Advancing liposome technology for innovative strategies against malaria

This review discusses the potential of liposomes as drug delivery systems for antimalarial therapies. Malaria continues to be a significant cause of mortality and morbidity, particularly among children and pregnant women. Drug resistance due to patient non-compliance and troublesome side effects remains a significant challenge in antimalarial treatment. Liposomes, as targeted and efficient drug carriers, have garnered attention owing to their ability to address these issues. Liposomes encapsulate hydrophilic and/or hydrophobic drugs, thus providing comprehensive and suitable therapeutic drug delivery. Moreover, the potential of passive and active drug delivery enables drug concentration in specific target tissues while reducing adverse effects. However, successful liposome formulation is influenced by various factors, including drug physicochemical characteristics and physiological barriers encountered during drug delivery. To overcome these challenges, researchers have explored modifications in liposome nanocarriers to achieve efficient drug loading, controlled release, and system stability. Computational approaches have also been adopted to predict liposome system stability, membrane integrity, and drug-liposome interactions, improving formulation development efficiency. By leveraging computational methods, optimizing liposomal drug delivery systems holds promise for enhancing treatment efficacy and minimizing side effects in malaria therapy. This review consolidates the current understanding and highlights the potential of liposome strategies against malaria.

Deep learning approaches for seizure video analysis: A review

Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded events in the clinical setting. To enhance the accuracy and consistency of evaluations, computer-aided video analysis of seizures has emerged as a natural avenue. In the field of medical applications, deep learning and computer vision approaches have driven substantial advancements. Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting. While vision-based technologies do not aim to replace clinical expertise, they can significantly contribute to medical decision-making and patient care by providing quantitative evidence and decision support. Behavior monitoring tools offer several advantages such as providing objective information, detecting challenging-to-observe events, reducing documentation efforts, and extending assessment capabilities to areas with limited expertise. The main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization. In this paper, we detail the foundation technologies used in vision-based systems in the analysis of seizure videos, highlighting their success in semiology detection and analysis, focusing on work published in the last 7 years. We systematically present these methods and indicate how the adoption of deep learning for the analysis of video recordings of seizures could be approached. Additionally, we illustrate how existing technologies can be interconnected through an integrated system for video-based semiology analysis. Each module can be customized and improved by adapting more accurate and robust deep learning approaches as these evolve. Finally, we discuss challenges and research directions for future studies.

An unprecedented global challenge, emerging trends and innovations in the fight against COVID-19: A comprehensive review

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a highly contagious and dangerous virus that caused the global COVID-19 pandemic in early 2020. It primarily affects the respiratory system, leading to severe illness and high rates of mortality worldwide. The virus enters the body by binding to a receptor called ACE2, which is present in specific cells of the lungs known as type 2 alveolar epithelial cells. Numerous studies have investigated the consequences of SARS-CoV-2 infection, revealing various impacts on the body. This review provides an overview of SARS-CoV-2, including its structure and how it infects cells. It also examines the different variants of concern, such as Alpha, Beta, Gamma, Delta, and the more recent Omicron variant, discussing their characteristics and the level of damage they cause. The usage of drugs to treat COVID-19 is another aspect that has been covered and compares the effectiveness and use of antiviral drugs in the treatment and its potential benefits in COVID-19 treatment. Furthermore, this review explores the consequences and abnormalities associated with SARS-CoV-2 infection, including its impact on various organs and systems in the body. And also discussing the different COVID-19 vaccines available and their effectiveness in preventing infection and reducing the severity of illness. The current review ensures the recent update of the COVID research with expert's knowledge, collection of numerous data from reliable sources and methodologies as well as update of findings based on reviews. This review also provided clear contextual explanations to aid the interpretation and application of the results. The main motto and limitation of this manuscript are to address the computational methods of drug discovery against the rapidly evolving SARS-CoV-2 virus, which has been discussed. Additionally, current computational approaches which are cost effective and can able to predict the therapeutic agents for the treatment against the virus have also been discussed.

Computational inhibition of S100A8 (calgranulin A) as a potential non-invasive biomarker for rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic joint pain and inflammation, loss of mobility, which affects the quality of life. The etiology of RA is unknown, and there is no isolated cause for the development of this illness. Synovial inflammation, autoantibody generation and bone degradation leading to deformity are classic characteristics of RA. The search for a non-invasive biomarker is crucial for helping patients receive standard therapies leading to faster treatments, as diagnosis depends on invasive biopsies. Therefore, in the present investigation, using transcriptomics and proteomic approaches, potential genes involved in crucial networks in RA were identified. Gene expression datasets of two tissue types i.e. whole blood and synovial tissue were retrieved from the GEO database and used for transcriptomic analyses. Using SWATH-MS analysis, differentially expressed proteins were identified from collected saliva of RA patients. Through bioinformatics analysis, S100A8, also known as Calprotectin (a complex of S100A8/A9) or Calgranulin A, was found to be common among all tissue types. S100A8 was then further quantified in saliva of healthy volunteers and RA patients, where it was found that the protein's level in healthy controls was lowered when compared to RA patients. Through in-silico characterization, potential plant-based inhibitors of S100A8 were also identified, to reduce its pro-inflammatory effects. Thus, it may be important in the pathophysiology of RA and, in the future, might help guide targeted treatment. as well as act as a non-invasive diagnostic biomarker platform.

Reimagining old drugs with new tricks: Mechanisms, strategies and notable success stories in drug repurposing for neurological diseases

Recent evolution in drug repurposing has brought new anticipation, especially in the conflict against neurodegenerative diseases (NDDs). The traditional approach to developing novel drugs for these complex disorders is laborious, time-consuming, and often abortive. However, drug reprofiling which is the implementation of illuminating novel therapeutic applications of existing approved drugs, has shown potential as a promising strategy to accelerate the hunt for therapeutics. The advancement of computational approaches and artificial intelligence has expedited drug repurposing. These progressive technologies have enabled scientists to analyse extensive datasets and predict potential drug-disease interactions. By prospecting into the existing pharmacological knowledge, scientists can recognise potential therapeutic candidates for reprofiling, saving precious time and resources. Preclinical models have also played a pivotal role in this field, confirming the effectiveness and mechanisms of action of repurposed drugs. Several studies have occurred in recent years, including the discovery of available drugs that demonstrate significant protective effects in NDDs, relieve debilitating symptoms, or slow down the progression of the disease. These findings highlight the potential of repurposed drugs to change the landscape of NDD treatment. Here, we present an overview of recent developments and major advances in drug repurposing intending to provide an in-depth analysis of traditional drug discovery and the strategies, approaches and technologies that have contributed to drug repositioning. In addition, this chapter attempts to highlight successful case studies of drug repositioning in various therapeutic areas related to NDDs and explore the clinical trials, challenges and limitations faced by researchers in the field. Finally, the importance of drug repositioning in drug discovery and development and its potential to address discontented medical needs is also highlighted.

Insights into the antimicrobial efficacy of Coleus aromaticus essential oil against food-borne microbes: Biochemical and molecular simulation approaches

The study reported the antimicrobial efficacy of chemically characterized Coleus aromaticus essential oil (CEO) against food-borne bacteria, molds (Aspergillus flavus), aflatoxin B1 (AFB1) and explored its mechanism of action using biochemical and molecular simulation approaches. The chemical profile of CEO was explored by Gas chromatography-mass spectrometry (GC-MS) analysis, which revealed thymol (46.0%) as the major compound. The minimum inhibitory concentration values of CEO for bacterial species Escherichia coli, Salmonella enterica, Bacillus cereus, and Shigella flexneri was found to be 0.9 μl/ml, 0.7 μl/ml, 0.16 μl/ml, and 0.12 μl/ml respectively. The MIC value for A. flavus and AFB1 contamination was 0.6 μl/ml. The DPPH radical scavenging activity of CEO was recorded with IC50 0.32 μl/ml. Biochemical and computational approaches (docking and dynamics simulation) have been performed to explore the multi-faceted antimicrobial inhibitory effects of CEO at the molecular level, which shows the impairment in membrane functioning, leakage of cellular contents, release of 260-nm absorbing materials, antioxidative defense, carbon catabolism and vital genes (7AP3, Nor1, Omt1, and Vbs). The findings indicated that CEO could be used as natural antimicrobial agents against food-spoilage bacteria, A. flavus and AFB1 contamination to extend the shelf-life of food product and prevention of food-borne diseases.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Computational approaches for simulating luminogenesis

Lumens, liquid-filled cavities surrounded by polarized tissue cells, are elementary units involved in the morphogenesis of organs. Theoretical modeling and computations, which can integrate various factors involved in biophysics of morphogenesis of cell assembly and lumens, may play significant roles to elucidate the mechanisms in formation of such complex tissue with lumens. However, up to present, it has not been documented well what computational approaches or frameworks can be applied for this purpose and how we can choose the appropriate approach for each problem. In this review, we report some typical lumen morphologies and basic mechanisms for the development of lumens, focusing on three keywords - mechanics, hydraulics and geometry - while outlining pros and cons of the current main computational strategies. We also describe brief guidance of readouts, i.e., what we should measure in experiments to make the comparison with the model's assumptions and predictions.

A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19

BACKGROUND

COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.

PURPOSE

This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.

METHODS

A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.

RESULTS

In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.

CONCLUSION

Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.

A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants

BACKGROUND

The field of pharmacogenomics focuses on the way a person's genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient's genome, hence rendering its incorporation into clinical routine practice a realistic possibility.

METHODS

This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would categorize novel variants according to their expected effect on protein functionality. This categorization is based on the available in silico prediction and/or conservation scores, which are selected with the use of recursive feature elimination process. Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation.

RESULTS

All models achieved similar performance toward making informed conclusions, with RF model achieving the highest accuracy (85%, 95% CI: 0.79, 0.90), as well as improved overall performance (precision 85%, sensitivity 84%, specificity 94%) and being used for subsequent analyses. When applied on real world WGS data, the selected RF model identified 2 missense variants, expected to lead to decreased function proteins and 1 to increased. As expected, a greater number of variants were highlighted when the approach was used on NGS data derived from targeted resequencing of coding regions. Specifically, 71 variants (out of 156 with sufficient annotation information) were classified as to "Decreased function," 41 variants as "No" function proteins, and 1 variant in "Increased function."

CONCLUSION

Overall, the proposed RF-based classification model holds promise to lead to an extremely useful variant prioritization and act as a scoring tool with interesting clinical applications in the fields of pharmacogenomics and personalized medicine.

Computational Approaches for the Design of (Mutant-)Selective Tyrosine Kinase Inhibitors: State-of-the-Art and Future Prospects

Kinases remain one of the major attractive therapeutic targets for a large number of indications such as cancer, rheumatoid arthritis, cardiac failure and many others. Design and development of kinase inhibitors (ATP-competitive, allosteric or covalent) is a clinically validated and successful strategy in the pharmaceutical industry. The perks come with limitations, particularly the development of resistance to highly potent and selective inhibitors. When this happens, the cycle needs to be repeated, i.e., the design and development of kinase inhibitors active against the mutated forms. The complexity of tumor milieu makes it awfully difficult for these molecularly-targeted therapies to work. Every year newer and better versions of these agents are introduced in the clinic. Several computational approaches such as structure-, ligand-based or hybrid ones continue to live up to their potential in discovering novel kinase inhibitors. New schools of thought in this area continue to emerge, e.g., development of dual-target kinase inhibitors. But there are fundamental issues with this approach. It is indeed difficult to selectively optimize binding at two entirely different or related kinases. In addition to the conventional strategies, modern technologies (machine learning, deep learning, artificial intelligence, etc.) started yielding the results and building success stories. Computational tools invariably played a critical role in catalysing the phenomenal progress in kinase drug discovery field. The present review summarized the progress in utilizing computational methods and tools for discovering (mutant-)selective tyrosine kinase inhibitor drugs in the last three years (2017-2019). Representative investigations have been discussed, while others are merely listed. The author believes that the enthusiastic reader will be inspired to dig out the cited literature extensively to appreciate the progress made so far and the future prospects of the field.

Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques

Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.

Drug Repurposing Approaches: Existing Leads For Novel Threats And Drug Targets

Drug Repurposing (DR) is an alternative to the traditional drug discovery process. It is cost and time effective, with high returns and low risk process that can tackle the increasing need for interventions for varied diseases and new outbreaks. Repurposing of old drugs for other diseases has gained a wider attention, as there have been several old drugs approved by FDA for new diseases. In the global emergency of COVID19 pandemic, this is one of the strategies implemented in repurposing of old anti-infective, anti-rheumatic and anti-thrombotic drugs. The goal of the current review is to elaborate the process of DR, its advantages, repurposed drugs for a plethora of disorders, and the evolution of related academic publications. Further, detailed are the computational approaches: literature mining and semantic inference, network-based drug repositioning, signature matching, retrospective clinical analysis, molecular docking and experimental phenotypic screening. We discuss the legal and economical potential barriers in DR, existent collaborative models and recommendations for overcoming these hurdles and leveraging the complete potential of DR in finding new indications.

Vaccine design of coronavirus spike (S) glycoprotein in chicken: immunoinformatics and computational approaches

BACKGROUND

Infectious bronchitis (IB) is a highly contagious respiratory disease in chickens and produces economic loss within the poultry industry. This disease is caused by a single stranded RNA virus belonging to Cronaviridae family. This study aimed to design a potential multi-epitopes vaccine against infectious bronchitis virus spike protein (S). Protein characterization was also performed for IBV spike protein.

METHODS

The present study used various tools in Immune Epitope Database (IEDB) to predict conserved B and T cell epitopes against IBV spike (S) protein that may perform a significant role in provoking the resistance response to IBV infection.

RESULTS

In B cell prediction methods, three epitopes ( ₁₁₃₉ KKSSYY ₁₁₄₄ , ₁₁₄₀ KSSYYT ₁₁₄₅ , ₁₁₄₁ SSYYT ₁₁₄₅ ) were selected as surface, linear and antigenic epitopes.Many MHCI and MHCII epitopes were predicted for IBV S protein. Among them ₉₈₂YYITARDMY₉₉₀ and ₉₈₃ YITARDMYM ₉₉₁ epitopes displayed high antigenicity, no allergenicity and no toxicity as well as great linkage with MHCI and MHCII alleles. Moreover, docking analysis of MHCI epitopes produced strong binding affinity with BF₂ alleles.

CONCLUSION

Five conserved epitopes were expected from spike glycoprotein of IBV as the best B and T cell epitopes due to high antigenicity, no allergenicity and no toxicity. In addition, MHC epitopes showed great linkage with MHC alleles as well as strong interaction with BF2 alleles. These epitopes should be designed and incorporated and then tested as multi-epitope vaccine against IBV.

Computational studies of potent covalent inhibitors on wild type or T790M/L858R mutant epidermal growth factor receptor

In this paper, we designed and synthesized two analog compounds M1 and T1 that have a Michael acceptor warhead. Although only slightly diversity existed in the structures of M1 and T1, their inhibitory activities against wild type epidermal growth factor receptor (EGFR^WT) and T790M/L858R mutant epidermal growth factor receptor (EGFR^T790M/L858R) were significant different. Thus, multiple computational approaches were applied to investigate the interactions between the compounds with EGFR^WT and EGFR^T790M/L858R in order to explore the effect of different compounds. The molecular docking and MD simulations were performed to study the intermolecular interactions between compounds and EGFR. The binding free energy revealed that M1-EGFR^WT and M1-EGFR^T790M/L858R complexes have stronger binding affinity compared with the corresponding T1-EGFR^WT and T1-EGFR^T790M/L858R complexes, respectively. And the binding free energy decompositions for each residue analysis indicated that the van der Waals interactions are the major contributor to enhance the compounds to bind with EGFR. In addition, covalent binding complexes of M1-EGFR^WT and M1-EGFR^T790M/L858R were constructed and studied. Moreover, quantum mechanics method was applied to investigate the reaction mechanism of covalent binding of the compound and EGFR. The results will provide the details of structural and energetic information to develop potent covalent EGFR inhibitors in the future.

Epitope-based peptide vaccine design and target site depiction against Middle East Respiratory Syndrome Coronavirus: an immune-informatics study

Background

Middle East Respiratory Syndrome Coronavirus (MERS-COV) is the main cause of lung and kidney infections in developing countries such as Saudi Arabia and South Korea. This infectious single-stranded, positive (+) sense RNA virus enters the host by binding to dipeptidyl-peptide receptors. Since no vaccine is yet available for MERS-COV, rapid case identification, isolation, and infection prevention strategies must be used to combat the spreading of MERS-COV infection. Additionally, there is a desperate need for vaccines and antiviral strategies.

Methods

The present study used immuno-informatics and computational approaches to identify conserved B- and T cell epitopes for the MERS-COV spike (S) protein that may perform a significant role in eliciting the resistance response to MERS-COV infection.

Results

Many conserved cytotoxic T-lymphocyte epitopes and discontinuous and linear B-cell epitopes were predicted for the MERS-COV S protein, and their antigenicity and interactions with the human leukocyte antigen (HLA) B7 allele were estimated. Among B-cell epitopes, QLQMGFGITVQYGT displayed the highest antigenicity-score, and was immensely immunogenic. Among T-cell epitopes, MHC class-I peptide YKLQPLTFL and MHC class-II peptide YCILEPRSG were identified as highly antigenic. Furthermore, docking analyses revealed that the predicted peptides engaged in strong bonding with the HLA-B7 allele.

Conclusion

The present study identified several MERS-COV S protein epitopes that are conserved among various isolates from different countries. The putative antigenic epitopes may prove effective as novel vaccines for eradication and combating of MERS-COV infection.

Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer

The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.

Rational construction of genome-reduced and high-efficient industrial Streptomyces chassis based on multiple comparative genomic approaches

BACKGROUND

Streptomyces chattanoogensis L10 is the industrial producer of natamycin and has been proved a highly efficient host for diverse natural products. It has an enormous potential to be developed as a versatile cell factory for production of heterologous secondary metabolites. Here we developed a genome-reduced industrial Streptomyces chassis by rational 'design-build-test' pipeline.

RESULTS

To identify candidate large non-essential genomic regions accurately and design large deletion rationally, we performed genome analyses of S. chattanoogensis L10 by multiple computational approaches, optimized Cre/loxP recombination system for high-efficient large deletion and constructed a series of universal suicide plasmids for rapid loxP or loxP mutant sites inserting into genome. Subsequently, two genome-streamlined mutants, designated S. chattanoogensis L320 and L321, were rationally constructed by depletion of 1.3 Mb and 0.7 Mb non-essential genomic regions, respectively. Furthermore, several biological performances like growth cycle, secondary metabolite profile, hyphae morphological engineering, intracellular energy (ATP) and reducing power (NADPH/NADP⁺) levels, transformation efficiency, genetic stability, productivity of heterologous proteins and secondary metabolite were systematically evaluated. Finally, our results revealed that L321 could serve as an efficient chassis for the production of polyketides.

CONCLUSIONS

Here we developed the combined strategy of multiple computational approaches and site-specific recombination system to rationally construct genome-reduced Streptomyces hosts with high efficiency. Moreover, a genome-reduced industrial Streptomyces chassis S. chattanoogensis L321 was rationally constructed by the strategy, and the chassis exhibited several emergent and excellent performances for heterologous expression of secondary metabolite. The strategy could be widely applied in other Streptomyces to generate miscellaneous and versatile chassis with minimized genome. These chassis can not only serve as cell factories for high-efficient production of valuable polyketides, but also will provide great support for the upgrade of microbial pharmaceutical industry and drug discovery.

In Silico Approaches for TRP Channel Modulation

The implication of several TRP ion channels (e.g., TRPV1) in diverse physiological and pathological processes has signaled them as pivotal drug targets. Consequently, the identification of selective and potent ligands for these channels is of great interest in pharmacology and biomedicine. However, a major challenge in the design of modulators is ensuring the specificity for their intended targets. In recent years, the emergence of high-resolution structures of ion channels facilitates the computer-assisted drug design at molecular levels. Here we describe some computational methods and general protocols to discover channel modulators, including homology modelling, docking and virtual screening, and structure-based peptide design.

Peptide vaccine against chikungunya virus: immuno-informatics combined with molecular docking approach

BACKGROUND

Chikungunya virus (CHIKV), causes massive outbreaks of chikungunya infection in several regions of Asia, Africa and Central/South America. Being positive sense RNA virus, CHIKV replication within the host resulting in its genome mutation and led to difficulties in creation of vaccine, drugs and treatment strategies. Vector control strategy has been a gold standard to combat spreading of CHIKV infection, but to eradicate a species from the face of earth is not an easy task. Therefore, alongside vector control, there is a dire need to prevent the infection through vaccine as well as through antiviral strategies.

METHODS

This study was designed to find out conserved B cell and T cell epitopes of CHIKV structural proteins through immuno-informatics and computational approaches, which may play an important role in evoking the immune responses against CHIKV.

RESULTS

Several conserved cytotoxic T-lymphocyte epitopes, linear and conformational B cell epitopes were predicted for CHIKV structural polyprotein and their antigenicity was calculated. Among B-cell epitopes "PPFGAGRPGQFGDI" showed a high antigenicity score and it may be highly immunogenic. In case of T cell epitopes, MHC class I peptides 'TAECKDKNL' and MHC class II peptides 'VRYKCNCGG' were found extremely antigenic.

CONCLUSION

The study led to the discovery of various epitopes, conserved among various strains belonging to different countries. The potential antigenic epitopes can be successfully utilized in designing novel vaccines for combating and eradication of CHIKV disease.

Biomolecular NMR: Past and future

The editors of this special volume suggested this topic, presumably because of the perspective lent by our combined >90-year association with biomolecular NMR. What follows is our personal experience with the evolution of the field, which we hope will illustrate the trajectory of change over the years. As for the future, one can confidently predict that it will involve unexpected advances. Our narrative is colored by our experience in using the NMR Facility for Biomedical Studies at Carnegie-Mellon University (Pittsburgh) and in developing similar facilities at Purdue (1977-1984) and the University of Wisconsin-Madison (1984-). We have enjoyed developing NMR technology and making it available to collaborators and users of these facilities. Our group's association with the Biological Magnetic Resonance data Bank (BMRB) and with the Worldwide Protein Data Bank (wwPDB) has also been rewarding. Of course, many groups contributed to the early growth and development of biomolecular NMR, and our brief personal account certainly omits many important milestones.

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting.

Computational investigations of hERG channel blockers: New insights and current predictive models

Identification of potential human Ether-a-go-go Related-Gene (hERG) potassium channel blockers is an essential part of the drug development and drug safety process in pharmaceutical industries or academic drug discovery centers, as they may lead to drug-induced QT prolongation, arrhythmia and Torsade de Pointes. Recent reports also suggest starting to address such issues at the hit selection stage. In order to prioritize molecules during the early drug discovery phase and to reduce the risk of drug attrition due to cardiotoxicity during pre-clinical and clinical stages, computational approaches have been developed to predict the potential hERG blockage of new drug candidates. In this review, we will describe the current in silico methods developed and applied to predict and to understand the mechanism of actions of hERG blockers, including ligand-based and structure-based approaches. We then discuss ongoing research on other ion channels and hERG polymorphism susceptible to be involved in LQTS and how systemic approaches can help in the drug safety decision.

A structure-based approach to predict predisposition to amyloidosis

BACKGROUND

Neurodegenerative diseases and other amyloidoses are linked to the formation of amyloid fibrils. It has been shown that the ability to form these fibrils is coded by the amino acid sequence. Existing methods for the prediction of amyloidogenicity generate an unsatisfactory high number of false positives when tested against sequences of the disease-related proteins.

METHODS

Recently, it has been shown that the three-dimensional structure of a majority of disease-related amyloid fibrils contains a β-strand-loop-β-strand motif called β-arch. Using this information, we have developed a novel bioinformatics approach for the prediction of amyloidogenicity.

RESULTS

The benchmark results show the superior performance of our method over the existing programs.

CONCLUSIONS

As genome sequencing becomes more affordable, our method provides an opportunity to create individual risk profiles for the neurodegenerative, age-related, and other diseases ushering in an era of personalized medicine. It will also be used in the large-scale analysis of proteomes to find new amyloidogenic proteins.

A review on the computational approaches for gene regulatory network construction

Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.

Investigation of interaction of nuclear fast red with human serum albumin by experimental and computational approaches

For the first time, interaction of nuclear fast red (NFR) with human serum albumin (HSA) was studied by experimental and computational approaches. Firstly, experimental measurements including fluorescence spectroscopy (F), UVvis spectrophotometry (UVvis), cyclic voltammetry (CV), differential pulse voltammetry (DPV) and linear sweep voltammetry (LSV) were separately used to investigate the interaction of NFR with HSA and interesting thermodynamics information was obtained from these studies. Secondly, new information including electrochemical behavior of NFR-HSA complex species, relative concentrations of the various reacting species and effects of NFR on the sub-structure of HSA was obtained by applying multivariate curve resolution-alternating least squares (MCR-ALS). In this case, a row- and column-wise augmented matrix was built with DPV, LSV, F and UVvis sub-matrices and resolved by MCR-ALS. Surprisingly, by this method two NFR-HSA complex species with different stoichiometries and different electrochemical behaviors were found. Furthermore, by the use of the recorded voltammetric and spectroscopic data the binding constants of complex species were computed by EQUISPEC (a hard-modeling algorithm). Finally, the binding of NFR to HSA was modeled by molecular modeling and molecular dynamics (MD) simulations methods. Excellent agreement was found between experimental and computational results. Both experimental and computational results suggested that the NFR binds mainly to the sub-domain IIA of HSA.