The presence of conflicting interactions, or frustration, determines how fast biomolecules can explore their configurational landscapes. Recent experiments have provided cases of systems with slow reconfiguration dynamics, perhaps arising from frustration. While it is well known that protein folding speed and mechanism are strongly affected by the protein native structure, it is still unknown how the response to frustration is modulated by the protein topology. We explore the effects of nonnative interactions in the reconfigurational and folding dynamics of proteins with different sizes and topologies. We find that structural correlations related to the folded state size and topology play an important role in determining the folding kinetics of proteins that otherwise have the same amount of nonnative interactions. In particular, we find that the reconfiguration dynamics of α-helical proteins are more susceptible to frustration than β-sheet proteins of the same size. Our results may explain recent experimental findings and suggest that attempts to measure the degree of frustration due to nonnative interactions might be more successful with α-helical proteins.
This paper presents AI-SNIPS (AI Support for Network Intelligence-based Pharmaceutical Security), a production-ready platform that enables stakeholder decision-making, secure data sharing, and interdisciplinary research in the fight against Illicit, Substandard, and Falsified Medical Products (ISFMP). AI-SNIPS takes as input cases: a case consists of one or more URLs suspected of ISFMP activity. Cases can be supplemented with ground-truth structured data (labeled keywords) such as seller PII or case notes. First, AI-SNIPS scrapes and stores relevant images and text from the provided URLs without any user intervention. Salient features for predicting case similarity are extracted from the aggregated data using a combination of rule-based and machine-learning techniques and used to construct a seller network, with the nodes representing cases (sellers) and the edges representing the similarity between two sellers. Network analysis and community detection techniques are applied to extract seller clusters ranked by profitability and their potential to harm society. Lastly, AI-SNIPS provides interpretability by distilling common word/image similarities for each cluster into signature vectors. We validate the importance of AI-SNIPS's features for distinguishing large pharmaceutical affiliate networks from small ISFMP operations using an actual ISFMP lead sheet.
Resolutions higher than the optical diffraction limit are often desired in the context of cellular imaging and the study of disease progression at the cellular level. However, three-dimensional super-resolution imaging without reliance on exogenous contrast agents has so far not been achieved. We present nanoscale photoacoustic tomography (nPAT), an imaging modality based on the photoacoustic effect. nPAT can achieve a dramatic improvement in the axial resolution of the photoacoustic imaging. We derive the theoretical resolution and sensitivity of nPAT and demonstrate that nPAT can achieve a maximum axial resolution of 9.2 nm. We also demonstrate that nPAT can theoretically detect smaller numbers of molecules (∼273) than conventional photoacoustic microscopy due to its ability to detect acoustic signals very close to the photoacoustic source. We simulate nPAT imaging of malaria-infected red blood cells (RBCs) using digital phantoms generated from real biological samples, showing nPAT imaging of the RBC at different stages of infection. These simulations show the potential of nPAT to nondestructively image RBCs at the nanometer resolutions for in vivo samples without the use of exogenous contrast agents. Simulations of nPAT-enabled functional imaging show that nPAT can yield insight into malarial metabolism and biocrystallization processes. We believe that the experimental realization of nPAT has important applications in biomedicine.
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