Unbound Protein Research Articles

Oleanolic acid purified from the stem bark of Olax subscorpioidea Oliv. inhibits the function and catalysis of human 17β-hydroxysteroid dehydrogenase 1

Cancer is a leading cause of global death. Medicinal plants have gained increasing attention in cancer drug discovery. In this study, the stem bark extract of Olax subscorpioidea, which is used in ethnomedicine to treat cancer, was subjected to phytochemical investigation leading to the isolation of oleanolic acid (OA). The structure was elucidated by 1-dimensional and 2-dimensional nuclear magnetic resonance spectroscopic (NMR) data, and by comparing its data with previously reported data. Molecular docking was used to investigate the interactions of OA with nine selected cancer-related protein targets. OA docked well with human 17β-hydroxysteroid dehydrogenase type-1 (17βHSD1), caspase-3, and epidermal growth factor receptor tyrosine kinase (binding affinities: −9.8, −9.3, and −9.1 kcal/mol, respectively). OA is a triterpenoid compound with structural similarity to steroids. This similarity with the substrates of 17βHSD1 gives the inhibitor candidate an excellent opportunity to bind to 17βHSD1. The structural and functional dynamics of OA-17βHSD1 were investigated by molecular dynamics simulations at 240 ns. Molecular mechanics/Poisson-Boltzmann surface area (MMPBSA) studies showed that OA had a binding free energy that is comparable with that of vincristine (–52.76, and −63.56 kcal/mol, respectively). The average C-α root mean square of deviation (RMSD) value of OA (1.69 Å) compared with the unbound protein (2.01 Å) indicated its high stability at the protein’s active site. The binding energy and stability at the active site of 17βHSD1 recorded in this study indicate that OA exhibited profound inhibitory potential. OA could be a good scaffold for developing new anti-breast cancer drugs.

Dual Fractions Proteomic Analysis of Silica Nanoparticle Interactions with Protein Extracts

Dual-fraction proteomics reveals a novel class of proteins impacted by nanoparticle exposure. Background: Nanoparticles (NPs) interact with cellular proteomes, altering biological processes. Understanding these interactions requires comprehensive analyses beyond solely characterizing the NP corona. Methods: We utilized a dual-fraction mass spectrometry (MS) approach to analyze both NP-bound and unbound proteins in Saccharomyces cerevisiae sp. protein extracts exposed to silica nanoparticles (SiNPs). We identified unique protein signatures for each fraction and quantified protein abundance changes using spectral counts. Results: Strong correlations were observed between protein profiles in each fraction and non-exposed controls, while minimal correlation existed between the fractions themselves. Linear models demonstrated equal contributions from both fractions in predicting control sample abundance. Combining both fractions revealed a larger proteomic response to SiNP exposure compared to single-fraction analysis. We identified 302/56 proteins bound/unbound to SiNPs and an additional 196 “impacted” proteins demonstrably affected by SiNPs. Conclusion: This dual-fraction MS approach provides a more comprehensive understanding of nanoparticle interactions with cellular proteomes. It reveals a novel class of “impacted” proteins, potentially undergoing conformational changes or aggregation due to NP exposure. Further research is needed to elucidate their biological functions and the mechanisms underlying their impact.

Fast Isolation and Sensitive Multicolor Visual Detection of Small Extracellular Vesicles by Multifunctional Polydopamine Nanospheres.

Small extracellular vesicles (sEVs) assume pivotal roles as vital messengers in intercellular communication, boasting a plethora of biological functions and promising clinical applications. However, efficient isolation and sensitive detection of sEVs continue to present formidable challenges. In this study, we report a novel method for fast isolation and highly sensitive multicolor visual detection of sEVs using aptamer-functionalized polydopamine nanospheres (SIMPLE). In the SIMPLE strategy, aptamer-functionalized polydopamine nanospheres (Apt-PDANS) with 170 nm diameters were synthesized and exhibited a remarkable ability to selectively bind to specific proteins on the surface of sEVs. The binding between sEVs and Apt-PDANS engenders an increase in the overall size of the sEVs, allowing fast isolation of sEVs by filtration (a filter membrane with a pore size of 200 nm). The fast isolation strategy not only circumvents the interference posed by unbound proteins and excessive probes as well as the intricacies associated with conventional ultracentrifugation methods but also expedites the separation of sEVs. Concurrently, the incorporation of Fe3+-doped PDANS permits the multicolor visual detection of sEVs, enabling quantitative analysis by the discernment of visual cues. The proposed strategy achieves a detection limit of 3.2 × 104 sEV mL-1 within 1 h, devoid of any reliance on instrumental apparatus. Furthermore, we showcase the potential application of this methodology in epithelial-mesenchymal transition monitoring and cancer diagnosis, while also envisioning its widespread adoption as a straightforward, rapid, sensitive, and versatile platform for disease monitoring and functional exploration.

Roemerine, a Phytoconstituent of Annona senegalensis, Targets MAO-A in Alzheimer’s Disease: Network Pharmacology Integrated with Molecular Docking and Dynamics Studies

Alzheimer’s disease (AD) is a common factor contributing to cognitive decline in aged people. The etiology of AD involves several mechanisms, including the breakdown of acetylcholine, the accumulation of amyloid beta, the formation of neurofibrillary tangles and inflammation. Several targets have been discovered in the study that are useful in reducing the cognitive decline associated with AD. By employing network pharmacology, molecular docking and MD simulations, we have ascertained that Roemerine can specifically interact with MAO-A. Network pharmacology identified a set of genes with a higher degree of interactions. MAO-A was identified as the gene with the highest interaction level with 37 other genes. MAO-A gene was interconnected with other top 10 genes with overall higher degrees of interactions. Roemerine molecule showed a binding energy of −5.27 kcal/mol, which was better than the reference ligand found to be −4.43 kcal/mol. MD simulations demonstrated that the average RMSD for Roemerine was 4.67 Å, whereas the average RMSF was 1.06 Å. Conversely, the unbound protein and the reference chemical exhibited average RMSD values of 5.57 Å and 5.74 Å, respectively. In addition, the average RMSF values for them were 1.30 Å and 1.00 Å, respectively. Based on these results, it may be inferred that Roemerine can inhibit MAO-A and potentially be used as a lead for AD.

Development of a fast and simple method for the isolation of superparamagnetic iron oxide nanoparticles protein corona from protein-rich matrices

The adsorption of proteins onto the surface of nanoparticle (NP) leads to the formation of the so-called “protein corona” as consisting both loosely and tightly bound proteins. It is well established that the biological identity of NPs that may be acquired after exposure to a biological matrix is mostly provided by the components of the hard corona as the pristine surface is generally less accessible for binding. For that reason, the isolation and the characterisation of the NP-corona complexes and identification of the associated biomolecules can help in understanding its biological behaviour. Established methods for the isolation of the NP-HC complexes are time-demanding and can lead to different results based on the isolation method applied. Herein, we have developed a fast and simple method using ferromagnetic beads isolated from commercial MACS column and used for the isolation of superparamagnetic NP following exposure to different types of biological milieu. We first demonstrated the ability to easily isolate superparamagnetic iron oxide NPs (IONPs) from different concentrations of human blood plasma, and also tested the method on the corona isolation using more complex biological matrices, such as culture medium containing pulmonary mucus where the ordinary corona methods cannot be applied. Our developed method showed less than 20% difference in plasma corona composition when compared with centrifugation. It also showed effective isolation of NP-HC complexes from mucus-containing culture media upon comparing with centrifugation and MACS columns, which failed to wash out the unbound proteins. Our study was supported with a full characterisation profile including dynamic light scattering, nanoparticle tracking analysis, analytical disk centrifuge, and zeta potentials. The biomolecules/ proteins composing the HC were separated by vertical gel electrophoresis and subsequently analysed by liquid chromatography-tandem mass spectrometry. In addition to our achievements in comparing different isolation methods to separate IONPs with corona from human plasma, this is the first study that provides a complete characterisation profile of particle protein corona after exposure in vitro to pulmonary mucus-containing culture media.

Drastic alterations in the loop structure around colchicine upon complex formation with an engineered lipocalin indicate a conformational selection mechanism.

Using Anticalin technology, a lipocalin protein dubbed Colchicalin, with the ability to bind the toxic plant alkaloid colchicine with picomolar affinity, has previously been engineered, thus offering a potential antidote in vivo and also allowing its sensitive detection in biological samples. To further analyze the mode of ligand recognition, the crystal structure of Colchicalin is now reported in its unliganded form and is compared with the colchicine complex. A superposition of the protein structures revealed major rearrangements in the four structurally variable loops of the engineered lipocalin. Notably, the binding pocket in the unbound protein is largely occupied by the inward-bent loop #3, in particular Ile97, as well as by the phenylalanine side chain at position 71 in loop #2. Upon binding of colchicine, a dramatic shift of loop #3 by up to 11.1 Å occurs, in combination with a side-chain flip of Phe71, thus liberating the necessary space within the ligand pocket. Interestingly, the proline residue at the neighboring position 72, which arose during the combinatorial engineering of Colchicalin, remained in a cis configuration in both structures. These findings provide a striking example of a conformational adaptation mechanism, which is a long-known phenomenon for antibodies in immunochemistry, during the recognition of a small ligand by an engineered lipocalin, thus illustrating the general similarity between the mode of antigen/ligand binding by immunoglobulins and lipocalins.

A novel thermostable TP-84 capsule depolymerase: a method for rapid polyethyleneimine processing of a bacteriophage-expressed proteins

BackgroundIn spite of the fact that recombinant enzymes are preferably biotechnologically obtained using recombinant clones, the purification of proteins from native microorganisms, including those encoded by bacteriophages, continues. The native bacteriophage protein isolation is often troubled by large volumes of the infected bacterial cell lysates needed to be processed, which is highly undesired in scaled-up industrial processing. A well-known ammonium sulphate fractionation is often a method of choice during purification of the native bacteriophage protein. However, this method is time-consuming and cumbersome, and requires large amounts of the relatively expensive reagent. Thus, other effective and inexpensive methods of reversible protein precipitation are highly desirable. We have previously characterized thermophilic TP-84 bacteriophage, defined a new genus TP84virus within Siphoviridae family, conducted the TP-84 genome annotation and proteomic analysis. The longest Open Reading Frame (ORF) identified in the genome is TP84_26. We have previously annotated this ORF as a hydrolytic enzyme depolymerizing the thick polysaccharides host’s capsule.ResultsThe TP84_26 ‘capsule depolymerase’ (depolymerase) is a large, 112 kDa protein, biosynthesized by the infected Geobacillus stearothermophilus 10 (G. stearothermophilus 10) cells. The TP84_26 protein biosynthesis was confirmed by three approaches: (i) purification of the protein of the expected size; (ii) mass spectrometry (LC–MS) analysis and (iii) detection of the enzymatic activity toward G. stearothermophilus polysaccharide capsules. Streptomycin-resistant mutant of the host was generated and microbiological aspects of both the TP-84 and G. stearothermophilus 10 were determined. A new variant of polyethyleneimine (PEI)-mediated purification method was developed, using the novel TP-84 depolymerase as a model. The enzyme was characterized. Three depolymerase forms were detected: soluble, unbound proteins in the bacteriophage/cells lysate and another integrated into the TP-84 virion.ConclusionsThe novel TP-84 depolymerase was purified and characterized. The enzyme exists in three forms. The soluble, unbound forms are probably responsible for the weakening of the capsules of the uninfected bacterial cells. The form integrated into virion particles may generate a local passage for the invading TP-84. The developed PEI purification method appears well suited for the scaled-up or industrial production of bacteriophage proteins.

In silico prediction of the possible antidiabetic and anti-inflammatory targets of Nymphaea lotus-derived phytochemicals and mechanistic insights by molecular dynamics simulations

Nymphaea lotus is used traditionally for the treatment of diabetes and its complications. However, the mode of action and the likely bioactive phytochemicals involved are not yet fully explored. GC-MS analysis was employed to identify the inherent compounds in N. lotus leaves. To gain an insight into the antidiabetic mode of action of this plant, the identified phytochemicals were subjected to computational studies against four molecular targets of diabetes, dipeptidyl peptidase-4, glycogen synthase kinase 3, NADPH oxidase (NOX), sodium-glucose co-transporter-2, and one target of inflammation, cyclooxygenase-2. Compounds with notable binding affinity were subjected to druggability test. Results from molecular docking showed that seven of the compounds investigated exhibited druggability properties and had outstanding binding affinity values for these targets relative to values obtained for the respective standards of each of the targets. Analysis of the MD trajectories from a 100 ns atomistic run shows that the integrities of the complex systems were more stable and preserved throughout the simulation than the unbound protein. These results indicated that the antidiabetic and anti-inflammatory effects of these compounds might be via the inhibition of these targets, laying the foundation for further studies, such as in vitro and in vivo studies to fully validate the anti-diabetic agents from this plant. Communicated by Ramaswamy H. Sarma

PointSite: A Point Cloud Segmentation Tool for Identification of Protein Ligand Binding Atoms.

Accurate identification of ligand binding sites (LBS) on a protein structure is critical for understanding protein function and designing structure-based drugs. As the previous pocket-centric methods are usually based on the investigation of pseudo-surface-points outside the protein structure, they cannot fully take advantage of the local connectivity of atoms within the protein, as well as the global 3D geometrical information from all the protein atoms. In this paper, we propose a novel point clouds segmentation method, PointSite, for accurate identification of protein ligand binding atoms, which performs protein LBS identification at the atom-level in a protein-centric manner. Specifically, we first transfer the original 3D protein structure to point clouds and then conduct segmentation through Submanifold Sparse Convolution based U-Net. With the fine-grained atom-level binding atoms representation and enhanced feature learning, PointSite can outperform previous methods in atom Intersection over Union (atom-IoU) by a large margin. Furthermore, our segmented binding atoms, that is, atoms with high probability predicted by our model can work as a filter on predictions achieved by previous pocket-centric approaches, which significantly decreases the false-positive of LBS candidates. Besides, we further directly extend PointSite trained on bound proteins for LBS identification on unbound proteins, which demonstrates the superior generalization capacity of PointSite. Through cascaded filter and reranking aided by the segmented atoms, state-of-the-art performance can be achieved over various canonical benchmarks, CAMEO hard targets, and unbound proteins in terms of the commonly used DCA criteria.

Prediction of Water Distributions and Displacement at Protein-Ligand Interfaces.

The retention and displacement of water molecules during formation of ligand-protein interfaces play a major role in determining ligand binding. Understanding these effects requires a method for positioning of water molecules in the bound and unbound proteins and for defining water displacement upon ligand binding. We describe an algorithm for water placement and a calculation of ligand-driven water displacement in >9000 protein-ligand complexes. The algorithm predicts approximately 38% of experimental water positions within 1.0 Å and about 83% within 1.5 Å. We further show that the predicted water molecules can complete water networks not detected in crystallographic structures of the protein-ligand complexes. The algorithm was also applied to solvation of the corresponding unbound proteins, and this allowed calculation of water displacement upon ligand binding based on differences in the water network between the bound and unbound structures. We illustrate use of this approach through comparison of water displacement by structurally related ligands at the same binding site. This method for evaluation of water displacement upon ligand binding may be of value for prediction of the effects of ligand modification in drug design.

Particle-by-Particle In Situ Characterization of the Protein Corona via Real-Time 3D Single-Particle-Tracking Spectroscopy*.

Nanoparticles (NPs) adsorb proteins when exposed to biological fluids, forming a dynamic protein corona that affects their fate in biological environments. A comprehensive understanding of the protein corona is lacking due to the inability of current techniques to precisely measure the full corona in situ at the single-particle level. Herein, we introduce a 3D real-time single-particle tracking spectroscopy to "lock-on" to single freely diffusing polystyrene NPs and probe their individual protein coronas, primarily using bovine serum albumin (BSA) as a model system. The fluorescence signals and diffusive motions of the tracked NPs enable quantification of the "hard corona" using mean-squared displacement analysis. Critically, this method's particle-by-particle nature enabled a lock-in-type frequency filtering approach to extract the full protein corona, despite the typically confounding effect of high background signal from unbound proteins. From these results, the dynamic in situ full protein corona is observed to contain twice the number of proteins compared to the ex situ-measured "hard" protein corona.

Particle‐by‐Particle In Situ Characterization of the Protein Corona via Real‐Time 3D Single‐Particle‐Tracking Spectroscopy**

AbstractNanoparticles (NPs) adsorb proteins when exposed to biological fluids, forming a dynamic protein corona that affects their fate in biological environments. A comprehensive understanding of the protein corona is lacking due to the inability of current techniques to precisely measure the full corona in situ at the single‐particle level. Herein, we introduce a 3D real‐time single‐particle tracking spectroscopy to “lock‐on” to single freely diffusing polystyrene NPs and probe their individual protein coronas, primarily using bovine serum albumin (BSA) as a model system. The fluorescence signals and diffusive motions of the tracked NPs enable quantification of the “hard corona” using mean‐squared displacement analysis. Critically, this method's particle‐by‐particle nature enabled a lock‐in‐type frequency filtering approach to extract the full protein corona, despite the typically confounding effect of high background signal from unbound proteins. From these results, the dynamic in situ full protein corona is observed to contain twice the number of proteins compared to the ex situ‐measured “hard” protein corona.

Purification, biotinylation, and testing of a monoclonal antibody to identify B cells in Trachemys scripta, the red-eared slider turtle

Abstract There is a shortage of research in reptile immunity which is further hampered by lack of reptile-specific reagents. Evidence suggests there are important differences between reptile and mammalian immune strategies and our laboratory is interested in reptile B cell development and function. Our undergraduate research project involved the preparation of a previously developed monoclonal antibody (HL673) that recognizes turtle light chain protein. To begin, culture supernatant from the HL673 mAb murine cell line was received and was applied to a protein A affinity column. Unbound proteins were then washed away, and the bound proteins were removed from the column using low pH glycine-HCl buffer. The purified antibody proteins were collected in fractions, OD at 280nm measured, then positive fractions were pooled and dialyzed. The concentration of the purified antibodies was determined, and reactivity tested using an ELISA plate coated with dilute turtle serum. Dilutions of the purified HL673 were detected by anti-mouse IgG-horseradish peroxidase (HRP). Furthermore, some of the antibody preparation was conjugated to biotin. After dialysis, the HL673-biotin was tested by ELISA with dilute turtle serum and detected by streptavidin-HRP. The newly biotinylated antibody was incubated with blood and spleen samples from both adult and hatchling red-eared slider turtles. Bound antibodies were detected using streptavidin-fluorochrome and B cell populations identified using flow cytometry. Our results showed successful detection of turtle B cells using the labeled mAb in both hatchling and adult turtle cell samples. Future studies will use this reagent to investigate the distribution and function of B cells in reptile gut immunity.

Unravelling the drugability of MSI2 RNA recognition motif (RRM) protein and the prediction of their effective antileukemia inhibitors from traditional herb concoctions

MSI2 is a homolog 2 of the Musashi RNA binding proteins (MSI) and is known to contribute to acute myeloid leukaemia (AML) and expressed up to 70% in AML patients. High expression of MSI2 has been found to lead to the lower overall survival of patients with AML. This study proposed the potential antagonists of MSI2 RNA-recognition motifs (MSI2 RRM1) derived from the LC-MS analysis of three traditional herbal samples. The LC-MS analysis of the three traditional herbs concoctions yields a total of 271 unique molecules of which 262 were screened against MSI2 RRM1 protein. After the dynamic study of the selected 8 top molecules from the virtual screening, the five most promising ligands emerged as potential MSI2 antagonists compare to the reference experimental molecule. The results show that the dynamic of MSI2 RRM1 protein is accompanied by a rare even of protein chain dissociation and re-association as evident in both the bound and unbound state of the protein. The unbound protein experience earlier chain dissociation compare to ligand-bound protein indicating that ligand binding to the protein slows down the dissociation time but thereafter increases the frequency of alternation between the protein chain association and dissociation after the first experience. Interestingly, the re-association of the protein chain is also accompanied by full restoration of the ligands to the binding site. The drug candidate Methotrexate (M3) and rescinnamine (M9) are listed among the promising antagonist of MSI2 with unique properties compared to a less promising molecule Ergotamine (M6). Communicated by Ramaswamy H. Sarma

A Coarse-Grained Methodology Identifies Intrinsic Mechanisms That Dissociate Interacting Protein Pairs.

We address the problem of triggering dissociation events between proteins that have formed a complex. We have collected a set of 25 non-redundant, functionally diverse protein complexes having high-resolution three-dimensional structures in both the unbound and bound forms. We unify elastic network models with perturbation response scanning (PRS) methodology as an efficient approach for predicting residues that have the propensity to trigger dissociation of an interacting protein pair, using the three-dimensional structures of the bound and unbound proteins as input. PRS reveals that while for a group of protein pairs, residues involved in the conformational shifts are confined to regions with large motions, there are others where they originate from parts of the protein unaffected structurally by binding. Strikingly, only a few of the complexes have interface residues responsible for dissociation. We find two main modes of response: In one mode, remote control of dissociation in which disruption of the electrostatic potential distribution along protein surfaces play the major role; in the alternative mode, mechanical control of dissociation by remote residues prevail. In the former, dissociation is triggered by changes in the local environment of the protein, e.g., pH or ionic strength, while in the latter, specific perturbations arriving at the controlling residues, e.g., via binding to a third interacting partner is required for decomplexation. We resolve the observations by relying on an electromechanical coupling model which reduces to the usual elastic network result in the limit of the lack of coupling. We validate the approach by illustrating the biological significance of top residues selected by PRS on select cases where we show that the residues whose perturbation leads to the observed conformational changes correspond to either functionally important or highly conserved residues in the complex.

Shedding light on disordered proteins

Protein Folding Disordered proteins often fold as they bind to a partner protein. There could be many different molecular trajectories between the unbound proteins and the bound complex. Most methods to measure transition paths rely on monitoring a single distance, making it difficult to resolve complex pathways. Kim and Chung used fast three-color single-molecule Foster resonance energy transfer (FRET) to simultaneously probe distance changes between the two ends of an unfolded protein and between each end and a probe on the partner protein. They show that binding can be initiated by diverse conformations and that the molecules are held together by non-native interactions as the disordered protein folds. This allows the association to be diffusion limited because most collisions lead to binding. Science , this issue p. [1253][1] [1]: /lookup/doi/10.1126/science.aba3854

Disordered proteins follow diverse transition paths as they fold and bind to a partner.

Transition paths of macromolecular conformational changes such as protein folding are predicted to be heterogeneous. However, experimental characterization of the diversity of transition paths is extremely challenging because it requires measuring more than one distance during individual transitions. In this work, we used fast three-color single-molecule Förster resonance energy transfer spectroscopy to obtain the distribution of binding transition paths of a disordered protein. About half of the transitions follow a path involving strong non-native electrostatic interactions, resulting in a transition time of 300 to 800 microseconds. The remaining half follow more diverse paths characterized by weaker electrostatic interactions and more than 10 times shorter transition path times. The chain flexibility and non-native interactions make diverse binding pathways possible, allowing disordered proteins to bind faster than folded proteins.

Geldanamycin Alters Abundance and Composition of Low Molecular Weight Proteins and Multiprotein Complexes as Observed by Size Exclusion Column Chromatography

Size exclusion chromatography (SEC) is a commonly used and minimally disruptive liquid chromatography‐based technique that separates unbound cytosolic proteins and multiprotein complexes according to their perceived molecular weights. The purpose of this project was to develop a method using SEC to partially purify and isolate biomolecular complexes, including those containing the essential molecular chaperone protein hsp90, in the presence and absence of the hsp90 inhibitor geldanamycin. Using an automated SEC program and S‐200 column, unbound proteins and protein complexes of differing molecular weights were isolated from reticulocyte lysate incubated in the presence and absence of geldanamycin. In addition to hsp90 eluting in a complex of ~250–300 kDa, consistent with it being part of a multiprotein chaperone machinery, other predominant protein elution peaks corresponded to ~26 kDa and ~16 kDa. Interestingly, incubating cell lysate with geldanamycin depleted nearly all of the ~26 kDa peak, indicating an unexpected hsp90 effect. Continued examination of eluate fractions from + and − geldanamycin‐incubated lysates has allowed us to further characterize these geldanamycin‐sensitive low molecular weight proteins as we examine the multitude of intracellular hsp90 functional activities.Support or Funding InformationFunding was provided by the Seattle University College of Science & Engineering undergraduate research program.

Protein Binding to Artificial Lipid Droplets Demonstrates the Importance of Phospholipid Composition

All cells and tissues store excess lipid in intracellular structures known as lipid droplets. Each lipid droplet consists of a neutral lipid core (primarily made of triacylglyceride and cholesteryl ester) surrounded by a phospholipid monolayer. Several proteins interact specifically with the surface of the lipid droplet and control their growth and degradation. Here, we share the development of an in vitro binding assay, that utilizes synthetic lipid droplets and partially purified lipid droplet proteins, to study protein binding to lipid droplets. We separate bound from unbound proteins with sucrose gradient ultracentrifugation and detect lipid droplet proteins following SDS‐PAGE and western blotting. We have used this assay to show that changing the phospholipid composition of the lipid droplet surface alters interactions with perilipin 2, a ubiquitously expressed lipid droplet protein that is predicted to interact with lipid droplets through an amphipathic alpha helix. Specifically, perilipin 2 association with synthetic lipid droplets was increased when the amount of phosphatidylethanolamine relative to phosphatidylcholine was increased. We hypothesize that the smaller headgroups of phosphatidylethanolamine allows additional interaction with the underlying neutral lipid. These results may be relevant to the formation of lipid droplets in alcoholic fatty liver disease, where lipid droplets form with altered levels of phosphatidylethanolamine and phosphatidylcholine.Support or Funding InformationFunding for this research was provided by an ASBMB Undergraduate Research Award and the Lowell and Doris Kispert Endowment at St. Olaf College.

Docking-based identification of small-molecule binding sites at protein-protein interfaces

Protein-protein interactions play an essential role in many biological processes, and their perturbation is a major cause of disease. The use of small molecules to modulate them is attracting increased attention, but protein interfaces generally do not have clear cavities for binding small compounds. A proposed strategy is to target interface hot-spot residues, but their identification through computational approaches usually require the complex structure, which is not often available. In this context, pyDock energy-based docking and scoring can predict hot-spots on the unbound proteins, thus not requiring the complex structure. Here, we have devised a new strategy to detect protein–protein inhibitor binding sites, based on the integration of molecular dynamics for the generation of transient cavities, and docking-based interface hot-spot prediction for the selection of the suitable cavities. This integrative approach has been validated on a test set formed by protein–protein complexes with known inhibitors for which complete structural data of unbound molecules and complexes is available. The results show that local conformational sampling with short molecular dynamics can generate transient cavities similar to the known inhibitor binding sites, and that docking simulations can identify the best cavities with similar predictive accuracy as when knowing the real interface. In a few cases, these predicted pockets are shown to be suitable for protein–ligand docking. The proposed strategy will be useful for many protein–protein complexes for which there is no available structure, as long as the the unbound proteins do not deviate dramatically from the bound conformations.