Protein conformational dynamics dictate the binding affinity for a ligand

Comparative molecular dynamics reveal the conformational dynamics of the IrtAB-cMBT complex in mycobacterial iron uptake

The IrtAB transporter in Mycobacterium tuberculosis (Mtb) is essential for iron acquisition through the import of iron-bound carboxymycobactin (cMBT), yet the molecular mechanisms governing substrate recognition and transport remain unresolved. Here, we employed 450-ns molecular dynamics simulations to elucidate the conformational dynamics of IrtAB in substrate-free (apo) and substrate-bound (holo) states, revealing key structural rearrangements and residue-specific interactions underpinning its transport cycle. Comparative analyses demonstrated enhanced conformational flexibility in the holo state, with asymmetric domain movements in IrtA and IrtB subunits facilitating cMBT translocation. Three histidine residues (His356, His402, His407) in IrtA undergone significant positional shifts (6-10 Å) upon substrate binding, forming a dynamic coordination network critical for cMBT recognition. The ligand exhibited complex behavior, including a 3.5-4.0 Å downward movement within the binding pocket and RMSD fluctuations (0.5-5.0 Å), indicative of multiple energetically favorable binding modes. Substrate-induced stabilization of the transmembrane domains correlates with progressive dehydration of the binding cavity, while RMSF profiles highlighted asymmetric flexibility in transmembrane helices during transport. These findings reveal how IrtAB's exporter-like architecture is repurposed for iron-siderophore import, balancing structural rigidity with conformational plasticity to enable efficient nutrient uptake. By delineating the mechanistic basis of IrtAB-mediated iron acquisition, this study provides a framework for targeting this pathway in Mtb, offering potential avenues for therapeutic intervention against tuberculosis.

Computational exploration of Eucommia ulmoides flavonoids as potential RANKL inhibitors via molecular docking and dynamics simulations

Osteoporosis, characterized by excessive osteoclast activation, is mediated through the RANKL/RANK/OPG signaling axis. While flavonoids from Eucommia ulmoides (EU) have demonstrated anti-osteoclastogenic activity, their atomic-level mechanisms remain elusive. Here, we investigated six EU-derived flavonoids (cyrtominetin, quercetin, syringetin, genistein, ombuin, and kaempferol) targeting RANKL using integrated computational approaches. Molecular docking revealed strong binding affinities (Total_Score > 4.0) for all compounds, with cyrtominetin exhibiting the highest affinity (-50.205 kJ/mol via MM-PBSA), primarily through hydrogen bonds with Gly178, His180, Lys181, and Asn295. Moreover, most flavonoids interacted with RANKL by forming strong hydrogen bonds with Gly178 and Asn295, exhibiting higher binding affinity that was identified as essential for the activity. All-atom molecular dynamics simulations (100 ns) confirmed complex stability, demonstrating: low RMSD fluctuations (< 4.0 Å) and compact Rg values (16.0-17.0 Å). Notably, binding free energy decomposition identified both electrostatic and van der Waals contributions as critical for stabilization. These results identify cyrtominetin as a promising lead compound for RANKL inhibition, providing structural insights for designing flavonoid-based therapeutics against osteoporosis.

Uncovering a novel binding trench in ERRα: insights from molecular simulations

Although estrogen-related receptor α (ERRα) holds significant therapeutic potential for treating various disorders, developing selective agonists remains challenging due to the poor pharmacokinetics and limited selectivity of current ligands. This study presents unconstrained molecular dynamics simulations of ERRα bound to an agonist ligand, uncovering dynamic ligand-binding behavior as the ligand shifts between two orientations: one in the orthosteric pocket and another in a newly identified trench adjacent to this site. The free energy landscape reveals that both binding orientations are comparably populated, with an accessible transition pathway between them. The identification of this novel binding trench expands our understanding of ERRα's ligand binding domain, offering new avenues for small-molecule drug discovery and selective modulation of ERRα activity.

Probing the interactions of genistein with HMGB1 through multi-spectroscopic and in-silico approaches

Functional regulation of proteins by ligand-protein interactions plays a crucial role in understanding biological processes and identifying potential drugs. High mobility group box 1 (HMGB1) plays a pivotal role in sterile inflammation as a key immunomodulatory protein. Genistein, a well-known isoflavone compound, has been shown to have neuroprotective effects. In this study, we investigated the genistein-HMGB1 interactions using experimental and computational approaches. Our results revealed that genistein binds to HMGB1 with a KD value of 6.06 × 10-5 M. The addition of genistein significantly quenched the fluorescence of HMGB1. Thermodynamic analyses demonstrated that hydrogen bonds and hydrophobic forces are the primary forces during the binding process. Furthermore, the interaction between genistein and HMGB1 led to changes in the microenvironment of protein chromogenic amino acids and subtle alterations in the protein secondary structure. Molecular modeling results indicate that Pro95, Pro98, and Lys154 are the major amino acid residues for genistein binding to HMGB1. Meanwhile, at the cellular level, an inhibitory effect of genistein on HMGB1-induced NO release from microglia was observed, demonstrating an inhibition rate of 42.1 %. Our studies demonstrated that genistein could be applied in treating neurological diseases through its interaction with HMGB1.

Structure-based molecular docking and molecular dynamics simulations study for the identification of dipeptidyl peptidase 4 inhibitors in type 2 diabetes

Inhibition of dipeptidyl peptidase-4 (DPP4) activity has emerged as a promising therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM). Bioinformatics-driven approaches have emerged as crucial tools in drug discovery. Molecular docking and molecular dynamics (MD) simulations are effective tools in drug discovery, as they reduce the time and cost associated with experimental screening. In this study, we employed structure-assisted in-silico methods, including molecular docking and MD simulations, to identify SRT2183, a small molecule that may potentially inhibit the activity of DPP4 enzyme. The interaction between the small molecule "SRT2183" and DPP4 exhibited a binding affinity of -9.9 Kcal/Mol, leading to the formation of hydrogen bonds with the amino acid residues MET348, SER376, and THR351 of DPP4. The MD simulations over a period of 100 ns indicated stable protein-ligand interactions, with no significant conformational rearrangements observed within the simulated timeframe. In conclusion, our results suggest that the small molecule SRT2183 may have the potential to inhibit the DPP4 enzyme and pave the way for the therapeutics of T2DM.Communicated by Ramaswamy H. Sarma.

Explicit water-ligand docking, drug-likeness and molecular dynamics simulation analysis to predict the potency of Boerhavia diffusa plant extract against mutant wilms tumor-1 protein responsible for type 4 nephrotic syndrome

Thestructure and function of a protein are closely connected. Changes in a protein structure can impact on its function. Nephrotic syndrome type 4 (NPHS4) is an uncommon genetic condition caused by mutations in the WT1 gene, which codes for the wilms tumor-1 protein. Several studies have discovered that patients with nephrotic syndromes are resistant to steroid therapy and are likely to develop end-stage renal failure. The use of phytochemicals-based therapeutics is in demand due to their high potential and low toxicity. Based on this context, we employed the Autodock raccoon to screen 67 distinct potent phytochemicals from the Boerhavia diffusa (B.diffusa) plant against the wild type and mutant model at position C388R (cysteine is replaced with arginine at position 388) of the C-terminal DNA binding domain of the wilms tumor-1 protein. Out of 67 active compounds, only 10 compounds (lunamarine, kaempferol, boeravinone B, boeravinone E, boeravinone A, boeravinone F, boeravinone J, boeravinone P, boerhaavic acid and 4',7-dihydroxy-3'-methylflavone) were screened based on drug-likeness properties and binding energy for explicit water ligand docking against wild and mutant model of C-terminal DNA binding domain of wilms tumor-1 protein. Consequently, the hydrated form of boeravinone F and boeravinone A demonstrated the highest binding energy against the protein mutant model described above, the binding energies were -9.56 and -8.96 Kcal/mol, respectively. Followed by explicit water ligand docking the microscopic properties of wild type, mutant, mutant-boeravinone F complex, and mutant-boeravinone A complex systems were evaluated using molecular dynamics simulation steps with 100 ns of trajectory. The findings indicate that, due to mutation the mutant model system had decreasing stability and decreasing compactness nature. However, boeravinone A effectively monitored the mutant system's stability and improved compactness nature after binding with the mutant model. Boeravinone A with the mutant model complex system was determined to have the lowest energy point as compared to other studied systems. The study revealed minimal structural alterations and reduced conformational mobility.

A Biochemical and Biophysical Analysis of the Interaction of nsp9 with nsp12 from SARS-CoV-2-Implications for Future Drug Discovery Efforts

The ongoing global pandemic of the coronavirus 2019 (COVID-19) disease is caused by the virus SARS-CoV-2, with very few highly effective antiviral treatments currently available. The machinery responsible for the replication and transcription of viral RNA during infection is made up of several important proteins. Two of these are nsp12, the catalytic subunit of the viral polymerase, and nsp9, a cofactor of nsp12 involved in the capping and priming of viral RNA. While several recent studies have determined the structural details of the interaction of nsp9 with nsp12 in the context of RNA capping, very few biochemical or biophysical details are currently available. In this study, we have used a combination of surface plasmon resonance (SPR) experiments, size exclusion chromatography (SEC) experiments, and biochemical assays to identify specific nsp9 residues that are critical for nsp12 binding as well as RNAylation, both of which are essential for the RNA capping process. Our data indicate that nsp9 dimerization is unlikely to play a significant functional role in the virus. We confirm that a set of recently discovered antiviral peptides inhibit nsp9-nsp12 interaction by specifically binding to nsp9; however, we find that these peptides do not impact RNAylation. In summary, our results have important implications for future drug discovery efforts to combat SARS-CoV-2 and any newly emerging coronaviruses.

Ensembling methods for protein-ligand binding affinity prediction

Protein-ligand binding affinity prediction is a key element of computer-aided drug discovery. Most of the existing deep learning methods for protein-ligand binding affinity prediction utilize single models and suffer from low accuracy and generalization capability. In this paper, we train 13 deep learning models from combinations of 5 input features. Then, we explore all possible ensembles of the trained models to find the best ensembles. Our deep learning models use cross-attention and self-attention layers to extract short and long-range interactions. Our method is named Ensemble Binding Affinity (EBA). EBA extracts information from various models using different combinations of input features, such as simple 1D sequential and structural features of the protein-ligand complexes rather than 3D complex features. EBA is implemented to accurately predict the binding affinity of a protein-ligand complex. One of our ensembles achieves the highest Pearson correlation coefficient (R) value of 0.914 and the lowest root mean square error (RMSE) value of 0.957 on the well-known benchmark test set CASF2016. Our ensembles show significant improvements of more than 15% in R-value and 19% in RMSE on both well-known benchmark CSAR-HiQ test sets over the second-best predictor named CAPLA. Furthermore, the superior performance of the ensembles across all metrics compared to existing state-of-the-art protein-ligand binding affinity prediction methods on all five benchmark test datasets demonstrates the effectiveness and robustness of our approach. Therefore, our approach to improving binding affinity prediction between proteins and ligands can contribute to improving the success rate of potential drugs and accelerate the drug development process.

Interdomain-linkers control conformational transitions in the SLC23 elevator transporter UraA

Uptake of nucleobases and ascorbate is an essential process in all living organisms mediated by SLC23 transport proteins. These transmembrane carriers operate via the elevator alternating-access mechanism, and are composed of two rigid domains whose relative motion drives transport. The lack of large conformational changes within these domains suggests that the interdomain-linkers act as flexible tethers. Here, we show that interdomain-linkers are not mere tethers, but have a key regulatory role in dictating the conformational space of the transporter and defining the rotation axis of the mobile transport domain. By resolving a wide inward-open conformation of the SLC23 elevator transporter UraA and combining biochemical studies using a synthetic nanobody as conformational probe with hydrogen-deuterium exchange mass spectrometry, we demonstrate that interdomain-linkers control the function of transport proteins by influencing substrate affinity and transport rate. These findings open the possibility to allosterically modulate the activity of elevator proteins by targeting their linkers.

Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and Inhibitor Trapping Models

Binding affinity is a fundamental parameter in drug design, describing the strength of the interaction between a molecule and its target protein. Accurately predicting binding affinity is crucial for the rapid development of novel therapeutics, the prioritization of promising candidates, and the optimization of their properties through rational design strategies. Binding affinity is determined by the mechanism of recognition between proteins and ligands. Various models, including the lock and key, induced fit, and conformational selection, have been proposed to explain this recognition process. However, current computational strategies to predict binding affinity, which are based on these models, have yet to produce satisfactory results. This article explores the connection between binding affinity and these protein-ligand interaction models, highlighting that they offer an incomplete picture of the mechanism governing binding affinity. Specifically, current models primarily center on the binding of the ligand and do not address its dissociation. In this context, the concept of ligand trapping is introduced, which models the mechanisms of dissociation. When combined with the current models, this concept can provide a unified theoretical framework that may allow for the accurate determination of the ligands' binding affinity.

Fast collective motions of backbone in transmembrane α helices are critical to water transfer of aquaporin

Fast collective motions are widely present in biomolecules, but their functional relevance remains unclear. Herein, we reveal that fast collective motions of backbone are critical to the water transfer of aquaporin Z (AqpZ) by using solid-state nuclear magnetic resonance (ssNMR) spectroscopy and molecular dynamics (MD) simulations. A total of 212 residue site-specific dipolar order parameters and 158 15N spin relaxation rates of the backbone are measured by combining the 13C- and 1H-detected multidimensional ssNMR spectra. Analysis of these experimental data by theoretic models suggests that the small-amplitude (~10°) collective motions of the transmembrane α helices on the nanosecond-to-microsecond timescales are dominant for the dynamics of AqpZ. The MD simulations demonstrate that these collective motions are critical to the water transfer efficiency of AqpZ by facilitating the opening of the channel and accelerating the water-residue hydrogen bonds renewing in the selectivity filter region.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications

Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski's rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.

Decision tree-based identification of important molecular fragments for protein-ligand binding

Fragment-based drug design is an emerging technology in pharmaceutical research and development. One of the key aspects of this technology is the identification and quantitative characterization of molecular fragments. This study presents a strategy for identifying important molecular fragments based on molecular fingerprints and decision tree algorithms and verifies its feasibility in predicting protein-ligand binding affinity. Specifically, the three-dimensional (3D) structures of protein-ligand complexes are encoded using extended-connectivity fingerprints (ECFP), and three decision tree models, namely Random Forest, XGBoost, and LightGBM, are used to quantitatively characterize the feature importance, thereby extracting important molecular fragments with high reliability. Few-shot learning reveals that the extracted molecular fragments contribute significantly and consistently to the binding affinity even with a small sample size. Despite the absence of location and distance information for molecular fragments in ECFP, 3D visualization, in combination with the reverse ECFP process, shows that the majority of the extracted fragments are located at the binding interface of the protein and the ligand. This alignment with the distance constraints critical for binding affinity further supports the reliability of the strategy for identifying important molecular fragments.

Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 Mpro

Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.

Sulfation Effects of Chondroitin Sulfate to Bind a Chemokine in Aqueous Medium: Conformational Heterogeneity and Dynamics from Molecular Simulation

The sulfation patterns and degree of sulfation of chondroitin sulfate (CS), an important class of glycosaminoglycans (GAG), and their interactions with chemokines are accountable for various diseases. To realize the underlying mechanism of such complex biological phenomena at a molecular level and their application in rational drug design, a study on conformations and dynamics of CSs is necessary. To explore this, in this study, we performed a series of atomistic molecular dynamics (MD) simulations with different sulfated variants of octadecasaccharide CS, like CS-C, CS-E, and CS-T, in their free forms and when bound to the protein chemokine CXCL8 dimer in an aqueous medium. The calculated binding free energy of CSs with the CXCL8 dimer is favorable, and the degree of sulfation favors the complexation process further with prominent hydrophobic and hydrogen-bonded interactions. We find that the recognition is associated with the configurational entropy loss of the CS molecules as calculated from the Gaussian mixture approach, which supports that the degree of sulfation regulates the process. Cluster analysis through the k-means algorithm and end-to-end distance measurement revealed that although the free CS molecules adopted linear conformations, the nonlinear conformations during binding with protein were noted. Adaptation of nonlinear forms in the bound forms is noteworthy for the less-sulfated CS-C and CS-E. Apart from favorable 4C1 conformations, the occasional appearance of skew-boat forms from the free-energy map of ring pucker for the GlcUA unit was observed, which remains unaffected by the sulfation. We find that during recognition, the average relaxation time of intra-CS and inter-CS-CXCL8 hydrogen bonds (HBs) is about a magnitude lesser than that of CS-water HBs, most prominent on the involvement of higher sulfated CS-T analogues. The translational motion of surrounded water molecules in CSs exhibited sublinear diffusion, and the degree of sublinearity increases around the heavily sulfated molecules due to the hindrance created by them as well as the presence of the chemokine and exhibited markedly slow heterogeneous diffusion.

Inhibitor Trapping in N-Myristoyltransferases as a Mechanism for Drug Potency

Predicting inhibitor potency is critical in drug design and development, yet it has remained one of computational biology's biggest unresolved challenges. Here, we show that in the case of the N-myristoyltransferase (NMT), this problem could be traced to the mechanisms by which the NMT enzyme is inhibited. NMT adopts open or closed conformations necessary for orchestrating the different steps of the catalytic process. The results indicate that the potency of the NMT inhibitors is determined by their ability to stabilize the enzyme conformation in the closed state, and that in this state, the small molecules themselves are trapped and locked inside the structure of the enzyme, creating a significant barrier for their dissociation. By using molecular dynamics simulations, we demonstrate that the conformational stabilization of the protein molecule in its closed form is highly correlated with the ligands activity and can be used to predict their potency. Hence, predicting inhibitor potency in silico might depend on modeling the conformational changes of the protein molecule upon binding of the ligand rather than estimating the changes in free binding energy that arise from their interaction.

Physiologically Based Pharmacokinetic Model of Brain Delivery of Plasma Protein Bound Drugs

INTRODUCTION

A physiologically based pharmacokinetic (PBPK) model is developed that focuses on the kinetic parameters of drug association and dissociation with albumin, alpha-1 acid glycoprotein (AGP), and brain tissue proteins, as well as drug permeability at the blood-brain barrier, drug metabolism, and brain blood flow.

GOAL

The model evaluates the extent to which plasma protein-mediated uptake (PMU) of drugs by brain influences the concentration of free drug both within the brain capillary compartment in vivo and the brain compartment. The model also studies the effect of drug binding to brain tissue proteins on the concentration of free drug in brain.

METHODS

The steady state and non-steady state PBPK models are comprised of 11-12 variables, and 18-23 parameters, respectively. Two model drugs are analyzed: propranolol, which undergoes modest PMU from the AGP-bound pool, and imipramine, which undergoes a high degree of PMU from both the albumin-bound and AGP-bound pools in plasma.

RESULTS

The free propranolol concentration in brain is under-estimated 2- to fourfold by in vitro measurements of free plasma propranolol, and the free imipramine concentration in brain is under-estimated by 18- to 31-fold by in vitro measurements of free imipramine in plasma. The free drug concentration in brain in vivo is independent of drug binding to brain tissue proteins.

CONCLUSIONS

In vitro measurement of free drug concentration in plasma under-estimates the free drug in brain in vivo if PMU in vivo from either the albumin and/or the AGP pools in plasma takes place at the BBB surface.

A protein engineering approach toward understanding FKBP51 conformational dynamics and mechanisms of ligand binding

Most proteins are flexible molecules that coexist in an ensemble of several conformations. Point mutations in the amino acid sequence of a protein can trigger structural changes that drive the protein population to a conformation distinct from the native state. Here, we report a protein engineering approach to better understand protein dynamics and ligand binding of the FK506-binding protein 51 (FKBP51), a prospective target for stress-related diseases, metabolic disorders, some types of cancers and chronic pain. By randomizing selected regions of its ligand-binding domain and sorting yeast display libraries expressing these variants, mutants with high affinity to conformation-specific FKBP51 selective ligands were identified. These improved mutants are valuable tools for the discovery of novel selective ligands that preferentially and specifically bind the FKBP51 active site in its open conformation state. Moreover, they will help us understand the conformational dynamics and ligand binding mechanics of the FKBP51 binding pocket.

Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network

Protein-ligand binding affinity prediction is an important task in structural bioinformatics for drug discovery and design. Although various scoring functions (SFs) have been proposed, it remains challenging to accurately evaluate the binding affinity of a protein-ligand complex with the known bound structure because of the potential preference of scoring system. In recent years, deep learning (DL) techniques have been applied to SFs without sophisticated feature engineering. Nevertheless, existing methods cannot model the differential contribution of atoms in various regions of proteins, and the relationship between atom properties and intermolecular distance is also not fully explored. We propose a novel empirical graph neural network for accurate protein-ligand binding affinity prediction (EGNA). Graphs of protein, ligand and their interactions are constructed based on different regions of each bound complex. Proteins and ligands are effectively represented by graph convolutional layers, enabling the EGNA to capture interaction patterns precisely by simulating empirical SFs. The contributions of different factors on binding affinity can thus be transparently investigated. EGNA is compared with the state-of-the-art machine learning-based SFs on two widely used benchmark data sets. The results demonstrate the superiority of EGNA and its good generalization capability.

Broadband single-molecule fluorescence enhancement based on self-assembled Ag@Au dimer plasmonic nanoantennas

Dimer optical antennas (OAs) enable great fluorescence enhancement and excitation volume reduction and hence potentially can be a very useful tool for single-molecule detection. The realization of broadband fluorescence enhancement with a dimer OA remains an essential step for its usage in multi-color single-molecule fluorescence (SMF) detection. Although silver dimer OAs have been shown to be able to yield broadband fluorescence enhancement over the visible spectrum, they are amenable to oxidization, hard to functionalize, and could cause cytotoxicity. To overcome these limitations, in this work, we took advantage of nano-sized silver due to its optical properties and gold due to its chemical properties and developed an ameliorated Ag@Au dimer OA in terms of its overall performance. The Ag@Au nanoparticle in the dimer OA contains a 70 nm silver core and an ultra-thin (∼1-5 nm) gold shell which play a key role in its optical responses. Furthermore, we employed three typical dyes, i.e., FAM, TAMRA and Cy5, representing the blue, yellow and red ranges, respectively, and characterized their single-molecule fluorescence enhancements in the presence of Au or Ag@Au OAs. Our results indicate that, in contrast to its Au counterpart, the Ag@Au dimer OA prepared here can greatly improve its optical response in the blue range and eventually achieve broadband fluorescence enhancement throughout almost the whole visible spectral range. Meanwhile, it also maintains good chemical stability and accessibility to functionalization. Such Ag@Au dimer OAs are thus expected to have many important applications in the future, including single-molecule sequencing and multi-color biosensing.

Programming xenon diffusion in maltose-binding protein

Protein interiors contain void space that can bind small gas molecules. Determination of gas pathways and kinetics in proteins has been an intriguing and challenging task. Here, we combined computational methods and the hyperpolarized xenon-129 chemical exchange saturation transfer (hyper-CEST) NMR technique to investigate xenon (Xe) exchange kinetics in maltose-binding protein (MBP). A salt bridge ∼9 Å from the Xe-binding site formed upon maltose binding and slowed the Xe exchange rate, leading to a hyper-CEST 129Xe signal from maltose-bound MBP. Xe dissociation occurred faster than dissociation of the salt bridge, as shown by 13C NMR spectroscopy and variable-B1 hyper-CEST experiments. "Xe flooding" molecular dynamics simulations identified a surface hydrophobic site, V23, that has good Xe binding affinity. Mutations at this site confirmed its role as a secondary exchange pathway in modulating Xe diffusion. This shows the possibility for site-specifically controlling xenon protein-solvent exchange. Analysis of the available MBP structures suggests a biological role of MBP's large hydrophobic cavity to accommodate structural changes associated with ligand binding and protein-protein interactions.

Controlling enzyme reactions by supramolecular protection and deprotection of oligosaccharide substrates

Protection/deprotection is a powerful strategy in the total synthesis of complex organic molecules but similar tools are nearly absent in enzymatic reactions. We here report supramolecular protective receptors that outcompete an enzyme in the binding of oligosaccharides. The strong binding inhibits the enzymatic reaction and addition of an even stronger ligand for the receptor releases the substrate. These receptors could be used to control products from the same substrate/enzyme mixture and regulate enzymatic reactions reversibly.

Flipping and other astonishing transporter dance moves in fungal drug resistance

In all domains of life, transmembrane proteins from the ATP-binding cassette (ABC) transporter family drive the translocation of diverse substances across lipid bilayers. In pathogenic fungi, the ABC transporters of the pleiotropic drug resistance (PDR) subfamily confer antibiotic resistance and so are of interest as therapeutic targets. They also drive the quest for understanding how ABC transporters can generally accommodate such a wide range of substrates. The Pdr5 transporter from baker's yeast is representative of the PDR group and, ever since its discovery more than 30 years ago, has been the subject of extensive functional analyses. A new perspective of these studies has been recently provided in the framework of the first electron cryo-microscopy structures of Pdr5, as well as emergent applications of machine learning in the field. Taken together, the old and the new developments have been used to propose a mechanism for the transport process in PDR proteins. This mechanism involves a "flippase" step that moves the substrates from one leaflet of the bilayer to the other, as a central element of cellular efflux.

Redesigned pMAL expression vector for easy and fast purification of active native antimicrobial peptides

AIMS

The aim of this study was to construct the improved pMAL expression vector in order to increase the efficacy of small native peptides purification and their clear-cut separation from MBP tag. The modifications we introduced can be applied to many expression vectors.

METHODS AND RESULTS

To improve the pMAL expression vector we introduced the His₆ tag and the enterokinase cleavage site (Ek) downstream from the MBP tag and Xa cleavage site on the original vector. For cloning of a desired peptide DNA, the enterokinase site contains a unique BsaBI restriction site adjacent to the original multi-cloning-site. This redesigned pMAL vector was optimized for the purification of cytoplasmic (pMALc5HisEk) and periplasmic (pMALp5HisEk) peptides. The purification of native and active peptide (P) was obtained following two-step affinity chromatography. In the first step the entire MBP-His₆ -Ek-P fusion protein is purified using the Ni-NTA agarose column. This fusion protein was cleaved with active His₆ tagged enterokinase. In the second step, the further purification was performed by column containing the mixture of amylose and Ni-NTA agarose resins. This removes both the MBP-His₆ and His₆ -enterokinase leaving pure native protein in solution. These new vectors and the two-step purification protocol was successfully applied in purification of active native small antimicrobial peptides (AMPs), lactococcin A and human β-defensin.

CONCLUSIONS

We constructed the improved pMAL expression vectors and established the pipeline and optimal conditions for their use in efficient purification of large amounts of active native small peptides.

SIGNIFICANCE AND IMPACT OF THE STUDY

Choice of expression vector impacts on the efficiency of expression and purification of desired proteins. The idea of redesigning pMAL vector was driven by need for rapid purification of larger amounts of active native AMPs. This newly improved pMAL vector, the cloning strategy, expression conditions and two-step purification protocol represent a unique simple approach which can be applied in every laboratory.

Resemblance-Ranking Peptide Library to Screen for Binders to Antibodies on a Peptidomic Scale

A novel resemblance-ranking peptide library with 160,000 10-meric peptides was designed to search for selective binders to antibodies. The resemblance-ranking principle enabled the selection of sequences that are most similar to the human peptidome. The library was synthesized with ultra-high-density peptide arrays. As proof of principle, screens for selective binders were performed for the therapeutic anti-CD20 antibody rituximab. Several features in the amino acid composition of antibody-binding peptides were identified. The selective affinity of rituximab increased with an increase in the number of hydrophobic amino acids in a peptide, mainly tryptophan and phenylalanine, while a total charge of the peptide remained relatively small. Peptides with a higher affinity exhibited a lower sum helix propensity. For the 30 strongest peptide binders, a substitutional analysis was performed to determine dissociation constants and the invariant amino acids for binding to rituximab. The strongest selective peptides had a dissociation constant in the hundreds of the nano-molar range. The substitutional analysis revealed a specific hydrophobic epitope for rituximab. To show that conformational binders can, in principle, be detected in array format, cyclic peptide substitutions that are similar to the target of rituximab were investigated. Since the specific binders selected via the resemblance-ranking peptide library were based on the hydrophobic interactions that are widespread in the world of biomolecules, the library can be used to screen for potential linear epitopes that may provide information about the cross-reactivity of antibodies.

Dissecting the Protein Dynamics Coupled Ligand Binding with Kinetic Models and Single-Molecule FRET

Protein-ligand interactions are crucial to many biological processes. Ligand binding and dissociation are the basic steps that allow proteins to function. Protein conformational dynamics have been shown to play important roles in ligand binding and dissociation. However, it is challenging to determine the ligand binding kinetics of dynamic proteins. Here, we undertook comprehensive single-molecule FRET (smFRET) measurements and kinetic model analysis to characterize the conformational dynamics coupled ligand binding of glutamine-binding protein (GlnBP). We showed that hinge and T118A mutations of GlnBP affect its conformational dynamics as well as the ligand binding affinity. Based on smFRET measurements, the kinetic model of ligand-GlnBP interactions was constructed. Using experimentally measured parameters, we solved the rate equations of the model and obtained the undetectable parameters of the model which allowed us to understand the ligand binding kinetics fully. Our results demonstrate that modulation of the conformational dynamics of GlnBP affects the ligand binding and dissociation rates. This study provides insights into the binding kinetics of ligands, which are related to the protein function itself.

GAPDH is involved in the heme-maturation of myoglobin and hemoglobin

GAPDH, a heme chaperone, has been previously implicated in the incorporation of heme into iNOS and soluble guanylyl cyclase (sGC). Since sGC is critical for myoglobin (Mb) heme-maturation, we investigated the role of GAPDH in the maturation of this globin, as well as hemoglobins α, β, and γ. Utilizing cell culture systems, we found that overexpression of wild-type GAPDH increased, whereas GAPDH mutants H53A and K227A decreased, the heme content of Mb and Hbα and Hbβ. Overexpression of wild-type GAPDH fully recovered the heme-maturation inhibition observed with the GAPDH mutants. Partial rescue was observed by overexpression of sGCβ1 but not by overexpression of a sGCΔβ1 deletion mutant, which is unable to bind the sGCα1 subunit required to form the active sGCα1β1 complex. Wild type and mutant GAPDH was found to be associated in a complex with each of the globins and Hsp90. GAPDH at endogenous levels was found to be associated with Mb in differentiating C2C12 myoblasts, and with Hbγ or Hbα in differentiating HiDEP-1 erythroid progenitor cells. Knockdown of GAPDH in C2C12 cells suppressed Mb heme-maturation. GAPDH knockdown in K562 erythroleukemia cells suppressed Hbα and Hbγ heme-maturation as well as Hb dimerization. Globin heme incorporation was not only dependent on elevated sGCα1β1 heterodimer formation, but also influenced by iron provision and magnitude of expression of GAPDH, d-aminolevulinic acid, and FLVCR1b. Together, our data support an important role for GAPDH in the maturation of myoglobin and γ, β, and α hemoglobins.

Importance of protein intrinsic conformational dynamics and transient nature of non-covalent interactions in ligand binding affinity

We have recently identified BEN1 as a protein interactor of seryl-tRNA synthetase (SerRS) from model plant Arabidopsis thaliana. BEN1 contains an NADP⁺ binding domain and possesses acidic N-terminal extension essential for interaction with A. thaliana SerRS. This extension, specific for BEN1 homologues from Brassicaceae family, is solvent-exposed and distant to the nucleotide-binding site. We prepared a truncated BEN1 variant ΔN17BEN1 lacking the first 17 amino acid of this N-terminal extension as well as full-length BEN1 to investigate how the truncation affects the binding affinity towards coenzyme NADP⁺. By performing microscale thermophoresis (MST) experiments we have shown that both BEN1 variants bind the NADP⁺ cofactor, however, truncated BEN1 showed 34-fold higher affinity towards NADP⁺ indicating that its core protein structure is not just preserved but it binds NADP⁺ even stronger. To further corroborate the obtained results, we opted for a computational approach based on classical molecular dynamics simulations of both complexes. Our results have shown that both truncated and intact BEN1 variants form the same number of interactions with the NADP⁺ cofactor; however, it was the interaction occupancy that was affected. Namely, three independent MD simulations showed that the ΔN17BEN1 variant in complex with NADP⁺ has significantly higher interaction occupancy thus binds NADP⁺ with more than one order of magnitude higher affinity. Contrary to our expectations, the truncation of this distant region that does not communicate with the nucleotide-binding site didn't result in the gain of interaction but affected the intrinsic conformational dynamics which in turn fine-tuned the binding affinity by increasing the interaction occupancy and strength of the key conserved cation-π interaction between Arg69 and adenine of NADP⁺ and hydrogen bond between Ser244 and phosphate of NADP⁺.

Conformational dynamics in peptide toxins: Implications for receptor interactions and molecular design

Peptide toxins are potent and often exquisitely selective probes of the structure and function of ion channels and receptors, and are therefore of significant interest to the pharmaceutical and biotech industries as both pharmacological tools and therapeutic leads. The three-dimensional structures of peptide toxins are essential as a basis for understanding their structure-activity relationships and their binding to target receptors, as well as in guiding the design of analogues with modified potency and/or selectivity for key targets. NMR spectroscopy has played a key role in elucidating the structures of peptide toxins and probing their structure-function relationships. In this article, we highlight the additional important contribution of NMR to characterising the dynamics of peptide toxins. We also compare the information available from NMR measurements with that afforded by molecular dynamics simulations. We describe several examples of the importance of dynamics measurements over a range of timescales for understanding the structure-function relationships of peptide toxins and their receptor engagement. Peptide toxins that inhibit the voltage-gated potassium channel K_V1.3 with pM affinities display different degrees of conformational flexibility, even though they contain multiple disulfide bonds, and this flexibility can affect the relative orientation of residues that have been shown to be critical for channel binding. Information on the dynamic properties of peptide toxins is important in the design of analogues or mimetics where receptor-bound structures are not available.

Backbone assignment of crystalline E. coli maltose binding protein

The E.coli maltose binding protein (MBP) is a 42.5 kDa molecule widely employed in many biotechnology applications. Because of its molecular size, it has become the main model system for the development of solution NMR methods adapted to large biomolecular targets. Here, we report virtually complete (~ 90%) backbone resonance assignments obtained on a microcrystalline sample of MBP with 1H-detected solid-state NMR at fast (> 100 kHz) magic-angle spinning. We additionally present the detailed description of the methodology employed for the preparation of the sample and the acquisition and analysis of the NMR spectra. The chemical shifts, obtained with a single uniformly 15N, 13C-labelled and fully-protonated sample and about 2 weeks on a 800 MHz NMR spectrometer, have been deposited to the BMRB under the accession number 50089.

HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid-protein interface with a case study

Protein-nucleic acid interactions play essential roles in many biological processes, such as transcription, replication and translation. In protein-nucleic acid interfaces, hotspot residues contribute the majority of binding affinity toward molecular recognition. Hotspot residues are commonly regarded as potential binding sites for compound molecules in drug design projects. The dynamic property is a considerable factor that affects the binding of ligands. Computational approaches have been developed to expedite the prediction of hotspot residues on protein-nucleic acid interfaces. However, existing approaches overlook hotspot dynamics, despite their essential role in protein function. Here, we report a web server named Hotspots In silico Scanning on Nucleic Acid and Protein Interface (HISNAPI) to analyze hotspot residue dynamics by integrating molecular dynamics simulation and one-step free energy perturbation. HISNAPI is capable of not only predicting the hotspot residues in protein-nucleic acid interfaces but also providing insights into their intensity and correlation of dynamic motion. Protein dynamics have been recognized as a vital factor that has an effect on the interaction specificity and affinity of the binding partners. We applied HISNAPI to the case of SARS-CoV-2 RNA-dependent RNA polymerase, a vital target of the antiviral drug for the treatment of coronavirus disease 2019. We identified the hotspot residues and characterized their dynamic behaviors, which might provide insight into the target site for antiviral drug design. The web server is freely available via a user-friendly web interface at http://chemyang.ccnu.edu.cn/ccb/server/HISNAPI/ and http://agroda.gzu.edu.cn:9999/ccb/server/HISNAPI/.

Unlocking the secret of lignin-enzyme interactions: Recent advances in developing state-of-the-art analytical techniques

Bioconversion of renewable lignocellulosics to produce liquid fuels and chemicals is one of the most effective ways to solve the problem of fossil resource shortage, energy security, and environmental challenges. Among the many biorefinery pathways, hydrolysis of lignocellulosics to fermentable monosaccharides by cellulase is arguably the most critical step of lignocellulose bioconversion. In the process of enzymatic hydrolysis, the direct physical contact between enzymes and cellulose is an essential prerequisite for the hydrolysis to occur. However, lignin is considered one of the most recalcitrant factors hindering the accessibility of cellulose by binding to cellulase unproductively, which reduces the saccharification rate and yield of sugars. This results in high costs for the saccharification of carbohydrates. The various interactions between enzymes and lignin have been explored from different perspectives in literature, and a basic lignin inhibition mechanism has been proposed. However, the exact interaction between lignin and enzyme as well as the recently reported promotion of some types of lignin on enzymatic hydrolysis is still unclear at the molecular level. Multiple analytical techniques have been developed, and fully unlocking the secret of lignin-enzyme interactions would require a continuous improvement of the currently available analytical techniques. This review summarizes the current commonly used advanced research analytical techniques for investigating the interaction between lignin and enzyme, including quartz crystal microbalance with dissipation (QCM-D), surface plasmon resonance (SPR), attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, atomic force microscopy (AFM), nuclear magnetic resonance (NMR) spectroscopy, fluorescence spectroscopy (FLS), and molecular dynamics (MD) simulations. Interdisciplinary integration of these analytical methods is pursued to provide new insight into the interactions between lignin and enzymes. This review will serve as a resource for future research seeking to develop new methodologies for a better understanding of the basic mechanism of lignin-enzyme binding during the critical hydrolysis process.

Stability of Ligand-induced Protein Conformation Influences Affinity in Maltose-binding Protein

Our understanding of what determines ligand affinity of proteins is poor, even with high-resolution structures available. Both the non-covalent ligand-protein interactions and the relative free energies of available conformations contribute to the affinity of a protein for a ligand. Distant, non-binding site residues can influence the ligand affinity by altering the free energy difference between a ligand-free and ligand-bound conformation. Our hypothesis is that when different ligands induce distinct ligand-bound conformations, it should be possible to tweak their affinities by changing the free energies of the available conformations. We tested this idea for the maltose-binding protein (MBP) from Escherichia coli. We used single-molecule Förster resonance energy transfer (smFRET) to distinguish several unique ligand-bound conformations of MBP. We engineered mutations, distant from the binding site, to affect the stabilities of different ligand-bound conformations. We show that ligand affinity can indeed be altered in a conformation-dependent manner. Our studies provide a framework for the tuning of ligand affinity, apart from modifying binding site residues.

Structural and biophysical characterization of the tandem substrate-binding domains of the ABC importer GlnPQ

The ATP-binding cassette transporter GlnPQ is an essential uptake system that transports glutamine, glutamic acid and asparagine in Gram-positive bacteria. It features two extra-cytoplasmic substrate-binding domains (SBDs) that are linked in tandem to the transmembrane domain of the transporter. The two SBDs differ in their ligand specificities, binding affinities and their distance to the transmembrane domain. Here, we elucidate the effects of the tandem arrangement of the domains on the biochemical, biophysical and structural properties of the protein. For this, we determined the crystal structure of the ligand-free tandem SBD1-2 protein from Lactococcus lactis in the absence of the transporter and compared the tandem to the isolated SBDs. We also used isothermal titration calorimetry to determine the ligand-binding affinity of the SBDs and single-molecule Förster resonance energy transfer (smFRET) to relate ligand binding to conformational changes in each of the domains of the tandem. We show that substrate binding and conformational changes are not notably affected by the presence of the adjoining domain in the wild-type protein, and changes only occur when the linker between the domains is shortened. In a proof-of-concept experiment, we combine smFRET with protein-induced fluorescence enhancement (PIFE–FRET) and show that a decrease in SBD linker length is observed as a linear increase in donor-brightness for SBD2 while we can still monitor the conformational states (open/closed) of SBD1. These results demonstrate the feasibility of PIFE–FRET to monitor protein–protein interactions and conformational states simultaneously.

Circadian clock modulating small molecules repurposing as inhibitors of SARS-CoV-2 Mpro for pharmacological interventions in COVID-19 pandemic

The COVID-19 pandemic caused by SARS-CoV-2 is a global health emergency warranting the development of targeted treatment. The main protease M^pro is considered as a key drug target in coronavirus infections because of its vital role in the proteolytic processing of two essential polyproteins required for the replication and transcription of viral RNA. Targeting and inhibiting the M^pro activity represents a valid approach to prevent the SARS-CoV-2 replication and spread. Based on the structure-assisted drug designing, here we report a circadian clock-modulating small molecule “SRT2183” as a potent inhibitor of M^pro to block the replication of SARS-CoV-2. The findings are expected to pave the way for the development of therapeutics for COVID-19.

Biotechnological applications of mammalian odorant-binding proteins

The olfactory system of mammals allows the detection and discrimination of thousands of odors from the environment. In mammals, odorant-binding proteins (OBPs) are considered responsible to carry odorant molecules across the aqueous nasal mucus to the olfactory receptors (ORs). The three-dimensional structure of these proteins presents eight antiparallel β-sheets and a short α-helical segment close to the C terminus, typical of the lipocalins family. The great ability of OBPs to bind differentiated ligand molecules has driven the research to understand the mechanisms underlying the OBP function in nature and the development of advanced biotechnological applications. This review describes the role of mammalian OBPs in the olfactory perception, highlighting the influence of several key parameters (amino acids, temperature, ionic strength, and pH) in the formation of the OBP/ligand complex. The information from the literature regarding OBP structure, affinity, the strength of binding, and stability inspiring the development of several applications herein detailed.

Carbohydrate-Protein Interactions: Advances and Challenges

A carbohydrate, also called saccharide in biochemistry, is a biomolecule consisting of carbon (C), hydrogen (H) and oxygen (O) atoms. For example, sugars are low molecular-weight carbohydrates, and starches are high molecular-weight carbohydrates. Carbohydrates are the most abundant organic substances in nature and essential constituents of all living things. Protein-carbohydrate interactions play important roles in many biological processes, such as cell growth, differentiation, and aggregation. They also have broad applications in pharmaceutical drug design. In this review, we will summarize the characteristic features of protein-carbohydrate interactions and review the computational methods for structure prediction, energy calculations, and kinetic studies of protein-carbohydrate complexes. Finally, we will discuss the challenges in this field.

Dynamic Instability from Non-equilibrium Structural Transitions on the Energy Landscape of Microtubule

Microtubules are the backbone of the cytoskeleton and vital to numerous cellular processes. The central dogma of microtubules is that all their functions are driven by dynamic instability, but its mechanism has remained unresolved for over 30 years because of conceptual difficulties inherent in the dominant GTP-cap framework. We present a physically rigorous structural mechanochemical model: dynamic instability is driven by non-equilibrium transitions between the bent (B), straight (S), and curved (C) forms of tubulin monomers and longitudinal interfaces in the two-dimensional lattice of microtubule. All the different phenomena (growth, shortening, catastrophe, rescue, and pausing) are controlled by the kinetic pathways for B↔S↔C transitions and corresponding energy landscapes. Different kinetics at minus end are due to different B↔S↔C pathways imposed by the polarity of microtubule lattice. This model enables us to reproduce all the observed phenomena of dynamic instability of purified tubulins in kinetic simulations.

Altered conformational sampling along an evolutionary trajectory changes the catalytic activity of an enzyme

Several enzymes are known to have evolved from non-catalytic proteins such as solute-binding proteins (SBPs). Although attention has been focused on how a binding site can evolve to become catalytic, an equally important question is: how do the structural dynamics of a binding protein change as it becomes an efficient enzyme? Here we performed a variety of experiments, including propargyl-DO3A-Gd(III) tagging and double electron-electron resonance (DEER) to study the rigid body protein dynamics of reconstructed evolutionary intermediates to determine how the conformational sampling of a protein changes along an evolutionary trajectory linking an arginine SBP to a cyclohexadienyl dehydratase (CDT). We observed that primitive dehydratases predominantly populate catalytically unproductive conformations that are vestiges of their ancestral SBP function. Non-productive conformational states, including a wide-open state, are frozen out of the conformational landscape via remote mutations, eventually leading to extant CDT that exclusively samples catalytically relevant compact states. These results show that remote mutations can reshape the global conformational landscape of an enzyme as a mechanism for increasing catalytic activity.

Protein Conformational Dynamics upon Association with the Surfaces of Lipid Membranes and Engineered Nanoparticles: Insights from Electron Paramagnetic Resonance Spectroscopy

Detailed study of conformational rearrangements and dynamics of proteins is central to our understanding of their physiological functions and the loss of function. This review outlines the applications of the electron paramagnetic resonance (EPR) technique to study the structural aspects of proteins transitioning from a solution environment to the states in which they are associated with the surfaces of biological membranes or engineered nanoobjects. In the former case these structural transitions generally underlie functional protein states. The latter case is mostly relevant to the application of protein immobilization in biotechnological industries, developing methods for protein purification, etc. Therefore, evaluating the stability of the protein functional state is particularly important. EPR spectroscopy in the form of continuous-wave EPR or pulse EPR distance measurements in conjunction with protein spin labeling provides highly versatile and sensitive tools to characterize the changes in protein local dynamics as well as large conformational rearrangements. The technique can be widely utilized in studies of both protein-membrane and engineered nanoobject-protein complexes.

A Potential Inhibition Process of Ricin Protein with the flavonoids Quercetin and Epigallocatechin Gallate. A Quantum-Chemical and Molecular Docking Study

Castor bean (Ricinus Communis) oil has been reported as one of the most important bio-based fuels; however, high amounts of toxic solid residue are generated in the production. This toxicity is due to several molecules, ricin protein being the most studied compound. The inhibition of the ricin protein is essential for eliminating its toxicity. The objective of this study is to predict the possible inhibition process via the interactions between the ricin protein and the flavonoids quercetin (Q) and epigallocatechin gallate (EGCG). The molecular structures of the complexes formed between the ricin protein and flavonoids were studied using quantum-chemical and molecular docking calculations to analyze the type of interaction, active site of the protein, binding energies, and different conformations in the inhibition process. Different methodologies were applied for the molecular structure determination; the best approximation was obtained with B3LYP/6-31G (d,p) theoretical methodology. Mappings of electrostatic potential (MEP) and frontier molecular orbitals were used for the identification of the probable sites of interaction, which were confirmed by molecular docking. The adjustment and alignment of flavonoid groups before and after the interaction, and charge transfer parameters, showed that Q and EGCG act as electron donors inside of the active site in ricin.

Inhibition of pancreatic elastase in silico and in vitro by Rubus rosifolius leaves extract and its constituents

Objective:

Elastases are protease enzymes, which mainly hydrolyze proteins of the connective tissue, so they have a significant impact on human disease. Rubus rosifolius is one of the Rubus species found in Indonesian mountains, and it has potential as an elastase inhibitor. The objective of this research was to examine the in vitro elastase inhibitor activity of R. rosifolius leaves and to dock different ligands of its constituents against target protein of Porcine Pancreatic Elastase (PPE) receptor.

Method:

Dried leaves powder of R. rosifolius was extracted using Soxhlet apparatus with n-hexane, ethyl acetate, and methanol. The extract was evaporated, and in vitro elastase inhibitor activity was determined using PPE with the quercetin used as control positive. Selected nine constituents of R. rosifolius were evaluated on the docking behavior of elastase receptor using Protein–Ligand ANT System (PLANTS) computational software with PPE enzyme with Protein Data Bank (PDB) file 1BRU.

Result:

The methanol extract showed significantly inhibited elastase with IC50 186.13 μg/mL, but ethyl acetate extract showed weak activity, and n-hexane extract did not show any activity. Docking studies and binding free energy calculations and hydrogen bonding with some amino acids revealed that ellagic acid showed the least binding energy for the target enzyme.

Conclusion:

This research has opened new insights into understanding that constituents of R. rosifolius methanol extract are potential inhibitors against elastase, and suggested the active compound is ellagic acid.

Mutational analysis confirms the presence of distal inhibitor-selectivity determining residues in B. stearothermophilus dihydrofolate reductase

Many antibacterial and antiparasitic drugs work by competitively inhibiting dihydrofolate reductase (DHFR), a vital enzyme in folate metabolism. The interactions between inhibitors and DHFR active site residues are known in many homologs but the contributions from distal residues are less understood. Identifying distal residues that aid in inhibitor binding can improve targeted drug development programs by accounting for distant influences that may be less conserved and subject to frequent resistance causing mutations. Previously, a novel, homology-based, computational approach that mines ligand inhibition data was used to predict residues involved in inhibitor selectivity in the DHFR family. Expectedly, some inhibitor selectivity determining residue positions were predicted to lie in the active site and coincide with experimentally known inhibitor selectivity determining positions. However, other residues that group spatially in clusters distal to the active site have not been previously investigated. In this study, the effect of introducing amino acid substitutions at one of these predicted clusters (His38-Ala39-Ile40) on the inhibitor selectivity profile in Bacillus stearothermophilus dihydrofolate reductase (Bs DHFR) was investigated. Mutations were introduced into these cluster positions to change sidechain chemistry and size. We determined kcat and KM values and measured KD values at equilibrium for two competitive DHFR inhibitors, trimethoprim (TMP) and pyrimethamine (PYR). Mutations in the His38-Ala39-Ile40 cluster significantly impacted inhibitor binding and TMP/PYR selectivity - seven out of nine mutations resulted in tighter binding to PYR when compared to TMP. These data suggest that the His38-Ala39-Ile40 cluster is a distal inhibitor selectivity determining region that favors PYR binding in Bs DHFR and, possibly, throughout the DHFR family.

Structure-guided rational design of the substrate specificity and catalytic activity of an enzyme

The quest for an enzyme with desired property is high for biocatalyic production of valuable products in industrial biotechnology. Synthetic biology and metabolic engineering also increasingly require an enzyme with unusual property in terms of substrate spectrum and catalytic activity for the construction of novel circuits and pathways. Structure-guided enzyme engineering has demonstrated a prominent utility and potential in generating such an enzyme, even though some limitations still remain. In this chapter, we present some issues regarding the implementation of the structural information to enzyme engineering, and exemplify the structure-guided rational approach to the design of an enzyme with desired functionality such as substrate specificity and catalytic efficiency.

Schistosomal Sulfotransferase Interaction with Oxamniquine Involves Hybrid Mechanism of Induced-fit and Conformational Selection

Background:

Sulfotransferase family comprises key enzymes involved in drug metabolism. Oxamniquine is a pro-drug converted into its active form by schistosomal sulfotransferase. The conformational dynamics of side-chain amino acid residues at the binding site of schistosomal sulfotransferase towards activation of oxamniquine has not received attention.

Objective:

The study investigated the conformational dynamics of binding site residues in free and oxamniquine bound schistosomal sulfotransferase systems and their contribution to the mechanism of oxamniquine activation by schistosomal sulfotransferase using molecular dynamics simulations and binding energy calculations.

Methods:

Schistosomal sulfotransferase was obtained from Protein Data Bank and both the free and oxamniquine bound forms were subjected to molecular dynamics simulations using GROMACS-4.5.5 after modeling it’s missing amino acid residues with SWISS-MODEL. Amino acid residues at its binding site for oxamniquine was determined and used for Principal Component Analysis and calculations of side-chain dihedrals. In addition, binding energy of the oxamniquine bound system was calculated using g_MMPBSA.

Results:

The results showed that binding site amino acid residues in free and oxamniquine bound sulfotransferase sampled different conformational space involving several rotameric states. Importantly, Phe45, Ile145 and Leu241 generated newly induced conformations, whereas Phe41 exhibited shift in equilibrium of its conformational distribution. In addition, the result showed binding energy of -130.091 ± 8.800 KJ/mol and Phe45 contributed -9.8576 KJ/mol.

Conclusion:

The results showed that schistosomal sulfotransferase binds oxamniquine by relying on hybrid mechanism of induced fit and conformational selection models. The findings offer new insight into sulfotransferase engineering and design of new drugs that target sulfotransferase.

Structure dictates the mechanism of ligand recognition in the histidine and maltose binding proteins

Two mechanisms, induced fit (IF) and conformational selection (CS), have been proposed to explain ligand recognition coupled conformational changes. The histidine binding protein (HisJ) adopts the CS mechanism, in which a pre-equilibrium is established between the open and the closed states with the ligand binding to the closed state. Despite being structurally similar to HisJ, the maltose binding protein (MBP) adopts the IF mechanism, in which the ligand binds the open state and induces a transition to the closed state. To understand the molecular determinants of this difference, we performed molecular dynamics (MD) simulations of coarse-grained dual structure based models. We find that intra-protein contacts unique to the closed state are sufficient to promote the conformational transition in HisJ, indicating a CS-like mechanism. In contrast, additional ligand-mimicking contacts are required to “induce” the conformational transition in MBP suggesting an IF-like mechanism. In agreement with experiments, destabilizing modifications to two structural features, the spine helix (SH) and the balancing interface (BI), present in MBP but absent in HisJ, reduce the need for ligand-mimicking contacts indicating that SH and BI act as structural restraints that keep MBP in the open state. We introduce an SH like element into HisJ and observe that this can impede the conformational transition increasing the importance of ligand-mimicking contacts. Similarly, simultaneous mutations to BI and SH in MBP reduce the barrier to conformational transitions significantly and promote a CS-like mechanism. Together, our results show that structural restraints present in the protein structure can determine the mechanism of conformational transitions and even simple models that correctly capture such structural features can predict their positions. MD simulations of such models can thus be used, in conjunction with mutational experiments, to regulate protein ligand interactions, and modulate ligand binding affinities.

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MBP operates by induced fit, HisJ by the conformational selection mechanism.

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Dual structure based models (dSBMs) encode two structures of a protein.

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MD simulations of dSBMs can identify the mechanism of conformational transitions.

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Locks, absent in HisJ, hold MBP open with ligand contacts required for closing.

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Binding mechanisms can be modified by altering such structural locks.