Journal articles: 'In silico methodologies'

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Hasan, Doaa Mohamed, Ahmed Sharaf Eldin, Ayman Elsayed Khedr, and Hanan Fahmy. "In-Silico Methodologies for Cancer Multidrug Optimization." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 2 (July 6, 2018): 7186–205. http://dx.doi.org/10.24297/ijct.v17i2.7168.

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear, which makes it not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer, and discuss the limitations and challenges of these methods.

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Scotti, Luciana, Jahan Ghasemi, and Marcus T. Scotti. "Editorial: In Silico Methodologies Applied to Drug Discovery." Combinatorial Chemistry & High Throughput Screening 21, no. 3 (April 23, 2018): 150–51. http://dx.doi.org/10.2174/138620732103180423125817.

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Scotti, Luciana, and Marcus T. Scotti. "In Silico Methodologies Applied to Anti-infections Drug Discovery." Combinatorial Chemistry & High Throughput Screening 23, no. 6 (October 5, 2020): 456–57. http://dx.doi.org/10.2174/138620732306200612101828.

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Remtulla, Raheem, Sanjoy Kumar Das, and Leonard A. Levin. "Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning." Molecules 26, no. 9 (April 25, 2021): 2505. http://dx.doi.org/10.3390/molecules26092505.

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Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

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Uysal, Sengul, Abdurrahman Aktumsek, Carene M. N. Picot, Alime Sahan, Adriano Mollica, Gokhan Zengin, and Mohamad Fawzi Mahomoodally. "A comparative in vitro and in silico study of the biological potential and chemical fingerprints of Dorcycinum pentapyllum subsp. haussknechtii using three extraction procedures." New Journal of Chemistry 41, no. 22 (2017): 13952–60. http://dx.doi.org/10.1039/c7nj03497k.

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Moura, Ana S., Amit K. Halder, and M. Natália DS Cordeiro. "From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling." Future Medicinal Chemistry 11, no. 17 (September 2019): 2313–31. http://dx.doi.org/10.4155/fmc-2018-0365.

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Bioactive peptides participate in numerous metabolic functions of living organisms and have emerged as potential therapeutics on a diverse range of diseases. Albeit peptide design does not go without challenges, overwhelming advancements on in silico methodologies have increased the scope of peptide-based drug design and discovery to an unprecedented amount. Within an in silico model versus an experimental validation scenario, this review aims to summarize and discuss how different in silico techniques contribute at present to the design of peptide-based molecules. Published in silico results from 2014 to 2018 were selected and discriminated in major methodological groups, allowing a transversal analysis, promoting a landscape vision and asserting its increasing value in drug design.

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Ball, Nicholas, Remi Bars, Philip A. Botham, Andreea Cuciureanu, Mark T. D. Cronin, John E. Doe, Tatsiana Dudzina, Timothy W. Gant, Marcel Leist, and Bennard van Ravenzwaay. "A framework for chemical safety assessment incorporating new approach methodologies within REACH." Archives of Toxicology 96, no. 3 (February 1, 2022): 743–66. http://dx.doi.org/10.1007/s00204-021-03215-9.

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AbstractThe long-term investment in new approach methodologies (NAMs) within the EU and other parts of the world is beginning to result in an emerging consensus of how to use information from in silico, in vitro and targeted in vivo sources to assess the safety of chemicals. However, this methodology is being adopted very slowly for regulatory purposes. Here, we have developed a framework incorporating in silico, in vitro and in vivo methods designed to meet the requirements of REACH in which both hazard and exposure can be assessed using a tiered approach. The outputs from each tier are classification categories, safe doses, and risk assessments, and progress through the tiers depends on the output from previous tiers. We have exemplified the use of the framework with three examples. The outputs were the same or more conservative than parallel assessments based on conventional studies. The framework allows a transparent and phased introduction of NAMs in chemical safety assessment and enables science-based safety decisions which provide the same level of public health protection using fewer animals, taking less time, and using less financial and expert resource. Furthermore, it would also allow new methods to be incorporated as they develop through continuous selective evolution rather than periodic revolution.

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Gimeno, Aleix, María Ojeda-Montes, Sarah Tomás-Hernández, Adrià Cereto-Massagué, Raúl Beltrán-Debón, Miquel Mulero, Gerard Pujadas, and Santiago Garcia-Vallvé. "The Light and Dark Sides of Virtual Screening: What Is There to Know?" International Journal of Molecular Sciences 20, no. 6 (March 19, 2019): 1375. http://dx.doi.org/10.3390/ijms20061375.

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Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.

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Kothandan, Gugan, Changdev G. Gadhe, Thirumurthy Madhavan, and Seung J. Cho. "Binding Site Analysis of CCR2 Through In Silico Methodologies: Docking, CoMFA, and CoMSIA." Chemical Biology & Drug Design 78, no. 1 (March 29, 2011): 161–74. http://dx.doi.org/10.1111/j.1747-0285.2011.01095.x.

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Gadhe, Changdev G., Gugan Kothandan, and Seung Joo Cho. "Binding site exploration of CCR5 using in silico methodologies: a 3D-QSAR approach." Archives of Pharmacal Research 36, no. 1 (January 2013): 6–31. http://dx.doi.org/10.1007/s12272-013-0001-1.

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Halder, Amit Kumar, and M. Natália Dias Soeiro Cordeiro. "Advanced in Silico Methods for the Development of Anti- Leishmaniasis and Anti-Trypanosomiasis Agents." Current Medicinal Chemistry 27, no. 5 (March 16, 2020): 697–718. http://dx.doi.org/10.2174/0929867325666181031093702.

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Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last decades, different in silico methods have been successfully implemented for supporting the lengthy and expensive drug discovery process. In the current review, we discuss recent advances pertaining to in silico analyses towards lead identification, lead modification and target identification of antileishmaniasis and anti-trypanosomiasis agents. We describe recent applications of some important in silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c) recent applications and advances in the last five years; (d) experimental validations of in silico predictions; (e) virtual screening tools; and (f) rationale or justification for the selection of these in silico methods.

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Cezário, Stephanie Priscila de Sousa, Gabriel Veloso Correa, and Luiz Frederico Motta. "<em>In silico</em> pharmacokinetic and toxicological study of Flavone analogues." Brazilian Journal of Development 8, no. 12 (December 27, 2022): 80782–99. http://dx.doi.org/10.34117/bjdv8n12-263.

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Flavone analogs are natural compounds of the flavonoid class that have a wide range of biological activities. The present study aimed to predict, with the aid of in silico methodologies, the oral bioavailability and pharmacokinetic and toxicological analyzes for three flavone analogues (apigenin, chrysin and luteonlin). The study revealed that the analogues have good oral availability, favorable pharmacokinetic and toxicological parameters. The Virtual Screening performed to predict oral bioavailability revealed that all analogues did not violate Lipinski's Rule. The in silico pharmacokinetic study revealed that all analogues have high intestinal absorption, do not cross the blood-brain barrier, are permeable by Caco-2 cells and do not inhibit P-glycoprotein. The in silico ADME study showed that all analogues inhibit the enzymes of the cytochrome P450 complex (CYP4501A2, CYP4502C9, CYP4502C19, CYP4503A4) and not only the CYP4502D6 enzyme. The in silico Toxicology study indicated that the analogues do not show toxicity by the AMES Test and are not carcinogenic. Apigenin and chrysin have low toxicity, while luteolin has moderate toxicity.

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Firman, James W., Mark T. D. Cronin, Philip H. Rowe, Elizaveta Semenova, and John E. Doe. "The use of Bayesian methodology in the development and validation of a tiered assessment approach towards prediction of rat acute oral toxicity." Archives of Toxicology 96, no. 3 (January 16, 2022): 817–30. http://dx.doi.org/10.1007/s00204-021-03205-x.

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AbstractThere exists consensus that the traditional means by which safety of chemicals is assessed—namely through reliance upon apical outcomes obtained following in vivo testing—is increasingly unfit for purpose. Whilst efforts in development of suitable alternatives continue, few have achieved levels of robustness required for regulatory acceptance. An array of “new approach methodologies” (NAM) for determining toxic effect, spanning in vitro and in silico spheres, have by now emerged. It has been suggested, intuitively, that combining data obtained from across these sources might serve to enhance overall confidence in derived judgment. This concept may be formalised in the “tiered assessment” approach, whereby evidence gathered through a sequential NAM testing strategy is exploited so to infer the properties of a compound of interest. Our intention has been to provide an illustration of how such a scheme might be developed and applied within a practical setting—adopting for this purpose the endpoint of rat acute oral lethality. Bayesian statistical inference is drawn upon to enable quantification of degree of confidence that a substance might ultimately belong to one of five LD50-associated toxicity categories. Informing this is evidence acquired both from existing in silico and in vitro resources, alongside a purposely-constructed random forest model and structural alert set. Results indicate that the combination of in silico methodologies provides moderately conservative estimations of hazard, conducive for application in safety assessment, and for which levels of certainty are defined. Accordingly, scope for potential extension of approach to further toxicological endpoints is demonstrated.

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Araujo, Laura Faria, Cacio Henrique de Souza Pinto, and Luiz Frederico Motta. "<em>In silico</em> pharmacokinetic and toxicological study of Cinnamic Acid analogues." Brazilian Journal of Development 8, no. 12 (December 27, 2022): 80800–80817. http://dx.doi.org/10.34117/bjdv8n12-264.

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Cinnamic acid analogs are natural phenolic compounds that have a wide range of biological and therapeutic activities. The present work aimed to predict, through in silico methodologies, the oral bioavailability and pharmacokinetic and toxicological analyzes for four cinnamic acid analogues (caffeic acid, ferulic acid, p-coumaric acid and synaptic acid). The study revealed that the analogues have good oral bioavailability, favorable pharmacokinetic and toxicological parameters. The Virtual Screening performed to predict oral bioavailability indicated that all analogues do not violate Lipinski's Rule. The in silico ADME study of pharmacokinetic parameters showed that all derivatives have high intestinal absorption, are permeable by Caco-2 cells, do not cross the blood-brain barrier, do not inhibit P-glycoprotein. There will be no inhibition of the cytochrome P450 complex isoenzymes (CYP450). The in silico Toxicological study revealed that the analogues do not have toxicity by the AMES Test, are not carcinogenic and do not present acute oral toxicity.

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Tsiaka, Thalia, Eftichia Kritsi, Konstantinos Tsiantas, Paris Christodoulou, Vassilia J. Sinanoglou, and Panagiotis Zoumpoulakis. "Design and Development of Novel Nutraceuticals: Current Trends and Methodologies." Nutraceuticals 2, no. 2 (April 23, 2022): 71–90. http://dx.doi.org/10.3390/nutraceuticals2020006.

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Over the past few years, nutraceuticals have gained substantial attention due to the health-promoting and disease-preventing functions behind their nutritional value. The global prevalence of nutraceuticals is reflected in the increasing number of commercially available nutraceuticals and their wide range of applications. Therefore, a unique opportunity emerges for their further exploration using innovative, reliable, accurate, low cost, and high hit rate methods to design and develop next generation nutraceuticals. Towards this direction, computational techniques constitute an influential trend for academic and industrial research, providing not only the chemical tools necessary for further mechanism characterization but also the starting point for the development of novel nutraceuticals. In the present review, an overview of nutraceuticals is discussed, underscoring the crucial role of chemoinformatic platforms, chemolibraries, and in silico techniques, as well as their perspectives in the development of novel nutraceuticals. This review also aims to record the latest advances and challenges in the area of nanonutraceuticals, an innovative field that capitalizes on the assets of nanotechnology for the encapsulation of bioactive components in order to improve their release profile and therapeutic efficacy.

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Johnson, David, Anthony J. Connor, Steve Mckeever, Zhihui Wang, Thomas S. Deisboeck, Tom Quaiser, and Eliezer Shochat. "Semantically Linking in Silico Cancer Models." Cancer Informatics 13s1 (January 2014): CIN.S13895. http://dx.doi.org/10.4137/cin.s13895.

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Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore combinations of models spanning multiple biological scales. We take the data model encoded by TumorML, an XML-based markup language for storing cancer models in online repositories, and transpose its model description elements into a graph-based representation. By taking such an approach, we can link domain models, such as controlled vocabularies, taxonomic schemes, and ontologies, with cancer model descriptions to better understand and explore relationships between models. The union of these graphs creates a connected property graph that links cancer models by categorizations, by computational compatibility, and by semantic interoperability, yielding a framework in which opportunities for exploration and discovery of combinations of models become possible.

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Pereira, Florbela, and Joao Aires-de-Sousa. "Computational Methodologies in the Exploration of Marine Natural Product Leads." Marine Drugs 16, no. 7 (July 13, 2018): 236. http://dx.doi.org/10.3390/md16070236.

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Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.

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Botton, Andrea, Gianmarco Barberi, and Pierantonio Facco. "Data Augmentation to Support Biopharmaceutical Process Development through Digital Models—A Proof of Concept." Processes 10, no. 9 (September 6, 2022): 1796. http://dx.doi.org/10.3390/pr10091796.

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In recent years, monoclonal antibodies (mAbs) are gaining a wide market share as the most impactful bioproducts. The development of mAbs requires extensive experimental campaigns which may last several years and cost billions of dollars. Following the paradigm of Industry 4.0 digitalization, data-driven methodologies are now used to accelerate the development of new biopharmaceutical products. For instance, predictive models can be built to forecast the productivity of the cell lines in the culture in such a way as to anticipate the identification of the cell lines to be progressed in the scale-up exercise. However, the number of experiments that can be performed decreases dramatically as the process scale increases, due to the resources required for each experimental run. This limits the availability of experimental data and, accordingly, the applicability of data-driven methodologies to support the process development. To address this issue in this work we propose the use of digital models to generate in silico data and augment the amount of data available from real (i.e., in vivo) experimental runs, accordingly. In particular, we propose two strategies for in silico data generation to estimate the endpoint product titer in mAbs manufacturing: one based on a first principles model and one on a hybrid semi-parametric model. As a proof of concept, the effect of in silico data generation was investigated on a simulated biopharmaceutical process for the production of mAbs. We obtained very promising results: the digital model effectively supports the identification of high-productive cell lines (i.e., high mAb titer) even when a very low number of real experimental batches (two or three) is available.

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Azzam, Khaldun AL. "SwissADME and pkCSM Webservers Predictors: an integrated Online Platform for Accurate and Comprehensive Predictions for In Silico ADME/T Properties of Artemisinin and its Derivatives." Kompleksnoe Ispolʹzovanie Mineralʹnogo syrʹâ/Complex Use of Mineral Resources/Mineraldik Shikisattardy Keshendi Paidalanu 325, no. 2 (November 28, 2022): 14–21. http://dx.doi.org/10.31643/2023/6445.13.

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In vivo ADME testing is costly, time-consuming, and puts animal lives at risk, whereas in silico ADME testing is safer, simpler, and faster. This study will use in silico methodologies from SwissADME and pkCSM as an integrated online platform for accurate and comprehensive predictions to determine In Silico ADME/T Properties of Artemisinin and its Derivatives. The investigated compounds' structures were translated into canonical SMILES format and then submitted to the SwissADME and pkCSM webserver tools, which provide free access to different properties of compounds. A compound's ADME/T characteristics are critical for future study and the results obtained will be of beneficial use for researchers. Additionally, the results of this study give great guidance and show that chemical alterations to the reference molecule artemisinin can enhance its ADMET capabilities. The webservers used in this work are free, and several comparison trials show that pkCSM and SwissADME performed are better than a number of other frequently used methods. The designing or engineering of a novel drug molecule primarily requires knowledge of the features of ADME/T of the new drug compound.

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Grumetto, Lucia, and Giacomo Russo. "cΔlog kwIAM: can we afford estimation of small molecules’ blood-brain barrier passage based upon in silico phospholipophilicity?" ADMET and DMPK 9, no. 4 (December 15, 2021): 267–81. http://dx.doi.org/10.5599/admet.1034.

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56 compounds, whose log BB values were known from the scientific literature, were considered and their phospholipophilicity values were calculated in silico. These values, along with either experimentally determined or calculated lipophilicity values, were used to extract cΔ/Δ’log kwIAM parameters. cΔ/Δ’log kwIAM values were found inversely related to data of blood-brain barrier passage, especially in the < -0.20 log BB range and on the IAM.PC.DD2 phase (r2 = 0.79). In multiple linear regression, satisfactory statistic models (r2 (n-1) = 0.76), based on cD/D’log kwIAM.MG along with other in silico calculated descriptors, were achieved. This method brings the potential to be applied, along with other methodologies, to filter out solutes whose BBB permeation is foreseen to be substandard, thus allowing pharmaceutical companies/research institutes to focus on candidates that are more likely to concentrate in the brain.

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Albuquerque, Pedro, Inês Ribeiro, Sofia Correia, Ana Paula Mucha, Paula Tamagnini, Andreia Braga-Henriques, Maria de Fátima Carvalho, and Marta V. Mendes. "Complete Genome Sequence of Two Deep-Sea Streptomyces Isolates from Madeira Archipelago and Evaluation of Their Biosynthetic Potential." Marine Drugs 19, no. 11 (November 1, 2021): 621. http://dx.doi.org/10.3390/md19110621.

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The deep-sea constitutes a true unexplored frontier and a potential source of innovative drug scaffolds. Here, we present the genome sequence of two novel marine actinobacterial strains, MA3_2.13 and S07_1.15, isolated from deep-sea samples (sediments and sponge) and collected at Madeira archipelago (NE Atlantic Ocean; Portugal). The de novo assembly of both genomes was achieved using a hybrid strategy that combines short-reads (Illumina) and long-reads (PacBio) sequencing data. Phylogenetic analyses showed that strain MA3_2.13 is a new species of the Streptomyces genus, whereas strain S07_1.15 is closely related to the type strain of Streptomyces xinghaiensis. In silico analysis revealed that the total length of predicted biosynthetic gene clusters (BGCs) accounted for a high percentage of the MA3_2.13 genome, with several potential new metabolites identified. Strain S07_1.15 had, with a few exceptions, a predicted metabolic profile similar to S. xinghaiensis. In this work, we implemented a straightforward approach for generating high-quality genomes of new bacterial isolates and analyse in silico their potential to produce novel NPs. The inclusion of these in silico dereplication steps allows to minimize the rediscovery rates of traditional natural products screening methodologies and expedite the drug discovery process.

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Gupta, Pawan, Prabha Garg, and Nilanjan Roy. "In silico screening for identification of novel HIV-1 integrase inhibitors using QSAR and docking methodologies." Medicinal Chemistry Research 22, no. 10 (February 5, 2013): 5014–28. http://dx.doi.org/10.1007/s00044-013-0490-y.

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Barlow, D. J., A. Buriani, T. Ehrman, E. Bosisio, I. Eberini, and P. J. Hylands. "In-silico studies in Chinese herbal medicines’ research: Evaluation of in-silico methodologies and phytochemical data sources, and a review of research to date." Journal of Ethnopharmacology 140, no. 3 (April 2012): 526–34. http://dx.doi.org/10.1016/j.jep.2012.01.041.

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Araújo, Gabrielle Luck de, Maria Augusta Amaral Campos, Maria Anete Santana Valente, Sarah Cristina Teixeira Silva, Flávia Dayrell França, Miriam Martins Chaves, and Carlos Alberto Tagliati. "Alternative methods in toxicity testing: the current approach." Brazilian Journal of Pharmaceutical Sciences 50, no. 1 (March 2014): 55–62. http://dx.doi.org/10.1590/s1984-82502011000100005.

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Alternative methods are being developed to reduce, refine, and replace (3Rs) animals used in experiments, aimed at protecting animal welfare. The present study reports alternative tests which are based on the principles of the 3Rs and the efforts made to validate these tests. In Europe, several methodologies have already been implemented, such as tests of irritability, cell viability, and phototoxicity as well as in vitro mathematical models together with the use of in silico tools. This is a complex process that spans from development to regulatory approval and subsequent adoption by various official entities. Within this regulatory framework is REACH, the European Community Regulation for chemicals and their safe use. In Brazil, the BraCVAM (Brazilian Center for the Validation of Alternative Methods) was recently established to validate alternative methods and stimulate incorporation of new methodologies. A new vision of toxicology is emerging for the 21st century (Tox-21), and the subsequent changes are shaping a new paradigm.

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Hussain, Michelle, Kun Tian, Luciano Mutti, Marija Krstic-Demonacos, and Jean-Marc Schwartz. "The Expanded p53 Interactome as a Predictive Model for Cancer Therapy." Genomics and Computational Biology 1, no. 1 (September 18, 2015): 20. http://dx.doi.org/10.18547/gcb.2015.vol1.iss1.e20.

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The tumor suppressor gene TP53 is implicated in the majority of all human cancers, thus pivotal to genomic integrity. There are more than 72,000 scientific publications describing p53’s function, yet, due to the complexity of its interactions we are still far from fully elucidating p53’s role in tumorigenesis. Computational methodologies are novel tools to depict and dissect complex disease networks. The Boolean PKT206 p53 – DNA damage model has previously demonstrated good predictive capability for p53 wild-type and null tumors in various in silico knockouts. Here, we have expanded PKT206 to generate a more clinically robust representation of p53 dynamics. The new PMH260 model incorporates 260 nodes representing genes, with 980 interactions between them representing inhibitions and activations. Additional biological outputs, including angiogenesis, cell cycle arrest and DNA repair were also amalgamated into the model. Three in silico knockouts of highly connected nodes (p53, MDM2 and FGF2) were generated and logical steady state analysis and dependency relationships applied. 71 % of predictions were considered true from superimposition of human osteosarcoma and HCT116 microarray profiles. In silico knockout analysis revealed 98 potential novel predictions, of which 13 were validated by literature; 83 % of them were overlapping with PKT206. Thus the expanded Boolean PMH260 model offers a promising platform for clinical potential in targeted cancer therapeutics.

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Aminpour, Maral, Carlo Montemagno, and Jack A. Tuszynski. "An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications." Molecules 24, no. 9 (April 30, 2019): 1693. http://dx.doi.org/10.3390/molecules24091693.

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In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

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Bisel, Blaine, Francesco S. Pavone, and Martino Calamai. "GM1 and GM2 gangliosides: recent developments." BioMolecular Concepts 5, no. 1 (March 1, 2014): 87–93. http://dx.doi.org/10.1515/bmc-2013-0039.

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AbstractGM1 and GM2 gangliosides are important components of the cell membrane and play an integral role in cell signaling and metabolism. In this conceptual overview, we discuss recent developments in our understanding of the basic biological functions of GM1 and GM2 and their involvement in several diseases. In addition to a well-established spectrum of disorders known as gangliosidoses, such as Tay-Sachs disease, more and more evidence points at an involvement of GM1 in Alzheimer’s and Parkinson’s diseases. New emerging methodologies spanning from single-molecule imaging in vivo to simulations in silico have complemented standard studies based on ganglioside extraction.

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Furxhi, Irini, Finbarr Murphy, Martin Mullins, Athanasios Arvanitis, and Craig A. Poland. "Practices and Trends of Machine Learning Application in Nanotoxicology." Nanomaterials 10, no. 1 (January 8, 2020): 116. http://dx.doi.org/10.3390/nano10010116.

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Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

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Piñeiro-Yáñez, Elena, María José Jiménez-Santos, Gonzalo Gómez-López, and Fátima Al-Shahrour. "In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome." Cancers 11, no. 9 (September 13, 2019): 1361. http://dx.doi.org/10.3390/cancers11091361.

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In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility.

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Iwaniak, Anna, Małgorzata Darewicz, Damir Mogut, and Piotr Minkiewicz. "Elucidation of the role of in silico methodologies in approaches to studying bioactive peptides derived from foods." Journal of Functional Foods 61 (October 2019): 103486. http://dx.doi.org/10.1016/j.jff.2019.103486.

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Ayaz, Shahid, and Vivek Asati. "In silico study for the identification of potential compounds as PIM-1 kinase inhibitors." Pharmaspire 14, no. 01 (2022): 01–09. http://dx.doi.org/10.56933/pharmaspire.2022.14101.

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PIM kinases are a group of serine/threonine kinases that are classified into three isoforms: PIM1, PIM2, and PIM3. Pim-1 kinase is a critical enzyme that is involved in cell growth, cell survival, differentiation, apoptosis, senescence and drug resistance. The PUBMED database has been taken for the screening of PIM-1 kinase inhibitor. This database, further, screened by Lipinski Rule of five, HTVS, standard precision (SP), and extra precision (XP) methodologies. 2OJF protein of PIM-1 kinase was taken for molecular docking. The compound 1a showed good docking scores, SP = −7.244 and XP = −8.6, whereas 1i showed minimal SP and XP scores. These studies may be used for the further development of potential compounds against PIM-1 kinase.

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Arooj, Qudsia, Gregory J. Wilson, and Feng Wang. "Methodologies in Spectral Tuning of DSSC Chromophores through Rational Design and Chemical-Structure Engineering." Materials 12, no. 24 (December 4, 2019): 4024. http://dx.doi.org/10.3390/ma12244024.

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The investigation of new photosensitizers for Grätzel-type organic dye-sensitized solar cells (DSSCs) remains a topic of interest for researchers of alternative solar cell materials. Over the past 20 years, considerable and increasing research efforts have been devoted to the design and synthesis of new materials, based on “donor, π-conjugated bridge, acceptor” (D–π–A) organic dye photosensitizers. In this paper, the computational chemistry methods are outlined and the design of organic sensitizers (compounds, dyes) is discussed. With reference to recent literature reports, rational molecular design is demonstrated as an effective process to study structure–property relationships. Examples from established organic dye sensitizer structures, such as TA-St-CA, Carbz-PAHTDDT (S9), and metalloporphyrin (PZn-EDOT), are used as reference structures for an examination of this concept applied to generate systematically modified structural derivatives and hence new photosensitizers (i.e., dyes). Using computer-aided rational design (CARD), the in silico design of new chromophores targeted an improvement in spectral properties via the tuning of electronic structures by substitution of molecular fragments, as evaluated by the calculation of absorption profiles. This mini review provides important rational design strategies for engineering new organic light-absorbing compounds towards improved spectral absorption and related optoelectronic properties of chromophores for photovoltaic applications, including the dye-sensitized solar cell (DSSC).

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Chikhale, Hemant U. "PERSPECTIVE INSIGHT AND APPLICATION OF IN-SILICO TOOL AS VIRTUAL SCREENING METHOD FOR LEAD DESIGNING AND DEVELOPMENT." Journal of Medical pharmaceutical and allied sciences 11, no. 6 (November 15, 2021): 16–24. http://dx.doi.org/10.22270/jmpas.v10i6.1908.

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Humans are now in a bioinformatics and chemo informatics century, where we can foresee data across domains like as healthcare, the environmental, technology, and public health. The use of information sharing in silico methodologies has impacted sickness administration by predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) patterns of synthetic compounds and efficient and environmentally succeeding pharmaceuticals upfront. The purpose of lead discovery and design is to create the appearance of novel drug candidates that can attach to a specific illness cause. The lead investigative process starts with the recognition of the lead structure, which is followed by the synthesis of its analogs and their estimation in order to produce a candidate for lead improvement. The finding of the proper lead exact is the fundamental and primary worked in the traditional lead discovery progression, and the use of computer (in silico) approaches is widely used in lead innovation. A medicinal chemist's passion for building lead structure is piqued by biomolecules, which are often made up of DNA, RNA, and proteins (such as enzymes, receptors, transporters, and ion channels). The underlying principle of such nuts and bolts is noteworthy to be acquainted with their pharmacological implication to the disease under examination. The motive of this review piece of writing is to emphasize several of the in silico methods that are used in lead discovery and to express the applications of these computational methods.

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Drummond, Michael L., Andrew Henry, Huifang Li, and Christopher I. Williams. "Improved Accuracy for Modeling PROTAC-Mediated Ternary Complex Formation and Targeted Protein Degradation via New In Silico Methodologies." Journal of Chemical Information and Modeling 60, no. 10 (September 24, 2020): 5234–54. http://dx.doi.org/10.1021/acs.jcim.0c00897.

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Santos, Joana, Miguel Cardoso, Irina S. Moreira, João Gonçalves, João D. G. Correia, Sandra Cabo Verde, and Rita Melo. "Integrated in Silico and Experimental Approach towards the Design of a Novel Recombinant Protein Containing an Anti-HER2 scFv." International Journal of Molecular Sciences 22, no. 7 (March 29, 2021): 3547. http://dx.doi.org/10.3390/ijms22073547.

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Biological therapies, such as recombinant proteins, are nowadays amongst the most promising approaches towards precision medicine. One of the most innovative methodologies currently available aimed at improving the production yield of recombinant proteins with minimization of costs relies on the combination of in silico studies to predict and deepen the understanding of the modified proteins with an experimental approach. The work described herein aims at the design and production of a biomimetic vector containing the single-chain variable domain fragment (scFv) of an anti-HER2 antibody fragment as a targeting motif fused with HIV gp41. Molecular modeling and docking studies were performed to develop the recombinant protein sequence. Subsequently, the DNA plasmid was produced and HEK-293T cells were transfected to evaluate the designed vector. The obtained results demonstrated that the plasmid construction is robust and can be expressed in the selected cell line. The multidisciplinary integrated in silico and experimental strategy adopted for the construction of a recombinant protein which can be used in HER2+-targeted therapy paves the way towards the production of other therapeutic proteins in a more cost-effective way.

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Yadav, Tara Chand, Amit Kumar Srivastava, Arpita Dey, Naresh Kumar, Navdeep Raghuwanshi, and Vikas Pruthi. "Application of Computational Techniques to Unravel Structure-Function Relationship and their Role in Therapeutic Development." Current Topics in Medicinal Chemistry 18, no. 20 (December 31, 2018): 1769–91. http://dx.doi.org/10.2174/1568026619666181120142141.

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Application of computational tools and techniques has emerged as an invincible instrument to unravel the structure-function relationship and offered better mechanistic insights in the designing and development of new drugs along with the treatment regime. The use of in silico tools equipped modern chemist with armamentarium of extensive methods to meticulously comprehend the structural tenacity of receptor-ligand interactions and their dynamics. In silico methods offers a striking property of being less resource intensive and economically viable as compared to experimental evaluation. These techniques have proved their mettle in the designing of potential lead compounds to combat life-threatening diseases such as AIDS, cancer, tuberculosis, malaria, etc. In the present scenario, computer-aided drug designing has ascertained an essential and indispensable gizmo in therapeutic development. This review will present a brief outline of computational methods used at different facets of drug designing and its latest advancements. The aim of this review article is to briefly highlight the methodologies and techniques used in structure-based/ ligand-based drug designing viz., molecular docking, pharmacophore modeling, density functional theory, protein-hydration and molecular dynamics simulation which helps in better understanding of macromolecular events and complexities.

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Di Lorenzo, Chiara, Mario Dell'Agli, Elisa Colombo, Enrico Sangiovanni, and Patrizia Restani. "Metabolic Syndrome and Inflammation: A Critical Review ofIn Vitroand Clinical Approaches for Benefit Assessment of Plant Food Supplements." Evidence-Based Complementary and Alternative Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/782461.

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Metabolic syndrome is defined as the clustering in an individual of several metabolic abnormalities associated with insulin resistance, type 2 diabetes, and obesity, in which low-grade chronic inflammatory activity is commonly observed. Part of the European Project PlantLIBRA is concerned with methods to assess the benefits of plant food supplements (PFSs) in countering inflammatory activity and metabolic syndrome. This paper summarizes the current methods used for benefit assessment of PFS, taking into consideration onlyin vitro, in silico, and clinical methodologies used to investigate the anti-inflammatory properties of plants. No in silico studies (using computer simulation) related to metabolic syndrome were found; these methods appear to be used exclusively for identifying or testing potentially effective compounds in drug development. Mostin vitromethods for the assessment of beneficial effects of botanicals or plant food supplements in diabetes were based on a quantitative polymerase chain reaction (PCR), whereas the preferred kind of clinical study was the double-blind randomized controlled clinical trial. Only two parameters were observed to change after treatment with botanicals in bothin vitroandin vivostudies: interleukin-6 and tumour necrosis factor-α, and these biomarkers should be carefully considered in future studies for PFS benefit assessment.

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Schaack, Dominik, Markus A. Weigand, and Florian Uhle. "Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data." PLOS ONE 16, no. 5 (May 17, 2021): e0251800. http://dx.doi.org/10.1371/journal.pone.0251800.

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We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.

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Chen, Qi, Xianwen Meng, Qi Liao, and Ming Chen. "Versatile interactions and bioinformatics analysis of noncoding RNAs." Briefings in Bioinformatics 20, no. 5 (June 4, 2019): 1781–94. http://dx.doi.org/10.1093/bib/bby050.

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Abstract Advances in RNA sequencing technologies and computational methodologies have provided a huge impetus to noncoding RNA (ncRNA) study. Once regarded as inconsequential results of transcriptional promiscuity, ncRNAs were later found to exert great roles in various aspects of biological functions. They are emerging as key players in gene regulatory networks by interacting with other biomolecules (DNA, RNA or protein). Here, we provide an overview of ncRNA repertoire and highlight recent discoveries of their versatile interactions. To better investigate the ncRNA-mediated regulation, it is necessary to make full use of innovative sequencing techniques and computational tools. We further describe a comprehensive workflow for in silico ncRNA analysis, providing up-to-date platforms, databases and tools dedicated to ncRNA identification and functional annotation.

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Agarwal, A., P. N. Pushparaj, G. Ahmad, M. Abu-Elmagd, M. Assidi, E. S. Sabanegh, and R. Sharma. "Deciphering the sperm proteins associated with infertility in men with hodgkin’s disease using mass spectrometry and in silico methodologies." Fertility and Sterility 108, no. 3 (September 2017): e192. http://dx.doi.org/10.1016/j.fertnstert.2017.07.567.

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Brogi, Simone, Mark Tristan Quimque, Kin Israel Notarte, Jeremiah Gabriel Africa, Jenina Beatriz Hernandez, Sophia Morgan Tan, Vincenzo Calderone, and Allan Patrick Macabeo. "Virtual Combinatorial Library Screening of Quinadoline B Derivatives against SARS-CoV-2 RNA-Dependent RNA Polymerase." Computation 10, no. 1 (January 12, 2022): 7. http://dx.doi.org/10.3390/computation10010007.

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The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest in discovering novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study wherein, using a virtual screening campaign, we identified a fumiquinazolinone alkaloid quinadoline B (Q3), an antiviral fungal metabolite with significant activity against SARS-CoV-2 RdRp, we applied in silico combinatorial methodologies for generating and screening a library of anti-SARS-CoV-2 candidates with strong in silico affinity for RdRp. For this study, the quinadoline pharmacophore was subjected to structural iteration, obtaining a Q3-focused library of over 900,000 unique structures. This chemical library was explored to identify binders of RdRp with greater affinity with respect to the starting compound Q3. Coupling this approach with the evaluation of physchem profile, we found 26 compounds with significant affinities for the RdRp binding site. Moreover, top-ranked compounds were submitted to molecular dynamics to evaluate the stability of the systems during a selected time, and to deeply investigate the binding mode of the most promising derivatives. Among the generated structures, five compounds, obtained by inserting nucleotide-like scaffolds (1, 2, and 5), heterocyclic thiazolyl benzamide moiety (compound 3), and a peptide residue (compound 4), exhibited enhanced binding affinity for SARS-CoV-2 RdRp, deserving further investigation as possible antiviral agents. Remarkably, the presented in silico procedure provides a useful computational procedure for hit-to-lead optimization, having implications in anti-SARS-CoV-2 drug discovery and in general in the drug optimization process.

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Shukla, Pratibha, Deepa Deswal, Chandra S. Azad, and Anudeep K. Narula. "Novel nucleosides as potential inhibitors of fungal lanosterol 14α-demethylase: an in vitro and in silico study." Future Medicinal Chemistry 11, no. 20 (October 2019): 2663–86. http://dx.doi.org/10.4155/fmc-2019-0014.

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Aim: The global burden of fungal infections has transitioned from a case–specific observation to a major cause of high human mortality. Therefore, novel compounds with innovative methodologies need to be synthesized and evaluated for their antifungal potential to keep pace with the current clinical demands. Results: An efficient synthetic pathway was developed for the synthesis of 21 synthetic novel nucleosides. Two compounds had significant antifungal effect on Aspergillus fumigatus 3007, which was comparable to fluconazole. The experimental data (confocal microscopy, ultrahigh-performance liquid chromatography and flow cytometry) demonstrated the inhibition of fungal lanosterol 14α-demethylase. Conclusion: Owing to the therapeutic relevance of the synthesized nucleosides and simplicity of the procedure, the method may find its potential application for synthesis of antifungal agents.

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Ribeiro, Frederico F., Francisco J. B. M. Junior, Marcelo S. da Silva, Marcus Tullius Scotti, and Luciana Scotti. "Computational and Investigative Study of Flavonoids Active against Trypanosoma cruzi and Leishmania spp." Natural Product Communications 10, no. 6 (June 2015): 1934578X1501000. http://dx.doi.org/10.1177/1934578x1501000630.

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Flavonoid compounds active against Trypanosoma cruzi and Leishmania species were submitted to several methodologies in silico: docking with the enzymes cruzain and trypanothione reductase (from T. cruzi), and N-myristoyltransferase, dihydroorotate dehydrogenase, and trypanothiona reductase (from Leishmania spp). Molecular maps of the complexes and the ligands were calculated. In order to compare and evaluate the antioxidant activity of the flavonoids with their antiprotozoal activity, quantum parameters were calculated. Considering the energies, interactions, and hydrophobic surfaces calculated, the flavonoids chrysin dimethyl ether against T. cruzi, and ladanein against Leishmania sp. presented the best results. The antioxidant activity did not show any correlation with anti-parasitic activity; only chrysin and its dimethyl ether showed favorable anti-parasitic results. This study hopes to contribute to existing research on these natural products against these tropical parasites.

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Ram, Rebecca N., Domenico Gadaleta, and Timothy E. H. Allen. "The role of ‘big data’ and ‘in silico’ New Approach Methodologies (NAMs) in ending animal use – A commentary on progress." Computational Toxicology 23 (August 2022): 100232. http://dx.doi.org/10.1016/j.comtox.2022.100232.

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Lee, Kyeonghee Monica, Richard Corley, Annie M. Jarabek, Nicole Kleinstreuer, Alicia Paini, Andreas O. Stucki, and Shannon Bell. "Advancing New Approach Methodologies (NAMs) for Tobacco Harm Reduction: Synopsis from the 2021 CORESTA SSPT—NAMs Symposium." Toxics 10, no. 12 (December 6, 2022): 760. http://dx.doi.org/10.3390/toxics10120760.

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New approach methodologies (NAMs) are emerging chemical safety assessment tools consisting of in vitro and in silico (computational) methodologies intended to reduce, refine, or replace (3R) various in vivo animal testing methods traditionally used for risk assessment. Significant progress has been made toward the adoption of NAMs for human health and environmental toxicity assessment. However, additional efforts are needed to expand their development and their use in regulatory decision making. A virtual symposium was held during the 2021 Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA) Smoke Science and Product Technology (SSPT) conference (titled “Advancing New Alternative Methods for Tobacco Harm Reduction”), with the goals of introducing the concepts and potential application of NAMs in the evaluation of potentially reduced-risk (PRR) tobacco products. At the symposium, experts from regulatory agencies, research organizations, and NGOs shared insights on the status of available tools, strengths, limitations, and opportunities in the application of NAMs using case examples from safety assessments of chemicals and tobacco products. Following seven presentations providing background and application of NAMs, a discussion was held where the presenters and audience discussed the outlook for extending the NAMs toxicological applications for tobacco products. The symposium, endorsed by the CORESTA In Vitro Tox Subgroup, Biomarker Subgroup, and NextG Tox Task Force, illustrated common ground and interest in science-based engagement across the scientific community and stakeholders in support of tobacco regulatory science. Highlights of the symposium are summarized in this paper.

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Cicaloni, Vittoria, Alfonso Trezza, Francesco Pettini, and Ottavia Spiga. "Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions." Current Topics in Medicinal Chemistry 19, no. 7 (May 31, 2019): 534–54. http://dx.doi.org/10.2174/1568026619666190304153901.

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Background:Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.Objective:Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.Methods:Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.Results:In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.Conclusion:A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

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Parladé, Eloi, Eric Voltà-Durán, Olivia Cano-Garrido, Julieta M. Sánchez, Ugutz Unzueta, Hèctor López-Laguna, Naroa Serna, et al. "An In Silico Methodology That Facilitates Decision Making in the Engineering of Nanoscale Protein Materials." International Journal of Molecular Sciences 23, no. 9 (April 29, 2022): 4958. http://dx.doi.org/10.3390/ijms23094958.

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Under the need for new functional and biocompatible materials for biomedical applications, protein engineering allows the design of assemblable polypeptides, which, as convenient building blocks of supramolecular complexes, can be produced in recombinant cells by simple and scalable methodologies. However, the stability of such materials is often overlooked or disregarded, becoming a potential bottleneck in the development and viability of novel products. In this context, we propose a design strategy based on in silico tools to detect instability areas in protein materials and to facilitate the decision making in the rational mutagenesis aimed to increase their stability and solubility. As a case study, we demonstrate the potential of this methodology to improve the stability of a humanized scaffold protein (a domain of the human nidogen), with the ability to oligomerize into regular nanoparticles usable to deliver payload drugs to tumor cells. Several nidogen mutants suggested by the method showed important and measurable improvements in their structural stability while retaining the functionalities and production yields of the original protein. Then, we propose the procedure developed here as a cost-effective routine tool in the design and optimization of multimeric protein materials prior to any experimental testing.

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Wu, Xunxun, Xiaokun Li, Chunxue Yang, and Yong Diao. "Target Characterization of Kaempferol against Myocardial Infarction Using Novel In Silico Docking and DARTS Prediction Strategy." International Journal of Molecular Sciences 22, no. 23 (November 29, 2021): 12908. http://dx.doi.org/10.3390/ijms222312908.

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Target identification is a crucial process for advancing natural products and drug leads development, which is often the most challenging and time-consuming step. However, the putative biological targets of natural products obtained from traditional prediction studies are also informatively redundant. Thus, how to precisely identify the target of natural products is still one of the major challenges. Given the shortcomings of current target identification methodologies, herein, a novel in silico docking and DARTS prediction strategy was proposed. Concretely, the possible molecular weight was detected by DARTS method through examining the protected band in SDS-PAGE. Then, the potential targets were obtained from screening and identification through the PharmMapper Server and TargetHunter method. In addition, the candidate target Src was further validated by surface plasmon resonance assay, and the anti-apoptosis effects of kaempferol against myocardial infarction were further confirmed by in vitro and in vivo assays. Collectively, these results demonstrated that the integrated strategy could efficiently characterize the targets, which may shed a new light on target identification of natural products.

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Johnson, Dale. "Biotherapeutics: Challenges and Opportunities for Predictive Toxicology of Monoclonal Antibodies." International Journal of Molecular Sciences 19, no. 11 (November 21, 2018): 3685. http://dx.doi.org/10.3390/ijms19113685.

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Biotherapeutics are a rapidly growing portion of the total pharmaceutical market accounting for almost one-half of recent new drug approvals. A major portion of these approvals each year are monoclonal antibodies (mAbs). During development, non-clinical pharmacology and toxicology testing of mAbs differs from that done with chemical entities since these biotherapeutics are derived from a biological source and therefore the animal models must share the same epitopes (targets) as humans to elicit a pharmacological response. Mechanisms of toxicity of mAbs are both pharmacological and non-pharmacological in nature; however, standard in silico predictive toxicological methods used in research and development of chemical entities currently do not apply to these biotherapeutics. Challenges and potential opportunities exist for new methodologies to provide a more predictive program to assess and monitor potential adverse drug reactions of mAbs for specific patients before and during clinical trials and after market approval.

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Zhou, Ying, Guoyou Gan, Jianhong Yi, Yumin Lai, Yingwu Wang, Jian Gao, and Zhiping Wang. "Research status of the rare and precious metals’ Materials Genome Initiative." Journal of Micromechanics and Molecular Physics 05, no. 02 (June 2020): 2040002. http://dx.doi.org/10.1142/s2424913020400020.

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The core philosophy of Materials Genome Initiative (MGI) is the transition of the way of new materials design from the traditional “trial-and-error” approach to the in-silico materials design approach which employs intensive computing and material informatics. In June 2011, President Barack Obama launched MGI alongside the Advanced Manufacturing Partnership to help businesses discover, develop and deploy new materials twice as fast. In this paper, the concept of rare and precious genome is presented first, followed by the progress of MGI. After that, we focus on the research status of the rare and precious metals’ MGI including the computational tools, the high-throughput experimental methodologies and the rare and precious metals database. We also introduce the application of MGI in the development of rare and precious metal materials, outline the remaining fundamental challenges and present an outlook on the future of the rare and precious metals’ MGI.

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Journal articles on the topic 'In silico methodologies'

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