Reconstitution of the heliobacterial photochemical reaction center and cytochrome c553 into a proteoliposome system.

The heliobacterial reaction center (HbRC) is the simplest known photochemical reaction center, in terms of its polypeptide composition. In the heliobacterial cells, its electron donor is a cytochrome (cyt) c553 attached to the membrane via a covalent linkage with a diacylglycerol. We have reconstituted purified HbRC into liposomes mimicking the phospholipid composition of heliobacterial membranes. We also incorporated a lipid with a headgroup containing Ni(II):nitrilotriacetate (NTA) to provide a binding site for the soluble version of the heliobacterial cyt c553 in which the N-terminal membrane attachment site is replaced by a hexahistidine tag. The HbRC was inserted into the liposomes with the donor side preferentially exposed to the exterior; this bias increased to nearly 100% with higher concentrations (≥ 10 mol%) of the Ni(II)-NTA lipid in the membrane, and is most likely due to the net negative charge of the surface of the membrane. The HbRC in proteoliposomes without the Ni(II)-NTA lipid exhibited normal charge separation and subsequent charge recombination of the P800+FX- state in 15 ms; however, the oxidized primary donor (P800+) was not significantly reduced by added H6-cyt c553. In contrast, with proteoliposomes containing the Ni(II)-NTA lipid, addition of H6-cyt c553 resulted in a new kinetic component resulting from fast reduction (2-5 ms) of P800+ by H6-cyt c553. The contribution of this kinetic component varied with the concentration of added H6-cyt c553 and could represent 80% or more of the total P800+ decay. Thus, the HbRC and its interaction with its native electron donor have been reconstituted into an artificial membrane system.

SAFE-MAC: Speed Aware Fairness Enabled MAC Protocol for Vehicular Ad-hoc Networks.

Highly dynamic geographical topology, two-direction mobility, and varying traffic density can lead to fairness issues in Vehicular Ad-hoc Networks (VANETs). The Medium Access Control (MAC) protocol plays a vital role in sharing the common wireless channel efficiently between vehicles in a VANET system. However, ensuring fairness between vehicles can be a challenge in designing MAC protocols for VANET systems. The existing protocol, IEEE 802.11 DCF, ensures that the packet transmission rate for a particular vehicle is directly proportional to the amount of time a vehicle spends within a service area, but it does not guarantee that faster vehicles will be able to send the minimum number of packets. Other existing MAC protocols based on IEEE 802.11 are able to provide a minimum amount of data transmission regardless of velocity, but are unable to provide an amount of data transmission that is more proportionate to the time a vehicle spends in the service area. To address the above limitations, we propose a Speed Aware Fairness Enabled MAC (SAFE-MAC) protocol that calculates the residence time of a vehicle in a service area by using mobility metrics such as position, direction, and speed to synthesize the transmission probability of each individual vehicle with respect to its residence time. This is achieved by dynamically altering the values of parameters such as minimum contention window, maximum backoff stage, and retransmission limit in the MAC protocol. We then develop an analytical model to compare the performance of our proposed protocol with contemporary MAC protocols. Numerical analysis results show that our proposed protocol significantly improves fairness among the speed-varying vehicles in VANET.

Long sequence insertion via CRISPR/Cas gene-editing with transposase, recombinase, and integrase.

CRISPR/Cas-based gene-editing technologies have emerged as one of the most transformative tools in genome science over the past decade, providing unprecedented possibilities for both fundamental and translational research. Following the initial wave of innovations for gene knock-out, epigenetic/RNA modulation, and nickase-mediated base-editing, recent efforts have pivoted towards long-sequence gene editing- specifically, the insertion of large fragments (>1 kb) into the endogenous genome. In this review, we survey the development of these CRISPR/Cas-based sequence insertion methodologies in conjunction with the emergence of novel families of editing enzymes, such as transposases, single-stranded DNA-annealing proteins, recombinases, and integrases. Despite facing a number of challenges, this field continues to evolve rapidly and holds the potential to catalyze a new wave of revolutionary biomedical applications.

Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge.

Deep learning neural networks have improved performance in many cancer informatics problems, including breast cancer subtype classification. However, many networks experience underspecificationwheremultiplecombinationsofparametersachievesimilarperformance, bothin training and validation. Additionally, certain parameter combinations may perform poorly when the test distribution differs from the training distribution. Embedding prior knowledge from the literature may address this issue by boosting predictive models that provide crucial, in-depth information about a given disease. Breast cancer research provides a wealth of such knowledge, particularly in the form of subtype biomarkers and genetic signatures. In this study, we draw on past research on breast cancer subtype biomarkers, label propagation, and neural graph machines to present a novel methodology for embedding knowledge into machine learning systems. We embed prior knowledge into the loss function in the form of inter-subject distances derived from a well-known published breast cancer signature. Our results show that this methodology reduces predictor variability on state-of-the-art deep learning architectures and increases predictor consistency leading to improved interpretation. We find that pathway enrichment analysis is more consistent after embedding knowledge. This novel method applies to a broad range of existing studies and predictive models. Our method moves the traditional synthesis of predictive models from an arbitrary assignment of weights to genes toward a more biologically meaningful approach of incorporating knowledge.

Bacterial expression, correct membrane targeting and functional folding of the HIV-1 membrane protein Vpu using a periplasmic signal peptide.

Viral protein U (Vpu) is a type-III integral membrane protein encoded by Human Immunodeficiency Virus-1 (HIV- 1). It is expressed in infected host cells and plays several roles in viral progeny escape from infected cells, including down-regulation of CD4 receptors. But key structure/function questions remain regarding the mechanisms by which the Vpu protein contributes to HIV-1 pathogenesis. Here we describe expression of Vpu in bacteria, its purification and characterization. We report the successful expression of PelB-Vpu in Escherichia coli using the leader peptide pectate lyase B (PelB) from Erwinia carotovora. The protein was detergent extractable and could be isolated in a very pure form. We demonstrate that the PelB signal peptide successfully targets Vpu to the cell membranes and inserts it as a type I membrane protein. PelB-Vpu was biophysically characterized by circular dichroism and dynamic light scattering experiments and was shown to be an excellent candidate for elucidating structural models.