Profile analysis

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

As personal genome sequencing becomes a reality, understanding the effects of genetic variants on phenotype-particularly the impact of germline variants on disease risk and the impact of somatic variants on cancer development and treatment-continues to increase in importance. Because of their clear potential for affecting phenotype, nonsynonymous genetic variants (variants that cause a change in the amino acid sequence of a protein encoded by a gene) have long been the target of efforts to predict the effects of genetic variation. Whole-genome sequencing is identifying large numbers of nonsynonymous variants in each genome, intensifying the need for computational methods that accurately predict which of these are likely to impact disease phenotypes. This review focuses on nonsynonymous variant prediction with two aims in mind: (1) to review the prioritization methods that have been developed to date and the principles on which they are based and (2) to discuss the challenges to further improving these methods.

Alternative approach to protein structure prediction based on sequential similarity of physical properties

The relationship between protein sequence and structure arises entirely from amino acid physical properties. An alternative method is therefore proposed to identify homologs in which residue equivalence is based exclusively on the pairwise physical property similarities of sequences. This approach, the property factor method (PFM), is entirely different from those in current use. A comparison is made between our method and PSI BLAST. We demonstrate that traditionally defined sequence similarity can be very low for pairs of sequences (which therefore cannot be identified using PSI BLAST), but similarity of physical property distributions results in almost identical 3D structures. The performance of PFM is shown to be better than that of PSI BLAST when sequence matching is comparable, based on a comparison using targets from CASP10 (89 targets) and CASP11 (51 targets). It is also shown that PFM outperforms PSI BLAST in informatically challenging targets.

Computational insights into the binding modes of Sr-Rex with cofactor NADH/NAD+ and operator DNA

The transcriptional repressor Rex plays key roles in modulating respiratory gene expression. It senses the redox poise of the NAD(H) pool. Rex from Streptomyces rimosus (Sr-Rex) is a newly identified protein. Its structure and complex with substrates are not determined yet. In this study, the three-dimensional (3D) structural models of Sr-Rex dimer and its complex with cofactors were constructed by homology modeling. The stability of the constructed Sr-Rex models and the detailed interactions between Sr-Rex and cofactors were further investigated by molecular dynamics simulations. The results demonstrated that the conformation of Sr-Rex changed a lot when binding with the reduced NADH or oxidized NAD(+). Once binding with NADH, the Sr-Rex dimer displayed an opener conformation, which would weaken the interaction of Sr-Rex with Rex operator DNA (ROP). Key residues responsible for the binding were then identified. The computational results were consistent with experimental results, and hence provided insights into the molecular mechanism of Sr-Rex binding with ROP and NADH/NAD(+), which might be helpful for the development of biosensor.

Physicochemical property consensus sequences for functional analysis, design of multivalent antigens and targeted antivirals

Background

Analysis of large sets of biological sequence data from related strains or organisms is complicated by superficial redundancy in the set, which may contain many members that are identical except at one or two positions. Thus a new method, based on deriving physicochemical property (PCP)-consensus sequences, was tested for its ability to generate reference sequences and distinguish functionally significant changes from background variability.

Methods

The PCP consensus program was used to automatically derive consensus sequences starting from sequence alignments of proteins from Flaviviruses (from the Flavitrack database) and human enteroviruses, using a five dimensional set of Eigenvectors that summarize over 200 different scalar values for the PCPs of the amino acids. A PCP-consensus protein of a Dengue virus envelope protein was produced recombinantly and tested for its ability to bind antibodies to strains using ELISA.

Results

PCP-consensus sequences of the flavivirus family could be used to classify them into five discrete groups and distinguish areas of the envelope proteins that correlate with host specificity and disease type. A multivalent Dengue virus antigen was designed and shown to bind antibodies against all four DENV types. A consensus enteroviral VPg protein had the same distinctive high pKa as wild type proteins and was recognized by two different polymerases.

Conclusions

The process for deriving PCP-consensus sequences for any group of aligned similar sequences, has been validated for sequences with up to 50% diversity. Ongoing projects have shown that the method identifies residues that significantly alter PCPs at a given position, and might thus cause changes in function or immunogenicity. Other potential applications include deriving target proteins for drug design and diagnostic kits.

Simplifying complex sequence information: a PCP-consensus protein binds antibodies against all four Dengue serotypes

Designing proteins that reflect the natural variability of a pathogen is essential for developing novel vaccines and drugs. Flaviviruses, including Dengue (DENV) and West Nile (WNV), evolve rapidly and can "escape" neutralizing monoclonal antibodies by mutation. Designing antigens that represent many distinct strains is important for DENV, where infection with a strain from one of the four serotypes may lead to severe hemorrhagic disease on subsequent infection with a strain from another serotype. Here, a DENV physicochemical property (PCP)-consensus sequence was derived from 671 unique sequences from the Flavitrack database. PCP-consensus proteins for domain 3 of the envelope protein (EdomIII) were expressed from synthetic genes in Escherichia coli. The ability of the purified consensus proteins to bind polyclonal antibodies generated in response to infection with strains from each of the four DENV serotypes was determined. The initial consensus protein bound antibodies from DENV-1-3 in ELISA and Western blot assays. This sequence was altered in 3 steps to incorporate regions of maximum variability, identified as significant changes in the PCPs, characteristic of DENV-4 strains. The final protein was recognized by antibodies against all four serotypes. Two amino acids essential for efficient binding to all DENV antibodies are part of a discontinuous epitope previously defined for a neutralizing monoclonal antibody. The PCP-consensus method can significantly reduce the number of experiments required to define a multivalent antigen, which is particularly important when dealing with pathogens that must be tested at higher biosafety levels.

AlignHUSH: alignment of HMMs using structure and hydrophobicity information

Background

Sensitive remote homology detection and accurate alignments especially in the midnight zone of sequence similarity are needed for better function annotation and structural modeling of proteins. An algorithm, AlignHUSH for HMM-HMM alignment has been developed which is capable of recognizing distantly related domain families The method uses structural information, in the form of predicted secondary structure probabilities, and hydrophobicity of amino acids to align HMMs of two sets of aligned sequences. The effect of using adjoining column(s) information has also been investigated and is found to increase the sensitivity of HMM-HMM alignments and remote homology detection.

Results

We have assessed the performance of AlignHUSH using known evolutionary relationships available in SCOP. AlignHUSH performs better than the best HMM-HMM alignment methods and is observed to be even more sensitive at higher error rates. Accuracy of the alignments obtained using AlignHUSH has been assessed using the structure-based alignments available in BaliBASE. The alignment length and the alignment quality are found to be appropriate for homology modeling and function annotation. The alignment accuracy is found to be comparable to existing methods for profile-profile alignments.

Conclusions

A new method to align HMMs has been developed and is shown to have better sensitivity at error rates of 10% and above when compared to other available programs. The proposed method could effectively aid obtaining clues to functions of proteins of yet unknown function.

A web-server incorporating the AlignHUSH method is available at http://crick.mbu.iisc.ernet.in/~alignhush/

Prioritizing predicted cis-regulatory elements for co-expressed gene sets based on Lasso regression models

Computational prediction of cis-regulatory elements for a set of co-expressed genes based on sequence analysis provides an overwhelming volume of potential transcription factor binding sites. It presents a challenge to prioritize transcription factors for regulatory functional studies. A novel approach based on the use of Lasso regression models is proposed to address this problem. We examine the ability of the Lasso model using time-course microarray data obtained from a comprehensive study of gene expression profiles in skin and mucosal wounds in mouse over all stages of wound healing.

Alignment of protein sequences by their profiles

The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences.

tacg--a grep for DNA

BACKGROUND

Pattern matching is the core of bioinformatics; it is used in database searching, restriction enzyme mapping, and finding open reading frames. It is done repeatedly over increasingly long sequences, thus codes must be efficient and insensitive to sequence length. Such patterns of interest include simple motifs with IUPAC degeneracies, regular expressions, patterns allowing mismatches, and probability matrices.

RESULTS

I describe a small application which allows searching for all the above pattern types individually, which further allows these atomic motifs to be assembled into logical rules for more sophisticated analysis.

CONCLUSION

tacg is small, portable, faster and more capable than most alternatives, relatively easy to modify, and freely available in source code.

Cloning and characterization of the mammalian brain-specific, Mg2+-dependent neutral sphingomyelinase

The enzymatic breakdown of sphingomyelin by sphingomyelinases is considered the major source of the second messenger ceramide. Studies on the contribution of the various described acidic and neutral sphingomyelinases to the signaling pool of ceramide have been hampered by the lack of molecular data on the neutral sphingomyelinases (nSMases). We recently identified a mammalian nSMase, an integral membrane protein with remote similarity to bacterial sphingomyelinases. However, its ubiquitous expression pattern is in contrast to previous findings that sphingomyelinase activity is found mainly in brain tissues. By using an improved database search method, combined with phylogenetic analysis, we identified a second mammalian nSMase (nSMase2) with predominant expression in the brain. The sphingomyelinase activity of nSMase2 has a neutral pH optimum, depends on Mg(2+) ions, and is activated by unsaturated fatty acids and phosphatidylserine. Immunofluorescence reveals a neuron-specific punctate perinuclear staining, which colocalizes with a Golgi marker in a number of cell lines. The likely identity of nSMase2 with cca1, a rat protein involved in contact inhibition of 3Y1 fibroblasts, suggests a role for this enzyme in cell cycle arrest. Both mammalian nSMases are members of a superfamily of Mg(2+)-dependent phosphohydrolases, which also contains nucleases, inositol phosphatases, and bacterial toxins.

OOTFD (Object-Oriented Transcription Factors Database): an object-oriented successor to TFD

ooTFD (object-oriented Transcription Factors Database) is a successor to TFD (Transcription Factors Database). ooTFD contains information represented in TFD but also allows the representation of containment, composite, and interaction relationships between transcription factor polypeptides. ooTFD is designed to represent information about all transcription factors, both eukaryotic and prokaryotic, basal as well as regulatory factors, and multiprotein complexes as well as monomers. ooTFD and associated tools and services can be accessed at http://www.isbi.net/

Extracting protein alignment models from the sequence database

Biologists often gain structural and functional insights into a protein sequence by constructing a multiple alignment model of the family. Here a program called Probe fully automates this process of model construction starting from a single sequence. Central to this program is a powerful new method to locate and align only those, often subtly, conserved patterns essential to the family as a whole. When applied to randomly chosen proteins, Probe found on average about four times as many relationships as a pairwise search and yielded many new discoveries. These include: an obscure subfamily of globins in the roundworm Caenorhabditis elegans ; two new superfamilies of metallohydrolases; a lipoyl/biotin swinging arm domain in bacterial membrane fusion proteins; and a DH domain in the yeast Bud3 and Fus2 proteins. By identifying distant relationships and merging families into superfamilies in this way, this analysis further confirms the notion that proteins evolved from relatively few ancient sequences. Moreover, this method automatically generates models of these ancient conserved regions for rapid and sensitive screening of sequences.