Mixed mechanism of conformational selection and induced fit as a molecular recognition process in the calreticulin family of proteins.

The fundamental question on the mechanism of molecular recognition during ligand binding has attracted a lot of scientific scrutiny. The two competing theories of ligand binding-"induced fit" and "conformational selection" have been proposed to explain biomolecular recognition. Since exploring a family of proteins with similar structural architectures and conserved functional roles can provide valuable insight into the significance of molecular structure and function, we performed molecular dynamics simulations on the calreticulin family of proteins, which specifically recognize monoglucosylated N-glycan during the protein folding process. Atomistic simulations of lectins in free and bound forms demonstrated that they exist in several conformations spanning from favorable to unfavorable for glycan binding. Our analysis was confined to the carbohydrate recognition domain (CRD) of these lectins to demonstrate the degree of conservation in protein sequence and structure and relate them with their function. Furthermore, we computed the lectin-glycan binding affinity using the mmPBSA approach to identify the most favorable lectin conformation for glycan binding and compared the molecular interaction fields in terms of noncovalent bond interactions. We also demonstrated the involvement of Tyr and Trp residues in the CRD with the non-reducing end glucose and central mannose residues, which contribute to some of the specific interactions. Furthermore, we analyzed the conformational changes in the CRD through SASA, RMSFs and protein surface topography mapping of electrostatic and hydrophobic potentials. Our findings demonstrate a hybrid mechanism of molecular recognition, initially driven by conformational selection followed by glycan-induced fluctuations in the key residues to strengthen the glycan binding interactions.

Structural Analysis of PfSec62-Autophagy Interacting Motifs (AIM) and PfAtg8 Interactions for Its Implications in RecovER-phagy in Plasmodium falciparum.

Autophagy is a degradative pathway associated with many pathological and physiological processes crucial for cell survival. During ER stress, while selective autophagy occurs via ER-phagy, the re-establishment of physiologic ER homeostasis upon resolution of a transient ER stress is mediated by recovER-phagy. Recent studies demonstrated that recovER-phagy is governed via association of Sec62 as an ER-resident autophagy receptor through its autophagy interacting motifs (AIM)/LC3-interacting region (LIR) toAtg8/LC3. Atg8 is an autophagy protein, which is central to autophagosome formation and maturation. Plasmodium falciparum Atg8 (PfAtg8) has both autophagic and non-autophagic functions critical for parasite survival. Since Plasmodium also has Sec62 in the ER membrane and is prone to ER stress due to drastic transformation during their complex intraerythrocytic cycle; hence, we initiated the studies to check whether recovER-phagy occurs in the parasite. To achieve this, a comprehensive study based on the computational approaches was carried out. This study embarks upon identification of AIM sequences in PfSec62 by carrying out peptide-protein docking simulations and comparing the interactions of these AIMs with PfAtg8, based on the molecular dynamic simulations. Detailed analysis is based on electrostatic surface complementarity, peptide-protein interaction strength, mapping of non-covalent bond interactions and rupture force calculated from steered MD simulations. Potential mean forces and unbinding free energies (ΔGdissociation) using Jarzynski's equality were also computed for the AIM/LIR motif complexes with PfAtg8/HsLC3 autophagy proteins to understand their dissociation free energy profiles and thereby their binding affinities and stability of the peptide-protein complexes. Through this study, we predict Sec62 mediated recovER-phagy in Plasmodium falciparum, which might open new avenues to explore novel drug targets for antimalarial drug discovery.

Multivariate PLS Modeling of Apicomplexan FabD-Ligand Interaction Space for Mapping Target-Specific Chemical Space and Pharmacophore Fingerprints.

Biomolecular recognition underlying drug-target interactions is determined by both binding affinity and specificity. Whilst, quantification of binding efficacy is possible, determining specificity remains a challenge, as it requires affinity data for multiple targets with the same ligand dataset. Thus, understanding the interaction space by mapping the target space to model its complementary chemical space through computational techniques are desirable. In this study, active site architecture of FabD drug target in two apicomplexan parasites viz. Plasmodium falciparum (PfFabD) and Toxoplasma gondii (TgFabD) is explored, followed by consensus docking calculations and identification of fifteen best hit compounds, most of which are found to be derivatives of natural products. Subsequently, machine learning techniques were applied on molecular descriptors of six FabD homologs and sixty ligands to induce distinct multivariate partial-least square models. The biological space of FabD mapped by the various chemical entities explain their interaction space in general. It also highlights the selective variations in FabD of apicomplexan parasites with that of the host. Furthermore, chemometric models revealed the principal chemical scaffolds in PfFabD and TgFabD as pyrrolidines and imidazoles, respectively, which render target specificity and improve binding affinity in combination with other functional descriptors conducive for the design and optimization of the leads.

Hierarchical sampling for metastable conformers determines biomolecular recognition: the case of malectin and diglucosylated N-glycan interactions.

Structural information over the entire course of binding interactions based on the analyses of energy landscapes is described, which provides a framework to understand the events involved during biomolecular recognition. Conformational dynamics of malectin's exquisite selectivity for diglucosylated N-glycan (Dig-N-glycan), a highly flexible oligosaccharide comprising of numerous dihedral torsion angles, are described as an example. For this purpose, a novel approach based on hierarchical sampling for acquiring metastable molecular conformations constituting low-energy minima for understanding the structural features involved in a biologic recognition is proposed. For this purpose, four variants of principal component analysis were employed recursively in both Cartesian space and dihedral angles space that are characterized by free energy landscapes to select the most stable conformational substates. Subsequently, k-means clustering algorithm was implemented for geometric separation of the major native state to acquire a final ensemble of metastable conformers. A comparison of malectin complexes was then performed to characterize their conformational properties. Analyses of stereochemical metrics and other concerted binding events revealed surface complementarity, cooperative and bidentate hydrogen bonds, water-mediated hydrogen bonds, carbohydrate-aromatic interactions including CH-π and stacking interactions involved in this recognition. Additionally, a striking structural transition from loop to ß-strands in malectin CRD upon specific binding to Dig-N-glycan is observed. The interplay of the above-mentioned binding events in malectin and Dig-N-glycan supports an extended conformational selection model as the underlying binding mechanism.