Whereas large-scale efforts have rapidly advanced the understanding and practical impact of human genomic variation, the practical impact of variation is largely unexplored in the human microbiome. We therefore developed a framework for metagenomic variation analysis and applied it to 252 faecal metagenomes of 207 individuals from Europe and North America. Using 7.4 billion reads aligned to 101 reference species, we detected 10.3 million single nucleotide polymorphisms (SNPs), 107,991 short insertions/deletions, and 1,051 structural variants. The average ratio of non-synonymous to synonymous polymorphism rates of 0.11 was more variable between gut microbial species than across human hosts. Subjects sampled at varying time intervals exhibited individuality and temporal stability of SNP variation patterns, despite considerable composition changes of their gut microbiota. This indicates that individual-specific strains are not easily replaced and that an individual might have a unique metagenomic genotype, which may be exploitable for personalized diet or drug intake.

The breakthrough of next generation sequencing-technologies has enabled large-scale studies of natural microbial communities and the 16S rRNA genes have been widely used as a phylogenetic marker to study community structure. However, major limitations of this approach are that neither strain-level resolution nor genomic context of microorganisms can be provided. This information, however, is crucial to answer fundamental questions about the temporal stability and distinctiveness of natural microbial communities.

The application of functional proteomics to important model organisms with unsequenced genomes is restricted because of the limited ability to identify proteins by conventional mass spectrometry (MS) methods. Here we applied MS and sequence-similarity database searching strategies to characterize the Xenopus laevis microtubule-associated proteome. We identified over 40 unique, and many novel, microtubule-bound proteins, as well as two macromolecular protein complexes involved in protein translation. This finding was corroborated by electron microscopy showing the presence of ribosomes on spindles assembled from frog egg extracts. Taken together, these results suggest that protein translation occurs on the spindle during meiosis in the Xenopus oocyte. These findings were made possible due to the application of sequence-similarity methods, which extended mass spectrometric protein identification capabilities by 2-fold compared to conventional methods.

A large-scale analysis of protein isoforms arising from alternative splicing shows that alternative splicing tends to insert or delete complete protein domains more frequently than expected by chance, whereas disruption of domains and other structural modules is less frequent. If domain regions are disrupted, the functional effect, as predicted from 3D structure, is frequently equivalent to removal of the entire domain. Also, short alternative splicing events within domains, which might preserve folded structure, target functional residues more frequently than expected. Thus, it seems that positive selection has had a major role in the evolution of alternative splicing.

The evolution of enzymes and pathways is under debate. Recent studies show that recruitment of single enzymes from different pathways could be the driving force for pathway evolution. Other mechanisms of evolution, such as pathway duplication, enzyme specialization, de novo invention of pathways or retro-evolution of pathways, appear to be less abundant. Twenty percent of enzyme superfamilies are quite variable, not only in changing reaction chemistry or metabolite type but in changing both at the same time. These variable superfamilies account for nearly half of all known reactions. The most frequently occurring metabolites provide a helping hand for such changes because they can be accommodated by many enzyme superfamilies. Thus, a picture is emerging in which new pathways are evolving from central metabolites by preference, thereby keeping the overall topology of the metabolic network.

The accumulation of genome-wide information on single nucleotide polymorphisms in humans provides an unprecedented opportunity to detect the evolutionary forces responsible for heterogeneity of the level of genetic variability across loci. Previous studies have shown that history of recombination events has produced long haplotype blocks in the human genome, which contribute to this heterogeneity. Other factors, however, such as natural selection or the heterogeneity of mutation rates across loci, may also lead to heterogeneity of genetic variability. We compared synonymous and non-synonymous variability within human genes with their divergence from murine orthologs. We separately analyzed the non-synonymous variants predicted to damage protein structure or function and the variants predicted to be functionally benign. The predictions were based on comparative sequence analysis and, in some cases, on the analysis of protein structure. A strong correlation between non-synonymous, benign variability and non-synonymous human-mouse divergence suggests that selection played an important role in shaping the pattern of variability in coding regions of human genes. However, the lack of correlation between deleterious variability and evolutionary divergence shows that a substantial proportion of the observed non-synonymous single-nucleotide polymorphisms reduces fitness and never reaches fixation. Evolutionary and medical implications of the impact of selection on human polymorphisms are discussed.

Human single nucleotide polymorphisms (SNPs) represent the most frequent type of human population DNA variation. One of the main goals of SNP research is to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. Non-synonymous coding SNPs (nsSNPs) comprise a group of SNPs that, together with SNPs in regulatory regions, are believed to have the highest impact on phenotype. Here we present a World Wide Web server to predict the effect of an nsSNP on protein structure and function. The prediction method enabled analysis of the publicly available SNP database HGVbase, which gave rise to a dataset of nsSNPs with predicted functionality. The dataset was further used to compare the effect of various structural and functional characteristics of amino acid substitutions responsible for phenotypic display of nsSNPs. We also studied the dependence of selective pressure on the structural and functional properties of proteins. We found that in our dataset the selection pressure against deleterious SNPs depends on the molecular function of the protein, although it is insensitive to several other protein features considered. The strongest selective pressure was detected for proteins involved in transcription regulation.

MALDI-quadrupole time-of-flight mass spectrometry was applied to identify proteins from organisms whose genomes are still unknown. The identification was carried out by successively searching a sequence database-first with a peptide mass fingerprint, then with a packet of noninterpreted MS/MS spectra, and finally with peptide sequences obtained by automated interpretation of the MS/MS spectra. A "MS BLAST" homology searching protocol was developed to overcome specific limitations imposed by mass spectrometric data, such as the limited accuracy of de novo sequence predictions. This approach was tested in a small-scale proteomic project involving the identification of 15 bands of gel-separated proteins from the methylotrophic yeast Pichia pastoris, whose genome has not yet been sequenced and which is only distantly related to other fungi.

With the massive amount of sequence and structural data being produced, new avenues emerge for exploiting the information therein for applications in several fields. Fold distributions can be mapped onto entire genomes to learn about the nature of the protein universe and many of the interactions between proteins can now be predicted solely on the basis of the genomic context of their genes. Furthermore, by utilising the new incoming data on single nucleotide polymorphisms by mapping them onto three-dimensional structures of proteins, problems concerning population, medical and evolutionary genetics can be addressed.

Single nucleotide polymorphisms (SNPs) constitute the bulk of human genetic variation, occurring with an average density of approximately 1/1000 nucleotides of a genotype. SNPs are either neutral allelic variants or are under selection of various strengths, and the impact of SNPs on fitness remains unknown. Identification of SNPs affecting human phenotype, especially leading to risks of complex disorders, is one of the key problems of medical genetics. SNPs in protein-coding regions that cause amino acid variants (non-synonymous cSNPs) are most likely to affect phenotypes. We have developed a straightforward and reliable method based on physical and comparative considerations that estimates the impact of an amino acid replacement on the three-dimensional structure and function of the protein. We estimate that approximately 20% of common human non-synonymous SNPs damage the protein. The average minor allele frequency of such SNPs in our data set was two times lower than that of benign non-synonymous SNPs. The average human genotype carries approximately 10(3) damaging non-synonymous SNPs that together cause a substantial reduction in fitness.

Sequence weighting techniques are aimed at balancing redundant observed information from subsets of similar sequences in multiple alignments. Traditional approaches apply the same weight to all positions of a given sequence, hence equal efficiency of phylogenetic changes is assumed along the whole sequence. This restrictive assumption is not required for the new method PSIC (position-specific independent counts) described in this paper. The number of independent observations (counts) of an amino acid type at a given alignment position is calculated from the overall similarity of the sequences that share the amino acid type at this position with the help of statistical concepts. This approach allows the fast computation of position-specific sequence weights even for alignments containing hundreds of sequences. The PSIC approach has been applied to profile extraction and to the fold family assignment of protein sequences with known structures. Our method was shown to be very productive in finding distantly related sequences and more powerful than Hidden Markov Models or the profile methods in WiseTools and PSI-BLAST in many cases. The profile extraction routine is available on the WWW (http://www.bork.embl-heidelberg. de/PSIC or http://www.imb.ac.ru/PSIC).

Analysis of human genetic variation can shed light on the problem of the genetic basis of complex disorders. Nonsynonymous single nucleotide polymorphisms (SNPs), which affect the amino acid sequence of proteins, are believed to be the most frequent type of variation associated with the respective disease phenotype. Complete enumeration of nonsynonymous SNPs in the candidate genes will enable further association studies on panels of affected and unaffected individuals. Experimental detection of SNPs requires implementation of expensive technologies and is still far from being routine. Alternatively, SNPs can be identified by computational analysis of a publicly available expressed sequence tag (EST) database following experimental verification. We performed in silico analysis of amino acid variation for 471 of proteins with a documented history of experimental variation studies and with confirmed association with human diseases. This allowed us to evaluate the level of completeness of the current knowledge of nonsynonymous SNPs in well studied, medically relevant genes and to estimate the proportion of new variants which can be added with the help of computer-aided mining in EST databases. Our results suggest that approx. 50% of frequent nonsynonymous variants are already stored in public databases. Computational methods based on the scan of an EST database can add significantly to the current knowledge, but they are greatly limited by the size of EST databases and the nonuniform coverage of genes by ESTs. Nevertheless, a considerable number of new candidate nonsynonymous SNPs in genes of medical interest were found by EST screening procedure.

Homology search techniques based on the iterative PSI-BLAST method in combination with various filters for low sequence complexity are applied to assign folds to all Mycoplasma genitalium proteins. The resulting procedure (implemented as a web server) is able to predict at least one domain in 37% of these proteins automatically, with an estimated accuracy higher than 98%. Taking structural features such as coiled coil or transmembrane regions aside, folds can be assigned to more than half of the globular proteins in a bacterium just by iterative sequence comparison.

The parametric description of residue environments through solvent accessibility, backbone conformation, or pairwise residue-residue distances is the key to the comparison between amino acid types at protein sequence positions and residue locations in structural templates (condition of protein sequence-structure match). For the first time, the research results presented in this study clarify and allow to quantify, on a rigorous statistical basis, to what extent the amino acid type-specific distributions of commonly used environment parameters are discriminative with respect to the 20 amino acid types. Relying on the Bahadur theory, we estimate the probability of error in a single-sequence-structure alignment based on weak or absent discriminative power in a learning database of protein structure. We present the results for many residue environment variables and demonstrate that each fold description parameter is sensitive with respect to only a few amino acid types while indifferent to most of the other amino acid types. Even complex structural characteristics combining solvent-accessible surface area, backbone conformation, and pairwise distances distinguish only some amino acid types, whereas the others remain nondiscriminated. We find that the knowledge-based potentials currently in use treat especially Ala, Asp, Gln, His, Ser, Thr, and Tyr as essentially "average" amino acids. Thus, highly discriminative amino acid types define the alignment register in gapless sequence-structure alignments. The introduction of gaps leads to alignment ambiguities at sequence positions occupied by nondiscriminated amino acid types. Therefore, local sequence-structure alignments produced by techniques with gaps cannot be reliable. Conceptionally new and more sensitive environment parameters must be invented.