trimeric PP2A complexes to a multitude of adaptor proteins that control their cellular functions. Spatial restraints guided molecular modeling of the binding interface between immunoglobulin binding protein 1 (IGBP1) and PP2A and revealed the topology of TCP1 ring complex (TRiC) chaperonin interacting with the PP2A regulatory subunit 2ABG. This study establishes XL-MS as an integral part of hybrid structural biology approaches for the analysis of endogenous protein complexes.

Automated prediction of CASP‐5 structures using the Robetta server

Robetta is a fully automated protein structure prediction server that uses the Rosetta fragment‐insertion method. It combines template‐based and de novo structure prediction methods in an attempt to produce high quality models that cover every residue of a submitted sequence. The first step in the procedure is the automatic detection of the locations of domains and selection of the appropriate modeling protocol for each domain. For domains matched to a homolog with an experimentally characterized structure by PSI‐BLAST or Pcons2, Robetta uses a new alignment method, called K*Sync, to align the query sequence onto the parent structure. It then models the variable regions by allowing them to explore conformational space with fragments in fashion similar to the de novo protocol, but in the context of the template. When no structural homolog is available, domains are modeled with the Rosetta de novo protocol, which allows the full length of the domain to explore conformational space via fragment‐insertion, producing a large decoy ensemble from which the final models are selected. The Robetta server produced quite reasonable predictions for targets in the recent CASP‐5 and CAFASP‐3 experiments, some of which were at the level of the best human predictions. Proteins 2003;53:524–533.

Assigning Function to Yeast Proteins by Integration of Technologies

Interpreting genome sequences requires the functional analysis of thousands of predicted proteins, many of which are uncharacterized and without obvious homologs. To assess whether the roles of large sets of uncharacterized genes can be assigned by targeted application of a suite of technologies, we used four complementary protein-based methods to analyze a set of 100 uncharacterized but essential open reading frames (ORFs) of the yeast Saccharomyces cerevisiae. These proteins were subjected to affinity purification and mass spectrometry analysis to identify copurifying proteins, two-hybrid analysis to identify interacting proteins, fluorescence microscopy to localize the proteins, and structure prediction methodology to predict structural domains or identify remote homologies. Integration of the data assigned function to 48 ORFs using at least two of the Gene Ontology (GO) categories of biological process, molecular function, and cellular component; 77 ORFs were annotated by at least one method. This combination of technologies, coupled with annotation using GO, is a powerful approach to classifying genes.

OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

Hannes L. Rost, 2, ∗ George Rosenberger, 2, ∗ Pedro Navarro, Ludovic Gillet, Sasa M. Miladinovic, 3 Olga T. Schubert, 2 Witold Wolski, Ben C. Collins, Johan Malmstrom, Lars Malmstrom, and Ruedi Aebersold 6, 7, † Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, CH-8093 Zurich, Switzerland Ph.D. Program in Systems Biology, University of Zurich and ETH Zurich, CH-8057 Zurich, Switzerland Biognosys AG, CH-8952 Schlieren, Switzerland SyBIT project of SystemsX.ch, ETH Zurich, CH-8092 Zurich, Switzerland Department of Immunotechnology, Lund University, S-22100 Lund, Sweden Competence Center for Systems Physiology and Metabolic Diseases, CH-8093 Zurich, Switzerland Faculty of Science, University of Zurich, CH-8057 Zurich, Switzerland (Dated: October 19, 2015)

De Novo Prediction of Three-dimensional Structures for Major Protein Families

We use the Rosetta de novo structure prediction method to produce three-dimensional structure models for all Pfam-A sequence families with average length under 150 residues and no link to any protein of known structure. To estimate the reliability of the predictions, the method was calibrated on 131 proteins of known structure. For approximately 60% of the proteins one of the top five models was correctly predicted for 50 or more residues, and for approximately 35%, the correct SCOP superfamily was identified in a structure-based search of the Protein Data Bank using one of the models. This performance is consistent with results from the fourth critical assessment of structure prediction (CASP4). Correct and incorrect predictions could be partially distinguished using a confidence function based on a combination of simulation convergence, protein length and the similarity of a given structure prediction to known protein structures. While the limited accuracy and reliability of the method precludes definitive conclusions, the Pfam models provide the only tertiary structure information available for the 12% of publicly available sequences represented by these large protein families.

Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home

We describe predictions made using the Rosetta structure prediction methodology for both template‐based modeling and free modeling categories in the Seventh Critical Assessment of Techniques for Protein Structure Prediction. For the first time, aggressive sampling and all‐atom refinement could be carried out for the majority of targets, an advance enabled by the Rosetta@home distributed computing network. Template‐based modeling predictions using an iterative refinement algorithm improved over the best existing templates for the majority of proteins with less than 200 residues. Free modeling methods gave near‐atomic accuracy predictions for several targets under 100 residues from all secondary structure classes. These results indicate that refinement with an all‐atom energy function, although computationally expensive, is a powerful method for obtaining accurate structure predictions. Proteins 2007.

Prediction of CASP6 structures using automated Robetta protocols.

The Robetta server and revised automatic protocols were used to predict structures for CASP6 targets. Robetta is a publicly available protein structure prediction server (http://robetta.bakerlab.org/ that uses the Rosetta de novo and homology modeling structure prediction methods. We incorporated some of the lessons learned in the CASP5 experiment into the server prior to participating in CASP6. We additionally tested new ideas that were amenable to full‐automation with an eye toward improving the server. We find that the Robetta server shows the greatest promise for the more challenging targets. The most significant finding from CASP5, that automated protocols can be roughly comparable in ability with the better human‐intervention predictors, is repeated here in CASP6. Proteins 2005;Suppl 7:157–166.

Free modeling with Rosetta in CASP6

We describe Rosetta predictions in the Sixth Community‐Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP), focusing on the free modeling category. Methods developed since CASP5 are described, and their application to selected targets is discussed. Highlights include improved performance on larger proteins (100–200 residues) and the prediction of a 70‐residue alpha–beta protein to near‐atomic resolution. Proteins 2005;Suppl 7:128–134.

Automated prediction of domain boundaries in CASP6 targets using Ginzu and RosettaDOM

Domain boundary prediction is an important step in both experimental and computational protein structure characterization. We have developed two fully automated domain parsing methods: the first, Ginzu, which we have described previously, utilizes information from homologous sequences and structures, while the second, RosettaDOM, which has not been described previously, uses only information in the query sequence. Ginzu iteratively assigns domains by homology to structures and sequence families using successively less confident methods. RosettaDOM uses the Rosetta de novo structure prediction method to build three‐dimensional models, and then applies Taylors structure based domain assignment method to parse the models into domains. Domain boundaries observed repeatedly in the models are predicted to be domain boundaries for the protein. Interestingly, RosettaDOM produced quite good domain predictions for proteins of a size typically considered to be beyond the reach of de novo structure prediction methods. For remote fold recognition targets and new folds, both Ginzu and RosettaDOM produced promising results, and in some cases where one method failed to detect the correct domain boundary, it was correctly identified by the other method. We describe here the successes and failures using both methods, and address the possibility of incorporating both protocols into an improved hybrid method. Proteins 2005;Suppl 7:193–200.

An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics

We present a computational pipeline for the quantification of peptides and proteins in label-free LC-MS/MS data sets. The pipeline is composed of tools from the OpenMS software framework and is applicable to the processing of large experiments (50+ samples). We describe several enhancements that we have introduced to OpenMS to realize the implementation of this pipeline. They include new algorithms for centroiding of raw data, for feature detection, for the alignment of multiple related measurements, and a new tool for the calculation of peptide and protein abundances. Where possible, we compare the performance of the new algorithms to that of their established counterparts in OpenMS. We validate the pipeline on the basis of two small data sets that provide ground truths for the quantification. There, we also compare our results to those of MaxQuant and Progenesis LC-MS, two popular alternatives for the analysis of label-free data. We then show how our software can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.