Frequency of gaps observed in a structurally aligned protein pair database suggests a simple gap penalty function

nPoRe: n-polymer realigner for improved pileup-based variant calling

Despite recent improvements in nanopore basecalling accuracy, germline variant calling of small insertions and deletions (INDELs) remains poor. Although precision and recall for single nucleotide polymorphisms (SNPs) now exceeds 99.5%, INDEL recall remains below 80% for standard R9.4.1 flow cells. We show that read phasing and realignment can recover a significant portion of false negative INDELs. In particular, we extend Needleman-Wunsch affine gap alignment by introducing new gap penalties for more accurately aligning repeated n-polymer sequences such as homopolymers ([Formula: see text]) and tandem repeats ([Formula: see text]). At the same precision, haplotype phasing improves INDEL recall from 63.76 to [Formula: see text] and nPoRe realignment improves it further to [Formula: see text].

New Approximate Statistical Significance of Gapped Alignments Based on the Greedy Extension Model

Sequence alignment is a fundamental concept in bioinformatics to distinguish regions of similarity among various sequences. The degree of similarity has been considered as a score. There are a number of various methods to find the statistical significance of similarity in the gapped and ungapped cases. In this article, we improve the statistical significance accuracy of the local score by introducing a new approximate p-value. This is developed according to Poisson clumping and the exact distribution of a partial sum of random variables. The efficiency of the proposed method is compared with that of previous methods on real and simulated data. The results yield a remarkable improvement in accuracy of the p-value in the gapped case. This is an evidence for the method to be considered as a prospective candidate for sequences comparison.

Dynamic Programming Used to Align Protein Structures with a Spectrum Is Robust

Several efficient algorithms to conduct pairwise comparisons among large databases of protein structures have emerged in the recent literature. The central theme is the design of a measure between the C_α atoms of two protein chains, from which dynamic programming is used to compute an alignment. The efficiency and efficacy of these algorithms allows large-scale computational studies that would have been previously impractical. The computational study herein shows that the structural alignment algorithm eigen-decomposition alignment with the spectrum (EIGAs) is robust against both parametric and structural variation.

Using structure to explore the sequence alignment space of remote homologs

Protein structure modeling by homology requires an accurate sequence alignment between the query protein and its structural template. However, sequence alignment methods based on dynamic programming (DP) are typically unable to generate accurate alignments for remote sequence homologs, thus limiting the applicability of modeling methods. A central problem is that the alignment that is “optimal” in terms of the DP score does not necessarily correspond to the alignment that produces the most accurate structural model. That is, the correct alignment based on structural superposition will generally have a lower score than the optimal alignment obtained from sequence. Variations of the DP algorithm have been developed that generate alternative alignments that are “suboptimal” in terms of the DP score, but these still encounter difficulties in detecting the correct structural alignment. We present here a new alternative sequence alignment method that relies heavily on the structure of the template. By initially aligning the query sequence to individual fragments in secondary structure elements and combining high-scoring fragments that pass basic tests for “modelability”, we can generate accurate alignments within a small ensemble. Our results suggest that the set of sequences that can currently be modeled by homology can be greatly extended.

It has been suggested that, for nearly every protein sequence, there is already a protein with a similar structure in current protein structure databases. However, with poor or undetectable sequence relationships, it is expected that accurate alignments and models cannot be generated. Here we show that this is not the case, and that whenever structural relationship exists, there are usually local sequence relationships that can be used to generate an accurate alignment, no matter what the global sequence identity. However, this requires an alternative to the traditional dynamic programming algorithm and the consideration of a small ensemble of alignments. We present an algorithm, S4, and demonstrate that it is capable of generating accurate alignments in nearly all cases where a structural relationship exists between two proteins. Our results thus constitute an important advance in the full exploitation of the information in structural databases. That is, the expectation of an accurate alignment suggests that a meaningful model can be generated for nearly every sequence for which a suitable template exists.

Patterns of insertion and deletion in Mammalian genomes

Nucleotide insertions and deletions (indels) are responsible for gaps in the sequence alignments. Indel is one of the major sources of evolutionary change at the molecular level. We have examined the patterns of insertions and deletions in the 19 mammalian genomes, and found that deletion events are more common than insertions in the mammalian genomes. Both the number of insertions and deletions decrease rapidly when the gap length increases and single nucleotide indel is the most frequent in all indel events. The frequencies of both insertions and deletions can be described well by power law.

The construction and use of log-odds substitution scores for multiple sequence alignment

Most pairwise and multiple sequence alignment programs seek alignments with optimal scores. Central to defining such scores is selecting a set of substitution scores for aligned amino acids or nucleotides. For local pairwise alignment, substitution scores are implicitly of log-odds form. We now extend the log-odds formalism to multiple alignments, using Bayesian methods to construct "BILD" ("Bayesian Integral Log-odds") substitution scores from prior distributions describing columns of related letters. This approach has been used previously only to define scores for aligning individual sequences to sequence profiles, but it has much broader applicability. We describe how to calculate BILD scores efficiently, and illustrate their uses in Gibbs sampling optimization procedures, gapped alignment, and the construction of hidden Markov model profiles. BILD scores enable automated selection of optimal motif and domain model widths, and can inform the decision of whether to include a sequence in a multiple alignment, and the selection of insertion and deletion locations. Other applications include the classification of related sequences into subfamilies, and the definition of profile-profile alignment scores. Although a fully realized multiple alignment program must rely upon more than substitution scores, many existing multiple alignment programs can be modified to employ BILD scores. We illustrate how simple BILD score based strategies can enhance the recognition of DNA binding domains, including the Api-AP2 domain in Toxoplasma gondii and Plasmodium falciparum.

Improving the performance of protein threading using insertion/deletion frequency arrays

As a protein evolves, not every part of the amino acid sequence has an equal probability of being deleted or for allowing insertions, because not every amino acid plays an equally important role in maintaining the protein structure. However, the most prevalent models in fold recognition methods treat every amino acid deletion and insertion as equally probable events. We have analyzed the alignment patterns for homologous and analogous sequences to determine patterns of insertion and deletion, and used that information to determine the statistics of insertions and deletions for different amino acids of a target sequence. We define these patterns as insertion/deletion (indel) frequency arrays (IFAs). By applying IFAs to the protein threading problem, we have been able to improve the alignment accuracy, especially for proteins with low sequence identity. We have also demonstrated that the application of this information can lead to an improvement in fold recognition.

SP5: improving protein fold recognition by using torsion angle profiles and profile-based gap penalty model

How to recognize the structural fold of a protein is one of the challenges in protein structure prediction. We have developed a series of single (non-consensus) methods (SPARKS, SP(2), SP(3), SP(4)) that are based on weighted matching of two to four sequence and structure-based profiles. There is a robust improvement of the accuracy and sensitivity of fold recognition as the number of matching profiles increases. Here, we introduce a new profile-profile comparison term based on real-value dihedral torsion angles. Together with updated real-value solvent accessibility profile and a new variable gap-penalty model based on fractional power of insertion/deletion profiles, the new method (SP(5)) leads to a robust improvement over previous SP method. There is a 2% absolute increase (5% relative improvement) in alignment accuracy over SP(4) based on two independent benchmarks. Moreover, SP(5) makes 7% absolute increase (22% relative improvement) in success rate of recognizing correct structural folds, and 32% relative improvement in model accuracy of models within the same fold in Lindahl benchmark. In addition, modeling accuracy of top-1 ranked models is improved by 12% over SP(4) for the difficult targets in CASP 7 test set. These results highlight the importance of harnessing predicted structural properties in challenging remote-homolog recognition. The SP(5) server is available at http://sparks.informatics.iupui.edu.

Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre

Structural and functional annotation of the large and growing database of genomic sequences is a major problem in modern biology. Protein structure prediction by detecting remote homology to known structures is a well-established and successful annotation technique. However, the broad spectrum of evolutionary change that accompanies the divergence of close homologues to become remote homologues cannot easily be captured with a single algorithm. Recent advances to tackle this problem have involved the use of multiple predictive algorithms available on the Internet. Here we demonstrate how such ensembles of predictors can be designed in-house under controlled conditions and permit significant improvements in recognition by using a concept taken from protein loop energetics and applying it to the general problem of 3D clustering. We have developed a stringent test that simulates the situation where a protein sequence of interest is submitted to multiple different algorithms and not one of these algorithms can make a confident (95%) correct assignment. A method of meta-server prediction (Phyre) that exploits the benefits of a controlled environment for the component methods was implemented. At 95% precision or higher, Phyre identified 64.0% of all correct homologous query-template relationships, and 84.0% of the individual test query proteins could be accurately annotated. In comparison to the improvement that the single best fold recognition algorithm (according to training) has over PSI-Blast, this represents a 29.6% increase in the number of correct homologous query-template relationships, and a 46.2% increase in the number of accurately annotated queries. It has been well recognised in fold prediction, other bioinformatics applications, and in many other areas, that ensemble predictions generally are superior in accuracy to any of the component individual methods. However there is a paucity of information as to why the ensemble methods are superior and indeed this has never been systematically addressed in fold recognition. Here we show that the source of ensemble power stems from noise reduction in filtering out false positive matches. The results indicate greater coverage of sequence space and improved model quality, which can consequently lead to a reduction in the experimental workload of structural genomics initiatives.

Comparison of protein active site structures for functional annotation of proteins and drug design

Rapid and accurate functional assignment of novel proteins is increasing in importance, given the completion of numerous genome sequencing projects and the vastly expanding list of unannotated proteins. Traditionally, global primary-sequence and structure comparisons have been used to determine putative function. These approaches, however, do not emphasize similarities in active site configurations that are fundamental to a protein's activity and highly conserved relative to the global and more variable structural features. The Comparison of Protein Active Site Structures (CPASS) database and software enable the comparison of experimentally identified ligand-binding sites to infer biological function and aid in drug discovery. The CPASS database comprises the ligand-defined active sites identified in the protein data bank, where the CPASS program compares these ligand-defined active sites to determine sequence and structural similarity without maintaining sequence connectivity. CPASS will compare any set of ligand-defined protein active sites, irrespective of the identity of the bound ligand.

Sequence comparison and protein structure prediction

Sequence comparison is a major step in the prediction of protein structure from existing templates in the Protein Data Bank. The identification of potentially remote homologues to be used as templates for modeling target sequences of unknown structure and their accurate alignment remain challenges, despite many years of study. The most recent advances have been in combining as many sources of information as possible--including amino acid variation in the form of profiles or hidden Markov models for both the target and template families, known and predicted secondary structures of the template and target, respectively, the combination of structure alignment for distant homologues and sequence alignment for close homologues to build better profiles, and the anchoring of certain regions of the alignment based on existing biological data. Newer technologies have been applied to the problem, including the use of support vector machines to tackle the fold classification problem for a target sequence and the alignment of hidden Markov models. Finally, using the consensus of many fold recognition methods, whether based on profile-profile alignments, threading or other approaches, continues to be one of the most successful strategies for both recognition and alignment of remote homologues. Although there is still room for improvement in identification and alignment methods, additional progress may come from model building and refinement methods that can compensate for large structural changes between remotely related targets and templates, as well as for regions of misalignment.

Variable gap penalty for protein sequence–structure alignment

The penalty for inserting gaps into an alignment between two protein sequences is a major determinant of the alignment accuracy. Here, we present an algorithm for finding a globally optimal alignment by dynamic programming that can use a variable gap penalty (VGP) function of any form. We also describe a specific function that depends on the structural context of an insertion or deletion. It penalizes gaps that are introduced within regions of regular secondary structure, buried regions, straight segments and also between two spatially distant residues. The parameters of the penalty function were optimized on a set of 240 sequence pairs of known structure, spanning the sequence identity range of 20-40%. We then tested the algorithm on another set of 238 sequence pairs of known structures. The use of the VGP function increases the number of correctly aligned residues from 81.0 to 84.5% in comparison with the optimized affine gap penalty function; this difference is statistically significant according to Student's t-test. We estimate that the new algorithm allows us to produce comparative models with an additional approximately 7 million accurately modeled residues in the approximately 1.1 million proteins that are detectably related to a known structure.