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.

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.

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.

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.

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.

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.

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.

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.

Maximum parsimony interpretation of chromatin capture experiments.

We present a new approach to characterizing the global geometric state of chromatin from HiC data. Chromatin conformation capture techniques (3C, and its variants: 4C, 5C, HiC, etc.) probe the spatial structure of the genome by identifying physical contacts between genomic loci within the nuclear space. In whole-genome conformation capture (HiC) experiments, the signal can be interpreted as spatial proximity between genomic loci and physical distances can be estimated from the data. However, observed spatial proximity signal does not directly translate into persistent contacts within the nuclear space. Attempts to infer a single conformation of the genome within the nuclear space lead to internal geometric inconsistencies, notoriously violating the triangle inequality. These inconsistencies have been attributed to the stochastic nature of chromatin conformation or to experimental artifacts. Here we demonstrate that it can be explained by a mixture of cells, each in one of only several conformational states, contained in the sample. We have developed and implemented a graph-theoretic approach that identifies the properties of such postulated subpopulations. We show that the geometrical conflicts in a standard yeast HiC dataset, can be explained by only a small number of homogeneous populations of cells (4 populations are sufficient to reconcile 95,000 most prominent impossible triangles, 8 populations can explain 375,000 top geometric conflicts). Finally, we analyze the functional annotations of genes differentially interacting between the populations, suggesting that each inferred subpopulation may be involved in a functionally different transcriptional program.

Crowding-Induced Elongated Conformation of Urea-Unfolded Apoazurin: Investigating the Role of Crowder Shape in Silico.

Here, we show by solution nuclear magnetic resonance measurements that the urea-unfolded protein apoazurin becomes elongated when the synthetic crowding agent dextran 20 is present, in contrast to the prediction from the macromolecular crowding effect based on the argument of volume exclusion. To explore the complex interactions beyond volume exclusion, we employed coarse-grained molecular dynamics simulations to explore the conformational ensemble of apoazurin in a box of monodisperse crowders under strong chemically denaturing conditions. The elongated conformation of unfolded apoazurin appears to result from the interplay of the effective attraction between the protein and crowders and the shape of the crowders. With a volume-conserving crowder model, we show that the crowder shape provides an anisotropic direction of the depletion force, in which a bundle of surrounding rodlike crowders stabilize an elongated conformation of unfolded apoazurin in the presence of effective attraction between the protein and crowders.

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.

Critical phenomena in the temperature-pressure-crowding phase diagram of a protein.

In the cell, proteins fold and perform complex functions through global structural rearrangements. Function requires a protein to be at the brink of stability to be susceptible to small environmental fluctuations, yet stable enough to maintain structural integrity. These apparently conflicting behaviors are exhibited by systems near a critical point, where distinct phases merge-a concept beyond previous studies indicating proteins have a well-defined folded/unfolded phase boundary in the pressure-temperature plane. Here, by modeling the protein phosphoglycerate kinase (PGK) on the temperature (T), pressure (P), and crowding volume-fraction (ϕ) phase diagram, we demonstrate a critical transition where phases merge, and PGK exhibits large structural fluctuations. Above the critical point, the difference between the intermediate and unfolded phases disappears. When ϕ increases, the critical point moves to lower T c. We verify the calculations with experiments mapping the T-P-ϕ space, which likewise reveal a critical point at 305 K and 170 MPa that moves to lower T c as ϕ increases. Crowding places PGK near a critical line in its natural parameter space, where large conformational changes can occur without costly free energy barriers. Specific structures are proposed for each phase based on simulation.

Molecular dynamics simulations suggest changes in electrostatic interactions as a potential mechanism through which serine phosphorylation inhibits DNA polymerase ß activity.

DNA polymerase ß is a 39 kDa enzyme that is a major component of Base Excision Repair in human cells. The enzyme comprises two major domains, a 31 kDa domain responsible for the polymerase activity and an 8 kDa domain, which bind ssDNA and has a deoxyribose phosphate (dRP) lyase activity. DNA polymerase ß was shown to be phosphorylated in vitro with protein kinase C (PKC) at serines 44 and 55 (S44 and S55), resulting in loss of its polymerase enzymic activity, but not its ability to bind ssDNA. In this study, we investigate the potential phosphorylation-induced structural changes for DNA polymerase ß using molecular dynamics simulations. The simulations show drastic conformational changes of the polymerase structure as a result of S44 phosphorylation. Phosphorylation-induced conformational changes transform the closed (active) enzyme structure into an open one. Further analysis of the results points to a key hydrogen bond and newly formed salt bridges as potential drivers of these structural fluctuations. The changes observed with S55/44 and S55 phosphorylation were less dramatic and the integrity of the H-bond was not compromised. Thus the phosphorylation of S44 is the major contributor to structural fluctuations that lead to loss of enzymatic activity.

Impact of hydrodynamic interactions on protein folding rates depends on temperature.

We investigated the impact of hydrodynamic interactions (HI) on protein folding using a coarse-grained model. The extent of the impact of hydrodynamic interactions, whether it accelerates, retards, or has no effect on protein folding, has been controversial. Together with a theoretical framework of the energy landscape theory (ELT) for protein folding that describes the dynamics of the collective motion with a single reaction coordinate across a folding barrier, we compared the kinetic effects of HI on the folding rates of two protein models that use a chain of single beads with distinctive topologies: a 64-residue α/ß chymotrypsin inhibitor 2 (CI2) protein, and a 57-residue ß-barrel α-spectrin Src-homology 3 domain (SH3) protein. When comparing the protein folding kinetics simulated with Brownian dynamics in the presence of HI to that in the absence of HI, we find that the effect of HI on protein folding appears to have a "crossover" behavior about the folding temperature. This means that at a temperature greater than the folding temperature, the enhanced friction from the hydrodynamic solvents between the beads in an unfolded configuration results in lowered folding rate; conversely, at a temperature lower than the folding temperature, HI accelerates folding by the backflow of solvent toward the folded configuration of a protein. Additionally, the extent of acceleration depends on the topology of a protein: for a protein like CI2, where its folding nucleus is rather diffuse in a transition state, HI channels the formation of contacts by favoring a major folding pathway in a complex free energy landscape, thus accelerating folding. For a protein like SH3, where its folding nucleus is already specific and less diffuse, HI matters less at a temperature lower than the folding temperature. Our findings provide further theoretical insight to protein folding kinetic experiments and simulations.

Molecular dynamics simulations suggest changes in electrostatic interactions as a potential mechanism through which serine phosphorylation inhibits DNA Polymerase ß's activity.

DNA polymerase ß is a 39kDa enzyme that is a major component of Base Excision Repair in human cells. The enzyme comprises two major domains, a 31kDa domain responsible for the polymerase activity and an 8kDa domain, which bind ssDNA and has a deoxyribose phosphate (dRP) lyase activity. DNA polymerase ß was shown to be phosphorylated in vitro with protein kinase C (PKC) at serines 44 and 55 (S44 and S55), resulting in loss of its polymerase enzymic activity, but not its ability to bind ssDNA. In this study, we investigate the potential phosphorylation-induced structural changes for DNA polymerase ß using molecular dynamics. The simulations show drastic conformational changes of the polymerase structure as a result of S44 phosphorylation. Phosphorylation-induced conformational changes transform the closed (active) enzyme structure into an open one. Further analysis of the results points to a key hydrogen bond and newly formed salt bridges as potential drivers of these structural fluctuations. The changes observed with S44/55 and S55 phosphorylation were less dramatic than S44 and the integrity of the H-bond was not compromised. Thus the phosphorylation of S44 is likely the major contributor to structural fluctuations that lead to loss of enzymatic activity.

Constructing Genetic Networks using Biomedical Literature and Rare Event Classification.

Text mining has become an important tool in bioinformatics research with the massive growth in the biomedical literature over the past decade. Mining the biomedical literature has resulted in an incredible number of computational algorithms that assist many bioinformatics researchers. In this paper, we present a text mining system called Gene Interaction Rare Event Miner (GIREM) that constructs gene-gene-interaction networks for human genome using information extracted from biomedical literature. GIREM identifies functionally related genes based on their co-occurrences in the abstracts of biomedical literature. For a given gene g, GIREM first extracts the set of genes found within the abstracts of biomedical literature associated with g. GIREM aims at enhancing biological text mining approaches by identifying the semantic relationship between each co-occurrence of a pair of genes in abstracts using the syntactic structures of sentences and linguistics theories. It uses a supervised learning algorithm, weighted logistic regression to label pairs of genes to related or un-related classes, and to reflect the population proportion using smaller samples. We evaluated GIREM by comparing it experimentally with other well-known approaches and a protein-protein interactions database. Results showed marked improvement.

Applying Monte Carlo Simulation to Biomedical Literature to Approximate Genetic Network.

Biologists often need to know the set of genes associated with a given set of genes or a given disease. We propose in this paper a classifier system called Monte Carlo for Genetic Network (MCforGN) that can construct genetic networks, identify functionally related genes, and predict gene-disease associations. MCforGN identifies functionally related genes based on their co-occurrences in the abstracts of biomedical literature. For a given gene g , the system first extracts the set of genes found within the abstracts of biomedical literature associated with g. It then ranks these genes to determine the ones with high co-occurrences with g . It overcomes the limitations of current approaches that employ analytical deterministic algorithms by applying Monte Carlo Simulation to approximate genetic networks. It does so by conducting repeated random sampling to obtain numerical results and to optimize these results. Moreover, it analyzes results to obtain the probabilities of different genes' co-occurrences using series of statistical tests. MCforGN can detect gene-disease associations by employing a combination of centrality measures (to identify the central genes in disease-specific genetic networks) and Monte Carlo Simulation. MCforGN aims at enhancing state-of-the-art biological text mining by applying novel extraction techniques. We evaluated MCforGN by comparing it experimentally with nine approaches. Results showed marked improvement.