Structural Insights into Phosphorylation-Mediated Polymerase Function Loss for DNA Polymerase ß Bound to Gapped DNA.

DNA polymerase ß is a member of the X-family of DNA polymerases, playing a critical role in the base excision repair (BER) pathway in mammalian cells by implementing the nucleotide gap-filling step. In vitro phosphorylation of DNA polymerase ß with PKC on S44 causes loss in the enzyme's DNA polymerase activity but not single-strand DNA binding. Although these studies have shown that single-stranded DNA binding is not affected by phosphorylation, the structural basis behind the mechanism underlying phosphorylation-induced activity loss remains poorly understood. Previous modeling studies suggested phosphorylation of S44 was sufficient to induce structural changes that impact the enzyme's polymerase function. However, the S44 phosphorylated-enzyme/DNA complex has not been modeled so far. To address this knowledge gap, we conducted atomistic molecular dynamics simulations of pol ß complexed with gapped DNA. Our simulations, which used explicit solvent and lasted for microseconds, revealed that phosphorylation at the S44 site, in the presence of Mg ions, induced significant conformational changes in the enzyme. Specifically, these changes led to the transformation of the enzyme from a closed to an open structure. Additionally, our simulations identified phosphorylation-induced allosteric coupling between the inter-domain region, suggesting the existence of a putative allosteric site. Taken together, our results provide a mechanistic understanding of the conformational transition observed due to phosphorylation in DNA polymerase ß interactions with gapped DNA. Our simulations shed light on the mechanisms of phosphorylation-induced activity loss in DNA polymerase ß and reveal potential targets for the development of novel therapeutics aimed at mitigating the effects of this post-translational modification.

Effect of Size and Temperature on Water Dynamics inside Carbon Nano-Tubes Studied by Molecular Dynamics Simulation.

Water transport inside carbon nano-tubes (CNTs) has attracted considerable attention due to its nano-fluidic properties, its importance in nonporous systems, and the wide range of applications in membrane desalination and biological medicine. Recent studies show an enhancement of water diffusion inside nano-channels depending on the size of the nano-confinement. However, the underlying mechanism of this enhancement is not well understood yet. In this study, we performed Molecular Dynamics (MD) simulations to study water flow inside CNT systems. The length of CNTs considered in this study is 20 nm, but their diameters vary from 1 to 10 nm. The simulations are conducted at temperatures ranging from 260 K to 320 K. We observe that water molecules are arranged into coaxial water tubular sheets. The number of these tubular sheets depends on the CNT size. Further analysis reveals that the diffusion of water molecules along the CNT axis deviates from the Arrhenius temperature dependence. The non-Arrhenius relationship results from a fragile liquid-like water component persisting at low temperatures with fragility higher than that of the bulk water.

Analyzing a co-occurrence gene-interaction network to identify disease-gene association.

BACKGROUND: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes. RESULTS: We evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes. CONCLUSIONS: The results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods.

Protein recognition and selection through conformational and mutually induced fit.

Protein-protein interactions drive most every biological process, but in many instances the domains mediating recognition are disordered. How specificity in binding is attained in the absence of defined structure contrasts with well-established experimental and theoretical work describing ligand binding to protein. The signaling protein calmodulin presents a unique opportunity to investigate mechanisms for target recognition given that it interacts with several hundred different targets. By advancing coarse-grained computer simulations and experimental techniques, mechanistic insights were gained in defining the pathways leading to recognition and in how target selectivity can be achieved at the molecular level. A model requiring mutually induced conformational changes in both calmodulin and target proteins was necessary and broadly informs how proteins can achieve both high affinity and high specificity.

Simulating protein folding in different environmental conditions.

Molecular dynamics simulations have become an invaluable tool in investigating the dynamics of protein folding. However, most computational studies of protein folding assume dilute aqueous simulation conditions in order to reduce the complexity of the system under study and enhance the efficiency. Nowadays, it is evident that environmental conditions encountered in vivo (or even in vitro) play a major role in regulating the dynamics of protein folding especially when one considers the highly condensed environment in the cellular cytoplasm. In order to factor in these conditions, we can utilize the high efficiency of well-designed low resolution (coarse-grained) simulation models to reduce the complexity of these added protein-milieu interactions involving different time and length scales. The goal of this chapter is to describe some recently developed coarse-grained simulation techniques that are specifically designed to go beyond traditional aqueous solvent conditions. The chapter also gives the reader a flavor of the things that we can study using such "smart" low resolution models.

Dynamics of confined water inside carbon nanotubes based on studying tetrahedral order parameters.

Water dynamics inside hydrophobic confinement, such as carbon nanotubes (CNTs), has garnered significant attention, focusing on water diffusion. However, a crucial aspect remains unexplored - the influence of confinement size on water ordering and intrinsic hydrogen bond dynamics. To address this gap, we conducted extensive molecular dynamics simulations to investigate local ordering and intrinsic hydrogen bond dynamics of water molecules within CNTs of various sizes (length:20 nm, diameters: 1.0 nm to 5.0 nm) over a wide range of temperatures (260K, 280K, 300K, and 320K). A striking observation emerged: in smaller CNTs, water molecules adopt an icy structure near tube walls while maintaining liquid state towards the center. Notably, water behavior within a 2.0 nm CNT stands out as an anomaly, distinct from other CNT sizes considered in this study. This anomaly was explained through the formation of water layers inside CNTs. The hydrogen bond correlation function of water within CNTs decayed more slowly than bulk water, with an increasing rate as CNT diameter increased. In smaller CNTs, water molecules hold onto their hydrogen bond longer than larger ones. Interestingly, in larger CNTs, the innermost layer's hydrogen bond lasts a shorter time compared to the other layers, and this changes with temperature.

Inhibition of the Early-Stage Cross-Amyloid Aggregation of Amyloid-ß and IAPP via EGCG: Insights from Molecular Dynamics Simulations.

Amyloid-ß (Aß) and islet amyloid polypeptide (IAPP) are small peptides that have the potential to not only self-assemble but also cross-assemble and form cytotoxic amyloid aggregates. Recently, we experimentally investigated the nature of Aß-IAPP coaggregation and its inhibition by small polyphenolic molecules. Notably, we found that epigallocatechin gallate (EGCG) had the ability to reduce heteroaggregate formation. However, the precise molecular mechanism behind the reduction of heteroaggregates remains unclear. In this study, the dimerization processes of Aß40 and IAPP peptides with and without EGCG were characterized by the enhanced sampling technique. Our results showed that these amyloid peptides exhibited a tendency to form a stable heterodimer, which represented the first step toward coaggregation. Furthermore, we also found that the EGCG regulated the dimerization process. In the presence of EGCG, well-tempered metadynamics simulation indicated a notable shift in the bound state toward a greater center of mass (COM) distance. Additionally, the presence of EGCG led to a significant increase in the free energy barrier height (∼15k B T) along the COM distance, and we observed a transition state between the bound and unbound states. Our findings also unveiled that the EGCG formed a greater number of hydrogen bonds with Aß40, effectively obstructing the dimer formation. In addition, we carried out microseconds of all-atom conventional molecular dynamics (cMD) simulations to investigate the formation of both hetero- and homo-oligomer states by these peptides. MD simulations illustrated that EGCG played a significant role in preventing oligomer formation by reducing the content of ß-sheets in the peptide. Collectively, our results offered valuable insight into the mechanism of cross-amyloid aggregation between Aß40 and IAPP and the inhibition effect of EGCG on the heteroaggregation process.

Analysis of Cancer-associated Mutations of POLB using Machine Learning and Bioinformatics.

DNA damage is a critical factor in the onset and progression of cancer. When DNA is damaged, the number of genetic mutations increases, making it necessary to activate DNA repair mechanisms. A crucial factor in the base excision repair process, which helps maintain the stability of the genome, is an enzyme called DNA polymerase [Formula: see text] (Pol[Formula: see text]) encoded by the POLB gene. It plays a vital role in the repair of damaged DNA. Additionally, variations known as Single Nucleotide Polymorphisms (SNPs) in the POLB gene can potentially affect the ability to repair DNA. This study uses bioinformatics tools that extract important features from SNPs to construct a feature matrix, which is then used in combination with machine learning algorithms to predict the likelihood of developing cancer associated with a specific mutation. Eight different machine learning algorithms were used to investigate the relationship between POLB gene variations and their potential role in cancer onset. This study not only highlights the complex link between POLB gene SNPs and cancer, but also underscores the effectiveness of machine learning approaches in genomic studies, paving the way for advanced predictive models in genetic and cancer research.

GRank: a middleware search engine for ranking genes by relevance to given genes.

BACKGROUND: Biologists may need to know the set of genes that are semantically related to a given set of genes. For instance, a biologist may need to know the set of genes related to another set of genes known to be involved in a specific disease. Some works use the concept of gene clustering in order to identify semantically related genes. Others propose tools that return the set of genes that are semantically related to a given set of genes. Most of these gene similarity measures determine the semantic similarities among the genes based solely on the proximity to each other of the GO terms annotating the genes, while overlook the structural dependencies among these GO terms, which may lead to low recall and precision of results. RESULTS: We propose in this paper a search engine called GRank, which overcomes the limitations of the current gene similarity measures outlined above as follows. It employs the concept of existence dependency to determine the structural dependencies among the GO terms annotating a given set of gene. After determining the set of genes that are semantically related to input genes, GRank would use microarray experiment to rank these genes based on their degree of relativity to the input genes. We evaluated GRank experimentally and compared it with a comparable gene prediction tool called DynGO, which retrieves the genes and gene products that are relatives of input genes. Results showed marked improvement. CONCLUSIONS: The experimental results demonstrated that GRank overcomes the limitations of current gene similarity measures. We attribute this performance to GRank's use of existence dependency concept for determining the semantic relationships among gene annotations. The recall and precision values for two benchmarking datasets showed that GRank outperforms DynGO tool, which does not employ the concept of existence dependency. The demo of GRank using 11000 KEGG yeast genes and a Gene Expression Omnibus (GEO) microarray file named "GSM34635.pad" is available at: http://ecesrvr.kustar.ac.ae:8080/ (click on the link labelled Gene Ontology 2).

Structure, function, and folding of phosphoglycerate kinase are strongly perturbed by macromolecular crowding.

We combine experiment and computer simulation to show how macromolecular crowding dramatically affects the structure, function, and folding landscape of phosphoglycerate kinase (PGK). Fluorescence labeling shows that compact states of yeast PGK are populated as the amount of crowding agents (Ficoll 70) increases. Coarse-grained molecular simulations reveal three compact ensembles: C (crystal structure), CC (collapsed crystal), and Sph (spherical compact). With an adjustment for viscosity, crowded wild-type PGK and fluorescent PGK are about 15 times or more active in 200 mg/ml Ficoll than in aqueous solution. Our results suggest a previously undescribed solution to the classic problem of how the ADP and diphosphoglycerate binding sites of PGK come together to make ATP: Rather than undergoing a hinge motion, the ADP and substrate sites are already located in proximity under crowded conditions that mimic the in vivo conditions under which the enzyme actually operates. We also examine T-jump unfolding of PGK as a function of crowding experimentally. We uncover a nonmonotonic folding relaxation time vs. Ficoll concentration. Theory and modeling explain why an optimum concentration exists for fastest folding. Below the optimum, folding slows down because the unfolded state is stabilized relative to the transition state. Above the optimum, folding slows down because of increased viscosity.

Hydrogen Bond Dynamics and Phase Transitions of Water inside Carbon Nanotubes.

Water dynamics in nanochannels are altered by confinement, particularly in small carbon nanotubes (CNTs). However, the mechanisms behind these effects remain unclear. To address these issues, we carried out extensive molecular dynamics (MD) simulations to investigate the structure and dynamics of water inside CNTs of different sizes (length of 20 nm and diameters vary from 0.8 nm to 5.0 nm) at different temperatures (from 200 K to 420 K). The radial density profile of water inside CNTs shows a single peak near the CNT walls for small nanotubes. For CNTs with larger sizes, water molecules are arranged into coaxial tubular sheets, the number of which increases with the CNT size. Subdiffusive behavior is observed for ultranarrow CNTs with diameters of 0.8 nm and 1 nm. As the size of CNTs increases, Fickian diffusion becomes evident. The hydrogen bond correlation function of water inside CNT decays slower than in bulk water, and the decay rate decreases as we increase the diameter of the CNTs. In large CNTs, the hydrogen bond lifetime of the innermost layer is shorter than the other layers and depends on temperature. Additional analysis of our results reveals that water molecules along the CNT axis show a non-Arrhenius to Arrhenius diffusion crossover. In general, the diffusion transition temperature is higher than that of bulk water, but it depends on the size of the CNT.

Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification.

Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score.

Phosphorylation Induced Conformational Transitions in DNA Polymerase ß.

DNA polymerase ß (pol ß) is a member of the X- family of DNA polymerases that catalyze the distributive addition of nucleoside triphosphates during base excision DNA repair. Previous studies showed that the enzyme was phosphorylated in vitro with PKC at two serines (44 and 55), causing loss of DNA polymerase activity but not DNA binding. In this work, we have investigated the phosphorylation-induced conformational changes in DNA polymerase ß in the presence of Mg ions. We report a comprehensive atomic resolution study of wild type and phosphorylated DNA polymerase using molecular dynamics (MD) simulations. The results are examined via novel methods of internal dynamics and energetics analysis to reveal the underlying mechanism of conformational transitions observed in DNA pol ß. The results show drastic conformational changes in the structure of DNA polymerase ß due to S44 phosphorylation. Phosphorylation-induced conformational changes transform the enzyme from a closed to an open structure. The dynamic cross-correlation shows that phosphorylation enhances the correlated motions between the different domains. Centrality network analysis reveals that the S44 phosphorylation causes structural rearrangements and modulates the information pathway between the Lyase domain and base pair binding domain. Further analysis of our simulations reveals that a critical hydrogen bond (between S44 and E335) disruption and the formation of three additional salt bridges are potential drivers of these conformational changes. In addition, we found that two of these additional salt bridges form in the presence of Mg ions on the active sites of the enzyme. These results agree with our previous study of DNA pol ß S44 phosphorylation without Mg ions which predicted the deactivation of DNA pol ß. However, the phase space of structural transitions induced by S44 phosphorylation is much richer in the presence of Mg ions.

Linking Alzheimer's Disease and Type 2 Diabetes: Characterization and Inhibition of Cytotoxic Aß and IAPP Hetero-Aggregates.

The cytotoxic self-aggregation of ß-amyloid (Aß) peptide and islet amyloid polypeptide (IAPP) is implicated in the pathogenesis of Alzheimer's disease (AD) and Type 2 diabetes (T2D), respectively. Increasing evidence, particularly the co-deposition of Aß and IAPP in both brain and pancreatic tissues, suggests that Aß and IAPP cross-interaction may be responsible for a pathological link between AD and T2D. Here, we examined the nature of IAPP-Aß40 co-aggregation and its inhibition by small molecules. In specific, we characterized the kinetic profiles, morphologies, secondary structures and toxicities of IAPP-Aß40 hetero-assemblies and compared them to those formed by their homo-assemblies. We demonstrated that monomeric IAPP and Aß40 form stable hetero-dimers and hetero-assemblies that further aggregate into ß-sheet-rich hetero-aggregates that are toxic (cell viability <50%) to both PC-12 cells, a neuronal cell model, and RIN-m5F cells, a pancreatic cell model for ß-cells. We then selected polyphenolic candidates to inhibit IAPP or Aß40 self-aggregation and examined the inhibitory effect of the most potent candidate on IAPP-Aß40 co-aggregation. We demonstrated that epigallocatechin gallate (EGCG) form inter-molecular hydrogen bonds with each of IAPP and Aß40. We also showed that EGCG reduced hetero-aggregate formation and resulted in lower ß-sheets content and higher unordered structures in IAPP-Aß40-EGCG samples. Importantly, we showed that EGCG is highly effective in reducing the toxicity of IAPP-Aß40 hetero-aggregates on both cell models, specifically at concentrations that are equivalent to or are 2.5-fold higher than the mixed peptide concentrations. To the best of our knowledge, this is the first study to report the inhibition of IAPP-Aß40 co-aggregation by small molecules. We conclude that EGCG is a promising candidate to prevent co-aggregation and cytotoxicity of IAPP-Aß40, which in turn, contribute to the pathological link between AD and T2D.

Crowded, cell-like environment induces shape changes in aspherical protein.

How the crowded environment inside cells affects the structures of proteins with aspherical shapes is a vital question because many proteins and protein-protein complexes in vivo adopt anisotropic shapes. Here we address this question by combining computational and experimental studies of a football-shaped protein (i.e., Borrelia burgdorferi VlsE) in crowded, cell-like conditions. The results show that macromolecular crowding affects protein-folding dynamics as well as overall protein shape. In crowded milieus, distinct conformational changes in VlsE are accompanied by secondary structure alterations that lead to exposure of a hidden antigenic region. Our work demonstrates the malleability of "native" proteins and implies that crowding-induced shape changes may be important for protein function and malfunction in vivo.

The Zero-Order Loop in Apoazurin Modulates Folding Mechanism In Silico.

Pseudomonas aeruginosa apoazurin (apo, without the copper cofactor) has a single disulfide bond between residues 3 and 26 and unfolds in a two-state reaction in vitro. The disulfide bond covalently connects the N-termini of ß-strands 1 and 3; thereby, it creates a zero-order loop or a "cinch" that restricts conformational space. Covalent loops and threaded topologies are emerging as important structural elements in folded proteins and may be important for function. In order to understand the role of a zero-order loop in the folding process of a protein, here we used coarse-grained molecular dynamics (CGMD) simulations in silico to compare two variants of apoazurin: one named "loop" which contained the disulfide, and another named "open" in which the disulfide bond between residues 3 and 26 was removed. CGMD simulations were performed to probe the stability and unfolding pathway of the two apoazurin variants at different urea concentrations and temperatures. Our results show that the covalent loop plays a prominent role in the unfolding mechanism of apoazurin; its removal alters both the folding-transition state and the unfolded-state ensemble of conformations. We propose that modulation of azurin's folding landscape by the disulfide bridge may be related to both copper capturing and redox sensing.

Multiscale investigation of chemical interference in proteins.

We developed a multiscale approach (MultiSCAAL) that integrates the potential of mean force obtained from all-atomistic molecular dynamics simulations with a knowledge-based energy function for coarse-grained molecular simulations in better exploring the energy landscape of a small protein under chemical interference such as chemical denaturation. An excessive amount of water molecules in all-atomistic molecular dynamics simulations often negatively impacts the sampling efficiency of some advanced sampling techniques such as the replica exchange method and it makes the investigation of chemical interferences on protein dynamics difficult. Thus, there is a need to develop an effective strategy that focuses on sampling structural changes in protein conformations rather than solvent molecule fluctuations. In this work, we address this issue by devising a multiscale simulation scheme (MultiSCAAL) that bridges the gap between all-atomistic molecular dynamics simulation and coarse-grained molecular simulation. The two key features of this scheme are the Boltzmann inversion and a protein atomistic reconstruction method we previously developed (SCAAL). Using MultiSCAAL, we were able to enhance the sampling efficiency of proteins solvated by explicit water molecules. Our method has been tested on the folding energy landscape of a small protein Trp-cage with explicit solvent under 8M urea using both the all-atomistic replica exchange molecular dynamics and MultiSCAAL. We compared computational analyses on ensemble conformations of Trp-cage with its available experimental NOE distances. The analysis demonstrated that conformations explored by MultiSCAAL better agree with the ones probed in the experiments because it can effectively capture the changes in side-chain orientations that can flip out of the hydrophobic pocket in the presence of urea and water molecules. In this regard, MultiSCAAL is a promising and effective sampling scheme for investigating chemical interference which presents a great challenge when modeling protein interactions in vivo.

Inferring Causation in Yeast Gene Association Networks With Kernel Logistic Regression.

Computational prediction of gene-gene associations is one of the productive directions in the study of bioinformatics. Many tools are developed to infer the relation between genes using different biological data sources. The association of a pair of genes deduced from the analysis of biological data becomes meaningful when it reflects the directionality and the type of reaction between genes. In this work, we follow another method to construct a causal gene co-expression network while identifying transcription factors in each pair of genes using microarray expression data. We adopt a machine learning technique based on a logistic regression model to tackle the sparsity of the network and to improve the quality of the prediction accuracy. The proposed system classifies each pair of genes into either connected or nonconnected class using the data of the correlation between these genes in the whole Saccharomyces cerevisiae genome. The accuracy of the classification model in predicting related genes was evaluated using several data sets for the yeast regulatory network. Our system achieves high performance in terms of several statistical measures.

Macromolecular crowding modulates folding mechanism of alpha/beta protein apoflavodoxin.

Protein dynamics in cells may be different from those in dilute solutions in vitro, because the environment in cells is highly concentrated with other macromolecules. This volume exclusion because of macromolecular crowding is predicted to affect both equilibrium and kinetic processes involving protein conformational changes. To quantify macromolecular crowding effects on protein folding mechanisms, we investigated the folding energy landscape of an alpha/beta protein, apoflavodoxin, in the presence of inert macromolecular crowding agents, using in silico and in vitro approaches. By means of coarse-grained molecular simulations and topology-based potential interactions, we probed the effects of increased volume fractions of crowding agents (phi(c)) as well as of crowding agent geometry (sphere or spherocylinder) at high phi(c). Parallel kinetic folding experiments with purified Desulfovibro desulfuricans apoflavodoxin in vitro were performed in the presence of Ficoll (sphere) and Dextran (spherocylinder) synthetic crowding agents. In conclusion, we identified the in silico crowding conditions that best enhance protein stability, and discovered that upon manipulation of the crowding conditions, folding routes experiencing topological frustrations can be either enhanced or relieved. Our test-tube experiments confirmed that apoflavodoxin's time-resolved folding path is modulated by crowding agent geometry. Macromolecular crowding effects may be a tool for the manipulation of protein-folding and function in living cells.

Hydrophobic interactions of hexane in nanosized water droplets.

We use all-atomistic molecular dynamics simulations to study hydrophobic interactions of hexane in nanosized water droplets where the hydrogen bonding network of water molecules is disrupted at the surface. As a result of the competition between the energetics of a hexane molecule and the distribution of water molecules that lost the ability to form hydrogen bonds at the boundary, all-trans-hexane molecules are statistically favored at the surface of a nanosized water droplet and such a statistical trend increases as the size of a nano water droplet decreases. Changes in the radial distribution and the orientation of water molecules surrounding hexane in nanosized water droplets over bulk water are indicative of the finite-size effects on the structural distribution of a short, topologically complex hydrocarbon chain.