Recent Advances on the Network Models in Target-based Drug Discovery.

As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria.Scientific contribution Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease.

A Decade of the Human Genome Sequence—How Does the Medicinal Chemist Benefit?

Colorectal cancer (CRC) is the second leading cause of cancer death in the United States, and research advances over the past 20 years have allowed us to better understand the molecular mechanisms underlying both tumorigenesis and resistance to therapy. As a result, there are a wide range of novel agents currently under development that we anticipate will enter the clinical arena within the coming years. This review seeks to discuss a spectrum of key pathways and agents that may be particularly relevant to the treatment of CRC in the near future, as well as briefly discuss some of the challenges we face in this new era of drug discovery.

In the past century, as medical research has become increasingly precise, it has become clear that the incidence and progression of many diseases involve multiple factors and pathologies; this is particularly true for the degenerative and metabolic diseases facing industrialized societies. At the same time, it becomes increasingly clear that single-target action drugs cannot effectively treat these diseases. Researchers are looking toward the chemical industry as well as traditional herbal medicines to find multi-target interventions. Thus, a new era in drug discovery has begun. Specifically, three approaches have proven effective in seeking multi-target drugs. These are: (1) designing drugs with multiple components; (2) discovering drugs through the study of synergistic compound-compound interactions in medicinal herbs or among chemical drugs and herbal components; and (3) developing drugs to tackle complex multi-component diseases. The authors conclude that there is an increasing need for multi-component remedies to treat the complex chronic diseases afflicting modern populations. Given this situation and the growing body of evidence that these new approaches are effective, multi-target intervention appears to have great potential for discovering, designing, and developing effective new drugs for today's diseases.

Although structure-based drug design (SBDD) has become an indispensable tool in drug discovery for a long time, it continues to pose major challenges to date. With the advancement of "omics" techniques, systems biology has enriched SBDD into a new era, called polypharmacology, in which multi-targets drug or drug combination is designed to fight complex diseases. As a preliminary tool in systems biology, protein structure networks (PSNs) treat a protein as a set of residues linked by edges corresponding to the intramolecular interactions existing in folded structures between the residues. The PSN offers a computationally efficient tool to study the structure and function of proteins, and thus may facilitate structurebased drug design. Herein, we provide an overview of recent advances in PSNs, from predicting functionally important residues, to charactering protein-protein interactions and allosteric communication paths. Furthermore, we discuss potential pharmacological applications of PSN concepts and tools, and highlight the application to two families of drug targets, GPCRs and Hsp90. Although the application of PSNs as a framework for computer-aided drug discovery has been limited to date, we put forward the potential utility value in the near future and propose the PSNs could also serve as a new tool for polypharmacology research.

Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm.

Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. The immune system involves >1,500 genes/proteins in many interconnected pathways and processes, and a systems-level approach is critical in broadening our understanding of the immune response to vaccination. Changes in molecular pathways can be detected using high-throughput omics datasets (e.g., transcriptomics, proteomics, and metabolomics) by using methods such as pathway enrichment, network analysis, machine learning, etc. Importantly, integration of multiple omic datasets is becoming key to revealing novel biological insights. In this perspective article, we highlight the use of protein-protein interaction (PPI) networks as a multi-omics integration approach to unravel information flow and mechanisms during complex biological events, with a focus on the immune system. This involves a combination of tools, including: InnateDB, a database of curated interactions between genes and protein products involved in the innate immunity; NetworkAnalyst, a visualization and analysis platform for InnateDB interactions; and MetaBridge, a tool to integrate metabolite data into PPI networks. The application of these systems techniques is demonstrated for a variety of biological questions, including: the developmental trajectory of neonates during the first week of life, mechanisms in host-pathogen interaction, disease prognosis, biomarker discovery, and drug discovery and repurposing. Overall, systems biology analyses of omics data have been applied to a variety of immunology-related questions, and here we demonstrate the numerous ways in which PPI network analysis can be a powerful tool in contributing to our understanding of the immune system and the study of vaccines.

The theory of complex networks has a wide range of applications in a variety of disciplines such as communications and power system engineering, the internet and worldwide web (www), food webs, human social networks, molecular biology, population biology and biological networks. The focus of this paper is on biological applications of the theory of graphs and networks. Graph theory and several graph theoretic properties serve as an ideal mathematical tool in the analysis of complex networks. We present the basic concepts and notations from graph theory which is widely used in the study of biological networks. Various biological networks such as Protein interaction networks, Metabolome based reaction network, Gene regulatory network, Gene coexpression network, Protein structure network, Structural brain network, Phylogenetic networks, Ecological networks and Food web networks are described. We also deal with various centrality measures which provide deep insight in the study of biological networks. Applications of biological network analysis in several areas are also discussed.

A First Course in Systems Biology is intended to serve as the textbook for a semester-long course in systems biology directed toward advanced undergraduate or graduate students. The book meets this goal admirably. Eberhard O. Voit writes in a style that is both interesting to read and highly informational. A First Course in Systems Biology manages to combine the fields of mathematics, engineering, and biology in a textbook that could be read and assimilated (with varying degrees of difficulty) by students of all three disciplines. The text is broken into three main sections: mathematical modeling, “molecular inventories” of the requisite biological systems, and case studies. Despite the extensive mathematical component, A First Course in Systems Biology is not set up like a traditional mathematics textbook. Instead of long proofs and stark equations, concepts are first introduced intuitively before the equations are formalized, and readers are walked through a concrete example. A number of plots and descriptive images accompany the math, ensuring that even a novice number-cruncher can grasp the basics of Boolean network models, stability analysis, and parameter estimation. The middle third of the text covers the most pertinent biological details required for studying gene regulatory systems, protein interaction networks, metabolic pathways, and cell signaling cascades from a systems biology perspective. The book closes with a series of case studies that focus on the proper selection and application of biological models. For example, in Chapter 12, Voit demonstrates the type of experimental data and computational simulation that would be required for modeling several aspects of heart function, from a physiological model of blood flow through the atria and ventricles to one that examines oscillations of calcium signaling. Throughout, Voit provides clear and concise explanations of the most important and basic modeling and biological concepts without resorting to oversimplification, though explanations can sometimes be vague. Further, some topics, particularly that of epigenetics, are disappointingly terse. However, the brevity is always compensated for with extensive references for the curious reader. Indeed, despite the approachability of the text, this book requires great concentration and interaction on the part of the reader. Frequently, Voit urges that “the reader should confirm” the mathematical conclusions stated in the text. Extensive and open-ended exercises are given at the end of each chapter. These problems are not for the faint of heart and often require additional research, computer coding, or essay writing to complete. While A First Course in Systems Biology does not teach students precisely how to code mathematical models or provide protocols for biological experiments, the book does grant students an entry into the proper mindset of systems biologists. The continual emphasis on determining how to choose an appropriate model for a given problem and understanding what type of information disparate models could provide prepares readers well for generating systems biology questions and models of their own.

DynOmics (dynomics.pitt.edu) is a portal developed to leverage rapidly growing structural proteomics data by efficiently and accurately evaluating the dynamics of structurally resolved systems, from individual molecules to large complexes and assemblies, in the context of their physiological environment. At the core of the portal is a newly developed server, ENM 1.0, which permits users to efficiently generate information on the collective dynamics of any structure in PDB format, user-uploaded or database-retrieved. ENM 1.0 integrates two widely used elastic network models (ENMs)—the Gaussian Network Model (GNM) and the Anisotropic Network Model (ANM), extended to take account of molecular environment. It enables users to assess potentially functional sites, signal transduction or allosteric communication mechanisms, and protein–protein and protein–DNA interaction poses, in addition to delivering ensembles of accessible conformers reconstructed at atomic details based on the global modes of motions predicted by the ANM. The ‘environment’ is defined in a flexible manner, from lipid bilayer and crystal contacts, to substrate or ligands bound to a protein, or surrounding subunits in a multimeric structure or assembly. User-friendly interactive features permit users to easily visualize how the environment alter the intrinsic dynamics of the query systems. ENM 1.0 can be accessed at http://enm.pitt.edu/ or http://dyn.life.nthu.edu.tw/oENM/.

A drug takes many years to develop and reach the market using the conventional drug discovery procedure. Computer-aided drug design (CADD) is an emerging technology that accelerates the process of drug discovery and minimizes the total expenditure associated with labour and resources. In the current scenario, the computational aided drug design (CADD) techniques play a significant role in the design and development of lead molecules for the treatment of various lethal pathological conditions. The prediction of the tertiary structure of a protein is a big concern in drug design and discovery. A typical drug discovery procedure starts with the tertiary structure of a protein. At present, a total of 184,407 protein structures are available in the protein data bank, which are determinedusing experimental methods. However, the procedures are difficult and time-consuming. A more advanced technique has been developed for the prediction of the 3D structure of a protein using a computational method. This technique has played a vital role in drug discovery. It has not only facilitated but also hastened the process of drug discovery. The method is named homology modeling since it involves the building of a protein model based on its homology to similar evolutionary proteins. The method is based on the fact that evolutionary related proteins have similar structures. In homology modelling, the 3D structure of a protein is derived from its primary sequence based on its similarity to the existing protein templates. There are many computational tools for homology modelling such as Modeller, Swiss model, Composer, 3D-JIGSAW, etc. The proposed book chapter will cover the introduction to homology modelling, step-by-step guide to building a protein model, various challenges and how to refine and validate the model, different algorithms related to sequence alignment, similarity search, and the applications of homology modelling in drug design and discovery. The chapter would be very fruitful to the readers to get insights into protein modelling, which will facilitate their research activities. It will be of great application in various disciplines,such as bioinformatics, physics, structural biology, and molecular biology. The content of the chapter will cover various research papers, review papers, and corresponding reference books.

Networks have been used to model many real-world phenomena to better understand the phenomena and to guide experiments in order to predict their behavior. Since incorrect models lead to incorrect predictions, it is vital to have as accurate a model as possible. As a result, new techniques and models for analyzing and modeling real-world networks have recently been introduced. One example of large and complex networks involves protein-protein interaction (PPI) networks. We analyze PPI networks of yeast Saccharomyces cerevisiae and fruitfly Drosophila melanogaster using a newly introduced measure of local network structure as well as the standardly used measures of global network structure. We examine the fit of four different network models, including Erdos-Renyi, scale-free and geometric random network models, to these PPI networks with respect to the measures of local and global network structure. We demonstrate that the currently accepted scale-free model of PPI networks fails to fit the data in several respects and show that a random geometric model provides a much more accurate model of the PPI data. We hypothesize that only the noise in these networks is scale-free. We systematically evaluate how well-different network models fit the PPI networks. We show that the structure of PPI networks is better modeled by a geometric random graph than by a scale-free model. Supplementary information is available at http://www.cs.utoronto.ca/~juris/data/data/ppiGRG04/

Deciphering Long-Range Effects of Mutations: An Integrated Approach Using Elastic Network Models and Protein Structure Networks

Essential proteins are crucial for the survival of cellular life and they are also important for many applications, such as drug design and the defense against human pathogens. Existing experimental approaches to identify essential proteins are time-consuming and expensive. Therefore, many computational methods to predict essential proteins are employed to address this issue. However, most of those approaches focus on topological features of protein-protein interaction (PPI) networks and they often fail to consider the low-connectivity essential proteins. In this paper, we present three indexes to measure biological features of low-connectivity essential proteins. And we also propose a centrality measure interaction-complex-function centrality (ICFC) to predict low-connectivity essential proteins, which combines topological structure information of the PPI network with the distinguishable biological properties of essential proteins. The experimental results demonstrate that the predicted precision of ICFC outperforms ten exiting centrality measures. The improvements of ICFC over the ten exiting centrality measures are up to 1.08∼3.31 times.

In this research, seven diseases OB (Obesity), SC (Stomach Cancer), CC (Colorectal Cancer), PC (Pancreatic Cancer), PRC (Prostate Cancer), LK (Leukemia), MD (Metabolic Disorder) has been encountered to analyze the network. When one or few diseases among them affects human there will be a possibility for them to be affected by the other diseases. Previous studies indicate that there are large numbers of similar biological and genetic characteristics among the seven disease. Common gene network models among these diseases have been explored for this reason. The genes obtained were compared and number of the gene has been decreased by the preprocessing and filtering process. After the filtering process, 9 common genes among seven related disease has been extracted. Topological properties, protein interactions network, enrichment analysis, co-expression network analysis, gene regulatory network, and physical interaction network are analyzed in this research. The substantial hub proteins based on biological, genetic and biochemical relationships among common genes has been identified by this analysis. Drug signature suggestion implies the 9 hub proteins which has been identified by PPI, GRN, gene interaction protein network. Finally, Protein chemical interaction and gene-disease interaction network indicated 6 significant genes and 9 significant genes consecutively for drug design. Network Enrichment shows the 9 common hub proteins interaction with other related genes. This analysis helps us to identify drug design pathway with chemical interactions. By applying further examining and studies those gene structures, it may be able to notify us to take precautionary steps to reduce the risk of Stomach Cancer and Associated disease.