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 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.

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.

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.

Analytic markovian rates for generalized protein structure evolution.

A general understanding of the complex phenomenon of protein evolution requires the accurate description of the constraints that define the sub-space of proteins with mutations that do not appreciably reduce the fitness of the organism. Such constraints can have multiple origins, in this work we present a model for constrained evolutionary trajectories represented by a markovian process throughout a set of protein-like structures artificially constructed to be topological intermediates between the structure of two natural occurring proteins. The number and type of intermediate steps defines how constrained the total evolutionary process is. By using a coarse-grained representation for the protein structures, we derive an analytic formulation of the transition rates between each of the intermediate structures. The results indicate that compact structures with a high number of hydrogen bonds are more probable and have a higher likelihood to arise during evolution. Knowledge of the transition rates allows for the study of complex evolutionary pathways represented by trajectories through a set of intermediate structures.

Protein topology from predicted residue contacts.

Residue contacts predicted from correlated positions in a multiple sequence alignment are often sparse and uncertain. To some extent, these limitations in the data can be overcome by grouping the contacts by secondary structure elements and enumerating the possible packing arrangements of these elements in a combinatorial manner. Strong interactions appear frequently but inconsistent interactions are down-weighted and missing interactions up-weighted. The resulting improved consistency in the predicted interactions has allowed the method to be successfully applied to proteins up to 200 residues in length which is larger than any structure previously predicted using sequence data alone.

Structural constraints on the covariance matrix derived from multiple aligned protein sequences.

Residue contact predictions were calculated based on the mutual information observed between pairs of positions in large multiple protein sequence alignments. Where previously only the statistical properties of these data have been considered important, we introduce new measures to impose constraints that make the contact map more consistent with a three dimensional structure. These included global (bulk) properties and local secondary structure properties. The latter allowed the contact constraints to be employed at the level of filtering pairs of secondary structure contacts which led to a more efficient (lower-level) implementation in the PLATO structure prediction server. Where previously the measure of success with this method had been whether the correct fold was predicted in the top 10 ranked models, with the current implementation, our summary statistic is the number of correct folds included in the top 10 models--which is on average over 50 percent.

Direct correlation analysis improves fold recognition.

The extraction of correlated mutations through the method of direct information (DI) provides predicted contact residue pairs that can be used to constrain the three dimensional structures of proteins. We apply this method to a large set of decoy protein folds consisting of many thousand well-constructed models, only tens of which have the correct fold. We find that DI is able to greatly improve the ranking of the true (native) fold but others still remain high scoring that would be difficult to discard due to small shifts in the core beta sheets.

Exploring the limits of fold discrimination by structural alignment: a large scale benchmark using decoys of known fold.

Protein structure comparison by pairwise alignment is commonly used to identify highly similar substructures in pairs of proteins and provide a measure of structural similarity based on the size and geometric similarity of the match. These scores are routinely applied in analyses of protein fold space under the assumption that high statistical significance is equivalent to a meaningful relationship, however the truth of this assumption has previously been difficult to test since there is a lack of automated methods which do not rely on the same underlying principles. As a resolution to this we present a method based on the use of topological descriptions of global protein structure, providing an independent means to assess the ability of structural alignment to maintain meaningful structural correspondances on a large scale. Using a large set of decoys of specified global fold we benchmark three widely used methods for structure comparison, SAP, TM-align and DALI, and test the degree to which this assumption is justified for these methods. Application of a topological edit distance measure to provide a scale of the degree of fold change shows that while there is a broad correlation between high structural alignment scores and low edit distances there remain many pairs of highly significant score which differ by core strand swaps and therefore are structurally different on a global level. Possible causes of this problem and its meaning for present assessments of protein fold space are discussed.

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.

Re-evaluating the "rules" of protein topology.

It is well known that the set of observed topological arrangements of secondary structures in globular proteins is highly limited. These limitations have been explained as the consequence of several rules of thumb including a strong preference for right-handed connections, against crossing loops and certain beta strand patterns. We present a critical evaluation of the power of these rules to distinguish known from possible topologies in a large set of two- and three-layer protein structures and determine that although these rules are still largely valid, an increasing number of exceptions can be found to many of them. The rules are then used to construct a generalised linear model for assessing the probability of occurrence of an arbitrary topology in the PDB. Application of the model to a large set of topologies generated during structure prediction showed that many had a similar probability of occurrence to known PDB folds.

Protein structures, folds and fold spaces.

There has been considerable progress towards the goal of understanding the space of possible tertiary structures adopted by proteins. Despite a greatly increased rate of structure determination and a deliberate strategy of sequencing proteins expected to be very different from those already known, it is now rare to see a genuinely new fold, leading to the conclusion that we have seen the majority of natural structural types. The increase in knowledge has also led to a critical examination of traditional fold-based classifications and their meaning for evolution and protein structures. We review these issues and discuss possible solutions.

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.

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.

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.

The implications of alternative splicing in the ENCODE protein complement.

Alternative premessenger RNA splicing enables genes to generate more than one gene product. Splicing events that occur within protein coding regions have the potential to alter the biological function of the expressed protein and even to create new protein functions. Alternative splicing has been suggested as one explanation for the discrepancy between the number of human genes and functional complexity. Here, we carry out a detailed study of the alternatively spliced gene products annotated in the ENCODE pilot project. We find that alternative splicing in human genes is more frequent than has commonly been suggested, and we demonstrate that many of the potential alternative gene products will have markedly different structure and function from their constitutively spliced counterparts. For the vast majority of these alternative isoforms, little evidence exists to suggest they have a role as functional proteins, and it seems unlikely that the spectrum of conventional enzymatic or structural functions can be substantially extended through alternative splicing.

Genome-wide analysis of repressor element 1 silencing transcription factor/neuron-restrictive silencing factor (REST/NRSF) target genes.

The completion of whole genome sequencing projects has provided the genetic instructions of life. However, whereas the identification of gene coding regions has progressed, the mapping of transcriptional regulatory motifs has moved more slowly. To understand how distinct expression profiles can be established and maintained, a greater understanding of these sequences and their trans-acting factors is required. Herein we have used a combined in silico and biochemical approach to identify binding sites [repressor element 1/neuron-restrictive silencer element (RE1/NRSE)] and potential target genes of RE1 silencing transcription factor/neuron-restrictive silencing factor (REST/NRSF) within the human, mouse, and Fugu rubripes genomes. We have used this genome-wide analysis to identify 1,892 human, 1,894 mouse, and 554 Fugu RE1/NRSEs and present their location and gene linkages in a searchable database. Furthermore, we identified an in vivo hierarchy in which distinct subsets of RE1/NRSEs interact with endogenous levels of REST/NRSF, whereas others function as bona fide transcriptional control elements only in the presence of elevated levels of REST/NRSF. These data show that individual RE1/NRSE sites interact differentially with REST/NRSF within a particular cell type. This combined bioinformatic and biochemical approach serves to illustrate the selective manner in which a transcription factor interacts with its potential binding sites and regulates target genes. In addition, this approach provides a unique whole-genome map for a given transcription factor-binding site implicated in establishing specific patterns of neuronal gene expression.

Automated generation and refinement of protein signatures: case study with G-protein coupled receptors.

MOTIVATION: Previous work had established that it was possible to derive sparse signatures (essentially sequence-length motifs) by examining points of contact between residues in proteins of known three-dimensional (3D) structure. Many interesting protein families have very little tertiary structural information. Methods for deriving signatures using only primary and secondary-structural information were therefore developed. RESULTS: Two methods for deriving protein signatures using protein sequence information and predicted secondary structures are described. One method is based on a scoring approach, the other on the Genetic Algorithm (GA). The effectiveness of the method was tested on the superfamily of GPCRs and compared with the established hidden Markov model (HMM) method. The signature method is shown to perform well, detecting 68% of superfamily members before the first false positive sequence and detecting several distant relationships. The GA population was used to provide information on alignment regions of particular importance for selection of key residues.