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Journal articles on the topic 'Biomolecular systems'

Author: Grafiati

Published: 6 September 2023

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1

Miró, Jesús M., and Alfonso Rodríguez-Patón. "Biomolecular Computing Devices in Synthetic Biology." International Journal of Nanotechnology and Molecular Computation 2, no. 2 (April 2010): 47–64. http://dx.doi.org/10.4018/978-1-59904-996-0.ch014.

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Synthetic biology and biomolecular computation are disciplines that fuse when it comes to designing and building information processing devices. In this chapter, we study several devices that are representative of this fusion. These are three gene circuits implementing logic gates, a DNA nanodevice and a biomolecular automaton. The operation of these devices is based on gene expression regulation, the so-called competitive hybridization and the workings of certain biomolecules like restriction enzymes or regulatory proteins. Synthetic biology, biomolecular computation, systems biology and standard molecular biology concepts are also defined to give a better understanding of the chapter. The aim is to acquaint readers with these biomolecular devices born of the marriage between synthetic biology and biomolecular computation.

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2

Katrusiak, Andrzej, Michalina Aniola, Kamil Dziubek, Kinga Ostrowska, and Ewa Patyk. "Biomolecular systems under pressure." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1188. http://dx.doi.org/10.1107/s2053273314088111.

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Biological systems are often regarded as the ultimate goal of all knowledge in this respect that they can provide the clue for understanding the origin of life and the means for improving the life conditions and healthcare. Hence the interest in high-pressure behavior of organic and biomolecular systems. Such simple organic systems were among the first structural studies at high pressure at all. They included chloroform by Roger Fourme in 1968 [1] and benzene by Piermarini et al. in 1969, still with the use of photographic technique. The efficient studies on bio-macromolecular crystals had to wait for several decades till synchrotron radiation became more accessible and Roger Fourme again stood in the avant-garde of these studies [2]. At the turn of centuries his innovations in the laboratory equipment and experimental setup let him exploring high-pressure conformations of proteins, viral capsids and the double-helix molecular architecture in nucleic acids. These directions of high-pressure studies are continued for simple and macromolecular systems of biological importance. Recently new surprising facts were revealed about the compression of urea, sucrose, and other organic compounds, as well as of macromolecular crystals. Sugars are the main energy carriers for animals as well as building blocks in the living tissue, they are also ideal models for studying pressure-induced changes of OH···O and CH···O interactions. Different types of transformations occur in compressed urea, the first organic compound synthesized in laboratory. Hen egg-white lysozyme was investigated at moderate pressure in a beryllium vessel and the compression of both tetragonal and orthorhombic modifications were measured to 1.0 GPa in a DAC; the high-pressure structure of the tetragonal form was determined and refined at still higher pressure by Fourme et al. [3] Can high pressure provide information about the remarkable polymorphism of lysozyme?

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3

Niranjan, Vidya, Purushotham Rao, Akshay Uttarkar, and Jitendra Kumar. "Protocol for the development of coarse-grained structures for macromolecular simulation using GROMACS." PLOS ONE 18, no. 8 (August 3, 2023): e0288264. http://dx.doi.org/10.1371/journal.pone.0288264.

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Coarse-grained simulations have emerged as a valuable tool in the study of large and complex biomolecular systems. These simulations, which use simplified models to represent complex biomolecules, reduce the computational cost of simulations and enable the study of larger systems for longer periods of time than traditional atomistic simulations. GROMACS is a widely used software package for performing coarse-grained simulations of biomolecules, and several force fields have been developed specifically for this purpose. In this protocol paper, we explore the advantages of using coarse-grained simulations in the study of biomolecular systems, focusing specifically on simulations performed using GROMACS. We discuss the force fields required for these simulations and the types of research questions that can be addressed using coarse-grained simulations. We also highlight the potential benefits of coarse-grained simulations for the development of new force fields and simulation methodologies. We then discuss the expected results from coarse-grained simulations using GROMACS and the various techniques that can be used to analyze these results. We explore the use of trajectory analysis tools, as well as thermodynamic and structural analysis techniques, to gain insight into the behavior of biomolecular systems.

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4

Emenecker, Ryan J., Alex S. Holehouse, and Lucia C. Strader. "Biological Phase Separation and Biomolecular Condensates in Plants." Annual Review of Plant Biology 72, no. 1 (June 17, 2021): 17–46. http://dx.doi.org/10.1146/annurev-arplant-081720-015238.

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A surge in research focused on understanding the physical principles governing the formation, properties, and function of membraneless compartments has occurred over the past decade. Compartments such as the nucleolus, stress granules, and nuclear speckles have been designated as biomolecular condensates to describe their shared property of spatially concentrating biomolecules. Although this research has historically been carried out in animal and fungal systems, recent work has begun to explore whether these same principles are relevant in plants. Effectively understanding and studying biomolecular condensates require interdisciplinary expertise that spans cell biology, biochemistry, and condensed matter physics and biophysics. As such, some involved concepts may be unfamiliar to any given individual. This review focuses on introducing concepts essential to the study of biomolecular condensates and phase separation for biologists seeking to carry out research in this area and further examines aspects of biomolecular condensates that are relevant to plant systems.

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5

Wang, Li, Coucong Gong, Xinzhu Yuan, and Gang Wei. "Controlling the Self-Assembly of Biomolecules into Functional Nanomaterials through Internal Interactions and External Stimulations: A Review." Nanomaterials 9, no. 2 (February 18, 2019): 285. http://dx.doi.org/10.3390/nano9020285.

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Biomolecular self-assembly provides a facile way to synthesize functional nanomaterials. Due to the unique structure and functions of biomolecules, the created biological nanomaterials via biomolecular self-assembly have a wide range of applications, from materials science to biomedical engineering, tissue engineering, nanotechnology, and analytical science. In this review, we present recent advances in the synthesis of biological nanomaterials by controlling the biomolecular self-assembly from adjusting internal interactions and external stimulations. The self-assembly mechanisms of biomolecules (DNA, protein, peptide, virus, enzyme, metabolites, lipid, cholesterol, and others) related to various internal interactions, including hydrogen bonds, electrostatic interactions, hydrophobic interactions, π–π stacking, DNA base pairing, and ligand–receptor binding, are discussed by analyzing some recent studies. In addition, some strategies for promoting biomolecular self-assembly via external stimulations, such as adjusting the solution conditions (pH, temperature, ionic strength), adding organics, nanoparticles, or enzymes, and applying external light stimulation to the self-assembly systems, are demonstrated. We hope that this overview will be helpful for readers to understand the self-assembly mechanisms and strategies of biomolecules and to design and develop new biological nanostructures or nanomaterials for desired applications.

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6

Smith, Paul E., and B. Montgomery Pettitt. "Modeling Solvent in Biomolecular Systems." Journal of Physical Chemistry 98, no. 39 (September 1994): 9700–9711. http://dx.doi.org/10.1021/j100090a002.

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7

Rhodes, William. "Coferent dynamics in biomolecular systems." Journal of Molecular Liquids 41 (October 1989): 165–80. http://dx.doi.org/10.1016/0167-7322(89)80076-5.

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8

Rowe, Rhianon K., and P. Shing Ho. "Relationships between hydrogen bonds and halogen bonds in biological systems." Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials 73, no. 2 (March 29, 2017): 255–64. http://dx.doi.org/10.1107/s2052520617003109.

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The recent recognition that halogen bonding (XB) plays important roles in the recognition and assembly of biological molecules has led to new approaches in medicinal chemistry and biomolecular engineering. When designing XBs into strategies for rational drug design or into a biomolecule to affect its structure and function, we must consider the relationship between this interaction and the more ubiquitous hydrogen bond (HB). In this review, we explore these relationships by asking whether and how XBs can replace, compete against or behave independently of HBs in various biological systems. The complex relationships between the two interactions inform us of the challenges we face in fully utilizing XBs to control the affinity and recognition of inhibitors against their therapeutic targets, and to control the structure and function of proteins, nucleic acids and other biomolecular scaffolds.

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9

Wang, Yue, Lei Ren, Hongzhen Peng, Linjie Guo, and Lihua Wang. "DNA-Programmed Biomolecular Spatial Pattern Recognition." Chemosensors 11, no. 7 (June 27, 2023): 362. http://dx.doi.org/10.3390/chemosensors11070362.

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Molecular recognition based on non-covalent interactions between two or more molecules plays a crucial role in biological systems. Specific biological molecule recognition has been widely applied in biotechnology, clinical diagnosis, and treatment. The efficiency and affinity of molecular recognition are greatly determined by the spatial conformation of biomolecules. The designability of DNA nanotechnology makes possible the precise programming of the spatial conformation of biomolecules including valency and spacing, further achieving spatial pattern recognition regulation between biomolecules. This review summarizes recent achievements with DNA-based molecular spatial pattern recognition systems, the important factors affecting spatial pattern recognition, and their applications in biosensing, bioimaging, and targeted therapy. The future challenges in and development of this field are discussed and prospected. This review will provide valuable guidance for the creation of new DNA tools to enhance the efficiency and specificity of biomolecular recognition.

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10

Ren, Pengyu, Jaehun Chun, Dennis G. Thomas, Michael J. Schnieders, Marcelo Marucho, Jiajing Zhang, and Nathan A. Baker. "Biomolecular electrostatics and solvation: a computational perspective." Quarterly Reviews of Biophysics 45, no. 4 (November 2012): 427–91. http://dx.doi.org/10.1017/s003358351200011x.

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AbstractAn understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.

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11

Yatskou, M. M., and V. V. Apanasovich. "Data analysis in complex biomolecular systems." Informatics 18, no. 1 (March 29, 2021): 105–22. http://dx.doi.org/10.37661/1816-0301-2021-18-1-105-122.

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The biomolecular technology progress is directly related to the development of effective methods and algorithms for processing a large amount of information obtained by modern high-throughput experimental equipment. The priority task is the development of promising computational tools for the analysis and interpretation of biophysical information using the methods of big data and computer models. An integrated approach to processing large datasets, which is based on the methods of data analysis and simulation modelling, is proposed. This approach allows to determine the parameters of biophysical and optical processes occurring in complex biomolecular systems. The idea of an integrated approach is to use simulation modelling of biophysical processes occurring in the object of study, comparing simulated and most relevant experimental data selected by dimension reduction methods, determining the characteristics of the investigated processes using data analysis algorithms. The application of the developed approach to the study of bimolecular systems in fluorescence spectroscopy experiments is considered. The effectiveness of the algorithms of the approach was verified by analyzing of simulated and experimental data representing the systems of molecules and proteins. The use of complex analysis increases the efficiency of the study of biophysical systems during the analysis of big data.

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12

Stayton, PS, MEH El-Sayed, N. Murthy, V. Bulmus, C. Lackey, C. Cheung, and AS Hoffman. "'Smart' delivery systems for biomolecular therapeutics." Orthodontics and Craniofacial Research 8, no. 3 (August 2005): 219–25. http://dx.doi.org/10.1111/j.1601-6343.2005.00336.x.

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13

Keenan, Thomas M., and Albert Folch. "Biomolecular gradients in cell culture systems." Lab Chip 8, no. 1 (2008): 34–57. http://dx.doi.org/10.1039/b711887b.

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14

Stoessel, James P., and Peter Nowak. "Absolute free energies in biomolecular systems." Macromolecules 23, no. 7 (April 1990): 1961–65. http://dx.doi.org/10.1021/ma00209a014.

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15

McGuigan, Kevin G. "Radiation Damage in Biomolecular Systems (RADAM07)." Journal of Physics: Conference Series 101 (March 1, 2008): 011001. http://dx.doi.org/10.1088/1742-6596/101/1/011001.

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16

Finney, J. L., J. M. Goodfellow, P. L. Howell, and F. Vovelle. "Computer Simulation of Aqueous Biomolecular Systems." Journal of Biomolecular Structure and Dynamics 3, no. 3 (December 1985): 599–622. http://dx.doi.org/10.1080/07391102.1985.10508447.

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17

Kuliński, Tadeusz. "Molecular Dynamics Simulations of Biomolecular Systems." Computational Methods in Science and Technology 1, no. 1 (1996): 43–54. http://dx.doi.org/10.12921/cmst.1996.01.01.43-54.

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18

Sen, Shaunak. "Tradeoffs in simple biomolecular signaling systems." Systems & Control Letters 61, no. 8 (August 2012): 834–38. http://dx.doi.org/10.1016/j.sysconle.2012.04.011.

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19

Barkai, Naama, and Ben-Zion Shilo. "Variability and Robustness in Biomolecular Systems." Molecular Cell 28, no. 5 (December 2007): 755–60. http://dx.doi.org/10.1016/j.molcel.2007.11.013.

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20

Zavadlav, Julija, Staš Bevc, and Matej Praprotnik. "Adaptive resolution simulations of biomolecular systems." European Biophysics Journal 46, no. 8 (September 13, 2017): 821–35. http://dx.doi.org/10.1007/s00249-017-1248-0.

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21

Senn, Hans Martin, and Walter Thiel. "QM/MM Methods for Biomolecular Systems." Angewandte Chemie International Edition 48, no. 7 (January 28, 2009): 1198–229. http://dx.doi.org/10.1002/anie.200802019.

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22

Fox, Jerome M., Mengxia Zhao, Michael J. Fink, Kyungtae Kang, and George M. Whitesides. "The Molecular Origin of Enthalpy/Entropy Compensation in Biomolecular Recognition." Annual Review of Biophysics 47, no. 1 (May 20, 2018): 223–50. http://dx.doi.org/10.1146/annurev-biophys-070816-033743.

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Biomolecular recognition can be stubborn; changes in the structures of associating molecules, or the environments in which they associate, often yield compensating changes in enthalpies and entropies of binding and no net change in affinities. This phenomenon—termed enthalpy/entropy (H/S) compensation—hinders efforts in biomolecular design, and its incidence—often a surprise to experimentalists—makes interactions between biomolecules difficult to predict. Although characterizing H/S compensation requires experimental care, it is unquestionably a real phenomenon that has, from an engineering perspective, useful physical origins. Studying H/S compensation can help illuminate the still-murky roles of water and dynamics in biomolecular recognition and self-assembly. This review summarizes known sources of H/ S compensation (real and perceived) and lays out a conceptual framework for understanding and dissecting—and, perhaps, avoiding or exploiting—this phenomenon in biophysical systems.

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23

Fujisaki, Hiroshi, Kei Moritsugu, and Yasuhiro Matsunaga. "Exploring Configuration Space and Path Space of Biomolecules Using Enhanced Sampling Techniques—Searching for Mechanism and Kinetics of Biomolecular Functions." International Journal of Molecular Sciences 19, no. 10 (October 15, 2018): 3177. http://dx.doi.org/10.3390/ijms19103177.

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To understand functions of biomolecules such as proteins, not only structures but their conformational change and kinetics need to be characterized, but its atomistic details are hard to obtain both experimentally and computationally. Here, we review our recent computational studies using novel enhanced sampling techniques for conformational sampling of biomolecules and calculations of their kinetics. For efficiently characterizing the free energy landscape of a biomolecule, we introduce the multiscale enhanced sampling method, which uses a combined system of atomistic and coarse-grained models. Based on the idea of Hamiltonian replica exchange, we can recover the statistical properties of the atomistic model without any biases. We next introduce the string method as a path search method to calculate the minimum free energy pathways along a multidimensional curve in high dimensional space. Finally we introduce novel methods to calculate kinetics of biomolecules based on the ideas of path sampling: one is the Onsager–Machlup action method, and the other is the weighted ensemble method. Some applications of the above methods to biomolecular systems are also discussed and illustrated.

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24

Harris, Sarah A., and Vivien M. Kendon. "Quantum-assisted biomolecular modelling." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1924 (August 13, 2010): 3581–92. http://dx.doi.org/10.1098/rsta.2010.0087.

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Our understanding of the physics of biological molecules, such as proteins and DNA, is limited because the approximations we usually apply to model inert materials are not, in general, applicable to soft, chemically inhomogeneous systems. The configurational complexity of biomolecules means the entropic contribution to the free energy is a significant factor in their behaviour, requiring detailed dynamical calculations to fully evaluate. Computer simulations capable of taking all interatomic interactions into account are therefore vital. However, even with the best current supercomputing facilities, we are unable to capture enough of the most interesting aspects of their behaviour to properly understand how they work. This limits our ability to design new molecules, to treat diseases, for example. Progress in biomolecular simulation depends crucially on increasing the computing power available. Faster classical computers are in the pipeline, but these provide only incremental improvements. Quantum computing offers the possibility of performing huge numbers of calculations in parallel, when it becomes available. We discuss the current open questions in biomolecular simulation, how these might be addressed using quantum computation and speculate on the future importance of quantum-assisted biomolecular modelling.

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25

Zhang, Jing, Li-Dong Gong, and Zhong-Zhi Yang. "Recent Development and Applications of the ABEEM/MM Polarizable Force Field." Journal of Computational Biophysics and Chemistry 21, no. 04 (May 16, 2022): 485–98. http://dx.doi.org/10.1142/s2737416521420084.

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In this paper, we review both development and applications of the atom-bond electronegativity equalization method fused into molecular mechanics, i.e., ABEEM/MM polarizable force field (FF). We will focus on the applications of the ABEEM/MM in pure water systems, chemical and biomolecular ion-containing systems, small molecules and biomolecules, etc. The results show that the performance of ABEEM/MM is generally better than that of the commonly used nonpolarizable force fields.

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26

Winter, Roland. "Interrogating the Structural Dynamics and Energetics of Biomolecular Systems with Pressure Modulation." Annual Review of Biophysics 48, no. 1 (May 6, 2019): 441–63. http://dx.doi.org/10.1146/annurev-biophys-052118-115601.

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High hydrostatic pressure affects the structure, dynamics, and stability of biomolecular systems and is a key parameter in the context of the exploration of the origin and the physical limits of life. This review lays out the conceptual framework for exploring the conformational fluctuations, dynamical properties, and activity of biomolecular systems using pressure perturbation. Complementary pressure-jump relaxation studies are useful tools to study the kinetics and mechanisms of biomolecular phase transitions and structural transformations, such as membrane fusion or protein and nucleic acid folding. Finally, the advantages of using pressure to explore biomolecular assemblies and modulate enzymatic reactions are discussed.

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27

Willner, Itamar, and Bilha Willner. "Molecular and biomolecular optoelectronics." Pure and Applied Chemistry 73, no. 3 (January 1, 2001): 535–42. http://dx.doi.org/10.1351/pac200173030535.

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Reversible electronic transduction of photonic processes occurring on electrodes is the conceptual method to develop molecular and biomolecular optoelectronic systems. Cyclic photochemical activation of molecular or biomolecular monolayer redox-functions provides a general methodology for the amperometric transduction of photonic information that is recorded by the chemical assembly. Alternatively, photoisomerizable monolayers associated with electrodes act as "command interfaces" for controlling the interfacial electron transfer between molecular redox-species or redox-proteins. The systems use a photonic input for the generation of an electronic output and act as information processing assemblies. Programmed arrays of photosensitizer/electron acceptor cross-linked Au-nanoparticle arrays are assembled on indium tin oxide (ITO) for photoelectrochemical applications.

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28

NAGY, NAYA, and SELIM G. AKL. "ASPECTS OF BIOMOLECULAR COMPUTING." Parallel Processing Letters 17, no. 02 (June 2007): 185–211. http://dx.doi.org/10.1142/s012962640700296x.

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This paper is intended as a survey of the state of the art of some branches of Biomolecular Computing. Biomolecular Computing aims to use biological hardware (biomare), rather than chips, to build a computer. We discuss the following three main research directions: DNA computing, membrane systems, and gene assembly in ciliates. DNA computing combines practical results together with theoretical algorithm design. Various search problems have been implemented using DNA strands. Membrane systems are a family of computational models inspired by the membrane structure of living cells. The process of gene assembly in ciliates has been formalized as an abstract computational model. Biomolecular Computing is a field in full development, with the promise of important results from the perspective of both Computer Science (models of computation) and Biology (understanding biological processes).

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29

Tolokonnikov, Georgy. "Modelling Biomolecular Structures in Categorical Systems Theory." EPJ Web of Conferences 248 (2021): 01015. http://dx.doi.org/10.1051/epjconf/202124801015.

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In the systemic movement there exist numerous approaches to systems, the most profound of which is the theory of functional systems by Anokhin, which remained largely intuitive science until his pioneering works. The basic principles of functional systems are formalized with the help of the convolutional polycategories in the form of categorical systems theory, which embraced the main systemic approaches, including the traditional mathematical theory of systems. Convolutional polycategories can be built using categorical splices that directly model the external and internal parts of systems. For an algebraic biology using the categorical theory of systems in relation to systemic constructions, the main task of which is to predict the properties of organisms from the genome using strict algebraic methods, new categorical methods are proposed that are widely used in categorical systems theory. These methods are based on the theory of categorical splices, with the help of which the behaviour of quantum-mechanical particles is modelled, in particular, within the framework of the proposed representation of molecules, including RNA and DNA, as categorical systems. Thus, new algebraic and categorical methods (associative algebras with identities, PROP, categorical splices) are involved in the analysis of the genome. The paper presents new results on these matters.

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30

D’Ascenzo, Luigi, and Pascal Auffinger. "106 Ion-π interactions in biomolecular systems." Journal of Biomolecular Structure and Dynamics 33, sup1 (May 18, 2015): 67. http://dx.doi.org/10.1080/07391102.2015.1032668.

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31

Brunner, Robert K., James C. Phillips, and Laxmikant V. Kalé. "Scalable Molecular Dynamics for Large Biomolecular Systems." Scientific Programming 8, no. 3 (2000): 195–207. http://dx.doi.org/10.1155/2000/750827.

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We present an optimized parallelization scheme for molecular dynamics simulations of large biomolecular systems, implemented in the production-quality molecular dynamics program NAMD. With an object-based hybrid force and spatial decomposition scheme, and an aggressive measurement-based predictive load balancing framework, we have attained speeds and speedups that are much higher than any reported in literature so far. The paper first summarizes the broad methodology we are pursuing, and the basic parallelization scheme we used. It then describes the optimizations that were instrumental in increasing performance, and presents performance results on benchmark simulations.

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32

Qiao, Yu, and Ben-Zhuo Lu. "Improvements in continuum modeling for biomolecular systems." Chinese Physics B 25, no. 1 (January 2016): 018705. http://dx.doi.org/10.1088/1674-1056/25/1/018705.

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33

Oishi, K., and E. Klavins. "Biomolecular implementation of linear I/O systems." IET Systems Biology 5, no. 4 (July 1, 2011): 252–60. http://dx.doi.org/10.1049/iet-syb.2010.0056.

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34

Dey, Abhishek, and Shaunak Sen. "Describing function-based approximations of biomolecular systems." IET Systems Biology 12, no. 3 (June 1, 2018): 93–100. http://dx.doi.org/10.1049/iet-syb.2017.0026.

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35

Noid, W. G. "Perspective: Coarse-grained models for biomolecular systems." Journal of Chemical Physics 139, no. 9 (September 7, 2013): 090901. http://dx.doi.org/10.1063/1.4818908.

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36

Leitner, David M., Hari Datt Pandey, and Korey M. Reid. "Energy Transport across Interfaces in Biomolecular Systems." Journal of Physical Chemistry B 123, no. 45 (September 11, 2019): 9507–24. http://dx.doi.org/10.1021/acs.jpcb.9b07086.

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37

Klein, ML, M. Marchi, and JC Smith. "Potential functions for simulation of biomolecular systems." Journal de Chimie Physique 94 (1997): 1305–12. http://dx.doi.org/10.1051/jcp/1997941305.

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38

Birch, David J. S. "Multiphoton excited fluorescence spectroscopy of biomolecular systems." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 57, no. 11 (September 2001): 2313–36. http://dx.doi.org/10.1016/s1386-1425(01)00487-5.

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39

Reinholdt, Peter, Frederik Kamper Jørgensen, Jacob Kongsted, and Jógvan Magnus Haugaard Olsen. "Polarizable Density Embedding for Large Biomolecular Systems." Journal of Chemical Theory and Computation 16, no. 10 (September 29, 2020): 5999–6006. http://dx.doi.org/10.1021/acs.jctc.0c00763.

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40

Khurgin, Yu I., V. A. Kudryashova, and V. A. Zavizion. "Interaction of EHF radiation with biomolecular systems." Radiophysics and Quantum Electronics 37, no. 1 (January 1994): 23–31. http://dx.doi.org/10.1007/bf01039298.

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41

Benenson, Yaakov. "Biomolecular computing systems: principles, progress and potential." Nature Reviews Genetics 13, no. 7 (June 12, 2012): 455–68. http://dx.doi.org/10.1038/nrg3197.

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42

Bowling, Alan, and Mahdi Haghshenas-Jaryani. "A multiscale modeling approach for biomolecular systems." Multibody System Dynamics 33, no. 4 (September 25, 2014): 333–65. http://dx.doi.org/10.1007/s11044-014-9431-x.

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43

Habeck, Michael. "Bayesian Structural Modeling of Large Biomolecular Systems." Biophysical Journal 116, no. 3 (February 2019): 330a. http://dx.doi.org/10.1016/j.bpj.2018.11.1793.

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44

Kučera, Ondřej, and Michal Cifra. "Radiofrequency and microwave interactions between biomolecular systems." Journal of Biological Physics 42, no. 1 (July 15, 2015): 1–8. http://dx.doi.org/10.1007/s10867-015-9392-1.

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45

Gilbert, D. "Biomolecular Interaction Network Database." Briefings in Bioinformatics 6, no. 2 (January 1, 2005): 194–98. http://dx.doi.org/10.1093/bib/6.2.194.

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Mogaki, Rina, P. K. Hashim, Kou Okuro, and Takuzo Aida. "Guanidinium-based “molecular glues” for modulation of biomolecular functions." Chem. Soc. Rev. 46, no. 21 (2017): 6480–91. http://dx.doi.org/10.1039/c7cs00647k.

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Hong, Seyoung, Dong Wook Choi, Hong Nam Kim, Chun Gwon Park, Wonhwa Lee, and Hee Ho Park. "Protein-Based Nanoparticles as Drug Delivery Systems." Pharmaceutics 12, no. 7 (June 29, 2020): 604. http://dx.doi.org/10.3390/pharmaceutics12070604.

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Nanoparticles have been extensively used as carriers for the delivery of chemicals and biomolecular drugs, such as anticancer drugs and therapeutic proteins. Natural biomolecules, such as proteins, are an attractive alternative to synthetic polymers commonly used in nanoparticle formulation because of their safety. In general, protein nanoparticles offer many advantages, such as biocompatibility and biodegradability. Moreover, the preparation of protein nanoparticles and the corresponding encapsulation process involved mild conditions without the use of toxic chemicals or organic solvents. Protein nanoparticles can be generated using proteins, such as fibroins, albumin, gelatin, gliadine, legumin, 30Kc19, lipoprotein, and ferritin proteins, and are prepared through emulsion, electrospray, and desolvation methods. This review introduces the proteins used and methods used in generating protein nanoparticles and compares the corresponding advantages and disadvantages of each.

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Zhu, Qiang, and Ray Luo. "Recent Advances in Biomolecular Recognition." International Journal of Molecular Sciences 24, no. 9 (May 5, 2023): 8310. http://dx.doi.org/10.3390/ijms24098310.

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Leinen, Margaret, Francisco Chavez, Raïssa Meyer, Pier Luigi Buttigieg, Neil Davies, Raffaella Casotti, and Astrid Fischer. "The Ocean Biomolecular Observing Network (OBON)." Marine Technology Society Journal 56, no. 3 (June 8, 2022): 106–7. http://dx.doi.org/10.4031/mtsj.56.3.20.

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Abstract Ocean life—from viruses to whales—is built from “biomolecules.” Biomolecules such as DNA infuse each drop of ocean water, grain of sediment, and breath of ocean air. The Ocean Biomolecular Observing Network (OBON) is developing a global collaboration that will allow science and society to understand ocean life like never before. The program will transform how we sense, harvest, protect, and manage ocean life using molecular techniques, as it faces multiple stresses including pollution, habitat loss, and climate change. It will also help communities detect biological hazards such as harmful algal blooms and pathogens, and be a key component of next-generation ocean observing systems. OBON will encourage continuous standardization and intercalibration of methods and data interoperability to help enhance and future-proof capabilities. OBON's objectives are: 1) to build a coastal-to-open ocean multi-omics biodiversity observing system; 2) to develop and transfer capacity between partners; 3) to enhance marine ecosystem digitization and modelling and 4) to coordinate action on pressing scientific, management, and policy questions.

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Matsuura, Kazunori. "Biomolecular Self-assembling Systems for Multivalent Ligand Display." Trends in Glycoscience and Glycotechnology 25, no. 146 (2013): 227–39. http://dx.doi.org/10.4052/tigg.25.227.

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