A KARRIKIN INSENSITIVE2 paralog in lettuce mediates highly sensitive germination responses to karrikinolide

Karrikins (KARs) are chemicals in smoke that can enhance germination of many plants. Lactuca sativa cv. Grand Rapids (lettuce), germinates in the presence of nanomolar karrikinolide (KAR1). We found that lettuce is much less responsive to KAR2 or a mixture of synthetic strigolactone analogs, rac-GR24. We investigated the molecular basis of selective and sensitive KAR1 perception in lettuce. The lettuce genome contains two copies of KARRIKIN INSENSITIVE2 (KAI2), a receptor that is required for KAR responses in Arabidopsis thaliana. LsKAI2b is more highly expressed than LsKAI2a in dry achenes and during early stages of seed imbibition. Through cross-species complementation assays in Arabidopsis we found that LsKAI2b confers robust responses to KAR1, but LsKAI2a does not. Therefore, LsKAI2b likely mediates KAR1 responses in lettuce. We compared homology models of the ligand-binding pockets of KAI2 proteins from lettuce and a fire follower, Emmenanthe penduliflora. This identified pocket residues 96, 124, 139, and 161 as candidates that influence the ligand-specificity of KAI2. Further support for the significance of these residues was found through a broader comparison of pocket residue conservation among 324 asterid KAI2 proteins. We tested the effects of substitutions at these four positions in Arabidopsis thaliana KAI2 and found that a broad array of responses to KAR1, KAR2, and rac-GR24 could be achieved.

Homology Modelling, Docking-based Virtual Screening, ADME Properties, and Molecular Dynamics Simulation for Identification of Probable Type II Inhibitors of AXL Kinase

Background: AXL kinase is an important member of the TAM family for kinases which is involved in a majority of cancers. Considering its role in different cancers due to its pro-tumorigenic effects and its involvement in the resistance, it has gained importance recently. Majority of research carried out is on Type I inhibitors and limited studies have been done for Type II inhibitors. Taking this into consideration, we have attempted to build Homology models to identify the Type II inhibitors for the AXL kinase. Methods: Homology Models for DFG-out C-helix-in/out state were developed using SWISS Model, PRIMO, and Prime. These models were validated by different methods and further evaluated for stability by molecular dynamics simulation using Desmond software. Selected models PED1-EB and PEDI1-EB were used for the docking-based virtual screening of four compound libraries using Glide software. The hits identified were subjected to interaction analysis and shortlisted compounds were subjected to Prime MM-GBSA studies for energy calculation. These compounds were also docked in the DFG-in state to check for binding and elimination of any compounds that may not be Type II inhibitors. The Prime energies were calculated for these complexes as well and some compounds were eliminated. ADMET studies were carried out using Qikprop. Some selected compounds were subjected to molecular dynamics simulation using Desmond for evaluating the stability of the complexes. Results: Out of the 78 models inclusive of both DFG-out C-helix-in and DFG-out C-helix-out, 5 models were identified after different types of evaluation as well as validation studies. 1 model representing each type (PED1-EB and PEDI1-EB) was selected for the screening studies. The screening studies resulted in identification of 29 compounds from the screen on PED1-EB and 10 compounds from the screen on PEDI1-EB. Hydrogen bonding interactions with Pro621, Met623, and Asp690 were observed for these compounds primarily. In some compounds, hydrogen bonding with Leu542, Glu544, Lys567, and Asn677 as well as pi-pi stacking interactions with either Phe622 or Phe691 was also seen. 4 compounds identified from PED1-EB screen were subjected to molecular dynamics simulation and their interactions were found to be consistent during the simulation. 2 compounds identified from PEDI1-EB screen were also subjected to the simulation studies however; their interactions with Asp690 were not observed for a significant time and in both cases differed from the docked pose. Conclusion: Multiple models of DFG-out conformations of AXL kinase were built, validated and used for virtual screening. Different compounds were identified in the virtual screening, which may possibly act as Type II inhibitors for AXL kinase. Some more experimental studies can be done to validate these findings in future. This study will play a guiding role further development of the newer Type II inhibitors of the AXL kinase for the probable treatment of cancer.

Locating ligand binding sites in G-protein coupled receptors using combined information from docking and sequence conservation

GPCRs (G-protein coupled receptors) are the largest family of drug targets and share a conserved structure. Binding sites are unknown for many important GPCR ligands due to the difficulties of GPCR recombinant expression, biochemistry, and crystallography. We describe our approach, ConDockSite, for predicting ligand binding sites in class A GPCRs using combined information from surface conservation and docking, starting from crystal structures or homology models. We demonstrate the effectiveness of ConDockSite on crystallized class A GPCRs such as the beta2 adrenergic and A2A adenosine receptors. We also demonstrate that ConDockSite successfully predicts ligand binding sites from high-quality homology models. Finally, we apply ConDockSite to predict the ligand binding sites on a structurally uncharacterized GPCR, GPER, the G-protein coupled estrogen receptor. Most of the sites predicted by ConDockSite match those found in other independent modeling studies. ConDockSite predicts that four ligands bind to a common location on GPER at a site deep in the receptor cleft. Incorporating sequence conservation information in ConDockSite overcomes errors introduced from physics-based scoring functions and homology modeling.

Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.

2D SIFt: a matrix of ligand-receptor interactions

Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d.

Best templates outperform homology models in predicting the impact of mutations on protein stability

Motivation: Prediction of protein stability change upon mutation (∆∆G) is crucial for facilitating protein engineering and understanding of protein folding principles. Robust prediction of protein folding free energy change requires the knowledge of protein three-dimensional (3D) structure. Unfortunately, protein 3D structure is not always available. In this case, one can still predict the protein stability change by constructing a homology model of the protein; however, the accuracy of homology model-based ∆∆G predictions is unknown. The perspectives of using 3D structures of the best templates are also unclear. Results: To investigate these questions, we used the most popular and accurate publicly available tools: FoldX for stability change prediction and I-Tasser for homology modeling. We found that both homology models and best templates worsen the ∆∆G prediction, with best templates performing 1.5 times better than homology models. For AlphaFold models, we also found that the best templates seem to outperform protein models. Our findings imply using the 3D structures of the best templates for ∆∆G prediction if the 3D protein structure is unavailable.

Structural Modeling of the TMPRSS Subfamily of Host Cell Proteases Reveals Potential Binding Sites

The transmembrane protease serine subfamily (TMPRSS) has at least eight members with known protein sequence: TMPRSS2, TMPRRS3, TMPRSS4, TMPRSS5, TMPRSS6, TMPRSS7, TMPRSS9, TMPRSS11, TMPRSS12 and TMPRSS13. A majority of these TMPRSS proteins have key roles in human hemostasis as well as promoting certain pathologies, including several types of cancer. In addition, TMPRSS proteins have been shown to facilitate the entrance of respiratory viruses into human cells, most notably TMPRSS2 and TMPRSS4 activate the spike protein of the SARS-CoV-2 virus. Despite the wide range of functions that these proteins have in the human body, none of them have been successfully crystallized. The lack of structural data has significantly hindered any efforts to identify potential drug candidates with high selectivity to these proteins. In this study, we present homology models for all members of the TMPRSS family including any known isoform (the homology model of TMPRSS2 is not included in this study as it has been previously published). The atomic coordinates for all homology models have been refined and equilibrated through molecular dynamic simulations. The structural data revealed potential binding sites for all TMPRSS as well as key amino acids that can be targeted for drug selectivity.

Predicting T cell receptor antigen specificity from structural features derived from homology models of receptor-peptide-major histocompatibility complexes.

The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.