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The low-complexity domain of hnRNPA1 (A1-LCD) phase separates in a salt-dependent manner. Unlike many intrinsically disordered proteins (IDPs) whose phase separation is suppressed by increasing salt concentrations, the phase separation of A1-LCD is promoted by >100 mM NaCl. To investigate the atypical salt effect on A1-LCD phase separation, we carried out all-atom molecular dynamics simulations of systems comprising multiple A1-LCD chains at NaCl concentrations from 50 to 1000 mM NaCl. The ions occupy first shell as well as more distant sites around the IDP chains, with Arg sidechains and backbone carbonyls the favored partners of Cl – and Na + , respectively. They play two direct roles in driving A1-LCD condensation. The first is to neutralize the high net charge of the protein (+9) by an excess of bound Cl – over Na + ; the second is to bridge between A1-LCD chains, thereby fortifying the intermolecular interaction networks in the dense phase. At high concentrations, NaCl also indirectly strengthens π–π, cation–π, and amino–π interactions, by drawing water away from the interaction partners. Therefore, at low salt, A1-LCD is prevented from phase separation by net charge repulsion; at intermediate concentrations, NaCl neutralizes enough of the net charge while also bridging IDP chains to drive phase separation. This drive becomes even stronger at high salt due to strengthened π-type interactions. Based on this understanding, four classes of salt dependence of IDP phase separation can be predicted from amino-acid composition.

Muscarinic receptor subtype 1 (M1) is a G protein-coupled receptor (GPCR) and a key pharmacological target for peripheral neuropathy, chronic obstructive pulmonary disease, nerve agent exposures, and cognitive disorders. Screening and identifying compounds with potential to interact with M1 will aid in rational drug design for these disorders. In this work, we developed machine learning-based M1 classification models utilizing publicly available bioactivity data. As inactive compounds are rarely reported in the literature, we encountered the problem of imbalanced datasets. We investigated two strategies to overcome this bottleneck: 1 ) transfer learning and 2 ) using generative models to oversample the inactive class. Our analysis shows that these approaches reduced misclassification of the inactive class not only for M1 but also for other GPCR targets. Overall, we have developed classification models for M1 receptor that will enable rapid screening of large chemical databases and advance drug discovery.

Melanomas that result from mutations in the gene encoding BRAF often become resistant to BRAF inhibition (BRAFi), with multiple mechanisms contributing to resistance. While therapy-induced autophagy promotes resistance to a number of therapies, especially those that target PI3K/mTOR signaling, its role as an adaptive resistance mechanism to BRAFi is not well characterized. Using tumor biopsies from BRAF(V600E) melanoma patients treated either with BRAFi or with combined BRAF and MEK inhibition, we found that BRAFi-resistant tumors had increased levels of autophagy compared with baseline. Patients with higher levels of therapy-induced autophagy had drastically lower response rates to BRAFi and a shorter duration of progression-free survival. In BRAF(V600E) melanoma cell lines, BRAFi or BRAF/MEK inhibition induced cytoprotective autophagy, and autophagy inhibition enhanced BRAFi-induced cell death. Shortly after BRAF inhibitor treatment in melanoma cell lines, mutant BRAF bound the ER stress gatekeeper GRP78, which rapidly expanded the ER. Disassociation of GRP78 from the PKR-like ER-kinase (PERK) promoted a PERK-dependent ER stress response that subsequently activated cytoprotective autophagy. Combined BRAF and autophagy inhibition promoted tumor regression in BRAFi-resistant xenografts. These data identify a molecular pathway for drug resistance connecting BRAFi, the ER stress response, and autophagy and provide a rationale for combination approaches targeting this resistance pathway.

Artificial intelligence recently achieved the breakthrough of predicting the three-dimensional structures of proteins. The next frontier is presented by intrinsically disordered proteins (IDPs), which, representing 30% to 50% of proteomes, readily access vast conformational space. Molecular dynamics (MD) simulations are promising in sampling IDP conformations, but only at extremely high computational cost. Here, we developed generative autoencoders that learn from short MD simulations and generate full conformational ensembles. An encoder represents IDP conformations as vectors in a reduced-dimensional latent space. The mean vector and covariance matrix of the training dataset are calculated to define a multivariate Gaussian distribution, from which vectors are sampled and fed to a decoder to generate new conformations. The ensembles of generated conformations cover those sampled by long MD simulations and are validated by small-angle X-ray scattering profile and NMR chemical shifts. This work illustrates the vast potential of artificial intelligence in conformational mining of IDPs. Generative autoencoders create full conformational ensembles of intrinsically disordered proteins from short molecular dynamics simulations.

Endometriosis (ENDO) is an estrogen-dependent chronic gynaecological manifestation of uterine fragments attached outside of the uterus, accompanied by debilitating chronic pelvic pain. The evoked assays often employed fail to capture the subtle behavioral nuances in the mouse model. Thus, this study proposes integrating a range of non-evoked assays alongside the ever-so-often used evoked test to better understand ethological disparities in the endometriosis mouse model. In this current study, we developed a syngeneic mouse model using C57BL/6J mice and injected donor uterine fragments into estrogen-primed recipient mice. The endometriotic (ENDO) mice were subjected to behavioral assays like marble burying (MB) and digging. In addition, findings of MB and digging were validated by standard non-evoked and evoked tests to capture the differential and subtle nuances in behavior post-endometriosis induction. The successful ENDO lesion development was confirmed via hematoxylin and eosin staining of lesions, higher M2 macrophage levels from the peritoneal fluid, and higher estrogen levels obtained from ENDO mice. Compared with control mice, ENDO mice exhibited diminished MB (***p value < 0.0001) and digging capacity (***p value < 0.0001). The reduced MB and digging positively correlate with diminished active burrowing, grooming, and exploratory behavior during the open-field test, but with increased anxiety as assessed by the elevated plus maze. The highest positive correlation for MB is with increased anxiety (r = 0.65), and that for digging is with the overnight burrow score (r = 0.62). On the contrary, diminished MB and digging negatively correlate with increased abdominal bouts [(r = -0.44) and (r = -0.46), respectively]. The ENDO mice also showed signs of mechanical and thermal hyperalgesia compared with control mice, as measured by evoked tests. The evaluation of the correlation between MB and evoked tests showed the highest positive correlation with tail flick (r = 0.66) and digging with Von Frey (r = 0.80). The non-evoked tests, such as MB, digging, and grooming patterns, are novel assays to evaluate in the mouse model of ENDO. When incorporated with other non-evoked and evoked tests, it provides a more detailed understanding of ethological behaviors in the ENDO mouse model.

In mammals, motility and fertilizing ability of spermatozoa develop during their passage through the epididymis. After ejaculation, sperm undergo capacitation and hyperactivation in the female reproductive tract - a motility transition that is required for sperm penetration of the egg. Both epididymal initiation of sperm motility and hyperactivation are essential for male fertility. Motility initiation in the epididymis and sperm hyperactivation involve changes in metabolism, cAMP (cyclic adenosine mono-phosphate), calcium and pH acting through protein kinases and phosphatases. Despite this knowledge, we still do not understand, in biochemical terms, how sperm acquire motility in the epididymis and how motility is altered in the female reproductive tract. Recent data show that the sperm specific protein phosphatase PP1γ2, glycogen synthase kinase 3 (GSK3), and the calcium regulated phosphatase calcineurin (PP2B), are involved in epididymal sperm maturation. The protein phosphatase PP1γ2 is present only in testis and sperm in mammals. PP1γ2 has a isoform-specific requirement for normal function of mammalian sperm. Sperm PP1γ2 is regulated by three proteins - inhibitor 2, inhibitor 3 and SDS22. Changes in phosphorylation of these three inhibitors and their binding to PP1γ2 are involved in initiation and activation of sperm motility. The inhibitors are phosphorylated by protein kinases, one of which is GSK3. The isoform GSK3α is essential for epididymal sperm maturation and fertility. Calcium levels dramatically decrease during sperm maturation and initiation of motility suggesting that the calcium activated sperm phosphatase (PP2B) activity also decreases. Loss of PP2B results in male infertility due to impaired sperm maturation in the epididymis. Thus the three signaling enzymes PP1γ2, GSK3, and PP2B along with the documented PKA (protein kinase A) have key roles in sperm maturation and hyperactivation. Significantly, all these four signaling enzymes are present as specific isoforms only in placental mammals, a testimony to their essential roles in the unique aspects of sperm function in mammals. These findings should lead to a better biochemical understanding of the basis of male infertility and should lead to novel approaches to a male contraception and managed reproduction.

In this paper, we are concerned with certain invariants of modules, called reducing invariants, which have been recently introduced and studied by Araya–Celikbas and Araya–Takahashi. We raise the question whether the residue field of each commutative Noetherian local ring has finite reducing projective dimension and obtain an affirmative answer for the question for a large class of local rings. Furthermore, we construct new examples of modules of infinite projective dimension that have finite reducing projective dimension and study several fundamental properties of reducing dimensions, especially properties under local homomorphisms of local rings.

Transporters are membrane proteins that are critical for normal cellular function and mediate the transport of endogenous and exogenous chemicals. Chemical interactions with these transporters have the potential to affect the pharmacokinetic properties of drugs. Inhibition of transporters can cause adverse drug-drug interactions and toxicity, whereas if a drug is a substrate of a transporter, it could lead to reduced therapeutic effects. The importance of transporters in drug efficacy and toxicity has led regulatory agencies, such as the U.S. Food and Drug Administration and the European Medicines Agency, to recommend screening of new molecular entities for potential transporter interactions. To aid in the rapid screening and prioritization of drug candidates without transporter liability, we developed a publicly available, web-based transporter profiler, MOlecular traNSporT inhibitoR and substrate predictOr Utility Server (MONSTROUS), that predicts the potential of a chemical to interact with transporters recommended for testing by regulatory agencies. We utilized publicly available data and developed machine learning or similarity-based classification models to predict inhibitors and substrates for 12 transporters. We used graph convolutional neural networks (GCNNs) to develop predictive models for transporters with sufficient bioactivity data, and we implemented two-dimensional similarity-based approach for those without sufficient data. The GCNN inhibitor models have an average five-fold cross-validated receiver operating characteristic area under the curve (ROC-AUC) of 0.85 ± 0.07, and the GCNN substrate models have an average ROC-AUC of 0.79 ± 0.08. We implemented the models along with applicability domain calculations in an easy-to-use web interface and made it publicly available at https://monstrous.bhsai.org/ .

Infinite impulse response (IIR) filter system recognition is a serious issue nowadays as it has many applications on a diversity of platforms. The whale optimization algorithm (WOA) is a novel nature-motivated population-based meta-heuristic algorithm where the hunting techniques of humpback whales are implemented to solve many optimization problems. But the main disadvantage of WOA is its stagnant convergence rate. As the algorithm is population based, the initialization process is very important in finding the best result and to enhance the convergence rate. In this paper, a novel chaotic oppositional-based initialization process is nominated before the start of conventional WOA to improve the performance. To effectively cover the entire search region, a chaotic-based logistic population map consists of both the actual numbers and its corresponding opposite numbers are incorporated into this opposition-based initialization process. When checked out with some classic model of examples, simulation performance authorizes chaotic oppositional-based whale optimization algorithm (COWOA) as a more convenient contender compared to the other evolutionary techniques in terms of accuracy and convergence speed. Convergence profile and mean square error are the performance specifications that are needed to inspect the performance of our recommended algorithm.

Early T precursor acute lymphoblastic leukemia (ETP-ALL) exhibits poor clinical outcomes and high relapse rates following conventional chemotherapeutic protocols. Extensive developmental flexibility of the multipotent ETP-ALL blasts with considerable intra-population heterogeneity in terms of immunophenotype and prognostic parameters might be a target for novel therapeutic interventions. Using a public gene expression dataset (GSE28703) from NCBI GEO DataSets with 12 ETP-ALL and 40 non-ETP-ALL samples, such heterogeneity was found to be reflected in their transcriptome as well. Hub genes were identified from the STRING-derived functional interaction network of genes showing differential expression between ETP-ALL and non-ETP-ALL as well as variable expression across ETP-ALL. Nine genes ( KIT , HGF , NT5E , PROM1 , CD33 , ANPEP , CDH2 , IL1B, and CXCL2 ) among the hubs were further validated as possible diagnostic ETP-ALL markers using another gene expression dataset (GSE78132) with 17 ETP-ALL and 27 non-ETP-ALL samples. Linear dimensionality reduction analysis with the expression levels of the hub genes in ETP-ALL revealed their divergent inclinations towards different hematopoietic lineages, proposing them as novel indicators of lineage specification in the incompletely differentiated ETP-ALL blasts. This further led to the formulation of a personalized lineage score calculation algorithm, which uncovered a considerable B-lineage-bias in a substantial fraction of ETP-ALL subjects from the GSE28703 and GSE78132 cohorts. In addition, STRING-derived physical interactome of the potential biomarkers displayed complete segregation of the B-lineage-skewed markers from other lineage-associated factors, highlighting their distinct functionality and possible druggability in ETP-ALL. A panel of these biomarkers might be useful in pinpointing the dominant lineage specification programmes in the ETP-ALL blasts on a personalized level, urging the development of novel lineage-directed precision therapies as well as repurposing of existing therapies against leukemia of different hematopoietic lineages; which might overcome the drawbacks of conventional chemotherapy.