Prediction of protein domain boundaries from inverse covariances.

It has been known even since relatively few structures had been solved that longer protein chains often contain multiple domains, which may fold separately and play the role of reusable functional modules found in many contexts. In many structural biology tasks, in particular structure prediction, it is of great use to be able to identify domains within the structure and analyze these regions separately. However, when using sequence data alone this task has proven exceptionally difficult, with relatively little improvement over the naive method of choosing boundaries based on size distributions of observed domains. The recent significant improvement in contact prediction provides a new source of information for domain prediction. We test several methods for using this information including a kernel smoothing-based approach and methods based on building alpha-carbon models and compare performance with a length-based predictor, a homology search method and four published sequence-based predictors: DOMCUT, DomPRO, DLP-SVM, and SCOOBY-DOmain. We show that the kernel-smoothing method is significantly better than the other ab initio predictors when both single-domain and multidomain targets are considered and is not significantly different to the homology-based method. Considering only multidomain targets the kernel-smoothing method outperforms all of the published methods except DLP-SVM. The kernel smoothing method therefore represents a potentially useful improvement to ab initio domain prediction.

Prediction of protein contacts from correlated sequence substitutions.

Recent work has led to a substantial improvement in the accuracy of predictions of contacts between amino acids using evolutionary information derived from multiple sequence alignments. Where large numbers of diverse sequence relatives are available and can be aligned to the sequence of a protein of unknown structure, it is now possible to generate high-resolution models without recourse to the structure of a template. In this review, we describe these exciting new techniques and critically assess the state of the art in contact prediction in light of them. We discuss areas for immediate research and development as well as potential future developments.

FOXRED1, encoding an FAD-dependent oxidoreductase complex-I-specific molecular chaperone, is mutated in infantile-onset mitochondrial encephalopathy.

Complex I is the first and largest enzyme in the respiratory chain and is located in the inner mitochondrial membrane. Complex I deficiency is the most commonly reported mitochondrial disorder presenting in childhood, but the molecular basis of most cases remains elusive. We describe a patient with complex I deficiency caused by mutation of the molecular chaperone FOXRED1. A combined homozygosity mapping and bioinformatics approach in a consanguineous Iranian-Jewish pedigree led to the identification of a homozygous mutation in FOXRED1 in a child who presented with infantile-onset encephalomyopathy. Silencing of FOXRED1 in human fibroblasts resulted in reduced complex I steady-state levels and activity, while lentiviral-mediated FOXRED1 transgene expression rescued complex I deficiency in the patient fibroblasts. This FAD-dependent oxidoreductase, which has never previously been associated with human disease, is now shown to be a complex I-specific molecular chaperone. The discovery of the c.1054C>T; p.R352W mutation in the FOXRED1 gene is a further contribution towards resolving the complex puzzle of the genetic basis of human mitochondrial disease.

A nonsense mutation in COQ9 causes autosomal-recessive neonatal-onset primary coenzyme Q10 deficiency: a potentially treatable form of mitochondrial disease.

Coenzyme Q(10) is a mobile lipophilic electron carrier located in the inner mitochondrial membrane. Defects of coenzyme Q(10) biosynthesis represent one of the few treatable mitochondrial diseases. We genotyped a patient with primary coenzyme Q(10) deficiency who presented with neonatal lactic acidosis and later developed multisytem disease including intractable seizures, global developmental delay, hypertrophic cardiomyopathy, and renal tubular dysfunction. Cultured skin fibroblasts from the patient had a coenzyme Q(10) biosynthetic rate of 11% of normal controls and accumulated an abnormal metabolite that we believe to be a biosynthetic intermediate. In view of the rarity of coenzyme Q(10) deficiency, we hypothesized that the disease-causing gene might lie in a region of ancestral homozygosity by descent. Data from an Illumina HumanHap550 array were analyzed with BeadStudio software. Sixteen regions of homozygosity >1.5 Mb were identified in the affected infant. Two of these regions included the loci of two of 16 candidate genes implicated in human coenzyme Q(10) biosynthesis. Sequence analysis demonstrated a homozygous stop mutation affecting a highly conserved residue of COQ9, leading to the truncation of 75 amino acids. Site-directed mutagenesis targeting the equivalent residue in the yeast Saccharomyces cerevisiae abolished respiratory growth.

De novo backbone scaffolds for protein design.

In recent years, there have been significant advances in the field of computational protein design including the successful computational design of enzymes based on backbone scaffolds from experimentally solved structures. It is likely that large-scale sampling of protein backbone conformations will become necessary as further progress is made on more complicated systems. Removing the constraint of having to use scaffolds based on known protein backbones is a potential method of solving the problem. With this application in mind, we describe a method to systematically construct a large number of de novo backbone structures from idealized topological forms in a top-down hierarchical approach. The structural properties of these novel backbone scaffolds were analyzed and compared with a set of high-resolution experimental structures from the protein data bank (PDB). It was found that the Ramachandran plot distribution and relative gamma- and beta-turn frequencies were similar to those found in the PDB. The de novo scaffolds were sequence designed with RosettaDesign, and the energy distributions and amino acid compositions were comparable with the results for redesigned experimentally solved backbones.

pGenTHREADER and pDomTHREADER: new methods for improved protein fold recognition and superfamily discrimination.

MOTIVATION: Generation of structural models and recognition of homologous relationships for unannotated protein sequences are fundamental problems in bioinformatics. Improving the sensitivity and selectivity of methods designed for these two tasks therefore has downstream benefits for many other bioinformatics applications. RESULTS: We describe the latest implementation of the GenTHREADER method for structure prediction on a genomic scale. The method combines profile-profile alignments with secondary-structure specific gap-penalties, classic pair- and solvation potentials using a linear combination optimized with a regression SVM model. We find this combination significantly improves both detection of useful templates and accuracy of sequence-structure alignments relative to other competitive approaches. We further present a second implementation of the protocol designed for the task of discriminating superfamilies from one another. This method, pDomTHREADER, is the first to incorporate both sequence and structural data directly in this task and improves sensitivity and selectivity over the standard version of pGenTHREADER and three other standard methods for remote homology detection.

An automatic method for assessing structural importance of amino acid positions.

BACKGROUND: A great deal is known about the qualitative aspects of the sequence-structure relationship, for example that buried residues are usually more conserved between structurally similar homologues, but no attempts have been made to quantitate the relationship between evolutionary conservation at a sequence position and change to global tertiary structure. In this paper we demonstrate that the Spearman correlation between sequence and structural change is suitable for this purpose. RESULTS: Buried residues, bends, cysteines, prolines and leucines were significantly more likely to occupy positions highly correlated with structural change than expected by chance. Some buried residues were found to be less informative than expected, particularly residues involved in active sites and the binding of small molecules. CONCLUSION: The correlation-based method generates predictions of structural importance for superfamily positions which agree well with previous results of manual analyses, and may be of use in automated residue annotation piplines. A PERL script which implements the method is provided.

Harnessing QbD, Programming Languages, and Automation for Reproducible Biology.

Building robust manufacturing processes from biological components is a task that is highly complex and requires sophisticated tools to describe processes, inputs, and measurements and administrate management of knowledge, data, and materials. We argue that for bioengineering to fully access biological potential, it will require application of statistically designed experiments to derive detailed empirical models of underlying systems. This requires execution of large-scale structured experimentation for which laboratory automation is necessary. This requires development of expressive, high-level languages that allow reusability of protocols, characterization of their reliability, and a change in focus from implementation details to functional properties. We review recent developments in these areas and identify what we believe is an exciting trend that promises to revolutionize biotechnology.

Prediction of contacts from correlated sequence substitutions.

Recent work has led to a substantial improvement in the accuracy of predictions of contacts between amino acids using evolutionary information derived from multiple sequence alignments. Where large numbers of diverse sequence relatives are available and can be aligned to the sequence of a protein of unknown structure it is now possible to generate high-resolution models without recourse to the structure of a template. In this review we describe these exciting new techniques and critically assess the state-of-the-art in contact prediction in the light of these. While concentrating on methods, we also discuss applications to protein and RNA structure prediction as well as potential future developments.