Improving your target-template alignment with MODalign

Summary: MODalign is an interactive web-based tool aimed at helping protein structure modelers to inspect and manually modify the alignment between the sequences of a target protein and of its template(s). It interactively computes, displays and, upon modification of the target-template alignment, updates the multiple sequence alignments of the two protein families, their conservation score, secondary structure and solvent accessibility values, and local quality scores of the implied three-dimensional model(s). Although it has been designed to simplify the target-template alignment step in modeling, it is suitable for all cases where a sequence alignment needs to be inspected in the context of other biological information. Availability and implementation: Freely available on the web at http://modorama.biocomputing.it/modalign. Website implemented in HTML and JavaScript with all major browsers supported. Contact: [email protected]

Improving your target-template alignment with MODalign

Summary: MODalign is an interactive web-based tool aimed at helping protein structure modelers to inspect and manually modify the alignment between the sequences of a target protein and of its template(s). It interactively computes, displays and, upon modification of the target-template alignment, updates the multiple sequence alignments of the two protein families, their conservation score, secondary structure and solvent accessibility values, and local quality scores of the implied three-dimensional model(s). Although it has been designed to simplify the target-template alignment step in modeling, it is suitable for all cases where a sequence alignment needs to be inspected in the context of other biological information

Viewing multiple sequence alignments with the JavaScript Sequence Alignment Viewer (JSAV)

The JavaScript Sequence Alignment Viewer (JSAV) is designed as a simple-to-use JavaScript component for displaying sequence alignments on web pages. The display of sequences is highly configurable with options to allow alternative coloring schemes, sorting of sequences and ’dotifying’ repeated amino acids. An option is also available to submit selected sequences to another web site, or to other JavaScript code. JSAV is implemented purely in JavaScript making use of the JQuery and JQuery-UI libraries. It does not use any HTML5-specific options to help with browser compatibility. The code is documented using JSDOC and is available from http://www.bioinf.org.uk/software/jsav/

Evaluation of MLH1 variants of unclear significance

Inactivating mutations in the MLH1 gene cause the cancer predisposition Lynch syndrome, but for small coding genetic variants it is mostly unclear if they are inactivating or not. Nine such MLH1 variants have been identified in South American colorectal cancer (CRC) patients (p.Tyr97Asp, p.His112Gln, p.Pro141Ala, p.Arg265Pro, p.Asn338Ser, p.Ile501del, p.Arg575Lys, p.Lys618del, p.Leu676Pro), and evidence of pathogenicity or neutrality was not available for the majority of these variants. We therefore performed biochemical laboratory testing of the variant proteins and compared the results to protein in silico predictions on structure and conservation. Additionally, we collected all available clinical information of the families to come to a conclusion concerning their pathogenic potential and facilitate clinical diagnosis in the affected families. We provide evidence that four of the alterations are causative for Lynch syndrome, four are likely neutral and one shows compromised activity which can currently not be classified with respect to its pathogenic potential. The work demonstrates that biochemical testing, corroborated by congruent evolutionary and structural information, can serve to reliably classify uncertain variants when other data are insufficient.Barretos Cancer Hospital was partially funded by FINEP‐CT‐INFRA, Grant Number: 02/2010, Radium Hospital Foundation (Oslo, Norway), Helse Sør‐Øst (Norway); Deutsche Forschungsgemeinschaft, Grant Number: PL688/2‐1info:eu-repo/semantics/publishedVersio

Efficient algorithms in protein modelling

Proteins are key players in the complex world of living cells. No matter whether they are involved in enzymatic reactions, inter-cell communication or numerous other processes, knowledge of their structure is vital for a detailed understanding of their function. However, structure determination by experiment is often a laborious process that cannot keep up with the ever increasing pace of sequencing methodologies. As a consequence, the gap between proteins where we only know the sequence and the proteins where we additionally have detailed structural information is growing rapidly. Computational modelling methods that extrapolate structural information from homologous structures have established themselves as a valuable complement to experiment and help bridging this gap. This thesis addresses two key aspects in protein modelling. (1) It investigates and improves methodologies that assign reliability estimates to protein models, so called quality estimation (QE) methods. Even a human expert cannot immediately detect errors introduced in the modelling process, thus the importance of automated methods performing this task. (2) It assesses the available methods that perform the modelling itself, discusses solutions for current shortcomings and provides efficient implementations thereof. When detecting errors in protein models, many knowledge based methods are biased towards the physio-chemical properties observed in soluble protein structures. This limits their applicability for the important class of membrane protein models. In an effort to improve the situation, QMEANBrane has been developed. QMEANBrane is specifically designed to detect local errors in membrane protein models by membrane specific statistical potentials of mean force that nowadays approach statistical saturation given the increase of available experimental data. Considering the improvement of quality estimation for soluble proteins, instead of solely applying the widely used statistical potentials of mean force, QMEANDisCo incorporates the observed structural variety of experimentally determined protein structures homologous to the model being assessed. Valuable ensemble information can be gathered without the need of actually depending on a large ensemble of protein models, thus circumventing a main limitation of consensus QE methods. Apart from improving QE methods, in an effort of implementing and extending state-of-the-art modelling algorithms, the lack of a free and efficient modelling engine became obvious. No available modelling engine provided an open-source codebase as a basis for novel, innovative algorithms and, at the same time, had no restrictions for usage. This contradicts our efforts to make protein modelling available to all biochemists and molecular biologists worldwide. As a consequence we implemented a new free and open modelling engine from scratch - ProMod3. ProMod3 allows to combine extremely efficient, state-of-the-art modelling algorithms in a flexible manner to solve various modelling problems. To weaken the dogma of one template one model, basic algorithms have been explored to incorporate structural information from multiple templates into one protein model. The algorithms are built using ProMod3 and have extensively been tested in the context of the CAMEO continuous evaluation platform. The result is a highly competitive modelling pipeline that excels with extremely low runtimes and excellent performance