molecules-logo

Journal Browser

Journal Browser

Special Issue "Molecular Computing and Bioinformatics"

A special issue of Molecules (ISSN 1420-3049).

Deadline for manuscript submissions: closed (1 March 2019).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Xiangxiang Zeng
E-Mail Website
Guest Editor
Department of Computer Science, Xiamen University, Xiamen 361005, China
Interests: molecular computing; membrane computing; neural computing; systems biology
Special Issues and Collections in MDPI journals
Prof. Dr. Alfonso Rodríguez-Patón
E-Mail Website
Guest Editor
Department of Artificial Intelligence, Universidad Politcnica de Madrid, Madrid 28660, Spain
Interests: DNA computing; molecular computing; synthetic biology
Special Issues and Collections in MDPI journals
Prof. Dr. Quan Zou
E-Mail
Guest Editor

Special Issue Information

Dear Colleagues,

Molecular Computing and Bioinformatics are two important interdisciplinary sciences on molecules and computers. Molecular Computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is the potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of—and to do it cheaply. Molecules are only a few nanometers in size, making possible chips containing billions—even trillions—of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary, bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular Computing and Bioinformatics pays attention to the same object, have close relationships, but work towards different orientations.

The Guest Editors look forward to collecting a set of recent advances in the related topics, to provide a platform for researchers, and bridge computer researchers, bioengineers and molecular biologists.

Prof. Dr. Xiangxiang Zeng
Prof. Dr. Alfonso Rodríguez-Patón
Prof. Dr. Quan Zou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • DNA Computing
  • neural computing
  • self-assembling and self-organizing systems
  • super-turing computation
  • cellular automata
  • evolutionary computation
  • swarm intelligence
  • ant algorithms
  • artificial immune systems
  • artificial life
  • membrane computing
  • amorphous computing
  • computational systems biology
  • computational neuroscience
  • synthetic biology
  • cellular (in-vivo) computing
  • protein disorder region
  • systems biology
  • protein inter-residue contacts prediction

Published Papers (26 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review

Open AccessEditorial
Molecular Computing and Bioinformatics
Molecules 2019, 24(13), 2358; https://doi.org/10.3390/molecules24132358 - 26 Jun 2019
Abstract
Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, [...] Read more.
Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of—and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions—even trillions—of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available

Research

Jump to: Editorial, Review

Open AccessArticle
Genome-Wide Identification of the LAC Gene Family and Its Expression Analysis Under Stress in Brassica napus
Molecules 2019, 24(10), 1985; https://doi.org/10.3390/molecules24101985 - 23 May 2019
Cited by 1
Abstract
Lignin is an important biological polymer in plants that is necessary for plant secondary cell wall ontogenesis. The laccase (LAC) gene family catalyzes lignification and has been suggested to play a vital role in the plant kingdom. In this study, we [...] Read more.
Lignin is an important biological polymer in plants that is necessary for plant secondary cell wall ontogenesis. The laccase (LAC) gene family catalyzes lignification and has been suggested to play a vital role in the plant kingdom. In this study, we identified 45 LAC genes from the Brassica napus genome (BnLACs), 25 LAC genes from the Brassica rapa genome (BrLACs) and 8 LAC genes from the Brassica oleracea genome (BoLACs). These LAC genes could be divided into five groups in a cladogram and members in same group had similar structures and conserved motifs. All BnLACs contained hormone- and stress- related elements determined by cis-element analysis. The expression of BnLACs was relatively higher in the root, seed coat and stem than in other tissues. Furthermore, BnLAC4 and its predicted downstream genes showed earlier expression in the silique pericarps of short silique lines than long silique lines. Three miRNAs (miR397a, miR397b and miR6034) target 11 BnLACs were also predicted. The expression changes of BnLACs under series of stresses were further investigated by RNA sequencing (RNA-seq) and quantitative real-time polymerase chain reaction (qRT-PCR). The study will give a deeper understanding of the LAC gene family evolution and functions in B. napus. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning
Molecules 2019, 24(7), 1409; https://doi.org/10.3390/molecules24071409 - 10 Apr 2019
Cited by 1
Abstract
Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability that may be essential in developing molecular systems that [...] Read more.
Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability that may be essential in developing molecular systems that function in dynamic environments. Here, we introduce an in vitro molecular algorithm that ‘learns’ molecular models from training data, opening the possibility of ‘machine learning’ in wet molecular systems. Our algorithm enables enzymatic weight update by targeting internal loop structures in DNA and ensemble learning, based on the hypernetwork model. This novel approach allows massively parallel processing of DNA with enzymes for specific structural selection for learning in an iterative manner. We also introduce an intuitive method of DNA data construction to dramatically reduce the number of unique DNA sequences needed to cover the large search space of feature sets. By combining molecular computing and machine learning the proposed algorithm makes a step closer to developing molecular computing technologies for future access to more intelligent molecular systems. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems
Molecules 2019, 24(7), 1235; https://doi.org/10.3390/molecules24071235 - 29 Mar 2019
Cited by 2
Abstract
Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time [...] Read more.
Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Therapeutic Effects of HIF-1α on Bone Formation around Implants in Diabetic Mice Using Cell-Penetrating DNA-Binding Protein
Molecules 2019, 24(4), 760; https://doi.org/10.3390/molecules24040760 - 20 Feb 2019
Cited by 2
Abstract
Patients with uncontrolled diabetes are susceptible to implant failure due to impaired bone metabolism. Hypoxia-inducible factor 1α (HIF-1α), a transcription factor that is up-regulated in response to reduced oxygen during bone repair, is known to mediate angiogenesis and osteogenesis. However, its function is [...] Read more.
Patients with uncontrolled diabetes are susceptible to implant failure due to impaired bone metabolism. Hypoxia-inducible factor 1α (HIF-1α), a transcription factor that is up-regulated in response to reduced oxygen during bone repair, is known to mediate angiogenesis and osteogenesis. However, its function is inhibited under hyperglycemic conditions in diabetic patients. This study thus evaluates the effects of exogenous HIF-1α on bone formation around implants by applying HIF-1α to diabetic mice and normal mice via a protein transduction domain (PTD)-mediated DNA delivery system. Implants were placed in the both femurs of diabetic and normal mice. HIF-1α and placebo gels were injected to implant sites of the right and left femurs, respectively. We found that bone-to-implant contact (BIC) and bone volume (BV) were significantly greater in the HIF-1α treated group than placebo in diabetic mice (p < 0.05). Bioinformatic analysis showed that diabetic mice had 216 differentially expressed genes (DEGs) and 21 target genes. Among the target genes, NOS2, GPNMB, CCL2, CCL5, CXCL16, and TRIM63 were found to be associated with bone formation. Based on these results, we conclude that local administration of HIF-1α via PTD may boost bone formation around the implant and induce gene expression more favorable to bone formation in diabetic mice. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
Identification of D Modification Sites by Integrating Heterogeneous Features in Saccharomyces cerevisiae
Molecules 2019, 24(3), 380; https://doi.org/10.3390/molecules24030380 - 22 Jan 2019
Cited by 2
Abstract
As an abundant post-transcriptional modification, dihydrouridine (D) has been found in transfer RNA (tRNA) from bacteria, eukaryotes, and archaea. Nonetheless, knowledge of the exact biochemical roles of dihydrouridine in mediating tRNA function is still limited. Accurate identification of the position of D sites [...] Read more.
As an abundant post-transcriptional modification, dihydrouridine (D) has been found in transfer RNA (tRNA) from bacteria, eukaryotes, and archaea. Nonetheless, knowledge of the exact biochemical roles of dihydrouridine in mediating tRNA function is still limited. Accurate identification of the position of D sites is essential for understanding their functions. Therefore, it is desirable to develop novel methods to identify D sites. In this study, an ensemble classifier was proposed for the detection of D modification sites in the Saccharomyces cerevisiae transcriptome by using heterogeneous features. The jackknife test results demonstrate that the proposed predictor is promising for the identification of D modification sites. It is anticipated that the proposed method can be widely used for identifying D modification sites in tRNA. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Integrating Multiple Interaction Networks for Gene Function Inference
Molecules 2019, 24(1), 30; https://doi.org/10.3390/molecules24010030 - 21 Dec 2018
Cited by 2
Abstract
In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information [...] Read more.
In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Genome-Wide Identification and Comparative Analysis for OPT Family Genes in Panax ginseng and Eleven Flowering Plants
Molecules 2019, 24(1), 15; https://doi.org/10.3390/molecules24010015 - 20 Dec 2018
Cited by 3
Abstract
Herb genomics and comparative genomics provide a global platform to explore the genetics and biology of herbs at the genome level. Panax ginseng C.A. Meyer is an important medicinal plant for a variety of bioactive chemical compounds of which the biosynthesis may involve [...] Read more.
Herb genomics and comparative genomics provide a global platform to explore the genetics and biology of herbs at the genome level. Panax ginseng C.A. Meyer is an important medicinal plant for a variety of bioactive chemical compounds of which the biosynthesis may involve transport of a wide range of substrates mediated by oligopeptide transporters (OPT). However, information about the OPT family in the plant kingdom is still limited. Only 17 and 18 OPT genes have been characterized for Oryza sativa and Arabidopsis thaliana, respectively. Additionally, few comprehensive studies incorporating the phylogeny, gene structure, paralogs evolution, expression profiling, and co-expression network between transcription factors and OPT genes have been reported for ginseng and other species. In the present study, we performed those analyses comprehensively with both online tools and standalone tools. As a result, we identified a total of 268 non-redundant OPT genes from 12 flowering plants of which 37 were from ginseng. These OPT genes were clustered into two distinct clades in which clade-specific motif compositions were considerably conservative. The distribution of OPT paralogs was indicative of segmental duplication and subsequent structural variation. Expression patterns based on two sources of RNA-Sequence datasets suggested that some OPT genes were expressed in both an organ-specific and tissue-specific manner and might be involved in the functional development of plants. Further co-expression analysis of OPT genes and transcription factors indicated 141 positive and 11 negative links, which shows potent regulators for OPT genes. Overall, the data obtained from our study contribute to a better understanding of the complexity of the OPT gene family in ginseng and other flowering plants. This genetic resource will help improve the interpretation on mechanisms of metabolism transportation and signal transduction during plant development for Panax ginseng. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach
Molecules 2018, 23(12), 3193; https://doi.org/10.3390/molecules23123193 - 04 Dec 2018
Cited by 2
Abstract
Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug side effects have and achieved initial success. However, most of [...] Read more.
Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug side effects have and achieved initial success. However, most of the prediction methods mainly rely on direct similarities inferred from drug information and cannot fully utilize the drug information about the impact of protein–protein interactions (PPI) on potential drug targets. Moreover, most of the methods are designed for specific tasks. In this work, we propose a novel heterogeneous network embedding approach for learning drug representations called SDHINE, which integrates PPI information into drug embeddings and is generic for different adverse drug reaction (ADR) prediction tasks. To integrate heterogeneous drug information and learn drug representations, we first design different meta-path-based proximities to calculate drug similarities, especially target propagation meta-path-based proximity based on PPI network, and then construct a semi-supervised stacking deep neural network model that is jointly optimized by the defined meta-path proximities. Extensive experiments with three state-of-the-art network embedding methods on three ADR prediction tasks demonstrate the effectiveness of the SDHINE model. Furthermore, we compare the drug representations in terms of drug differentiation by mapping the representations into 2D space; the results show that the performance of our approach is superior to that of the comparison methods. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
An Efficient Classifier for Alzheimer’s Disease Genes Identification
Molecules 2018, 23(12), 3140; https://doi.org/10.3390/molecules23123140 - 29 Nov 2018
Cited by 6
Abstract
Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at [...] Read more.
Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
8-Bit Adder and Subtractor with Domain Label Based on DNA Strand Displacement
Molecules 2018, 23(11), 2989; https://doi.org/10.3390/molecules23112989 - 15 Nov 2018
Cited by 2
Abstract
DNA strand displacement, which plays a fundamental role in DNA computing, has been widely applied to many biological computing problems, including biological logic circuits. However, there are many biological cascade logic circuits with domain labels based on DNA strand displacement that have not [...] Read more.
DNA strand displacement, which plays a fundamental role in DNA computing, has been widely applied to many biological computing problems, including biological logic circuits. However, there are many biological cascade logic circuits with domain labels based on DNA strand displacement that have not yet been designed. Thus, in this paper, cascade 8-bit adder/subtractor with a domain label is designed based on DNA strand displacement; domain t and domain f represent signal 1 and signal 0, respectively, instead of domain t and domain f are applied to representing signal 1 and signal 0 respectively instead of high concentration and low concentration high concentration and low concentration. Basic logic gates, an amplification gate, a fan-out gate and a reporter gate are correspondingly reconstructed as domain label gates. The simulation results of Visual DSD show the feasibility and accuracy of the logic calculation model of the adder/subtractor designed in this paper. It is a useful exploration that may expand the application of the molecular logic circuit. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Inferring microRNA-Environmental Factor Interactions Based on Multiple Biological Information Fusion
Molecules 2018, 23(10), 2439; https://doi.org/10.3390/molecules23102439 - 24 Sep 2018
Cited by 5
Abstract
Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. [...] Read more.
Accumulated studies have shown that environmental factors (EFs) can regulate the expression of microRNA (miRNA) which is closely associated with several diseases. Therefore, identifying miRNA-EF associations can facilitate the study of diseases. Recently, several computational methods have been proposed to explore miRNA-EF interactions. In this paper, a novel computational method, MEI-BRWMLL, is proposed to uncover the relationship between miRNA and EF. The similarities of miRNA-miRNA are calculated by using miRNA sequence, miRNA-EF interaction, and the similarities of EF-EF are calculated based on the anatomical therapeutic chemical information, chemical structure and miRNA-EF interaction. The similarity network fusion is used to fuse the similarity between miRNA and the similarity between EF, respectively. Further, the multiple-label learning and bi-random walk are employed to identify the association between miRNA and EF. The experimental results show that our method outperforms the state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures
Molecules 2018, 23(8), 2055; https://doi.org/10.3390/molecules23082055 - 16 Aug 2018
Cited by 4
Abstract
Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational [...] Read more.
Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Putative Iron Acquisition Systems in Stenotrophomonas maltophilia
Molecules 2018, 23(8), 2048; https://doi.org/10.3390/molecules23082048 - 16 Aug 2018
Cited by 4
Abstract
Iron has been shown to regulate biofilm formation, oxidative stress response and several pathogenic mechanisms in Stenotrophomonas maltophilia. Thus, the present study is aimed at identifying various iron acquisition systems and iron sources utilized during iron starvation in S. maltophilia. The [...] Read more.
Iron has been shown to regulate biofilm formation, oxidative stress response and several pathogenic mechanisms in Stenotrophomonas maltophilia. Thus, the present study is aimed at identifying various iron acquisition systems and iron sources utilized during iron starvation in S. maltophilia. The annotations of the complete genome of strains K279a, R551-3, D457 and JV3 through Rapid Annotations using Subsystems Technology (RAST) revealed two putative subsystems to be involved in iron acquisition: the iron siderophore sensor and receptor system and the heme, hemin uptake and utilization systems/hemin transport system. Screening for these acquisition systems in S. maltophilia showed the presence of all tested functional genes in clinical isolates, but only a few in environmental isolates. NanoString nCounter Elements technology, applied to determine the expression pattern of the genes under iron-depleted condition, showed significant expression for FeSR (6.15-fold), HmuT (12.21-fold), Hup (5.46-fold), ETFb (2.28-fold), TonB (2.03-fold) and Fur (3.30-fold). The isolates, when further screened for the production and chemical nature of siderophores using CAS agar diffusion (CASAD) and Arnows’s colorimetric assay, revealed S. maltophilia to produce catechol-type siderophore. Siderophore production was also tested through liquid CAS assay and was found to be greater in the clinical isolate (30.8%) compared to environmental isolates (4%). Both clinical and environmental isolates utilized hemoglobin, hemin, transferrin and lactoferrin as iron sources. All data put together indicates that S. maltophilia utilizes siderophore-mediated and heme-mediated systems for iron acquisition during iron starvation. These data need to be further confirmed through several knockout studies. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
Correcting Errors in Image Encryption Based on DNA Coding
Molecules 2018, 23(8), 1878; https://doi.org/10.3390/molecules23081878 - 27 Jul 2018
Cited by 7
Abstract
As a primary method, image encryption is widely used to protect the security of image information. In recent years, image encryption pays attention to the combination with DNA computing. In this work, we propose a novel method to correct errors in image encryption, [...] Read more.
As a primary method, image encryption is widely used to protect the security of image information. In recent years, image encryption pays attention to the combination with DNA computing. In this work, we propose a novel method to correct errors in image encryption, which results from the uncertainty of DNA computing. DNA coding is the key step for DNA computing that could decrease the similarity of DNA sequences in DNA computing as well as correct errors from the process of image encryption and decryption. The experimental results show our method could be used to correct errors in image encryption based on DNA coding. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme
Molecules 2018, 23(7), 1820; https://doi.org/10.3390/molecules23071820 - 22 Jul 2018
Cited by 4
Abstract
P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood–brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range [...] Read more.
P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood–brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure–activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r2 = 0.96, qCV2 = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q2 = 0.80–0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
The Cartesian Product and Join Graphs on Edge-Version Atom-Bond Connectivity and Geometric Arithmetic Indices
Molecules 2018, 23(7), 1731; https://doi.org/10.3390/molecules23071731 - 16 Jul 2018
Cited by 11
Abstract
The Cartesian product and join are two classical operations in graphs. Let dL(G)(e) be the degree of a vertex e in line graph L(G) of a graph G. The edge versions of [...] Read more.
The Cartesian product and join are two classical operations in graphs. Let dL(G)(e) be the degree of a vertex e in line graph L(G) of a graph G. The edge versions of atom-bond connectivity (ABCe) and geometric arithmetic (GAe) indices of G are defined as efE(L(G))dL(G)(e)+dL(G)(f)2dL(G)(e)×dL(G)(f) and efE(L(G))2dL(G)(e)×dL(G)(f)dL(G)(e)+dL(G)(f), respectively. In this paper, ABCe and GAe indices for certain Cartesian product graphs (such as PnPm, PnCm and PnSm) are obtained. In addition, ABCe and GAe indices of certain join graphs (such as Cm+Pn+Sr, Pm+Pn+Pr, Cm+Cn+Cr and Sm+Sn+Sr) are deduced. Our results enrich and revise some known results. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Causal Discovery Combining K2 with Brain Storm Optimization Algorithm
Molecules 2018, 23(7), 1729; https://doi.org/10.3390/molecules23071729 - 16 Jul 2018
Abstract
Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on [...] Read more.
Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. However, when the graph was sufficiently large, it did not work well. To alleviate this problem, an improved causal structure learning algorithm named brain storm optimization (BSO), is presented in this paper, combining K2 with brain storm optimization (K2-BSO). Here BSO is used to search optimal topological order of nodes instead of graph space. This paper assumes that dataset is generated by conforming to a causal diagram in which each variable is generated from its parent based on a causal mechanism. We designed an elaborate distance function for clustering step in BSO according to the mechanism of K2. The graph space therefore was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method. The experimental results on various real-world datasets showed our methods outperformed the traditional search and score methods and the state-of-the-art genetic algorithm-based methods. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Scoring Amino Acid Mutations to Predict Avian-to-Human Transmission of Avian Influenza Viruses
Molecules 2018, 23(7), 1584; https://doi.org/10.3390/molecules23071584 - 29 Jun 2018
Cited by 3
Abstract
Avian influenza virus (AIV) can directly cross species barriers and infect humans with high fatality. Using machine learning methods, the present paper scores the amino acid mutations and predicts interspecies transmission. Initially, 183 signature positions in 11 viral proteins were screened by the [...] Read more.
Avian influenza virus (AIV) can directly cross species barriers and infect humans with high fatality. Using machine learning methods, the present paper scores the amino acid mutations and predicts interspecies transmission. Initially, 183 signature positions in 11 viral proteins were screened by the scores of five amino acid factors and their random forest rankings. The most important amino acid factor (Factor 3) and the minimal range of signature positions (50 amino acid residues) were explored by a supporting vector machine (the highest-performing classifier among four tested classifiers). Based on these results, the avian-to-human transmission of AIVs was analyzed and a prediction model was constructed for virology applications. The distributions of human-origin AIVs suggested that three molecular patterns of interspecies transmission emerge in nature. The novel findings of this paper provide important clues for future epidemic surveillance. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein–Protein Interaction Network
Molecules 2018, 23(6), 1460; https://doi.org/10.3390/molecules23061460 - 15 Jun 2018
Cited by 2
Abstract
High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease [...] Read more.
High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein–protein interaction (PPI) networks. In this study, based on penalized matrix decomposition (PMD), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMDpc) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMDpc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR). Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Discovering Structural Motifs in miRNA Precursors from the Viridiplantae Kingdom
Molecules 2018, 23(6), 1367; https://doi.org/10.3390/molecules23061367 - 06 Jun 2018
Cited by 4
Abstract
A small non-coding molecule of microRNA (19–24 nt) controls almost every biological process, including cellular and physiological, of various organisms’ lives. The amount of microRNA (miRNA) produced within an organism is highly correlated to the organism’s key processes, and determines whether the system [...] Read more.
A small non-coding molecule of microRNA (19–24 nt) controls almost every biological process, including cellular and physiological, of various organisms’ lives. The amount of microRNA (miRNA) produced within an organism is highly correlated to the organism’s key processes, and determines whether the system works properly or not. A crucial factor in plant biogenesis of miRNA is the Dicer Like 1 (DCL1) enzyme. Its responsibility is to perform the cleavages in the miRNA maturation process. Despite everything we already know about the last phase of plant miRNA creation, recognition of miRNA by DCL1 in pre-miRNA structures of plants remains an enigma. Herein, we present a bioinformatic procedure we have followed to discover structure patterns that could guide DCL1 to perform a cleavage in front of or behind an miRNA:miRNA* duplex. The patterns in the closest vicinity of microRNA are searched, within pre-miRNA sequences, as well as secondary and tertiary structures. The dataset consists of structures of plant pre-miRNA from the Viridiplantae kingdom. The results confirm our previous observations based on Arabidopsis thaliana precursor analysis. Hereby, our hypothesis was tested on pre-miRNAs, collected from the miRBase database to show secondary structure patterns of small symmetric internal loops 1-1 and 2-2 at a 1–10 nt distance from the miRNA:miRNA* duplex. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
Small Universal Bacteria and Plasmid Computing Systems
Molecules 2018, 23(6), 1307; https://doi.org/10.3390/molecules23061307 - 29 May 2018
Cited by 7
Abstract
Bacterial computing is a known candidate in natural computing, the aim being to construct “bacterial computers” for solving complex problems. In this paper, a new kind of bacterial computing system, named the bacteria and plasmid computing system (BP system), is proposed. We investigate [...] Read more.
Bacterial computing is a known candidate in natural computing, the aim being to construct “bacterial computers” for solving complex problems. In this paper, a new kind of bacterial computing system, named the bacteria and plasmid computing system (BP system), is proposed. We investigate the computational power of BP systems with finite numbers of bacteria and plasmids. Specifically, it is obtained in a constructive way that a BP system with 2 bacteria and 34 plasmids is Turing universal. The results provide a theoretical cornerstone to construct powerful bacterial computers and demonstrate a concept of paradigms using a “reasonable” number of bacteria and plasmids for such devices. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessArticle
ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers
Molecules 2018, 23(5), 1028; https://doi.org/10.3390/molecules23051028 - 27 Apr 2018
Cited by 4
Abstract
A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In [...] Read more.
A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Graphical abstract

Open AccessArticle
To Decipher the Mycoplasma hominis Proteins Targeting into the Endoplasmic Reticulum and Their Implications in Prostate Cancer Etiology Using Next-Generation Sequencing Data
Molecules 2018, 23(5), 994; https://doi.org/10.3390/molecules23050994 - 24 Apr 2018
Cited by 6
Abstract
Cancer was initially considered a genetic disease. However, recent studies have revealed the connection between bacterial infections and growth of different types of cancer. The enteroinvasive strain of Mycoplasma hominis alters the normal behavior of host cells that may result in the growth [...] Read more.
Cancer was initially considered a genetic disease. However, recent studies have revealed the connection between bacterial infections and growth of different types of cancer. The enteroinvasive strain of Mycoplasma hominis alters the normal behavior of host cells that may result in the growth of prostate cancer. The role of M. hominis in the growth and development of prostate cancer still remains unclear. The infection may regulate several factors that influence prostate cancer growth in susceptible individuals. The aim of this study was to predict M. hominis proteins targeted into the endoplasmic reticulum (ER) of the host cell, and their potential role in the induction of prostate cancer. From the whole proteome of M. hominis, 19 proteins were predicted to be targeted into the ER of host cells. The results of our study predict that several proteins of M. hominis may be targeted to the host cell ER, and possibly alter the normal pattern of protein folding. These predicted proteins can modify the normal function of the host cell. Thus, the intercellular infection of M. hominis in host cells may serve as a potential factor in prostate cancer etiology. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Review

Jump to: Editorial, Research

Open AccessReview
Application of Molecular Methods in the Identification of Ingredients in Chinese Herbal Medicines
Molecules 2018, 23(10), 2728; https://doi.org/10.3390/molecules23102728 - 22 Oct 2018
Cited by 2
Abstract
There are several kinds of Chinese herbal medicines originating from diverse sources. However, the rapid taxonomic identification of large quantities of Chinese herbal medicines is difficult using traditional methods, and the process of identification itself is prone to error. Therefore, the traditional methods [...] Read more.
There are several kinds of Chinese herbal medicines originating from diverse sources. However, the rapid taxonomic identification of large quantities of Chinese herbal medicines is difficult using traditional methods, and the process of identification itself is prone to error. Therefore, the traditional methods of Chinese herbal medicine identification must meet higher standards of accuracy. With the rapid development of bioinformatics, methods relying on bioinformatics strategies offer advantages with respect to the speed and accuracy of the identification of Chinese herbal medicine ingredients. This article reviews the applicability and limitations of biochip and DNA barcoding technology in the identification of Chinese herbal medicines. Furthermore, the future development of the two technologies of interest is discussed. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Figure 1

Open AccessReview
Machine Learning for Drug-Target Interaction Prediction
Molecules 2018, 23(9), 2208; https://doi.org/10.3390/molecules23092208 - 31 Aug 2018
Cited by 14
Abstract
Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve [...] Read more.
Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers. Full article
(This article belongs to the Special Issue Molecular Computing and Bioinformatics) Printed Edition available
Show Figures

Graphical abstract

Back to TopTop