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Nanoinformatics: An Emerging Trend in Cancer Therapeutics
Published in Rajesh Singh Tomar, Anurag Jyoti, Shuchi Kaushik, Nanobiotechnology, 2020
Medha Pandya, Snehal Jani, Vishakha Dave, Rakesh Rawal
Native atomic bonds and interactions to drug binding sites mainly express the mechanisms and effectiveness of therapeutic action. The protein structure prediction is accurately comparable to experimental residues is challenging [55]. The critical assessment of protein structure prediction (CASP) community provides an opportunity to research groups through objectively test their structure prediction methods. It presents an independent evaluation of the cutting- edge protein structure modeling to the research community and software users. The variety of methods generates protein models. Though it is unclear what exactly done, the methods roughly classified the following groups: “Modeler,” “Raptor,” “Zhang” group, “Rosetta,” “Lee” group, and others. The use of physics-based methods in protein structure refinement is highly encouraging. It is highly complementary to knowledge-based methods and allows the future repetitive refinement of protein structures to experimental accuracies. In this study, MD simulations and loop refinements further polished predicted models of fusion proteins [25] in CASP11 recommend that the refinement performs significant dynamics, where the averaged models have very poor MolProbity scores. Molecular dynamics (MD) simulations used in combination with a better-quality selection and averaging protocol. The preliminary 3D model of AF9-MLL fusion protein acquired from homology modeling and further refined by MD simulation to improve the accuracy of the structure.
Structures
Published in Thomas M. Nordlund, Peter M. Hoffmann, Quantitative Understanding of Biosystems, 2019
Thomas M. Nordlund, Peter M. Hoffmann
Dozens of more sophisticated rules for protein structure have been devised since the 1980s. Such sets of rules lie in the domain of biophysics called protein structure prediction, usually based on the amino acid sequence. These sets of rules are sometimes computational, invoking energy minimization techniques, molecular dynamics (Newton’s laws of motion), database guidelines for local structure, and other tools. Besides purchasing software tools, students and researchers may use online services such as www.predictprotein.org. “PredictProtein is a service for sequence analysis, structure and function prediction. When you submit any protein sequence PredictProtein retrieves similar sequences in the database and predicts aspects of protein structure and function.” This service is based on software developed under the auspices of the National Library of Medicine, National Institutes of Health.7 There are many other such services: https://cmm.cit.nih.gov/,http://www.expasy.org,
Proteins and proteomics
Published in Firdos Alam Khan, Biotechnology Fundamentals, 2018
The protein-folding phenomenon was largely an experimental endeavor until the formulation of the energy landscape theory by Joseph Bryngelson and Peter Wolynes in the late 1980s and early 1990s. This approach introduced the principle of minimal frustration, which asserts that evolution has selected the amino acid sequences of natural proteins so that interactions between side chains largely favor the molecule’s acquisition of the folded state. Interactions that do not favor folding are selected against, although some residual frustration is expected to exist. This “folding funnel” landscape allows the protein to fold to the native state through any of a large number of pathways and intermediates, rather than being restricted to a single mechanism. The theory is supported by both computational simulations of model proteins and numerous experimental studies, and it has been used to improve methods for protein structure prediction and design.
Uncertainty modeling and applications for operating data-driven inverse design
Published in Journal of Engineering Design, 2023
Shijiang Li, Liang Hou, Zebo Chen, Shaojie Wang, Xiangjian Bu
The inverse problem is a very active research area with applications in both science and engineering. It is an interdisciplinary field that matches the mathematical model of a problem with its experimental data (Neto and da Silva Neto 2012). Yang et al. applied the inverse method to the design of hydraulic machinery by calculating the blade shape based on data such as the flow rate and flow shear conditions (Yang, Liu, and Xiao 2019). In the field of material design, Liao and Li pointed out that trial and error is no longer a viable method for discovering new materials with desirable properties, and proposed a metaheuristic-based material inverse design method (Liao and Li 2020). In the aerospace field, Wang et al. determined the aerodynamic and geometric parameters of a turbine by inverse design (Wang et al. 2022). In the field of thermodynamics, García – Esteban et al. modelled and analysed radiation heat transfer processes using the inverse design approach combined with deep learning algorithms (García-Esteban, Bravo-Abad, and Cuevas 2021). In the fields of optics and electronics, Yu et al. used the inverse design approach to design an optical through-hole for integrated circuits (Yu et al. 2018). In the field of biomedicine, Kuhlman and Bradley reported that the inverse approach is an effective way to study protein structure prediction and design (Kuhlman and Bradley 2019). In the field of microscopy, Gebauer et al. emphasised the importance of inverse design in molecular design (Gebauer et al. 2022).
Characterization of marine bacterial carbonic anhydrase and their CO2 sequestration abilities based on a soil microcosm
Published in Preparative Biochemistry & Biotechnology, 2019
Panchami Jaya, Vinod Kumar Nathan, Parvathi Ammini
The CA of B. safensis isolate AS-75 was α-helix rich based on the secondary structure prediction. They are the first component formed during protein folding and is the most stable element.[50,51] The CA secondary protein structure prediction result showed that the protein has 49.22% alpha helix, 16.06% extended strand, 9.33% beta-turn and 25.33% random coil (Fig. 4a). Although β-CAs were found to be essential for microbial growth of Escherichia coli,[55]Helicobacter pylori,[56] and C. glutamicum,[57] their full physiological role in the biosphere still remains unclear.[58] Certain CA enzymes have a compact structure with a β-sheet core with 5 anti-parallel strands, 4 or more α-helices and a shallow. They can function in the dimers or larger multi-oligomeric states.[5]
Challenges and opportunities of the spatiotemporal responses to the global pandemic of COVID-19
Published in Annals of GIS, 2022
Chaowei Phil Yang, Shuming Bao, Wendy Guan, Kate Howell, Tao Hu, Hai Lan, Yun Li, Qian Liu, Jennifer Smith, Anusha Srirenganathan, Theo Trefonides, Kevin Wang, Zifu Wang
Computing and network research have played a major role in the COVID-19 research. Since the onset of the pandemic, computing and network applications have been applied towards a variety of different use cases ranging from deep learning for telemedicine applications to network tracing to curb viral transmissions. The field of deep learning alone contains many COVID-19 applications including natural language processing for information retrieval and misinformation detection, computer vision applications for medical image analysis and vision-based robotics, and life science applications for precision diagnostics, protein structure prediction, and drug repurposing (Shorten, Khoshgoftaar, and Furht 2021). With the increased amount of environmental data available since the start of the pandemic, computing and network research have enabled findings such as significant reductions in environmental pollution in countries with severe COVID-19 transmissions (Shakil et al. 2020). Bioinformatics computing research enabled much pandemic research such as tracking with epidemiological models and sharing datasets to boost discovery (Hufsky et al. 2021). Computing and network research continues to be important for researchers in the COVID-19 research with several common pitfalls, potential limitations, and gaps. Many computing and network research methodologies rely on high-quality datasets for optimal results, but the lack of quality public COVID-19 personal-level datasets may result in limitations for researchers. Ethical oversight is necessary for the protection of patient privacy in applications such as contact tracing, and the computing and network research field must adapt to meet the ethical guidelines present (Morley et al. 2020).