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Protein-protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein-protein interactions and produce high-quality multimeric structural models.Application of our method to the Human and Yeast genomes yield protein-protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2’s non-structural protein 3 (Nsp3). We also produced models of SARS-CoV2’s spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor (MOG) and dipeptidyl peptidase-4 (DPP4). The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation.The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.

Genome-wide protein–protein interaction (PPI) determination remains a significant unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (HTEs) have often a relatively high false-positive rate in assigning PPIs, and PPI quaternary structures are more difficult to solve than tertiary structures using traditional structural biology techniques. We proposed a uniform pipeline, Threpp, to address both problems. Starting from a pair of monomer sequences, Threpp first threads both sequences through a complex structure library, where the alignment score is combined with HTE data using a naive Bayesian classifier model to predict the likelihood of two chains to interact with each other. Next, quaternary complex structures of the identified PPIs are constructed by reassembling monomeric alignments with dimeric threading frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs which is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks show a scale-free cluster size distribution, consistent with previous studies, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Furthermore, complex structure models were constructed for all predicted E. coli PPIs based on the quaternary threading alignments, where 6771 of them were found to have a high confidence score that corresponds to the correct fold of the complexes with a TM-score >0.5, and 39 showed a close consistency with the later released experimental structures with an average TM-score = 0.73. These results demonstrated the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.

Genome-wide protein-protein interaction (PPI) determination remains a significant unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (HTEs) have often a high false-positive rate in assigning PPIs, and PPI quaternary structures are more difficult to solve than tertiary structures using traditional structural biology techniques. We proposed a uniform pipeline to address both problems, which first recognizes PPIs by combining multi-chain threading alignments with HTE results using naïve Bayesian classifiers, where the quaternary complex structures are then constructed by mapping the monomer models with the dimeric threading frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs which is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks revealed a scale-free cluster size distribution, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Furthermore, complex structure models were constructed for all predicted E. coli PPIs based on the quaternary threading alignments, where 6,771 of them were found to have a high confidence score that corresponds to the correct fold of the complexes with a TM-score >0.5 and 93 showed a close consistency with the later released experimental structures with an average TM-score=0.73. These results demonstrated the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

A web service for analysis of protein structures that are sequentially or non-sequentially similar was generated. Recently, the non-sequential structure alignment algorithm GANGSTA+ was introduced. GANGSTA+ can detect non-sequential structural analogs for proteins stated to possess novel folds. Since GANGSTA+ ignores the polypeptide chain connectivity of secondary structure elements (i.e. alpha-helices and beta-strands), it is able to detect structural similarities also between proteins whose sequences were reshuffled during evolution. GANGSTA+ was applied in an all-against-all comparison on the ASTRAL40 database (SCOP version 1.75), which consists of >10,000 protein domains yielding about 55 x 10(6) possible protein structure alignments. Here, we provide the resulting protein structure alignments as a public web-based service, named GANGSTA+ Internet Services (GIS). We also allow to browse the ASTRAL40 database of protein structures with GANGSTA+ relative to an externally given protein structure using different constraints to select specific results. GIS allows us to analyze protein structure families according to the SCOP classification scheme. Additionally, users can upload their own protein structures for pairwise protein structure comparison, alignment against all protein structures of the ASTRAL40 database (SCOP version 1.75) or symmetry analysis. GIS is publicly available at http://agknapp.chemie.fu-berlin.de/gplus.

Finding and identifying circular permuted protein pairs (CPP) is one of the harder tasks for structure alignment programs, because of the different location of the break in the polypeptide chain connectivity. The protein structure alignment tool GANGSTA+ was used to search for CPPs in a database of nearly 10,000 protein structures. It also allows determination of the statistical significance of the occurrence of circular permutations in the protein universe. The number of detected CPPs was found to be higher than expected, raising questions about the evolutionary processes leading to CPPs. The GANGSTA+ protein structure alignment tool is available online via the web server at http://gangsta.chemie.fu-berlin.de. On the same webpage the complete data base of similar protein structure pairs based on the ASTRAL40 set of protein domains is provided and one can select CPPs specifically. Proteins 2010. © 2009 Wiley-Liss, Inc.

This paper describes a new semi-flexible docking approach named Fado ( f lexible a lignment and do cking), which incorporates flexibility by using an ensemble of precomputed ligand conformers. A primary ligand is defined as a linear combination over all input conformers. An optimization with regard to the linear coefficients makes the ligand flexible. Initially, a point matching problem utilizing the Merck Molecular Force Field (MMFF) is modeled in order to compute the correct orientation of the ligand with respect to the target. The problem is then solved through a local optimization approach (RPROP). This is done for 20 randomized ligand orientations, yielding 20 binding modes per complex. Evaluating these modes illustrates that our method is able to reproduce the binding modes of molecules within a few minutes of CPU time. A representative dataset of diverse protein–ligand complexes could be reproduced with 78% accuracy below 2 A RMSD distance to the reference crystal structure. Fado is available upon request to the authors (see also http://www.zib.eu/Numerik/projects/docking/projectlong.en.html ).

Due to the large number of available protein structure alignment algorithms, a lot of effort has been made to define robust measures to evaluate their performances and the quality of generated alignments. Most quality measures involve the number of aligned residues and the RMSD. In this work, we analyze how these two properties are influenced by different residue assignment strategies as employed in common non-sequential structure alignment algorithms. Therefore, we implemented different residue assignment strategies into our non-sequential structure alignment algorithm GANGSTA+. We compared the resulting numbers of aligned residues and RMSDs for each residue assignment strategy and different alignment algorithms on a benchmark set of circular-permuted protein pairs. Unfortunately, differences in the residue assignment strategies are often ignored when comparing the performances of different algorithms. However, our results clearly show that this may strongly bias the observations. Bringing residue assignment strategies in line can explain observed performance differences between entirely different alignment algorithms. Our results suggest that performance comparison of non-sequential protein structure alignment algorithms should be based on the same residue assignment strategy.

Insights in structural biology can be gained by analyzing protein architectures and characterizing their structural similarities. Current computational approaches enable a comparison of a variety of structural and physicochemical properties in protein space. Here we describe the automated detection of rotational symmetries within a representative set of nearly 10,000 nonhomologous protein structures. To find structural symmetries in proteins initially, equivalent pairs of secondary structure elements (SSE), i.e., alpha-helices and beta-strands, are assigned. Thereby, we also allow SSE pairs to be assigned in reverse sequential order. The results highlight that the generation of symmetric, i.e., repetitive, protein structures is one of nature's major strategies to explore the universe of possible protein folds. This way structurally separated 'islands' of protein folds with a significant amount of symmetry were identified. The complete results of the present study are available at http://agknapp.chemie.fu-berlin.de/gplus, where symmetry analysis of new protein structures can also be performed.

Protein-protein docking is a major task in structural biology. In general, the geometries of protein pairs are sampled by generating docked conformations, analyzing them with scoring functions and selecting appropriate geometries for further refinement. Here, we present an algorithm in real space to sample geometries of protein pairs. Therefore, we initially determine uniformly distributed points on the surfaces of the two protein structures to be docked and additionally define a set of uniformly distributed rotations. Then, the sampling method generates structures of protein pairs as follows: (i) We rotate one protein of the protein pair according to a selected rotation and (ii) translate it along a line connecting two surface points belonging to different proteins such that these surface points coincide. The resulting protein pair geometries are then analyzed and selected using a scoring function that considers residues and atom pairs. We applied this approach to a set of 22 enzyme-inhibitor complexes and demonstrate that a discretisation of the rigid-body search in real space provides an efficient and robust sampling scheme. Our method generates decoy sets with a considerable fraction of near-native geometries for all considered enzyme-inhibitor complexes.

Critical illness polyneuropathy (CIP), a neurologic complication which may occur secondary to surgery, trauma and coma, is associated with sepsis or multiple organ failure (MOF). CIP is characterized by an axonal distal degeneration of sensory and motor fibres. The patients will often become neurologically conspicious when weaning from mechanical ventilation is unexpectedly difficult. In such cases electrophysiologic examinations must be performed. CIP following cardiac surgery is widely unrecognized. The most important aspect of CIP therapy is treatment of the underlying disease, because no specific treatment for CIP exists. We report on a 64-year old patient who developed sepsis and CIP following cardiovascular surgery. The neurological complication was initially misinterpreted as hypoxic brain damage.

Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.