PPICT: an integrated deep neural network for predicting inter-protein PTM cross-talk.

Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk. As a challenging question, less attention has been given to PTM dynamics underlying cross-talk residue pairs and structural information underlying protein-protein interaction (PPI) graph, limiting the progress in this PTM functional research. Here we propose a novel integrated deep neural network PPICT (Predictor for PTM Inter-protein Cross-Talk), which predicts PTM cross-talk by combining protein sequence-structure-dynamics information and structural information for PPI graph. We find that cross-talk events preferentially occur among residues with high co-evolution and high potential in allosteric regulation. To make full use of the complex associations between protein evolutionary and biophysical features, and protein pair features, a heterogeneous feature combination net is introduced in the final prediction of PPICT. The comprehensive test results show that the proposed PPICT method significantly improves the prediction performance with an AUC value of 0.869, outperforming the existing state-of-the-art methods. Additionally, the PPICT method can capture the potential PTM cross-talks involved in the functional regulatory PTMs on modifying enzymes and their catalyzed PTM substrates. Therefore, PPICT represents an effective tool for identifying PTM cross-talk between proteins at the proteome level and highlights the hints for cross-talk between different signal pathways introduced by PTMs.

Protein Interaction Network Reconstruction with a Structural Gated Attention Deep Model by Incorporating Network Structure Information.

Protein-protein interactions (PPIs) provide a physical basis of molecular communications for a wide range of biological processes in living cells. Establishing the PPI network has become a fundamental but essential task for a better understanding of biological events and disease pathogenesis. Although many machine learning algorithms have been employed to predict PPIs, with only protein sequence information as the training features, these models suffer from low robustness and prediction accuracy. In this study, a new deep-learning-based framework named the Structural Gated Attention Deep (SGAD) model was proposed to improve the performance of PPI network reconstruction (PINR). The improved predictive performances were achieved by augmenting multiple protein sequence descriptors, the topological features and information flow of the PPI network, which were further implemented with a gating mechanism to improve its robustness to noise. On 11 independent test data sets and one combined data set, SGAD yielded area under the curve values of approximately 0.83-0.93, outperforming other models. Furthermore, the SGAD ensemble can learn more characteristics information on protein pairs through a two-layer neural network, serving as a powerful tool in the exploration of PPI biological space.

Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models.

Accurate prediction of post-translational modifications (PTMs) is of great significance in understanding cellular processes, by modulating protein structure and dynamics. Nowadays, with the rapid growth of protein data at different "omics" levels, machine learning models largely enriched the prediction of PTMs. However, most machine learning models only rely on protein sequence and little structural information. The lack of the systematic dynamics analysis underlying PTMs largely limits the PTM functional predictions. In this research, we present two dynamics-centric deep learning models, namely, cDL-PAU and cDL-FuncPhos, by incorporating sequence, structure, and dynamics-based features to elucidate the molecular basis and underlying functional landscape of PTMs. cDL-PAU achieved satisfactory area under the curve (AUC) scores of 0.804-0.888 for predicting phosphorylation, acetylation, and ubiquitination (PAU) sites, while cDL-FuncPhos achieved an AUC value of 0.771 for predicting functional phosphorylation (FuncPhos) sites, displaying reliable improvements. Through a feature selection, the dynamics-based coupling and commute ability show large contributions in discovering PAU sites and FuncPhos sites, suggesting the allosteric propensity for important PTMs. The application of cDL-FuncPhos in three oncoproteins not only corroborates its strong performance in FuncPhos prioritization but also gains insight into the physical basis for the functions. The source code and data set of cDL-PAU and cDL-FuncPhos are available at https://github.com/ComputeSuda/PTM_ML.

Low-Altitude Windshear Estimation Method Based on Four-Dimensional Frequency Domain Compensation for Fuselage Frustum Conformal Array.

In this paper, a low-altitude wind speed estimation method based on the fuselage frustum conformal array system is proposed. Firstly, based on the signal model of the fuselage conformal array radar, the four-dimensional joint phase compensation of the echo data in the Doppler domain and three-dimensional space-frequency domain is performed by using the four-dimensional frequency domain compensation method. Secondly, the clutter covariance matrix is estimated by the compensated echo data, and a space-time Adaptive Processing (STAP) processor suitable for low-altitude windshear target is constructed to suppress clutter. Finally, the maximum Doppler value of each distance cell is extracted, and the wind velocity is estimated. Simulation results show that the proposed method can effectively suppress clutter and accurately estimate wind speed.

Drug design targeting active posttranslational modification protein isoforms.

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

IOData: A python library for reading, writing, and converting computational chemistry file formats and generating input files.

IOData is a free and open-source Python library for parsing, storing, and converting various file formats commonly used by quantum chemistry, molecular dynamics, and plane-wave density-functional-theory software programs. In addition, IOData supports a flexible framework for generating input files for various software packages. While designed and released for stand-alone use, its original purpose was to facilitate the interoperability of various modules in the HORTON and ChemTools software packages with external (third-party) molecular quantum chemistry and solid-state density-functional-theory packages. IOData is designed to be easy to use, maintain, and extend; this is why we wrote IOData in Python and adopted many principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. This article is the official release note of the IOData library.

Integrating molecular characterization and metabolites profile revealed CtCHI1's significant role in Carthamus tinctorius L.

BACKGROUND: As a traditional Chinese herb, safflower (Carthamus tinctorius L.) is valued for its florets to prevent cardiovascular and cerebrovascular diseases. Basing on previous chemical analysis, the main active compounds are flavonoids in its florets. Although flavonoid biosynthetic pathway has been well-documented in many model species, unique biosynthetic pathway remains to be explored in safflower. Of note, as an important class of transitional enzymes, chalcone isomerase (CHI) has not been characterized in safflower. RESULTS: According to our previous research, CHIs were identified in a safflower transcriptome library built by our lab. To characterize CHI in safflower, a CHI gene named CtCHI1 was identified. A multiple sequences alignment and phylogenetic tree demonstrate that CtCHI1 shares 92% amino acid identity and close relationship with CHI to Saussurea medusa. Additionally, subcellular localization analysis indicated CtCHI1-GFP fusion protein was mainly in the cell nucleus. Further, we purified CtCHI1 protein from E. coli which can effectively catalyze isomerization of 2',4',4,6'-tetrahydroxychalcone into naringenin in vitro. Via genetic engineer technology, we successfully obtained transgenic tobacco and safflower lines. In transgenic tobacco, overexpression of CtCHI1 significantly inhibited main secondary metabolites accumulation, including quercetin (~ 79.63% for ovx-5 line) and anthocyanins (~ 64.55% for ovx-15 line). As shown in transgenic safflower, overexpression of CtCHI1 resulted in upstream genes CtPAL3 and CtC4H1 increasing dramatically (up to ~ 3.9fold) while Ct4CL3, CtF3H and CtDFR2 were inhibited. Also, comparing the whole metabolomics database by PCA and PLS-DA between transgenic and control group, 788 potential differential metabolites were marked and most of them displayed up-regulated trends. In parallel, some isolated secondary metabolites, such as hydroxysafflor yellow A (HSYA), rutin, kaempferol-3-O-ß-rutinoside and dihydrokaempferol, accumulated in transgenic safflower plants. CONCLUSIONS: In this study, we found that CtCHI1 is an active, functional, catalytic protein. Moreover, CtCHI1 can negatively and competitively regulate anthocyanins and quercetin pathway branches in tobacco. By contrast, CtCHI1 can positively regulate flavonol and chalcone metabolic flow in safflower. This research provides some clues to understand CHI's differential biochemical functional characterization involving in flavonoid pathway. More molecular mechanisms of CHI remain to be explored in the near future.

Identification of Novel Disruptor of Telomeric Silencing 1-like (DOT1L) Inhibitors through Structure-Based Virtual Screening and Biological Assays.

Histone methyltransferases are involved in many important biological processes, and abnormalities in these enzymes are associated with tumorigenesis and progression. Disruptor of telomeric silencing 1-like (DOT1L), a key hub in histone lysine methyltransferases, has been reported to play an important role in the processes of mixed-lineage leukemia (MLL)-rearranged leukemias and validated to be a potential therapeutic target. In this study, we identified a novel DOT1L inhibitor, DC_L115 (CAS no. 1163729-79-0), by combining structure-based virtual screening with biochemical analyses. This potent inhibitor DC_L115 shows high inhibitory activity toward DOT1L (IC50 = 1.5 µM). Through a process of surface plasmon resonance-based binding assays, DC_L115 was founded to bind to DOT1L with a binding affinity of 0.6 µM in vitro. Moreover, this compound selectively inhibits MLL-rearranged cell proliferation with an IC50 value of 37.1 µM. We further predicted the binding modes of DC_L115 through molecular docking analysis and found that the inhibitor competitively occupies the binding site of S-adenosylmethionine. Overall, this study demonstrates the development of potent DOT1L inhibitors with novel scaffolds.

PTP1B inhibitors from stems of Angelica keiskei (Ashitaba).

Three new chalcones, xanthoangelols K-M (1-3), together with 19 known compounds were isolated from the stems of Angelica keiskei Koidzumi, a well-known rejuvenated and anti-diabetic plant originated from Japan. The structures of compounds 1-3 were elucidated on the basis of spectroscopic data and Mosher's method. All compounds were evaluated for their inhibitory activity against protein tyrosine phosphatase 1B (PTP1B). Among them, six chalcones, xanthoangelol K (1), xanthoangelol (4), xanthoangelol F (5), 4-hydroxyderricin (6), xanthoangelol D (7), xanthoangelol E (8), and a coumarin, methoxsalen (17), showed strong PTP1B inhibitory effect with IC50 values of 0.82, 1.97, 1.67, 2.47, 3.97, 1.43, and 2.53µg/mL, respectively. A kinetic study revealed that compound 1 inhibited PTP1B with characteristics typical of a competitive inhibitor. Molecular docking simulations elucidated that ring B of 1 may anchor in a pocket of PTP1B and the molecule is stabilized by hydrogen bonds with Arg47, Asp48, and π-π interaction with Phe182 of PTP1B.

Plantadeprate A, a Tricyclic Monoterpene Zwitterionic Guanidium, and Related Derivatives from the Seeds of Plantago depressa.

Two new alkaloids, plantadeprate A (1) and 1'-(4″-hydroxybutyl)plantagoguanidinic acid (2), along with three known compounds, were isolated from the seeds of Plantago depressa. Their structures were elucidated by physical data analyses including NMR, MS, and electronic circular dichroism (ECD) methods. Plantadeprate A (1), a monoterpene zwitterionic guanidium, possesses a unique 5/5/6-tricyclic ring system. Its absolute configuration was determined by X-ray crystallography and computational methods. Compound 1, plumbagine D (3), and plantagoguanidinic acid (4) exhibited potential antihyperglycemic properties attributed to suppression of hepatic gluconeogenesis with inhibitory rates of 8.2%, 18.5%, and 12.5% at 40 µM, respectively.

A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors.

The highly-selective blood-brain barrier (BBB) prevents neurotoxic substances in blood from crossing into the extracellular fluid of the central nervous system (CNS). As such, the BBB has a close relationship with CNS disease development and treatment, so predicting whether a substance crosses the BBB is a key task in lead discovery for CNS drugs. Machine learning (ML) is a promising strategy for predicting the BBB permeability, but existing studies have been limited by small datasets with limited chemical diversity. To mitigate this issue, we present a large benchmark dataset, B3DB, complied from 50 published resources and categorized based on experimental uncertainty. A subset of the molecules in B3DB has numerical log BB values (1058 compounds), while the whole dataset has categorical (BBB+ or BBB-) BBB permeability labels (7807). The dataset is freely available at https://github.com/theochem/B3DB and https://doi.org/10.6084/m9.figshare.15634230.v3 (version 3). We also provide some physicochemical properties of the molecules. By analyzing these properties, we can demonstrate some physiochemical similarities and differences between BBB+ and BBB- compounds.

Dynamics of Post-Translational Modification Inspires Drug Design in the Kinase Family.

Post-translational modification (PTM) on protein plays important roles in the regulation of cellular function and disease pathogenesis. The systematic analysis of PTM dynamics presents great opportunities to enlarge the target space by PTM allosteric regulation. Here, we presented a framework by integrating the sequence, structural topology, and particular dynamics features to characterize the functional context and druggabilities of PTMs in the well-known kinase family. The machine learning models with these biophysical features could successfully predict PTMs. On the other hand, PTMs were identified to be significantly enriched in the reported allosteric pockets and the allosteric potential of PTM pockets were thus proposed through these biophysical features. In the end, the covalent inhibitor DC-Srci-6668 targeting the PTM pocket in c-Src kinase was identified, which inhibited the phosphorylation and locked c-Src in the inactive state. Our findings represent a crucial step toward PTM-inspired drug design in the kinase family.

The dynamic conformational landscape of the protein methyltransferase SETD8.

Elucidating the conformational heterogeneity of proteins is essential for understanding protein function and developing exogenous ligands. With the rapid development of experimental and computational methods, it is of great interest to integrate these approaches to illuminate the conformational landscapes of target proteins. SETD8 is a protein lysine methyltransferase (PKMT), which functions in vivo via the methylation of histone and nonhistone targets. Utilizing covalent inhibitors and depleting native ligands to trap hidden conformational states, we obtained diverse X-ray structures of SETD8. These structures were used to seed distributed atomistic molecular dynamics simulations that generated a total of six milliseconds of trajectory data. Markov state models, built via an automated machine learning approach and corroborated experimentally, reveal how slow conformational motions and conformational states are relevant to catalysis. These findings provide molecular insight on enzymatic catalysis and allosteric mechanisms of a PKMT via its detailed conformational landscape.

Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases.

BACKGROUND: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine. METHODS: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed. RESULTS: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases. CONCLUSION: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).

Discovery of 3-(5'-Substituted)-Benzimidazole-5-(1-(3,5-dichloropyridin-4-yl)ethoxy)-1H-indazoles as Potent Fibroblast Growth Factor Receptor Inhibitors: Design, Synthesis, and Biological Evaluation.

Fibroblast growth factor receptor (FGFR) represents an attractive oncology target for cancer therapy in view of its critical role in promoting cancer formation and progression, as well as causing resistance to approved therapies. In this article, we describe the identification of the potent pan-FGFR inhibitor (R)-21c (FGFR1-4 IC50 values of 0.9, 2.0, 2.0, and 6.1 nM, respectively). Compound (R)-21c exhibited excellent in vitro inhibitory activity against a panel of FGFR-amplified cell lines. Western blot analysis demonstrated that (R)-21c suppressed FGF/FGFR and downstream signaling pathways at nanomolar concentrations. Moreover, (R)-21c provided nearly complete inhibition of tumor growth (96.9% TGI) in NCI-H1581 (FGFR1-amplified) xenograft mice model at the dose of 10 mg/kg/qd via oral administration.

Discovery and Optimization of Novel, Selective Histone Methyltransferase SET7 Inhibitors by Pharmacophore- and Docking-Based Virtual Screening.

Histone methyltransferases are involved in various biological functions, and these methylation regulating enzymes' abnormal expression or activity has been noted in several human cancers. Within this context, SET domain-containing (lysine methyltransferase) 7 (SET7, also called KMT7, SETD7, SET9) is of increasing significance due to its diverse roles in biological functions and diseases, such as diabetes, cancers, alopecia areata, atherosclerotic vascular disease, HIV, and HCV. In this study, DC-S100, which was discovered by pharmacophore- and docking-based virtual screening, was identified as the hit compound of SET7 inhibitor. Structure-activity relationship (SAR) analysis was performed on analogs of DC-S100 and according to the putative binding mode of DC-S100, structure modifications were made to improve its activity. Of note, compounds DC-S238 and DC-S239, with IC50 values of 4.88 and 4.59 µM, respectively, displayed selectivity for DNMT1, DOT1L, EZH2, NSD1, SETD8, and G9a. Taken together, DC-S238 and DC-S239 can serve as leads for further investigation as SET7 inhibitors and the chemical toolkits for functional biology studies of SET7.