Editing Protein design (section)

===Energy function===
[[File:PEF comparison.png|thumb|400px|right|Comparison of various potential energy functions. The most accurate energy are those that use quantum mechanical calculations, but these are too slow for protein design. On the other extreme, heuristic energy functions are based on statistical terms and are very fast. In the middle are molecular mechanics energy functions that are physically based but are not as computationally expensive as quantum mechanical simulations.<ref name="Boas"/>]]

Rational protein design techniques must be able to discriminate sequences that will be stable under the target fold from those that would prefer other low-energy competing states. Thus, protein design requires accurate [[force field (chemistry)|energy functions]] that can rank and score sequences by how well they fold to the target structure. At the same time, however, these energy functions must consider the computational [[#As an optimization problem|challenges]] behind protein design.  One of the most challenging requirements for successful design is an energy function that is both accurate and simple for computational calculations.

The most accurate energy functions are those based on quantum mechanical simulations. However, such simulations are too slow and typically impractical for protein design. Instead, many protein design algorithms use either physics-based energy functions adapted from [[molecular mechanics]] simulation programs, [[statistical potential|knowledge based energy-functions]], or a hybrid mix of both. The trend has been toward using more physics-based potential energy functions.<ref name="Boas">{{cite journal |last1=Boas |first1=F. E. |last2=Harbury |first2=P. B. |name-list-style=amp |year=2007 |title=Potential energy functions for protein design |journal=Current Opinion in Structural Biology |volume=17 |issue=2 |pages=199–204 |doi=10.1016/j.sbi.2007.03.006 |pmid=17387014}}</ref>

Physics-based energy functions, such as [[AMBER]] and [[CHARMM]], are typically derived from quantum mechanical simulations, and experimental data from thermodynamics, crystallography, and spectroscopy.<ref name="boas2007">{{cite journal|last=Boas|first=FE|author2=Harbury, PB |title=Potential energy functions for protein design.|journal=Current Opinion in Structural Biology|date=April 2007|volume=17|issue=2|pages=199–204|pmid=17387014|doi=10.1016/j.sbi.2007.03.006}}</ref> These energy functions typically simplify physical energy function and make them pairwise decomposable, meaning that the total energy of a protein conformation can be calculated by adding the pairwise energy between each atom pair, which makes them attractive for optimization algorithms. Physics-based energy functions typically model an attractive-repulsive [[Lennard-Jones]] term between atoms and a pairwise [[electrostatics]] coulombic term<ref>{{cite journal|last=Vizcarra|first=CL|author2=Mayo, SL |title=Electrostatics in computational protein design.|journal=Current Opinion in Chemical Biology|date=December 2005|volume=9|issue=6|pages=622–6|pmid=16257567|doi=10.1016/j.cbpa.2005.10.014}}</ref> between non-bonded atoms.

[[File:Water-hbond-vrc01-gp120.png|thumb|left|Water-mediated hydrogen bonds play a key role in protein–protein binding. One such interaction is shown between residues D457, S365 in the heavy chain of the HIV-broadly-neutralizing antibody VRC01 (green) and residues N58 and Y59 in the HIV envelope protein GP120 (purple).<ref name="wu2010">{{cite journal|last=Zhou|first=T|author2=Georgiev, I|author3=Wu, X|author4=Yang, ZY|author5=Dai, K|author6=Finzi, A|author7=Kwon, YD|author8=Scheid, JF|author9=Shi, W|author10=Xu, L|author11=Yang, Y|author12=Zhu, J|author13=Nussenzweig, MC|author14=Sodroski, J|author15=Shapiro, L|author16=Nabel, GJ|author17=Mascola, JR|author18=Kwong, PD|title=Structural basis for broad and potent neutralization of HIV-1 by antibody VRC01.|journal=Science|date=August 13, 2010|volume=329|issue=5993|pages=811–7|pmid=20616231|bibcode= 2010Sci...329..811Z |doi= 10.1126/science.1192819|pmc=2981354}}</ref>]]

Statistical potentials, in contrast to physics-based potentials, have the advantage of being fast to compute, of accounting implicitly of complex effects and being less sensitive to small changes in the protein structure.<ref>{{cite journal|last=Mendes|first=J|author2=Guerois, R |author3=Serrano, L |title=Energy estimation in protein design.|journal=Current Opinion in Structural Biology|date=August 2002|volume=12|issue=4|pages=441–6|pmid=12163065|doi=10.1016/s0959-440x(02)00345-7}}</ref> These energy functions are [[:File:knowledge based potential.png|based on deriving energy values]] from frequency of appearance on a structural database.

Protein design, however, has requirements that can sometimes be limited in molecular mechanics force-fields. Molecular mechanics force-fields, which have been 
used mostly in molecular dynamics simulations, are optimized for the simulation of single sequences, but protein design searches through many conformations of many sequences. Thus, molecular mechanics force-fields must be tailored for protein design. In practice, protein design energy functions often incorporate both statistical terms and physics-based terms. For example, the Rosetta energy function, one of the most-used energy functions, incorporates physics-based energy terms originating in the CHARMM energy function, and statistical energy terms, such as rotamer probability and knowledge-based electrostatics. Typically, energy functions are highly customized between laboratories, and specifically tailored for every design.<ref name="boas2007" />

====Challenges for effective design energy functions====
Water makes up most of the molecules surrounding proteins and is the main driver of protein structure. Thus, modeling the interaction between water and protein is vital in protein design. The number of water molecules that interact with a protein at any given time is huge and each one has a large number of degrees of freedom and interaction partners. Instead, protein design programs model most of such water molecules as a continuum, modeling both the hydrophobic effect and solvation polarization.<ref name="boas2007" />

Individual water molecules can sometimes have a crucial structural role in the core of proteins, and in protein–protein or protein–ligand interactions. Failing to model such waters can result in mispredictions of the optimal sequence of a protein–protein interface. As an alternative, water molecules can be added to rotamers.
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