Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction.

Significant efforts in wet and dry laboratories are devoted to resolving molecular structures. In particular, computational methods can now compute thousands of tertiary structures that populate the structure space of a protein molecule of interest. These advances are now allowing us to turn our attention to analysis methodologies that are able to organize the computed structures in order to highlight functionally relevant structural states. In this paper, we propose a methodology that leverages community detection methods, designed originally to detect communities in social networks, to organize computationally probed protein structure spaces. We report a principled comparison of such methods along several metrics on proteins of diverse folds and lengths. We present a rigorous evaluation in the context of decoy selection in template-free protein structure prediction. The results make the case that network-based community detection methods warrant further investigation to advance analysis of protein structure spaces for automated selection of functionally relevant structures.

Mapping trends and hotspots of mitochondrial dysfunction in Alzheimer's disease from 2013 to 2022: a bibliometric analysis of global research.

Objective: Alzheimer's disease (AD), a prevalent neurodegenerative affliction that predominantly affects the elderly population, imposes a substantial burden on not only patients but also their families and society at large. Mitochondrial dysfunction plays an important role in its pathogenesis. In this study, we conducted a bibliometric analysis of research on mitochondrial dysfunction and AD over the past 10 years, with the aim of summarizing current research hotspots and trends in this field. Methods: On February 12, 2023, we searched for publications about mitochondrial dysfunction and AD in the Web of Science Core Collection database from 2013 to 2022. VOSview software, CiteSpace, SCImago, and RStudio were used to analyze and visualize countries, institutions, journals, keywords, and references. Results: The number of publications on mitochondrial dysfunction and AD were on the rise until 2021 and decreased slightly in 2022. The United States ranks first in the number of publications, H-index, and intensity of international cooperation in this research. In terms of institutions, Texas Tech University in the United States has the most publications. The Journal of Alzheimer's Disease has the most publications in this field of research, while Oxidative Medicine and Cellular Longevity have the highest number of citations. Mitochondrial dysfunction is still an important direction of current research. Autophagy, mitochondrial autophagy, and neuroinflammation are new hotspots. The article from Lin MT is the most cited by analyzing references. Conclusion: Research on mitochondrial dysfunction in AD is gaining significant momentum as it provides a crucial research avenue for the treatment of this debilitating condition. This study sheds light on the present research trajectory concerning the molecular mechanisms underlying mitochondrial dysfunction in AD.

Learning Organizations of Protein Energy Landscapes: An Application on Decoy Selection in Template-Free Protein Structure Prediction.

The protein energy landscape, which lifts the protein structure space by associating energies with structures, has been useful in improving our understanding of the relationship between structure, dynamics, and function. Currently, however, it is challenging to automatically extract and utilize the underlying organization of an energy landscape to the link structural states it houses to biological activity. In this chapter, we first report on two computational approaches that extract such an organization, one that ignores energies and operates directly in the structure space and another that operates on the energy landscape associated with the structure space. We then describe two complementary approaches, one based on unsupervised learning and another based on supervised learning. Both approaches utilize the extracted organization to address the problem of decoy selection in template-free protein structure prediction. The presented results make the case that learning organizations of protein energy landscapes advances our ability to link structures to biological activity.