Biomedical subjects

Orkun S Soyer

Publications and source records attributed to Orkun S Soyer.

9 recordsLinked to original sources

PubMed2006

Evolution of complexity in signaling pathways.

It is not clear how biological pathways evolve to mediate a certain physiological response and why they show a level of complexity that is generally above the minimum required to achieve such a response. One possibility is that pathway complexity increases due to the nature of evolutionary mechanisms. Here, we analyze this possibility by using mathematical models of biological pathways and evolutionary simulations. Starting with a population of small pathways of three proteins, we let the population evolve with mutations that affect pathway structure through duplication or deletion of existing proteins, deletion or creation of interactions among them, or addition of new proteins. Our simulations show that such mutational events, coupled with a selective pressure, leads to growth of pathways. These results indicate that pathways could be driven toward complexity via simple evolutionary mechanisms and that complexity can arise without any specific selective pressure for it. Furthermore, we find that the level of complexity that pathways evolve toward depends on the selection criteria. In general, we find that final pathway size tends to be lower when pathways evolve under stringent selection criteria. This leads to the counterintuitive conclusion that simple response requirements on a pathway would facilitate its evolution toward higher complexity.

Biological Evolution↗

PubMed2006

Simulating the evolution of signal transduction pathways.

We use a generic model of a network of proteins that can activate or deactivate each other to explore the emergence and evolution of signal transduction networks and to gain a basic understanding of their general properties. Starting with a set of non-interacting proteins, we evolve a signal transduction network by random mutation and selection to fulfill a complex biological task. In order to validate this approach we base selection on a fitness function that captures the essential features of chemotactic behavior as seen in bacteria. We find that a system of as few as three proteins can evolve into a network mediating chemotaxis-like behavior by acting as a "derivative sensor". Furthermore, we find that the dynamics and topology of such networks show many similarities to the natural chemotaxis pathway, that the response magnitude can increase with increasing network size and that network behavior shows robustness towards variations in some of the internal parameters. We conclude that simulating the evolution of signal transduction networks to mediate a certain behavior may be a promising approach for understanding the general properties of the natural pathway for that behavior.

Animals↗

PubMed2005

Signal transduction networks: topology, response and biochemical processes.

Conventionally, biological signal transduction networks are analysed using experimental and theoretical methods to describe specific protein components, interactions, and biochemical processes and to model network behavior under various conditions. While these studies provide crucial information on specific networks, this information is not easily converted to a broader understanding of signal transduction systems. Here, using a specific model of protein interaction we analyse small network topologies to understand their response and general properties. In particular, we catalogue the response for all possible topologies of a given network size to generate a response distribution, analyse the effects of specific biochemical processes on this distribution, and analyse the robustness and diversity of responses with respect to internal fluctuations or mutations in the network. The results show that even three- and four-protein networks are capable of creating diverse and biologically relevant responses, that the distribution of response types changes drastically as a function of biochemical processes at protein level, and that certain topologies strongly pre-dispose a specific response type while others allow for diverse types of responses. This study sheds light on the response types and properties that could be expected from signal transduction networks, provides possible explanations for the role of certain biochemical processes in signal transduction and suggests novel approaches to interfere with signaling pathways at the molecular level. Furthermore it shows that network topology plays a key role on determining response type and properties and that proper representation of network topology is crucial to discover and understand so-called building blocks of large networks.

Animals↗

PubMed2005

The evolution of connectivity in metabolic networks.

Processes in living cells are the result of interactions between biochemical compounds in highly complex biochemical networks. It is a major challenge in biology to understand causes and consequences of the specific design of these networks. A characteristic design feature of metabolic networks is the presence of hub metabolites such as ATP or NADH that are involved in a high number of reactions. To study the emergence of hub metabolites, we implemented computer simulations of a widely accepted scenario for the evolution of metabolic networks. Our simulations indicate that metabolic networks with a large number of highly specialized enzymes may evolve from a few multifunctional enzymes. During this process, enzymes duplicate and specialize, leading to a loss of biochemical reactions and intermediary metabolites. Complex features of metabolic networks such as the presence of hubs may result from selection of growth rate if essential biochemical mechanisms are considered. Specifically, our simulations indicate that group transfer reactions are essential for the emergence of hubs.

Biomass↗

PubMed2004

Predicting functional sites in proteins: site-specific evolutionary models and their application to neurotransmitter transporters.

Currently there exist several computational methods for predicting the functional sites in a set of homologous proteins based on their sequences. Due to difficulties in defining the functional site in a protein, it is not trivial to compare the performance of these methods, evaluate their limitations and quantify improvements by new approaches. Here, we use extensive mutation data from two proteins, Lac repressor and subtilisin, to perform such an analysis. Along with the evaluation of existing approaches, we describe a site class model of evolution as a tool to predict functional sites in proteins. The results indicate that this model, which simulates the evolution process at the amino acid level using site-specific substitution matrices, provides the most accurate information on functional sites in a given protein family. Secondly, we present an application of this model to neurotransmitter transporters, a superfamily of proteins of which we have limited experimental knowledge. Based on this application we present testable hypotheses regarding the mechanism of action of these proteins.

Amino Acid Transport Systems↗

PubMed2003

Dimerization in aminergic G-protein-coupled receptors: application of a hidden-site class model of evolution.

G-Protein-coupled receptors (GPCRs) are an important superfamily of transmembrane proteins involved in cellular communication. Recently, it has been shown that dimerization is a widely occurring phenomenon in the GPCR superfamily, with likely important physiological roles. Here we use a novel hidden-site class model of evolution as a sequence analysis tool to predict possible dimerization interfaces in GPCRs. This model aims to simulate the evolution of proteins at the amino acid level, allowing the analysis of their sequences in an explicitly evolutionary context. Applying this model to aminergic GPCR sequences, we first validate the general reasoning behind the model. We then use the model to perform a family specific analysis of GPCRs. Accounting for the family structure of these proteins, this approach detects different evolutionarily conserved and accessible patches on transmembrane (TM) helices 4-6 in different families. On the basis of these findings, we propose an experimentally testable dimerization mechanism, involving interactions among different combinations of these helices in different families of aminergic GPCRs.

Amino Acid Substitution↗

PubMed2003

Depicting a protein's two faces: GPCR classification by phylogenetic tree-based HMMs.

Related proteins with similar biological functions generally share common features, allowing us to extract the common sequence features. These common features enable us to build statistical models that can be used to classify proteins, to predict new members, and to study the sequence-function relationship of this protein function group. Although evolution underlies the basis of multiple sequence analysis methods, most methods ignore phylogenetic relationships and the evolutionary process in building these statistical models. Previously we have shown that a phylogenetic tree-based profile hidden Markov model (T-HMM) is superior in generating a profile for a group of similar proteins. In this study we used the method to generate common features of G protein-coupled receptors (GPCRs). The profile generated by T-HMM gives high accuracy in GPCR function classification, both by ligand and by coupled G protein.

Animals↗

PubMed2003

Probing conformational changes in neurotransmitter transporters: a structural context.

The Na+/Cl-dependent neurotransmitter transporters, a family of proteins responsible for the reuptake of neurotransmitters and other small molecules from the synaptic cleft, have been the focus of intensive research in recent years. The biogenic amine transporters, a subset of this larger family, are especially intriguing as they are the targets for many psychoactive compounds, including cocaine and amphetamines, as well as many antidepressants. In the absence of a high-resolution structure for any transporter in this family, research into the structure-function relationships of these transporters has relied on analysis of the effects of site-directed mutagenesis as well as of chemical modification of reactive residues. The aim of this review is to establish a structural context for the experimental study of these transporters through various computational approaches and to highlight what is known about the conformational changes associated with function in these transporters. We also present a novel numbering scheme to assist in the comparison of aligned positions between sequences of the neurotransmitter transporter family, a comparison that will be of increasing importance as additional experimental data is amassed.

Amino Acid Sequence↗

PubMed2002

NMR structure of the second intracellular loop of the alpha 2A adrenergic receptor: evidence for a novel cytoplasmic helix.

A major, unresolved question in signal transduction by G protein coupled receptors (GPCRs) is to understand how, at atomic resolution, a GPCR activates a G protein. A step toward answering this question was made with the determination of the high-resolution structure of rhodopsin; we now know the intramolecular interactions that characterize the resting conformation of a GPCR. To what degree does this structure represent a structural paradigm for other GPCRs, especially at the cytoplasmic surface where GPCR-G protein interaction occurs and where the sequence homology is low among GPCRs? To address this question, we performed NMR studies on approximately 35-residue-long peptides including the critical second intracellular loop (i2) of the alpha 2A adrenergic receptor (AR) and of rhodopsin. To stabilize the secondary structure of the peptide termini, 4-12 residues from the adjacent transmembrane helices were included and structures determined in dodecylphosphocholine micelles. We also characterized the effects on an alpha 2A AR peptide of a D130I mutation in the conserved DRY motif. Our results show that in contrast to the L-shaped loop in the i2 of rhodopsin, the i2 of the alpha 2A AR is predominantly helical, supporting the hypothesis that there is structural diversity within GPCR intracellular loops. The D130I mutation subtly modulates the helical structure. The spacing of nonpolar residues in i2 with helical periodicity is a predictor of helical versus loop structure. These data should lead to more accurate models of the intracellular surface of GPCRs and of receptor-mediated G protein activation.

Amino Acid Sequence↗