Random Parametric Perturbations of Gene Regulatory Circuit Uncover State Transitions in Cell Cycle.

Many biological processes involve precise cellular state transitions controlled by complex gene regulation. Here, we use budding yeast cell cycle as a model system and explore how a gene regulatory circuit encodes essential information of state transitions. We present a generalized random circuit perturbation method for circuits containing heterogeneous regulation types and its usage to analyze both steady and oscillatory states from an ensemble of circuit models with random kinetic parameters. The stable steady states form robust clusters with a circular structure that are associated with cell cycle phases. This circular structure in the clusters is consistent with single-cell RNA sequencing data. The oscillatory states specify the irreversible state transitions along cell cycle progression. Furthermore, we identify possible mechanisms to understand the irreversible state transitions from the steady states. We expect this approach to be robust and generally applicable to unbiasedly predict dynamical transitions of a gene regulatory circuit

Structural interpretation of protein-protein interaction network

Background Currently a huge amount of protein-protein interaction data is available from high throughput experimental methods. In a large network of protein-protein interactions, groups of proteins can be identified as functional clusters having related functions where a single protein can occur in multiple clusters. However experimental methods are error-prone and thus the interactions in a functional cluster may include false positives or there may be unreported interactions. Therefore correctly identifying a functional cluster of proteins requires the knowledge of whether any two proteins in a cluster interact, whether an interaction can exclude other interactions, or how strong the affinity between two interacting proteins is. Methods In the present work the yeast protein-protein interaction network is clustered using a spectral clustering method proposed by us in 2006 and the individual clusters are investigated for functional relationships among the member proteins. 3D structural models of the proteins in one cluster have been built – the protein structures are retrieved from the Protein Data Bank or predicted using a comparative modeling approach. A rigid body protein docking method (Cluspro) is used to predict the protein-protein interaction complexes. Binding sites of the docked complexes are characterized by their buried surface areas in the docked complexes, as a measure of the strength of an interaction. Results The clustering method yields functionally coherent clusters. Some of the interactions in a cluster exclude other interactions because of shared binding sites. New interactions among the interacting proteins are uncovered, and thus higher order protein complexes in the cluster are proposed. Also the relative stability of each of the protein complexes in the cluster is reported. Conclusions Although the methods used are computationally expensive and require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. These theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes

Discovery of Functional Motifs from the Interface Region of Oligomeric Proteins using Frequent Subgraph Mining

Modeling the interface region of a protein complex paves the way for understanding its dynamics and functionalities. Existing works model the interface region of a complex by using different approaches, such as, the residue composition at the interface region, the geometry of the interface residues, or the structural alignment of interface regions. These approaches are useful for ranking a set of docked conformation or for building scoring function for protein-protein docking, but they do not provide a generic and scalable technique for the extraction of interface patterns leading to functional motif discovery. In this work, we model the interface region of a protein complex by graphs and extract interface patterns of the given complex in the form of frequent subgraphs. To achieve this we develop a scalable algorithm for frequent subgraph mining. We show that a systematic review of the mined subgraphs provides an effective method for the discovery of functional motifs that exist along the interface region of a given protein complex

Building and simulating protein machines

Glycolysis is a central metabolic pathway, present in almost all organisms, that produces energy. The pathway has been extensively investigated by biochemists. There is a significant body of structural and biochemical information about this pathway. The complete pathway is a ten step process. At each step, a specific chemical reaction is catalyzed by a specific enzyme. Fructose bisphosphate aldolase (FBA) and triosephosphate isomerase (TIM) catalyze the fourth and the fifth steps on the pathway. This thesis investigates the possible substrate transfer mechanism between FBA and TIM. FBA cleaves its substrate, the six-carbon fructose-1,6-bisphosphate (FBP), into two three-carbon products - glyceraldehydes 3-phosphate (GAP) and dihydroxy acetone phosphate (DHAP). One component of these two products, DHAP, is the substrate for TIM and the other component GAP goes directly to GAPDH, the subsequent enzyme on the pathway. TIM converts DHAP to GAP and delivers the product to GAPDH. I employ Elastic Network Models (ENM) to investigate the mechanistic and dynamic aspects of the functionality of FBA and TIM enzymes - (1) the effects of the oligomerization of these two enzymes on their functional dynamics and the coordination of the individual protein's structural components along the functional region; and (2) the mechanistic synchrony of these two protein machines that may enable them to operate in a coordinated fashion as a conjugate machine - transferring the product from FBA as substrate to TIM. A macromolecular machine comprised of FBA and TIM will facilitate the substrate catalysis mechanism and the product flow between FBA and TIM. Such a machine could be used as a functional unit in building a larger a machine for the structural modeling of the whole glycolysis pathway. Building such machines for the glycolysis pathway may reveal the interplay of the enzymes as a complete machine. Also the methods and insights developed from the efforts to build such large machines could be applied to build macromolecular structures for other biologically important clusters of interacting enzymes centered around individual metabolic pathways.</p

Aldolases Utilize Different Oligomeric States To Preserve Their Functional Dynamics

Aldolases are essential enzymes in the glycolysis pathway and catalyze the reaction cleaving fructose/tagatose 1,6-bisphosphate into dihydroxyacetone phosphate and glyceraldehyde 3-phosphate. To determine how the aldolase motions relate to its catalytic process, we studied the dynamics of three different class II aldolase structures through simulations. We employed coarse-grained elastic network normal mode analyses to investigate the dynamics of E.coli fructose 1,6-bisphosphate aldolase, E.coli tagatose 1,6-bisphosphate aldolase, and T.aquaticus fructose 1,6-bisphosphate aldolase, and compared their motions in different oligomeric states. The first one is a dimer, and the second and third ones are tetramers. Our analyses suggest that oligomerization not only stabilizes the aldolase structures, showing reduced fluctuations at the subunit interfaces, it further enables the enzyme to achieve the required dynamics for its functional loops. The essential mobility of these loops in the functional oligomeric states can facilitate the enzymatic mechanism – substrate recruitment in the open state, bringing the catalytic residues into their required configuration in the closed bound state, and moving back to the open state to release the catalytic products and re-positioning the enzyme for its next catalytic cycle. These findings suggest that the aldolase global motions are conserved among aldolases having different oligomeric states in order to preserve its catalytic mechanism. The coarse-grained approaches taken permit an unprecedented view of the changes in the structural dynamics and how these relate to the critical structural stabilities essential for catalysis. The results are supported by experimental findings from many previous studies.This is a manuscript of an article published as Katebi, Ataur R., and Robert L. Jernigan. "Aldolases utilize different oligomeric states to preserve their functional dynamics." Biochemistry 54, no. 22 (2015): 3543-3554. doi:10.1021/acs.biochem.5b00042. Posted with permission.</p

Structural interpretation of protein-protein interaction network

Background Currently a huge amount of protein-protein interaction data is available from high throughput experimental methods. In a large network of protein-protein interactions, groups of proteins can be identified as functional clusters having related functions where a single protein can occur in multiple clusters. However experimental methods are error-prone and thus the interactions in a functional cluster may include false positives or there may be unreported interactions. Therefore correctly identifying a functional cluster of proteins requires the knowledge of whether any two proteins in a cluster interact, whether an interaction can exclude other interactions, or how strong the affinity between two interacting proteins is. Methods In the present work the yeast protein-protein interaction network is clustered using a spectral clustering method proposed by us in 2006 and the individual clusters are investigated for functional relationships among the member proteins. 3D structural models of the proteins in one cluster have been built – the protein structures are retrieved from the Protein Data Bank or predicted using a comparative modeling approach. A rigid body protein docking method (Cluspro) is used to predict the protein-protein interaction complexes. Binding sites of the docked complexes are characterized by their buried surface areas in the docked complexes, as a measure of the strength of an interaction. Results The clustering method yields functionally coherent clusters. Some of the interactions in a cluster exclude other interactions because of shared binding sites. New interactions among the interacting proteins are uncovered, and thus higher order protein complexes in the cluster are proposed. Also the relative stability of each of the protein complexes in the cluster is reported. Conclusions Although the methods used are computationally expensive and require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. These theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes.This article is published as Katebi, Ataur R., Andrzej Kloczkowski, and Robert L. Jernigan. "Structural interpretation of protein-protein interaction network." BMC structural biology 10, no. 1 (2010): S4. doi:10.1186/1472-6807-10-S1-S4. Posted with permission.</p

Topological diversity of chromatin fibers: Interplay between nucleosome repeat length, DNA linking number and the level of transcription

The spatial organization of nucleosomes in 30-nm fibers remains unknown in detail. To tackle this problem, we analyzed all stereochemically possible configurations of two-start chromatin fibers with DNA linkers L = 10-70 bp (nucleosome repeat length NRL = 157-217 bp). In our model, the energy of a fiber is a sum of the elastic energy of the linker DNA, steric repulsion, electrostatics, and the H4 tail-acidic patch interaction between two stacked nucleosomes. We found two families of energetically feasible conformations of the fibers—one observed earlier, and the other novel. The fibers from the two families are characterized by different DNA linking numbers—that is, they are topologically different. Remarkably, the optimal geometry of a fiber and its topology depend on the linker length: the fibers with linkers L = 10n and 10n + 5 bp have DNA linking numbers per nucleosome DLk >>-1.5 and -1.0, respectively. In other words, the level of DNA supercoiling is directly related to the length of the inter-nucleosome linker in the chromatin fiber (and therefore, to NRL). We hypothesize that this topological polymorphism of chromatin fibers may play a role in the process of transcription, which is known to generate different levels of DNA supercoiling upstream and downstream from RNA polymerase. A genome-wide analysis of the NRL distribution in active and silent yeast genes yielded results consistent with this assumption