Tomasz Kosciolek

MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins

Motivation: Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Results: Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts—around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. Availability and implementation: MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Contact: d.t.jones@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts

The advent of high accuracy residue-residue intra-protein contact prediction methods enabled a significant boost in the quality of de novo structure predictions. Here, we investigate the potential benefits of combining a well-established fragment-based folding algorithm – FRAGFOLD, with PSICOV, a contact prediction method which uses sparse inverse covariance estimation to identify co-varying sites in multiple sequence alignments. Using a comprehensive set of 150 diverse globular target proteins, up to 266 amino acids in length, we are able to address the effectiveness and some limitations of such approaches to globular proteins in practice. Overall we find that using fragment assembly with both statistical potentials and predicted contacts is significantly better than either statistical potentials or contacts alone. Results show up to nearly 80% of correct predictions (TM-score ≥0.5) within analysed dataset and a mean TM-score of 0.54. Unsuccessful modelling cases emerged either from conformational sampling problems, or insufficient contact prediction accuracy. Nevertheless, a strong dependency of the quality of final models on the fraction of satisfied predicted long-range contacts was observed. This not only highlights the importance of these contacts on determining the protein fold, but also (combined with other ensemble-derived qualities) provides a powerful guide as to the choice of correct models and the global quality of the selected model. A proposed quality assessment scoring function achieves 0.93 precision and 0.77 recall for the discrimination of correct folds on our dataset of decoys. These findings suggest the approach is well-suited for blind predictions on a variety of globular proteins of unknown 3D structure, provided that enough homologous sequences are available to construct a large and accurate multiple sequence alignment for the initial contact prediction step.

Protein binding site analysis by means of structural interaction fingerprint patterns.

We introduce a new approach to the known concept of interaction profiles, based on Structural Interaction Fingerprints (SIFt), for precise and rapid binding site description. A set of scripts for batch generation and analysis of SIFt were prepared, and the implementation is computationally efficient and supports parallelization. It is based on a 9-digit binary interaction pattern that describes physical ligand-protein interactions in structures and models of ligand-protein complexes. The tool performs analysis and identifies binding site residues (crucial and auxiliary) and classifies interactions according to type (hydrophobic, aromatic, charge, polar, side chain, and backbone). It is convenient and easy to use, and gives manageable output data for both, interpretation and further processing. In the presented Letter, SIFts are applied to analyze binding sites in models of antagonist-5-HT7 receptor complexes and structures of cyclin dependent kinase 2-ligand complexes.

Accurate contact predictions using covariation techniques and machine learning

Here we present the results of residue–residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effective sequences, our server achieved an average top‐L/5 long‐range contact precision of 27%. MetaPSICOV method bases on a combination of classical contact prediction features, enhanced with three distinct covariation methods embedded in a two‐stage neural network predictor. Some unique features of our approach are (1) the tuning between the classical and covariation features depending on the depth of the input alignment and (2) a hybrid approach to generate deepest possible multiple‐sequence alignments by combining jackHMMer and HHblits. We discuss the CONSIP2 pipeline, our results and show that where the method underperformed, the major factor was relying on a fixed set of parameters for the initial sequence alignments and not attempting to perform domain splitting as a preprocessing step. Proteins 2016; 84(Suppl 1):145–151.

Impact of Template Choice on Homology Model Efficiency in Virtual Screening

Homology modeling is a reliable method of predicting the three-dimensional structures of proteins that lack NMR or X-ray crystallographic data. It employs the assumption that a structural resemblance exists between closely related proteins. Despite the availability of many crystal structures of possible templates, only the closest ones are chosen for homology modeling purposes. To validate the aforementioned approach, we performed homology modeling of four serotonin receptors (5-HT1AR, 5-HT2AR, 5-HT6R, 5-HT7R) for virtual screening purposes, using 10 available G-Protein Coupled Receptors (GPCR) templates with diverse evolutionary distances to the targets, with various approaches to alignment construction and model building. The resulting models were further validated in two steps by means of ligand docking and enrichment calculation, using Glide software. The final quality of the models was determined in virtual screening-like experiments by the AUROC score of the resulting ROC curves. The outcome of this research showed that no correlation between sequence identity and model quality was found, leading to the conclusion that the closest phylogenetic relative is not always the best template for homology modeling.

Best practices for analysing microbiomes

Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.Complex microbial communities shape the dynamics of various environments. In this Review, Knight and colleagues discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets.

Overview and systematic review of studies of microbiome in schizophrenia and bipolar disorder

Schizophrenia and bipolar disorder are among the leading causes of disability, morbidity, and mortality worldwide. In addition to being serious mental illnesses, these disorders are associated with considerable systemic physiological dysfunction, including chronic inflammation and elevated oxidative stress. The advent of sophisticated sequencing techniques has led to a growing interest in the potential role of gut microbiota in human health and disease. Advances in this area have transformed our understanding of a number of medical conditions and have generated a new perspective suggesting that gut microbiota might be involved in the development and maintenance of brain/mental health. Animal models have demonstrated strong though indirect evidence for a contributory role of intestinal microbiota in psychiatric symptomatology and have linked the microbiome with neuropsychiatric conditions. We present a systematic review of clinical studies of microbiome in schizophrenia and bipolar disorder. The published literature has a number of limitations; however, the investigations suggest that these disorders are associated with reduced microbial diversity and show global community differences compared to non-psychiatric comparison samples. In some reports, specific microbial taxa were associated with clinical disease characteristics, including physical health, depressive and psychotic symptoms, and sleep, but little information on the functional potential of those community changes. Studies also suggest increased intestinal inflammation and permeability, which may be among the principal mechanisms by which microbial dysbiosis impacts systemic physiological functioning. We highlight gaps in the current literature and implications for diagnosis and therapeutic interventions, and outline future directions for microbiome research in psychiatry.

Gut Instinct: Creating Scientific Theories with Online Learners

Learners worldwide collectively spend millions of hours per week testing their skills on assignments with known answers. Might some of this time fruitfully be spent posing and exploring novel questions? This paper investigates an approach for learners to contribute scientific ideas. The Gut Instinct system embodies this approach, hosting online learning materials and invites learners to collaboratively brainstorm potential influences on peoples microbiome. A between-subjects experiment compared the performance of participants who engaged in just learning, just contributing, or a combination. Participants in the learning condition scored highest on a summative test. Participants in both the contribution and combined conditions generated novel, useful questions; there was not a significant difference between the two. Though participants in the combined condition both learned and contributed, this setting did not exhibit an additive benefit, such as better learning in the combined condition. These results highlight the promise and difficulty of double-bottom-line learning experiences.

Opportunities and limitations in applying coevolution-derived contacts to protein structure prediction

Abstract The prospect of identifying contacts in protein structures purely from aligned protein sequences has lured researchers for a long time, but progress has been modest until recently. Here, we reviewed the most successful methods for identifying structural contacts from sequence and how these methods differ and made an initial assessment of the overlap of predicted contacts by alternative approaches. We then discussed the limitations of these methods and possibilities for future development and highlighted the recent applications of contacts in tertiary structure prediction, identifying the residues at the interfaces of protein-protein interactions, and the use of these methods in disentangling alternative conformational states. Finally, we identified the current challenges in the field of contact prediction, concentrating on the limitations imposed by available data, dependencies on the sequence alignments, and possible future developments.

Homology modelling of metabotropic glutamate receptor 2

Many studies show involvement of metabotropic glutamate receptors (mGluRs) in synaptic excitation transudation. The mGluR family consists of eight proteins divided into three groups corresponding to sequence similarities, pharmacology and physiological role. These groups are: I (mGluR1, -5), II (mGluR2, -3) and III (mGluR4, -6, -7, -8). Group II lies in field of our interest due to its potential as therapeutic target for stroke and pain drugs. Primary goal of this research is to create viable virtual model of transmembrane domain of mGluR2 receptor capable of binding reference ligands. This model will be used for further research. Our approach is based on homology modeling. Rhodopsin crystal structure has been used as a template for creating mGluR2 models. Due to inconsistencies between sequence alignments found in literature our alignment has been prepared basing on experimental secondary structure prediction for mGluR1 and mGluR3 [1]. So created models were then tested against available mutational data [2,3] and by flexible docking of known active/inactive compounds.