Biomedical subjects

A S Lapedes

Publications and source records attributed to A S Lapedes.

7 recordsLinked to original sources

PubMed2001

Exploring protein sequence space using knowledge-based potentials.

Knowledge-based potentials can be used to decide whether an amino acid sequence is likely to fold into a prescribed native protein structure. We use this idea to survey the sequence-structure relations in protein space. In particular, we test the following two propositions which were found to be important for efficient evolution: the sequences folding into a particular native fold form extensive neutral networks that percolate through sequence space. The neutral networks of any two native folds approach each other to within a few point mutations. Computer simulations using two very different potential functions, M. Sippl's PROSA pair potential and a neural network based potential, are used to verify these claims.

Amino Acid Sequence↗

PubMed2001

Complex systems analysis: a tool for shock research.

For the past century, students of shock have focused research efforts to illuminate specific mechanisms that cause, or fail as a consequence of, circulatory collapse. Although clinical strategies aimed at supporting or restoring individual organ systems have proven effective, many patients succumb to more generalized multiple organ system failure. We suggest that general biological systems failure cannot be interpreted through reliance on reductionist science. We propose that complex systems analysis is an essential tool for shock research and we evaluate its application to genomic technologies.

Animals↗

PubMed2001

SAMIE: statistical algorithm for modeling interaction energies.

We are investigating the rules that govern protein-DNA interactions, using a statistical mechanics based formalism that is related to the Boltzmann Machine of the neural net literature. Our approach is data-driven, in which probabilistic algorithms are used to model protein-DNA interactions, given SELEX and/or phage data as input. In the current report, we trained the network using SELEX data, under the "one-to-one" model of interactions (i.e. one amino acid contacts one base). The trained network was able to successfully identify the wild-type binding sites of EGR and MIG protein families. The predictions using our method are the same or better than that of methods existing in the literature. However our methodology offers the potential to capitalise in quantitative detail, as well as to be used to explore more general model of interactions, given availability of data.

Algorithms↗

PubMed1999

Superadditive correlation.

The fact that correlation does not imply causation is well known. Correlation between variables at two sites does not imply that the two sites directly interact, because, e.g., correlation between distant sites may be induced by chaining of correlation between a set of intervening, directly interacting sites. Such "noncausal correlation" is well understood in statistical physics: an example is long-range order in spin systems, where spins which have only short-range direct interactions, e.g., the Ising model, display correlation at a distance. It is less well recognized that such long-range "noncausal" correlations can in fact be stronger than the magnitude of any causal correlation induced by direct interactions. We call this phenomenon superadditive correlation (SAC). We demonstrate this counterintuitive phenomenon by explicit examples in (i) a model spin system and (ii) a model continuous variable system, where both models are such that two variables have multiple intervening pathways of indirect interaction. We apply the technique known as decimation to explain SAC as an additive, constructive interference phenomenon between the multiple pathways of indirect interaction. We also explain the effect using a definition of the collective mode describing the intervening spin variables. Finally, we show that the SAC effect is mirrored in information theory, and is true for mutual information measures in addition to correlation measures. Generic complex systems typically exhibit multiple pathways of indirect interaction, making SAC a potentially widespread phenomenon. This affects, e.g., attempts to deduce interactions by examination of correlations, as well as, e.g., hierarchical approximation methods for multivariate probability distributions, which introduce parameters based on successive orders of correlation.

Journal Article↗

PubMed1999

Genetic correlations in mutation processes.

We study the role of phylogenetic trees on correlations in mutation processes. Generally, correlations decay exponentially with the generation number. We find that two distinct regimes of behavior exist. For mutation rates smaller than a critical rate, the underlying tree morphology is almost irrelevant, while mutation rates higher than this critical rate lead to strong tree-dependent correlations. We show analytically that identical critical behavior underlies all multiple point correlations. This behavior generally characterizes branching processes undergoing mutation.

Genetics↗

PubMed1994

Neural networks for determining protein specificity and multiple alignment of binding sites.

We use a quantitative definition of specificity to develop a neural network for the identification of common protein binding sites in a collection of unaligned DNA fragments. We demonstrate the equivalence of the method to maximizing Information Content of the aligned sites when simple models of the binding energy and the genome are employed. The network method subsumes those simple models and is capable of working with more complicated ones. This is demonstrated using a Markov model of the E. coli genome and a sampling method to approximate the partition function. A variation of Gibbs' sampling aids in avoiding local minima.

Bacterial Proteins↗

PubMed1993

Covariation of mutations in the V3 loop of human immunodeficiency virus type 1 envelope protein: an information theoretic analysis.

The V3 loop of the human immunodeficiency virus type 1 (HIV-1) envelope protein is a highly variable region that is both functionally and immunologically important. Using available amino acid sequences from the V3 region, we have used an information theoretic quantity called mutual information, a measure of covariation, to quantify dependence between mutations in the loop. Certain pairs of sites, including non-contiguous sites along the sequence, do not have independent mutations but display considerable, statistically significant, covarying mutations as measured by mutual information. For the pairs of sites with the highest mutual information, specific amino acids were identified that were highly predictive of amino acids in the linked site. The observed interdependence between variable sites may have implications for structural or functional relationships; separate experimental evidence indicates functional linkage between some of the pairs of sites with high mutual information. Further specific mutational studies of the V3 loop's role in determining viral phenotype are suggested by our analyses. Also, the implications of our results may be important to consider for V3 peptide vaccine design. The methods used here are generally applicable to the study of variable proteins.

Amino Acid Sequence↗