SimShiftDB; local conformational restraints derived from chemical shift similarity searches on a large synthetic database

A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core

The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) is a free, one-stop web service for protein bioinformatic analysis. It currently offers 34 interconnected external and in-house tools, whose functionality covers sequence similarity searching, alignment construction, detection of sequence features, structure prediction, and sequence classification. This breadth has made the Toolkit an important resource for experimental biology and for teaching bioinformatic inquiry. Recently, we replaced the first version of the Toolkit, which was released in 2005 and had served around 2.5 million queries, with an entirely new version, focusing on improved features for the comprehensive analysis of proteins, as well as on promoting teaching. For instance, our popular remote homology detection server, HHpred, now allows pairwise comparison of two sequences or alignments and offers additional profile HMMs for several model organisms and domain databases. Here, we introduce the new version of our Toolkit and its application to the analysis of proteins.

The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis

The MPI Bioinformatics Toolkit (http://toolkit.tuebingen.mpg.de) is an open, interactive web service for comprehensive and collaborative protein bioinformatic analysis. It offers a wide array of interconnected, state-of-the-art bioinformatics tools to experts and non-experts alike, developed both externally (e.g. BLAST+, HMMER3, MUSCLE) and internally (e.g. HHpred, HHblits, PCOILS). While a beta version of the Toolkit was released 10 years ago, the current production-level release has been available since 2008 and has serviced more than 1.6 million external user queries. The usage of the Toolkit has continued to increase linearly over the years, reaching more than 400 000 queries in 2015. In fact, through the breadth of its tools and their tight interconnection, the Toolkit has become an excellent platform for experimental scientists as well as a useful resource for teaching bioinformatic inquiry to students in the life sciences. In this article, we report on the evolution of the Toolkit over the last ten years, focusing on the expansion of the tool repertoire (e.g. CS-BLAST, HHblits) and on infrastructural work needed to remain operative in a changing web environment.

Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks

A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed (ϕ, ψ) torsion angles of ca 12º. TALOS-N also reports sidechain χ(1) rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.

Mechanism of regulation of receptor histidine kinases

Bacterial transmembrane receptors regulate an intracellular catalytic output in response to extracellular sensory input. To investigate the conformational changes that relay the regulatory signal, we have studied the HAMP domain, a ubiquitous intracellular module connecting input to output domains. HAMP forms a parallel, dimeric, four-helical coiled coil, and rational substitutions in our model domain (Af1503 HAMP) induce a transition in its interhelical packing, characterized by axial rotation of all four helices (the gearbox signaling model). We now illustrate how these conformational changes are propagated to a downstream domain by fusing Af1503 HAMP variants to the DHp domain of EnvZ, a bacterial histidine kinase. Structures of wild-type and mutant constructs are correlated with ligand response in vivo, clearly associating them with distinct signaling states. We propose that altered recognition of the catalytic domain by DHp, rather than a shift in position of the phospho-accepting histidine, forms the basis for regulation of kinase activity.

Effective techniques for protein structure mining

Retrieval and characterization of protein structure relationships are instrumental in a wide range of tasks in structural biology. The classification of protein structures (COPS) is a web service that provides efficient access to structure and sequence similarities for all currently available protein structures. Here, we focus on the application of COPS to the problem of template selection in homology modeling.

Optimized measurement temperature gives access to the solution structure of a 49 kDa homohexameric β-propeller

Ph1500 is a homohexameric, two-domain protein of unknown function from the hyperthermophilic archaeon Pyrococcus horikoshii. The C-terminal hexamerization domain (Ph1500C) is of particular interest, as it lacks sequence homology to proteins of known structure. However, it resisted crystallization for X-ray analysis, and proteins of this size (49 kDa) present a considerable challenge to NMR structure determination in solution. We solved the high-resolution structure of Ph1500C, exploiting the hyperthermophilic nature of the protein to minimize unfavorable relaxation properties by high-temperature measurement. Thus, the side chain assignment (97%) and structure determination became possible at full proton density. To our knowledge, Ph1500C is the largest protein for which this has been achieved. To minimize detrimental fast water exchange of amide protons at increased temperature, we employed a strategy where the temperature was optimized separately for backbone and side chain experiments.

4D prediction of protein (1)H chemical shifts

A 4D approach for protein (1)H chemical shift prediction was explored. The 4th dimension is the molecular flexibility, mapped using molecular dynamics simulations. The chemical shifts were predicted with a principal component model based on atom coordinates from a database of 40 protein structures. When compared to the corresponding non-dynamic (3D) model, the 4th dimension improved prediction by 6-7%. The prediction method achieved RMS errors of 0.29 and 0.50 ppm for Halpha and HN shifts, respectively. However, for individual proteins the RMS errors were 0.17-0.34 and 0.34-0.65 ppm for the Halpha and HN shifts, respectively. X-ray structures gave better predictions than the corresponding NMR structures, indicating that chemical shifts contain invaluable information about local structures. The (1)H chemical shift prediction tool 4DSPOT is available from http://www.uku.fi/kemia/4dspot .

Biophysical characterizations of human mitochondrial transcription factor A and its binding to tumor suppressor p53

Human mitochondrial transcription factor A (TFAM) is a multi-functional protein, involved in different aspects of maintaining mitochondrial genome integrity. In this report, we characterized TFAM and its interaction with tumor suppressor p53 using various biophysical methods. DNA-free TFAM is a thermally unstable protein that is in equilibrium between monomers and dimers. Self-association of TFAM is modulated by its basic C-terminal tail. The DNA-binding ability of TFAM is mainly contributed by its first HMG-box, while the second HMG-box has low-DNA-binding capability. We also obtained backbone resonance assignments from the NMR spectra of both HMG-boxes of TFAM. TFAM binds primarily to the N-terminal transactivation domain of p53, with a K_d of 1.95 ± 0.19 μM. The C-terminal regulatory domain of p53 provides a secondary binding site for TFAM. The TFAM–p53-binding interface involves both TAD1 and TAD2 sub-domains of p53. Helices α1 and α2 of the HMG-box constitute the main p53-binding region. Since both TFAM and p53 binds preferentially to distorted DNA, the TFAM–p53 interaction is implicated in DNA damage and repair. In addition, the DNA-binding mechanism of TFAM and biological relevance of the TFAM–p53 interaction are discussed.

CheckShift improved: fast chemical shift reference correction with high accuracy

The construction of a consistent protein chemical shift database is an important step toward making more extensive use of this data in structural studies. Unfortunately, progress in this direction has been hampered by the quality of the available data, particularly with respect to chemical shift referencing, which is often either inaccurate or inconsistently annotated. Preprocessing of the data is therefore required to detect and correct referencing errors. In an earlier study we developed CheckShift, a program for performing this task automatically. Now we spent substantial effort in improving the running time of the CheckShift algorithm, which resulted in an running time decrease of 90%, thereby achieving equivalent quality to the former version of CheckShift. The reason for the running time decrease is twofold. Firstly we improved the search for the optimal re-referencing offset considerably. Secondly, as CheckShift is based on a secondary structure prediction from the amino acid sequence (formally PsiPred was used), we evaluated a wide range of available secondary structure prediction programs focusing on the special needs of the CheckShift algorithm. The results of this evaluation prove empirically that we can use faster secondary structure prediction programs than PsiPred without sacrificing CheckShift's accuracy. Very recently Wang and Markley (2009) gave a small list of extreme outliers of the former version of the CheckShift web-server. Those were due to the empirical reduction of the search space implemented in the old version. The new version of CheckShift now gives very similar results to RefDB and LACS for all outliers mentioned in Table 1 of Wang and Markley (2009).