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Results for “binding affinities”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots
The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions. In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ΔSASsa 1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods. Here, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/.
Microscale thermophoresis as a powerful tool for screening glycosyltransferases involved in cell wall biosynthesis
Background: Identification and characterization of key enzymes associated with cell wall biosynthesis and modification is fundamental to gain insights into cell wall dynamics. However, it is a challenge that activity assays of glycosyltransferases are very low throughput and acceptor substrates are generally not available. Results: We optimized and validated microscale thermophoresis (MST) to achieve high throughput screening for glycosyltransferase substrates. MST is a powerful method for the quantitative analysis of protein-ligand interactions with low sample consumption. The technique is based on the motion of molecules along local temperature gradients, measured by fluorescence changes. We expressed glycosyltransferases as YFP-fusion proteins in tobacco and optimized the MST method to allow the determination of substrate binding affinity without purification of the target protein from the cell lysate. The application of this MST method to the β-1,4-galactosyltransferase AtGALS1 validated the capability to screen both nucleotide-sugar donor substrates and acceptor substrates. We also expanded the application to members of glycosyltransferase family GT61 in sorghum for substrate screening and function prediction. Conclusions: This method is rapid and sensitive to allow determination of both donor and acceptor substrates of glycosyltransferases. MST enables high throughput screening of glycosyltransferases for likely substrates, which will narrow down their in vivo function and help to select candidates for further studies. Additionally, this method gives insight into biochemical mechanism of glycosyltransferase function.
Adaptive language model training for molecular design
Abstract The vast size of chemical space necessitates computational approaches to automate and accelerate the design of molecular sequences to guide experimental efforts for drug discovery. Genetic algorithms provide a useful framework to incrementally generate molecules by applying mutations to known chemical structures. Recently, masked language models have been applied to automate the mutation process by leveraging large compound libraries to learn commonly occurring chemical sequences (i.e., using tokenization) and predict rearrangements (i.e., using mask prediction). Here, we consider how language models can be adapted to improve molecule generation for different optimization tasks. We use two different generation strategies for comparison, fixed and adaptive. The fixed strategy uses a pre-trained model to generate mutations; the adaptive strategy trains the language model on each new generation of molecules selected for target properties during optimization. Our results show that the adaptive strategy allows the language model to more closely fit the distribution of molecules in the population. Therefore, for enhanced fitness optimization, we suggest the use of the fixed strategy during an initial phase followed by the use of the adaptive strategy. We demonstrate the impact of adaptive training by searching for molecules that optimize both heuristic metrics, drug-likeness and synthesizability, as well as predicted protein binding affinity from a surrogate model. Our results show that the adaptive strategy provides a significant improvement in fitness optimization compared to the fixed pre-trained model, empowering the application of language models to molecular design tasks.
Unraveling the mechanism of recognition of the 3’ splice site of the adenovirus major late promoter intron by the alternative splicing factor PUF60
Pre-mRNA splicing is critical for achieving required amounts of a transcript at a given time and for regulating production of encoded protein. A given pre-mRNA may be spliced in many ways, or not at all, giving rise to multiple gene products. Numerous splicing factors are recruited to pre-mRNA splice sites to ensure proper splicing. One such factor, the 60 kDa poly(U)-binding splicing factor (PUF60), is recruited to sites that are not always spliced, but rather function as alternative splice sites. In this study, we characterized the interaction of PUF60 with a splice site from the adenovirus major late promoter (the AdML 3' splice site, AdML3’). We found that the PUF60–AdML3’ dissociation constants are in the micromolar range, with the binding affinity predominantly provided by PUF60’s two central RNA recognition motifs (RRMs). A 1.95 Å crystal structure of the two PUF60 RRMs in complex with AdML3’ revealed a dimeric organization placing two stretches of nucleic acid tracts in opposing directionalities, which can cause looping of nucleic acid and explain how PUF60 affects pre-mRNA geometry to effect splicing. Solution characterization of this complex by light-scattering and UV/Vis spectroscopy suggested a potential 2:1 (PUF60 2 :AdML3’) stoichiometry, consistent with the crystal structure. This work defines the sequence specificity of the alternative splicing factor PUF60 at the pre-mRNA 3’ splice site. Our observations suggest that control of pre-mRNA directionality is important in the early stage of spliceosome assembly, and advance our understanding of the molecular mechanism by which alternative and constitutive splicing factors differentiate among 3’ splice sites.
Actinide N-Donor Thermodynamics: Expanding the f-element Covalency Dialogue. Final report
Resolving the chemistry and physics of f-electrons is one of the grand challenges of science for energy technology. A central component of this challenge is describing covalency in actinide-ligand interactions. Crystallography, x-ray spectroscopy and computational techniques have provided reasonable, and in some cases remarkable, support for covalency in actinide-ligand interactions. Courtesy the quality of the results obtained, a significant portion of the covalency dialogue has been forwarded through solid-state and computational studies of the more stable actinides (Th, U, Pu, Np). However; recent reports have questioned whether covalency in actinide interactions increases or decreases across the series. Examination of less-stable trans-actinides (Am, Cm, Bk, Cf and Es) by the aforementioned approaches can be rapidly limited due to material availability, radiological hazards and experimental data to validate computational models. Furthermore, limited thermodynamic data exists to confirm covalency in actinide solution phase interactions that are highly relevant to the remediation and reprocessing of used nuclear fuel. The best-defined interactions of actinides with soft donors in aqueous solutions involve (poly)aminopolycarboxylate (APC) ligands. The chelate effect and binding affinity with the acetic acid APCs subgroups encourages amine interactions with the actinide metal center. In the absence of these factors, amine interactions with actinides are too weak to overcome the protective hydration shell of the dissolved ion. The ability for APCs to force actinide interactions with soft nitrogen donors (as defined by Pearson’s Hard Soft Acid Base theory) encourages the application of these ligands in a variety of processes for actinide recovery from nearly chemically identical lanthanides. The ability to functionalize the amine center of the ligand in a variety of capacities (adding additional amine groups, exchanging the conventional acetate group for an acetate group, etcetera), allows for the APC ligand to serve as a thermodynamic probe for actinide-nitrogen interactions. Perhaps the single most significant breakthrough during the previous funding cycle was the observation that covalency for the transplutonium part of the actinide series can be increasingly influenced with energy degeneracy driven covalency as the actinides become heavier. This was observed most predominantly with dipicolinic acid, but extensions of f-orbital degeneracy were found to affect aliphatic aminopolycarboxylate-actinide complexes through einsteinium.
Facilitated Direct Liquid Fuel Cells with High Temperature Membrane Electrode Assemblies
Dimethyl ether (DME) is a liquid fuel of great potential impact due to its exceptionally high energy density. However, it has received minimal prior investigation as an alternative to either purified hydrogen or other liquid fuels, including methanol (MeOH). In the limited published literature work on direct dimethyl ether fuel cells, regardless of operating temperature, PtRu (either supported or unsupported on carbon) has been established as the standard catalyst of choice. The majority of the work in this program also utilized a Johnson Matthey (JM) HiSPEC ® 12100 PtRu/C (nominally 50% Pt, 25% Ru) while looking at electrode optimizations, beginning of life (BoL) performance, pressure- and temperature-dependent studies to look at the effect of binding affinity of DME oxidation intermediates, mass transport effects, crossover studies, and durability. However, it does also investigate some promising alternatives to PtRu/C as well, which should be investigated in more detail in further work. Those catalysts include a pair of ternary PtRuPd/C catalysts (from Los Alamos National Laboratory (LANL) and Pajarito Powder, LLC. (PP)) as well as a Pt 2 Bi Black catalyst from Professor Anastasios Angelopoulos of the University of Cincinnati (UC). This work achieved several project objectives, including an optimization of the membrane electrode assembly (MEA) process using PtRu/C anode catalyst. Additionally, these direct dimethyl ether fuel cells (DDFCs) were able to match or exceed many performance metrics for the state-of-the-art (SOA) direct methanol fuel cells (DMFCs), a primary and more evolved competitor to direct dimethyl ether fuel cells. This included peak specific power, total platinum group metal (PGM) loading, crossover current, degradation rate, start/stop cycling losses, and anode specific current.
Streptavidin Affinity Grids for the Preparation of Biological Samples for Cryogenic Electron Microscopy (CRADA Final Report)
Currently, cryo-EM is the most powerful tool for structural analysis of large biomacromolecules, but there are still problems in that biomacromolecules are denatured at the air-interface and their orientations are limited in a specific orientation. We solved this problem at LBL by developing a streptavidin-coated EM grid. Use of such affinity-binding grids can minimize exposure to air-interface by keeping biomacromolecules bound to streptavidin. It is urgently needed to make such grids readily available to the cryo-EM community, but they are somewhat difficult to make. The goal of the CRADA with Hummingbird company was to mass-produce the streptavidin grids so that they would become readily available to the cryo-EM community. Our contribution to the CRADA was to first help Hummingbird to replicate the manual process that we use, and this was completed successfully.
Probing the Structure and Dynamics of Fluid Mixtures in Porous Materials Through Ultrafast Vibrational Spectro-Microscopy and Many-Body Molecular Dynamics
This research program focused on the characterization of both molecular structure and dynamics of aqueous solutions in various metal-organic frameworks (MOFs) and organic polymers that have recently been proposed for applications in water treatment technologies. This was accomplished by integrating ultrafast vibrational spectroscopy and microscopy, which are sensitive to local environments, molecular orientation, and dynamical couplings, with many-body molecular dynamics (MB-MD) simulations, which enable realistic modeling of aqueous systems and porous materials under different thermodynamic conditions. Our studies allowed for identifying the physical mechanisms and characterizing the underlying molecular interactions between water and host materials that determine the adsorption and transport processes of water in various MOFs and β-cyclodextrin polymers – prototypical examples of porous materials and polymers, that have recently been proposed for technological applications in water purification. By combining bulk- and surface-sensitive, spatially resolved ultrafast vibrational spectroscopy with MB-MD simulations, it was possible to gain broad insight into the structure and mobility of water under heterogeneous confinement at various length scales. In particular, measurements of surface- and bulk-sensitive vibrational spectra at single crystal level, integrated with MB-MD simulations were used to characterize domain-specific, structure–binding affinity relationships. Besides providing specific information about the behavior of aqueous solutions in prototypical MOFs and organic polymers for potential applications in water treatment technologies, our studies also provided fundamental insights into the properties of aqueous solutions in confined heterogeneous environments, which have broad implications in many areas, ranging from heterogeneous catalysis to oil recovery, ion transport processes, and reaction-diffusion processes in crowded systems.
Machine Learning-driven Molecular Design for Therapeutic Discovery
The ongoing novel coronavirus pandemic (COVID-19) has highlighted the need for new therapeutics to counter the threat of emerging viral pathogens. The main proteases are a promising target for developing antiviral inhibitors. In this work, we utilized a novel combination of artificial intelligence-driven iterative design of covalent inhibitor candidates, physics-based computational modeling of protein-inhibitor interactions, and “All in One” Native MS biophysical assay screening and characterization of therapeutic candidates. With our existing expertise in hit generation using a particular scaffold as a starting point, we first generated tens of thousands of compounds that preserve the key scaffold. In order to optimize the candidates, we calculated about 136 descriptors consisting of 2D and 3D features for molecules targeting the SARS-CoV-2 Main protease (Mpro). These compounds were initially filtered according to properties and further sorted by predicted binding affinity using our automated docking modeling and machine learning methods. We tested a handful of candidates and identified two as inhibitors of Mpro with micromolar affinities.
Using CRISPR/Cas13a technologies to detect miRNA [DOE SULI Internship Report]
Micro ribonucleic acids (miRNA) give our immune systems the ability to recognize viruses and other pathogens by their complementary single-stranded RNA (ssRNA) produced in the reproduction of the pathogen in our cells. When miRNA of a specific sequence is detected in a cell sample, it can be assumed that the immune system is activated and attempting to track down the infection. This pathway can be utilized to diagnose infection from a pathogen before the individual even develops symptoms, aiding in early disease detection and proper treatment. One of the ways that we can detect miRNA is through an assay of clustered regularly interspaced short palindromic repeats or “CRISPR” and the bacterial protein Cas13a. This report details discoveries made while attempting to optimize this assay for miRNA detection. After looking at several different factors within the assay, it was determined that some factors, such as reporter type and metallic ion concentration, are more impactful on the overall assay sensitivity than other factors, such as the overall concentration of Cas13a, CRISPR RNA (crRNA), or ssRNA reporter. It was also discovered that different sequences with different lengths require renewed optimization efforts, as each target has a unique binding affinity determined by the sequence length and composition. This information is crucial in the development of point of care molecular detection devices as they become sensitive enough to identify pathogens before they spread.
Automated AI-driven Molecular Design for Therapeutic Discovery
In recent years, artificial intelligence and machine learning (AI/ML) approaches have revolutionized the process of designing new therapeutics, enabling scientists to rapidly respond to emerging threats from various pathogens. A prime example is the SARS-CoV-2 main protease, a key target for the development of antiviral inhibitors. In this study, we employed a novel, integrated approach that combines AI-driven iterative design of inhibitor candidates, screening based on physio-chemical properties and toxicity, physics-based computational modeling of protein-inhibitor interactions, and AI-assisted analysis of Native MS biophysical assay and characterization of designed candidates. Our deep learning 3D-scaffold model, which uses an input scaffold as a starting point, generated tens of thousands of compounds while preserving the key scaffold. To optimize these candidates, we calculated a comprehensive set of 136 descriptors, including both 2D and 3D molecular features, for compounds targeting the SARS-CoV-2 Main protease (Mpro) and a neurodegenerative disease-associated protein, cyclophilin (Cyp). The generated compounds were initially filtered based on their properties and then ranked according to their predicted binding affinity using our automated modeling and ML methods. Experimental validation of the Mpro candidates showing inhibitory activity demonstrates that our workflow can expedite the therapeutic discovery.
SuFEx-enabled high-throughput medicinal chemistry for developing potent tamoxifen analogs as Ebola virus entry inhibitors
Ebola virus (EBOV) causes severe hemorrhagic fever with a high mortality rate in humans. In acute infection, an abnormal immune response results in excessive inflammatory cytokines and uncontrolled systemic inflammation that can result in organ damage and multi-organ failure. While vaccines and monoclonal antibody therapies are available, there is an urgent need for effective small-molecule antivirals against EBOV. Here, we report on the optimization of tamoxifen, an EBOV-glycoprotein (GP) binder that inhibits viral entry, using our Sulfur-Fluoride Exchange (SuFEx) click chemistry-based high-throughput medicinal chemistry (HTMC) strategy. Using a “Direct-to-Biology” approach, we generated a focused library of 2,496 tamoxifen analogs overnight and screened them in a cell-based pseudo-EBOV infection assay. The HTMC workflow enabled the development of a potent EBOV entry inhibitor with submicromolar EC 50 cellular antiviral activity and more than 50-fold improvement in binding affinity against EBOV-GP compared to the parent compound. Our findings underscore the use of SuFEx-enabled HTMC for rapidly generating and assessing potential therapeutic candidates against viral and immune-mediated diseases in a cell-based assay.
The Antioxidant Enzyme Methionine Sulfoxide Reductase A (MsrA) Interacts with Jab1/CSN5 and Regulates Its Function
Methionine sulfoxide (MetO) is an oxidative posttranslational modification that primarily occurs under oxidative stress conditions, leading to alteration of protein structure and function. This modification is regulated by MetO reduction through the evolutionarily conserved methionine sulfoxide reductase (Msr) system. The Msr type A enzyme (MsrA) plays an important role as a cellular antioxidant and promotes cell survival. The ubiquitin- (Ub) like neddylation pathway, which is controlled by the c-Jun activation domain-binding protein-1 (Jab1), also affects cell survival. Jab1 negatively regulates expression of the cell cycle inhibitor cyclin-dependent kinase inhibitor 1B (P27) through binding and targeting P27 for ubiquitination and degradation. Here we report the finding that MsrA interacts with Jab1 and enhances Jab1's deneddylase activity (removal of Nedd8). In turn, an increase is observed in the level of deneddylated Cullin-1 (Cul-1, a component of E3 Ub ligase complexes). Furthermore, the action of MsrA increases the binding affinity of Jab1 to P27, while MsrA ablation causes a dramatic increase in P27 expression. Thus, an interaction between MsrA and Jab1 is proposed to have a positive effect on the function of Jab1 and to serve as a means to regulate cellular resistance to oxidative stress and to enhance cell survival.
Structures of FOX-4 Cephamycinase in Complex with Transition-State Analog Inhibitors
Boronic acid transition-state analog inhibitors (BATSIs) are partners with β-lactam antibiotics for the treatment of complex bacterial infections. Herein, microbiological, biochemical, and structural findings on four BATSIs with the FOX-4 cephamycinase, a class C β-lactamase that rapidly hydrolyzes cefoxitin, are revealed. FOX-4 is an extended-spectrum class C cephalosporinase that demonstrates conformational flexibility when complexed with certain ligands. Like other β-lactamases of this class, studies on FOX-4 reveal important insights into structure–activity relationships. We show that SM23, a BATSI, shows both remarkable flexibility and affinity, binding similarly to other β-lactamases, yet retaining an IC 50 value < 0.1 μM. Our analyses open up new opportunities for the design of novel transition-state analogs of class C enzymes.
Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors
A rational therapeutic strategy is urgently needed for combating SARS-CoV-2 infection. Viral infection initiates when the SARS-CoV-2 receptor-binding domain (RBD) binds to the ACE2 receptor, and thus, inhibiting RBD is a promising therapeutic for blocking viral entry. In this study, the structure of lead antiviral candidate binder (LCB1), which has three alpha-helices (H1, H2, and H3), is used as a template to design and simulate several miniprotein RBD inhibitors. LCB1 undergoes two modifications: structural modification by truncation of the H3 to reduce its size, followed by single and double amino acid substitutions to enhance its binding with RBD. We use molecular dynamics (MD) simulations supported by ab initio density functional theory (DFT) calculations. Complete binding profiles of all miniproteins with RBD have been determined. The MD investigations reveal that the H3 truncation results in a small inhibitor with a -1.5 kcal/mol tighter binding to RBD than original LCB1, while the best miniprotein with higher binding affinity involves D17R or E11V + D17R mutation. DFT calculations provide atomic-scale details on the role of hydrogen bonding and partial charge distribution in stabilizing the minibinder:RBD complex. This study provides insights into general principles for designing potential therapeutics for SARS-CoV-2.
Dexamethasone effects on creatine kinase activity and insulin-like growth factor receptors in cultured muscle cells
The effect of dexamethasone on the activity of creatine kinase (CK) and the insulin-like growth factor I (IGF-I) binding were investigated using skeletal- and cardiac-muscle-derived cultured cell lines (mouse, C2C12; rat, L6 and H9c2). It was found that, in skeletal muscle cells, dexamethasone treatment during differentiation of skeletal-muscle cells caused dose-dependent increases in CK activity and increases in the degree of myotube formation, whereas cardiac cells (H9c2) exhibited very low CK activity during culture or dexamethasone treatment. Results for IGF-I binding were similar in all three cell lines. The IGF-I binding to dexamethasone-treated cells (50 nM for 24 hr on the day prior to confluence) resulted in an increased number of available binding sites, with no effect on the binding affinities.
Dermal Aged and Fetal Fibroblasts Realign in Response to Mechanical Strain
Integrins specifically recognize and bind extracellular matrix components, providing physical anchor points and functional setpoints. Focal adhesion complexes, containing integrin and cytoskeletal proteins, are potential mechanoreceptors, poised to distribute applied forces through the cytoskeleton. Pursuing the hypothesis that cells both perceive and respond to external force, we applied a stretch/relaxation regimen to normal human fetal and aged dermal fibroblast monolayers cultured on flexible membranes. The frequency and magnitude of the applied force is precisely controlled by the Flexercell Unit(Trademark). A protocol of stretch (20% elongation of the monolayer) at a frequency of 6 cycles/min caused a progressive change from a randomly distributed pattern of cells to a symmetric, radial distribution with cells aligned parallel to the applied force. We have coined the term 'orienteering' as the process of active alignment of cells in response to applied force. Cytochalasin D was added in graded doses to investigate the role of the actin cytoskeleton in force perception and transmission. A clear dose response was found; at high concentrations orienteering was abolished; and the drug's impact was reversible. The two cell strains used were similar in their alignment behavior and in their responses to cytochalasin D. Orienteering was influenced by cell density, and the cell strains studied differed in this respect. Fetal cells, unlike their aged counterparts, failed to orient at high cell density. In both cell strains, mid-density cultures aligned rapidly and sparse cultures lagged. These results indicate that both cell-cell adhesion and cytoskeleton integrity are critical in mediating the orienteering response. Differences between these two cell strains may relate to their expression of extracellular matrix molecules (fibronectin, collagen type 1) integrins and their relative binding affinities.