Proteomic Profile of in Response to Heavy Metal Pollution in Lakes of Northern Patagonia.

Over recent decades, Northern Patagonia in Chile has seen significant growth in agriculture, livestock, forestry, and aquaculture, disrupting lake ecosystems and threatening native species. These environmental changes offer a chance to explore how anthropization impacts zooplankton communities from a molecular-ecological perspective. This study assessed the anthropogenic impact on by comparing its proteomes from two lakes: Llanquihue (anthropized) and Icalma (oligotrophic).

Structural basis for the transport and regulation mechanism of the multidrug resistance-associated protein 2.

Multidrug resistance-associated protein 2 (MRP2) is an ATP-powered exporter important for maintaining liver homeostasis and a potential contributor to chemotherapeutic resistance. Using cryogenic electron microscopy (cryo-EM), we determine the structures of human MRP2 in three conformational states: an autoinhibited state, a substrate-bound pre-translocation state, and an ATP-bound post-translocation state. In the autoinhibited state, the cytosolic regulatory (R) domain plugs into the transmembrane substrate-binding site and extends into the cytosol to form a composite ATP-binding site at the surface of nucleotide-binding domain 2.

Engineering plant biosensors: recent advances in design and applications.

Plants have evolved sophisticated molecular switches to perceive and respond to both endogenous and exogenous signals, enabling changes in form and function in response to environmental and developmental cues. As autotrophic, multicellular organisms, plants represent promising platforms for designing and engineering sense-and-report modules. Advances in protein engineering and functional screening have facilitated the reprogramming of native switches into biosensors capable of detecting novel small molecules.

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.

Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus-host interactions. Leveraging advancements in computational biology, we present VirusHound-II, a novel tool that utilizes machine learning techniques to predict viral proteins evading the innate immune response with high accuracy.

Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques.

Viral oncoproteins play crucial roles in transforming normal cells into cancer cells, representing a significant factor in the etiology of various cancers. Traditionally, identifying these oncoproteins is both time-consuming and costly. With advancements in computational biology, bioinformatics tools based on machine learning have emerged as effective methods for predicting biological activities.