Biology subjects

Nakazawa, H.

Publications and source records attributed to Nakazawa, H..

3 recordsLinked to original sources

bioRxiv2023

Low-flux electron diffraction study on the intercellular lipid organization in the human lip stratum corneum

OBJECTIVEChapped lips, characterized by drying and cracking, remain a prevalent concern. Identifying the root causes of lip chapping is crucial for developing effective treatments. We examined the lateral packing structure of intercellular lipids (ICL) in the lip stratum corneum (SC) by low-flux electron diffraction (LFED) to obtain new insights into the causes of high transepidermal water loss (TEWL) and low water retention, which may increase the vulnerability of the lip to chapping. METHODSTwenty-one healthy subjects participated in this study. After water content and TEWL measurements, a layer of corneocytes was collected from each lip vermilion surface by the grid-stripping technique. The lateral packing structure of ICL on the collected corneocytes was analyzed by LFED. RESULTSSimilar to skin SC-ICLs, we found coexistence of orthorhombic and hexagonal phases in lip SC-ICLs. We also found that electron diffraction (ED) images with no sharp peaks and a relatively small broad peak at around 2.2 nm-1 appeared frequently, unlike skin SC-ICLs. This suggests that a large fraction of corneocytes in the lip SC is surrounded by thin ICL layers in the fluid phase. Such structural features of lip SC-ICLs can explain its inferior barrier function. Furthermore, we calculated the frequency of appearance of ED images with no sharp peaks, Af, and quantitatively analyzed its correlation with water content and TEWL. The analysis showed a negative correlation between Af and water content when Af > 50%. CONCLUSIONThis is the first report on the detailed analysis of lipid organization in lip SC-ICLs. We showed that the LFED method in combination with quasi-noninvasive sample collection by the grid-stripping technique is useful for statistical study of the fine structures in lip SC. We also found that the proportion of ICLs in a fluid phase was much higher in lip SC than in skin SC, which may be related to lower water content and vulnerability of lip to chapping. Our findings provide a promising approach for obtaining clues to the structural factors regulating the water content and TEWL in lip SC, leading to more effective lip care products.

biophysics↗

bioRxiv2021

Machine-learning-guided library design cycle for directed evolution of enzymes: the effects of training data composition on sequence space exploration

Machine learning (ML) is becoming an attractive tool in mutagenesis-based protein engineering because of its ability to design a variant library containing proteins with a desired function. However, it remains unclear how ML guides directed evolution in sequence space depending on the composition of training data. Here, we present a ML-guided directed evolution study of an enzyme to investigate the effects of a known "highly positive" variant (i.e., variant known to have high enzyme activity) in training data. We performed two separate series of ML-guided directed evolution of Sortase A with and without a known highly positive variant called 5M in training data. In each series, two rounds of ML were conducted: variants predicted by the first round were experimentally evaluated, and used as additional training data for the second-round prediction. The improvements in enzyme activity were comparable between the two series, both achieving enzyme activity 2.2-2.5 times higher than 5M. Intriguingly, the sequences of the improved variants were largely different between the two series, indicating that ML guided the directed evolution to the distinct regions of sequence space depending on the presence/absence of the highly positive variant in the training data. This suggests that the sequence diversity of improved variants can be expanded not only by conventional ML using the whole training data, but also by ML using a subset of the training data even when it lacks highly positive variants. In summary, this study demonstrates the importance of regulating the composition of training data in ML-guided directed evolution.

bioengineering↗

bioRxiv2018

A machine-learning-guided mutagenesis platform for accelerated discovery of novel functional proteins

Molecular evolution based on mutagenesis is widely used in protein engineering. However, optimal proteins are often difficult to obtain due to a large sequence space that requires high costs for screening experiments. Here, we propose a novel approach that combines molecular evolution with machine learning. In this approach, we conduct two rounds of mutagenesis where an initial library of protein variants is used to train a machine-learning model to guide mutagenesis for the second-round library. This enables to prepare a small library suited for screening experiments with high enrichment of functional proteins. We demonstrated a proof-of-concept of our approach by altering the reference green fluorescent protein (GFP) so that its fluorescence is changed to yellow while improving its fluorescence intensity. Using 155 and 78 variants for the initial and the second-round libraries, respectively, we successfully obtained a number of proteins showing yellow fluorescence, 12 of which had better fluorescence performance than the reference yellow fluorescent protein (YFP). These results show the potential of our approach as a powerful platform for accelerated discovery of functional proteins.

bioengineering↗