For the negative set, protein variants that were never found (693 variants) or found only once( 323 variants) in human cancer have been selected (no_cancer p53 variants)
[TP53_PROF:a machine learning model to predict impact of missense mutations inTP53]
A diagram of the calculated values for each set of p53 protein mutations, which we will further analyze.

Cancer-related Mutations
TP53 mutation data was aggregated from multiple studies

Effects of Common Cancer Mutations on Stability

Silent and oncological mutations in p53

Heat map of reliable electrostatic interaction energy between pairwise amino acid residues of the p53 monomer
The difference in the calculations obtained for the Positive and Negative set of p53 mutations

Start calculate Effects of Single Mutations
on Protein Stability right now!
For Monomer and Dimer Protein only

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stay in touch with specialists, or cancel them at any time.


  1. Collection of materials,
  2. Technical assignment,
  3. Calculations up to N variants,
  4. Obtaining data
  5. Data processing,
  6. Sorting,
  7. Plotting graphs,
  8. Dependencies,
  9. Explanatory note for each calculation
  10. Starting with 50 mutations, machine learning methods will be applied.

If you need 200 mutations or more, then the price increases proportionally with a decreasing coefficient, i.e. the calculation of one mutation when ordering 200 mutations will be cheaper than the cost of 1 mutation when ordering 10 mutations.

Preliminary set
Small amount up to 10
$50
Explanatory notes for each calculation
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Second Set
Average amount up to 20
$70
Calculations up to 20 variants.
Explanatory notes for each calculation
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Second Set
Average amount up to 50
$130
Calculations up to 50 variants.
Explanatory notes for each calculation
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Larch amount up to 100
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Calculations up to 100 variants.
Explanatory notes for each calculation
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Interplay between physical quantities
on the affinity of the complex

The best way to improve your professional skills and increase your value
  • 45%
    Free energy
    1 year
  • 67%
    Entropy change
    3 years
  • 15%
    Entalpy change
    2 years
  • 20%
    Hydrophobicity
  • 30%
    Solvatation
  • 55%
    Stability
Characteristics of physical quantities
Relationships and influences of physical quantities among themselves and correlation with affinity.
  • Entropy change
    The entropy change accompanying a chemical reaction is defined as the difference between the sum of the entropies of all the products and the sum of the entropies of all reactants the entropy change is given by:
    dS = (cSc + dSD + • • •) - (aSA + bSB+ • • ')
  • Gibbs energy
    the thermodynamic potential that is minimized when a system reaches chemical equilibrium at constant pressure and temperature when not driven by an applied electrolytic voltage
  • Entalpy change
    The lattice energy of an ionic crystal is the enthalpy change,
    required to decompose 1 mole of the crystal into its constituent gaseous ions at any temperature T.
  • Hydrophobicity
    It is commonly understood to be the tendency of non-polar molecules to form aggregates in order to reduce their surface of contact with polar molecules such as water

Lock-and-Key:
An Entropy-Dominated Binding Process

Therefore, for the lock-and-key binding to proceed, the solvent entropy gain should be large enough to overcompensate for not only the positive enthalpy change arising from the desolvation process, but also the negative entropy change caused by the loss of rotational and translational motions of the ligand.
Indeed, the negative enthalpy change arising
from the favorable interactions (such as van der Waals forces, hydrogen bonding, electrostatic, and dipole–dipole interactions) can also contribute to the lowering of the system’s free energy, but the solvent entropy gain arising from the displacement of the water molecules plays a dominant role in lowering the free energy. Therefore, it is reasonable to conclude that the lock-and-key binding is a entropy-dominated process.

Conformational Selection:
A Process in Which Entropy and Enthalpy Play Roles in a Sequential Manner

the selective binding may be dominated by the solvent entropy gain.
the presence of the conformational flexibility in the protein allows for the conformational adjustments of the residue side chaine
For the conformational selection binding scenario,
it is difficult to distinguish which factor (the entropy or the enthalpy) contributes more to the lowering of the system’s free energy because the large solvent entropy gain in the first step could be offset by the loss of the rotational and translational entropy and the decrease of the conformational entropy in the subsequent step, and the negative enthalpy change in the second step could be offset by the positive enthalpy changes due to the desolvation energy penalty and the disruption of the original noncovalent interactions surrounding the binding sites.

Nevertheless, the selective binding and the following conformational adjustments are dominated by the solvent entropy gain and the system enthalpy decrease, respectively, suggesting that they play a role, in a sequential manner, in lowering the system’s free energy.
Thermodynamic profiles of N-HSP90 inhibitors measured by ITC. The enthalpic and entropic components of the binding free energy are shown in a and b for for WT N-HSP90, and the L107A mutant, respectively. The dashed diagonal line (ΔH=−TΔS) divides the plot into two main areas where the enthalpy (gray) or the entropy (red) dominate the binding free energy (ΔG).
Thermodynamic profiles of N-HSP90 inhibitors
The thermodynamic profiles of the binding of the 20 resorcinol ligands to N-HSP90 obtained by isothermal titration calorimetry (ITC) are depicted in Fig. 3 and quantify the energetic differences between two states in equilibrium (free state and bound state).
[Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods]
[Some Binding-Related Drug Properties are Dependent on Thermodynamic Signature]
Thermodynamic profiles for three pairs of HIV-1 proteinase inhibitors that vary by only a single group: (a) KNI-10033-KNI-10075 pair within which an apolar group thioether on KNI-10033 is replaced by a polar group sulfonyl to form KNI-10075; (b) KNI-10052-KNI-10054 pair within which an apolar methyl group is replaced by a polar hydroxyl group; and (c) KNI-10046-KNI-10030 pair within which a hydrogen atom on the former is replaced by an apolar methyl group to form the latter. The binding free energy (∆G), enthalpy (∆H), and entropy (T∆S) are shown
[Freire, E. The binding thermodynamics of drug candidate. In Thermodynamics and Kinetics of Drug Binding; Keserü, G.M., Swinney, D.C., Eds.; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2015; pp. 1–13.]
[Lafont, V.; Armstrong, A.A.; Ohtaka, H.; Kiso, Y.; Mario Amzel, L.; Freire, E. Compensating enthalpic and entropic changes hinder binding affinity optimization. Chem. Biol. Drug Des. 2007, 69, 413–422.]
[Kawasaki, Y.; Freire, E. Finding a better path to drug selectivity. Drug Discov. Today 2011, 16, 985–990.]
NBD-556 is a competitive inhibitor of CD4 characterized by a binding affinity of 3.7 μM. Despite the small size, NBD-556 binds with a thermodynamic signature that resembles that of CD4 (ΔH = -24.5 kcal/mol, -TΔS = 17.1 kcal/mol at 25 °C
dH, kcal/mol
dH, kcal/mol
-TΔS, kcal/mol
-TΔS, kcal/mol
The thermodynamic signatures of sCD4 and NBD-556 at 25°C. The large conformational structuring of gp120 triggered by CD4 binding is reflected in a thermodynamic signature characterized by an unusually large favorable change in enthalpy and a very large unfavorable entropy change. Except for a lower affinity, the binding of NBD-556 to gp120 is also associated with enthalpy and entropy changes similar to those observed for CD4. ΔG is represented by blue bars, ΔH by green bars and -TΔS by red bars.
Free energy calculations of protein-ligand complexes
As introduced above, two thermodynamic quantities, the enthalpy change and entropy change, determine the sign and magnitude of the binding free energy.

We therefore consider ΔH and ΔS as the driving factors for protein–ligand binding. The contributions of ΔH and ΔS to ΔG are closely related.

For instance, the tight binding resulting from multiple favorable noncovalent interactions between association partners will lead to a large negative enthalpy change, but this is usually accompanied by a negative entropy change due to the restriction of the mobility of the interacting partners, ultimately resulting in a medium-magnitude change in binding free energy.

Similarly, a large entropy gain is usually accompanied by an enthalpic penalty (positive enthalpy change) due to the energy required for disrupting noncovalent interactions. This phenomenon—the medium-magnitude free energy change caused by the complementary changes between enthalpy and entropy—is called the enthalpy–entropy compensation.
The main criticisms are that the compensation could be
  • a misleading interpretation of the data obtained from a relatively narrow temperature range or from a limited range for the free energies
  • the result of random experimental and systematic errors
  • and (iii) the result of data selection bias

Например, прочное связывание, возникающее в результате множественных благоприятных нековалентных взаимодействий между партнерами ассоциации, приведет к значительному отрицательному изменению энтальпии, но это обычно сопровождается отрицательным изменением энтропии из-за ограничения подвижности взаимодействующих партнеров, что в конечном итоге приводит к изменение средней величины свободной энергии связи.

Точно так же большой прирост энтропии обычно сопровождается энтальпийным штрафом (положительным изменением энтальпии) из-за энергии, необходимой для разрушения нековалентных взаимодействий. Это явление — изменение свободной энергии средней величины, вызванное дополнительными изменениями между энтальпией и энтропией, — называется энтальпийно-энтропийной компенсацией.
Correctly identifying the true driver mutations in a patient’s tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium-and low-frequency variants mostly unaddressed. For TP53, this identification is crucial for both somatic and germline mutations, a multi organ cancer predisposition.

Here we combine calculated physical quantities and the application of machine learning methods to their processing to separate oncogenic mutations from neutral mutations of the P53 protein.

Most of p53 mutations in cancers are missense mutations, which produce the full-length mutant p53 (mutp53) protein
Fechner Correlation

The FECHNER CORRELATION command calculates the Fechner signs correlation coefficient between all the pairs of variables. Fechner correlation coefficient is used to check relationship for small samples.

How To
Run: STATISTICS->NONPARAMETRIC STATISTICS-> FECHNER CORRELATION...
Select the variables you want to correlate.
 Pairwise deletion is default for missing values removal (use the MISSING VALUES option in the PREFERENCES window to force casewise deletion).
Results:
Matrix with Fechner correlation coefficients between each pair of variables is calculated.
Fechner correlation coefficient is defined by
Negative Set of p53 mutations
p.A119P,p.A119S, p.A129G, p.A129P, p.A129S, p.A138G, p.A189S, p.C124F, p.C124W, p.C182F, p.C229G, p.C229W, p.D148A, p.D148V, p.D184A, p.D184E, p.D184V, p.D186A, p.D186E, p.D186Y p.D207A, p.D207V p.E171A, p.E171V, p.E180A, p.E180V, p.E198A, p.E198V, p.E224Q, p.E287A, ,p.F109Y, p.F113Y, p.F134Y, p.F212C, p.G108A, p.G108C, p.G112A, p.G112C, p.G112R, p.G112V, p.G117A, p.G117V, p.G117W, p.G154A, p.G154R, p.G187A, p.G226C, p.G262A, p.G262C p.G262R, p.G279A, p.H115D, p.H115L, p.H115N, p.H115P, p.H115Q, p.H115R p.H168Q, p.H178L, p.H214N, p.H233N, p.I162L, p.I195L, p.I195V, p.I255L, p.K101I, p.K101N, p.K101Q, p.K101T, p.K120T, p.L111V, p.L114F, p.L114M, p.L114S, p.L114V, p.L114W, p.L137R, p.L188Q, p.L188R, p.L201M, p.L201W, p.L206F, p.L206M, p.L206V, p.L206W, p.L252R, p.L257M, p.L264P, p.L264Q p.L264V, p.L265V, p.M169L, p.S106T, p.S116A, p.S116T, p.S116Y, p.S121A, p.S121C, p.S121T, p.S121Y, p.S127A, p.S149A, p.S149C, p.S183A, p.S183T, p.S185C, p.S185T, p.S227A, p.S227Y, p.S261I, p.T102N, p.T102P, p.T118K, p.T118P, p.T118R, p.T118S, p.T123N p.T123P, p.T123S, p.T125S, p.T140N, p.T150S, p.T170K, p.T256R, p.T284R, p.V122E, p.V122G, p.V122M, p.V147L, p.V203G, p.V225L, p.Y103C, p.Y103D, p.Y103F, p.Y103H, p.Y103N, p.Y103S, p.Y107F, p.Y107N, p.Y107S
Positive Set of p53 mutations
p.A138V, p.A159P, p.A159V, p.A161S, p.A161T, p.A276D, p.A276G, p.A276P, p.C124G, p.C135F, p.C135R, pC135S, p.C135W, p.C135Y, p.C141G, p.C141R, p.C141W, p.C141Y, p.C176F, p.C176G, p.C176R, p.C176W, p.C176Y, p.C238F, p.C238R, p.C238Y, p.C242F, p.C242G, p.C242S, p.C242Y, p.C275F, p.C275G, p.C275R, p.C275W, p.C275Y, p.C277F, p.C277Y, p.D259V, p.D259Y, p.D281E, p.D281H, p.D281N, p.D281V, p.D281Y, p.E180K, p.E224D, p.E258A, p.E258K, p.E258Q, p.E271K, p.E271V, p.E285K,p.E285V, p.E286A, p.E286G, p.E286K, p.E286Q, p.E286V, p.E287D, p.F109C, p.F109V, p.F113C, p.F113V, p.F134C, p.F134L, p.F134V, p.F270C, p.F270L, p.F270S, p.G105C, p.G105D, p.G105V, p.G154V, p.G199V, p.G244C, p.G244D, p.G244S, p.G244V, p.G245C, p.G245D, p.G245R, p.G245S, p.G245V, p.G262V, p.G266E, .G266R, p.G266V, p.G279E
The fact is that the yi values for Negative Set practically coincide with the obtained average value; as a result of the difference between the average and yi values, we get a very small value tending to zero.
Neutral Mutations
Oncological mutations
WOW! affect
Difference in calculated characteristics for Positive and Negative sets of P53 protein mutations.
To determine the correlation coefficient, we use Boolean variables and the Fechner coefficient.
rb(integr) is a Fechner coefficient
enthalpy change
N1-N60
N1-N60
Negative set
Negative set
Positive set
Positive set
Positive set
Negative set
N53-N140
N2-N89
p.A119P
p.A119S
p.A129G
p.A129P
p.A129S
p.A138G
p.A189S
p.C124F
p.C124W
p.C182F
p.C229G
p.C229W
p.D148A
p.D148V
p.D184A
p.D184E
p.D184V
p.D186A
p.D186E
p.D186Y
p.D207A
p.D207V
p.E171A
p.E171V
p.E180A
p.E180V
p.E198A
p.E198V
p.E224Q
p.E287A
p.F109Y
p.F113Y
p.F134Y
p.F212C
p.G108A
p.G108C
p.G112A
p.G112C
p.G112R
p.G112V
p.G117A
p.G117V
p.G117W
p.G154A
p.G154R
p.G187A
p.G226C
p.G262A
p.G262C
p.G262R
p.G279A
p.H115D
p.H115L
p.H115N
p.H115P
p.H115Q
p.H115R
p.H168Q
p.H178L
p.H214N
0.73993787
0.73993397
0.7399646
0.73995984
0.73995537
0.73985441
0.7399354
0.7375981
0.73758668
0.73957824
0.73991268
0.73990698
0.73998011
0.73997562
0.74010023
0.74000325
0.73998995
0.74002283
0.74000103
0.73997485
0.74000402
0.73997575
0.74002357
0.73997244
0.74015927
0.73999621
0.74005368
0.73997331
0.74003208
0.73998698
0.73999584
0.73997822
0.7399458
0.74006543
0.73999537
0.74001586
0.74005163
0.74015792
0.74014309
0.73999474
0.74011601
0.74000749
0.74000731
0.74000035
0.74007487
0.74001805
0.74003
0.74
0.74002
0.74008
0.74007
0.74
0.73998
0.73999
0.73998
0.74007
0.74013
0.74006
0.74008
0.73999
p.A138V
p.A159P
p.A159V
p.A161S
p.A161T
p.A276D
p.A276G
p.A276P
p.C124G
p.C135F
p.C135R
p.C135S
p.C135W
p.C135Y
p.C141G
p.C141R
p.C141W
p.C141Y
p.C176F
p.C176G
p.C176R
p.C176W
p.C176Y
p.C238F
p.C238R
p.C238Y
p.C242F
p.C242G
p.C242S
p.C242Y
p.C275F
p.C275G
p.C275R
p.C275W
p.C275Y
p.C277F
p.C277Y
p.D259V
p.D259Y
p.D281E
p.D281H
p.D281N
p.D281V
p.D281Y
p.E180K
p.E224D
p.E258A
p.E258K
p.E258Q
p.E271K
p.E271V
p.E285K
p.E285V
p.E286A
p.E286G
p.E286K
p.E286Q
p.E286V
p.E287D
p.F109C
0.73979859
0.73992935
0.73992999
0.73990309
0.73991134
0.739866
0.73989952
0.7398477
0.73790811
0.73676158
0.73993565
0.73758584
0.73644711
0.73674552
0.73650341
0.73801977
0.73835483
0.73771861
0.73755947
0.73683127
0.73725951
0.73756511
0.73681525
0.73657199
0.73682142
0.73757876
0.73656484
0.73762472
0.73793314
0.73740206
0.73744179
0.73944582
0.73950486
0.73972086
0.73944381
0.73941679
0.73973285
0.73972298
0.73997548
0.73997854
0.73998977
0.73998112
0.73998263
0.73997477
0.73997529
0.73999009
0.7399824
0.74008994
0.74003253
0.7402447
0.73997668
0.74017278
0.73997055
0.74000908
0.73997871
0.74014231
0.74005099
0.73996981
0.73998965
0.74006329
Negative set
Positive set
Histogram of values distribution ​​for the magnitude of enthalpy change
Сalculated data obtained for two sets of mutations. Plot of differential entropy changes for two data sets. The graphs show Boolean variables and the final Fechner coefficient.
Positive set
N1-N60
Positive set
Negative set
Negative set
Negativ set
p.A119P
p.A119S
p.A129G
p.A129P
p.A129S
p.A138G
p.A189S
p.C124F
p.C124W
p.C182F
p.C229G
p.C229W
p.D148A
p.D148V
p.D184A
p.D184E
p.D184V
p.D186A
p.D186E
p.D186Y
p.D207A
p.D207V
p.E171A
p.E171V
p.E180A
p.E180V
p.E198A
p.E198V
p.E224Q
p.E287A
p.F109Y
p.F113Y
p.F134Y
p.F212C
p.G108A
p.G108C
p.G112A
p.G112C
p.G112R
p.G112V
p.G117A
p.G117V
p.G117W
p.G154A
p.G154R
p.G187A
p.G226C
p.G262A
p.G262C
p.G262R
p.G279A
p.H115D
p.H115L
p.H115N
p.H115P
p.H115Q
p.H115R
p.H168Q
p.H178L
lg[Kd],Mol/L
-0.614253
-0.61426091
-0.61419881
-0.61420846
-0.61421752
-0.6144222
-0.61425801
-0.61899628
-0.61901944
-0.61498207
-0.61430407
-0.61431562
-0.61416737
-0.61417648
-0.61392386
-0.61412046
-0.61414743
-0.61408077
-0.61412496
-0.61417803
-0.6141189
-0.61417621
-0.61407927
-0.61418292
-0.61380418
-0.61413473
-0.61401822
-0.61418116
-0.61406201
-0.61415345
-0.61413549
-0.61417121
-0.61423694
-0.6139944
-0.61413644
-0.61409489
-0.61402239
-0.61380691
-0.61383698
-0.61413771
-0.61389187
-0.61411186
-0.61411223
-0.61412633
-0.61397528
-0.61409047
-0.61407
-0.61413
-0.61408
-0.61397
-0.61399
-0.61413
-0.61416
-0.61415
-0.61416
-0.61398
-0.61385
-0.61401
-0.61397
Positiv set
p.A138V
p.A159P
p.A159V
p.A161S
p.A161T
p.A276D
p.A276G
p.A276P
p.C124G
p.C135F
p.C135R
p.C135S
p.C135W
p.C135Y
p.C141G
p.C141R
p.C141W
p.C141Y
p.C176F
p.C176G
p.C176R
p.C176W
p.C176Y
p.C238F
p.C238R
p.C238Y
p.C242F
p.C242G
p.C242S
p.C242Y
p.C275F
p.C275G
p.C275R
p.C275W
p.C275Y
p.C277F
p.C277Y
p.D259V
p.D259Y
p.D281E
p.D281H
p.D281N
p.D281V
p.D281Y
p.E180K
p.E224D
p.E258A
p.E258K
p.E258Q
p.E271K
p.E271V
p.E285K
p.E285V
p.E286A
p.E286G
p.E286K
p.E286Q
p.E286V
p.E287D
lg[Kd],Mol/L
-0.614535352
-0.614270272
-0.614268987
-0.614323517
-0.614306795
-0.614398712
-0.614330745
-0.61443581
-0.618367833
-0.620692116
-0.614257509
-0.619021144
-0.62132964
-0.620724674
-0.621215486
-0.618141465
-0.617462206
-0.618751991
-0.619074608
-0.620550837
-0.619682692
-0.619063178
-0.620583317
-0.621076466
-0.620570816
-0.619035497
-0.621090966
-0.618942322
-0.618317076
-0.619393704
-0.619313177
-0.615250519
-0.615130812
-0.614692929
-0.615254583
-0.615309361
-0.614668627
-0.614688648
-0.614176768
-0.614170555
-0.614147785
-0.614165335
-0.614162272
-0.614178203
-0.61417715
-0.61414715
-0.614162733
-0.613944716
-0.614061115
-0.613630991
-0.614174336
-0.613776781
-0.614186748
-0.614108647
-0.614170203
-0.613838552
-0.614023677
-0.614188258
-0.614148036
N1-N60
Positive set
Negative set
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Mathematical Methods Determination of the Biomolecular Complexes Stability

iASPP, a member of the ASPP (Ankyrin repeat domain, SH3 domain and Proline rich sequence containing Protein) family of proteins , binds the DNA binding domain of p53 and regulates its target selective transcription. In vitro and xenograft studies have shown that iASPP has pro-proliferative and chemoresistant properties. These observations led to the hypothesis that iASPP deficiency would enhance WT p53 activity to inhibit tumorigenesis


PDB: 6RZ3

Графики полученные для позитивного и негативного набора Р53 мутаций.
Графики наглядно представляют полученную разницу для негативного набора и позитивного набора р53 мутаций при расчете различных физических параметров.
Graph of physical quantities for two sets of mutations in the P53 protein: a negative set of mutations and a positive set of mutations.

Different physical characteristics have different probabilities in determining the nature of the mutation and its subsequent impact on the cellular response.

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