Designing a Novel Approach Using a Greedy and Information-Theoretic Clustering-Based Algorithm for Anonymizing Microdata Sets.

Data anonymization is a technique that safeguards individuals' privacy by modifying attribute values in published data. However, increased modifications enhance privacy but diminish the utility of published data, necessitating a balance between privacy and utility levels. K-Anonymity is a crucial anonymization technique that generates k-anonymous clusters, where the probability of disclosing a record is 1/k. However, k-anonymity fails to protect against attribute disclosure when the diversity of sensitive values within the anonymous cluster is insufficient. Several techniques have been proposed to address this issue, among which t-closeness is considered one of the most robust privacy techniques. In this paper, we propose a novel approach employing a greedy and information-theoretic clustering-based algorithm to achieve strict privacy protection. The proposed anonymization algorithm commences by clustering the data based on both the similarity of quasi-identifier values and the diversity of sensitive attribute values. In the subsequent adjustment phase, the algorithm splits and merges the clusters to ensure that they each possess at least k members and adhere to the t-closeness requirements. Finally, the algorithm replaces the quasi-identifier values of the records in each cluster with the values of the cluster center to attain k-anonymity and t-closeness. Experimental results on three microdata sets from Facebook, Twitter, and Google+ demonstrate the proposed algorithm's ability to preserve the utility of released data by minimizing the modifications of attribute values while satisfying the k-anonymity and t-closeness constraints.

A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks.

Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.

Tracing drugs from discovery to disposal.

The life cycle of a drug begins with discovery and ends with its disposal. Drug discovery companies, drug manufacturers, regulatory agencies, suppliers, pharmacies, patients, healthcare providers, and many more are involved in this process. Transparency, traceability, automation, and data security are some of the most crucial factors affecting how effectively and safely the transactions are conducted across all parties involved in the cycle. By contrast, scalability, energy consumption, regulation, standards, and complexity hamper the adoption of new technology that is expected to fulfil these requirements. Here, we highlight how blockchain technology can track, accelerate, and boost the efficiency of incredibly complicated operations, such as pharmaceutical development.

In silico targeting breast cancer biomarkers by applying rambutan (Nephelium lappaceum) phytocompounds.

Worldwide, breast cancer is the leading type of cancer among women. Overexpression of various prognostic indicators, including nuclear receptors, is linked to breast cancer features. To date, no effective drug has been discovered to block the proliferation of breast cancer cells. This study has been designed to discover target-based small molecular-like natural drug candidates that have anti-cancer potential without causing any serious side effects. A comprehensive substrate-based drug design was carried out to discover the potential plant compounds against the target breast cancer biomarkers including phytochemicals screening, active site identification, molecular docking, pharmacokinetic (PK) properties prediction, toxicity prediction, and molecular dynamics (MD) simulation approaches. Twenty plant compounds extracted from the rambutan (Nephelium lappaceum) were obtained from PubChem Database; and screened against the breast cancer biomarkers including estrogen receptor (ER), progesterone receptor (PR), and androgen receptor (AR). The best docking interaction was chosen based on the higher binding affinity. Analyzing the pharmacokinetic properties and toxicity prediction results indicated that the fifteen selected plant compounds have good potency without toxicity and are safe for humans. Four phytochemicals with a higher binding affinity were chosen for each breast cancer biomarker to study their stability in interaction with the target proteins using MD simulation. Among the above compounds, Ellagic acid showed the high binding affinity against all three breast cancer biomarkers.Communicated by Ramaswamy H. Sarma.

GADP-align: A genetic algorithm and dynamic programming-based method for structural alignment of proteins.

Introduction: Similarity analysis of protein structure is considered as a fundamental step to give insight into the relationships between proteins. The primary step in structural alignment is looking for the optimal correspondence between residues of two structures to optimize the scoring function. An exhaustive search for finding such a correspondence between two structures is intractable. Methods: In this paper, a hybrid method is proposed, namely GADP-align, for pairwise protein structure alignment. The proposed method looks for an optimal alignment using a hybrid method based on a genetic algorithm and an iterative dynamic programming technique. To this end, the method first creates an initial map of correspondence between secondary structure elements (SSEs) of two proteins. Then, a genetic algorithm combined with an iterative dynamic programming algorithm is employed to optimize the alignment. Results: The GADP-align algorithm was employed to align 10 'difficult to align' protein pairs in order to evaluate its performance. The experimental study shows that the proposed hybrid method produces highly accurate alignments in comparison with the methods using exactly the dynamic programming technique. Furthermore, the proposed method prevents the local optimal traps caused by the unsuitable initial guess of the corresponding residues. Conclusion: The findings of this paper demonstrate that employing the genetic algorithm along with the dynamic programming technique yields highly accurate alignments between a protein pair by exploring the global alignment and avoiding trapping in local alignments.

Pharmacoinformatics-based phytochemical screening for anticancer impacts of yellow sweet clover, Melilotus officinalis (Linn.) Pall.

To date, much attention has been paid to phytochemicals because of their diverse pharmacological effects on a variety of diseases such as cancer. In this regard, computer-aided drug design, as a cost- and time-effective approach, is primarily applied to investigate the drug candidates before their further costly in vitro and in vivo experimental evaluations. Accordingly, different signaling pathways and proteins can be targeted using such strategies. As a key protein for the initiation of eukaryotic DNA replication, mini-chromosome maintenance complex component 7 (MCM7) overexpression is related to the initiation and progression of aggressive malignancies. The current study was conducted to identify new potential natural compounds from the yellow sweet clover, Melilotus officinalis (Linn.) Pall, by examining the potential of 40 isolated phytochemicals against MCM7 protein. A structure-based pharmacophore model to the protein active site cavity was generated and followed by virtual screening and molecular docking. Overall, four compounds were selected for further evaluation based on their binding affinities. Our analyses revealed that two novel compounds, namely rosmarinic acid (PubChem CID:5281792) and melilotigenin (PubChem CID:14059499) might be druggable and offer safe usage in human. The stability of these two protein-ligand complex structures was confirmed through molecular dynamics simulation. The findings of this study reveal the potential of these two phytochemicals to serve as anticancer agents, while further pharmacological experiments are required to confirm their effectiveness against human cancers.

Psychrophilic enzymes: structural adaptation, pharmaceutical and industrial applications.

Psychrophiles are cold-living microorganisms synthesizing enzymes that are permanently active at almost near-zero temperatures. Psychrozymes are supposed to be structurally more flexible than their homologous proteins. This structural flexibility enables these proteins to undergo conformational changes during catalysis and improve catalytic efficiency at low temperatures. The outstanding characteristics of the psychrophilic enzymes have attracted the attention of the scientific community to utilize them in a wide variety of industrial and pharmaceutical applications. In this review, we first highlight the current knowledge of the cold-adaptation mechanisms of the psychrophiles. In the sequel, we describe the potential applications of the enzymes in different biotechnological processes specifically, in the production of industrial and pharmaceutical products. KEY POINTS: • Methods that organisms have evolved to survive and proliferate at cold environments. • The economic benefits due to their high activity at low and moderate temperatures. • Applications of the psychrophiles in biotechnological and pharmaceutical industry.

Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches.

Computational epitope-based vaccine design is the cornerstone of vaccine development. Owing to the selection of proper compositions [antigens (Ags), epitopes, peptide linkers, and intramolecular adjuvants], epitope-based vaccines are considered a cost- and time-effective approach resulting in the development of vaccines with maximal therapeutic efficacy and minimal adverse reactions. In this review, we provide insights into in silico epitope-based vaccine design and highlight vaccinology procedures used for the development of the next-generation vaccines with high effectiveness.

In silico design of a triple-negative breast cancer vaccine by targeting cancer testis antigens.

Introduction: Triple-negative breast cancer (TNBC) is an important subtype of breast cancer, which occurs in the absence of estrogen, progesterone and HER-2 receptors. According to the recent studies, TNBC may be a cancer testis antigen (CTA)-positive tumor, indicating that the CTA-based cancer vaccine can be a treatment option for the patients bearing such tumors. Of these antigens (Ags), the MAGE-A family and NY-ESO-1 as the most immunogenic CTAs are the potentially relevant targets for the development of an immunotherapeutic way of the breast cancer treatment. Methods: In the present study, immunoinformatics approach was used to design a multi-epitope peptide vaccine to combat the TNBC. The vaccine peptide was constructed by the fusion of three crucial components, including the CD8+ cytotoxic T lymphocytes (CTLs) epitopes, helper epitopes and adjuvant. The epitopes were predicted from the MAGE-A and NY-ESO-1 Ags. In addition, the granulocyte-macrophage-colony-stimulating factor (GM-CSF) was used as an adjuvant to promote the CD4+ T cells towards the T-helper for more strong induction of CTL responses. The components were conjugated by proper linkers. Results: The vaccine peptide was examined for different physiochemical characteristics to confirm the safety and immunogenic behavior. Furthermore, the 3D-structure of the vaccine peptide was predicted based on the homology modeling approach using the MODELLER v9.17 program. The vaccine structure was also subjected to the molecular dynamics simulation study for structure refinement. The results verified the immunogenicity and safety profile of the constructed vaccine as well as its capability for stimulating both the cellular and humoral immune responses. Conclusion: Based on our in-silico analyses, the proposed vaccine may be considered for the immunotherapy of TNBC.

The two mutations of actin-myosin interface and their effect on the dynamics, structures, and functions of skeletal muscle actin.

Congenital myopathy is a broad category of muscular diseases with symptoms appearing at the time of birth. One type of congenital myopathy is Congenital Fiber Type Disproportion (CFTD), a severely debilitating disease. The G48D and G48C mutations in the D-loop and the actin-myosin interface are the two causes of CFTD. These mutations have been shown to significantly affect the structure and function of muscle fibers. To the author's knowledge, the effects of these mutations have not yet been studied. In this work, the power stroke structure of the head domain of myosin and the wild and mutated types of actin were modeled. Then, a MD simulation was run for the modeled structures to study the effects of these mutations on the structure, function, and molecular dynamics of actin. The wild and mutated actins docked with myosin showed differences in hydrogen bonding patterns, free binding energies, and hydrogen bond occupation frequencies. The G48D and G48C mutations significantly impacted the conformation of D-loops because of their larger size compared to Glycine and their ability to interfere with the polarity or hydrophobicity of this neutralized and hydrophobic loop. Therefore, the mutated loops were unable to fit properly into the hydrophobic groove of the adjacent G-actin. The abnormal structure of D-loops seems to result in the abnormal assembly of F-actins, giving rise to the symptoms of CFTD. It was also noted that G48C and G48D did not form hydrogen bonds with myosin in the residue 48 location. Nevertheless, in this case, muscles are unable to contract properly due to muscle atrophy.

Breast cancer vaccination comes to age: impacts of bioinformatics.

Introduction: Breast cancer, as one of the major causes of cancer death among women, is the central focus of this study. The recent advances in the development and application of computational tools and bioinformatics in the field of immunotherapy of malignancies such as breast cancer have emerged the new dominion of immunoinformatics, and therefore, next generation of immunomedicines . Methods: Having reviewed the most recent works on the applications of computational tools, we provide comprehensive insights into the breast cancer incidence and its leading causes as well as immunotherapy approaches and the future trends. Furthermore, we discuss the impacts of bioinformatics on different stages of vaccine design for the breast cancer, which can be used to produce much more efficient vaccines through a rationalized time- and cost-effective in silico approaches prior to conducting costly experiments. Results: The tools can be significantly used for designing the immune system-modulating drugs and vaccines based on in silico approaches prior to in vitro and in vivo experimental evaluations. Application of immunoinformatics in the cancer immunotherapy has shown its success in the pre-clinical models. This success returns back to the impacts of several powerful computational approaches developed during the last decade. Conclusion: Despite the invention of a number of vaccines for the cancer immunotherapy, more computational and clinical trials are required to design much more efficient vaccines against various malignancies, including breast cancer.

Structure Prediction of a Novel Exo-ß-1,3-Glucanase: Insights into the Cold Adaptation of Psychrophilic Yeast Glaciozyma antarctica PI12.

We report a detailed structural analysis of the psychrophilic exo-ß-1,3-glucanase (GaExg55) from Glaciozyma antarctica PI12. This study elucidates the structural basis of exo-1,3-ß-1,3-glucanase from this psychrophilic yeast. The structural prediction of GaExg55 remains a challenge because of its low sequence identity (37 %). A 3D model was constructed for GaExg55. Threading approach was employed to determine a suitable template and generate optimal target-template alignment for establishing the model using MODELLER9v15. The primary sequence analysis of GaExg55 with other mesophilic exo-1,3-ß-glucanases indicated that an increased flexibility conferred to the enzyme by a set of amino acids substitutions in the surface and loop regions of GaExg55, thereby facilitating its structure to cold adaptation. A comparison of GaExg55 with other mesophilic exo-ß-1,3-glucanases proposed that the catalytic activity and structural flexibility at cold environment were attained through a reduced amount of hydrogen bonds and salt bridges, as well as an increased exposure of the hydrophobic side chains to the solvent. A molecular dynamics simulation was also performed using GROMACS software to evaluate the stability of the GaExg55 structure at varying low temperatures. The simulation result confirmed the above findings for cold adaptation of the psychrophilic GaExg55. Furthermore, the structural analysis of GaExg55 with large catalytic cleft and wide active site pocket confirmed the high activity of GaExg55 to hydrolyze polysaccharide substrates.

Elderly fall risk prediction based on a physiological profile approach using artificial neural networks.

Falls play a critical role in older people's life as it is an important source of morbidity and mortality in elders. In this article, elders fall risk is predicted based on a physiological profile approach using a multilayer neural network with back-propagation learning algorithm. The personal physiological profile of 200 elders was collected through a questionnaire and used as the experimental data for learning and testing the neural network. The profile contains a series of simple factors putting elders at risk for falls such as vision abilities, muscle forces, and some other daily activities and grouped into two sets: psychological factors and public factors. The experimental data were investigated to select factors with high impact using principal component analysis. The experimental results show an accuracy of ≈90 percent and ≈87.5 percent for fall prediction among the psychological and public factors, respectively. Furthermore, combining these two datasets yield an accuracy of ≈91 percent that is better than the accuracy of single datasets. The proposed method suggests a set of valid and reliable measurements that can be employed in a range of health care systems and physical therapy to distinguish people who are at risk for falls.

Structural prediction of a novel laminarinase from the psychrophilic Glaciozyma antarctica PI12 and its temperature adaptation analysis.

Here, we present a novel psychrophilic ß-glucanase from Glaciozyma antarctica PI12 yeast that has been structurally modeled and analyzed in detail. To our knowledge, this is the first attempt to model a psychrophilic laminarinase from yeast. Because of the low sequence identity (<40%), a threading method was applied to predict a 3D structure of the enzyme using the MODELLER9v12 program. The results of a comparative study using other mesophilic, thermophilic, and hyperthermophilic laminarinases indicated several amino acid substitutions on the surface of psychrophilic laminarinase that totally increased the flexibility of its structure for efficient catalytic reactions at low temperatures. Whereas several structural factors in the overall structure can explain the weak thermal stability, this research suggests that the psychrophilic adaptation and catalytic activity at low temperatures were achieved through existence of longer loops and shorter or broken helices and strands, an increase in the number of aromatic and hydrophobic residues, a reduction in the number of hydrogen bonds and salt bridges, a higher total solvent accessible surface area, and an increase in the exposure of the hydrophobic side chains to the solvent. The results of comparative molecular dynamics simulation and principal component analysis confirmed the above strategies adopted by psychrophilic laminarinase to increase its catalytic efficiency and structural flexibility to be active at cold temperature.

Structural and functional analysis of a novel psychrophilic ß-mannanase from Glaciozyma antarctica PI12.

The structure of a novel psychrophilic ß-mannanase enzyme from Glaciozyma antarctica PI12 yeast has been modelled and analysed in detail. To our knowledge, this is the first attempt to model a psychrophilic ß-mannanase from yeast. To this end, a 3D structure of the enzyme was first predicted using a threading method because of the low sequence identity (<30%) using MODELLER9v12 and simulated using GROMACS at varying low temperatures for structure refinement. Comparisons with mesophilic and thermophilic mannanases revealed that the psychrophilic mannanase contains longer loops and shorter helices, increases in the number of aromatic and hydrophobic residues, reductions in the number of hydrogen bonds and salt bridges and numerous amino acid substitutions on the surface that increased the flexibility and its efficiency for catalytic reactions at low temperatures.

A context evaluation approach for structural comparison of proteins using cross entropy over n-gram modelling.

The structural comparison of proteins is a vital step in structural biology that is used to predict and analyse a new unknown protein function. Although a number of different techniques have been explored, the study to develop new alternative methods is still an active research area. The present paper introduces a text modelling-based technique for the structural comparison of proteins. The method models the secondary and tertiary structure of proteins in two linear sequences and then applies them to the comparison of two structures. The technique used for pairwise comparison of the sequences has been adopted from computational linguistics and its well-known techniques for analysing and quantifying textual sequences. To this end, an n-gram modelling technique is used to capture regularities between sequences, and then, the cross-entropy concept is employed to measure their similarities. Several experiments are conducted to evaluate the performance of the method and compare it with other commonly used programs. The assessments for information retrieval evaluation demonstrate that the technique has a high running speed, which is similar to other linear encoding methods, such as 3D-BLAST, SARST, and TS-AMIR, whereas its accuracy is comparable to CE and TM-align, which are high accuracy comparison tools. Accordingly, the results demonstrate that the algorithm has high efficiency compared with other state-of-the-art methods.

Artificial signal peptide prediction by a hidden markov model to improve protein secretion via Lactococcus lactis bacteria.

A hidden Markov model (HMM) has been utilized to predict and generate artificial secretory signal peptide sequences. The strength of signal peptides of proteins from different subcellular locations via Lactococcus lactis bacteria correlated with their HMM bit scores in the model. The results show that the HMM bit score +12 are determined as the threshold for discriminating secreteory signal sequences from the others. The model is used to generate artificial signal peptides with different bit scores for secretory proteins. The signal peptide with the maximum bit score strongly directs proteins secretion.

TS-AMIR: a topology string alignment method for intensive rapid protein structure comparison.

BACKGROUND: In structural biology, similarity analysis of protein structure is a crucial step in studying the relationship between proteins. Despite the considerable number of techniques that have been explored within the past two decades, the development of new alternative methods is still an active research area due to the need for high performance tools. RESULTS: In this paper, we present TS-AMIR, a Topology String Alignment Method for Intensive Rapid comparison of protein structures. The proposed method works in two stages: In the first stage, the method generates a topology string based on the geometric details of secondary structure elements, and then, utilizes an n-gram modelling technique over entropy concept to capture similarities in these strings. This initial correspondence map between secondary structure elements is submitted to the second stage in order to obtain the alignment at the residue level. Applying the Kabsch method, a heuristic step-by-step algorithm is adopted in the second stage to align the residues, resulting in an optimal rotation matrix and minimized RMSD. The performance of the method was assessed in different information retrieval tests and the results were compared with those of CE and TM-align, representing two geometrical tools, and YAKUSA, 3D-BLAST and SARST as three representatives of linear encoding schemes. It is shown that the method obtains a high running speed similar to that of the linear encoding schemes. In addition, the method runs about 800 and 7200 times faster than TM-align and CE respectively, while maintaining a competitive accuracy with TM-align and CE. CONCLUSIONS: The experimental results demonstrate that linear encoding techniques are capable of reaching the same high degree of accuracy as that achieved by geometrical methods, while generally running hundreds of times faster than conventional programs.