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Protein design
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===Design for affinity=== [[Protein–protein interaction]]s are involved in most biotic processes. Many of the hardest-to-treat diseases, such as [[Alzheimer]]'s, many forms of [[cancer]] (e.g., [[TP53]]), and human immunodeficiency virus ([[HIV]]) infection involve protein–protein interactions. Thus, to treat such diseases, it is desirable to design protein or protein-like therapeutics that bind one of the partners of the interaction and, thus, disrupt the disease-causing interaction. This requires designing protein-therapeutics for ''affinity'' toward its partner. Protein–protein interactions can be designed using protein design algorithms because the principles that rule protein stability also rule protein–protein binding. Protein–protein interaction design, however, presents challenges not commonly present in protein design. One of the most important challenges is that, in general, the interfaces between proteins are more polar than protein cores, and binding involves a tradeoff between desolvation and hydrogen bond formation.<ref name="kuhlman2009">{{cite journal|last=Karanicolas|first=J|author2=Kuhlman, B |title=Computational design of affinity and specificity at protein–protein interfaces.|journal=Current Opinion in Structural Biology|date=August 2009|volume=19|issue=4|pages=458–63|pmid=19646858|doi=10.1016/j.sbi.2009.07.005|pmc=2882636}}</ref> To overcome this challenge, Bruce Tidor and coworkers developed a method to improve the affinity of antibodies by focusing on electrostatic contributions. They found that, for the antibodies designed in the study, reducing the desolvation costs of the residues in the interface increased the affinity of the binding pair.<ref name=kuhlman2009 /><ref>{{cite journal|last=Shoichet|first=BK|title=No free energy lunch.|journal=Nature Biotechnology|date=October 2007|volume=25|issue=10|pages=1109–10|pmid=17921992|doi=10.1038/nbt1007-1109|s2cid=5527226}}</ref><ref>{{cite journal|last=Lippow|first=SM|author2=Wittrup, KD |author3=Tidor, B |title=Computational design of antibody-affinity improvement beyond in vivo maturation.|journal=Nature Biotechnology|date=October 2007|volume=25|issue=10|pages=1171–6|pmid=17891135|doi=10.1038/nbt1336|pmc=2803018}}</ref> ====Scoring binding predictions==== Protein design energy functions must be adapted to score binding predictions because binding involves a trade-off between the lowest-[[Thermodynamic free energy|energy]] conformations of the free proteins (<var>E<sub>P</sub></var> and <var>E<sub>L</sub></var>) and the lowest-energy conformation of the bound complex (<var>E<sub>PL</sub></var>): : <math>\Delta_G = E_{PL} - E_P - E_L </math>. The K* algorithm approximates the binding constant of the algorithm by including conformational entropy into the free energy calculation. The K* algorithm considers only the lowest-energy conformations of the free and bound complexes (denoted by the sets <var>P</var>, <var>L</var>, and <var>PL</var>) to approximate the partition functions of each complex:<ref name=donald10 /> : <math>K^* = \frac{\sum\limits_{x\in PL} e^{-E(x)/RT}}{\sum\limits_{x\in P} e^{-E(x)/RT}\sum\limits_{x\in L} e^{-E(x)/RT}}</math>
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