Combination Of Computational Tools Research Articles

Design of functional intrinsically disordered proteins.

Many proteins do not fold into a fixed three-dimensional structure, but rather function in a highly disordered state. These intrinsically disordered proteins pose a unique challenge to protein engineering and design: How can proteins be designed de novo if not by tailoring their structure? Here, we will review the nascent field of design of intrinsically disordered proteins with focus on applications in biotechnology and medicine. The design goals should not necessarily be the same as for de novo design of folded proteins as disordered proteins have unique functional strengths and limitations. We focus on functions where intrinsically disordered proteins are uniquely suited including disordered linkers, desiccation chaperones, sensors of the chemical environment, delivery of pharmaceuticals, and constituents of biomolecular condensates. Design of functional intrinsically disordered proteins relies on a combination of computational tools and heuristics gleaned from sequence-function studies. There are few cases where intrinsically disordered proteins have made it into industrial applications. However, we argue that disordered proteins can perform many roles currently performed by organic polymers, and that these proteins might be more designable due to their modularity.

Structural Stability Analysis of Multistory Building Structure under Wind Load Conditions

In the current research the effect of wind load on structural stability of building structure is evaluate using techniques of Computational Fluid Dynamics. The CFD analysis on building is conducted using ANSYS simulation package. The FSI simulations considered the complex interactions between the structural components and the surrounding fluid flow, providing valuable insights into the structural response and its vulnerability to external loading conditions. Through a combination of computational tools and numerical methods, the study was able to accurately predict the structural response, including deformations, stresses, and critical failure points.

Smartphones and the Neuroscience of Mental Health.

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Machine learning approach to integrated endometrial transcriptomic datasets reveals biomarkers predicting uterine receptivity in cattle at seven days after estrous

The main goal was to apply machine learning (ML) methods on integrated multi-transcriptomic data, to identify endometrial genes capable of predicting uterine receptivity according to their expression patterns in the cow. Public data from five studies were re-analyzed. In all of them, endometrial samples were obtained at day 6–7 of the estrous cycle, from cows or heifers of four different European breeds, classified as pregnant (n = 26) or not (n = 26). First, gene selection was performed through supervised and unsupervised ML algorithms. Then, the predictive ability of potential key genes was evaluated through support vector machine as classifier, using the expression levels of the samples from all the breeds but one, to train the model, and the samples from that one breed, to test it. Finally, the biological meaning of the key genes was explored. Fifty genes were identified, and they could predict uterine receptivity with an overall 96.1% accuracy, despite the animal’s breed and category. Genes with higher expression in the pregnant cows were related to circadian rhythm, Wnt receptor signaling pathway, and embryonic development. This novel and robust combination of computational tools allowed the identification of a group of biologically relevant endometrial genes that could support pregnancy in the cattle.

Soil prokaryotic community resilience, fungal colonisation and increased cross-domain co-occurrence in response to a plant-growth enhancing organic amendment

In agricultural systems based on organic fertilisers, the activity of prokaryotes and fungi is essential for degradation of complex substrates and release of nutrients for plant uptake. Understanding the dynamics of microbial communities in these systems is, therefore, desirable for designing successful management strategies aiming to optimise nutrient availability and improve plant productivity. Of particular interest is how the microbial inoculum provided by an organic substrate persists in the soil and interacts with soil and plant microbiomes, as these processes may affect the long-term benefits of organic amendments. We aimed to investigate how these dynamics occurred in soil treated with stabilised spent mushroom substrate (SMS), a soil amendment rich in nutrients and complex organic matter. We carried out a 14 week soil trial to assess the plant growth promoting properties of the SMS and to monitor the successional processes of the resulting SMS-soil communities compared to a control treatment containing soil amended with mineral fertiliser. Bacterial and fungal communities were analysed by high-throughput sequencing at both DNA and RNA (cDNA) levels. Using a combination of computational tools, including SourceTracker and Network analysis, we assessed the persistence of SMS-derived taxa in soil, and the changes in co-occurrence patterns and microbial community structure over time. Prokaryotic and fungal communities presented remarkably distinct trajectories following SMS treatment. The soil prokaryotic communities displayed higher levels of resilience to the changes introduced by SMS treatment and rapidly tended toward a soil-like profile, with low persistence of SMS-derived prokaryotes. By contrast, the SMS fungal community had greater success in soil colonisation during the time monitored. SMS treatment promoted an increase in the participation of fungi in the highly connected fraction of the active community, including fungal taxa of SMS origin. We observed the presence of highly connected microbial guilds, composed by fungal and bacterial taxa with reported capabilities of complex organic matter degradation. Many of these taxa were also significantly correlated with soil organic matter content and plant yield, suggesting that these highly connected taxa may play key roles not only in the community structure, but also in the plant-soil system under organic fertilisation.

Machine-learning-assisted prediction of magnetic double perovskites

Magnetism is an important property of materials that plays a key role in many different applications. In the present paper, we use a combination of computational tools: a machine-learning technique for screening of stable candidates, an evolutionary algorithm for crystal structure determination, and first-principles calculations for characterization of electronic and magnetic properties to make predictions on magnetic double perovskites, which are yet to be synthesized. Out of 412 scanned candidates of ${A}_{2}{BB}^{\ensuremath{'}}{O}_{6}$ composition with $3d$ and $4d$ or $5d$ transition metals at B and ${B}^{\ensuremath{'}}$ sites, we found 33 compounds to form stable double-perovskite structures, 25 of which were further considered for characterization of their structure and properties. Our exercise predicted 21 double perovskites of varying magnetic and electronic properties, ranging from ferromagnetic half metals to ferri- and antiferromagnetic insulators to ferromagnetic metals and a rare example of antiferromagnetic metals. Our computational study is expected to help in discovering new magnetic double perovskites.

Retooling Computational Techniques for EEG-Based Neurocognitive Modeling of Children's Data, Validity and Prospects for Learning and Education.

This paper describes continuing research on the building of neurocognitive models of the internal mental and brain processes of children using a novel adapted combination of existing computational approaches and tools, and using electro-encephalographic (EEG) data to validate the models. The guiding working model which was pragmatically selected for investigation was the established and widely used Adaptive Control of Thought-Rational (ACT-R) modeling architecture from cognitive science. The anatomo-functional circuitry covered by ACT-R is validated by MRI-based neuroscience research. The present experimental data was obtained from a cognitive neuropsychology study involving preschool children (aged 4–6), which measured their visual selective attention and word comprehension behaviors. The collection and analysis of Event-Related Potentials (ERPs) from the EEG data allowed for the identification of sources of electrical activity known as dipoles within the cortex, using a combination of computational tools (Independent Component Analysis, FASTICA; EEG-Lab DIPFIT). The results were then used to build neurocognitive models based on Python ACT-R such that the patterns and the timings of the measured EEG could be reproduced as simplified symbolic representations of spikes, built through simplified electric-field simulations. The models simulated ultimately accounted for more than three-quarters of variations spatially and temporally in all electrical potential measurements (fit of model to dipole data expressed as R2 ranged between 0.75 and 0.98; P < 0.0001). Implications for practical uses of the present work are discussed for learning and educational applications in non-clinical and special needs children's populations, and for the possible use of non-experts (teachers and parents).

Clinical characteristics of Algerian subjects with MODY p.R85W glucokinase mutation- in silico assessment of p.R85W effect on glucokinase structure and function

AimMaturity-onset diabetes of the young (MODY) is a genetically and clinically heterogeneous group of autosomal dominantly inherited nonketotic diabetes. MODY2 or (GCK-MODY) subtype is caused by heterozygous mutations in the GCK gene coding for glucokinase. In our current study, we aim to confirm a clinically suspected GCK-MODY phenotype at molecular level and subsequently, to estimate co-segregation of the detected mutation with hyperglycemia among Algerian related subjects. Lastly, assessment of mutation effect was performed using a combination of computational tools. MethodsSixteen related subjects were implicated in this study (proband and 15 relatives). Blood samples were used for biochemical, immunological and genetic assays. The proband was screened for mutation in the GCK gene using PCR-sequencing technique. Mutation screening among proband's relatives was performed by PCR-Restriction fragment length polymorphism (PCR-RFLP) technique. Computational analysis was performed with a combination of predictive tools to assess effect of the detected mutation on glucokinase structure/function and stability. ResultsGenetic analysis results revealed the presence of c.253A>T (p.R85W) GCK mutation at heterozygous state in 11 subjects, while it was absent in 5 others. Biochemical analysis showed that all subjects carrying the p.R85W mutation were affected with diabetes or impaired fasting glucose (fasting glucose 6.94 ± 0.63 mmol/l, HbA1c 6.92 ± 0.77%), whereas the five non-carriers were normoglycemic (fasting glucose 5.01 ± 0.26 mmol/l, HbA1c 5.36 ± 0.17%). All computational tools predict the detected mutation to be ‘damaging’ or ‘probably damaging’ with high scores. ConclusionThe p.R85W GCK mutation segregates with diabetes or IFG in a large pedigree. Clinical features of Algerian GCK-MODY patients are similar to those of other populations; in case of double diabetes the co-occurring diabetes overcomes the GCK-MODY phenotype. In silico predictions results are consistent with metabolic study.

In silico screening and identification of potential GSK3β inhibitors

Glycogen synthase kinase-3β (GSK3β) has been reported for its impact on multitude biological processes from cell proliferation to apoptosis. The increase in the ratio of active/inactive GSK3β is the major factor associated in the etiology of several psychiatric diseases, diabetes, muscle hypertrophy, neurodegenerative diseases, and some cancers. These findings made GSK3β a promising therapeutic target, and the interest in the discovery, synthesis of novel drugs to effectively attenuate its function with probably no side effects has been increasing in the chronology of GSK3β drug discovery. In the present study, we applied a combination of computational tools on a chemical library for the virtual discovery of their potency to inhibit GSK3β. The chemical library was screened against a set of filters at different levels. Finally, five compounds in the chemical library were found to potentially inhibit GSK3β with no toxic effects. Furthermore, binding mode analysis revealed that all the compounds bound to the ATP site and most of the hydrogen bonding interactions are conserved as in GSK3β structures deposited in PDB.

In silico tools and transcriptomics analyses in the mutagenicity assessment of cosmetic ingredients: a proof-of-principle on how to add weight to the evidence.

Prior to the downstream development of chemical substances, including pharmaceuticals and cosmetics, their influence on the genetic apparatus has to be tested. Several in vitro and in vivo assays have been developed to test for genotoxicity. In a first tier, a battery of two to three in vitro tests is recommended to cover mutagenicity, clastogenicity and aneugenicity as main endpoints. This regulatory in vitro test battery is known to have a high sensitivity, which is at the expense of the specificity. The high number of false positive in vitro results leads to excessive in vivo follow-up studies. In the case of cosmetics it may even induce the ban of the particular compound since in Europe the use of experimental animals is no longer allowed for cosmetics. In this article, an alternative approach to derisk a misleading positive Ames test is explored. Hereto we first tested the performance of five existing computational tools to predict the potential mutagenicity of a data set of 132 cosmetic compounds with a known genotoxicity profile. Furthermore, we present, as a proof-of-principle, a strategy in which a combination of computational tools and mechanistic information derived from in vitro transcriptomics analyses is used to derisk a misleading positive Ames test result. Our data shows that this strategy may represent a valuable tool in a weight-of-evidence approach to further evaluate a positive outcome in an Ames test.

Prioritization of Candidate Genes for Periodontitis Using Multiple Computational Tools

Both genetic and environmental factors contribute to the development of periodontitis. Genetic studies identified a variety of candidate genes for periodontitis. The aim of the present study is to identify the most promising candidate genes for periodontitis using an integrative gene ranking method. Seed genes that were confirmed to be associated with periodontitis were identified using text mining. Three types of candidate genes were then extracted from different resources (expression profiles, genome-wide association studies). Combining the seed genes, four freely available bioinformatics tools (ToppGene, DIR, Endeavour, and GPEC) were integrated for prioritization of candidate genes. Candidate genes that identified with at least three programs and ranked in the top 20 by each program were considered the most promising. Prioritization analysis resulted in 21 promising genes involved or potentially involved in periodontitis. Among them, IL18 (interleukin 18), CD44 (CD44 molecule), CXCL1 (chemokine [CXC motif] ligand 1), IL6ST (interleukin 6 signal transducer), MMP3 (matrix metallopeptidase 3), MMP7, CCR1 (chemokine [C-C motif] receptor 1), MMP13, and TLR9 (Toll-like receptor 9) had been associated with periodontitis. However, the roles of other genes, such as CSF3 (colony stimulating factor 3 receptor), CD40, TNFSF14 (tumor necrosis factor receptor superfamily, member 14), IFNB1 (interferon-β1), TIRAP (toll-interleukin 1 receptor domain containing adaptor protein), IL2RA (interleukin 2 receptor α), ETS1 (v-ets avian erythroblastosis virus E26 oncogene homolog 1), GADD45B (growth arrest and DNA-damage-inducible 45 β), BIRC3 (baculoviral IAP repeat containing 3), VAV1 (vav 1 guanine nucleotide exchange factor), COL5A1 (collagen, type V, α1), and C3 (complement component 3), have not been investigated thoroughly in the process of periodontitis. These genes are mainly involved in bacterial infection, immune response, and inflammatory reaction, suggesting that further characterizing their roles in periodontitis will be important. A combination of computational tools will be useful in mining candidate genes for periodontitis. These theoretical results provide new clues for experimental biologists to plan targeted experiments.

Comments on Special Issue

Selection of numerical and experimental tools for finding solutions to problems of interest to these deep-water offshore structures is a difficult task. It is important to determine how the combination of computational tools with experimental methods can play a role in design decisions. The application of CFD is an extensive and lengthy process. Model tests are expensive and simulation of experiments in facilities of limited depth offers a challenge to the experimenters. However, model testing often reveals physical phenomenon that helps the numerical tool development. Similarly, numerical results help experimental investigations in the sense that they help select experiments that are useful in the design process. Therefore, it is important that the experts from the two disciplines are brought together on discussions of advantages of the two methods and how one can compliment the other in a successful design of the deep-water offshore structures of today. Besides, many new concepts yet unproven in the field are still under development. The actual behavior of these structures in practice is unknown. Model testing, when adequately designed, may provide a means to anticipate and experience any unforeseen or overlooked behavior of these structures, which are unlike known systems. During the OMAE 2001 Conference in Rio de Janeiro, a special session was held primarily made up of invitees on the above subject. The purpose of the session was to stimulate discussions on the present challenges in the hydrodynamic design of offshore structures. In particular, the state of the experimental versus numerical methods in deep-water offshore structure design was discussed. Six papers were presented at the session, three of which were numerical and one experimental method. Others included both numerical and experimental approaches. One paper provided the state of the art in the development of ship hydrodynamics. Besides the presenters, several expert participants in the audience interactively offered discussions on the general subject of modeling. It was clearly identified that in today’s deep-water structure development, the interaction of the theoreticians and experimenters is critically important for their success so that the development and operation of these structures are safe and affordable. The papers were further reviewed for the journal and have gone through revisions based on the reviews. This special issue publishes these papers along with a few selected discussions based on the general subject on the theme of the special issue.