Binding kinetic properties of protein–ligand complexes are crucial factors affecting the drug potency.Nevertheless,the current in silico techniques are insufficient in providing accurate and robust predictions for bi...Binding kinetic properties of protein–ligand complexes are crucial factors affecting the drug potency.Nevertheless,the current in silico techniques are insufficient in providing accurate and robust predictions for binding kinetic properties.To this end,this work develops a variety of binding kinetic models for predicting a critical binding kinetic property,dissociation rate constant,using eight machine learning(ML)methods(Bayesian Neural Network(BNN),partial least squares regression,Bayesian ridge,Gaussian process regression,principal component regression,random forest,support vector machine,extreme gradient boosting)and the descriptors of the van der Waals/electrostatic interaction energies.These eight models are applied to two case studies involving the HSP90 and RIP1 kinase inhibitors.Both regression results of two case studies indicate that the BNN model has the state-of-the-art prediction accuracy(HSP90:R^(2)_(test)=0:947,MAE_(test)=0.184,rtest=0.976,RMSE_(test)=0.220;RIP1 kinase:R^(2)_(test)=0:745,MAE_(test)=0.188,rtest=0.961,RMSE_(test)=0.290)in comparison with other seven ML models.展开更多
Since its establishment in 2013,BioLiP has become one of the widely used resources for protein-ligand interactions.Nevertheless,several known issues occurred with it over the past decade.For example,the protein-ligand...Since its establishment in 2013,BioLiP has become one of the widely used resources for protein-ligand interactions.Nevertheless,several known issues occurred with it over the past decade.For example,the protein-ligand interactions are represented in the form of single chain-based tertiary structures,which may be inappropriate as many interactions involve multiple protein chains(known as quaternary structures).We sought to address these issues,resulting in Q-BioLiP,a comprehensive resource for quaternary structure-based protein-ligand interactions.The major features of Q-BioLiP include:(1)representing protein structures in the form of quaternary structures rather than single chain-based tertiary structures;(2)pairing DNA/RNA chains properly rather than separation;(3)providing both experimental and predicted binding affinities;(4)retaining both biologically relevant and irrelevant interactions to alleviate the wrong justification of ligands’biological relevance;and(5)developing a new quaternary structure-based algorithm for the modelling of protein-ligand complex structure.With these new features,Q-BioLiP is expected to be a valuable resource for studying biomolecule interactions,including protein-small molecule interaction,protein-metal ion interaction,protein-peptide interaction,protein-protein interaction,protein-DNA/RNA interaction,and RNA-small molecule interaction.Q-BioLiP is freely available at https://yanglab.qd.sdu.edu.cn/Q-BioLiP/.展开更多
In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. The machine learning methods greatly offer an efficient prediction ability in diagnosis system alter...In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. The machine learning methods greatly offer an efficient prediction ability in diagnosis system alternative with the clinicians. Most of the systems operate on the extracted features from the patients and most of the predicted cases are accurate. However, in recent time, the prevalence of COVID-19 has emerged the global healthcare industry to find a new drug that suppresses the pandemic outbreak. In this paper, we design a Deep Neural Network(DNN)model that accurately finds the protein-ligand interactions with the drug used. The DNN senses the response of protein-ligand interactions for a specific drug and identifies which drug makes the interaction that combats effectively the virus. With limited genome sequence of Indian patients submitted to the GISAID database, we find that the DNN system is effective in identifying the protein-ligand interactions for a specific drug.展开更多
Because of their important roles in cellular functions, life activities, drug screening, and disease treatment, the development of efficient methods for monitoring protein-ligand interaction is essential. In this stud...Because of their important roles in cellular functions, life activities, drug screening, and disease treatment, the development of efficient methods for monitoring protein-ligand interaction is essential. In this study, inspired by our previous studies on DNA conformation-selective fluorescent indicators, we developed a new sensing platform for monitoring protein-ligand interaction and detecting protein activity based on binding-mediated DNA protection and the dsDNA-lighted fluorophore, ethyl-4-[3,6-bis(1-methyl-4-vinylpyridium iodine)-9 H-carbazol-9-yl)] butanoate(EBCB). The ligand was purposefully linked to the 3?-terminal of a hairpin DNA probe to selectively bind with the target protein and protect the DNA from cleavage by exonuclease III. By virtue of EBCB's outstanding capacity to discriminate DNA conformation, the protein-ligand interaction could be effectively monitored through a fluorescence change in EBCB. A high fluorescence signal was detected when the hairpin DNA was protected in the presence of the target protein, whereas a much lower signal was observed in the presence of nontarget proteins.Our results demonstrated that the proposed strategy had high potential, such as high selectivity and relatively high sensitivity, for monitoring protein-ligand interaction and detecting protein activity. We believe these results will pave the way for applying dsDNA-lighted fluorophore EBCB as an effective signal transducer for DNA conformation transformation-mediated biochemical sensing.展开更多
This is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model(FPCM)and rational drug design.Computational modeling has become a powerf...This is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model(FPCM)and rational drug design.Computational modeling has become a powerful tool in understanding detailed protein-ligand interactions at molecular level and in rational drug design.To study the binding of a protein with multiple molecular species of a ligand,one must accurately determine both the relative free energies of all of the molecular species in solution and the corresponding microscopic binding free energies for all of the molecular species binding with the protein.In this paper,we aim to provide a brief overview of the recent development in computational modeling of the solvent effects on the detailed protein-ligand interactions involving multiple molecular species of a ligand related to rational drug design.In particular,we first briefly discuss the main challenges in computational modeling of the detailed protein-ligand interactions involving the multiple molecular species and then focus on the FPCM model and its applications.The FPCM method allows accurate determination of the solvent effects in the first-principles quantum mechanism(QM)calculations on molecules in solution.The combined use of the FPCM-based QM calculations and other computational modeling and simulations enables us to accurately account for a protein binding with multiple molecular species of a ligand in solution.Based on the computational modeling of the detailed protein-ligand interactions,possible new drugs may be designed rationally as either small-molecule ligands of the protein or engineered proteins that bind/metabolize the ligand.The computational drug design has successfully led to discovery and development of promising drugs.展开更多
Accurately estimating protein–ligand binding free energy is crucial for drug design and biophysics, yet remains a challenging task. In this study, we applied the screening molecular mechanics/Poisson–Boltzmann surfa...Accurately estimating protein–ligand binding free energy is crucial for drug design and biophysics, yet remains a challenging task. In this study, we applied the screening molecular mechanics/Poisson–Boltzmann surface area(MM/PBSA)method in combination with various machine learning techniques to compute the binding free energies of protein–ligand interactions. Our results demonstrate that machine learning outperforms direct screening MM/PBSA calculations in predicting protein–ligand binding free energies. Notably, the random forest(RF) method exhibited the best predictive performance,with a Pearson correlation coefficient(rp) of 0.702 and a mean absolute error(MAE) of 1.379 kcal/mol. Furthermore, we analyzed feature importance rankings in the gradient boosting(GB), adaptive boosting(Ada Boost), and RF methods, and found that feature selection significantly impacted predictive performance. In particular, molecular weight(MW) and van der Waals(VDW) energies played a decisive role in the prediction. Overall, this study highlights the potential of combining machine learning methods with screening MM/PBSA for accurately predicting binding free energies in biosystems.展开更多
基金financial supports of“the Fundamental Research Funds for the Central Universities”(DUT22YG218),NSFC(22278053,22078041)China Postdoctoral Science Foundation(2022M710578)“the Dalian High-level Talents Innovation Support Program”(2021RQ105).
文摘Binding kinetic properties of protein–ligand complexes are crucial factors affecting the drug potency.Nevertheless,the current in silico techniques are insufficient in providing accurate and robust predictions for binding kinetic properties.To this end,this work develops a variety of binding kinetic models for predicting a critical binding kinetic property,dissociation rate constant,using eight machine learning(ML)methods(Bayesian Neural Network(BNN),partial least squares regression,Bayesian ridge,Gaussian process regression,principal component regression,random forest,support vector machine,extreme gradient boosting)and the descriptors of the van der Waals/electrostatic interaction energies.These eight models are applied to two case studies involving the HSP90 and RIP1 kinase inhibitors.Both regression results of two case studies indicate that the BNN model has the state-of-the-art prediction accuracy(HSP90:R^(2)_(test)=0:947,MAE_(test)=0.184,rtest=0.976,RMSE_(test)=0.220;RIP1 kinase:R^(2)_(test)=0:745,MAE_(test)=0.188,rtest=0.961,RMSE_(test)=0.290)in comparison with other seven ML models.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.T2225007 and T2222012)the Foundation for Innovative Research Groups of State Key Laboratory of Microbial Technology,China(Grant No.WZCX2021-03).
文摘Since its establishment in 2013,BioLiP has become one of the widely used resources for protein-ligand interactions.Nevertheless,several known issues occurred with it over the past decade.For example,the protein-ligand interactions are represented in the form of single chain-based tertiary structures,which may be inappropriate as many interactions involve multiple protein chains(known as quaternary structures).We sought to address these issues,resulting in Q-BioLiP,a comprehensive resource for quaternary structure-based protein-ligand interactions.The major features of Q-BioLiP include:(1)representing protein structures in the form of quaternary structures rather than single chain-based tertiary structures;(2)pairing DNA/RNA chains properly rather than separation;(3)providing both experimental and predicted binding affinities;(4)retaining both biologically relevant and irrelevant interactions to alleviate the wrong justification of ligands’biological relevance;and(5)developing a new quaternary structure-based algorithm for the modelling of protein-ligand complex structure.With these new features,Q-BioLiP is expected to be a valuable resource for studying biomolecule interactions,including protein-small molecule interaction,protein-metal ion interaction,protein-peptide interaction,protein-protein interaction,protein-DNA/RNA interaction,and RNA-small molecule interaction.Q-BioLiP is freely available at https://yanglab.qd.sdu.edu.cn/Q-BioLiP/.
文摘In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. The machine learning methods greatly offer an efficient prediction ability in diagnosis system alternative with the clinicians. Most of the systems operate on the extracted features from the patients and most of the predicted cases are accurate. However, in recent time, the prevalence of COVID-19 has emerged the global healthcare industry to find a new drug that suppresses the pandemic outbreak. In this paper, we design a Deep Neural Network(DNN)model that accurately finds the protein-ligand interactions with the drug used. The DNN senses the response of protein-ligand interactions for a specific drug and identifies which drug makes the interaction that combats effectively the virus. With limited genome sequence of Indian patients submitted to the GISAID database, we find that the DNN system is effective in identifying the protein-ligand interactions for a specific drug.
基金supported by the National Natural Science Foundation of China (21605008, 21735001, 21575018, 21505006)the Hunan Provincial Natural Science Foundation (2016JJ3001)
文摘Because of their important roles in cellular functions, life activities, drug screening, and disease treatment, the development of efficient methods for monitoring protein-ligand interaction is essential. In this study, inspired by our previous studies on DNA conformation-selective fluorescent indicators, we developed a new sensing platform for monitoring protein-ligand interaction and detecting protein activity based on binding-mediated DNA protection and the dsDNA-lighted fluorophore, ethyl-4-[3,6-bis(1-methyl-4-vinylpyridium iodine)-9 H-carbazol-9-yl)] butanoate(EBCB). The ligand was purposefully linked to the 3?-terminal of a hairpin DNA probe to selectively bind with the target protein and protect the DNA from cleavage by exonuclease III. By virtue of EBCB's outstanding capacity to discriminate DNA conformation, the protein-ligand interaction could be effectively monitored through a fluorescence change in EBCB. A high fluorescence signal was detected when the hairpin DNA was protected in the presence of the target protein, whereas a much lower signal was observed in the presence of nontarget proteins.Our results demonstrated that the proposed strategy had high potential, such as high selectivity and relatively high sensitivity, for monitoring protein-ligand interaction and detecting protein activity. We believe these results will pave the way for applying dsDNA-lighted fluorophore EBCB as an effective signal transducer for DNA conformation transformation-mediated biochemical sensing.
基金supported by the National Science Foundation(grant CHE-1111761)the National Institutes of Health(grants R01 DA032910,R01 DA013930,R01 DA025100,R01 DA021416,and RC1 MH088480)+1 种基金Alzheimer’s Drug Discovery Foundation(ADDA)Institute for the Study of Aging(ISOA).
文摘This is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model(FPCM)and rational drug design.Computational modeling has become a powerful tool in understanding detailed protein-ligand interactions at molecular level and in rational drug design.To study the binding of a protein with multiple molecular species of a ligand,one must accurately determine both the relative free energies of all of the molecular species in solution and the corresponding microscopic binding free energies for all of the molecular species binding with the protein.In this paper,we aim to provide a brief overview of the recent development in computational modeling of the solvent effects on the detailed protein-ligand interactions involving multiple molecular species of a ligand related to rational drug design.In particular,we first briefly discuss the main challenges in computational modeling of the detailed protein-ligand interactions involving the multiple molecular species and then focus on the FPCM model and its applications.The FPCM method allows accurate determination of the solvent effects in the first-principles quantum mechanism(QM)calculations on molecules in solution.The combined use of the FPCM-based QM calculations and other computational modeling and simulations enables us to accurately account for a protein binding with multiple molecular species of a ligand in solution.Based on the computational modeling of the detailed protein-ligand interactions,possible new drugs may be designed rationally as either small-molecule ligands of the protein or engineered proteins that bind/metabolize the ligand.The computational drug design has successfully led to discovery and development of promising drugs.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 12222506, 12347102, 12447164, and 12174184)。
文摘Accurately estimating protein–ligand binding free energy is crucial for drug design and biophysics, yet remains a challenging task. In this study, we applied the screening molecular mechanics/Poisson–Boltzmann surface area(MM/PBSA)method in combination with various machine learning techniques to compute the binding free energies of protein–ligand interactions. Our results demonstrate that machine learning outperforms direct screening MM/PBSA calculations in predicting protein–ligand binding free energies. Notably, the random forest(RF) method exhibited the best predictive performance,with a Pearson correlation coefficient(rp) of 0.702 and a mean absolute error(MAE) of 1.379 kcal/mol. Furthermore, we analyzed feature importance rankings in the gradient boosting(GB), adaptive boosting(Ada Boost), and RF methods, and found that feature selection significantly impacted predictive performance. In particular, molecular weight(MW) and van der Waals(VDW) energies played a decisive role in the prediction. Overall, this study highlights the potential of combining machine learning methods with screening MM/PBSA for accurately predicting binding free energies in biosystems.
