Allosteric regulation in STAT3 interdomains is mediated by a rigid core: SH2 domain regulation by CCD in D170A variant.

Signal Transducer and Activator of Transcription 3 (STAT3) plays a crucial role in cancer development and thus is a viable target for cancer treatment. STAT3 functions as a dimer mediated by phosphorylation of the SRC-homology 2 (SH2) domain, a key target for therapeutic drugs. While great efforts have been employed towards the development of compounds that directly target the SH2 domain, no compound has yet been approved by the FDA due to a lack of specificity and pharmacologic efficacy. Studies have shown that allosteric regulation of SH2 via the coiled-coil domain (CCD) is an alternative drug design strategy. Several CCD effectors have been shown to modulate SH2 binding and affinity, and at the time of writing at least one drug candidate has entered phase I clinical trials. However, the mechanism for SH2 regulation via CCD is poorly understood. Here, we investigate structural and dynamic features of STAT3 and compare the wild type to the reduced function variant D170A in order to delineate mechanistic differences and propose allosteric pathways. Molecular dynamics simulations were employed to explore conformational space of STAT3 and the variant, followed by structural, conformation, and dynamic analysis. The trajectories explored show distinctive conformational changes in the SH2 domain for the D170A variant, indicating long range allosteric effects. Multiple analyses provide evidence for long range communication pathways between the two STAT3 domains, which seem to be mediated by a rigid core which connects the CCD and SH2 domains via the linker domain (LD) and transmits conformational changes through a network of short-range interactions. The proposed allosteric mechanism provides new insight into the understanding of intramolecular signaling in STAT3 and potential pharmaceutical control of STAT3 specificity and activity.

Allosteric control of ACE2 peptidase domain dynamics.

The Angiotensin Converting Enzyme 2 (ACE2) assists the regulation of blood pressure and is the main target of the coronaviruses responsible for SARS and COVID19. The catalytic function of ACE2 relies on the opening and closing motion of its peptidase domain (PD). In this study, we investigated the possibility of allosterically controlling the ACE2 PD functional dynamics. After confirming that ACE2 PD binding site opening-closing motion is dominant in characterizing its conformational landscape, we observed that few mutations in the viral receptor binding domain fragments were able to impart different effects on the binding site opening of ACE2 PD. This showed that binding to the solvent exposed area of ACE2 PD can effectively alter the conformational profile of the protein, and thus likely its catalytic function. Using a targeted machine learning model and relative entropy-based statistical analysis, we proposed the mechanism for the allosteric perturbation that regulates the ACE2 PD binding site dynamics at atomistic level. The key residues and the source of the allosteric regulation of ACE PD dynamics are also presented.

Cryptochromes: Photochemical and structural insight into magnetoreception.

Cryptochromes (CRYs) function as blue light photoreceptors in diverse physiological processes in nearly all kingdoms of life. Over the past several decades, they have emerged as the most likely candidates for light-dependent magnetoreception in animals, however, a long history of conflicts between in vitro photochemistry and in vivo behavioral data complicate validation of CRYs as a magnetosensor. In this review, we highlight the origins of conflicts regarding CRY photochemistry and signal transduction, and identify recent data that provides clarity on potential mechanisms of signal transduction in magnetoreception. The review primarily focuses on examining differences in photochemistry and signal transduction in plant and animal CRYs, and identifies potential modes of convergent evolution within these independent lineages that may identify conserved signaling pathways.

Predicting Potential SARS-COV-2 Drugs-In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking.

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as well as the discovery of multiple vaccines. During an urgent crisis, rapidly identifying potential new treatments requires global and cross-discipline cooperation, together with an enhanced open-access research model to distribute new ideas and leads. Herein, we introduce an application of a deep neural network based drug screening method, validating it using a docking algorithm on approved drugs for drug repurposing efforts, and extending the screen to a large library of 750,000 compounds for de novo drug discovery effort. The results of large library screens are incorporated into an open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed and de novo design of ACE2-regulatory compounds. Through these efforts we demonstrate the utility of a new machine learning algorithm for drug discovery, SSnet, that can function as a tool to triage large molecular libraries to identify classes of molecules with possible efficacy.

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.

Steric and Electronic Interactions at Gln154 in ZEITLUPE Induce Reorganization of the LOV Domain Dimer Interface.

Plants measure light quality, intensity, and duration to coordinate growth and development with daily and seasonal changes in environmental conditions; however, the molecular details linking photochemistry to signal transduction remain incomplete. Two closely related light, oxygen, or voltage (LOV) domain-containing photoreceptor proteins, ZEITLUPE (ZTL) and FLAVIN-BINDING, KELCH REPEAT, F-BOX 1 (FKF1), divergently regulate the protein stability of circadian clock and photoperiodic flowering components to mediate daily and seasonal development. Using structural approaches, we identified that mutations at the Gly46 position led to global rearrangements of the ZTL dimer interface in the isolated ZTL-LOV domain. Specifically, G46S and G46A variants induce a 180° rotation about the ZTL-LOV dimer interface that is coupled to ordering of N- and C-terminal signaling elements. These conformational changes hinge upon rotation of a C-terminal Gln residue (Gln154) analogous to that present in light-state structures of ZTL. In contrast to other LOV proteins, a Q154L variant retains light-state interactions with GIGANTEA (GI), thereby indicating N5 protonation is not required for ZTL signaling. The results presented herein confirm a divergent signaling mechanism within ZTL, whereby steric and electronic effects following adduct formation can be sufficient for signal propagation in LOV proteins containing a Gly residue at position 46. Examination of bacterial LOV structures with Gly residues at the equivalent position suggests that mechanisms of signal transduction in LOV proteins may be fluid across the LOV protein family.

Metabolically Distinct Pools of Phosphatidylcholine Are Involved in Trafficking of Fatty Acids out of and into the Chloroplast for Membrane Production.

The eukaryotic pathway of galactolipid synthesis involves fatty acid synthesis in the chloroplast, followed by assembly of phosphatidylcholine (PC) in the endoplasmic reticulum (ER), and then turnover of PC to provide a substrate for chloroplast galactolipid synthesis. However, the mechanisms and classes of lipids transported between the chloroplast and the ER are unclear. PC, PC-derived diacylglycerol, phosphatidic acid, and lyso-phosphatidylcholine (LPC) have all been implicated in ER-to-chloroplast lipid transfer. LPC transport requires lysophosphatidylcholine acyltransferase (LPCAT) activity at the chloroplast to form PC before conversion to galactolipids. However, LPCAT has also been implicated in the opposite chloroplast-to-ER trafficking of newly synthesized fatty acids through PC acyl editing. To understand the role of LPC and LPCAT in acyl trafficking we produced and analyzed the Arabidopsis (Arabidopsis thaliana) act1 lpcat1 lpcat2 triple mutant. LPCAT1 and LPCAT2 encode the major lysophospholipid acyltransferase activity of the chloroplast, and it is predominantly for incorporation of nascent fatty acids exported form the chloroplast into PC by acyl editing. In vivo acyl flux analysis revealed eukaryotic galactolipid synthesis is not impaired in act1 lpcat1 lpcat2 and uses a PC pool distinct from that of PC acyl editing. We present a model for the eukaryotic pathway with metabolically distinct pools of PC, suggesting an underlying spatial organization of PC metabolism as part of the ER-chloroplast metabolic interactions.

Chemical and structural analysis of a photoactive vertebrate cryptochrome from pigeon.

Computational and biochemical studies implicate the blue-light sensor cryptochrome (CRY) as an endogenous light-dependent magnetosensor enabling migratory birds to navigate using the Earth's magnetic field. Validation of such a mechanism has been hampered by the absence of structures of vertebrate CRYs that have functional photochemistry. Here we present crystal structures of Columba livia (pigeon) CRY4 that reveal evolutionarily conserved modifications to a sequence of Trp residues (Trp-triad) required for CRY photoreduction. In ClCRY4, the Trp-triad chain is extended to include a fourth Trp (W369) and a Tyr (Y319) residue at the protein surface that imparts an unusually high quantum yield of photoreduction. These results are consistent with observations of night migratory behavior in animals at low light levels and could have implications for photochemical pathways allowing magnetosensing.

The effect of light conditions on interpreting oil composition engineering in Arabidopsis seeds.

Arabidopsis thaliana is the most developed and utilized model plant. In particular, it is an excellent model for proof-of-concept seed oil engineering studies because it accumulates approximately 37% seed oil by weight, and it is closely related to important Brassicaceae oilseed crops. Arabidopsis can be grown under a wide variety of conditions including continuous light; however, the amount of light is strongly correlated with total seed oil accumulation. In addition, many attempts to engineer novel seed oil fatty acid compositions in Arabidopsis have reported significant reductions in oil accumulation; however, the relative reduction from the nontransgenic controls varies greatly within the literature. A set of experiments were conducted to systematically analyze the effect of light conditions (including day/night cycle vs. continuous light, and different light intensities) on the relative accumulation of seed oil between three different transgenic lines producing novel hydroxy fatty acids and their nontransgenic background. Oil content was measured per seed and as a percentage of seed weight. Our results indicate the relative amount of seed oil between transgenic lines and nontransgenic controls is dependent on both the light conditions and the type of oil content measurement utilized. In addition, the light conditions effect the relative accumulation of the novel fatty acids between various transgenic lines. Therefore, the success of novel fatty acid proof-of-concept engineering strategies on both oil accumulation and fatty acid composition in Arabidopsis seeds should be considered in light of the select growth and measurement conditions prior to moving engineering strategies into crop plants.