Hendricks, P. | Search bioRxiv

bioRxiv2023

Conformational coupling of the sialic acid TRAP transporter HiSiaQM with its substrate binding protein HiSiaP

The tripartite ATP-independent periplasmic (TRAP) transporters use an extra cytoplasmic substrate binding protein (SBP) to transport a wide variety of substrates in bacteria and archaea. The SBP can adopt an open or closed state depending on the presence of substrate. The two transmembrane domains of TRAP transporters form a monomeric elevator whose function is strictly dependent on the presence of a sodium ion gradient. Insights from experimental structures, structural predictions and molecular modeling have suggested a conformational coupling between the membrane elevator and the substrate binding protein. Here, we use a disulfide engineering approach to lock the TRAP transporter HiSiaPQM from Haemophilus influenzae in different conformational states. The SBP, HiSiaP, was locked in its substrate-bound form and the transmembrane elevator, HiSiaQM, was locked in either its predicted inward- or outward-facing states. We characterized the disulfide-locked variants and used single-molecule total internal reflection fluorescence (TIRF) microscopy to study their interactions. Our experiments demonstrate that the SBP and the transmembrane elevator are indeed conformationally coupled, meaning that the open and closed state of the SBP recognize specific conformational states of the transporter and vice versa.

biochemistry↗

bioRxiv2017

Easyml: Easily Build And Evaluate Machine Learning Models

The easyml (easy machine learning) package lowers the barrier to entry to machine learning and is ideal for undergraduate/graduate students, and practitioners who want to quickly apply machine learning algorithms to their research without having to worry about the best practices of implementing each algorithm. The package provides standardized recipes for regression and classification algorithms in R and Python and implements them in a functional, modular, and extensible framework. This package currently implements recipes for several common machine learning algorithms (e.g., penalized linear models, random forests, and support vector machines) and provides a unified interface to each one. Importantly, users can run and evaluate each machine learning algorithm with a single line of coding. Each recipe is robust, implements best practices specific to each algorithm, and generates a report with details about the model, its performance, as well as journal-quality visualizations. The packages functional, modular, and extensible framework also allows researchers and more advanced users to easily implement new recipes for other algorithms.

neuroscience↗