Generalized dead-end elimination algorithms make large-scale protein side-chain structure prediction tractable: implications for protein design and structural genomics

2001 ◽  
Vol 307 (1) ◽  
pp. 429-445 ◽  
Author(s):  
Loren L. Looger ◽  
Homme W. Hellinga
Keyword(s):  
Protein Design ◽  
Structural Genomics ◽  
Structure Prediction ◽  
Large Scale ◽  
Chain Structure ◽  
Side Chain ◽  
Dead End

scholarly journals DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins

2021 ◽  
Author(s):  
Mikita Misiura ◽  
Raghav Shroff ◽  
Ross Thyer ◽  
Anatoly Kolomeisky
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Protein Design ◽  
Structure Prediction ◽  
Deep Neural Networks ◽  
Side Chain ◽  
Amino Acid Side Chain ◽  
Image Transformation ◽  
Hydrophobic Amino Acids ◽  
Chain Conformations

Prediction of side chain conformations of amino acids in proteins (also termed 'packing') is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this work we evaluate the potential of Deep Neural Networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr and Trp being up to 50% smaller.

scholarly journals FASPR: an open-source tool for fast and accurate protein side-chain packing

2020 ◽  
Vol 36 (12) ◽  
pp. 3758-3765 ◽  
Author(s):  
Xiaoqiang Huang ◽  
Robin Pearce ◽  
Yang Zhang
Keyword(s):  
Protein Structure ◽  
Protein Design ◽  
Structure Prediction ◽  
Protein Structures ◽  
Scoring Function ◽  
Supplementary Information ◽  
Side Chain ◽  
Chain Packing ◽  
And Function ◽  
Side Chain Packing

Abstract Motivation Protein structure and function are essentially determined by how the side-chain atoms interact with each other. Thus, accurate protein side-chain packing (PSCP) is a critical step toward protein structure prediction and protein design. Despite the importance of the problem, however, the accuracy and speed of current PSCP programs are still not satisfactory. Results We present FASPR for fast and accurate PSCP by using an optimized scoring function in combination with a deterministic searching algorithm. The performance of FASPR was compared with four state-of-the-art PSCP methods (CISRR, RASP, SCATD and SCWRL4) on both native and non-native protein backbones. For the assessment on native backbones, FASPR achieved a good performance by correctly predicting 69.1% of all the side-chain dihedral angles using a stringent tolerance criterion of 20°, compared favorably with SCWRL4, CISRR, RASP and SCATD which successfully predicted 68.8%, 68.6%, 67.8% and 61.7%, respectively. Additionally, FASPR achieved the highest speed for packing the 379 test protein structures in only 34.3 s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on I-TASSER predicted backbones and the backbones perturbed from experimental structures. Detailed analyses showed that the major advantage of FASPR lies in the optimal combination of the dead-end elimination and tree decomposition with a well optimized scoring function, which makes FASPR of practical use for both protein structure modeling and protein design studies. Availability and implementation The web server, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/FASPR and https://github.com/tommyhuangthu/FASPR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Designing Peptides on a Quantum Computer

2019 ◽  
Author(s):  
Vikram Khipple Mulligan ◽  
Hans Melo ◽  
Haley Irene Merritt ◽  
Stewart Slocum ◽  
Brian D. Weitzner ◽  
...  
Keyword(s):  
Protein Design ◽  
Real World ◽  
Large Scale ◽  
Quantum Computer ◽  
Research Area ◽  
Side Chain ◽  
Quantum Computers ◽  
Amino Acid Side Chain ◽  
Processing Unit ◽  
D Wave

AbstractAlthough a wide variety of quantum computers are currently being developed, actual computational results have been largely restricted to contrived, artificial tasks. Finding ways to apply quantum computers to useful, real-world computational tasks remains an active research area. Here we describe our mapping of the protein design problem to the D-Wave quantum annealer. We present a system whereby Rosetta, a state-of-the-art protein design software suite, interfaces with the D-Wave quantum processing unit to find amino acid side chain identities and conformations to stabilize a fixed protein backbone. Our approach, which we call the QPacker, uses a large side-chain rotamer library and the full Rosetta energy function, and in no way reduces the design task to a simpler format. We demonstrate that quantum annealer-based design can be applied to complex real-world design tasks, producing designed molecules comparable to those produced by widely adopted classical design approaches. We also show through large-scale classical folding simulations that the results produced on the quantum annealer can inform wet-lab experiments. For design tasks that scale exponentially on classical computers, the QPacker achieves nearly constant runtime performance over the range of problem sizes that could be tested. We anticipate better than classical performance scaling as quantum computers mature.

scholarly journals Relationship of 25-hydroxyvitamin D3 side chain structure to biological activity.

1975 ◽  
Vol 250 (1) ◽  
pp. 226-230
Author(s):  
M F Holick ◽  
M Garabedian ◽  
H K Schnoes ◽  
H F DeLuca
Keyword(s):  
Biological Activity ◽  
Chain Structure ◽  
Side Chain ◽  
Relationship Of

scholarly journals cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design

2016 ◽  
Vol 23 (9) ◽  
pp. 737-749 ◽  
Author(s):  
Yuchao Pan ◽  
Yuxi Dong ◽  
Jingtian Zhou ◽  
Mark Hallen ◽  
Bruce R. Donald ◽  
...  
Keyword(s):  
Protein Design ◽  
Distributed Algorithm ◽  
Large Scale ◽  
Computational Protein Design
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