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Gotowa bibliografia na temat „Prediction of binding affinity”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Prediction of binding affinity"

Wang, Debby D., Haoran Xie, and Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity." Bioinformatics 37, no. 17 (2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.

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Abstract Motivation Reliable predictive models of protein–ligand binding affinity are required in many areas of biomedical research. Accurate prediction based on current descriptors or molecular fingerprints (FPs) remains a challenge. We develop novel interaction FPs (IFPs) to encode protein–ligand interactions and use them to improve the prediction. Results Proteo-chemometrics IFPs (PrtCmm IFPs) formed by combining extended connectivity fingerprints (ECFPs) with the proteo-chemometrics concept. Combining PrtCmm IFPs with machine-learning models led to efficient scoring models, which were validated on the PDBbind v2019 core set and CSAR-HiQ sets. The PrtCmm IFP Score outperformed several other models in predicting protein–ligand binding affinities. Besides, conventional ECFPs were simplified to generate new IFPs, which provided consistent but faster predictions. The relationship between the base atom properties of ECFPs and the accuracy of predictions was also investigated. Availability PrtCmm IFP has been implemented in the IFP Score Toolkit on github (https://github.com/debbydanwang/IFPscore). Supplementary information Supplementary data are available at Bioinformatics online.

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Kondabala, Rajesh, Vijay Kumar, Amjad Ali, and Manjit Kaur. "A novel astrophysics-based framework for prediction of binding affinity of glucose binder." Modern Physics Letters B 34, no. 31 (2020): 2050346. http://dx.doi.org/10.1142/s0217984920503467.

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In this paper, a novel astrophysics-based prediction framework is developed for estimating the binding affinity of a glucose binder. The proposed framework utilizes the molecule properties for predicting the binding affinity. It also uses the astrophysics-learning strategy that incorporates the concepts of Kepler’s law during the prediction process. The proposed framework is compared with 10 regression algorithms over ZINC dataset. Experimental results reveal that the proposed framework provides 99.30% accuracy of predicting binding affinity. However, decision tree provides the prediction with 97.14% accuracy. Cross-validation results show that the proposed framework provides better accuracy than the other existing models. The developed framework enables researchers to screen glucose binder rapidly. It also reduces computational time for designing small glucose binding molecule.

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Antunes, Dinler A., Jayvee R. Abella, Didier Devaurs, Maurício M. Rigo, and Lydia E. Kavraki. "Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes." Current Topics in Medicinal Chemistry 18, no. 26 (2019): 2239–55. http://dx.doi.org/10.2174/1568026619666181224101744.

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Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

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Kwon, Yongbeom, Woong-Hee Shin, Junsu Ko, and Juyong Lee. "AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks." International Journal of Molecular Sciences 21, no. 22 (2020): 8424. http://dx.doi.org/10.3390/ijms21228424.

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Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

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Shar, Piar Ali, Weiyang Tao, Shuo Gao, et al. "Pred-binding: large-scale protein–ligand binding affinity prediction." Journal of Enzyme Inhibition and Medicinal Chemistry 31, no. 6 (2016): 1443–50. http://dx.doi.org/10.3109/14756366.2016.1144594.

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Nguyen, Austin, Abhinav Nellore, and Reid F. Thompson. "Discordant results among major histocompatibility complex binding affinity prediction tools." F1000Research 12 (June 7, 2023): 617. http://dx.doi.org/10.12688/f1000research.132538.1.

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Background: Human leukocyte antigen (HLA) alleles are critical components of the immune system’s ability to recognize and eliminate tumors and infections. A large number of machine learning-based major histocompatibility complex (MHC) binding affinity (BA) prediction tools have been developed and are widely used for both investigational and therapeutic applications, so it is important to explore differences in tool outputs. Methods: We examined predictions of four popular tools (netMHCpan, HLAthena, MHCflurry, and MHCnuggets) across a range of possible peptide sources (human, viral, and randomly generated) and MHC class I alleles. Results: We uncovered inconsistencies in predictions of BA, allele promiscuity and the relationship between physical properties of peptides by source and BA predictions, as well as quality of training data. We found amount of training data does not explain inconsistencies between tools and yet for all tools, predicted binding quantities are similar between human and viral proteomes. Lastly, we find peptide physical properties are associated with allele-specific binding predictions. Conclusions: Our work raises fundamental questions about the fidelity of peptide-MHC binding prediction tools and their real-world implications. The real-world use of these prediction tools for theoretical binding of peptides to alleles is worrying, as the range of allele promiscuity is substantial yet does not differentiate between potential foreign versus self-antigens. Evaluating more viruses – as well as bacteria, fungi, and other pathogens – and linking these analyses with metrics such as evolutionary distance may give greater insight into the relationship between HLA evolution and disease.

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Langham, James J., Ann E. Cleves, Russell Spitzer, Daniel Kirshner, and Ajay N. Jain. "Physical Binding Pocket Induction for Affinity Prediction." Journal of Medicinal Chemistry 52, no. 19 (2009): 6107–25. http://dx.doi.org/10.1021/jm901096y.

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Öztürk, Hakime, Arzucan Özgür, and Elif Ozkirimli. "DeepDTA: deep drug–target binding affinity prediction." Bioinformatics 34, no. 17 (2018): i821—i829. http://dx.doi.org/10.1093/bioinformatics/bty593.

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Wang, Xun, Dayan Liu, Jinfu Zhu, Alfonso Rodriguez-Paton, and Tao Song. "CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction." Biomolecules 11, no. 5 (2021): 643. http://dx.doi.org/10.3390/biom11050643.

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The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, we propose a novel deep learning method, namely CSConv2d, for protein-ligand interactions’ prediction. The proposed method is improved by a DEEPScreen model using 2-D structural representations of compounds as input. Furthermore, a channel and spatial attention mechanism (CS) is added in feature abstractions. Data experiments conducted on ChEMBLv23 datasets show that CSConv2d performs better than the original DEEPScreen model in predicting protein-ligand binding affinity, as well as some state-of-the-art DTIs (drug-target interactions) prediction methods including DeepConv-DTI, CPI-Prediction, CPI-Prediction+CS, DeepGS and DeepGS+CS. In practice, the docking results of protein (PDB ID: 5ceo) and ligand (Chemical ID: 50D) and a series of kinase inhibitors are operated to verify the robustness.

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Pantsar, Tatu, and Antti Poso. "Binding Affinity via Docking: Fact and Fiction." Molecules 23, no. 8 (2018): 1899. http://dx.doi.org/10.3390/molecules23081899.

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In 1982, Kuntz et al. published an article with the title “A Geometric Approach to Macromolecule-Ligand Interactions”, where they described a method “to explore geometrically feasible alignment of ligands and receptors of known structure”. Since then, small molecule docking has been employed as a fast way to estimate the binding pose of a given compound within a specific target protein and also to predict binding affinity. Remarkably, the first docking method suggested by Kuntz and colleagues aimed to predict binding poses but very little was specified about binding affinity. This raises the question as to whether docking is the right tool to estimate binding affinity. The short answer is no, and this has been concluded in several comprehensive analyses. However, in this opinion paper we discuss several critical aspects that need to be reconsidered before a reliable binding affinity prediction through docking is realistic. These are not the only issues that need to be considered, but they are perhaps the most critical ones. We also consider that in spite of the huge efforts to enhance scoring functions, the accuracy of binding affinity predictions is perhaps only as good as it was 10–20 years ago. There are several underlying reasons for this poor performance and these are analyzed. In particular, we focus on the role of the solvent (water), the poor description of H-bonding and the lack of the systems’ true dynamics. We hope to provide readers with potential insights and tools to overcome the challenging issues related to binding affinity prediction via docking.

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Rozprawy doktorskie na temat "Prediction of binding affinity"

Jovanovic, Srdan. "Rapid, precise and reproducible binding affinity prediction : applications in drug discovery." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10053853/.

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As we move towards an era of personalised medicine, the identification of lead compounds requires years of research and considerable financial backing, in the development of targeted therapies for cancer. We use molecular modelling and simulation to screen a library of active compounds, and understand the ligand-protein interaction at the molecular level in appropriate protein targets, in a bid to identify the most active lead drug candidates. In recent times, good progress has been made in accurately predicting binding affinities for drug candidates. Advances in high-performance computation (HPC), mean it is now possible to run a larger number of calculations in parallel, paving the way for multiple replica simulations from which binding affinities are obtained. This, then, allows for a tighter control of errors and in turn, a higher confidence in the binding affinity predictions. Here, we present ESMACS (Enhanced Sampling of Molecular dynamics with Approximation of Continuum Solvent) and TIES (Thermodynamic Integration with Enhanced Sampling); a new framework from which binding affinities are calculated. ESMACS performs 25 replica simulations of the same ligand-receptor system with the only difference being the initial momentum of each atom. From this ensemble of trajectories, an extended MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) free energy method is employed. The TIES protocol constitutes 5 replicas simulations per lambda state followed by the integration of the potential derivatives of each lambda state, generating a relative binding affinity. This is all tied together using the BAC (Binding Affinity Calculator) which automates the ESMACS and TIES workflow. ESMACS and TIES, given suitable access to HPC resources, can compute binding affinities in a matter of hours on a supercomputer; the size of such machines therefore means that we can reach the industrial scale of demand necessary to impact drug discovery programmes.

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Uslan, Volkan. "Support vector machine-based fuzzy systems for quantitative prediction of peptide binding affinity." Thesis, De Montfort University, 2015. http://hdl.handle.net/2086/11170.

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Reliable prediction of binding affinity of peptides is one of the most challenging but important complex modelling problems in the post-genome era due to the diversity and functionality of the peptides discovered. Generally, peptide binding prediction models are commonly used to find out whether a binding exists between a certain peptide(s) and a major histocompatibility complex (MHC) molecule(s). Recent research efforts have been focused on quantifying the binding predictions. The objective of this thesis is to develop reliable real-value predictive models through the use of fuzzy systems. A non-linear system is proposed with the aid of support vector-based regression to improve the fuzzy system and applied to the real value prediction of degree of peptide binding. This research study introduced two novel methods to improve structure and parameter identification of fuzzy systems. First, the support-vector based regression is used to identify initial parameter values of the consequent part of type-1 and interval type-2 fuzzy systems. Second, an overlapping clustering concept is used to derive interval valued parameters of the premise part of the type-2 fuzzy system. Publicly available peptide binding affinity data sets obtained from the literature are used in the experimental studies of this thesis. First, the proposed models are blind validated using the peptide binding affinity data sets obtained from a modelling competition. In that competition, almost an equal number of peptide sequences in the training and testing data sets (89, 76, 133 and 133 peptides for the training and 88, 76, 133 and 47 peptides for the testing) are provided to the participants. Each peptide in the data sets was represented by 643 bio-chemical descriptors assigned to each amino acid. Second, the proposed models are cross validated using mouse class I MHC alleles (H2-Db, H2-Kb and H2-Kk). H2-Db, H2-Kb, and H2-Kk consist of 65 nona-peptides, 62 octa-peptides, and 154 octa-peptides, respectively. Compared to the previously published results in the literature, the support vector-based type-1 and support vector-based interval type-2 fuzzy models yield an improvement in the prediction accuracy. The quantitative predictive performances have been improved as much as 33.6\% for the first group of data sets and 1.32\% for the second group of data sets. The proposed models not only improved the performance of the fuzzy system (which used support vector-based regression), but the support vector-based regression benefited from the fuzzy concept also. The results obtained here sets the platform for the presented models to be considered for other application domains in computational and/or systems biology. Apart from improving the prediction accuracy, this research study has also identified specific features which play a key role(s) in making reliable peptide binding affinity predictions. The amino acid features "Polarity", "Positive charge", "Hydrophobicity coefficient", and "Zimm-Bragg parameter" are considered as highly discriminating features in the peptide binding affinity data sets. This information can be valuable in the design of peptides with strong binding affinity to a MHC I molecule(s). This information may also be useful when designing drugs and vaccines.

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Bodnarchuk, Michael. "Predicting the location and binding affinity of small molecules in protein binding sites." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/348170/.

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In this thesis, various methods for locating and scoring the binding affinity of water molecules and molecular fragments in protein binding sites are described. The primary aim of this work is to understand how different methodologies compare to one another and how, by carefully choosing the correct method, they can be used to extract information on how small molecules interact with proteins. Three different methods are used to predict the location and affinity of water molecules; Just Add Water Molecules (JAWS), Grand Canonical Monte Carlo (GCMC) and double-decoupling. By applying the methods to the N9-Neuraminidase system, it can be shown that all of the methods predict the same binding free energy of the water molecules to within error. The JAWS method was shown to be advantageous for the rapid prediction of the binding free energy of water molecules, whilst GCMC was preferred for the prediction of hydration sites. The combination of the methods were used on a variety of novel test cases, including hydrophobic cavities and protein kinases. These test cases highlight how the methods can be used to accurately predict hydration patterns as a function of the binding free energy in GCMC simulations, and how these patterns can be used to dictate ligand design in a drug discovery context. The approaches described are likely to be of interest to the pharmaceutical industry. A JAWS based fragment based drug discovery methodology is also described, which takes into account key features commonly neglected by existing computational approaches such as fragment-solvent competition and fragment desolvation. This method is used upon the Kinesin Spindle Protein and factor Xa, and demonstrates that the method is able to accurately locate the position of molecular fragments and water molecules compared to crystallographic ligands.

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Erdas, Ozlem. "Modelling And Predicting Binding Affinity Of Pcp-like Compounds Using Machine Learning Methods." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608792/index.pdf.

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Machine learning methods have been promising tools in science and engineering fields. The use of these methods in chemistry and drug design has advanced after 1990s. In this study, molecular electrostatic potential (MEP) surfaces of PCP-like compounds are modelled and visualized in order to extract features which will be used in predicting binding affinity. In modelling, Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map. Resulting maps are visualized by using values of electrostatic potential. These values also provide features for prediction system. Support vector machines and partial least squares method are used for predicting binding affinity of compounds, and results are compared.

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Guedes, Isabella Alvim. "Development of empirical scoring funcions forn predicting proteinligand binding affinity." Laboratório Nacional de Computação Científica, 2016. https://tede.lncc.br/handle/tede/247.

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Submitted by Maria Cristina (library@lncc.br) on 2017-04-12T19:05:59Z No. of bitstreams: 1 tese_isabella_vfinal.pdf: 6145955 bytes, checksum: e3ed369e970ad7eb06b79a77ef921a9b (MD5) Approved for entry into archive by Maria Cristina (library@lncc.br) on 2017-04-12T19:06:11Z (GMT) No. of bitstreams: 1 tese_isabella_vfinal.pdf: 6145955 bytes, checksum: e3ed369e970ad7eb06b79a77ef921a9b (MD5) Made available in DSpace on 2017-04-12T19:06:22Z (GMT). No. of bitstreams: 1 tese_isabella_vfinal.pdf: 6145955 bytes, checksum: e3ed369e970ad7eb06b79a77ef921a9b (MD5) Previous issue date: 2016-07-29 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes) Molecular docking is a methodology that aims to predict the binding modes and affinity of a small molecule within the binding site of the receptor target of interest. It is an approach widely used by the pharmaceutical industry and the academic community for identification and optimization of lead compounds, contributing to the reduction of cost, time and failures in the development of new drugs. Current docking methods and the associated scoring functions exhibit good performances in identifying experimental binding modes. However, the detection of active compounds among a decoy set of ligands and the accurate prediction of binding affinity remain challenging tasks. The DockThor program developed in our group has obtained promising results in comparative studies with other well established and widely used protein-ligand docking programs for predicting experimental binding modes. Despite useful for pose prediction, the current scoring function implemented in DockThor is not suitable for predicting binding affinities of protein-ligand complexes, obtaining no correlation with measured affinity data. In this work, we develop several scoring functions with physically-based features for predicting binding affinities of protein-ligand complexes trained with diverse machine learning techniques. The final scoring functions consist of force-field based terms related to the intermolecular interactions (electrostatic and van der Waals potentials), an original term for the ligand entropy (number of frozen rotatable bonds), ligand and protein desolvation and the hydrophobic effect. Then, we developed general and target-classes scoring functions, the last to account for binding characteristics associated with a target class of interest, focusing on proteases, kinases and protein-protein interactions complexes (PPIs). The scoring functions were derived using linear regression (MLR) and seven more advanced machine learning techniques for nonlinear problems. The training and testing were carried out using high-quality datasets composed of experimental structures of diverse protein-ligand complexes with binding affinities data available (Kd or Ki). Additionally, we also derived general scoring functions trained with redocking results from the DockThor program. The scoring functions trained with docking results obtained promising performances when evaluated in both experimental and docking structures, indicating that they are reliable to be used in real virtual screening experiments. The scoring functions developed in this work have demonstrated to be competitive with the best-evaluated linear and nonlinear scoring functions in benchmarking studies described in the literature. The scoring functions derived for specific classes of targets also exhibited promising performances, achieving great improvements when using nonlinear approaches compared to the linear models. Moreover, the consensus scoring strategy investigated in this work exhibited impressive results, ranking among the top-three models with the best predictive performances on all cases. The development of the scoring functions implemented in this thesis is a crucial step to make the DockThor an even more competitive program, enabling the development of the high-throughput virtual screening program and portal DockThor-VS. Atracamento molecular é uma metodologia que tem por objetivo prever a conformação e a afinidade de uma pequena molécula no sítio de ligação do receptor alvo de interesse. É uma abordagem amplamente utilizada pela indústria farmacêutica e pela comunidade acadêmica para identificação e otimização de compostos líderes, contribuindo para a redução de custo, tempo e falhas no desenvolvimento de novos fármacos. As metodologias atuais de atracamento molecular e as funções de avaliação associadas possuem bom desempenho em identificar modos de ligação. Entretanto, a detecção de compostos ativos dentre inativos e a predição acurada da afinidade de ligação ainda são grandes desafios. O programa DockThor, desenvolvido pelo nosso grupo de pesquisa, tem obtido resultados promissores em estudos comparativos com outros programas de atracamento molecular bem estabelecidos e amplamente utilizados pela comunidade científica para a predição de modos de ligação. Apesar de ser bastante útil para predição de poses, a função de avaliação atualmente implementada no DockThor não é adequada para prever afinidade de complexos proteína-ligante, não obtendo correlação com dados experimentais. Neste trabalho, nós desenvolvemos diversas funções de avaliação com características baseadas na física para prever afinidade de ligação de complexos proteína-ligante, treinadas com diversas técnicas de aprendizagem de máquina. As funções de avaliação finais consistem de termos baseados em campo de força relacionados com as interações intermoleculares (potenciais eletrostático e de van der Waals), um termo original para a entropia do ligante (número de ligações rotacionáveis congeladas), dessolvatação do ligante e da proteína e o efeito hidrofóbico. Desenvolvemos então funções de avaliação gerais e específicas para classes de alvos, esta para considerar características específicas associadas com a classe de alvo de interesse, focando em proteases, cinases e complexos de interações proteína-proteína (PPIs). As funções de avaliação foram derivadas utilizando regressão linear (MLR) e sete outras técnicas mais avançadas de aprendizagem de máquina para problemas não lineares. O processo de treinamento e teste foi realizado utilizando conjuntos de dados de alta qualidade compostos de estruturas experimentais de diversos complexos proteína-ligante com dados de afinidade de ligação disponíveis (Kd ou Ki). Adicionalmente, também derivamos funções de avaliação gerais treinadas com resultados do atracamento molecular com o programa DockThor. As funções treinadas com resultados de atracamento obtiveram desempenho promissor quando avaliadas tanto em estruturas experimentais quanto provenientes de atracamento molecular, indicando que elas são confiáveis para serem usadas em experimentos reais de triagem virtual. As funções desenvolvidas neste trabalho demonstraram ser competitivas com as melhores funções de avaliação lineares e não lineares em estudos comparativos descritas na literatura. As funções específicas para classes de alvos também exibiram desempenhos promissores, alcançando significativa melhoria quando utilizando abordagens não lineares comparadas com os modelos lineares. Além disso, a estratégia de avaliação consenso investigada neste trabalho exibiu resultados impressionantes, ficando entre os três melhores modelos com melhores desempenhos preditivos em todos os casos. O desenvolvimento das funções de avaliação implementadas nesta tese é um passo crucial para tornar o programa DockThor ainda mais competitivo, possibilitando o desenvolvimento do programa e do portal de triagem virtual em larga escala DockThor-VS.

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Matereke, Lavious Tapiwa. "Analysis of predictive power of binding affinity of PBM-derived sequences." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1018666.

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A transcription factor (TF) is a protein that binds to specific DNA sequences as part of the initiation stage of transcription. Various methods of finding these transcription factor binding sites (TFBS) have been developed. In vivo technologies analyze DNA binding regions known to have bound to a TF in a living cell. Most widely used in vivo methods at the moment are chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) and DNase I hypersensitive sites sequencing. In vitro methods derive TFBS based on experiments with TFs and DNA usually in artificial settings or computationally. An example is the Protein Binding Microarray which uses artificially constructed DNA sequences to determine the short sequences that are most likely to bind to a TF. The major drawback of this approach is that binding of TFs in vivo is also dependent on other factors such as chromatin accessibility and the presence of cofactors. Therefore TFBS derived from the PBM technique might not resemble the true DNA binding sequences. In this work, we use PBM data from the UniPROBE motif database, ChIP-seq data and DNase I hypersensitive sites data. Using the Spearman’s rank correlation and area under receiver operating characteristic curve, we compare the enrichment scores which the PBM approach assigns to its identified sequences and the frequency of these sequences in likely binding regions and the human genome as a whole. We also use central motif enrichment analysis (CentriMo) to compare the enrichment of UniPROBE motifs with in vivo derived motifs (from the JASPAR CORE database) in their respective TF ChIP-seq peak region. CentriMo is applied to 14 TF ChIP-seq peak regions from different cell lines. We aim to establish if there is a relationship between the occurrences of UniPROBE 8-mer patterns in likely binding regions and their enrichment score and how well the in vitro derived motifs match in vivo binding specificity. We did not come out with a particular trend showing failure of the PBM approach to predict in vivo binding specificity. Our results show Ets1, Hnf4a and Tcf3 show prediction failure by the PBM technique in terms of our Spearman’s rank correlation for ChIP-seq data and central motif enrichment analysis. However, the PBM technique also matched the in vivo binding specificities of FoxA2, Pou2f2 and Mafk. Failure of the PBM approach was found to be a result of variability in the TF’s binding specificity, the presence of cofactors, narrow binding specificity and the presence ubiquitous binding patterns.

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Yoldas, Mine. "Predicting The Effect Of Hydrophobicity Surface On Binding Affinity Of Pcp-like Compounds Using Machine Learning Methods." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613215/index.pdf.

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This study aims to predict the binding affinity of the PCP-like compounds by means of molecular hydrophobicity. Molecular hydrophobicity is an important property which affects the binding affinity of molecules. The values of molecular hydrophobicity of molecules are obtained on three-dimensional coordinate system. Our aim is to reduce the number of points on the hydrophobicity surface of the molecules. This is modeled by using self organizing maps (SOM) and k-means clustering. The feature sets obtained from SOM and k-means clustering are used in order to predict binding affinity of molecules individually. Support vector regression and partial least squares regression are used for prediction.

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Shoemake, Claire. "The use of static and dynamic models for the prediction of ligand binding affinity using oestrogen and androgen nuclear receptors as case studies." Thesis, University of Nottingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.478985.

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Atkovska, Kalina, Sergey A. Samsonov, Maciej Paszkowski-Rogacz, and M. Teresa Pisabarro. "Multipose Binding in Molecular Docking." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-147177.

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Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e., the property of certain protein-ligand complexes to exhibit different ligand binding modes, has been shown to occur in nature for a variety of molecules. We conducted a high-throughput docking study and implemented multipose binding in the scoring procedure by considering multiple docking solutions in binding affinity prediction. In general, improvement of the agreement between docking scores and experimental data was observed, and this was most pronounced in complexes with large and flexible ligands and high binding affinities. Further developments of the selection criteria for docking solutions for each individual complex are still necessary for a general utilization of the multipose binding concept for accurate binding affinity prediction by molecular docking.

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Nordesjö, Olle. "Searching for novel protein-protein specificities using a combined approach of sequence co-evolution and local structural equilibration." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-275040.

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Greater understanding of how we can use protein simulations and statistical characteristics of biomolecular interfaces as proxies for biological function will make manifest major advances in protein engineering. Here we show how to use calculated change in binding affinity and coevolutionary scores to predict the functional effect of mutations in the interface between a Histidine Kinase and a Response Regulator. These proteins participate in the Two-Component Regulatory system, a system for intracellular signalling found in bacteria. We find that both scores work as proxies for functional mutants and demonstrate a ~30 fold improvement in initial positive predictive value compared with choosing randomly from a sequence space of 160 000 variants in the top 20 mutants. We also demonstrate qualitative differences in the predictions of the two scores, primarily a tendency for the coevolutionary score to miss out on one class of functional mutants with enriched frequency of the amino acid threonine in one position.

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Książki na temat "Prediction of binding affinity"

Krishna, Mallia A., and Smith Paul K, eds. Immobilized affinity ligand techniques. Academic Press, 1992.

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Naples, Mark. Determinants of high affinity ligand binding to the group III metabotropic glutamate receptors. National Library of Canada, 2001.

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1958-, McMahon Robert Joseph, ed. Avidin-biotin interactions: Methods and applications. Humana, 2008.

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Marles, Jennifer Anne. Significance of the ligand-binding affinity of the Sho1 SH3 domain for in vivo function. National Library of Canada, 2003.

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Puvvada, Madhu. Investigation into the relationship between DNA-binding affinity, sequence-specificity and biological activity in the pyrrolo[2,1-c][1,4]benzodiazepine group of antitumour antibiotics. University of Portsmouth, Division of Medicinal Chemistry, 1995.

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Marelius, John. Computational Prediction of Receptor-Ligand Binding Affinity in Drug Discovery. Uppsala Universitet, 2000.

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Mallia, A. Krishna. Immobilized Affinity Ligand Techniques. Elsevier Science & Technology Books, 2012.

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Affinity and Efficacy. World Scientific Publishing Company, 2011.

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Verotoxin-globotriosylceramide binding: Receptor affinity purification and the effect of membrane environment on toxin binding. National Library of Canada, 1993.

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Banks, Robert C. Oligodeoxynucleotide affinity column for the isolation of sequence specific DNA binding proteins. 1989.

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Części książek na temat "Prediction of binding affinity"

Takaba, Kenichiro. "Application of FMO for Protein–ligand Binding Affinity Prediction." In Recent Advances of the Fragment Molecular Orbital Method. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9235-5_13.

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Lu, Yaoyao, Junkai Liu, Tengsheng Jiang, Shixuan Guan, and Hongjie Wu. "Protein-Ligand Binding Affinity Prediction Based on Deep Learning." In Intelligent Computing Theories and Application. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13829-4_26.

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Asha, P. R., and M. S. Vijaya. "Binding Affinity Prediction Models for Spinocerebellar Ataxia Using Supervised Learning." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1423-0_17.

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Liu, Wen, Ji Wan, Xiangshan Meng, Darren R. Flower, and Tongbin Li. "In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC." In Methods in Molecular Biology. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-60327-118-9_20.

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Li, Xueling, Min Zhu, Xiaolai Li, Hong-Qiang Wang, and Shulin Wang. "Protein-Protein Binding Affinity Prediction Based on an SVR Ensemble." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31588-6_19.

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Nikam, Rahul, K. Yugandhar, and M. Michael Gromiha. "Discrimination and Prediction of Protein-Protein Binding Affinity Using Deep Learning Approach." In Intelligent Computing Theories and Application. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_89.

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Xia, Minghao, Jing Hu, Xiaolong Zhang, and Xiaoli Lin. "Drug-Target Binding Affinity Prediction Based on Graph Neural Networks and Word2vec." In Intelligent Computing Theories and Application. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13829-4_43.

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Yang, Yuedong, Huiying Zhao, Jihua Wang, and Yaoqi Zhou. "SPOT-Seq-RNA: Predicting Protein–RNA Complex Structure and RNA-Binding Function by Fold Recognition and Binding Affinity Prediction." In Methods in Molecular Biology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0366-5_9.

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Azzopardi, Joseph, and Jean Paul Ebejer. "LigityScore: A CNN-Based Method for Binding Affinity Predictions." In Biomedical Engineering Systems and Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20664-1_2.

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Li, Hongjian, Kwong-Sak Leung, Man-Hon Wong, and Pedro J. Ballester. "The Impact of Docking Pose Generation Error on the Prediction of Binding Affinity." In Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24462-4_20.

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Streszczenia konferencji na temat "Prediction of binding affinity"

Atwereboannah, Abena Achiaa, Wei-Ping Wu, Lei Ding, Sophyani B. Yussif, and Edwin Kwadwo Tenagyei. "Protein-Ligand Binding Affinity Prediction Using Deep Learning." In 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2021. http://dx.doi.org/10.1109/iccwamtip53232.2021.9674118.

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Li, Yanjun, Mohammad A. Rezaei, Chenglong Li, and Xiaolin Li. "DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8982964.

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Li, Mei, Sihan Xu, Xiangrui Cai, Zhong Zhang, and Hua Ji. "Contrastive Meta-Learning for Drug-Target Binding Affinity Prediction." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995372.

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D'Souza, Sofia, K. V. Prema, and S. Balaji. "Hierarchical Modeling of Binding Affinity Prediction Using Machine LearningTechniques." In 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). IEEE, 2021. http://dx.doi.org/10.1109/discover52564.2021.9663690.

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Cong, Chunyu, Pingping Sun, and Zhiqiang Ma. "Predicting binding affinity using differential evolution." In 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2012. http://dx.doi.org/10.1109/bmei.2012.6513124.

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Zhao, Qichang, Fen Xiao, Mengyun Yang, Yaohang Li, and Jianxin Wang. "AttentionDTA: prediction of drug–target binding affinity using attention model." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983125.

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Chyan, Jeffrey, Mark Moll, and Lydia E. Kavraki. "Improving the Prediction of Kinase Binding Affinity Using Homology Models." In BCB'13: ACM-BCB2013. ACM, 2013. http://dx.doi.org/10.1145/2506583.2506704.

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Yaseen, Adiba, Wajid Arshad Abbasi, and Fayyaz ul Amir Afsar Minhas. "Protein binding affinity prediction using support vector regression and interfecial features." In 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2018. http://dx.doi.org/10.1109/ibcast.2018.8312222.

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Zhao, Lingling, Peijin Xie, Lingfeng Hao, Tiantian Li, and Chunyu Wang. "Gene Ontology aided Compound Protein Binding Affinity Prediction Using BERT Encoding." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9312985.

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Feng, Xianbing, Jingwei Qu, Tianle Wang, Bei Wang, Xiaoqing Lyu, and Zhi Tang. "Attention-enhanced Graph Cross-convolution for Protein-Ligand Binding Affinity Prediction." In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669341.

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Raporty organizacyjne na temat "Prediction of binding affinity"

Warren, H. S. Purification of LPS Binding Factors in Tolerant Serum by Affinity Chromatography. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada233638.

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Reiff, Emily A., and Gunda I. Georg. Construction of Affinity Probes to Study the Epothilone Binding Site on Tubulin. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada416670.

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Stratis-Cullum, Dimitra N., Sun McMasters, and Paul M. Pellegrino. Affinity Probe Capillary Electrophoresis Evaluation of Aptamer Binding to Campylobacter jejuni Bacteria. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada512469.

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Fresco, Jacques R. Development of affinity technology for isolating individual human chromosomes by third strand binding. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/820632.

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Pattabiraman, Nagarajan, Carolyn Chambers, Ayesha Adil, and Gregory E. Garcia. Identification of Small Molecules against Botulinum Neurotoxin B Binding to Neuronal Cells at Ganglioside GT1b Binding Site with Low to Moderate Affinity. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada612876.

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Chefetz, Benny, Baoshan Xing, and Yona Chen. Interactions of engineered nanoparticles with dissolved organic matter (DOM) and organic contaminants in water. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7699863.bard.

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Background: Engineered carbon nanotubes (CNTs) are expected to be increasingly released into the environment with the rapid increase in their production and use. The discharged CNTs may interact with coexisting contaminants and subsequently change environmental behaviors and ecological effects of both the CNTs themselves and the contaminants. Dissolved organic matter (DOM) plays a critical role in the transport of CNTs in the aquatic environment, affecting both CNT's surface properties through adsorption, and its colloidal stability in solution. Therefore, CNT-bound DOM complexes may interact with coexisting contaminants, thus affecting their environmental fate. With increasing production and use of CNTs, there is an increasing risk that humans could be exposed to CNTs mainly through ingestion and inhalation. Since CNTs can be carriers of contaminants due to their high adsorption affinity and capacity, the distribution of these nanoparticles in the environment holds a potential environmental and health risk. Project objectives: The overall goal of this project was to gain a better understanding of the environmental behavior of engineered nanoparticles with DOM and organic pollutant in aqueous systems. The scope of this study includes: characterizing various types of engineered nanoparticles and their interaction with DOM; binding studies of organic contaminants by nanoparticles and DOM-nanoparticle complexes; and examining interactions in DOM-nanoparticles-contaminant systems. Major conclusions, solutions and achievements: DOM has a pronounced effect on colloidal stability of CNTs in solution and on their surface chemistry and reactivity toward associated contaminants. The structure and chemical makeup of both CNTs and DOM determine their interactions and nature of formed complexes. CNTs, contaminants and DOM can co-occur in the aquatic environment. The occurrence of co-contaminants, as well as of co-introduction of DOM, was found to suppress the adsorption of organic contaminants to CNTs through both competition over adsorption sites and direct interactions in solution. Furthermore, the release of residual contaminants from CNTs could be enhanced by biomolecules found in the digestive as well as the respiratory tracts, thus increasing the bioaccessibility of adsorbed contaminants and possibly the overall toxicity of contaminant-associated CNTs. Contaminant desorption could be promoted by both solubilization and sorptive competition by biological surfactants. Scientific and agricultural implications: The information gained in the current project may assist in predicting the transport and fate of both CNTs and associated contaminants in the natural environment. Furthermore, the results imply a serious health risk from contaminant-associated CNTs.

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Bennion, B., K. Kulp, M. Cosman, and F. Lightstone. Computational Characterization and Prediction of Estrogen Receptor Coactivator Binding Site Inhibitors. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/900142.

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Bennion, Brian J., Kris Kulp, Monique Cosman, and Felice Lightstone. Computational Characterization and Prediction of Estrogen Receptor Coactivator Binding Site Inhibitors. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada446323.

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Mills, Gordon B. Detection of Serum Lysophosphatidic Acids Using Affinity Binding and Surface Enhanced Laser Desorption/Ionization (SELDI) Time of Flight Mass Spectrometry. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada437186.

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Mills, Gordon B. Detection of Serum Lysophosphatidic Acids Using Affinity Binding and Surface Enhanced Laser Deorption/Ionization (SELDI) Time of Flight Mass Spectrometry. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada455094.

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