Insights into the selectivity of a brain-penetrant CDK4/6 vs CDK1/2 inhibitor for glioblastoma used in multiple replica molecular dynamics simulations

Cyclin dependent kinases (CDKs) play an important role in cell cycle regulation and their dysfunction is associated with many cancers. That is why CDKs have been attractive targets for the treatment of cancer. Glioblastoma is a cancer caused by the aberrant expression of CDK4/6, so exploring the mechanism of the selection of CDK4/6 toward inhibitors relative to the other family members CDK1/2 is essential. In this work, multiple replica molecular dynamics (MRMD) simulations, principal component analysis (PCA), free energy landscapes (FELs), molecular mechanics Poisson-Boltzmann/Generalized Born surface area (MM-PB/GBSA) and other methods were integrated to decipher the selectively binding mechanism of the inhibitor N1J to CDK4/6 and CDK1/2. Molecular electrostatic potential (MESP) analysis provides an explanation for the N1J selectivity. Residue-based free energy decomposition reveals that most of the hot residues are located at the same location of CDKs proteins, but the different types of residues in different proteins cause changes in binding energy, which is considered as a potential developmental direction to improve the selectivity of inhibitors to CDK4/6. These results provide insights into the source of inhibitor and CDK4/6 selectivity for the future development of more selective inhibitors.

Molecular insights and optimization strategies for the competitive binding of engineered ACE2 proteins: a multiple replica molecular dynamics study

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to spread globally, and rapid viral evolution and the emergence of new variants pose challenges to pandemic control. During infection, the spike protein of SARS-CoV-2 interacts with the human ACE2 protein via its receptor binding domain (RBD), and it is known that engineered forms of ACE2 can compete with wild-type (WT) ACE2 for binding to inhibit infection. Here, we conducted multiple replica molecular dynamics (MRMD) simulations to study the mechanisms of the engineered ACE2 variants 3N39 and 3N94 and provide directions for optimization. Our findings reveal that engineered ACE2 is notably more efficacious in systems that show weaker binding to WT ACE2 (i.e., WT and BA.1 RBD), but also faces immune escape as the virus evolves. Moreover, by modifying residue types near the binding interface, engineered ACE2 alters the electrostatic potential distribution and reconfigures the hydrogen bonding network, which results in modified binding to the RBD. However, this structural rearrangement does not occur in all RBD variants. In addition, we identified potentially engineerable beneficial residues and potentially engineerable detrimental residues in both ACE2 and RBD. Functional conservation can thus enable the optimization of these residues and improve the binding competitiveness of engineered ACE2, which therefore provides additional immune escape prevention. Finally, we conclude that these findings have implications for understanding the mechanisms responsible for engineered ACE2 and can help us to develop engineered ACE2 proteins that show superior performance.

A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research

Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.

ConSIG: consistent discovery of molecular signature from OMIC data

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

Biological activities of drug inactive ingredients

In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs' activity data, which was the first evaluation on the possibility to predict DIG's activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.

PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods

Bioinformatic annotation of protein function is essential but extremely sophisticated, which asks for extensive efforts to develop effective prediction method. However, the existing methods tend to amplify the representativeness of the families with large number of proteins by misclassifying the proteins in the families with small number of proteins. That is to say, the ability of the existing methods to annotate proteins in the 'rare classes' remains limited. Herein, a new protein function annotation strategy, PFmulDL, integrating multiple deep learning methods, was thus constructed. First, the recurrent neural network was integrated, for the first time, with the convolutional neural network to facilitate the function annotation. Second, a transfer learning method was introduced to the model construction for further improving the prediction performances. Third, based on the latest data of Gene Ontology, the newly constructed model could annotate the largest number of protein families comparing with the existing methods. Finally, this newly constructed model was found capable of significantly elevating the prediction performance for the 'rare classes' without sacrificing that for the 'major classes'. All in all, due to the emerging requirements on improving the prediction performance for the proteins in 'rare classes', this new strategy would become an essential complement to the existing methods for protein function prediction. All the models and source codes are freely available and open to all users at: https://github.com/idrblab/PFmulDL.

Molecular insights into the allosteric coupling mechanism between an agonist and two different transducers for μ-opioid receptors

G protein-coupled receptors (GPCRs) as the most important class of pharmacological targets regulate G-protein and β-arrestin-mediated signaling through allosteric interplay, which are responsible for different biochemical and physiological actions like therapeutic efficacy and side effects. However, the allosteric mechanism underlying preferentially recruiting one transducer versus the other has been poorly understood, limiting drug design. Motivated by this issue, we utilize accelerated molecular dynamics simulation coupled with potential of mean force (PMF), molecular mechanics Poisson Boltzmann surface area (MM/PBSA) and protein structure network (PSN) to study two ternary complex systems of a representative class A GPCR (μ-opioid receptor (μOR)) bound by an agonist and one specific transducer (G-protein and β-arrestin). The results show that no significant difference exists in the whole structure of μOR between two transducer couplings, but displays transducer-dependent changes in the intracellular binding region of μOR, where the β-arrestin coupling results in a narrower crevice with TM7 inward movement compared with the G-protein. In addition, both the G-protein and β-arrestin coupling can increase the binding affinity of the agonist to the receptor. However, the interactions between the agonist and μOR also exhibit transducer-specific changes, in particular for the interaction with ECL2 that plays an important role in recruiting β-arrestin. The allosteric network analysis further indicates that Y148^3.33, F152^3.37, F156^3.41, N191^4.49, T160^3.45, Y106^2.42, W293^6.48, F289^6.44, I248^5.54 and Y252^5.58 play important roles in equally activating G-protein and β-arrestin. In contrast, M161^3.46 and R165^3.50 devote important contributions to preferentially recruit G-protein while D164^3.49 and R179^ICL2 are revealed to be important for selectively activating β-arrestin. The observations provide useful information for understanding the biased activation mechanism.

Binding mechanism of inhibitors to SARS-CoV-2 main protease deciphered by multiple replica molecular dynamics simulations

The outbreak caused by SARS-CoV-2 has received extensive worldwide attention. As the main protease (M^pro) in SARS-CoV-2 has no human homologues, it is feasible to reduce the possibility of targeting the host protein by accidental drugs. Thus, M^pro has been an attractive target of efficient drug design for anti-SARS-CoV-2 treatment. In this work, multiple replica molecular dynamics (MRMD) simulations, principal component analysis (PCA), free energy landscapes (FELs), and the molecular mechanics-generalized Born surface area (MM-GBSA) method were integrated together to decipher the binding mechanism of four inhibitors masitinib, O6K, FJC and GQU to M^pro. The results indicate that the binding of four inhibitors clearly affects the structural flexibility and internal dynamics of M^pro along with dihedral angle changes of key residues. The analysis of FELs unveils that the stability in the relative orientation and geometric position of inhibitors to M^pro is favorable for inhibitor binding. Residue-based free energy decomposition reveals that the inhibitor-M^pro interaction networks involving hydrogen bonding interactions and hydrophobic interactions provide significant information for the design of potent inhibitors against M^pro. The hot spot residues including H41, M49, F140, N142, G143, C145, H163, H164, M165, E166 and Q189 identified by computational alanine scanning are considered as reliable targets of clinically available inhibitors inhibiting the activities of M^pro.