Relevant bibliographies by topics / Protein-Protein Docking - Algorithms

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Academic literature on the topic 'Protein-Protein Docking - Algorithms'

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Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Protein-Protein Docking - Algorithms"

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Vreven, Thom, Howook Hwang, and Zhiping Weng. "Exploring Angular Distance in Protein-Protein Docking Algorithms." PLoS ONE 8, no. 2 (February 21, 2013): e56645. http://dx.doi.org/10.1371/journal.pone.0056645.

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Cakici, Serdar, Selcuk Sumengen, Ugur Sezerman, and Selim Balcisoy. "DockPro: A VR-Based Tool for Protein-Protein Docking Problem." International Journal of Virtual Reality 8, no. 2 (January 1, 2009): 19–23. http://dx.doi.org/10.20870/ijvr.2009.8.2.2720.

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Proteins are large molecules that are vital for all living organisms and they are essential components of many industrial products. The process of binding a protein to another is called protein-protein docking. Many automated algorithms have been proposed to find docking configurations that might yield promising protein-protein complexes. However, these automated methods are likely to come up with false positives and have high computational costs. Consequently, Virtual Reality has been used to take advantage of user's experience on the problem; and proposed applications can be further improved. Haptic devices have been used for molecular docking problems; but they are inappropriate for protein-protein docking due to their workspace limitations. Instead of haptic rendering of forces, we provide a novel visual feedback for simulating physicochemical forces of proteins. We propose an interactive 3D application, DockPro, which enables domain experts to come up with dockings of protein-protein couples by using magnetic trackers and gloves in front of a large display.
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Ruiz Echartea, Maria Elisa, Isaure Chauvot de Beauchêne, and David W. Ritchie. "EROS-DOCK: protein–protein docking using exhaustive branch-and-bound rotational search." Bioinformatics 35, no. 23 (May 24, 2019): 5003–10. http://dx.doi.org/10.1093/bioinformatics/btz434.

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Abstract Motivation Protein–protein docking algorithms aim to predict the 3D structure of a binary complex using the structures of the individual proteins. This typically involves searching and scoring in a 6D space. Many docking algorithms use FFT techniques to exhaustively cover the search space and to accelerate the scoring calculation. However, FFT docking results often depend on the initial protein orientations with respect to the Fourier sampling grid. Furthermore, Fourier-transforming a physics-base force field can involve a serious loss of precision. Results Here, we present EROS-DOCK, an algorithm to rigidly dock two proteins using a series of exhaustive 3D rotational searches in which non-clashing orientations are scored using the ATTRACT coarse-grained force field model. The rotational space is represented as a quaternion ‘π-ball’, which is systematically sub-divided in a ‘branch-and-bound’ manner, allowing efficient pruning of rotations that will give steric clashes. The algorithm was tested on 173 Docking Benchmark complexes, and results were compared with those of ATTRACT and ZDOCK. According to the CAPRI quality criteria, EROS-DOCK typically gives more acceptable or medium quality solutions than ATTRACT and ZDOCK. Availability and implementation The EROS-DOCK program is available for download at http://erosdock.loria.fr. Supplementary information Supplementary data are available at Bioinformatics online.
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Cherfils, Jacqueline, and Joël Janin. "Protein docking algorithms: simulating molecular recognition." Current Opinion in Structural Biology 3, no. 2 (April 1993): 265–69. http://dx.doi.org/10.1016/s0959-440x(05)80162-9.

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Sunny, Sharon, and P. B. Jayaraj. "A Geometric Complementarity-Based Tool for Protein–Protein Docking." Journal of Computational Biophysics and Chemistry 21, no. 01 (December 9, 2021): 35–46. http://dx.doi.org/10.1142/s273741652250003x.

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The computationally hard protein–protein complex structure prediction problem is continuously fascinating to the scientific community due to its biological impact. The field has witnessed the application of geometric algorithms, randomized algorithms, and evolutionary algorithms to name a few. These techniques improve either the searching or scoring phase. An effective searching strategy does not generate a large conformation space that perhaps demands computational power. Another determining factor is the parameter chosen for score calculation. The proposed method is an attempt to curtail the conformations by limiting the search procedure to probable regions. In this method, partial derivatives are calculated on the coarse-grained representation of the surface residues to identify the optimal points on the protein surface. Contrary to the existing geometric-based algorithms that align the convex and concave regions of both proteins, this method aligns the concave regions of the receptor with convex regions of the ligand only and thus reduces the size of conformation space. The method’s performance is evaluated using the 55 newly added targets in Protein–Protein Docking Benchmark v 5 and is found to be successful for around 47% of the targets.
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Sukhwal, Anshul, and Ramanathan Sowdhamini. "PPCheck: A Webserver for the Quantitative Analysis of Protein-Protein Interfaces and Prediction of Residue Hotspots." Bioinformatics and Biology Insights 9 (January 2015): BBI.S25928. http://dx.doi.org/10.4137/bbi.s25928.

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Background Modeling protein-protein interactions (PPIs) using docking algorithms is useful for understanding biomolecular interactions and mechanisms. Typically, a docking algorithm generates a large number of docking poses, and it is often challenging to select the best native-like pose. A further challenge is to recognize key residues, termed as hotspots, at protein-protein interfaces, which contribute more in stabilizing a protein-protein interface. Results We had earlier developed a computer algorithm, called PPCheck, which ascribes pseudoenergies to measure the strength of PPIs. Native-like poses could be successfully identified in 27 out of 30 test cases, when applied on a separate set of decoys that were generated using FRODOCK. PPCheck, along with conservation and accessibility scores, was able to differentiate ‘native-like and non-native-like poses from 1883 decoys of Critical Assessment of Prediction of Interactions (CAPRI) targets with an accuracy of 60%. PPCheck was trained on a 10-fold mixed dataset and tested on a 10-fold mixed test set for hotspot prediction. We obtain an accuracy of 72%, which is in par with other methods, and a sensitivity of 59%, which is better than most existing methods available for hotspot prediction that uses similar datasets. Other relevant tests suggest that PPCheck can also be reliably used to identify conserved residues in a protein and to perform computational alanine scanning. Conclusions PPCheck webserver can be successfully used to differentiate native-like and non-native-like docking poses, as generated by docking algorithms. The webserver can also be a convenient platform for calculating residue conservation, for performing computational alanine scanning, and for predicting protein-protein interface hotspots. While PPCheck can differentiate the generated decoys into native-like and non-native-like decoys with a fairly good accuracy, the results improve dramatically when features like conservation and accessibility are included. The method can be successfully used in ranking/scoring the decoys, as obtained from docking algorithms.
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Zheng, Jinfang, Xu Hong, Juan Xie, Xiaoxue Tong, and Shiyong Liu. "P3DOCK: a protein–RNA docking webserver based on template-based and template-free docking." Bioinformatics 36, no. 1 (June 7, 2019): 96–103. http://dx.doi.org/10.1093/bioinformatics/btz478.

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AbstractMotivationThe main function of protein–RNA interaction is to regulate the expression of genes. Therefore, studying protein–RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein–protein docking. Therefore, integrating these two methods may improve the prediction accuracy.ResultsIn this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3–20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK’s decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein–RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html.Availability and implementationwww.rnabinding.com/P3DOCK/P3DOCK.html.Supplementary informationSupplementary data are available at Bioinformatics online.
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DUAN, YUHUA, BOOJALA V. B. REDDY, and YIANNIS N. KAZNESSIS. "RESIDUE CONSERVATION INFORMATION FOR GENERATING NEAR-NATIVE STRUCTURES IN PROTEIN–PROTEIN DOCKING." Journal of Bioinformatics and Computational Biology 04, no. 04 (August 2006): 793–806. http://dx.doi.org/10.1142/s0219720006002223.

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Motivation: Protein–protein docking algorithms typically generate large numbers of possible complex structures with only a few of them resembling the native structure. Recently (Duan et al., Protein Sci, 14:316–218, 2005), it was observed that the surface density of conserved residue positions is high at the interface regions of interacting protein surfaces, except for antibody–antigen complexes, where a lesser number of conserved positions than average is observed at the interface regions. Using this observation, we identified putative interacting regions on the surface of interacting partners and significantly improved docking results by assigning top ranks to near-native complex structures. In this paper, we combine the residue conservation information with a widely used shape complementarity algorithm to generate candidate complex structures with a higher percentage of near-native structures (hits). What is new in this work is that the conservation information is used early in the generation stage and not only in the ranking stage of the docking algorithm. This results in a significantly larger number of generated hits and an improved predictive ability in identifying the native structure of protein–protein complexes. Results: We report on results from 48 well-characterized protein complexes, which have enough residue conservation information from the same 59 benchmark complexes used in our previous work. We compute conservation indices of residue positions on the surfaces of interacting proteins using available homologous sequences from UNIPROT and calculate the solvent accessible surface area. We combine this information with shape-complementarity scores to generate candidate protein–protein complex structures. When compared with pure shape-complementarity algorithms, performed by FTDock, our method results in significantly more hits, with the improvement being over 100% in many instances. We demonstrate that residue conservation information is useful not only in refinement and scoring of docking solutions, but also helpful in enrichment of near-native-structures during the generation of candidate geometries of complex structures.
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Han, Ye, Simin Zhang, and Fei He. "A Point Cloud-Based Deep Learning Model for Protein Docking Decoys Evaluation." Mathematics 11, no. 8 (April 11, 2023): 1817. http://dx.doi.org/10.3390/math11081817.

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Protein-protein docking reveals the process and product in protein interactions. Typically, a protein docking works with a docking model sampling, and then an evaluation method is used to rank the near-native models out from a large pool of generated decoys. In practice, the evaluation stage is the bottleneck to perform accurate protein docking. In this paper, PointNet, a deep learning algorithm based on point cloud, is applied to evaluate protein docking models. The proposed architecture is able to directly learn deep representations carrying the geometrical properties and atomic attributes from the 3D structural data of protein decoys. The experimental results show that the informative representations can benefit our proposed method to outperform other algorithms.
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Chen, Jui-Le, Chun-Wei Tsai, Ming-Chao Chiang, and Chu-Sing Yang. "A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/909717.

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The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking prediction algorithm (FCPLDPA), is presented to accelerate the docking prediction algorithm. The proposed algorithm works by leveraging two high-performance operators: (1) thenovelmigration (information exchange) operator is designed specially for cloud-based environments to reduce the computation time; (2) theefficientoperator is aimed at filtering out the worst search directions. Our simulation results illustrate that the proposed method outperforms the other docking algorithms compared in this paper in terms of both the computation time and the quality of the end result.
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Dissertations / Theses on the topic "Protein-Protein Docking - Algorithms"

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Derevyanko, Georgy. "Structure-based algorithms for protein-protein interactions." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENY070/document.

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Les phénotypes de tous les organismes vivants connus sont déterminés par les interactions compliquées entre les protéines produites dans ces organismes. La compréhension des réponses des organismes aux stimuli externes ou internes est basée sur la compréhension des interactions des protéines individuelles et des structures de ses complexes. La prédiction d'un complexe de deux ou plus protéines est le problème du domaine du docking protéine-protéine. Les algorithmes du docking ont habituellement deux étapes majeurs: recherche 6D exhaustive suivi par le scoring. Dans ce travail, nous avons contribués aux deus étapes sus indiquées. Nous avons développés le nouvel algorithme pour la recherche 6D exhaustive, HermiteFit. Cela est basé sur la décomposition des fonctions 3D en base Hermite. Nous avons implémenté cet algorithme dans le programme pour le fitting (l'ajustement des donnés) des cartes de densité électronique de résolution faible. Nous avons montrés qu'il surpasse les algorithmes existants en terme de temps par point tandis qu'il maintient la même précision du modèle sortant. Nous avons aussi développés la nouvelle approche de calculation de la fonction du scoring, qui est basé sur les arguments logique simples et qui évite la calculation ambiguë de l'état de référence. Nous avons comparés cela aux fonctions de scoring existantes avec l'aide du docking protéines-protéines benchmarks bien connues. Enfin, nous avons développés une approche permettant l'inclusion des interactions eau-protéine à la fonction du scoring et nous avons validés notre méthode pendant le CAPRI (Critical Assessment of Protein Interactions) tour 47
The phenotype of every known living organism is determined mainly by the complicated interactions between the proteins produced in this organism. Understanding the orchestration of the organismal responses to the external or internal stimuli is based on the understanding of the interactions of individual proteins and their complexes structures. The prediction of a complex of two or more proteins is the problem of the protein-protein docking field. Docking algorithms usually have two major steps: exhaustive 6D rigid-body search followed by the scoring. In this work we made contribution to both of these steps. We developed a novel algorithm for 6D exhaustive search, HermiteFit. It is based on Hermite decomposition of 3D functions into the Hermite basis. We implemented this algorithm in the program for fitting low-resolution electron density maps. We showed that it outperforms existing algorithms in terms of time-per-point while maintaining the same output model accuracy. We also developed a novel approach to computation of a scoring function, which is based on simple logical arguments and avoids an ambiguous computation of the reference state. We compared it to the existing scoring functions on the widely used protein-protein docking benchmarks. Finally, we developed an approach to include water-protein interactions into the scoring functions and validated our method during the Critical Assessment of Protein Interactions round 47
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Sajjadi, Sajdeh [Verfasser]. "Step by step in fast protein-protein docking algorithms / Sajdeh Sajjadi." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2014. http://d-nb.info/1060276887/34.

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Jiménez, García Brian. "Development and optimization of high-performance computational tools for protein-protein docking." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/398790.

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Computing has pushed a paradigm shift in many disciplines, including structural biology and chemistry. This change has been mainly driven by the increase in performance of computers, the capacity of dealing with huge amounts of experimental and analysis data and the development of new algorithms. Thanks to these advances, our understanding on the chemistry that supports life has increased and it is even more sophisticated that we had never imagined before. Proteins play a major role in nature and are often described as the factories of the cell as they are involved in virtually all important function in living organisms. Unfortunately, our understanding of the function of many proteins is still very poor due to the actual limitations in experimental techniques which, at the moment, they can not provide crystal structure for many protein complexes. The development of computational tools as protein-protein docking methods could help to fill this gap. In this thesis, we have presented a new protein-protein docking method, LightDock, which supports the use of different custom scoring functions and it includes anisotropic normal analysis to model backbone flexibility upon binding process. Second, several interesting web-based tools for the scientific community have been developed, including a web server for protein-protein docking, a web tool for the characterization of protein-protein interfaces and a web server for including SAXS experimental data for a better prediction of protein complexes. Moreover, the optimizations made in the pyDock protocol and the increase in th performance helped our group to score in the 5th position among more than 60 participants in the past two CAPRI editions. Finally, we have designed and compiled the Protein-Protein (version 5.0) and Protein-RNA (version 1.0) docking benchmarks, which are important resources for the community to test and to develop new methods against a reference set of curated cases.
Gràcies als recents avenços en computació, el nostre coneixement de la química que suporta la vida ha incrementat enormement i ens ha conduït a comprendre que la química de la vida és més sofisticada del que mai haguéssim pensat. Les proteïnes juguen un paper fonamental en aquesta química i són descrites habitualment com a les fàbriques de les cèl·lules. A més a més, les proteïnes estan involucrades en gairebé tots els processos fonamentals en els éssers vius. Malauradament, el nostre coneixement de la funció de moltes proteïnes és encara escaig degut a les limitacions actuals de molts mètodes experimentals, que encara no són capaços de proporcionar-nos estructures de cristall per a molts complexes proteïna-proteïna. El desenvolupament de tècniques i eines informàtiques d’acoblament proteïna-proteïna pot ésser crucial per a ajudar-nos a reduir aquest forat. En aquesta tesis, hem presentat un nou mètode computacional de predicció d’acoblament proteïna-proteïna, LightDock, que és capaç de fer servir diverses funcions energètiques definides per l’usuari i incloure un model de flexibilitat de la cadena principal mitjançant la anàlisis de modes normals. Segon, diverses eines d’interès per a la comunitat científica i basades en tecnologia web han sigut desenvolupades: un servidor web de predicció d’acoblament proteïna-proteïna, una eina online per a caracteritzar les interfícies d’acoblament proteïna-proteïna i una eina web per a incloure dades experimentals de tipus SAXS. A més a més, les optimitzacions fetes al protocol pyDock i la conseqüent millora en rendiment han propiciat que el nostre grup de recerca obtingués la cinquena posició entre més de 60 grups en les dues darreres avaluacions de l’experiment internacional CAPRI. Finalment, hem dissenyat i compilat els banc de proves d’acoblament proteïna-proteïna (versió 5) i proteïna-ARN (versió 1), molt importants per a la comunitat ja que permeten provar i desenvolupar nous mètodes i analitzar-ne el rendiment en aquest marc de referència comú.
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Gonçalves, Reinaldo Bellini. "Development and validation of new methods of distribution of initial population on genetic algorithms for the problem of protein-ligand docking." Laboratório Nacional de Computação Científica, 2008. http://www.lncc.br/tdmc/tde_busca/arquivo.php?codArquivo=154.

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The methods of protein-ligand docking are computational methods usedto predict the mode of binding of molecules into drug candidates for its receptor. The docking allows tests of hundreds of compounds in ashort space of time, assisting in the discovery of new drug candidates. The great complexity that involves the binding of protein-ligand complex, makes the problem of docking computationally difficult to be solved. In this work, we used the Genetic Algorithms which is a technique of optimization based on the theory of biological evolution of Darwin. The proposed algorithm was implemented and tested initially by Camila S. de Magalhães in her doctoral thesis, with the Group of Molecular Modeling of Biological Systems at LNCC, with a range of 5 ligands of HIV-1 protease. It was built a new set used for test with 49 structures with several physico-chemical properties, distributed in 22 different families of protein, allowing for a broader test of the algorithm It was conducted a detailed study of the dependence of the genetic algorithm in relation to the distribution of its initial population and it was also investigated ways more efficient and robust to generate the same. Among these, the proposal to distribute the initial population based on the coordinates of individuals of lower energy in the population (proposal 5), it is very promising. This distribution has allowed the algorithm to obtain good results, finding solutions of lower energy in the population very close to experimental structure optimized, without having specific information about the experimental structure. This fact is very important, because the algorithm makes it more realistic in view that in the rational design of drugs, it has not the trial structure.
Os métodos de docking proteína-ligante, são métodos computacionais usados para predizer o modo de ligação de moléculas candidatas a fármaco em seu receptor. O docking permite o teste de centenas de compostos em um curto espaço de tempo, auxiliando na descoberta de novos candidatos a fármacos. A grande complexidade que envolve a ligação do complexo ligante-proteína, torna o problema de docking difícil de ser resolvido computacionalmente. Neste trabalho, são usados os Algoritmos Genéticos, que são uma técnica de otimização baseada na teoria da evolução biológica de Darwin. O algoritmo proposto foi implementado e testado inicialmente por Camila S. de Magalhães em sua tese de doutorado, junto ao Grupo de Modelagem Molecular de Sistemas Biológicos do LNCC, com um conjunto de 5 ligantes de HIV-1 protease. Foi construido um novo conjunto utilizado para teste, agora com 49 estruturas com propriedades físico-químicas diversas, distribuidos em 22 famílias distintas de proteínas, permitindo um teste mais amplo do algoritmo. Foi realizado um estudo aprofundado sobre a dependência do Algoritmo Genético em relação à distribuição da sua população inicial e investigou-se formas mais eficientes e robustas de gerar a mesma. Dentre estas, a proposta de distribuir a população inicial baseada nas coordenadas dos indivíduos de menor energia na população (proposta 5), é muito promissora. Esta distribuição permitiu o algoritmo obter bons resultados, encontrando soluções de menor energia na população muito próximas a estrutura experimental otimizada, sem possuir informações específicas sobre a estrutura experimental. Este fato é muito importante, pois torna o algoritmo mais realista, tendo em vista que no desenho racional de fármacos real não se dispoe da estrutura experimental.
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Oledzki, Peter Richard. "Developing a protein-ligand docking algorithm : FlexLigDock." Thesis, University of Leeds, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435818.

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Betts, Matthew James. "Analysis and prediction of protein-protein recognition." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313795.

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Tai, Hio Kuan. "Protein-ligand docking and virtual screening based on chaos-embedded particle swarm optimization algorithm." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3948431.

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Ruiz, Echartea Maria Elisa. "Pairwise and Multi-Component Protein-Protein Docking Using Exhaustive Branch-and-Bound Tri-Dimensional Rotational Searches." Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0306.

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La détermination des structures tri-dimensionnelles (3D) des complexes protéiques est cruciale pour l’avancement des recherches sur les processus biologiques qui permettent, par exemple, de comprendre le développement de certaines maladies et, si possible, de les prévenir ou de les traiter. Face à l’intérêt des complexes protéiques pour la recherche, les difficultés et le coût élevé des méthodes expérimentales de détermination des structures 3D des protéines ont encouragé l’utilisation de l’informatique pour développer des outils capables de combler le fossé, comme par exemple les algorithmes d’amarrage protéiques. Le problème de l’amarrage protéique a été étudié depuis plus de 40 ans. Cependant, le développement d’algorithmes d’amarrages précis et efficaces demeure un défi à cause de la taille de l’espace de recherche, de la nature approximée des fonctions de score utilisées, et souvent de la flexibilité inhérente aux structures de protéines à amarrer. Cette thèse présente un algorithme pour l’amarrage rigide des protéines, qui utilise une série de recherches exhaustives rotationnelles au cours desquelles seules les orientations sans clash sont quantifiées par ATTRACT. L’espace rotationnel est représenté par une hyper-sphère à quaternion, qui est systématiquement subdivisée par séparation et évaluation, ce qui permet un élagage efficace des rotations qui donneraient des clashs stériques entre les deux protéines. Les contributions de cette thèse peuvent être décrites en trois parties principales comme suit. 1) L’algorithme appelé EROS-DOCK, qui permet d’amarrer deux protéines. Il a été testé sur 173 complexes du jeu de données “Docking Benchmark”. Selon les critères de qualité CAPRI, EROS-DOCK renvoie typiquement plus de solutions de qualité acceptable ou moyenne que ATTRACT et ZDOCK. 2) L’extension de l’algorithme EROS-DOCK pour permettre d’utiliser les contraintes de distance entre atomes ou entre résidus. Les résultats montrent que le fait d’utiliser une seule contrainte inter-résidus dans chaque interface d’interaction est suffisant pour faire passer de 51 à 121 le nombre de cas présentant une solution dans le top-10, sur 173 cas d’amarrages protéine-protéine. 3) L’extension de EROSDOCK à l’amarrage de complexes trimériques. Ici, la méthode proposée s’appuie sur l’hypothèse selon laquelle chacune des trois interfaces de la solution finale doit être similaire à au moins l’une des interfaces trouvées dans les solutions des amarrages pris deux-à-deux. L’algorithme a été testé sur un benchmark de 11 complexes à 3 protéines. Sept complexes ont obtenu au moins une solution de qualité acceptable dans le top-50 des solutions. À l’avenir, l’algorithme EROS-DOCK pourra encore évoluer en intégrant des fonctions de score améliorées et d’autres types de contraintes. De plus il pourra être utilisé en tant que composant dans des workflows élaborés pour résoudre des problèmes complexes d’assemblage multi-protéiques
Determination of tri-dimensional (3D) structures of protein complexes is crucial to increase research advances on biological processes that help, for instance, to understand the development of diseases and their possible prevention or treatment. The difficulties and high costs of experimental methods to determine protein 3D structures and the importance of protein complexes for research have encouraged the use of computer science for developing tools to help filling this gap, such as protein docking algorithms. The protein docking problem has been studied for over 40 years. However, developing accurate and efficient protein docking algorithms remains a challenging problem due to the size of the search space, the approximate nature of the scoring functions used, and often the inherent flexibility of the protein structures to be docked. This thesis presents an algorithm to rigidly dock proteins using a series of exhaustive 3D branch-and-bound rotational searches in which non-clashing orientations are scored using ATTRACT. The rotational space is represented as a quaternion “π-ball”, which is systematically sub-divided in a “branch-and-bound” manner, allowing efficient pruning of rotations that will give steric clashes. The contribution of this thesis can be described in three main parts as follows. 1) The algorithm called EROS-DOCK to assemble two proteins. It was tested on 173 Docking Benchmark complexes. According to the CAPRI quality criteria, EROS-DOCK typically gives more acceptable or medium quality solutions than ATTRACT and ZDOCK. 2)The extension of the EROS-DOCK algorithm to allow the use of atom-atom or residue-residue distance restraints. The results show that using even just one residue-residue restraint in each interaction interface is sufficient to increase the number of cases with acceptable solutions within the top-10 from 51 to 121 out of 173 pairwise docking cases. Hence, EROS-DOCK offers a new improved search strategy to incorporate experimental data, of which a proof-of-principle using data-driven computational restraints is demonstrated in this thesis, and this might be especially important for multi-body complexes. 3)The extension of the algorithm to dock trimeric complexes. Here, the proposed method is based on the premise that all of the interfaces in a multi-body docking solution should be similar to at least one interface in each of the lists of pairwise docking solutions. The algorithm was tested on a home-made benchmark of 11 three-body cases. Seven complexes obtained at least one acceptable quality solution in the top-50. In future, the EROS-DOCK algorithm can evolve by integrating improved scoring functions and other types of restraints. Moreover, it can be used as a component in elaborate workflows to efficiently solve complex problems of multi-protein assemblies
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Mucs, Daniel. "Computational methods for prediction of protein-ligand interactions." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/computational-methods-for-prediction-of-proteinligand-interactions(33ad0b24-ef7b-4dff-8e28-597a2f34e079).html.

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This thesis contains three main sections. In the first section, we examine methodologies to discriminate Type II protein kinase inhibitors from the Type I inhibitors. We have studied the structure of 55 Type II kinase inhibitors and have notice specific descriptive geometric features. Using this information we have developed a pharmacophore and a shape based screening approach. We have found that these methods did not effectively discriminate between the two inhibitor types used independently, but when combined in a consecutive way – pharmacophore search first, then shape based screening, we have found a method that successfully filtered out all Type I molecules. The effect of protonation states and using different conformer generators were studied as well. This method was then tested on a freely available database of decoy molecules and again shown to be discriminative. In the second section of the thesis, we implement and assess swarm-based docking methods. We implement a repulsive particle swarm optimization (RPSO) based conformational search approach into Autodock 3.05. The performance of this approach with different parameters was then tested on a set of 51 protein ligand complexes. The effect of using different factoring for the cognitive, social and repulsive terms and the importance of the inertia weight were explored. We found that the RPSO method gives similar performance to the particle swarm optimization method. Compared to the genetic algorithm approach used in Autodock 3.05, our RPSO method gives better results in terms of finding lower energy conformations. In the final, third section we have implemented a Monte Carlo (MC) based conformer searching approach into Gaussian03. This enables high level quantum mechanics/molecular mechanics (QM/MM) potentials to be used in docking molecules in a protein active site. This program was tested on two Zn2+ ion-containing complexes, carbonic anhydrase II and cytidine deaminase. The effects of different QM region definitions were explored in both systems. A consecutive and a parallel docking approach were used to study the volume of the active site explored by the MC search algorithm. In case of the carbonic anhydrase II complex, we have used 1,2-difluorobenzene as a ligand to explore the favourable interactions within the binding site. With the cytidine deaminase complex, we have evaluated the ability of the approach to discriminate the native pose from other higher energy conformations during the exploration of the active site of the protein. We find from our initial calculations, that our program is able to perform a conformational search in both cases, and the effect of QM region definition is noticeable, especially in the description of the hydrophobic interactions within the carbonic anhydrase II system. Our approach is also able to find poses of the cytidine deaminase ligand within 1 Å of the native pose.
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Skone, Gwyn S. "Stratagems for effective function evaluation in computational chemistry." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:8843465b-3e5f-45d9-a973-3b27949407ef.

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In recent years, the potential benefits of high-throughput virtual screening to the drug discovery community have been recognized, bringing an increase in the number of tools developed for this purpose. These programs have to process large quantities of data, searching for an optimal solution in a vast combinatorial range. This is particularly the case for protein-ligand docking, since proteins are sophisticated structures with complicated interactions for which either molecule might reshape itself. Even the very limited flexibility model to be considered here, using ligand conformation ensembles, requires six dimensions of exploration - three translations and three rotations - per rigid conformation. The functions for evaluating pose suitability can also be complex to calculate. Consequently, the programs being written for these biochemical simulations are extremely resource-intensive. This work introduces a pure computer science approach to the field, developing techniques to improve the effectiveness of such tools. Their architecture is generalized to an abstract pattern of nested layers for discussion, covering scoring functions, search methods, and screening overall. Based on this, new stratagems for molecular docking software design are described, including lazy or partial evaluation, geometric analysis, and parallel processing implementation. In addition, a range of novel algorithms are presented for applications such as active site detection with linear complexity (PIES) and small molecule shape description (PASTRY) for pre-alignment of ligands. The various stratagems are assessed individually and in combination, using several modified versions of an existing docking program, to demonstrate their benefit to virtual screening in practical contexts. In particular, the importance of appropriate precision in calculations is highlighted.
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Book chapters on the topic "Protein-Protein Docking - Algorithms"

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Mishra, Nikita, and Negin Forouzesh. "Protein-Ligand Binding with Applications in Molecular Docking." In Algorithms and Methods in Structural Bioinformatics, 1–16. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-05914-8_1.

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Cavasotto, Claudio N. "Handling Protein Flexibility in Docking and High-Throughput Docking: From Algorithms to Applications." In Methods and Principles in Medicinal Chemistry, 245–62. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527633326.ch9.

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Liu, Zhuoran, Changsheng Zhang, Qidong Zhao, Bin Zhang, and Wenjuan Sun. "Comparative Study of Evolutionary Algorithms for Protein-Ligand Docking Problem on the AutoDock." In Simulation Tools and Techniques, 598–607. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32216-8_58.

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Lenhof, Hans-Peter. "An Algorithm for the Protein Docking Problem." In Bioinformatics: From Nucleic Acids and Proteins to Cell Metabolism, 125–39. Weinheim, Germany: Wiley-VCH Verlag GmbH, 2007. http://dx.doi.org/10.1002/9783527615193.ch10.

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Hurwitz, Naama, and Haim J. Wolfson. "Memdock: An α-Helical Membrane Protein Docking Algorithm." In Methods in Molecular Biology, 111–17. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1468-6_7.

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Choi, Vicky, and Navin Goyal. "A Combinatorial Shape Matching Algorithm for Rigid Protein Docking." In Combinatorial Pattern Matching, 285–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27801-6_21.

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Reddy, S. V. G., K. Thammi Reddy, and V. Valli Kumari. "The Computational Analysis of Protein – Ligand Docking with Diverse Genetic Algorithm Parameters." In Advances in Intelligent Systems and Computing, 129–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13728-5_14.

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Hasani, Horia Jalily, and Khaled H. Barakat. "Protein-Protein Docking." In Methods and Algorithms for Molecular Docking-Based Drug Design and Discovery, 173–95. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0115-2.ch007.

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Protein-protein docking algorithms are powerful computational tools, capable of analyzing the protein-protein interactions at the atomic-level. In this chapter, we will review the theoretical concepts behind different protein-protein docking algorithms, highlighting their strengths as well as their limitations and pointing to important case studies for each method. The methods we intend to cover in this chapter include various search strategies and scoring techniques. This includes exhaustive global search, fast Fourier transform search, spherical Fourier transform-based search, direct search in Cartesian space, local shape feature matching, geometric hashing, genetic algorithm, randomized search, and Monte Carlo search. We will also discuss the different ways that have been used to incorporate protein flexibility within the docking procedure and some other future directions in this field, suggesting possible ways to improve the different methods.
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Hasani, Horia Jalily, and Khaled H. Barakat. "Protein-Protein Docking." In Pharmaceutical Sciences, 1092–114. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1762-7.ch042.

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Protein-protein docking algorithms are powerful computational tools, capable of analyzing the protein-protein interactions at the atomic-level. In this chapter, we will review the theoretical concepts behind different protein-protein docking algorithms, highlighting their strengths as well as their limitations and pointing to important case studies for each method. The methods we intend to cover in this chapter include various search strategies and scoring techniques. This includes exhaustive global search, fast Fourier transform search, spherical Fourier transform-based search, direct search in Cartesian space, local shape feature matching, geometric hashing, genetic algorithm, randomized search, and Monte Carlo search. We will also discuss the different ways that have been used to incorporate protein flexibility within the docking procedure and some other future directions in this field, suggesting possible ways to improve the different methods.
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Afanasyeva, Arina, Chioko Nagao, and Kenji Mizuguchi. "Docking algorithms and scoring functions." In Protein Interactions, 257–69. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811211874_0010.

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Conference papers on the topic "Protein-Protein Docking - Algorithms"

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Li, Zhao, Zhang Tianchi, and Zhang Jing. "Optimization Algorithms for Flexible Protein-Protein Docking." In 2012 Third International Conference on Digital Manufacturing and Automation (ICDMA). IEEE, 2012. http://dx.doi.org/10.1109/icdma.2012.135.

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Atilgan, Emrah, and Jianjun Hu. "Efficient protein-ligand docking using sustainable evolutionary algorithms." In 2010 10th International Conference on Hybrid Intelligent Systems (HIS 2010). IEEE, 2010. http://dx.doi.org/10.1109/his.2010.5600082.

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Lo, Ying-Tsang, Yueh-Lin Tsai, Hsin-Wei Wang, Yu-Ping Hsu, and Tun-Wen Pai. "Using Solid Angles to Detect Protein Docking Regions by CUDA Parallel Algorithms." In 2010 International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2010. http://dx.doi.org/10.1109/ispa.2010.66.

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Sunny, Sharon, Gautham Sreekumar, and Jayaraj P. B. "SFLADock: A Memetic Protein-Protein Docking Algorithm." In 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2022. http://dx.doi.org/10.1109/icdcece53908.2022.9793129.

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Hashmi, Irina, and Amarda Shehu. "A basin hopping algorithm for protein-protein docking." In 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2012. http://dx.doi.org/10.1109/bibm.2012.6392725.

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Atilgan, Emrah, and Jianjun Hu. "Efficient protein-ligand docking using sustainable evolutionary algorithm." In the 12th annual conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830483.1830521.

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Ao, Meng Chi, and Shirley W. I. Siu. "Evaluating Variants of Firefly Algorithm for Ligand Pose Prediction in Protein-ligand Docking Program." In ICBBT 2020: 2020 12th International Conference on Bioinformatics and Biomedical Technology. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3405758.3405761.

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Moghadasi, Mohammad, Dima Kozakov, Pirooz Vakili, Sandor Vajda, and Ioannis C. Paschalidis. "A new distributed algorithm for side-chain positioning in the process of protein docking." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6759970.

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Rondon, Paola, Henry Arguello, and Rodrigo Torres. "Development of a zoned genetic algorithm for semi-flexible protein-ligand docking in drug design." In 2011 6th Colombian Computing Congress (CCC). IEEE, 2011. http://dx.doi.org/10.1109/colomcc.2011.5936321.

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Ruiz-Tagle, Benjamin, Manuel Villalobos-Cid, Marcio Dorn, and Mario Inostroza-Ponta. "Evaluating the use of local search strategies for a memetic algorithm for the protein-ligand docking problem." In 2017 36th International Conference of the Chilean Computer Science Society (SCCC). IEEE, 2017. http://dx.doi.org/10.1109/sccc.2017.8405141.

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