Who Watches the Watchmen? An Appraisal of Benchmarks for Multiple Sequence Alignment

Multiple sequence alignment (MSA) is a fundamental and ubiquitous technique in bioinformatics used to infer related residues among biological sequences. Thus alignment accuracy is crucial to a vast range of analyses, often in ways difficult to assess in those analyses. To compare the performance of different aligners and help detect systematic errors in alignments, a number of benchmarking strategies have been pursued. Here we present an overview of the main strategies--based on simulation, consistency, protein structure, and phylogeny--and discuss their different advantages and associated risks. We outline a set of desirable characteristics for effective benchmarking, and evaluate each strategy in light of them. We conclude that there is currently no universally applicable means of benchmarking MSA, and that developers and users of alignment tools should base their choice of benchmark depending on the context of application--with a keen awareness of the assumptions underlying each benchmarking strategy.Comment: Revie

ConSole: using modularity of contact maps to locate solenoid domains in protein structures.

BackgroundPeriodic proteins, characterized by the presence of multiple repeats of short motifs, form an interesting and seldom-studied group. Due to often extreme divergence in sequence, detection and analysis of such motifs is performed more reliably on the structural level. Yet, few algorithms have been developed for the detection and analysis of structures of periodic proteins.ResultsConSole recognizes modularity in protein contact maps, allowing for precise identification of repeats in solenoid protein structures, an important subgroup of periodic proteins. Tests on benchmarks show that ConSole has higher recognition accuracy as compared to Raphael, the only other publicly available solenoid structure detection tool. As a next step of ConSole analysis, we show how detection of solenoid repeats in structures can be used to improve sequence recognition of these motifs and to detect subtle irregularities of repeat lengths in three solenoid protein families.ConclusionsThe ConSole algorithm provides a fast and accurate tool to recognize solenoid protein structures as a whole and to identify individual solenoid repeat units from a structure. ConSole is available as a web-based, interactive server and is available for download at http://console.sanfordburnham.org

FFAS server: novel features and applications.

The Fold and Function Assignment System (FFAS) server [Jaroszewski et al. (2005) FFAS03: a server for profile-profile sequence alignments. Nucleic Acids Research, 33, W284-W288] implements the algorithm for protein profile-profile alignment introduced originally in [Rychlewski et al. (2000) Comparison of sequence profiles. Strategies for structural predictions using sequence information. Protein Science: a Publication of the Protein Society, 9, 232-241]. Here, we present updates, changes and novel functionality added to the server since 2005 and discuss its new applications. The sequence database used to calculate sequence profiles was enriched by adding sets of publicly available metagenomic sequences. The profile of a user's protein can now be compared with ∼20 additional profile databases, including several complete proteomes, human proteins involved in genetic diseases and a database of microbial virulence factors. A newly developed interface uses a system of tabs, allowing the user to navigate multiple results pages, and also includes novel functionality, such as a dotplot graph viewer, modeling tools, an improved 3D alignment viewer and links to the database of structural similarities. The FFAS server was also optimized for speed: running times were reduced by an order of magnitude. The FFAS server, http://ffas.godziklab.org, has no log-in requirement, albeit there is an option to register and store results in individual, password-protected directories. Source code and Linux executables for the FFAS program are available for download from the FFAS server

The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs

A Three-dimensional Deformable Brain Atlas for DBS Targeting. I. Methodology for Atlas Creation and Artifact Reduction.

BackgroundTargeting in deep brain stimulation (DBS) relies heavily on the ability to accurately localize particular anatomic brain structures. Direct targeting of subcortical structures has been limited by the ability to visualize relevant DBS targets.Methods and resultsIn this work, we describe the development and implementation, of a methodology utilized to create a three dimensional deformable atlas for DBS surgery. This atlas was designed to correspond to the print version of the Schaltenbrand-Bailey atlas structural contours. We employed a smoothing technique to reduce artifacts inherent in the print version.ConclusionsWe present the methodology used to create a three dimensional patient specific DBS atlas which may in the future be tested for clinical utility