Micelle-based fluorogenic sensing of trypsin: a sensitive method in pancreatic disease diagnosis.

Protamine-mediated micellar aggregates, featuring an AIE-based fluorescent sensor, facilitate efficient detection of trypsin activity. This method enables the detection of trypsin at exceptionally low concentrations (0.01-0.1 µg mL-1) in urine, demonstrating its potential for early clinical diagnosis of trypsin-related pancreatic diseases.

Non-covalent Lasso Entanglements in Folded Proteins: Prevalence, Functional Implications, and Evolutionary Significance.

One-third of protein domains in the CATH database contain a recently discovered tertiary topological motif: non-covalent lasso entanglements, in which a segment of the protein backbone forms a loop closed by non-covalent interactions between residues and is threaded one or more times by the N- or C-terminal backbone segment. Unknown is how frequently this structural motif appears across the proteomes of organisms. And the correlation of these motifs with various classes of protein function and biological processes have not been quantified. Here, using a combination of protein crystal structures, AlphaFold2 predictions, and Gene Ontology terms we show that in E. coli, S. cerevisiae and H. sapiens that 71%, 52% and 49% of globular proteins contain one-or-more non-covalent lasso entanglements in their native fold, and that some of these are highly complex with multiple threading events. Further, proteins containing these tertiary motifs are consistently enriched in certain functions and biological processes across these organisms and depleted in others, strongly indicating an influence of evolutionary selection pressures acting positively and negatively on the distribution of these motifs. Together, these results demonstrate that non-covalent lasso entanglements are widespread and indicate they may be extensively utilized for protein function and subcellular processes, thus impacting phenotype.

Inferring protein fitness landscapes from laboratory evolution experiments.

Directed laboratory evolution applies iterative rounds of mutation and selection to explore the protein fitness landscape and provides rich information regarding the underlying relationships between protein sequence, structure, and function. Laboratory evolution data consist of protein sequences sampled from evolving populations over multiple generations and this data type does not fit into established supervised and unsupervised machine learning approaches. We develop a statistical learning framework that models the evolutionary process and can infer the protein fitness landscape from multiple snapshots along an evolutionary trajectory. We apply our modeling approach to dihydrofolate reductase (DHFR) laboratory evolution data and the resulting landscape parameters capture important aspects of DHFR structure and function. We use the resulting model to understand the structure of the fitness landscape and find numerous examples of epistasis but an overall global peak that is evolutionarily accessible from most starting sequences. Finally, we use the model to perform an in silico extrapolation of the DHFR laboratory evolution trajectory and computationally design proteins from future evolutionary rounds.

Flexible Organic Photodetectors with Mechanically Robust Zinc Oxide Nanoparticle Thin Films.

Zinc oxide nanoparticle (ZnO-NP) thin films have been intensively used as electron transport layers (ETLs) in organic optoelectronic devices, but their moderate mechanical flexibility hinders their application to flexible electronic devices. This study reveals that the multivalent interaction between ZnO-NPs and multicharged conjugated electrolytes, such as diphenylfluorene pyridinium bromide derivative (DFPBr-6), can significantly improve the mechanical flexibility of ZnO-NP thin films. Intermixing ZnO-NPs and DFPBr-6 facilitates the coordination between bromide anions (from the DFPBr-6) and zinc cations on ZnO-NP surfaces, forming Zn2+-Br- bonds. Different from a conventional electrolyte (e.g., KBr), DFPBr-6 with six pyridinium ionic side chains holds the Br--chelated ZnO-NPs adjacent to DFP+ through Zn2+-Br--N+ bonds. Consequently, ZnO-NP:DFPBr-6 thin films exhibit improved mechanical flexibility with a critical bending radius as low as 1.5 mm under tensile bending conditions. Flexible organic photodetectors with ZnO-NP:DFPBr-6 thin films as ETLs demonstrate reliable device performances with high R (0.34 A/W) and D* (3.03 × 1012 Jones) even after 1000 times repetitive bending at a bending radius of 4.0 mm, whereas devices with ZnO-NP and ZnO-NP:KBr ETLs yield >85% reduction in R and D* under the same bending condition.

Efficient Imidazolium-Biomolecule Interaction-Assisted Amplified Quenching for Ultrasensitive Detection of Heparin.

Detection of heparin (HP) under physiological conditions is difficult due to the presence of biological obstructions including proteins and lipids. Thus, it is highly challenging to selectively detect HP and to increase its sensitivity in complex systems. Here, we report the detection of HP at nanomolar levels via efficient imidazolium-HP interaction-assisted fluorescence quenching amplification. The self-assembled pyrenyl aggregates are devised as a conduit for efficient exciton transport, which induces amplified fluorescence quenching for HP detection. This amplified quenching is enhanced by introducing an imidazolium receptor designed to have a high affinity to HP via electrostatic and/or additional interactions with C2 protons, resulting in a very high Stern-Volmer quenching constant of approximately 1.17×108  M-1 .

Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning.

Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It is challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related methods. DMS data often contain high-dimensional and correlated sequence variables, experimental sampling error and bias, and the presence of missing data. Notably, most DMS data do not contain examples of negative sequences, making it challenging to directly estimate how sequence affects function. Here, we develop a positive-unlabeled (PU) learning framework to infer sequence-function relationships from large-scale DMS data. Our PU learning method displays excellent predictive performance across ten large-scale sequence-function datasets, representing proteins of different folds, functions, and library types. The estimated parameters pinpoint key residues that dictate protein structure and function. Finally, we apply our statistical sequence-function model to design highly stabilized enzymes.

Graph-based regularization for regression problems with alignment and highly-correlated designs.

Sparse models for high-dimensional linear regression and machine learning have received substantial attention over the past two decades. Model selection, or determining which features or covariates are the best explanatory variables, is critical to the interpretability of a learned model. Much of the current literature assumes that covariates are only mildly correlated. However, in many modern applications covariates are highly correlated and do not exhibit key properties (such as the restricted eigenvalue condition, restricted isometry property, or other related assumptions). This work considers a high-dimensional regression setting in which a graph governs both correlations among the covariates and the similarity among regression coefficients - meaning there is alignment between the covariates and regression coefficients. Using side information about the strength of correlations among features, we form a graph with edge weights corresponding to pairwise covariances. This graph is used to define a graph total variation regularizer that promotes similar weights for correlated features. This work shows how the proposed graph-based regularization yields mean-squared error guarantees for a broad range of covariance graph structures. These guarantees are optimal for many specific covariance graphs, including block and lattice graphs. Our proposed approach outperforms other methods for highly-correlated design in a variety of experiments on synthetic data and real biochemistry data.

The bias of isotonic regression.

We study the bias of the isotonic regression estimator. While there is extensive work characterizing the mean squared error of the isotonic regression estimator, relatively little is known about the bias. In this paper, we provide a sharp characterization, proving that the bias scales as O(n -ß/3) up to log factors, where 1 ≤ ß ≤ 2 is the exponent corresponding to Hölder smoothness of the underlying mean. Importantly, this result only requires a strictly monotone mean and that the noise distribution has subexponential tails, without relying on symmetric noise or other restrictive assumptions.

Tyrosine Kinase Inhibitor Dosing Patterns in Elderly Patients With Chronic Myeloid Leukemia.

INTRODUCTION: Tyrosine kinase inhibitors (TKIs) improve the survival rate of patients with chronic myeloid leukemia (CML). However, elderly patients often experience adverse events and require dose adjustments, leading to dose interruptions or treatment discontinuation. We therefore investigated TKI dosing patterns and subsequent outcomes in elderly CML patients. PATIENTS AND METHODS: Using the National Health Information Database, we identified patients with CML aged ≥ 70 years who were prescribed TKIs (imatinib, dasatinib, nilotinib, or radotinib) during 2007-2013. Data on age, sex, prescribed medication, and date of death were extracted. RESULTS: Among the 378 patients, the median age was 75 (range, 70-92) years; the median follow-up period was 53 (range, 1-133) months. Imatinib, dasatinib, nilotinib, and radotinib were prescribed to 324 (85.7%), 110 (29.1%), 93 (24.6%), and 15 (4.0%) patients, respectively. In 42 patients (12.2%), the initial dose was lower than the recommended dose for chronic-phase CML. At last follow-up, 249 patients (65.9%) were receiving a reduced dose. The mean ± standard deviation dose densities of imatinib, dasatinib, nilotinib, and radotinib were 207 ± 121.6, 29 ± 26.7, 235 ± 197, and 123 ± 95.4 mg/day, respectively. The estimated 5-year overall survival probability was 61.0%. Initial TKI dose or dose reduction within first year did not affect the overall survival (P = .0571 and .1826, respectively). CONCLUSION: Dose reduction was observed in 65.9% of the patients at their last visit; except for imatinib, TKI dose densities were < 50% of the recommended dose for the chronic phase. Therefore, the recommended TKI doses might be too high for elderly patients with CML.

PUlasso: High-Dimensional Variable Selection With Presence-Only Data.

In various real-world problems, we are presented with classification problems with positive and unlabeled data, referred to as presence-only responses. In this article we study variable selection in the context of presence only responses where the number of features or covariates p is large. The combination of presence-only responses and high dimensionality presents both statistical and computational challenges. In this article, we develop the PUlasso algorithm for variable selection and classification with positive and unlabeled responses. Our algorithm involves using the majorization-minimization framework which is a generalization of the well-known expectation-maximization (EM) algorithm. In particular to make our algorithm scalable, we provide two computational speed-ups to the standard EM algorithm. We provide a theoretical guarantee where we first show that our algorithm converges to a stationary point, and then prove that any stationary point within a local neighborhood of the true parameter achieves the minimax optimal mean-squared error under both strict sparsity and group sparsity assumptions. We also demonstrate through simulations that our algorithm outperforms state-of-the-art algorithms in the moderate p settings in terms of classification performance. Finally, we demonstrate that our PUlasso algorithm performs well on a biochemistry example. Supplementary materials for this article are available online.