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Biomedical subjects

E Domany

Publications and source records attributed to E Domany.

At least 19 recordsLinked to original sources
PubMed2001

DNA microarrays identification of primary and secondary target genes regulated by p53.

The transcriptional program regulated by the tumor suppressor p53 was analysed using oligonucleotide microarrays. A human lung cancer cell line that expresses the temperature sensitive murine p53 was utilized to quantitate mRNA levels of various genes at different time points after shifting the temperature to 32 degrees C. Inhibition of protein synthesis by cycloheximide (CHX) was used to distinguish between primary and secondary target genes regulated by p53. In the absence of CHX, 259 and 125 genes were up or down-regulated respectively; only 38 and 24 of these genes were up and down-regulated by p53 also in the presence of CHX and are considered primary targets in this cell line. Cluster analysis of these data using the super paramagnetic clustering (SPC) algorithm demonstrate that the primary genes can be distinguished as a single cluster among a large pool of p53 regulated genes. This procedure identified additional genes that co-cluster with the primary targets and can also be classified as such genes. In addition to cell cycle (e.g. p21, TGF-beta, Cyclin E) and apoptosis (e.g. Fas, Bak, IAP) related genes, the primary targets of p53 include genes involved in many aspects of cell function, including cell adhesion (e.g. Thymosin, Smoothelin), signaling (e.g. H-Ras, Diacylglycerol kinase), transcription (e.g. ATF3, LISCH7), neuronal growth (e.g. Ninjurin, NSCL2) and DNA repair (e.g. BTG2, DDB2). The results suggest that p53 activates concerted opposing signals and exerts its effect through a diverse network of transcriptional changes that collectively alter the cell phenotype in response to stress.

Animals↗
PubMed2000

Comparison of two optimization methods to derive energy parameters for protein folding: perceptron and Z score.

Two methods were proposed recently to derive energy parameters from known native protein conformations and corresponding sets of decoys. One is based on finding, by means of a perceptron learning scheme, energy parameters such that the native conformations have lower energies than the decoys. The second method maximizes the difference between the native energy and the average energy of the decoys, measured in terms of the width of the decoys' energy distribution (Z-score). Whereas the perceptron method is sensitive mainly to "outlier" (i.e., extremal) decoys, the Z-score optimization is governed by the high density regions in decoy-space. We compare the two methods by deriving contact energies for two very different sets of decoys: the first obtained for model lattice proteins and the second by threading. We find that the potentials derived by the two methods are of similar quality and fairly closely related. This finding indicates that standard, naturally occurring sets of decoys are distributed in a way that yields robust energy parameters (that are quite insensitive to the particular method used to derive them). The main practical implication of this finding is that it is not necessary to fine-tune the potential search method to the particular set of decoys used.

Algorithms↗
PubMed2000

Coupled two-way clustering analysis of gene microarray data.

We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.

Cluster Analysis↗
PubMed2000

Toward an energy function for the contact map representation of proteins.

We analyzed several energy functions for predicting the native state of proteins from an energy minimization procedure. We derived the parameters of a given energy function by imposing the basic requirement that the energy of the native conformation of a protein is lower than that of any conformation chosen from a set of decoys. Our work is motivated by a recent result which proved that the simple pairwise contact approximation of the energy is insufficient to satisfy simultaneously such a basic requirement for all the proteins in a database. Here, we investigate the reasons of such negative results and show how to improve the predictive power of methods based on energy minimization. We generated decoys by gapless threading, and we derive energy parameters by perceptron learning. We first considered hydrophobic contributions to the energy, defined in several ways, and showed that the additional hydrophobic terms enlarge slightly the number of proteins that can be stabilized together. Next, we performed various modifications of the pairwise energy term. We introduced (1) a distinction between inter-residue contacts on the surface and in the core of a protein and (2) a simple distance-dependent pairwise interaction in which a two-tier definition of contact replaces the original (single-tier) one. Our results suggest that a detailed treatment of the pairwise potential is likely to be more relevant than the consideration of other forces.

Algorithms↗
PubMed2000

Can a pairwise contact potential stabilize native protein folds against decoys obtained by threading?

We present a method to derive contact energy parameters from large sets of proteins. The basic requirement on which our method is based is that for each protein in the database the native contact map has lower energy than all its decoy conformations that are obtained by threading. Only when this condition is satisfied one can use the proposed energy function for fold identification. Such a set of parameters can be found (by perceptron learning) if Mp, the number of proteins in the database, is not too large. Other aspects that influence the existence of such a solution are the exact definition of contact and the value of the critical distance Rc, below which two residues are considered to be in contact. Another important novel feature of our approach is its ability to determine whether an energy function of some suitable proposed form can or cannot be parameterized in a way that satisfies our basic requirement. As a demonstration of this, we determine the region in the (Rc, Mp) plane in which the problem is solvable, i.e., we can find a set of contact parameters that stabilize simultaneously all the native conformations. We show that for large enough databases the contact approximation to the energy cannot stabilize all the native folds even against the decoys obtained by gapless threading.

Algorithms↗
PubMed2000

Protein folding using contact maps.

We discuss the problem of representations of protein structure and give the definition of contact maps. We present a method to obtain a three-dimensional polypeptide conformation from a contact map. We also explain how to deal with the case of nonphysical contact maps. We describe a stochastic method to perform dynamics in contact map space. We explain how the motion is restricted to physical regions of the space. First, we introduce the exact free energy of a contact map and discuss two simple approximations to it. Second, we present a method to derive energy parameters based on perception learning. We prove in an extensive number of situations that the pairwise contact approximation both when alone and when supplemented with a hydrophobic term is unsuitable for stabilizing proteins' native states.

Aprotinin↗
PubMed1999

Folding Lennard-Jones proteins by a contact potential.

We studied the possibility to approximate a Lennard-Jones interaction by a pairwise contact potential. First we used a Lennard-Jones potential to design off-lattice, protein-like heteropolymer sequences, whose lowest energy (native) conformations were then identified by molecular dynamics. Then we turned to investigate whether one can find a pairwise contact potential, whose ground states are the contact maps associated with these native conformations. We show that such a requirement cannot be satisfied exactly, i.e., no such contact parameters exist. Nevertheless, we found that one can find contact energy parameters for which an energy minimization procedure, acting in the space of contact maps, yields maps whose corresponding structures are close to the native ones. Finally, we show that when these structures are used as the initial point of a molecular dynamics energy minimization process, the correct native folds are recovered with high probability.

Drug Design↗
PubMed1998

Efficient dynamics in the space of contact maps.

BACKGROUND: Two problems are of major importance in protein fold prediction: how to generate plausible conformations, and how to choose an energy function to identify the native state. Contact maps are a simple representation of protein structure and offer a promising framework to address these two issues. RESULTS: In this work we develop Monte Carlo dynamics in contact map space. The procedure is divided into four steps: non-local dynamics, in which large-scale "cluster" moves are performed (clusters are in approximate correspondence with secondary structure elements); local dynamics, in which secondary structure location is optimized; reconstruction, in which the physicality of the contact map is restored; and refinement, which consists of a further Monte Carlo energy minimization in real space. We demonstrate that such a dynamical procedure is effective in producing uncorrelated low-energy states. CONCLUSIONS: The procedure introduced in this paper very effectively generates a representative ensemble of conformations. We are able to show that existing sets of pairwise contact energy parameters are not suitable to single out the native state within this ensemble. The remaining outstanding issue in protein folding is to find an energy function that can discriminate the native state from decoys.

Models, Chemical↗
PubMed1997

Recovery of protein structure from contact maps.

BACKGROUND: Prediction of a protein's structure from its amino acid sequence is a key issue in molecular biology. While dynamics, performed in the space of two-dimensional contact maps, eases the necessary conformational search, it may also lead to maps that do not correspond to any real three-dimensional structure. To remedy this, an efficient procedure is needed to reconstruct three-dimensional conformations from their contact maps. RESULTS: We present an efficient algorithm to recover the three-dimensional structure of a protein from its contact map representation. We show that when a physically realizable map is used as target, our method generates a structure whose contact map is essentially similar to the target. furthermore, the reconstructed and original structures are similar up to the resolution of the contact map representation. Next, we use nonphysical target maps, obtained by corrupting a physical one; in this case, our method essentially recovers the underlying physical map and structure. Hence, our algorithm will help to fold proteins, using dynamics in the space of contact maps. Finally, we investigate the manner in which the quality of the recovered structure degrades when the number of contacts is reduced. CONCLUSIONS: The procedure is capable of assigning quickly and reliably a three-dimensional structure to a given contact map. It is well suited for use in parallel with dynamics in contact map space to project a contact map onto its closest physically allowed structural counterpart.

Algorithms↗
PubMed1996

Protein fold recognition and dynamics in the space of contact maps.

We introduce an energy function for contact maps of proteins. In addition to the standard term, that takes into account pair-wise interactions between amino acids, our potential contains a new hydrophobic energy term. Parameters of the energy function were obtained from a statistical analysis of the contact maps of known structures. The quality of our energy function was tested extensively in a variety of ways. In particular, fold recognition experiments revealed that for a fixed sequence the native map is identified correctly in an overwhelming majority of the cases tested. We succeeded in identifying the structure of some proteins that are known to pose difficulties for such tests (BPTI, spectrin, and cro-protein). In addition, many known pairs of homologous structures were correctly identified, even when the two sequences had relatively low sequence homology. We also introduced a dynamic Monte Carlo procedure in the space of contact maps, taking topological and polymeric constraints into account by restrictive dynamic rules. Various aspects of protein dynamics, including high-temperature melting and refolding, were simulated. Perspectives of application of the energy function and the method for structure checking and fold prediction are discussed.

Amino Acids↗
PubMed1995

On the equivalence of two-layered perceptrons with binary neurons.

We consider two-layered perceptions consisting of N binary input units, K binary hidden units and one binary output unit, in the limit N >> K > or = 1. We prove that the weights of a regular irreducible network are uniquely determined by its input-output map up to some obvious global symmetries. A network is regular if its K weight vectors from the input layer to the K hidden units are linearly independent. A (single layered) perceptron is said to be irreducible if its output depends on every one of its input units; and a two-layered perceptron is irreducible if the K + 1 perceptrons that constitute such network are irreducible. By global symmetries we mean, for instance, permuting the labels of the hidden units. Hence, two irreducible regular two-layered perceptrons that implement the same Boolean function must have the same number of hidden units, and must be composed of equivalent perceptrons.

Computer Simulation↗