Editing Protein folding (section)

=== Modeling of protein folding ===
 [[File:ACBP MSM from Folding@home.tiff|right|thumb|350px|Folding@home uses [[Markov state model]]s, like the one diagrammed here, to model the possible shapes and folding pathways a protein can take as it condenses from its initial randomly coiled state (left) into its native 3D structure (right).]]

''[[wiktionary:de novo|De novo]]'' or ''[[ab initio]]'' techniques for computational [[protein structure prediction]] can be used for simulating various aspects of protein folding. [[Molecular dynamics]] (MD) was used in simulations of protein folding and dynamics [[in silico]].<ref name="Rizzuti">{{cite journal | vauthors = Rizzuti B, Daggett V | title = Using simulations to provide the framework for experimental protein folding studies | journal = Archives of Biochemistry and Biophysics | volume = 531 | issue = 1–2 | pages = 128–35 | date = March 2013 | pmid = 23266569 | pmc = 4084838 | doi = 10.1016/j.abb.2012.12.015 }}</ref> First equilibrium folding simulations were done using implicit solvent model and [[umbrella sampling]].<ref>{{cite journal | vauthors = Schaefer M, Bartels C, Karplus M | title = Solution conformations and thermodynamics of structured peptides: molecular dynamics simulation with an implicit solvation model | journal = Journal of Molecular Biology | volume = 284 | issue = 3 | pages = 835–48 | date = December 1998 | pmid = 9826519 | doi = 10.1006/jmbi.1998.2172 }}</ref> Because of computational cost, ab initio MD folding simulations with explicit water are limited to peptides and small proteins.<ref>{{cite web | url = http://www.cs.ucl.ac.uk/staff/d.jones/t42morph.html | title = Fragment-based Protein Folding Simulations | first = David | last = Jones | name-list-style = vanc | publisher = University College London }}</ref><ref>{{cite web | url = http://www.biomolecular-modeling.com/Abalone/Protein-folding.html | title = Protein folding | format = by Molecular Dynamics }}</ref> MD simulations of larger proteins remain restricted to dynamics of the experimental structure or its high-temperature unfolding. Long-time folding processes (beyond about 1 millisecond), like folding of larger proteins (>150 residues)  can be accessed using [[Coarse-grained modeling|coarse-grained models]].<ref>{{cite journal | vauthors = Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A | title = Coarse-Grained Protein Models and Their Applications | journal = Chemical Reviews | volume = 116 | issue = 14 | pages = 7898–936 | date = July 2016 | pmid = 27333362 | doi = 10.1021/acs.chemrev.6b00163 | doi-access = free }}</ref><ref name="Kmiecik">{{cite journal | vauthors = Kmiecik S, Kolinski A | title = Characterization of protein-folding pathways by reduced-space modeling | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 104 | issue = 30 | pages = 12330–5 | date = July 2007 | pmid = 17636132 | pmc = 1941469 | doi = 10.1073/pnas.0702265104 | bibcode = 2007PNAS..10412330K | doi-access = free }}</ref><ref name="teritfix">{{cite journal | vauthors = Adhikari AN, Freed KF, Sosnick TR | title = De novo prediction of protein folding pathways and structure using the principle of sequential stabilization | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 109 | issue = 43 | pages = 17442–7 | date = October 2012 | pmid = 23045636 | pmc = 3491489 | doi = 10.1073/pnas.1209000109 | bibcode = 2012PNAS..10917442A | doi-access = free }}</ref>

Several large-scale computational projects, such as [[Rosetta@home]],<ref>{{Cite web|url=http://boinc.bakerlab.org/rosetta/|title=Rosetta@home|website=boinc.bakerlab.org|accessdate=14 March 2023}}</ref> [[Folding@home]]<ref>{{Cite web|url=https://foldingathome.org/about-2/the-foldinghome-consortium/|title=The Folding@home Consortium (FAHC) – Folding@home|accessdate=14 March 2023}}</ref> and [[Foldit]],<ref>{{Cite web|url=http://fold.it/portal/info/science|title=Foldit|website=fold.it|accessdate=14 March 2023}}</ref> target protein folding.

Long continuous-trajectory simulations have been performed on [[Anton (computer)|Anton]], a massively parallel supercomputer designed and built around custom [[ASIC]]s and interconnects by [[D. E. Shaw Research]]. The longest published result of a simulation performed using Anton as of 2011 was a 2.936 millisecond simulation of NTL9 at 355 K.<ref name="pmid22034434">{{cite journal | vauthors = Lindorff-Larsen K, Piana S, Dror RO, Shaw DE | title = How fast-folding proteins fold | journal = Science | volume = 334 | issue = 6055 | pages = 517–20 | date = October 2011 | pmid = 22034434 | doi = 10.1126/science.1208351 | bibcode = 2011Sci...334..517L | s2cid = 27988268 }}</ref> Such simulations are currently able to unfold and refold small proteins (<150 amino acids residues) in equilibrium and predict how mutations affect folding kinetics and stability. <ref name="pmid20974152">{{cite journal | vauthors = Piana S, Piana S, Sarkar K, Lindorff-Larsen K, Guo M, Gruebele M, Shaw DE | title = Computational Design and Experimental Testing of the Fastest-Folding β-Sheet Protein | journal = J. Mol. Biol. | volume = 405 | pages = 43–48 | date = 2010 | issue = 1 | doi = 10.1016/j.jmb.2010.10.023 | pmid = 20974152 }}</ref>

In 2020 a team of researchers that used [[AlphaFold]], an [[artificial intelligence]] (AI) protein structure prediction program developed by [[DeepMind]] placed first in [[CASP]], a long-standing structure prediction contest.<ref name=cnbc20201130>{{Cite news |last=Shead|first=Sam |date=2020-11-30 |title=DeepMind solves 50-year-old 'grand challenge' with protein folding A.I. |url=https://www.cnbc.com/2020/11/30/deepmind-solves-protein-folding-grand-challenge-with-alphafold-ai.html |access-date=2020-11-30|website=CNBC|language=en}}</ref> The team achieved a level of accuracy much higher than any other group.<ref name="Stoddart">{{cite journal |last1=Stoddart |first1=Charlotte |title=Structural biology: How proteins got their close-up |journal=Knowable Magazine |date=1 March 2022 |doi=10.1146/knowable-022822-1|s2cid=247206999 |doi-access=free |url=https://knowablemagazine.org/article/living-world/2022/structural-biology-how-proteins-got-their-closeup |access-date=25 March 2022}}</ref> It scored above 90% for around two-thirds of the proteins in CASP's [[Global distance test|global distance test (GDT)]], a test that measures the degree of similarity between the structure predicted by a computational program, and the empirical structure determined experimentally in a lab. A score of 100 is considered a complete match, within the distance cutoff used for calculating GDT.<ref name=science20201130>Robert F. Service, [https://www.science.org/content/article/game-has-changed-ai-triumphs-solving-protein-structures 'The game has changed.' AI triumphs at solving protein structures], ''[[Science (magazine)|Science]]'', 30 November 2020</ref>

AlphaFold's protein structure prediction results at CASP were described as "transformational" and "astounding".<ref name=Callaway2020>{{cite journal |last1=Callaway |first1=Ewen |title='It will change everything': DeepMind's AI makes gigantic leap in solving protein structures |journal=Nature |date=30 November 2020 |volume=588 |issue=7837 |pages=203–204 |doi=10.1038/d41586-020-03348-4 |pmid=33257889 |bibcode=2020Natur.588..203C |s2cid=227243204 }}</ref><ref>{{cite tweet|user=MoAlQuraishi|number=1333383634649313280|title=CASP14 #s just came out and they're astounding}}</ref> Some researchers noted that the accuracy is not high enough for a third of its predictions, and that it does not reveal the physical mechanism of protein folding for the [[protein folding problem]] to be considered solved.<ref>{{cite web |url=https://www.chemistryworld.com/opinion/behind-the-screens-of-alphafold/4012867.article |title=Behind the screens of AlphaFold |first= Phillip |last= Balls|date=9 December 2020|work=Chemistry World }}</ref> Nevertheless, it is considered a significant achievement in [[computational biology]]<ref name=science20201130/> and great progress towards a decades-old grand challenge of biology, predicting the structure of proteins.<ref name=Callaway2020/>