ProQM-resample: improved model quality assessment for membrane proteins by limited conformational sampling

Intestinal serotonin and fluoxetine exposure modulate bacterial colonization in the gut

The gut microbiota regulates levels of serotonin (5-hydroxytryptamine (5-HT)) in the intestinal epithelium and lumen^1-5. However, whether 5-HT plays a functional role in bacteria from the gut microbiota remains unknown. We demonstrate that elevating levels of intestinal lumenal 5-HT by oral supplementation or genetic deficiency in the host 5-HT transporter (SERT) increases the relative abundance of spore-forming members of the gut microbiota, which were previously reported to promote host 5-HT biosynthesis. Within this microbial community, we identify Turicibacter sanguinis as a gut bacterium that expresses a neurotransmitter sodium symporter-related protein with sequence and structural homology to mammalian SERT. T. sanguinis imports 5-HT through a mechanism that is inhibited by the selective 5-HT reuptake inhibitor fluoxetine. 5-HT reduces the expression of sporulation factors and membrane transporters in T. sanguinis, which is reversed by fluoxetine exposure. Treating T. sanguinis with 5-HT or fluoxetine modulates its competitive colonization in the gastrointestinal tract of antibiotic-treated mice. In addition, fluoxetine reduces the membership of T. sanguinis in the gut microbiota of conventionally colonized mice. Host association with T. sanguinis alters intestinal expression of multiple gene pathways, including those important for lipid and steroid metabolism, with corresponding reductions in host systemic triglyceride levels and inguinal adipocyte size. Together, these findings support the notion that select bacteria indigenous to the gut microbiota signal bidirectionally with the host serotonergic system to promote their fitness in the intestine.

Structural Model of the ETR1 Ethylene Receptor Transmembrane Sensor Domain

The structure, mechanism of action and copper stoichiometry of the transmembrane sensor domain of the plant ethylene receptor ETR1 and homologs have remained elusive, hampering the understanding on how the perception of the plant hormone ethylene is transformed into a downstream signal. We generated the first structural model of the transmembrane sensor domain of ETR1 by integrating ab initio structure prediction and coevolutionary information. To refine and independently validate the model, we determined protein-related copper stoichiometries on purified receptor preparations and explored the helix arrangement by tryptophan scanning mutagenesis. All-atom molecular dynamics simulations of the dimeric model reveal how ethylene can bind proximal to the copper ions in the receptor, illustrating the initial stages of the ethylene perception process.

MyPMFs: a simple tool for creating statistical potentials to assess protein structural models

Evaluating the model quality of protein structures that evolve in environments with particular physicochemical properties requires scoring functions that are adapted to their specific residue compositions and/or structural characteristics. Thus, computational methods developed for structures from the cytosol cannot work properly on membrane or secreted proteins. Here, we present MyPMFs, an easy-to-use tool that allows users to train statistical potentials of mean force (PMFs) on the protein structures of their choice, with all parameters being adjustable. We demonstrate its use by creating an accurate statistical potential for transmembrane protein domains. We also show its usefulness to study the influence of the physical environment on residue interactions within protein structures. Our open-source software is freely available for download at https://github.com/bibip-impmc/mypmfs.

TMPL: a database of experimental and theoretical transmembrane protein models positioned in the lipid bilayer

Knowing the position of protein structures within the membrane is crucial for fundamental and applied research in the field of molecular biology. Only few web resources propose coordinate files of oriented transmembrane proteins, and these exclude predicted structures, although they represent the largest part of the available models. In this article, we present TMPL (http://www.dsimb.inserm.fr/TMPL/), a database of transmembrane protein structures (α-helical and β-sheet) positioned in the lipid bilayer. It is the first database to include theoretical models of transmembrane protein structures, making it a large repository with more than 11 000 entries. The TMPL database also contains experimentally solved protein structures, which are available as either atomistic or coarse-grained models. A unique feature of TMPL is the possibility for users to update the database by uploading, through an intuitive web interface, the membrane assignments they can obtain with our recent OREMPRO web server.

Sorting protein decoys by machine-learning-to-rank

Much progress has been made in Protein structure prediction during the last few decades. As the predicted models can span a broad range of accuracy spectrum, the accuracy of quality estimation becomes one of the key elements of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, and these methods could be roughly divided into three categories: the single-model methods, clustering-based methods and quasi single-model methods. In this study, we develop a single-model method MQAPRank based on the learning-to-rank algorithm firstly, and then implement a quasi single-model method Quasi-MQAPRank. The proposed methods are benchmarked on the 3DRobot and CASP11 dataset. The five-fold cross-validation on the 3DRobot dataset shows the proposed single model method outperforms other methods whose outputs are taken as features of the proposed method, and the quasi single-model method can further enhance the performance. On the CASP11 dataset, the proposed methods also perform well compared with other leading methods in corresponding categories. In particular, the Quasi-MQAPRank method achieves a considerable performance on the CASP11 Best150 dataset.

A Hybrid Approach to Structure and Function Modeling of G Protein-Coupled Receptors

The recent GPCR Dock 2013 assessment of serotonin receptor 5-HT1B and 5-HT2B, and smoothened receptor SMO targets, exposed the strengths and weaknesses of the currently used computational approaches. The test cases of 5-HT1B and 5-HT2B demonstrated that both the receptor structure and the ligand binding mode can be predicted with the atomic-detail accuracy, as long as the target-template sequence similarity is relatively high. On the other hand, the observation of a low target-template sequence similarity, e.g., between SMO from the frizzled GPCR family and members of the rhodopsin family, hampers the GPCR structure prediction and ligand docking. Indeed, in GPCR Dock 2013, accurate prediction of the SMO target was still beyond the capabilities of most research groups. Another bottleneck in the current GPCR research, as demonstrated by the 5-HT2B target, is the reliable prediction of global conformational changes induced by activation of GPCRs. In this work, we report details of our protocol used during GPCR Dock 2013. Our structure prediction and ligand docking protocol was especially successful in the case of 5-HT1B and 5-HT2B-ergotamine complexes for which we provide one of the most accurate predictions. In addition to a description of the GPCR Dock 2013 results, we propose a novel hybrid computational methodology to improve GPCR structure and function prediction. This computational methodology employs two separate rankings for filtering GPCR models. The first ranking is ligand-based while the second is based on the scoring scheme of the recently published BCL method. In this work, we prove that the use of knowledge-based potentials implemented in BCL is an efficient way to cope with major bottlenecks in the GPCR structure prediction. Thereby, we also demonstrate that the knowledge-based potentials for membrane proteins were significantly improved, because of the recent surge in available experimental structures.

ProQ2: estimation of model accuracy implemented in Rosetta

Motivation: Model quality assessment programs are used to predict the quality of modeled protein structures. They can be divided into two groups depending on the information they are using: ensemble methods using consensus of many alternative models and methods only using a single model to do its prediction. The consensus methods excel in achieving high correlations between prediction and true quality measures. However, they frequently fail to pick out the best possible model, nor can they be used to generate and score new structures. Single-model methods on the other hand do not have these inherent shortcomings and can be used both to sample new structures and to improve existing consensus methods.

Results: Here, we present an implementation of the ProQ2 program to estimate both local and global model accuracy as part of the Rosetta modeling suite. The current implementation does not only make it possible to run large batch runs locally, but it also opens up a whole new arena for conformational sampling using machine learned scoring functions and to incorporate model accuracy estimation in to various existing modeling schemes. ProQ2 participated in CASP11 and results from CASP11 are used to benchmark the current implementation. Based on results from CASP11 and CAMEO-QE, a continuous benchmark of quality estimation methods, it is clear that ProQ2 is the single-model method that performs best in both local and global model accuracy.

Availability and implementation:https://github.com/bjornwallner/ProQ_scripts

Contact:bjornw@ifm.liu.se

Supplementary information:Supplementary data are available at Bioinformatics online.

Membrane positioning for high- and low-resolution protein structures through a binary classification approach

The critical importance of algorithms for orienting proteins in the lipid bilayer stems from the extreme difficulty in obtaining experimental data about the membrane boundaries. Here, we present a computational method for positioning protein structures in the membrane, based on the sole alpha carbon coordinates and, therefore, compatible with both high and low structural resolutions. Our algorithm follows a new and simple approach, by treating the membrane assignment problem as a binary classification. Compared with the state-of-the-art algorithms, our method achieves similar accuracy, while being faster. Finally, our open-source software is also capable of processing coarse-grained models of protein structures.

ProTSAV: A protein tertiary structure analysis and validation server

Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. The server is validated on ~64,446 protein structures including experimental structures from RCSB and predicted model structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures and achieves a specificity of 88%and a sensitivity of 91% on predicted protein structures of CASP11 targets under 2Å.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp.

ResQ: An Approach to Unified Estimation of B-Factor and Residue-Specific Error in Protein Structure Prediction

Computer-based structure prediction becomes a major tool to provide large-scale structure models for annotating biological function of proteins. Information of residue-level accuracy and thermal mobility (or B-factor), which is critical to decide how biologists utilize the predicted models, is however missed in most structure prediction pipelines. We developed ResQ for unified residue-level model quality and B-factor estimations by combining local structure assembly variations with sequence-based and structure-based profiling. ResQ was tested on 635 non-redundant proteins with structure models generated by I-TASSER, where the average difference between estimated and observed distance errors is 1.4Å for the confidently modeled proteins. ResQ was further tested on structure decoys from CASP9-11 experiments, where the error of local structure quality prediction is consistently lower than or comparable to other state-of-the-art predictors. Finally, ResQ B-factor profile was used to assist molecular replacement, which resulted in successful solutions on several proteins that could not be solved from constant B-factor settings.