Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums.

AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module's focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms.

Computational Studies of Relative Stabilities of Low-Spin d6 cis- and trans-[M(en)2X2]+ Complexes (M = Co, Rh, Ir): Steric and Electronic Effects in the Context of the Structural Trans Influence.

Computational studies of low spin d6 cis- and trans-[M(en)2X2]+ complexes (M = Co, Rh, Ir) employing multiple model chemistries find that isomer preferences fall into three categories. Complexes where X is largely a σ-donor (H-, CH3-, CF3-) prefer cis geometries, in keeping with predictions associated with the trans influence series. Complexes where this donor characteristic is augmented by π acceptor behavior (B(CF3)2-, BCl2-, SiCl3-) evince even greater preference for cis geometries. QTAIM charge data suggest this is marked by lower positive charge on the metal in cis complexes. In contrast, complexes where X is a π donor and low in the trans influence series (X = OH-, F-, Cl-, I-) prefer trans geometries to varying degrees. QTAIM calculations indicate that this arises because the cis complexes are destabilized by distortions of the electron density in the M-X bonds. This can be viewed conceptually as resulting from repulsions between lone pair electrons on the ligands. Complexes where the X ligands are moderately trans-influencing and can interact conjugatively (CN-, NC-, NO2-, C≡CH-) prefer trans geometries because they combine destabilization of cis geometries with enhanced stabilization of trans geometries resulting from conjugation.

Method for isolation of high molecular weight genomic DNA from Botryococcus biomass.

The development of high molecular weight (HMW) genomic DNA (gDNA) extraction protocols for non-model species is essential to fully exploit long-read sequencing technologies in order to generate genome assemblies that can help answer complex questions about these organisms. Obtaining enough high-quality HMW gDNA can be challenging for these species, especially for tissues rich in polysaccharides such as biomass from species within the Botryococcus genus. The existing protocols based on column-based DNA extraction and biochemical lysis kits can be inefficient and may not be useful due to variations in biomass polysaccharide content. We developed an optimized protocol for the efficient extraction of HMW gDNA from Botryococcus biomass for use in long-read sequencing technologies. The protocol utilized an initial wash step with sorbitol to remove polysaccharides and yielded HMW gDNA concentrations up to 220 ng/µL with high purity. We then demonstrated the suitability of the HMW gDNA isolated from this protocol for long-read sequencing on the Oxford Nanopore PromethION platform for three Botryococcus species. Our protocol can be used as a standard for efficient HMW gDNA extraction in microalgae rich in polysaccharides and may be adapted for other challenging species.

Disruption of Circadian Clock Induces Abnormal Mammary Morphology and Aggressive Basal Tumorigenesis by Enhancing LILRB4 Signaling.

Epidemiological studies have shown that circadian rhythm disruption (CRD) is associated with the risk of breast cancer. However, the role of CRD in mammary gland morphology and aggressive basal mammary tumorigenesis and the molecular mechanisms underlying CRD and cancer risk remain unknown. To investigate the effect of CRD on aggressive tumorigenesis, a genetically engineered mouse model that recapitulates the human basal type of breast cancer was used for this study. The effect of CRD on mammary gland morphology was investigated using wild-type mice model. The impact of CRD on the tumor microenvironment was investigated using the tumors from LD12:12 and CRD mice via scRNA seq. ScRNA seq was substantiated by multiplexing immunostaining, flow cytometry, and realtime PCR. The effect of LILRB4 immunotherapy on CRD-induced tumorigenesis was also investigated. Here we identified the impact of CRD on basal tumorigenesis and mammary gland morphology and identified the role of LILRB4 on CRD-induced lung metastasis. We found that chronic CRD disrupted mouse mammary gland morphology and increased tumor burden, and lung metastasis and induced an immunosuppressive tumor microenvironment by enhancing LILRB4a expression. Moreover, CRD increased the M2-macrophage and regulatory T-cell populations but decreased the M1-macrophage populations. Furthermore, targeted immunotherapy against LILRB4 reduced CRD-induced immunosuppressive microenvironment and lung metastasis. These findings identify and implicate LILRB4a as a link between CRD and aggressive mammary tumorigenesis. This study also establishes the potential role of the targeted LILRB4a immunotherapy as an inhibitor of CRD-induced lung metastasis.