Pairing Affinity Research Articles

Biochemical Affinities of some Species of <i>Ficus</i>

Polyphenolic constituents of eighteen Ficus species were studied as separated by paper chromatography. Their inter-relationship was examined on the basis of their paired affinity (PA) indices. A comparative relationship of each Species in respect of PA values with others, are shown in polygonal graphs. Each species posses a distinct shape and area of these polygons. F. eriobotryoides, the only species native of Tropical Africa included, has distinctly smaller PA values thereby indicating its remote relationship with others.About 25-30% of the polyphenolic constituents are common in all the species, probably an indication of common heritage. At least two phenolics are found to be common to all the species studied.PA indices between 30 and 60% are shown by a fairly large group of species in each case. Considering PA values above 60% or less than 30% as indicating more nearness or remoteness respectively, it is possible to show inter-relationship among them. The various species of the subgenus Urostigma clearly isolate themselves into one group. F. virens, F. elastica and F. drupaceae var. pubescens appear to be the connecting links between the species of the subgenus Urostigma and Ficus. Details of the relationship are discussed.

Predicting TCR sequences for unseen antigen epitopes using structural and sequence features.

T-cell receptor (TCR) recognition of antigens is fundamental to the adaptive immune response. With the expansion of experimental techniques, a substantial database of matched TCR-antigen pairs has emerged, presenting opportunities for computational prediction models. However, accurately forecasting the binding affinities of unseen antigen-TCR pairs remains a major challenge. Here, we present convolutional-self-attention TCR (CATCR), a novel framework tailored to enhance the prediction of epitope and TCR interactions. Our approach utilizes convolutional neural networks to extract peptide features from residue contact matrices, as generated by OpenFold, and a transformer to encode segment-based coded sequences. We introduce CATCR-D, a discriminator that can assess binding by analyzing the structural and sequence features of epitopes and CDR3-β regions. Additionally, the framework comprises CATCR-G, a generative module designed for CDR3-β sequences, which applies the pretrained encoder to deduce epitope characteristics and a transformer decoder for predicting matching CDR3-β sequences. CATCR-D achieved an AUROC of 0.89 on previously unseen epitope-TCR pairs and outperformed four benchmark models by a margin of 17.4%. CATCR-G has demonstrated high precision, recall and F1 scores, surpassing 95% in bidirectional encoder representations from transformers score assessments. Our results indicate that CATCR is an effective tool for predicting unseen epitope-TCR interactions. Incorporating structural insights enhances our understanding of the general rules governing TCR-epitope recognition significantly. The ability to predict TCRs for novel epitopes using structural and sequence information is promising, and broadening the repository of experimental TCR-epitope data could further improve the precision of epitope-TCR binding predictions.

Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images.

Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection (CD) in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pixels to the learning of the translation function. Many existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. We propose to extract relational pixel information captured by domain-specific affinity matrices at the input and use this to enforce alignment of the code spaces and reduce the impact of change pixels on the learning objective. A change prior is derived in an unsupervised fashion from pixel pair affinities that are comparable across domains. To achieve code space alignment, we enforce pixels with similar affinity relations in the input domains to be correlated also in code space. We demonstrate the utility of this procedure in combination with cycle consistency. The proposed approach is compared with the state-of-the-art machine learning and deep learning algorithms. Experiments conducted on four real and representative datasets show the effectiveness of our methodology.

Extensive preclinical evaluation of combined mangiferin and glycyrrhizic acid for restricting synovial neovascularization in rheumatoid arthritis

BackgroundSynovial neovascularization promotes rheumatoid arthritis (RA) progression. Baihu guizhi decoction (BHGZD) has a potential in restricting this pathological change of RA.PurposeTo identify bioactive compounds (BACs) of BHGZD and to elucidate the underlying mechanisms in restricting synovial neovascularization of RA.MethodThrough transcriptomic profiling, the chemical profiling of BHGZD and its effective transcriptomic profiling against RA were identified. Then, candidate targets and the corresponding BACs against synovial neovascularization were screened by “disease gene-drug target” interaction network analysis and in silico molecular docking. The binding affinities of candidate BAC-target pairs were verified using surface plasmon resonance, and the pharmacokinetic characteristics of BACs in vivo after BHGZD administration at different time points were detected by Ultra Performance Liquid Chromatography-Mass spectrum/Mass spectrum. After that, in vivo experiments based on adjuvant-induced arthritis (AIA-M) rats, and in vitro experiments based on human umbilical vein endothelial cells (HUVEC) and arthritic synovial fibroblasts (MH7A) were carried out to evaluate the pharmacological effects of BHGZD and the two-BACs-combination, and to verify the associated mechanisms.ResultVEGFA/VEGFR2/SRC/PI3K/AKT signal axis was screened as one of the key network targets of BHGZD against synovial neovascularization in RA. Mangiferin (MG) and glycyrrhizic acid (GA) were identified as the representative BACs of BHGZD for their strong binding affinities with components of the VEGFA/VEGFR2/SRC/PI3K/AKT signal axis, and their high exposed quantity in vivo. Both BHGZD and the two-BAC combination of MG and GA were demonstrated to be effective in restricting disease severity, reducing synovial inflammation and decreasing the formation of vascular opacities in AIA-M rats, and also reducing the migrative and invasive activities of HUVEC and MH7A cells and attenuating the lumen formation ability of HUVEC cells significantly. Mechanically, both BHGZD and the two-BAC combination markedly reduced the expression of VEGFA in synovial tissues, the serum levels of VEGF and NO, and the enzymatic activity of eNOS, increased the content of endostatin, and also reversed the abnormal alterations in the VEGFA/VEGFR2/SRC/PI3K/AKT signal axis in vivo and in vitro.ConclusionMG and GA may be the representative BACs of BHGZD for restricting excessive synovial vascularization in RA via regulating VEGFA/VEGFR2/SRC/PI3K/AKT signal axis.

TAG-DTA: Binding-region-guided strategy to predict drug-target affinity using transformers

The proper assessment of target-specific compound selectivity is paramount in the drug discovery context, promoting the identification of drug-target interactions (DTIs) and the discovery of potential leads. On that account, the accurate prediction of an unbiased drug-target binding affinity (DTA) metric is pivotal to understanding the binding process. Most in silico computational approaches, however, neglect the inter-dependency of the proteomics, chemical, and pharmacological spaces and the explainability during the model construction. Furthermore, these methods have yet to actively include information associated with binding pockets during the learning process, which is essential to DTA prediction performance and model explainability. In this study, we propose an end-to-end binding-region-guided Transformer-based architecture that simultaneously predicts the 1D binding pocket and the binding affinity of DTI pairs, where the prediction of the 1D binding pocket guides and conditions the prediction of DTA. This architecture uses 1D raw sequential and structural data to represent the proteins and compounds, respectively, and combines multiple Transformer-Encoder blocks to capture and learn the proteomics, chemical, and pharmacological contexts. The predicted 1D binding pocket conditions the attention mechanism of the Transformer-Encoder used to learn the pharmacological space in order to model the inter-dependency amongst binding-related positions. The results show that the proposed architecture, TAG-DTA, achieved the best performance in DTA prediction compared to state-of-the-art benchmarks, including in unknown subsets of the proteomics and chemical representation spaces. Moreover, the 1D binding pocket prediction increases the discriminative power and robustness of the aggregate representation of the pharmacological space and improves the DTA prediction performance. Overall, this research study validates the applicability of an end-to-end Transformer-based architecture in the context of drug discovery, and that combining computationally different yet contextually related tasks is critical to new findings in the DTI domain. Additionally, it shows that TAG-DTA is capable of providing increasing DTI and prediction understanding due to the nature of the attention blocks and prediction of the 1D binding pocket. The data and source code used in this study are available at: https://github.com/larngroup/TAG-DTA.

DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction.

Accurate prediction of drug-target binding affinity (DTA) is crucial for drug discovery. The increase in the publication of large-scale DTA datasets enables the development of various computational methods for DTA prediction. Numerous deep learning-based methods have been proposed to predict affinities, some of which only utilize original sequence information or complex structures, but the effective combination of various information and protein-binding pockets have not been fully mined. Therefore, a new method that integrates available key information is urgently needed to predict DTA and accelerate the drug discovery process. In this study, we propose a novel deep learning-based predictor termed DataDTA to estimate the affinities of drug-target pairs. DataDTA utilizes descriptors of predicted pockets and sequences of proteins, as well as low-dimensional molecular features and SMILES strings of compounds as inputs. Specifically, the pockets were predicted from the three-dimensional structure of proteins and their descriptors were extracted as the partial input features for DTA prediction. The molecular representation of compounds based on algebraic graph features was collected to supplement the input information of targets. Furthermore, to ensure effective learning of multiscale interaction features, a dual-interaction aggregation neural network strategy was developed. DataDTA was compared with state-of-the-art methods on different datasets, and the results showed that DataDTA is a reliable prediction tool for affinities estimation. Specifically, the CI of DataDTA is 0.806 and the R value is 0.814 on the test dataset, which is higher than other methods. The codes of DataDTA are available at https://github.com/YanZhu06/DataDTA. Supplementary data are available at Bioinformatics online.

Label-Free Quantification of DNA Hybridization Kinetics and Affinity Enabled by an Electro-Optical Modulation on Single Nanoparticles.

Since the formation of a DNA duplex plays a vital role in gene expression and regulation across all kinds of organisms, quantifying the interaction during DNA hybridization is essential for understanding various biological processes in living systems on the molecular scale of nucleic acids. Here, we developed a label-free method to measure the binding kinetics and affinity of DNA hybridization with an electro-optical modulation on the individual gold nanorods based on a total internal reflection dark-field imaging microscopy. Under the electrochemical modulation, a Fourier transform filter was utilized to extract the optical scattering of the nanorods, which varies with the DNA binding due to their impedance change. We validated the imaging principle and established an analytical model to monitor the optical response during the DNA hybridization. Using the presented platform, we measured the binding kinetics and affinities of different DNA pairs and demonstrated its capability to distinguish the DNA hybridization with only single-base mismatch, which may provide guidance to explore the etiology and pathogenesis of diseases associated with single point gene mutations. Furthermore, the method allows for simultaneous imaging of multiple nanoparticles, thus enabling a high-throughput detection and opening possibilities for the study of the interfacial heterogeneity.

PLA-MoRe: A Protein-Ligand Binding Affinity Prediction Model via Comprehensive Molecular Representations.

Accurately predicting the binding affinity of protein-ligand pairs is an essential part of drug discovery. Since wet laboratory experiments to determine the binding affinity are expensive and time-consuming, several computational methods for binding affinity prediction have been proposed. In the representation of compounds, most methods only focus on the structural properties such as SMILES and ignore the bioactive properties. In this study, we proposed a novel model named PLA-MoRe to predict protein-ligand binding affinity, which represents compounds based on both structural and bioactive properties and mainly contains three feature extractors. First, a structure feature extractor based on the graph isomorphism network was constructed to learn the representations of the molecular graphs. Second, we designed an Autoencoder-based bioactive feature extractor to integrate the multisource bioactive information including chemical, target, network, cellular, and clinical. The above two parts aimed to learn representations of compounds in terms of structures and bioactivities, respectively. Then, we constructed a sequence feature extractor to learn embeddings for protein sequences. The output of the three extractors was concatenated and fed into a fully connected network for affinity prediction. We compared PLA-MoRe with three state-of-the-art methods, and an ablation study was conducted to test the role of each part of the model. Further attention visualization showed that our model had the potential to locate the binding sites, which might help explain the mechanism of interaction. These results prove that PLA-MoRe is competitive and reliable. The resource codes are freely available at the GitHub repository https://github.com/QingyuLiaib/PLA-MoRe.

Sorting Nanoparticles by Valency with DNA Barcoding.

Self-assembly of DNA-labeled nanoparticles is an effective strategy to fabricate new nanocomposite materials and nanoscale devices from the bottom-up. To tailor the properties of the resulting material or device, one requires access to a wide range of nanoparticle sizes and shapes, as well as control over the number (valency) of DNA molecules on the nanoparticle surface. Currently, nanoparticles with a defined DNA valency can only be obtained in a narrow range of sizes, and in small quantities, limiting the properties of the resulting composite structures and their applications. Here, we leverage the digital information encoded in the number and sequence of short DNA barcodes to generate preparatory amounts of nanoparticles bearing a specific number of DNA molecules, irrespective of the identity of the nanocomponent. We show that this DNA valency sorting chromatography, which is driven by the selective affinity of Watson-Crick base pairs, is applicable to arbitrary DNA sequences and a broad range of nanoparticle sizes, shapes, and material compositions. To further demonstrate this fact, we use valency-sorted large gold nanospheres directly in self-assembly schemes to create, in one synthesis step, large amounts of several previously inaccessible molecule-like dimer and trimer nanostructures with unique optical properties. We anticipate that the expanded scope of DNA valency-defined nanoparticle reagents, and the increased scale at which they can be produced, will open new avenues for the molecularly precise manipulation of nanoscale matter.

ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding

MotivationDrug-target binding affinity (DTA) reflects the strength of the drug-target interaction; therefore, predicting the DTA can considerably benefit drug discovery by narrowing the search space and pruning drug-target (DT) pairs with low binding affinity scores. Representation learning using deep neural networks has achieved promising performance compared with traditional machine learning methods; hence, extensive research efforts have been made in learning the feature representation of proteins and compounds. However, such feature representation learning relies on a large-scale labelled dataset, which is not always available.ResultsWe present an end-to-end deep learning framework, ELECTRA-DTA, to predict the binding affinity of drug-target pairs. This framework incorporates an unsupervised learning mechanism to train two ELECTRA-based contextual embedding models, one for protein amino acids and the other for compound SMILES string encoding. In addition, ELECTRA-DTA leverages a squeeze-and-excitation (SE) convolutional neural network block stacked over three fully connected layers to further capture the sequential and spatial features of the protein sequence and SMILES for the DTA regression task. Experimental evaluations show that ELECTRA-DTA outperforms various state-of-the-art DTA prediction models, especially with the challenging, interaction-sparse BindingDB dataset. In target selection and drug repurposing for COVID-19, ELECTRA-DTA also offers competitive performance, suggesting its potential in speeding drug discovery and generalizability for other compound- or protein-related computational tasks.

Insight into the interatomic competitive mechanism for interfacial stability of room temperature liquid GaInSn/Cu electrode

Room temperature Ga-based alloys have received more and more attentions due to their unique advantages in these applications of stretchable and wearable electronics. In this paper, the interfacial stability of liquid GaInSn/Cu interface was systematically investigated. From the perspective of crystallography, the orientation relationship between the nucleation and growth of intermetallic compounds and Cu substrate are discussed according to plane-on-plane matching model and edge-to-edge matching model. The required standard formation enthalpy and bond enthalpy of as-formed intermetallic compounds are estimated based on the elemental condensed bond enthalpy and crystal cluster theory. For the ternary GaInSn system, the competitive reactive mechanism between different reactive atomic pairs was revealed and discussed from the thermodynamic view, which verified that the rationality of the formed intermetallic compounds only consisting of Cu and Ga. Besides, we confirmed that the wetting behavior of GaInSn on Cu substrate was determined by the affinity and close contact probability of different atomic pairs. • Atomic competitive mechanism of Cu–Ga, Cu–In, Cu–Sn atomic pairs is revealed. • Growth of CuGa 2 was analyzed from thermodynamic and crystallographic view. • Wetting behavior is determined by the affinity and close contact probability of atomic pairs. • Orientation relationship is defined according to plane-on-plane matching and edge-to-edge matching models. • Interfacial stability of GaInSn/Cu are destroyed during room temperature long-term service.

A signal conversion system using binding-induced strand displacement for disease biomarker assay.

Using the principle of binding-induced DNA strand displacement (BINSD), a DNAzyme-powered nanomachine biosensor for multiple biomarkers via magnetic beads-based signal conversion was designed. This sensor can convert multiple biomarker recognition into release of predesigned output nucleic acids tagged with streptavidin proteins (SA-DNA) for activation of DNA nanomachines. In general, we adopted complementary base pairing rules and affinity ligand specific recognition, and three types of signal conversion systems were constructed that realized universal, sensitive, accurate, and specific detection of multiple biomarkers. Taking the advantage of the strong anti-interference capability of magnetic separation, this strategy could be used for detection of various biomarkers in clinical practice.

Deep drug-target binding affinity prediction with multiple attention blocks

Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studies (i) ignore the essential correlations between atoms when encoding drug compounds and (ii) model the interaction of drug-target pairs simply by concatenation. Based on those observations, in this study, we propose an end-to-end model with multiple attention blocks to predict the binding affinity scores of drug-target pairs. Our proposed model offers the abilities to (i) encode the correlations between atoms by a relation-aware self-attention block and (ii) model the interaction of drug representations and target representations by the multi-head attention block. Experimental results of DTI prediction on two benchmark datasets show our approach outperforms existing methods, which are benefit from the correlation information encoded by the relation-aware self-attention block and the interaction information extracted by the multi-head attention block. Moreover, we conduct the experiments on the effects of max relative position length and find out the best max relative position length value n}{}k in {3, 5}. Furthermore, we apply our model to predict the binding affinity of Corona Virus Disease 2019 (COVID-19)-related genome sequences and n}{}3137 FDA-approved drugs.

Ultrahigh molecular recognition specificity of competing DNA oligonucleotide strands in thermal equilibrium: a cooperative transition to order

The specificity of molecular recognition is important for molecular self-organization. A prominent example is the biological cell where a myriad of different molecular receptor pairs recognize their binding partners with astonishing accuracy within a highly crowded molecular environment. In thermal equilibrium it is usually admitted that the affinity of recognizer pairs only depends on the nature of the two binding molecules. Accordingly, Boltzmann factors of binding energy differences relate the molecular affinities among different target molecules that compete for the same probe. Here, we consider the molecular recognition of short DNA oligonucleotide single strands. We show that a better matching oligonucleotide can prevail against a disproportionally more concentrated competitor with reduced affinity due to a mismatch. We investigate the situation using fluorescence-based techniques, among them Förster resonance energy transfer and total internal reflection fluorescence excitation. We find that the affinity of certain strands appears considerably reduced only as long as a better matching competitor is present. Compared to the simple Boltzmann picture above we observe increased specificity, up to several orders of magnitude. We interpret our observations based on an energy-barrier of entropic origin that occurs if two competing oligonucleotide strands occupy the same probe simultaneously. Due to their differences in binding microstate distributions, the barrier affects the binding affinities of the competitors differently. Based on a mean field description, we derive a resulting expression for the free energy landscape, a formal analogue to a Landau description of phase transitions reproducing the observations in quantitative agreement as a result of a cooperative transition. The advantage of improved molecular recognition comes at no energetic cost other than the design of the molecular ensemble and the presence of the competitor. As a possible application, binding assays for the detection of single nucleotide polymorphisms in DNA strands could be improved by adding competing strands. It will be interesting to see if mechanisms along similar lines as exposed here contribute to the molecular synergy that occurs in biological systems.

Three founding ancestral genomes involved in the origin of sugarcane.

Background and AimsModern sugarcane cultivars (Saccharum spp.) are high polyploids, aneuploids (2n = ~12x = ~120) derived from interspecific hybridizations between the domesticated sweet species Saccharum officinarum and the wild species S. spontaneum.MethodsTo analyse the architecture and origin of such a complex genome, we analysed the sequences of all 12 hom(oe)ologous haplotypes (BAC clones) from two distinct genomic regions of a typical modern cultivar, as well as the corresponding sequence in Miscanthus sinense and Sorghum bicolor, and monitored their distribution among representatives of the Saccharum genus.Key ResultsThe diversity observed among haplotypes suggested the existence of three founding genomes (A, B, C) in modern cultivars, which diverged between 0.8 and 1.3 Mya. Two genomes (A, B) were contributed by S. officinarum; these were also found in its wild presumed ancestor S. robustum, and one genome (C) was contributed by S. spontaneum. These results suggest that S. officinarum and S. robustum are derived from interspecific hybridization between two unknown ancestors (A and B genomes). The A genome contributed most haplotypes (nine or ten) while the B and C genomes contributed one or two haplotypes in the regions analysed of this typical modern cultivar. Interspecific hybridizations likely involved accessions or gametes with distinct ploidy levels and/or were followed by a series of backcrosses with the A genome. The three founding genomes were found in all S. barberi, S. sinense and modern cultivars analysed. None of the analysed accessions contained only the A genome or the B genome, suggesting that representatives of these founding genomes remain to be discovered.ConclusionsThis evolutionary model, which combines interspecificity and high polyploidy, can explain the variable chromosome pairing affinity observed in Saccharum. It represents a major revision of the understanding of Saccharum diversity.

Statistical approach to obtaining vacancy formation energies in high-entropy crystals from first principles calculations: Application to a high-entropy diboride

A reduced pair approximation model for vacancy formation energy in multicomponent materials is proposed as an alternative to the commonly used cluster expansion method. By imposing physical constraints to the interaction coefficients, lower rank models are obtained with improved accuracy as measured by Bayesian information criterion and cross validation. Additionally, reduced models can outperform the full parametrization in high-entropy compounds with as much as 50% less training data. The results are presented for cation vacancies in the high-entropy diboride ${\mathrm{Hf}}_{0.2}{\mathrm{Zr}}_{0.2}{\mathrm{Ti}}_{0.2}{\mathrm{Ta}}_{0.2}{\mathrm{Nb}}_{0.2}{\mathrm{B}}_{2}$ calculated by density functional theory simulations of large cell special quasirandom structures. Further, the calculation of vacancy concentrations from a distribution of energies is considered, wherein the chemical disorder on lattice sites gives rise to non-Arrhenius temperature dependence. Preferential clustering and the possibility of short-range order in the high-entropy lattice are explored through pair affinities derived from model coefficients.

Molecular Mechanism for the Interactions of Hofmeister Cations with Macromolecules in Aqueous Solution.

Ion identity and concentration influence the solubility of macromolecules. To date, substantial effort has been focused on obtaining a molecular level understanding of specific effects for anions. By contrast, the role of cations has received significantly less attention and the underlying mechanisms by which cations interact with macromolecules remain more elusive. To address this issue, the solubility of poly(N-isopropylacrylamide), a thermoresponsive polymer with an amide moiety on its side chain, was studied in aqueous solutions with a series of nine different cation chloride salts as a function of salt concentration. Phase transition temperature measurements were correlated to molecular dynamics simulations. The results showed that although all cations were on average depleted from the macromolecule/water interface, more strongly hydrated cations were able to locally accumulate around the amide oxygen. These weakly favorable interactions helped to partially offset the salting-out effect. Moreover, the cations approached the interface together with chloride counterions in solvent-shared ion pairs. Because ion pairing was concentration-dependent, the mitigation of the dominant salting-out effect became greater as the salt concentration was increased. Weakly hydrated cations showed less propensity for ion pairing and weaker affinity for the amide oxygen. As such, there was substantially less mitigation of the net salting-out effect for these ions, even at high salt concentrations.

A molecular odorant transduction model and the complexity of spatio-temporal encoding in the Drosophila antenna.

Over the past two decades, substantial amount of work has been conducted to characterize different odorant receptors, neuroanatomy and odorant response properties of the early olfactory system of Drosophila melanogaster. Yet many odorant receptors remain only partially characterized, and the odorant transduction process and the axon hillock spiking mechanism of the olfactory sensory neurons (OSNs) have yet to be fully determined. Identity and concentration, two key characteristics of the space of odorants, are encoded by the odorant transduction process. Detailed molecular models of the odorant transduction process are, however, scarce for fruit flies. To address these challenges we advance a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process (OTP) and a biophysical spike generator (BSG). We model odorant identity and concentration using an odorant-receptor binding rate tensor, modulated by the odorant concentration profile, and an odorant-receptor dissociation rate tensor, and quantitatively describe the mechanics of the molecular ligand binding/dissociation of the OTP. We model the BSG as a Connor-Stevens point neuron. The resulting spatio-temporal encoding model of the Drosophila antenna provides a theoretical foundation for understanding the neural code of both odorant identity and odorant concentration and advances the state-of-the-art in a number of ways. First, it quantifies on the molecular level the spatio-temporal level of complexity of the transformation taking place in the antennae. The concentration-dependent spatio-temporal code at the output of the antenna circuits determines the level of complexity of olfactory processing in the downstream neuropils, such as odorant recognition and olfactory associative learning. Second, the model is biologically validated using multiple electrophysiological recordings. Third, the model demonstrates that the currently available data for odorant-receptor responses only enable the estimation of the affinity of the odorant-receptor pairs. The odorant-dissociation rate is only available for a few odorant-receptor pairs. Finally, our model calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

Graph-based Recommendation Meets Bayes and Similarity Measures

Graph-based approaches provide an effective memory-based alternative to latent factor models for collaborative recommendation. Modern approaches rely on either sampling short walks or enumerating short paths starting from the target user in a user-item bipartite graph. While the effectiveness of random walk sampling heavily depends on the underlying path sampling strategy, path enumeration is sensitive to the strategy adopted for scoring each individual path. In this article, we demonstrate how both strategies can be improved through Bayesian reasoning. In particular, we propose to improve random walk sampling by exploiting distributional aspects of items’ ratings on the sampled paths. Likewise, we extend existing path enumeration approaches to leverage categorical ratings and to scale the score of each path proportionally to the affinity of pairs of users and pairs of items on the path. Experiments on several publicly available datasets demonstrate the effectiveness of our proposed approaches compared to state-of-the-art graph-based recommenders.

Oriented Assembly of Cell-Mimicking Nanoparticles via a Molecular Affinity Strategy for Targeted Drug Delivery.

Cell membrane cloaking is an emerging field in drug delivery in which specific functions of parent cells are conferred to newly formed biomimetic vehicles. A growing variety of delivery systems with diverse surface properties have been utilized for this strategy, but it is unclear whether the affinity of membrane-core pairs could guarantee effective and proper camouflaging. In this study, we propose a concise and effective "molecular affinity" strategy using the intracellular domain of transmembrane receptors as "grippers" during membrane coating. Red blood cell (RBC) membranes and cationic liposomes were adopted for fabrication, and a peptide ligand derived from the cytoplasmic protein P4.2 was prepared to specifically recognize the cytoplasmic domain of band 3, a key transmembrane receptor of erythrocytes. Once anchored onto the liposome surface, the P4.2-derived peptide would interact with the isolated RBC membrane, forming a "hidden peptide button", which ensures the right-side-out orientation. The membrane-coated liposomes exhibited an appropriate size distribution around 100 nm and high stability, with superior circulation durations compared with those of conventional PEGylated liposomes. Importantly, they possessed the ability to target Candida albicans by the interaction between the pathogenic fungus and host erythrocytes and to neutralize hemotoxin secreted by the pathogenic fungi. The curative effect of the model drug was thus substantially improved. In summary, the "molecular affinity" strategy may provide a powerful and universal approach for the construction of cell membrane-coated biomaterials and nanomedicines at both laboratory and industrial scales.