Influence of pH on indole-dependent heterodimeric interactions between Anopheles gambiae odorant-binding proteins OBP1 and OBP4.

Insect Odorant Binding Proteins (OBPs) constitute important components of their olfactory apparatus, as they are essential for odor recognition. OBPs undergo conformational changes upon pH change, altering their interactions with odorants. Moreover, they can form heterodimers with novel binding characteristics. Anopheles gambiae OBP1 and OBP4 were found capable of forming heterodimers possibly involved in the specific perception of the attractant indole. In order to understand how these OBPs interact in the presence of indole and to investigate the likelihood of a pH-dependent heterodimerization mechanism, the crystal structures of OBP4 at pH 4.6 and 8.5 were determined. Structural comparison to each other and with the OBP4-indole complex (3Q8I, pH 6.85) revealed a flexible N-terminus and conformational changes in the α4-loop-α5 region at acidic pH. Fluorescence competition assays showed a weak binding of indole to OBP4 that becomes further impaired at acidic pH. Additional Molecular Dynamic and Differential Scanning Calorimetry studies displayed that the influence of pH on OBP4 stability is significant compared to the modest effect of indole. Furthermore, OBP1-OBP4 heterodimeric models were generated at pH 4.5, 6.5, and 8.5, and compared concerning their interface energy and cross-correlated motions in the absence and presence of indole. The results indicate that the increase in pH may induce the stabilization of OBP4 by increasing its helicity, thereby enabling indole binding at neutral pH that further stabilizes the protein and possibly promotes the creation of a binding site for OBP1. A decrease in interface stability and loss of correlated motions upon transition to acidic pH may provoke the heterodimeric dissociation allowing indole release. Finally, we propose a potential OBP1-OBP4 heterodimer formation/disruption mechanism induced by pH change and indole binding.

Exploring the medicinally important secondary metabolites landscape through the lens of transcriptome data in fenugreek (Trigonella foenum graecum L.).

Fenugreek (Trigonella foenum-graecum L.) is a self-pollinated leguminous crop belonging to the Fabaceae family. It is a multipurpose crop used as herb, spice, vegetable and forage. It is a traditional medicinal plant in India attributed with several nutritional and medicinal properties including antidiabetic and anticancer. We have performed a combined transcriptome assembly from RNA sequencing data derived from leaf, stem and root tissues. Around 209,831 transcripts were deciphered from the assembly of 92% completeness and an N50 of 1382 bases. Whilst secondary metabolites of medicinal value, such as trigonelline, diosgenin, 4-hydroxyisoleucine and quercetin, are distributed in several tissues, we report transcripts that bear sequence signatures of enzymes involved in the biosynthesis of such metabolites and are highly expressed in leaves, stem and roots. One of the antidiabetic alkaloid, trigonelline and its biosynthesising enzyme, is highly abundant in leaves. These findings are of value to nutritional and the pharmaceutical industry.

DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity.

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to "learn" intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.