Low Structural Similarity Research Articles

Understanding and Predicting Ligand Efficacy in the μ-Opioid Receptor through Quantitative Dynamical Analysis of Complex Structures.

The μ-opioid receptor (MOR) is a G-protein coupled receptor involved in nociception and the primary target of opioid drugs. Understanding the relationships among the ligand structure, receptor dynamics, and efficacy in activating MOR is crucial for drug discovery and development. Here, we use coarse-grained normal-mode analysis to predict ligand-induced changes in receptor dynamics with the Quantitative Dynamics Activity Relationship (QDAR) DynaSig-ML methodology, training a LASSO regression model on the entropic signatures (ESs) computed from ligand-receptor complexes. We train and validate the methodology using a data set of 179 MOR ligands with experimentally measured efficacies split into strictly chemically different cross-validation sets. By analyzing the coefficients of the ES LASSO model, we identified key residues involved in MOR activation, several of which have mutational data supporting their role in MOR activation. Additionally, we explored a contact-only LASSO model based on ligand-protein interactions. While the model showed predictive power, it failed at predicting efficacy for ligands with low structural similarity to the training set, emphasizing the importance of receptor dynamics for predicting ligand-induced receptor activation. Moreover, the low computational cost of our approach, at 3 CPU s per ligand-receptor complex, opens the door to its application in large-scale virtual screening contexts. Our work contributes to a better understanding of dynamics-function relationships in the μ-opioid receptor and provides a framework for predicting ligand efficacy based on ligand-induced changes in receptor dynamics.

Two novel plasmids harbouring the multiresistance gene cfr in porcine Staphylococcus equorum

BackgroundThe emergence and transmission of the multidrug resistance gene cfr have raised public health concerns worldwide. ObjectivesMultidrug-resistant Staphylococcus equorum isolates can pose a threat to public health. In this study, we have characterised the whole-genome of one Staphylococcus equorum isolate harbouring two distinct cfr-carrying plasmids. MethodsAntimicrobial susceptibility testing was performed by broth microdilution. Genomic DNA was sequenced using both the Illumina HiSeq X Ten and Nanopore MinION platforms. De novo hybrid assembly was performed by Unicycler. Genomic data were assessed by in silico prediction and bioinformatic tools. ResultsStaphylococcus equorum isolate SN42 exhibited resistance or high MICs to linezolid, erythromycin, tetracycline, oxacillin, clindamycin, virginiamycin, tiamulin, chloramphenicol and florfenicol. It carried two cfr-harbouring plasmids: the RepA N-family plasmid pSN42–51 K and the Inc18-family plasmid pSN42–50 K. These two plasmids exhibited low structural similarities to the so far reported cfr-carrying plasmids. Both plasmids harboured an arsenic resistance operon, copper and cadmium resistance genes as well as the lincosamide-pleuromutilin-streptogramin A resistance gene lsa(B). In addition, plasmid pSN42–51 K carried two erm(B) genes for macrolide-lincosamide-streptogramin B resistance, the streptomycin resistance gene ant(6)-Ia as well as mercury resistance genes while pSN42–50 K was associated with the heavy metal translocating P-type ATPase gene hmtp. The co-carriage and co-existence of these antimicrobial resistance and heavy metal resistance genes increases the likelihood of co-selection of the cfr-carrying plasmids. ConclusionThis is the first report of S. equorum carrying two distinct cfr-carrying plasmids, underscoring the need for ongoing surveillance to address the potential dissemination of multi-drug resistance in bacteria from food-producing animals to ensure food safety and public health.

Discovery of megapolipeptins by genome mining of a Burkholderiales bacteria collection.

Burkholderiales bacteria have emerged as a promising source of structurally diverse natural products that are expected to play important ecological and industrial roles. This order ranks in the top three in terms of predicted natural product diversity from available genomes, warranting further genome sequencing efforts. However, a major hurdle in obtaining the predicted products is that biosynthetic genes are often 'silent' or poorly expressed. Here we report complementary strain isolation, genomics, metabolomics, and synthetic biology approaches to enable natural product discovery. First, we built a collection of 316 rhizosphere-derived Burkholderiales strains over the course of five years. We then selected 115 strains for sequencing using the mass spectrometry pipeline IDBac to avoid strain redundancy. After predicting and comparing the biosynthetic potential of each strain, a biosynthetic gene cluster that was silent in the native Paraburkholderia megapolitana and Paraburkholderia acidicola producers was cloned and activated by heterologous expression in a Burkholderia sp. host, yielding megapolipeptins A and B. Megapolipeptins are unusual polyketide, nonribosomal peptide, and polyunsaturated fatty acid hybrids that show low structural similarity to known natural products, highlighting the advantage of our Burkholderiales genomics-driven and synthetic biology-enabled pipeline to discover novel natural products.

Two‐stage diffusion model deriving FDG‐PET from T1 Weighted Magnetic Resonance Images for diagnosis of Alzheimer’s disease

AbstractBackgroundAlzheimer’s Disease (AD) is a neurodegenerative disease which is characterized by cognitive and functional impairment, and defined by triad of Amyloid status, Tau pathology, and Neuroimaging marker of neurodegeneration or neuronal injury. An early diagnosis of AD can be challenging and it can be detected earlier in brain functional imaging than brain structural imaging.MethodWe develop a pipeline for synthesizing FDG‐PET/MRI from source MRI‐T1‐Weighted (MRI‐T1W) images based on denoising diffusion mechanism [1]. The pipeline includes a forward pass which encodes the source MRI‐T1WI images to gaussian noise space, and a backward pass which generates the corresponding FDG‐PET from the derived noises with repeated denoising steps. Furthermore, to validate the effectiveness of the synthesized FDG‐PET images, we also develop multiple types of classifiers and the experiments for both single input of MRI‐T1WI and joint input of MRI‐T1WI and synthesized FDG‐PET.ResultThe experiments are performed on 1036 of FDG‐PET/MRI pairs (286,474, 276 for Cognitive Normal, Mild Cognitive Impaiement and Alzheimer’s Disease each) are obtained from ADNI [2] datasets and display the promising results with low Mean Absolute Error (MAE) and high Structural Similarity Index Measure (SSIM) for the two stage diffusion denoising based FDG‐PET synthesis pipeline. The results for the additional classification experiments also show that in most cases the joint input enhances the classifiers' performance in accuracy and F1‐score. The results also indicate that the synthesized FDG‐PET images are not only visually similar to the ground truth images, but also improve the performance of machine learning based AD diagnosis.ConclusionThis work provides support for the notion that machine learning‐derived image analysis may be a useful approach to improving the diagnosis of AD.Reference:[1] Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840‐6851.[2] Petersen, R., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., … & Toga, A. W. (2010). Alzheimer’s disease neuroimaging initiative (ADNI). Neurology, 74(3), 201‐209.

Investigation of rare earth-based magnetic nanocomposites for specific enrichment of exosomes from human plasma

Exosomes, also known as small extracellular vesicles, are widely present in a variety of body fluids (e.g., blood, urine, and saliva). Exosomes are becoming an alternative promising source of diagnostic markers for disease rich in cargo of metabolites, proteins, and nucleic acids. However, due to the low abundance and structure similarity with protein complex, the efficient isolation of exosomes is one of the most important issues for biomedical applications. With a higher order of f-orbitals in rare earth element, it will have strong adsorption toward the phosphate group on the surface of the phospholipid bilayer of exosomes. In this study, we systematically investigated the ability of various rare earths interacting with phosphate-containing molecules and plasma exosomes. One of the best binding europium was selected and used to synthesize core-shell magnetic nanomaterials (Fe3O4@SiO2@Eu2O3) for the enrichment of exosomes from human plasma. The developed nanomaterials exhibited higher enrichment capacity, less time consumption and more convenient handling compared to commonly used ultracentrifugation method. The nanomaterials were applied to separate exosomes from the plasma of patients with hepatocellular carcinoma and healthy controls for metabolomics study with high-resolution mass spectrometry, where 70 differentially expressed metabolites were identified, involving amino acid and lipid metabolic pathway. We anticipated the rare earth-based materials to be an alternative approach on exosome isolation for disease diagnosis or postoperative clinical monitoring.

Conserved active site architecture between bacterial cellulose and chitin synthases.

Glycosyltransferases (GTs) are a large and diverse group of enzymes responsible for catalyzing the formation of a glycosidic bond between a donor molecule, usually a monosaccharide, and a wide range of acceptor molecules, thus, playing critical roles in various essential biological processes. Chitin and cellulose synthases are two inverting processive integral membrane GTs, belonging to the type-2 family involved in the biosynthesis of chitin and cellulose, respectively. Herein, we report that bacterial cellulose and chitin synthases share an E-D-D-ED-QRW-TK common motif spatially co-localized among distant bacterial evolutionary species despite their low amino acid sequence and structural similarities. This theoretical framework offers a new perspective to the current view that bacterial cellulose and chitin synthases are substrate specific and that chitin and cellulose are organism specific. It lays the ground for future in vivo and in silico experimental assessment of cellulose synthase catalytic promiscuity against uridine diphosphate N-acetylglucosamine and chitin synthase against uridine diphosphate glucose, respectively.

MELD-Bracket Ranks Binding Affinities of Diverse Sets of Ligands.

Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns.

Using carpet plots to analyze blood transit times in the brain during hypercapnic challenge magnetic resonance imaging.

Blood arrival time and blood transit time are useful metrics in characterizing hemodynamic behaviors in the brain. Functional magnetic resonance imaging in combination with a hypercapnic challenge has been proposed as a non-invasive imaging tool to determine blood arrival time and replace dynamic susceptibility contrast (DSC) magnetic resonance imaging, a current gold-standard imaging tool with the downsides of invasiveness and limited repeatability. Using a hypercapnic challenge, blood arrival times can be computed by cross-correlating the administered CO2 signal with the fMRI signal, which increases during elevated CO2 due to vasodilation. However, whole-brain transit times derived from this method can be significantly longer than the known cerebral transit time for healthy subjects (nearing 20s vs. the expected 5-6s). To address this unrealistic measurement, we here propose a novel carpet plot-based method to compute improved blood transit times derived from hypercapnic blood oxygen level dependent fMRI, demonstrating that the method reduces estimated blood transit times to an average of 5.32s. We also investigate the use of hypercapnic fMRI with cross-correlation to compute the venous blood arrival times in healthy subjects and compare the computed delay maps with DSC-MRI time to peak maps using the structural similarity index measure (SSIM). The strongest delay differences between the two methods, indicated by low structural similarity index measure, were found in areas of deep white matter and the periventricular region. SSIM measures throughout the remainder of the brain reflected a similar arrival sequence derived from the two methods despite the exaggerated spread of voxel delays computed using CO2 fMRI.

A colour transfer method of interior design based on machine learning

In order to overcome the problems of large colour range, low structure similarity and poor objective evaluation index in the process of interior design colour transfer, this paper proposes a new method of interior design colour transfer based on machine learning. In this method, K-means algorithm is introduced to eliminate the uneven brightness area of the target image. The target image is processed by initial clustering, and the iterative threshold segmentation method is used to obtain the final clustering accurate target image. Combined with machine learning, the corresponding samples are selected on the two images to finish colouring, and the colour transfer of interior design is realised by referring to the coloured sample block. The experimental results show that the colour degree of the proposed method is maintained between 33 and 45, the structural similarity is always above 95%, and the comprehensive objective evaluation index value is close to 100%.

Fabric Defect Segmentation System Based on a Lightweight GAN for Industrial Internet of Things

Machine vision systems based on deep learning play an important role in the industrial Internet of things (IIoT) and Industry 4.0 applications, especially for product quality monitoring. Fabric defect detection is an important task in the industrial production of textiles and is crucial for product quality assurance. In actual production, the detection of many small and weak target defects remains challenging. Furthermore, industrial production requires high production rates and small model sizes in practice. This study proposes a lightweight segmentation system that meets real-time industrial production requirements. Herein, first, the defect sample image was repaired based on the image repair mechanism of the generative adversarial network model. Then, the difference between the defect sample and the repaired sample was obtained and subsequent processing, such as denoising and enhancement, was done. Finally, the defect areas were segmented. Our model was specifically designed for the segmentation of weak and small defects. This was achieved through adversarial training, optimization of an objective function, and image processing. Experimental comparisons show that the intersection over union of the three different datasets is 77.84%, 77.85%, and 73.6% and that our model is superior to the conventional semantic segmentation model. Furthermore, our model has good image restoration quality with a low mean absolute error and high structural similarity index. Additionally, our model is lightweight, has good real-time performance, and is suitable for applications in the IIoT and industrial production lines, such as embedded systems.

A primer-initiated strand displacement amplification strategy for sensitive detection of 5-Hydroxymethylcytosine in genomic DNA

5-Hydroxymethylcytosine (5hmC), an intermediate product of DNA demethylation, is important for the regulation of gene expression during development and even tumorigenesis. The challenges associated with determination of 5hmC level include its extremely low abundance and high structural similarity with other cytosine derivatives, which resulted in sophisticated treatment with large amount of sample input. Herein, we developed a primer-initiated strand displacement amplification (PISDA) strategy to quantify the global 5hmC in genomic DNA from mammalian tissues with high sensitivity/selectivity, low input and simple operation. This sensitive fluorescence method is based on 5hmC-specific glucosylation, primer ligation and DNA amplification. After the primer was labeled on 5hmC site, DNA polymerase and nicking enzyme will repeatedly act on each primer, causing a significant increase of fluorescence signal to magnify the minor difference of 5hmC content from other cytosine derivatives. This method enables highly sensitive analysis of 5hmC with a detection limit of 0.003% in DNA (13.6 fmol, S/N = 3) from sample input of only 150 ng, which takes less than 15 min for determination. Further determination of 5hmC in different tissues not only confirms the widespread presence of 5hmC but also indicates its significant variation in different tissues and ages. Importantly, this PISDA strategy exhibits distinct advantages of bisulfite-free treatment, mild conditions and simple operation without the involvement of either expensive equipment or large amount of DNA sample. This method can be easily performed in almost all research and medical laboratories, and would provide a promising prospect to detect global 5hmC in mammalian tissues.

Accurate Identification of Bioactive Meliaceae Limonoids by UHPLC-MS/MS Based Structure-Fragment Relationships (SFRs).

Limonoids are bioactive plant specialized metabolites found in the Meliaceae family. The basic limonoids, i.e., azadiradione, epoxyazadiradione, and gedunin have been exploited for various bioactivities and therefore are the potential drug leads for tomorrow. However, their low abundance, structural similarity, and lack of adequate mass fragmentation data have hampered their accurate identification and quantification from various sources. In the present study, basic limonoids such as azadirone, azadiradione, epoxyazadiradione, and gedunin isolated from Neem were utilized for the synthesis of their derivatives and isotopologs. A total of 30 one compounds were used in this study among which five were isolated, two were biotransformed, and 24 were synthesized. Among the synthesized compounds nine are novel compounds including six deuterated analogs/isotopologs which are (1,3-2H)-1,2-dihydro-3β-hydroxyazadiradione (9), (1,3,16-2H)-1,2-dihydro-3β-16β-dihydroxyazadiradione (10), 3β-hydroxyazadiradione (11), 3β-16β-dihydroxyazadiradione (12), (3-2H)-3β-hydroxyazadiradione (13), (3,16-2H)-3β-16β-dihydroxyazadiradione (14), (1,3,7-2H)-1,2-dihydro-3β-hydroxy-7-deacetylazadiradione (15), 1,2,20,21,22,23-hexahydroazadiradione (17), and (1,3-2H)-1,2-dihydro-3β-hydroxygedunin (29). These limonoids along with their semisynthesized derivatives were subjected to ultra high performance liquid chromatography mass spectrometry (UHPLC–MS/MS) and the fragmentation pathway was established based on structure–fragment relationships (SFRs), utilizing high resolution MS/MS data. We have developed a most reliable and easily reproducible protocol describing in depth analysis of SFRs based on the structural modifications and synthesis of isotopologs. Also, the MS/MS fragment library of these basic limonoids generated in this study acts as a fingerprint for accurate identification and quantification of limonoids by MS/MS analysis in various plant tissue extracts, phytopharmaceutical formulations and biological samples.

Floristic and structure of different strata in an urban Semideciduous Forest in Jataí, Goiás state, Brazil

Estudos comparativos entre diferentes estratos de florestas em sucessão ecológica são importantes para entender a dinâmica florestal. O estudo avaliou aspectos florísticos, fitossociológicos e ecológicos entre os estratos adulto e regenerante de um fragmento urbano de floresta estacional semidecidual, em Jataí, Goiás. Nas últimas décadas, a vegetação que era tipicamente savânica tem se tornado uma floresta. As árvores adultas (diâmetro a altura do peito, DAP ≥ 5 cm) foram amostradas em 12 parcelas permanentes de 200m². Em cada uma delas, sub-parcelas de 25m² foram amostradas para o estrato regenerante, que compreendeu indivíduos com DAP 5cm e altura ≥ 1m. Regenerantes tiveram maior riqueza (31 espécies), com duas espécies a mais do que os adultos. Houve baixa similaridade florística e estrutural entre os estratos, indicando uma substituição de espécies ao longo do tempo. A diversidade de Shannon foi significativamente menor para o estrato arbóreo (teste t), visto que apresentou baixa equabilidade, com dominância ecológica de poucas espécies. Ambos os estratos tiveram elevada proporção de espécies pioneiras, quando comparado com outros estudos. Zoocoria e espécies de ambientes florestais foram expressivamente mais abundantes no estrato regenerante. O estudo revelou um fragmento em franco processo de sucessão secundária e vulnerabilidade diante de pressões antrópicas, sendo importante os cuidados quanto à sua conservação.

Targeted LC-MS/MS platform for the comprehensive determination of peptides in the kallikrein-kinin system

The kallikrein-kinin system (KKS) is involved in many physiological and pathophysiological processes and is assumed to be connected to the development of clinical symptoms of angioedema or COVID-19, among other diseases. However, despite its diverse role in the regulation of physiological and pathophysiological functions, knowledge about the KKS in vivo remains limited. The short half-lives of kinins, their low abundance and structural similarities and the artificial generation of the kinin bradykinin greatly hinder reliable and accurate determination of kinin levels in plasma. To address these issues, a sensitive LC-MS/MS platform for the comprehensive and simultaneous determination of the four active kinins bradykinin, kallidin, des-Arg(9)-bradykinin and des-Arg(10)-kallidin and their major metabolites bradykinin 2-9, bradykinin 1-7 and bradykinin 1-5 was developed. This platform was validated according to the bioanalytical guideline of the US Food and Drug Administration regarding linearity, accuracy, precision, sensitivity, carry-over, recovery, parallelism, matrix effects and stability in plasma of healthy volunteers. The validated platform encompassed a broad calibration curve range from 2.0–15.3 pg/mL (depending on the kinin) up to 1000 pg/mL, covering the expected concentrations in disease states. No source-dependent matrix effects were identified, and suitable stability of the analytes in plasma was observed. The applicability of the developed platform was proven by the determination of endogenous levels in healthy volunteers, whose plasma kinin levels were successfully detected in the low pg/mL range. The established platform facilitates the investigation of kinin-mediated diseases (e.g. angioedema, COVID-19) and enables the assessment of the impact of altered enzyme activities on the formation or degradation of kinins.Graphical abstract

Protein structure search to support the development of protein structure prediction methods.

Protein structure prediction is a long-standing unsolved problem in molecular biology that has seen renewed interest with the recent success of deep learning with AlphaFold at CASP13. While developing and evaluating protein structure prediction methods, researchers may want to identify the most similar known structures to their predicted structures. These predicted structures often have low sequence and structure similarity to known structures. We show how RUPEE, a purely geometric protein structure search, is able to identify the structures most similar to structure predictions, regardless of how they vary from known structures, something existing protein structure searches struggle with. RUPEE accomplishes this through the use of a novel linear encoding of protein structures as a sequence of residue descriptors. Using a fast Needleman-Wunsch algorithm, RUPEE is able to perform alignments on the sequences of residue descriptors for every available structure. This is followed by a series of increasingly accurate structure alignments from TM-align alignments initialized with the Needleman-Wunsch residue descriptor alignments to standard TM-align alignments of the final results. By using alignment normalization effectively at each stage, RUPEE also can execute containment searches in addition to full-length searches to identify structural motifs within proteins. We compare the results of RUPEE to the protein structure searches mTM-align, SSM, CATHEDRAL, and VAST using a benchmark derived from the protein structure predictions submitted to CASP13. RUPEE identifies better alignments on average with respect to TM-score as well as scores specific to SSM and CATHEDRAL, Q-score and SSAP-score, respectively.

Possibilities of in silico estimations for the development of pharmaceutical composition phytoladaptogene cytotoxic for bladder cancer cells

Based on the prediction of biological activity spectra for several secondary metabolites of medicinal plants using the PASS computer program and validation in vitro of the predictions results the priority direction of the pharmaceutical composition Phytoladaptogene (PLA) development was determined. PLA is a complex of structurally diverse small organic compounds including biologically active substances of phytoadaptogenes (ginsenosides from Panax ginseng, rhodionin from Rhodiola rosea and others) compiled considering previously developed pharmaceutical compositions. Two variants of the pharmaceutical composition were studied: - the major and minor variants included 22 and 13 compounds, respectively. The probability of activity exceeds the probability of inactivity for 1400 out of 1945 pharmacological effects and mechanisms predicted by PASS for the major variant of PLA. The wide range of predicted activities is mainly due to the low structural similarity of constituent compounds. An in silico prediction indicates the possibilities of antitumor properties against bladder, stomach, colon, ovarian and cervical cancers both for minor and major PLA compositions. It was found that the highest probability values of activity were predicted for three mechanisms: apoptosis agonist, caspase-3 stimulant, and transcription factor NF-κB inhibitor. According to the PharmaExpert program they are associated with the antitumor effect against bladder cancer. Experimental validation was using the human bladder cancer cell line RT-112. The results of the MTT test have shown that the cytotoxicity of the major PLA variant is higher than that of the minor PLA variant. In vitro experiments performed using two methods (double staining with annexin V and propidium iodide and detection of active caspase-3 in cells) confirmed that the death of bladder cancer cells occurred via the apoptotic mechanism. The data obtained correspond to the results of the prediction and indicate advantages of the major PLA composition. Thus, PLA can become the basis for the development of a drug with the antitumor activity against bladder cancer. The antitumor activity predicted by PASS for other cancers may be the subject of further studies.

Dynamic target image correction method of digital media based on virtual reality

In order to overcome the problems of low peak signal-to-noise ratio (PSNR), low structure similarity index and resolution coefficient not close to 1, a digital media dynamic target image correction method based on virtual reality is proposed. This method uses virtual reality, image neighbourhood information statistical information, fuzzy information and human visual characteristics to carry out grey value statistics, then combines with the statistical characteristics of grey value to realise image denoising processing; combined with rational function model, obtains the digital media dynamic target image pixel coordinates representing the ground point coordinates, obtains the target image correction model, completes the digital media dynamic target image correction. The experimental results show that the peak signal-to-noise ratio (PSNR) is close to 400 dB, the maximum structure similarity index is 0.6025, and the decision coefficient is close to 1, which can quickly and accurately realise the dynamic target image correction of digital media.

Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening

BackgroundDespite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods.ResultsThe chemical binding similarity, receptor-based pharmacophore, chemical structure similarity, and molecular docking methods were evaluated to identify an effective virtual screening procedure for desired target proteins. When we tested the chemical binding similarity method with test sets of 51 kinases, it outperformed the traditional structural similarity-based methods as well as structure-based methods, such as molecular docking and receptor-based pharmacophore modeling, in terms of finding active compounds. We further validated the results by performing virtual screening (using the chemical binding similarity and receptor-based pharmacophore methods) against a completely blind dataset for mitogen-activated protein kinase kinase 1 (MEK1), ephrin type-B receptor 4 (EPHB4) and wee1-like protein kinase (WEE1). The in vitro kinase binding assay confirmed that 6 out of 13 (46.2%) for MEK1 and 2 out of 12 (16.7%) for EPHB4 were newly identified only by the chemical binding similarity model.ConclusionsWe report that the virtual screening results could further be improved by combining the chemical binding similarity model with 3D-QSAR pharmacophore and molecular docking models. Not only the new inhibitors are identified in this study, but also many of the identified molecules have low structural similarity scores against already reported inhibitors and that show the revelation of novel scaffolds.

AFP-LSE: Antifreeze Proteins Prediction Using Latent Space Encoding of Composition of k-Spaced Amino Acid Pairs

Species living in extremely cold environments resist the freezing conditions through antifreeze proteins (AFPs). Apart from being essential proteins for various organisms living in sub-zero temperatures, AFPs have numerous applications in different industries. They possess very small resemblance to each other and cannot be easily identified using simple search algorithms such as BLAST and PSI-BLAST. Diverse AFPs found in fishes (Type I, II, III, IV and antifreeze glycoproteins (AFGPs)), are sub-types and show low sequence and structural similarity, making their accurate prediction challenging. Although several machine-learning methods have been proposed for the classification of AFPs, prediction methods that have greater reliability are required. In this paper, we propose a novel machine-learning-based approach for the prediction of AFP sequences using latent space learning through a deep auto-encoder method. For latent space pruning, we use the output of the auto-encoder with a deep neural network classifier to learn the non-linear mapping of the protein sequence descriptor and class label. The proposed method outperformed the existing methods, yielding excellent results in comparison. A comprehensive ablation study is performed, and the proposed method is evaluated in terms of widely used performance measures. In particular, the proposed method demonstrated a high Matthews correlation coefficient of 0.52, F-score of 0.49, and Youden’s index of 0.81 on an independent test dataset, thereby outperforming the existing methods for AFP prediction.

A Molecular Mechanism for Gating Polarity in Non-Domain-Swapped Kv Channels

The molecular basis of ion channel gating polarity, whether a channel opens via depolarization or hyperpolarization, is poorly understood. This is a consequence of multiple factors but can be traced to the inherent difficulties in generalizing channel behaviors between channels sharing low sequence and structural similarity. We address this issue by focusing on the properties that define gating polarity in the plant Kv channels KAT1 and SKOR. These are hyperpolarization (KAT1) and depolarization (SKOR ) activated Kv channels sharing, respectively, 47% identity within their transmembrane domains. Using the recently determined cryo-EM structure of KAT1 as a baseline, we conducted a mutagenic walk between KAT1 and SKOR, monitoring macroscopic currents of the mutants and always referencing the results to the 3D structure. Our results uncover several crucial positions at the intracellular interface between the VSD and the pore, in the S5, S6, and proximal C-linker that shift the gating polarity of KAT1, turning it in an outward rectifying channel. These results are analyzed in the context of a mechanistic model where the interaction between the downwards-moving S4 and the C-linker defines electromechanical coupling in these channels. GFC and MC are equally contributed to this work.