ArticleAIChE journal. American Institute of Chemical Engineers2012
ASTRO-FOLD 2.0: an Enhanced Framework for Protein Structure Prediction.
Article in AIChE journal. American Institute of Chemical Engineers, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- A Guide to In Silico Drug Design.Pharmaceutics · 2022Review
- Protein Structure Prediction: Conventional and Deep Learning Perspectives.The protein journal · 2021Review
- COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization.Membranes · 2021Article
- Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure.BMC bioinformatics · 2020Article
- Improved contact predictions using the recognition of protein like contact patterns.PLoS computational biology · 2014Article
- Protein folding and de novo protein design for biotechnological applications.Trends in biotechnology · 2014Review
- Forcefield_PTM:Journal of chemical theory and computation · 2013Article
- Protein WISDOM: a workbench for in silico de novo design of biomolecules.Journal of visualized experiments : JoVE · 2013Article
- Structure prediction of loops with fixed and flexible stems.The journal of physical chemistry. B · 2012Article
- β-sheet topology prediction with high precision and recall for β and mixed α/β proteins.PloS one · 2012Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles-based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) β-sheet topology prediction using mixed-integer linear optimization (MILP), (3) Residue-to-residue contact prediction using a high-resolution distance-dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non-linear optimization (NLP), (5) 3-D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full-atomistic ECEPP/3 potential, (6) Near-native structure selection using a traveling salesman problem-based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO-FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.