The structure of genetic linkage data: from LIPED to 1M SNPs

There are three assumptions of independence or conditional independence that underlie linkage likelihood computations on sets of related individuals. The first is the independence of meioses, which gives rise to the conditional independence of haplotypes carried by offspring, given those of their parents. The second derives from the assumption of absence of genetic interference, which gives rise to the conditional independence of inheritance vectors, given the inheritance vector at an intermediate location. The third is the assumption of independence of allelic types, at the population level, both among haplotypes of unrelated individuals and also over the loci along a given haplotype. These three assumptions have been integral to likelihood computations since the first lod scores were computed, and remain key components in analysis of modern genetic data. In this paper we trace the role of these assumptions through the history of linkage likelihood computation, through to a new framework of genetic linkage analysis in the era of dense genomic marker data.     

Protein function from sequence and structure data

With the large amount of genomics and proteomics data that we are confronted with, computational support for the elucidation of protein function becomes more and more pressing. Many different kinds of biological data harbour signals of protein function, but these signals are often concealed. Computational methods that use protein sequence and structure data can be used for discovering these signals. They provide information that can substantially speed up experimental function elucidation. In this review we concentrate on such methods.     

Mapping membrane protein structure with fluorescence

Membrane proteins regulate many cellular processes including signaling cascades, ion transport, membrane fusion, and cell-to-cell communications. Understanding the architecture and conformational fluctuations of these proteins is critical to understanding their regulation and functions. Fluorescence methods including intensity mapping, fluorescence resonance energy transfer (FRET), and photo-induced electron transfer, allow for targeted measurements of domains within membrane proteins. These methods can reveal how a protein is structured and how it transitions between different conformational states. Here, I will review recent work done using fluorescence to map the structures of membrane proteins, focusing on how each of these methods can be applied to understanding the dynamic nature of individual membrane proteins and protein complexes.     

An Integrated Sequence-Structure Database incorporating matching mRNA sequence, amino acid sequence and protein three-dimensional structure data.

We have constructed a non-homologous database, termed the Integrated Sequence-Structure Database (ISSD) which comprises the coding sequences of genes, amino acid sequences of the corresponding proteins, their secondary structure and straight phi,psi angles assignments, and polypeptide backbone coordinates. Each protein entry in the database holds the alignment of nucleotide sequence, amino acid sequence and the PDB three-dimensional structure data. The nucleotide and amino acid sequences for each entry are selected on the basis of exact matches of the source organism and cell environment. The current version 1.0 of ISSD is available on the WWW at http://www.protein.bio.msu.su/issd/ and includes 107 non-homologous mammalian proteins, of which 80 are human proteins. The database has been used by us for the analysis of synonymous codon usage patterns in mRNA sequences showing their correlation with the three-dimensional structure features in the encoded proteins. Possible ISSD applications include optimisation of protein expression, improvement of the protein structure prediction accuracy, and analysis of evolutionary aspects of the nucleotide sequence-protein structure relationship.     

The insulin receptor: from protein sequence to structure

Sequences of the insulin receptor (IR), the type-I insulin-like growth-factor receptor (IGFR) and the insulin-receptor-related receptor show that they belong to a homologous family but, until recently, have given few clues about their structures. Three repeats of fibronectin type III have been identified close to the membrane. Although the N-terminal L1, Cys-rich and L2 domains of the IGFR have been identified from their sequences and their structures determined by X-ray crystallography, little is known of their relative positions in the complete receptor in vivo. Here, we ask what can be learnt further from the analysis of sequences, about the structure, organization and function of the extracellular regions of the IR family.     

Prediction of protein function from protein sequence and structure

The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.     

Predicting protein function from sequence and structure

While the number of sequenced genomes continues to grow, experimentally verified functional annotation of whole genomes remains patchy. Structural genomics projects are yielding many protein structures that have unknown function. Nevertheless, subsequent experimental investigation is costly and time-consuming, which makes computational methods for predicting protein function very attractive. There is an increasing number of noteworthy methods for predicting protein function from sequence and structural data alone, many of which are readily available to cell biologists who are aware of the strengths and pitfalls of each available technique.     

Compression of nucleic acid and protein sequence data

This paper describes the application of text compression methodsto machine-readable files of nucleic acid and protein sequencedata. Two main methods are used to reduce the storage requirementsof such files, these being n-gram coding and run-length coding.A Pascal program combining both of these techniques resultedin a compression figure of 74.6% for the GenBank database anda program that used only n-gram coding gave a compression figureof 42.8% for the Protein Identification Resource database. Received on November 29, 1985; accepted on February 24, 1986     

Calling SNPs without a reference sequence

Background  

The most common application for the next-generation sequencing technologies is resequencing, where short reads from the genome of an individual are aligned to a reference genome sequence for the same species. These mappings can then be used to identify genetic differences among individuals in a population, and perhaps ultimately to explain phenotypic variation. Many algorithms capable of aligning short reads to the reference, and determining differences between them have been reported. Much less has been reported on how to use these technologies to determine genetic differences among individuals of a species for which a reference sequence is not available, which drastically limits the number of species that can easily benefit from these new technologies.     

Mapping Insertions,Deletions and SNPs on Venter's Chromosomes

Background

The very recent availability of fully sequenced individual human genomes is a major revolution in biology which is certainly going to provide new insights into genetic diseases and genomic rearrangements.

Results

We mapped the insertions, deletions and SNPs (single nucleotide polymorphisms) that are present in Craig Venter''s genome, more precisely on chromosomes 17 to 22, and compared them with the human reference genome hg17. Our results show that insertions and deletions are almost absent in L1 and generally scarce in L2 isochore families (GC-poor L1+L2 isochores represent slightly over half of the human genome), whereas they increase in GC-rich isochores, largely paralleling the densities of genes, retroviral integrations and Alu sequences. The distributions of insertions/deletions are in striking contrast with those of SNPs which exhibit almost the same density across all isochore families with, however, a trend for lower concentrations in gene-rich regions.

Conclusions

Our study strongly suggests that the distribution of insertions/deletions is due to the structure of chromatin which is mostly open in gene-rich, GC-rich isochores, and largely closed in gene-poor, GC-poor isochores. The different distributions of insertions/deletions and SNPs are clearly related to the two different responsible mechanisms, namely recombination and point mutations.     

Mapping the distribution of conformational information throughout a protein sequence

The three-dimensional structure of protein is encoded in the sequence, but many amino acid residues carry no essential conformational information, and the identity of those that are structure-determining is elusive. By circular permutation and terminal deletion, we produced and purified 25 Bacillus licheniformis beta-lactamase (ESBL) variants that lack 5-21 contiguous residues each, and collectively have 82% of the sequence and 92% of the non-local atom-atom contacts eliminated. Circular dichroism and size-exclusion chromatography showed that most of the variants form conformationally heterogeneous mixtures, but by measuring catalytic constants, we found that all populate, to a greater or lesser extent, conformations with the essential features of the native fold. This suggests that no segment of the ESBL sequence is essential to the structure as a whole, which is congruent with the notion that local information and modular organization can impart most of the tertiary fold specificity and cooperativity.     

Simulating protein evolution in sequence and structure space

Naturally occurring proteins comprise a special subset of all plausible sequences and structures selected through evolution. Simulating protein evolution with simplified and all-atom models has shed light on the evolutionary dynamics of protein populations, the nature of evolved sequences and structures, and the extent to which today's proteins are shaped by selection pressures on folding, structure and function. Extensive mapping of the native structure, stability and folding rate in sequence space using lattice proteins has revealed organizational principles of the sequence/structure map important for evolutionary dynamics. Evolutionary simulations with lattice proteins have highlighted the importance of fitness landscapes, evolutionary mechanisms, population dynamics and sequence space entropy in shaping the generic properties of proteins. Finally, evolutionary-like simulations with all-atom models, in particular computational protein design, have helped identify the dominant selection pressures on naturally occurring protein sequences and structures.     

Inferring social network structure from bacterial sequence data

Using DNA sequence data from pathogens to infer transmission networks has traditionally been done in the context of epidemics and outbreaks. Sequence data could analogously be applied to cases of ubiquitous commensal bacteria; however, instead of inferring chains of transmission to track the spread of a pathogen, sequence data for bacteria circulating in an endemic equilibrium could be used to infer information about host contact networks. Here, we show--using simulated data--that multilocus DNA sequence data, based on multilocus sequence typing schemes (MLST), from isolates of commensal bacteria can be used to infer both local and global properties of the contact networks of the populations being sampled. Specifically, for MLST data simulated from small-world networks, the small world parameter controlling the degree of structure in the contact network can robustly be estimated. Moreover, we show that pairwise distances in the network--degrees of separation--correlate with genetic distances between isolates, so that how far apart two individuals in the network are can be inferred from MLST analysis of their commensal bacteria. This result has important consequences, and we show an example from epidemiology: how this result could be used to test for infectious origins of diseases of unknown etiology.     

Relations between protein sequence and structure and their significance

The relation between amino acid sequence and local structure in proteins is investigated. The local structures considered are either the four classes of secondary structure (H, E, T and C) or four classes of local conformations defined using measures of conformational similarity based on distances between C alpha atoms. The classes are obtained by applying an automatic clustering procedure to short polypeptide fragments of uniform length from a database of 75 known protein structures. The thrust of our investigation consists of systematically searching the database for simple amino acid patterns of the type Gly-X-Ala-X-X-Val, where X denotes an arbitrary residue. Patterns that are nearly always associated with the same structure are retained. Finding many such associations, we then evaluate by a statistical approach how many among them are non-random and compare the results for different definitions of local structure. A similar comparison is made for the predictive value of retained associations, which is assessed using an internal test based on dividing the database into "learning" and "test" subsets. While we find that local structures defined by conformational similarity are not superior to secondary structure for prediction purposes, they help us gain insight into the factors that influence the predictive value of derived associations. A major conclusion is that the number of retained associations is in large excess over the number expected from a random correlation between sequence and structure, irrespective of how local conformation is defined. However, only a very small number of these associations can be earmarked as reliable using statistical criteria, due to the limited size of the database. We find, for instance, that the pattern Ala-Ala-X-X-Lys reliably characterizes helix, and the pattern Val-X-Val-X-X-X-Ala reliably characterizes extended structure and beta-strand. The possibility is discussed that these and other reliable associations correspond to regions of the polypeptide chain whose conformations are locally determined and that these regions may play a role in folding.     

Exploiting protein structure data to explore the evolution of protein function and biological complexity

New directions in biology are being driven by the complete sequencing of genomes, which has given us the protein repertoires of diverse organisms from all kingdoms of life. In tandem with this accumulation of sequence data, worldwide structural genomics initiatives, advanced by the development of improved technologies in X-ray crystallography and NMR, are expanding our knowledge of structural families and increasing our fold libraries. Methods for detecting remote sequence similarities have also been made more sensitive and this means that we can map domains from these structural families onto genome sequences to understand how these families are distributed throughout the genomes and reveal how they might influence the functional repertoires and biological complexities of the organisms. We have used robust protocols to assign sequences from completed genomes to domain structures in the CATH database, allowing up to 60% of domain sequences in these genomes, depending on the organism, to be assigned to a domain family of known structure. Analysis of the distribution of these families throughout bacterial genomes identified more than 300 universal families, some of which had expanded significantly in proportion to genome size. These highly expanded families are primarily involved in metabolism and regulation and appear to make major contributions to the functional repertoire and complexity of bacterial organisms. When comparisons are made across all kingdoms of life, we find a smaller set of universal domain families (approx. 140), of which families involved in protein biosynthesis are the largest conserved component. Analysis of the behaviour of other families reveals that some (e.g. those involved in metabolism, regulation) have remained highly innovative during evolution, making it harder to trace their evolutionary ancestry. Structural analyses of metabolic families provide some insights into the mechanisms of functional innovation, which include changes in domain partnerships and significant structural embellishments leading to modulation of active sites and protein interactions.     

MultiSeq: unifying sequence and structure data for evolutionary analysis

Background  

Since the publication of the first draft of the human genome in 2000, bioinformatic data have been accumulating at an overwhelming pace. Currently, more than 3 million sequences and 35 thousand structures of proteins and nucleic acids are available in public databases. Finding correlations in and between these data to answer critical research questions is extremely challenging. This problem needs to be approached from several directions: information science to organize and search the data; information visualization to assist in recognizing correlations; mathematics to formulate statistical inferences; and biology to analyze chemical and physical properties in terms of sequence and structure changes.     

SnapDRAGON: a method to delineate protein structural domains from sequence data

We describe a method to identify protein domain boundaries from sequence information alone based on the assumption that hydrophobic residues cluster together in space. SnapDRAGON is a suite of programs developed to predict domain boundaries based on the consistency observed in a set of alternative ab initio three-dimensional (3D) models generated for a given protein multiple sequence alignment. This is achieved by running a distance geometry-based folding technique in conjunction with a 3D-domain assignment algorithm. The overall accuracy of our method in predicting the number of domains for a non-redundant data set of 414 multiple alignments, representing 185 single and 231 multiple-domain proteins, is 72.4 %. Using domain linker regions observed in the tertiary structures associated with each query alignment as the standard of truth, inter-domain boundary positions are delineated with an accuracy of 63.9 % for proteins comprising continuous domains only, and 35.4 % for proteins with discontinuous domains. Overall, domain boundaries are delineated with an accuracy of 51.8 %. The prediction accuracy values are independent of the pair-wise sequence similarities within each of the alignments. These results demonstrate the capability of our method to delineate domains in protein sequences associated with a wide variety of structural domain organisation.