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.

A RhoA structure with switch II flipped outward revealed the conformational dynamics of switch II region.

Small GTPase RhoA switches from GTP-bound state to GDP-bound state by hydrolyzing GTP, which is accelerated by GTPases activating proteins (GAPs). However, less study of RhoA structural dynamic changes was conducted during this process, which is essential for understanding the molecular mechanism of GAP dissociation. Here, we solved a RhoA structure in GDP-bound state with switch II flipped outward. Because lacking the intermolecular interactions with guanine nucleotide, we proposed this conformation of RhoA could be an intermediate after GAP dissociation. Further molecular dynamics simulations found the conformational changes of switch regions are indeed existing in RhoA and involved in the regulation of GAP dissociation and GEF recognition. Besides, the guanine nucleotide binding pocket extended to switch II region, indicating a potential "druggable" cavity for RhoA. Taken together, our study provides a deeper understanding of the dynamic properties of RhoA switch regions and highlights the direction for future drug development.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications.

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein-DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

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.

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.

Insights into Conformational Dynamics and Allostery in DNMT1-H3Ub/USP7 Interactions.

DNA methyltransferases (DNMTs) including DNMT1 are a conserved family of cytosine methylases that play crucial roles in epigenetic regulation. The versatile functions of DNMT1 rely on allosteric networks between its different interacting partners, emerging as novel therapeutic targets. In this work, based on the modeling structures of DNMT1-ubiquitylated H3 (H3Ub)/ubiquitin specific peptidase 7 (USP7) complexes, we have used a combination of elastic network models, molecular dynamics simulations, structural residue perturbation, network modeling, and pocket pathway analysis to examine their molecular mechanisms of allosteric regulation. The comparative intrinsic and conformational dynamics analysis of three DNMT1 systems has highlighted the pivotal role of the RFTS domain as the dynamics hub in both intra- and inter-molecular interactions. The site perturbation and network modeling approaches have revealed the different and more complex allosteric interaction landscape in both DNMT1 complexes, involving the events caused by mutational hotspots and post-translation modification sites through protein-protein interactions (PPIs). Furthermore, communication pathway analysis and pocket detection have provided new mechanistic insights into molecular mechanisms underlying quaternary structures of DNMT1 complexes, suggesting potential targeting pockets for PPI-based allosteric drug design.

Deciphering the role of dimer interface in intrinsic dynamics and allosteric pathways underlying the functional transformation of DNMT3A.

BACKGROUND: DNMT3A, as de novo DNA methyltransferase, is essential for regulating gene expression through cellular development and differentiation. The functions of DNMT3A rely on its oligomeric states and allosteric regulations between its catalytic domain and binding partners. Despite recent resolution of autoinhibitory and active DNMT3A/3L crystal structures, the mechanism of their functional motions and interdomain allostery in regulating the activity remains to be established. METHODS: The hybrid approach, comprising Elastic Network Models coupled with information theory, Protein Structure Network, and sequence evolution analysis was employed to investigate intrinsic dynamics and allosteric properties of DNMT3A resolved in autoinhibitory and active states. RESULTS: The conformational transition between two states is characterized by global motions, and the homo-dimer displays the similar dynamic properties as tetramer, acting as the basic functional unit. The hinge residues with restricted fluctuations are clustered at the dimer interface, which are predicted to enjoy remarkably efficient signal transduction properties. The allosteric pathways through the dimer interface are achieved by a cascade of interactions predominantly involving conserved and co-evolved residues. CONCLUSIONS: Our results suggest that structural topology coupled with global motions indicates the structural origin of the functional transformation of DNMT3A. The comprehensive analysis further highlights the pivotal role of the dimer interface of DNMT3A both in defining the quaternary structure dynamics and establishing interdomain communications. GENERAL SIGNIFICANCE: Understanding the global motions of DNMT3As not only provides mechanical insights into the functions of such molecular machines, but also reveals the mediators that determine their allosteric regulations.

Node-Weighted Amino Acid Network Strategy for Characterization and Identification of Protein Functional Residues.

The study of functional residues (FRs) is essential for understanding protein functions and biological processes. The amino acid network (AAN) has become an emerging paradigm for studying FRs during the past decade. Current AAN models ignore the heterogeneity of nodes and treat amino acids in the AAN as the same. However, the properties of each amino acid node are of fundamental importance. We here proposed a node-weighted AAN strategy termed the node-weighted amino acid contact energy network (NACEN) to characterize and predict three types of FRs, namely, hot spots, catalytic residues, and allosteric residues. We first constructed NACENs with their nodes weighted based on structural, sequence, physicochemical, and dynamical properties of the amino acids and then characterized the FRs with the NACEN parameters. We finally built machine learning predictors to identify each type of FR. The results revealed that residues characterized with NACEN parameters are more distinguishable between FRs and non-FRs than those with unweighted network ones. With few features for classification, NACEN yields comparable performance for FR identification and provides residue level prediction for allosteric regulation. The proposed strategy can be easily implemented to other functional residue identification. An R package is also provided for NACEN construction and analysis at http://sysbio.suda.edu.cn/NACEN/index.html .

Molecular Mechanism underlying PRMT1 Dimerization for SAM Binding and Methylase Activity.

Protein arginine methyltransferases (PRMTs) catalyze the posttranslational methylation of arginine, which is important in a range of biological processes, including epigenetic regulation, signal transduction, and cancer progression. Although previous studies of PRMT1 mutants suggest that the dimerization arm and the N-terminal region of PRMT1 are important for activity, the contributions of these regions to the structural architecture of the protein and its catalytic methylation activity remain elusive. Molecular dynamics (MD) simulations performed in this study showed that both the dimerization arm and the N-terminal region undergo conformational changes upon dimerization. Because a correlation was found between the two regions despite their physical distance, an allosteric pathway mechanism was proposed based on a network topological analysis. The mutation of residues along the allosteric pathways markedly reduced the methylation activity of PRMT1, which may be attributable to the destruction of dimer formation and accordingly reduced S-adenosyl-L-methionine (SAM) binding. This study provides the first demonstration of the use of a combination of MD simulations, network topological analysis, and biochemical assays for the exploration of allosteric regulation upon PRMT1 dimerization. These findings illuminate the results of mechanistic studies of PRMT1, which have revealed that dimer formation facilitates SAM binding and catalytic methylation, and provided direction for further allosteric studies of the PRMT family.

Protein cysteine phosphorylation of SarA/MgrA family transcriptional regulators mediates bacterial virulence and antibiotic resistance.

Protein posttranslational modifications (PTMs), particularly phosphorylation, dramatically expand the complexity of cellular regulatory networks. Although cysteine (Cys) in various proteins can be subject to multiple PTMs, its phosphorylation was previously considered a rare PTM with almost no regulatory role assigned. We report here that phosphorylation occurs to a reactive cysteine residue conserved in the staphylococcal accessary regulator A (SarA)/MarR family global transcriptional regulator A (MgrA) family of proteins, and is mediated by the eukaryotic-like kinase-phosphatase pair Stk1-Stp1 in Staphylococcus aureus. Cys-phosphorylation is crucial in regulating virulence determinant production and bacterial resistance to vancomycin. Cell wall-targeting antibiotics, such as vancomycin and ceftriaxone, inhibit the kinase activity of Stk1 and lead to decreased Cys-phosphorylation of SarA and MgrA. An in vivo mouse model of infection established that the absence of stp1, which results in elevated protein Cys-phosphorylation, significantly reduces staphylococcal virulence. Our data indicate that Cys-phosphorylation is a unique PTM that can play crucial roles in bacterial signaling and regulation.

Virtual screening and biological evaluation of novel small molecular inhibitors against protein arginine methyltransferase 1 (PRMT1).

Protein arginine methylation is a common post-translational modification which is crucial for a variety of biological processes. Dysregulation of protein arginine methyltransferases (PRMTs) activity has been implicated in cancer and other serious diseases. Thus, small molecule inhibitors against PRMT have great potential for therapeutic development. Herein, through the combination of virtual screening and bioassays, six small molecular compounds were identified as PRMT1 inhibitors. Amongst them, the binding affinity of compounds DCLX069 and DCLX078 with PRMT1 was further validated by T1ρ and saturation transfer difference (STD) NMR experiments. Most important of all, both compounds effectively blocked cell proliferation in breast cancer, liver cancer and acute myeloid leukemia cell lines. The binding mode analysis from molecular docking simulations theoretically indicated that both inhibitors occupied the SAM binding pocket to exert the inhibitory effect. Taken together, our compounds enriched the structural scaffolds as PRMT1 inhibitors and afforded clues for further optimization.

FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics.

Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhos-STR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.

Structure-based computational study of the hydrolysis of New Delhi metallo-ß-lactmase-1.

New Delhi metallo-ß-lactmase-1 (NDM-1) is an enzyme that confers antibiotic resistance to bacteria and is thus a serious threat to human health. Almost all clinically available ß-lactam antibiotics can be hydrolyzed by NDM-1. To determine the mechanism behind the wide substrate diversity and strong catalytic ability of NDM-1, we explored the molecular interactions between NDM-1 and different ß-lactam antibiotics using computational methods. Molecular dynamics simulations and binding free energy calculations were performed on enzyme-substrate (ES) complex models of NDM-1-Meropenem, NDM-1-Nitrocefin, and NDM-1-Ampicillin constructed by molecular docking. Our computational results suggest that mutant residues Ile35 and Lys216, and active site loop L1 residues 65-73 in NDM-1 play crucial roles in substrate recognition and binding. The results of our study provide new insights into the mechanism behind the enhanced substrate binding and wider substrate spectrum of NDM-1 compared with its homologous enzymes CcrA and IMP-1. These insights may be useful in the discovery and design of specific and potent inhibitors against NDM-1.

Novel 5-(benzyloxy)pyridin-2(1H)-one derivatives as potent c-Met inhibitors.

A series of novel 5-(benzyloxy)pyridin-2(1H)-ones were designed, synthesized and biologically evaluated for c-Met inhibition. Various amides and benzoimidazoles at C-3 position were investigated. A potent compound 12b with a c-Met IC50 of 12nM was identified. This compound exhibited potent inhibition of EBC-1 cell associated with c-Met constitutive activation and showed high selectivity for c-Met than other tested 11 kinases. The binding model 12b with c-Met was disclosed by docking analysis.

A quantum mechanics/molecular mechanics study on the hydrolysis mechanism of New Delhi metallo-ß-lactamase-1.

New Delhi metallo-ß-lactamase-1 (NDM-1) has emerged as a major global threat to human health for its rapid rate of dissemination and ability to make pathogenic microbes resistant to almost all known ß-lactam antibiotics. In addition, effective NDM-1 inhibitors have not been identified to date. In spite of the plethora of structural and kinetic data available, the accurate molecular characteristics of and details on the enzymatic reaction of NDM-1 hydrolyzing ß-lactam antibiotics remain incompletely understood. In this study, a combined computational approach including molecular docking, molecular dynamics simulations and quantum mechanics/molecular mechanics calculations was performed to characterize the catalytic mechanism of meropenem catalyzed by NDM-1. The quantum mechanics/molecular mechanics results indicate that the ionized D124 is beneficial to the cleavage of the C-N bond within the ß-lactam ring. Meanwhile, it is energetically favorable to form an intermediate if no water molecule coordinates to Zn2. Moreover, according to the molecular dynamics results, the conserved residue K211 plays a pivotal role in substrate binding and catalysis, which is quite consistent with previous mutagenesis data. Our study provides detailed insights into the catalytic mechanism of NDM-1 hydrolyzing meropenem ß-lactam antibiotics and offers clues for the discovery of new antibiotics against NDM-1 positive strains in clinical studies.

Synthesis and biological evaluation of 2-amino-5-aryl-3-benzylthiopyridine scaffold based potent c-Met inhibitors.

A series of 2-amino-N-benzylpyridine-3-carboxnamides, 2-amino-N-benzylpyridine-3-sulfonamides and 2-amino-3-benzylthiopyridines against c-Met were designed by means of bioisosteric replacement and docking analysis. Optimization of the 2-amino-3-benzylthiopyridine scaffold led to the identification of compound (R)-10b displaying c-Met inhibition with an IC50 up to 7.7nM. In the cytotoxic evaluation, compound (R)-10b effectively inhibited the proliferation of c-Met addictive human cancer cell lines (IC50 from 0.19 to 0.71µM) and c-Met activation-mediated cell metastasis. At a dose of 100mg/Kg, (R)-10b evidently inhibited tumor growth (45%) in NIH-3T3/TPR-Met xenograft model. Of note, (R)-10b could overcome c-Met-activation mediated gefitinib-resistance, which indicated its potential use for drug combination. Taken together, 2-amino-3-benzylthiopyridine scaffold was first disclosed and exhibited promising pharmacological profiles against c-Met, which left room for further exploration.

Molecular dynamic simulation to explore the molecular basis of Btk-PH domain interaction with Ins(1,3,4,5)P4.

Bruton's tyrosine kinase contains a pleckstrin homology domain, and it specifically binds inositol 1,3,4,5-tetrakisphosphate (Ins(1,3,4,5)P4), which is involved in the maturation of B cells. In this paper, we studied 12 systems including the wild type and 11 mutants, K12R, S14F, K19E, R28C/H, E41K, L11P, F25S, Y40N, and K12R-R28C/H, to investigate any change in the ligand binding site of each mutant. Molecular dynamics simulations combined with the method of molecular mechanics/Poisson-Boltzmann solvent-accessible surface area have been applied to the twelve systems, and reasonable mutant structures and their binding free energies have been obtained as criteria in the final classification. As a result, five structures, K12R, K19E, R28C/H, and E41K mutants, were classified as "functional mutations," whereas L11P, S14F, F25S, and Y40N were grouped into "folding mutations." This rigorous study of the binding affinity of each of the mutants and their classification provides some new insights into the biological function of the Btk-PH domain and related mutation-causing diseases.

Protectin DX Relieve Hyperoxia-induced Lung Injury by Protecting Pulmonary Endothelial Glycocalyx.

Background: Bronchopulmonary dysplasia (BPD) is a common chronic lung disease in premature infants with limited treatments and poor prognosis. Damaged endothelial glycocalyx leads to vascular permeability, lung edema and inflammation. However, whether hyperoxia increases neonatal pulmonary microvascular permeability by degrading the endothelial glycocalyx remains unknown. Methods: Newborn mice were maintained in 60-70% O2 for 7 days. Protectin DX (PDX), an endogenous lipid mediator, was injected intraperitoneally on postnatal d 0, 2, 4 and 6. Lung samples and bronchoalveolar lavage fluid were taken at the end of the study. Primary human umbilical vein endothelial cells (HUVECs) were cultured in 80%O2. Results: Hyperoxia exposure for 7 days led to neonatal mice alveolar simplification with less radial alveolar count (RAC), mean linear intercept (MLI) and mean alveolar diameter (MAD) compared to the control group. Hyperoxia exposure increased lung vascular permeability with more fluid and proteins and inflammatory factors, including TNF-α and IL-1ß, in bronchoalveolar lavage fluid while reducing the heparan sulfate (HS), the most abundant component of the endothelial glycocalyx, in the pulmonary endothelial cells. PDX relieve these changes. PDX attenuated hyperoxia-induced high expression of heparanase (HPA), the endoglycosidase that shed endothelial glycocalyx, p-P65, P65, and low expression of SIRT1. BOC-2 and EX527 abolished the affection of PDX both in vivo and intro. Conclusion: In summary, our findings indicate that PDX treatment relieves hyperoxia-induced alveolar simplification, vascular leakage and lung inflammation by attenuating pulmonary endothelial glycocalyx injury via the SIRT1/NF-κB/ HPA pathway.

HNRNPA2B1 as a potential therapeutic target for thymic epithelial tumor recurrence: An integrative network analysis.

Thymic epithelial tumors (TETs) are rare malignant tumors, and the molecular mechanisms of both primary and recurrent TETs are poorly understood. Here we established comprehensive proteomic signatures of 15 tumors (5 recurrent and 10 non-recurrent) and 15 pair wised tumor adjacent normal tissues. We then proposed an integrative network approach for studying the proteomics data by constructing protein-protein interaction networks based on differentially expressed proteins and a machine learning-based score, followed by network modular analysis, functional enrichment annotation and shortest path inference analysis. Network modular analysis revealed that primary and recurrent TETs shared certain common molecular mechanisms, including a spliceosome module consisting of RNA splicing and RNA processing, but the recurrent TET was specifically related to the ribosome pathway. Applying the shortest path inference to the collected seed gene module identified that the ribonucleoprotein hnRNPA2B1 probably serves as a potential target for recurrent TET therapy. The drug repositioning combined molecular dynamics simulations suggested that the compound ergotamine could potentially act as a repurposing drug to treat recurrent TETs by targeting hnRNPA2B1. Our study demonstrates the value of integrative network analysis to understand proteotype robustness and its relationships with genotype, and provides hits for further research on cancer therapeutics.