The Experts below are selected from a list of 24225 Experts worldwide ranked by ideXlab platform
Jianlin Cheng - One of the best experts on this subject based on the ideXlab platform.
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Improving Protein Tertiary Structure prediction by deep learning and distance prediction in CASP14
2021Co-Authors: Jian Liu, Zhiye Guo, Jie Hou, Jianlin ChengAbstract:Substantial progresses in Protein Structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM Protein Structure prediction system in three main aspects: (1) a new deep learning based Protein inter-residue distance predictor (DeepDist) to improve template-free (ab initio) Tertiary Structure prediction, (2) an enhanced template-based Tertiary Structure prediction method, and (3) distance-based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked 7th out of 146 predictors in Protein Tertiary Structure prediction and ranked 3rd out of 136 predictors in inter-domain Structure predic-tion. The results of MULTICOM demonstrate that the template-free modeling based on deep learning and residue-residue distance prediction can predict the correct topology for almost all template-based modeling targets and a majority of hard targets (template-free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. The performance of template-free Tertiary Structure prediction largely depends on the accuracy of distance pre-dictions that is closely related to the quality of multiple sequence alignments. The structural model quality assessment works reasonably well on targets for which a sufficient number of good models can be predicted, but may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed.
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Improving Protein Tertiary Structure prediction by deep learning and distance prediction in CASP14
2021Co-Authors: Jian Liu, Zhiye Guo, Jie Hou, Jianlin ChengAbstract:Substantial progresses in Protein Structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM Protein Structure prediction system in the three main aspects: (1) a new deep-learning based Protein inter-residue distance predictor (DeepDist) to improve template-free (ab initio) Tertiary Structure prediction, (2) an enhanced template-based Tertiary Structure prediction method, and (3) distance-based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked 7th out of 146 predictors in Protein Tertiary Structure prediction and ranked 3rd out of 136 predictors in inter-domain Structure prediction. The results of MULTICOM demonstrate that the template-free modeling based on deep learning and residue-residue distance prediction can predict the correct topology for almost all template-based modeling targets and a majority of hard targets (template-free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. The performance of template-free Tertiary Structure prediction largely depends on the accuracy of distance predictions that is closely related to the quality of multiple sequence alignments. The structural model quality assessment works reasonably well on targets for which a sufficient number of good models can be predicted, but may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed.
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Protein Tertiary Structure modeling driven by deep learning and contact distance prediction in casp13
International Conference on Bioinformatics, 2019Co-Authors: Jianlin ChengAbstract:Ab initio prediction of Protein Structure from sequence is one of the most challenging and important problems in bioinformatics and computational biology. After a long period of stagnancy, ab initio Protein Structure prediction is undergoing a revolution driven by inter-residue contact distance prediction empowered by deep learning. In this talk, I will present the deep learning and contact distance prediction methods of our MULTICOM Protein Structure prediction system that was ranked among the top three best methods in the 13th community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP13) in 2018 [1]. MULTICOM was able to correctly fold Structures of numerous hard Protein targets from scratch in CASP13, which was an unprecedented progress. The success clearly demonstrates that contact distance prediction is the key direction to tackle the Protein Structure prediction challenge and deep learning is the key technology to solve it. However, to completely solve the problem, more advanced deep learning methods are needed to accurately predict inter-residue distances when few homologous sequences are available to calculate residue-residue co-evolution scores, fold Proteins from noisy inter-residue distances, and rank the structural models of hard Protein targets.
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Protein Tertiary Structure modeling driven by deep learning and contact distance prediction in casp13
Proteins, 2019Co-Authors: Tianqi Wu, Jianlin ChengAbstract:Predicting residue-residue distance relationships (eg, contacts) has become the key direction to advance Protein Structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM Protein Structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, distance-driven template-free (ab initio) modeling, and Protein model ranking empowered by deep learning and contact prediction. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both template-free and template-based Structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residue-residue features such as coevolution scores to substantially improve contact distance prediction, which played a decisive role in correctly folding some free modeling and hard template-based modeling targets. Deep learning also successfully integrated one-dimensional structural features, two-dimensional contact information, and three-dimensional structural quality scores to improve Protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of Protein models for the first time. The success of MULTICOM system clearly shows that Protein contact distance prediction and model selection driven by deep learning holds the key of solving Protein Structure prediction problem. However, there are still challenges in accurately predicting Protein contact distance when there are few homologous sequences, folding Proteins from noisy contact distances, and ranking models of hard targets.
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Protein Tertiary Structure modeling driven by deep learning and contact distance prediction in casp13
bioRxiv, 2019Co-Authors: Tianqi Wu, Jianlin ChengAbstract:Prediction of residue-residue distance relationships (e.g. contacts) has become the key direction to advance Protein Tertiary Structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM Protein Structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, contact distance-driven template-free (ab initio) modeling, and Protein model ranking empowered by deep learning and contact prediction, in addition to an update of other components such as template library, sequence database, and alignment tools. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both template-free and template-based Protein Structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residue-residue features such as co-evolution scores to substantially improve inter-residue contact distance prediction, which played a decisive role in correctly folding some free modeling and hard template-based modeling targets from scratch. Deep learning also successfully integrated 1D structural features, 2D contact information, and 3D structural quality scores to improve Protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of Protein models for the first time. The success of MULTICOM system in the CASP13 experiment clearly shows that Protein contact distance prediction and model selection driven by powerful deep learning holds the key of solving Protein Structure prediction problem. However, there are still major challenges in accurately predicting Protein contact distance when there are few homologous sequences to generate co-evolutionary signals, folding Proteins from noisy contact distances, and ranking models of hard targets.
Bhyravabhotla Jayaram - One of the best experts on this subject based on the ideXlab platform.
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ProTSAV: A Protein Tertiary Structure analysis and validation server Proteins and proteomics
Biochimica et Biophysica Acta, 2016Co-Authors: Ankita Singh, Rahul Kaushik, Avinash Mishra, Asheesh Shanker, Bhyravabhotla JayaramAbstract:Quality assessment of predicted model Structures of Proteins is as important as the Protein Tertiary Structure prediction. A highly efficient quality assessment of predicted model Structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model Structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual Protein Structure along with a graphical representation and ranking in case of multiple Protein Structure assessment. The server is validated on ~64,446 Protein Structures including experimental Structures from RCSB and predicted model Structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of Protein Structures with a specificity of 100% and a sensitivity of 98% on experimentally solved Structures and achieves a specificity of 88%and a sensitivity of 91% on predicted Protein Structures of CASP11 targets under 2A.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment.ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp
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ProTSAV: A Protein Tertiary Structure analysis and validation server
Biochimica et biophysica acta, 2015Co-Authors: Ankita Singh, Rahul Kaushik, Avinash Mishra, Asheesh Shanker, Bhyravabhotla JayaramAbstract:Quality assessment of predicted model Structures of Proteins is as important as the Protein Tertiary Structure prediction. A highly efficient quality assessment of predicted model Structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model Structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual Protein Structure along with a graphical representation and ranking in case of multiple Protein Structure assessment. The server is validated on ~64,446 Protein Structures including experimental Structures from RCSB and predicted model Structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of Protein Structures with a specificity of 100% and a sensitivity of 98% on experimentally solved Structures and achieves a specificity of 88%and a sensitivity of 91% on predicted Protein Structures of CASP11 targets under 2A.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp.
Andrzej Kloczkowski - One of the best experts on this subject based on the ideXlab platform.
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Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques.
Molecules (Basel Switzerland), 2020Co-Authors: Óscar Álvarez-machancoses, Juan Luis Fernández-martínez, Andrzej KloczkowskiAbstract:We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in Protein Tertiary Structure prediction, followed by Structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of Protein templates before constructing a lower dimensional subspace via a regularized LDA. The Protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive–regressive PSO (RR-PSO). The obtained Structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a Structure refinement. The final Structures are similar to those predicted by best Structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of Protein Structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set.
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predicting Protein Tertiary Structure and its uncertainty analysis via particle swarm sampling
Journal of Molecular Modeling, 2019Co-Authors: Oscar Alvarez, Juan Luis Fernandezmartinez, Ana Cernea Corbeanu, Zulima Fernandezmuniz, Andrzej KloczkowskiAbstract:We discuss the relationship between the problem of Protein Tertiary Structure prediction from the amino acid sequence and the uncertainty analysis. The algorithm presented in this paper belongs to the category of decoy-based modeling, where different known Protein models are used to establish a low dimensional space via principal component analysis. The low dimensional space is utilized to perform an energy optimization via a family of very explorative particle swarm optimizers to find the global minimum. The aim of this procedure is to get a representative sample of the nonlinear equivalent region, that is, Protein models that have their energy lower than a certain energy bound. The posterior analysis of this family provides very valuable information about the backbone Structure of the native conformation and its possible alternate states. This methodology has the advantage of being simple and fast and can help refine the Tertiary Protein Structure. We comprehensively illustrate the performance of our algorithm on one Protein from the CASP-9 Protein Structure prediction experiment. We also provide a theoretical analysis of the energy landscape found in the Tertiary Structure Protein inverse problem, explaining why model reduction techniques (principal component analysis in this case) serve to alleviate the ill-posed character of this high dimensional optimization problem. In addition, we expand the computational benchmark with a summary of other CASP-9 Proteins in the Appendix.
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on the use of principal component analysis and particle swarm optimization in Protein Tertiary Structure prediction
International Conference on Artificial Intelligence and Soft Computing, 2018Co-Authors: Oscar Alvarez, Juan Luis Fernandezmartinez, Zulima Fernandezmuniz, Andrzej Kloczkowski, Ana Cernea, Celia FernandezbrilletAbstract:We discuss applicability of Principal Component Analysis and Particle Swarm Optimization in Protein Tertiary Structure prediction. The proposed algorithm is based on establishing a low-dimensional space where the sampling (and optimization) is carried out via Particle Swarm Optimizer (PSO). The reduced space is found via Principal Component Analysis (PCA) performed for a set of previously found low-energy Protein models. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. Our results show that PSO improves the energy of the best decoy used in the PCA considering an adequate number of PCA terms.
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ICAISC (2) - On the Use of Principal Component Analysis and Particle Swarm Optimization in Protein Tertiary Structure Prediction
Artificial Intelligence and Soft Computing, 2018Co-Authors: Oscar Alvarez, Juan Luis Fernández-martínez, Ana Cernea, Celia Fernández-brillet, Zulima Fernández-muñiz, Andrzej KloczkowskiAbstract:We discuss applicability of Principal Component Analysis and Particle Swarm Optimization in Protein Tertiary Structure prediction. The proposed algorithm is based on establishing a low-dimensional space where the sampling (and optimization) is carried out via Particle Swarm Optimizer (PSO). The reduced space is found via Principal Component Analysis (PCA) performed for a set of previously found low-energy Protein models. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. Our results show that PSO improves the energy of the best decoy used in the PCA considering an adequate number of PCA terms.
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Protein Tertiary Structure prediction via svd and pso sampling
International Conference on Bioinformatics and Biomedical Engineering, 2018Co-Authors: Oscar Alvarez, Juan Luis Fernandezmartinez, Zulima Fernandezmuniz, Ana Cernea, Andrzej KloczkowskiAbstract:We discuss the use of the Singular Value Decomposition as a model reduction technique in Protein Tertiary Structure prediction, alongside to the uncertainty analysis associated to the Tertiary Protein predictions via Particle Swarm Optimization (PSO). The algorithm presented in this paper corresponds to the category of the decoy-based modelling, since it first finds a good Protein model located in the low energy region of the Protein energy landscape, that is used to establish a three-dimensional space where the free-energy optimization and search is performed via an exploratory version of PSO. The ultimate goal of this algorithm is to get a representative sample of the Protein backbone Structure and the alternate states in an energy region equivalent or lower than the one corresponding to the Protein model that is used to establish the expansion (model reduction), obtaining as result other Protein Structures that are closer to the native Structure and a measure of the uncertainty in the Protein Tertiary Protein reconstruction. The strength of this methodology is that it is simple and fast, and serves to alleviate the ill-posed character of the Protein Structure prediction problem, which is very highly dimensional, improving the results when it is performed in a good Protein model of the low energy region. To prove this fact numerically we present the results of the application of the SVD-PSO algorithm to a set of Proteins of the CASP competition whose native’s Structures are known.
Peter G. Wolynes - One of the best experts on this subject based on the ideXlab platform.
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Protein Tertiary Structure recognition using optimized hamiltonians with local interactions
Proceedings of the National Academy of Sciences of the United States of America, 1992Co-Authors: Richard A. Goldstein, Zaida Lutheyschulten, Peter G. WolynesAbstract:Abstract Protein folding codes embodying local interactions including surface and secondary Structure propensities and residue-residue contacts are optimized for a set of training Proteins by using spin-glass theory. A screening method based on these codes correctly matches the Structure of a set of test Proteins with Proteins of similar topology with 100% accuracy, even with limited sequence similarity between the test Proteins and the structural homologs and the absence of any structurally similar Proteins in the training set.
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Protein Tertiary Structure recognition usingoptimized Hamiltonians withlocal interactions
1992Co-Authors: Richard A. Goldstein, Zaidaa . Luthey-schulten, Peter G. WolynesAbstract:Protein folding codes embodying local inter- actions including surface andsecondary Structure propensities andresidue-residue contacts areoptimized forasetoftraining Proteins byusing spin-glass theory. Ascreening method based onthese codes correctly matches theStructure ofasetoftest Proteins withProteins ofsimilar topology with100%accuracy, evenwithlimited sequence similarity between thetest Proteins andthestructural homologs andtheabsence ofanystructurally similar Proteins inthetraining set. Theability topredict thenative Tertiary Structure ofaProtein based solely onits aminoacidsequence haslongbeenagoal ofcomputational biophysics. Theroughness ofarealistic free-energy landscape withitsattendant numerous local minima combined withthelarge numberofconformational degrees offreedom foraProtein chain haveledtoattempts tocreate alternative energy functions intermsofreduced descriptions oftheProtein configuration. Inprevious work, weexplored theuseofassociative memoryHamiltonians, a particular typeoffolding codeintroduced byFriedrichs and Wolynes (1), whichencodes correlations between these- quence ofthetarget Protein whoseStructure istobedeter- minedandthesequences andStructures ofasetof"memo- ry"Proteins. Useoftheassociative memoryformulation allowed ustoapply thetheory ofspinglasses, whoserele- vancetoProtein folding hasbeenexplored (2-7), tocreate a nonslavishly realistic energy function forProtein Tertiary Structure prediction optimized soastofacilitate rapid folding while avoiding local energy minima. We demonstrated the ability ofanoptimized associative memoryHamiltonian to correctly predict low-resolution Structures oftarget Proteins withlowsequence similarity tothememoryProteins by either ascreening method ormolecular dynamics withsim-
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Generalized Protein Tertiary Structure recognition using associative memory Hamiltonians.
Journal of molecular biology, 1991Co-Authors: Mark S. Friedrichs, Richard A. Goldstein, Peter G. WolynesAbstract:Abstract In previous papers, a method of Protein Tertiary Structure recognition was described based on the construction of an associative memory Hamiltonian, which encoded the amino acid sequence and the Cα co-ordinates of a set of database Proteins. Using molecular dynamics with simulated annealing, the ability of the Hamiltonian to successfully recall the Structure of a Protein in the memory database was successfully demonstrated, as long as the total number of database Proteins did not exceed a characteristic value, called the capacity of the Hamiltonian, equal to 0·5N to 0·7N, where N is the number of amino acid residues in the Protein to be recalled. In this paper, we describe the development of additional methods to increase the capacity of the Hamiltonian, including use of a more complete representation of the Protein backbone and the incorporation of contextual information into the Hamiltonian through the use of secondary Structure prediction. In addition, we further extend the ability of associative memory models to predict the Tertiary Structures of Proteins not present in the Protein data set, by making the Hamiltonian invariant with respect to biological symmetries that represent site mutations and insertions and deletions. The ability of the Hamiltonian to generalize from homologous Proteins to an unknown Protein in the presence of other unrelated Proteins in the data set is demonstrated.
Ankita Singh - One of the best experts on this subject based on the ideXlab platform.
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ProTSAV: A Protein Tertiary Structure analysis and validation server Proteins and proteomics
Biochimica et Biophysica Acta, 2016Co-Authors: Ankita Singh, Rahul Kaushik, Avinash Mishra, Asheesh Shanker, Bhyravabhotla JayaramAbstract:Quality assessment of predicted model Structures of Proteins is as important as the Protein Tertiary Structure prediction. A highly efficient quality assessment of predicted model Structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model Structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual Protein Structure along with a graphical representation and ranking in case of multiple Protein Structure assessment. The server is validated on ~64,446 Protein Structures including experimental Structures from RCSB and predicted model Structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of Protein Structures with a specificity of 100% and a sensitivity of 98% on experimentally solved Structures and achieves a specificity of 88%and a sensitivity of 91% on predicted Protein Structures of CASP11 targets under 2A.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment.ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp
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ProTSAV: A Protein Tertiary Structure analysis and validation server
Biochimica et biophysica acta, 2015Co-Authors: Ankita Singh, Rahul Kaushik, Avinash Mishra, Asheesh Shanker, Bhyravabhotla JayaramAbstract:Quality assessment of predicted model Structures of Proteins is as important as the Protein Tertiary Structure prediction. A highly efficient quality assessment of predicted model Structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model Structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual Protein Structure along with a graphical representation and ranking in case of multiple Protein Structure assessment. The server is validated on ~64,446 Protein Structures including experimental Structures from RCSB and predicted model Structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of Protein Structures with a specificity of 100% and a sensitivity of 98% on experimentally solved Structures and achieves a specificity of 88%and a sensitivity of 91% on predicted Protein Structures of CASP11 targets under 2A.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp.
