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Relevant bibliographies by topics / Coevolutionary domains

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Journal articles

Academic literature on the topic 'Coevolutionary domains'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Coevolutionary domains"

1

Krepel, Dana, Ryan R. Cheng, Michele Di Pierro, and José N. Onuchic. "Deciphering the structure of the condensin protein complex." Proceedings of the National Academy of Sciences 115, no. 47 (2018): 11911–16. http://dx.doi.org/10.1073/pnas.1812770115.

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Protein assemblies consisting of structural maintenance of chromosomes (SMC) and kleisin subunits are essential for the process of chromosome segregation across all domains of life. Prokaryotic condensin belonging to this class of protein complexes is composed of a homodimer of SMC that associates with a kleisin protein subunit called ScpA. While limited structural data exist for the proteins that comprise the (SMC)–kleisin complex, the complete structure of the entire complex remains unknown. Using an integrative approach combining both crystallographic data and coevolutionary information, we

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2

Croce, Giancarlo, Thomas Gueudré, Maria Virginia Ruiz Cuevas, et al. "A multi-scale coevolutionary approach to predict interactions between protein domains." PLOS Computational Biology 15, no. 10 (2019): e1006891. http://dx.doi.org/10.1371/journal.pcbi.1006891.

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3

Cartlidge, John, and Seth Bullock. "Combating Coevolutionary Disengagement by Reducing Parasite Virulence." Evolutionary Computation 12, no. 2 (2004): 193–222. http://dx.doi.org/10.1162/106365604773955148.

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While standard evolutionary algorithms employ a static, absolute fitness metric, co-evolutionary algorithms assess individuals by their performance relative to populations of opponents that are themselves evolving. Although this arrangement offers the possibility of avoiding long-standing difficulties such as premature convergence, it suffers from its own unique problems, cycling, over-focusing and disengagement. Here, we introduce a novel technique for dealing with the third and least explored of these problems. Inspired by studies of natural host-parasite systems, we show that disengagement

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4

Zheng, Wei, Xiaogen Zhou, Qiqige Wuyun, Robin Pearce, Yang Li, and Yang Zhang. "FUpred: detecting protein domains through deep-learning-based contact map prediction." Bioinformatics 36, no. 12 (2020): 3749–57. http://dx.doi.org/10.1093/bioinformatics/btaa217.

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Abstract Motivation Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence. Results We developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural

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5

Xue, Xingsi, Jie Chen, Junfeng Chen, and Dongxu Chen. "Using Compact Coevolutionary Algorithm for Matching Biomedical Ontologies." Computational Intelligence and Neuroscience 2018 (October 8, 2018): 1–8. http://dx.doi.org/10.1155/2018/2309587.

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Over the recent years, ontologies are widely used in various domains such as medical records annotation, medical knowledge representation and sharing, clinical guideline management, and medical decision-making. To implement the cooperation between intelligent applications based on biomedical ontologies, it is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Although Evolutionary algorithms (EAs) are one of the state-of-the-art methodologies to match the heterogeneous ontologies, huge mem

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6

Solan, Ron, Joana Pereira, Andrei N. Lupas, Rachel Kolodny та Nir Ben-Tal. "Gram-negative outer-membrane proteins with multiple β-barrel domains". Proceedings of the National Academy of Sciences 118, № 31 (2021): e2104059118. http://dx.doi.org/10.1073/pnas.2104059118.

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Outer-membrane beta barrels (OMBBs) are found in the outer membrane of gram-negative bacteria and eukaryotic organelles. OMBBs fold as antiparallel β-sheets that close onto themselves, forming pores that traverse the membrane. Currently known structures include only one barrel, of 8 to 36 strands, per chain. The lack of multi-OMBB chains is surprising, as most OMBBs form oligomers, and some function only in this state. Using a combination of sensitive sequence comparison methods and coevolutionary analysis tools, we identify many proteins combining multiple beta barrels within a single chain;

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7

Donges, Jonathan F., Wolfgang Lucht, Finn Müller-Hansen, and Will Steffen. "The technosphere in Earth System analysis: A coevolutionary perspective." Anthropocene Review 4, no. 1 (2017): 23–33. http://dx.doi.org/10.1177/2053019616676608.

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Earth System analysis is the study of the joint dynamics of biogeophysical, social and technological processes on our planet. To advance our understanding of possible future development pathways and identify management options for navigating to safe operating spaces while avoiding undesirable domains, computer models of the Earth System are developed and applied. These models hardly represent dynamical properties of technological processes despite their great planetary-scale influence on the biogeophysical components of the Earth System and the associated risks for human societies posed, e.g.

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8

Granata, Daniele, Luca Ponzoni, Cristian Micheletti, and Vincenzo Carnevale. "Patterns of coevolving amino acids unveil structural and dynamical domains." Proceedings of the National Academy of Sciences 114, no. 50 (2017): E10612—E10621. http://dx.doi.org/10.1073/pnas.1712021114.

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Patterns of interacting amino acids are so preserved within protein families that the sole analysis of evolutionary comutations can identify pairs of contacting residues. It is also known that evolution conserves functional dynamics, i.e., the concerted motion or displacement of large protein regions or domains. Is it, therefore, possible to use a pure sequence-based analysis to identify these dynamical domains? To address this question, we introduce here a general coevolutionary coupling analysis strategy and apply it to a curated sequence database of hundreds of protein families. For most fa

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9

García, Enol, José R. Villar, Qing Tan, Javier Sedano, and Camelia Chira. "An efficient multi-robot path planning solution using A* and coevolutionary algorithms." Integrated Computer-Aided Engineering 30, no. 1 (2022): 41–52. http://dx.doi.org/10.3233/ica-220695.

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Multi-robot path planning has evolved from research to real applications in warehouses and other domains; the knowledge on this topic is reflected in the large amount of related research published in recent years on international journals. The main focus of existing research relates to the generation of efficient routes, relying the collision detection to the local sensory system and creating a solution based on local search methods. This approach implies the robots having a good sensory system and also the computation capabilities to take decisions on the fly. In some controlled environments,

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10

Dharna, Aaron, Julian Togelius, and L. B. Soros. "Co-Generation of Game Levels and Game-Playing Agents." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (2020): 203–9. http://dx.doi.org/10.1609/aiide.v16i1.7431.

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Open-endedness, a longstanding cornerstone of artificial life research, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generation in games. The Paired Open-Ended Trailblazer (POET) algorithm, heretofore explored only in a biped walking domain, is a coevolutionary system that simultaneously generates environments and agents that can solve them. This paper introduces a POET-Inspired Neuroevolutionary S

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