Datamining and Drug Design - Semantic Scholar
Computational Modeling of Proteins
Datamining and Drug Design L. Ridgway Scott Departments of Computer Science and Mathematics, Computation Institute, and Institute for Biophysical Dynamics, University of Chicago ´ This talk is based on joint work with Ariel Ferndandez (Argentine Institute ´ and Collegium Basilea, Institute of Mathematics “Alberto Pedro Calderon” for Advanced Study, Basel, Switzerland), Chris Fraser, Bioanalytical Computing Inc. (formerly UChicago), Harold Scheraga (Cornell), and Kristina Rogale Plazonic (Princeton); and at U. Chicago: Steve Berry, Tatiana Orlova. Digital Biology 2016
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Computational Models
100 nanometers 10 nanometers
continuum
nanometer quantum mechanics
Angstrom
molecular dynamics
electrostatics
10 picometers attosec femtosec picosec nanosec microsec msec Figure 1: Computational models at multiple scales
Nonbonded interactions involved in all scales. Digital Biology 2016
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Failure of drug designs in clinical trials [2]
Figure 2: FDA approvals of new molecule entities (NME), numbers of phase III clinical trials completed and pharmaceutical R&D spending from 1990 to 2005. NME approvals reached a peak in the period from 1996 to 1999 and are now dropping in spite of continually increasing R&D spending and phase III clinical trials. Digital Biology 2016
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Aligned backbones for two paralog kinases
Dehydrons for Chk1 marked in green and those for Pdk1 in red [8].
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HIV-1 protease with ‘dehydron wrapper’ inhibitor
Detail of the protease cavity, pattern of packing defects, and inhibitor positioned as dehydron wrapper. Digital Biology 2016
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Desolvation spheres for flap
Desolvation spheres for flap Gly-49–Gly-52 dehydron containing nonpolar groups of the wrapping inhibitor. Digital Biology 2016
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Body composition
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Proteins as digital components
Proteins are the essential components of life: • used to build complexes, e.g., viruses (bricks and mortar) • involved in signalling (information transmission) • enzymes essential in catalysis (chemical machines)
In all these cases, protein-ligand interaction is essential. These interactions are deterministic (always the same). Proteins function as discrete components not as analog devices. Digital Biology 2016
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The hydrophobic effect
Hydrophobic effect crucial in protein-ligand association. Water is essential to life as we know it, but hostile to proteins. The role of water in protein biophysics: to modulate electric forces via the dielectric effect. Hydrophobicity fosters water removal and supports protein-ligand interaction, but it also modulates the dielectric effect. Water is a strong dielectric, and protein sidechains are a complex mix of charged, polar, and hydrophobic parts. But the hydrophobic effect is non-specific in action. What makes proteins interact in a repeatable way? Digital Biology 2016
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What sidechains are found at interfaces?
By examining interfaces in PDB structures, we can see which residues are most likely to be found at interfaces. Asparagine Threonine Glycine Serine CH2
H
C
C CH3
OH
H H
C
Aspartic acid Alanine Cysteine CH2 SH
C
H
❅ ❅ ❅ ❅
NH2
CH3
OH CH2
❅ ❅ ❅ ❅
O
O−
O
Sidechains most likely to be involved in interactions, ordered from the left (asparagine), are not hydrophobic. Digital Biology 2016
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Electronic forces
The only force of significance in biochemistry is the electric force. — But often modulated by indirection or induction. In terrestrial biology, water plays a significant role as a dielectric which mediates non-covalent interactions (hydrogen bonds, salt bridges, cation-pi interactions). But the dielectric effect of water is modulated by hydrophobic components of proteins. Moreover, a ligand can change the hydrophobic environment upon binding. In protein-ligand interactions, this makes intramolecular bonds as important as intermolecular interactions. Digital Biology 2016
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Our technology
Interaction between physical chemistry and data mining in biophysical data bases. • Data mining can lead to new results in physical chemistry that are significant in biology. • Using physical chemistry to look at data provides insights regarding function. We review recent results regarding protein-ligand interaction based on insights about hydrophobic effects. We show that sidechain configurations modulate dielectric effect. We discuss how these can be used to understand a novel factor that supports protein-ligand binding. Digital Biology 2016
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Competing effects: why this is so hard
Protein sidechains have large electrostatic gradients Water is a strong dielectric Hydrophobic groups modify the water structure Large electrostatic gradients
Screening by dielectric effect
Modulation of dielectric strength by hydrophobic effect Figure 3: Three competing effects that determine protein behavior. These conspire to weaken interactive forces, making biological relationships more tenuous and amenable to mutation.
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Protein basics
Proteins are sequences of amino acids which are covalently bonded along a “backbone.” Proteins of biological significance fold into a three-dimensional structure by adding hydrogen bonds between carbonyl and amide groups on the backbone of different amino acids. In addition, other bonds, such as a salt bridge or a disulfide bond can form between particular amino acids (Cysteine has sulfur atoms in its sidechain). But the hydrogen bond is the primary mode of structure formation in proteins. Digital Biology 2016
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Chains of amino acid residues
Proteins are chains of amino acid residues whose basic unit is the peptide group. ✧✧
H ❜ ❜
Cαi+1
+
N Ri
✧ ❅ ❅ ✧ i
Cα
(a)
C
i+1 C ✟ α
✟
❅ ❅ i+1 R
Ri+1 Ri
❜❜
O−
❜❜
✧ ❅ ❅ ✧ i
Cα
N+ C
✧ H ✧
❜❜
O−
(b)
Figure 4: The rigid state of the peptide bond: (a) trans form, (b) cis form. The double bond between the central carbon and nitrogen keeps the peptide bond planar. Digital Biology 2016
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Linear (primary) structure of proteins R R
R
R
R
R
Figure 5: Cartoon of peptide sequence where all peptides are in trans form (cf. Figure 4). Small boxes represent C-alpha carbons, arrow heads represent amide groups NH, arrow tails represent carbonyl groups CO, and thin rectangular boxes are double bond between backbone C and N. The different residues are indicated by R’s. The numbering scheme is increasing from left to right, so that the arrow formed by the carbonyl-amide pair points in the direction of increasing residue number. The three-dimensional nature of the protein is left to the imagination. Digital Biology 2016
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Hydrogen bonds and secondary structure Proteins have a hierarchy of structure, the next being secondary structure consisting of two primary types: alpha-helices and beta-sheets (a.k.a., α-helices and β-sheets). Alpha helices are helical arrangements of the subsequent peptide complexes with a distinctive hydrogen bond arrangement between the amide (NH) and carbonyl (OC) groups in peptides separated by k steps in the sequence, where primarily k = 4 but with k = 3 and k = 5 also occurring less frequently:
(a) Digital Biology 2016
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Beta sheets Beta sheets represent different hydrogen bond arrangements: (b) is the anti-parallel arrangement and (c) is the parallel.
(b)
(c)
Both structures are essentially flat, in contrast to the helical structure in (a).
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Dehydrons in human hemoglobin
Dehydrons in human hemoglobin From PNAS 100: 6446-6451 (2003) Ariel Fernandez, Jozsef Kardos, L. Ridgway Scott, Yuji Goto, and R. Stephen Berry. Structural defects and the diagnosis of amyloidogenic propensity.
Well-wrapped hydrogen bonds are grey, and dehydrons are green. The standard ribbon model of “structure” lacks indicators of electrostatic environment. Digital Biology 2016
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A quote
from Nature’s Robots ....
“The exact and definite determination of life phenomena which are common to plants and animals is only one side of the physiological problem of today. The other side is the construction of a mental picture of the constitution of living matter from these general qualities. In this portion of our work we need the aid of physical chemistry.” Jacques Loeb, The biological problems of today: physiology. Science 7, 154-156 (1897).
so our theme is not so new .... Digital Biology 2016
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Data mining definition
WHATIS.COM: Data mining is sorting through data to identify patterns and establish relationships. Data mining parameters include: • Association - looking for patterns where one event is connected to another event • Sequence or path analysis - looking for patterns where one event leads to another later event • Classification - looking for new patterns (May result in a change in the way the data is organized but that’s ok) • Clustering - finding and visually documenting groups of facts not previously known Conclusion: Data mining involves looking at data. Digital Biology 2016
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Data mining lens
If data mining is looking at data then ☛
✟
What type of lens do we use? ✡ ✠
• All of these have chemical representations, e.g., C400 H620 N100 O120 P1 S1 • Alphabetic sequences describe much of biology: DNA, RNA, proteins. • All of these have three-dimensional structure. • But structure alone does not explain how they function. Physical chemistry clarifies the picture and allows function to be more easily interpreted. Digital Biology 2016
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Sequences can tell a story
Protein sequences aardvarkateatavisticallyacademicianaccelerative acetylglycineachievementacidimetricallyacridity actressadamantadhesivenessadministrativelyadmit afflictiveafterdinneragrypniaaimlessnessairlift and DNA sequences actcatatactagagtacttagacttatactagagcattacttagat can be studied using automatically determined lexicons. Joint work with John Goldsmith, Terry Clark, Jing Liu.
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Sequences can tell a story
Protein sequences (a linguistic lens) aardvarkateatavisticallyacademicianaccelerative acetylglycineachievementacidimetricallyacridity actressadamantadhesivenessadministrativelyadmit afflictiveafterdinneragrypniaaimlessnessairlift and DNA sequences actcatatactagagtacttagacttatactagagcattacttagat can be studied using automatically determined lexicons. Joint work with John Goldsmith, Terry Clark, Jing Liu.
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What sidechains are found at interfaces?
By examining interfaces in PDB structures, we can see which residues are most likely to be found at interfaces. Asparagine Threonine Glycine Serine CH2
H
C
C CH3
OH
H H
C
Aspartic acid Alanine Cysteine OH CH2 C
H
❅ ❅ ❅ ❅
NH2
CH3
CH2 SH
❅ ❅ ❅ ❅
O
O−
O
Sidechains most likely to be involved in interactions, ordered from the left (asparagine), are not hydrophobic.
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Amino acid sidechains have different properties
Carbonaceous groups on sidechains are hydrophobic: Valine
Leucine
CH2
CH2
Isoleucine H
C
❅ ❅
CH2
Proline
CH3 CH2
CH2
CH ❅ ❅
CH3
CH3
CH3
CH2
CH2
❅ ❅
CH2
Phenylalanine
CH2
✟❍ ✟ ✟✟ ❍ ❍❍ ✟ ❍ ❍✟
Amino acid residues (sidechains only shown) having only carbonaceous groups.
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Charges in a dielectric
Charges in a dielectric are like lights in a fog.
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Wrapping modifies dielectric effect
Hydrophobic (CHn ) groups remove water locally. This causes a reduction in ε locally. (Resulting increase in φ makes dehydrons sticky.) This can be quantified and used to predict binding sites. The placement of hydrophobic groups near an electrostatic bond is called wrapping. Like putting insulation on an electrical wire. We can see this effect on a single hydrogen bond. Digital Biology 2016
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Unit of hydrophobicity
A single carbonaceous group CHn can enhance the strength and stability of a hydrogen bond. Consider the effect of such a group in • methyl alchohol versus ethyl alchohol • ethylene glycol versus propylene glycol • (deadly versus drinkable) Can we see a molecular-level effect analogous to the change in dielectric permittivity? What can a simple model of dielectric modulation predict?
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Wrapping protects hydrogen bond from water
CHn CHn C
CHn H
O
N
CHn
CHn
CHn
CHn H
C
O O
H
H Well wrapped hydrogen bond Digital Biology 2016
CHn
H
O
H
N
O
H O
H
H
H Underwrapped hydrogen bond 30/64
Extent of wrapping changes nature of hydrogen bond Hydrogen bonds (B) not protected from water do not persist.
From De Simone, et al., PNAS 102 no 21 7535-7540 (2005) Digital Biology 2016
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Dynamics of hydrogen bonds and wrapping
900 800 700 600 500 400 300 200 100 0 0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Figure 6: Distribution of bond lengths for two hydrogen bonds formed in a structure of the sheep prion [4]. Horizontal axis measured in nanometers, vertical axis represents numbers of occurrences taken from a simulation ˚ with 20, 000 data points with bin widths of 0.1 Angstrom. Distribution for the well-wrapped hydrogen bond (H3) has smaller mean value but a longer (exponential) tail, whereas distribution for the underwrapped hydrogen bond (H1) has larger mean but Gaussian tail. Digital Biology 2016
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Ligand binding removes water
CHn
CHn CHn
CHn C
O
H
11111 00000 00000 11111 00000 11111 00000 N 11111 00000 11111 00000 11111
CHn C
O
CHn H
N
O
H O
H
H
L IG A N D
H
Binding of ligand changes underprotected hydrogen bond (high dielectric) to strong bond (low dielectric)
No intermolecular bonds needed! Digital Biology 2016
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Inter- versus intra-molecular
Intermolecular bonds are like the power cord on my computer.
Figure 7: Wireless Charging (from Technology Review).
Intramolecular bonds are like the charger on electric toothbrush. Digital Biology 2016
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Intermolecular versus intramolecular H bonds
CHn
CHn CHn
CHn
H
C
O
CHn N
C
O
CHn H
N
L IG A N D
Energetic contribution to binding comparable but can be better for intramolecular. Digital Biology 2016
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Dehydrons in human hemoglobin
Dehydrons in human hemoglobin From PNAS 100: 6446-6451 (2003) Ariel Fernandez, Jozsef Kardos, L. Ridgway Scott, Yuji Goto, and R. Stephen Berry. Structural defects and the diagnosis of amyloidogenic propensity.
Well-wrapped hydrogen bonds are grey, and dehydrons are green. The standard ribbon model of “structure” lacks indicators of electrostatic environment. Digital Biology 2016
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Wrapping made quantitative
Wrapping made quantitative by counting carbonaceous groups in the neighborhood of a hydrogen bond.
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Distribution of wrapping
Distribution of wrapping for an antibody complex. PDB file 1P2C: Light chain, A, dotted line; Heavy chain, B, dashed line; HEL, C, solid line 12 line 2 line 3 line 4
frequency of occurrence
10
8
6
4
2
0 0
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5 10 15 20 25 30 35 number of noncarbonaceous groups in each desolvation sphere: radius=6.0 Angstroms
40
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Stickiness of dehydrons
Attractive force of dehydrons predicted and measured in Ariel Fernandez and L. Ridgway Scott. Adherence of packing defects in soluble proteins. Phys. Rev. Lett. 2003 91:18102(4)
by considering rates of adhesion to phospholipid (DLPC) bilayer. Deformation of phospholipid bilayer by dehydrons measured in Ariel Fernandez and L. Ridgway Scott. Under-wrapped soluble proteins as signals triggering membrane morphology. Journal of Chemical Physics 119(13), 6911-6915 (2003).
Single molecule measurement of dehydronic force in Ariel Fernandez. Direct nanoscale dehydration of hydrogen bonds. Journal of Physics D: Applied Physics 38, 2928-2932, 2005. Fine print: careful definition of dehydron requires assessing modification of dielectric enviroment by test hydrophobe. That is, geometry of carbon groups matters, although counting gets it right ≈ 90% of the time [7].
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Charge-force relationship
Here’s the math.... Charges ρ induce an electric field e = ∇φ given by ∇· (ε∇φ) = ∇· (εe) = ρ, where ε is permittivity of medium. Energy =
(1) R
ρφ dx.
In vacuum, ε is permittivity of free space, ε0 . In other media (e.g., water) the value of ε is much larger. ε measures strength of dielectric enviroment. Water removal decreases ε in (1), and increases φ. Hydrophilic groups contribute to right-hand side ρ in (1). Digital Biology 2016
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HIV protease dehydron
The HIV protease has a dehydron at an antibody binding site.
When the antibody binds at the dehydron, it wraps it with hydrophobic groups.
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A model for protein-protein interaction
Foot-and-mouth disease virus assembly from small proteins. Digital Biology 2016
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Dehydrons guide binding
Dehydrons guide binding of component proteins VP1, VP2 and VP3 of foot-and-mouth disease virus. Digital Biology 2016
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Extreme interaction: amyloid formation
Standard application of bioinformatics: look at distribution tails. If some is good, more may be better, but too many may be bad. Too many dehydrons signals trouble: the human prion.
From PNAS 100: 6446-6451 (2003) Ariel Fernandez, Jozsef Kardos, L. Ridgway Scott, Yuji Goto, and R. Stephen Berry. Structural defects and the diagnosis of amyloidogenic propensity. Digital Biology 2016
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Dehydrons as indicators of protein interactivity
If dehydrons provide mechanism for proteins to interact, then more interactive proteins should have more dehydrons, and vice versa. We only expect a correlation since there are (presumably) other ways for proteins to interact. The DIP database collects information about protein interactions, based on individual protein domains: can measure interactivity of different regions of a given protein.
Result: Interactivity of proteins correlates strongly with number of dehydrons. PNAS 101(9):2823-7 (2004) The nonconserved wrapping of conserved protein folds reveals a trend toward increasing connectivity in proteomic networks. ´ Ariel Fernandez, L. R. Scott and R. Steve Berry Digital Biology 2016
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Dehydron counts correlate with interactivity
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Dehydron variation over different species Species (common name) Aplysia limacina (mollusc) Chironomus thummi thummi (insect) Thunnus albacares (tuna) Caretta caretta (sea turtle) Physeter catodon (whale) Sus scrofa (pig) Equus caballus (horse) Elephas maximus (Asian elephant) Phoca vitulina (seal) H. sapiens (human)
peptides 146 136 146 153 153 153 152 153 153 146
H bonds 106 101 110 110 113 113 112 115 109 102
dehydrons 0 3 8 11 11 12 14 15 16 16
Number of dehydrons in Myoglobin of different species
1LHT (sea turtle) [11]
1MBA (mollusc) [0] 1ECA (insect) [4] 1MYT (yellow−fin tuna) [8]
1MBS (seal) [16] 1BZ6 (sperm whale) [11] 1DRW (horse) [14] 1MWC (wild boar) [12] 2MM1 (human) [16] Digital Biology 2016
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Anecdotal evidence
Anecdotal evidence: basic structure is similar, dehydron number increases. SH3 domains are from nematode C. elegans (a) H. sapiens (b); ubiquitin from E. coli (c) and H. sapiens (d); hemoglobin from Paramecium (e) and H. sapiens-subunit (f). Digital Biology 2016
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Wrapping technology in drug design Synopsis of “Modulating drug impact by wrapping target proteins” by Ariel ´ Fernandez and L. Ridgway Scott, Expert Opinion on Drug Discovery 2007.
Drug ligands bind to proteins near dehydrons, enhancing wrapping upon attachment. Drug side effects often caused by binding to proteins with structure similar to target. We can exploit the differences in dehydron patterns in homologous proteins to make drugs more specific. Digital Biology 2016
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Drug ligand non-polar groups Drug ligand provides additional non-polar carbonaceous group(s) in desolvation domain, enhancing wrapping of hydrogen bond.
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HIV-1 protease with ‘dehydron wrapper’ inhibitor
Detail of the protease cavity, pattern of packing defects, and inhibitor positioned as dehydron wrapper. Digital Biology 2016
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Desolvation spheres for flap
Desolvation spheres for flap Gly-49–Gly-52 dehydron containing nonpolar groups of the wrapping inhibitor. Digital Biology 2016
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Drug specificity
Tyrosine kinases: a family of proteins with very similar structure. • Called paralogous because they are similar proteins within a given species. • Presumed to have evolved from a common source. • Crucial target of cancer drug therapy. Gleevec targets particular tyrosine kinases and has been one of the most successful cancer drugs. However, it also targets similar proteins and can cause unwanted side effects (it is cardiotoxic). Differences between the dehydron patters in similar proteins can be used to differentiate them and guide the re-design of drug ligands. Digital Biology 2016
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Aligned paralog kinases
Aligned backbones for two paralog kinases; dehydrons for Chk1 are marked in green and those for Pdk1 are in red.
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Dehydron in C-Kit
Dehydron Cys673-Gly676 in C-Kit not conserved in paralogs Bcr-Abl, Lck, Chk1 and Pdk1. By methylating Gleevec at para position (1), inhibitor becomes selective wrapper of C-Kit dehydron. Digital Biology 2016
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Wrapping technology: WBZ drugs
Two variants of imatinib, WBZ-4 and WBZ-7, were developed using wrapping technology: “WBZ-4, unlike imatinib, targets C-Kit but not Bcr-Abl.... potential for cardiotoxicity is even lower, researchers found in laboratory, animal, and computer testing. WBZ-4 appears to be as effective against GIST as imatinib” “Gleevec is far more effective against a drug-resistant strain of cancer when the drug wraps the target .... modified version of the drug, WBZ-7, ... seals out water from a critical area.” Digital Biology 2016
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Genetic code Genetic code minimizes changes of polarity due to single-letter codon mutations, but it facilitates changes in wrapping due to single-letter codon mutations. Second Position
u
c a g
Phe 7
||
Leu 4
||
Leu 4 Ile
4
|| ||
Met 1 + − Val 3
||
ucu ucc uca ucg ccu ccc cca ccg acu acc aca acg gcu gcc gca gcg
a
Ser 0 + −
Pro 2
||
Thr 1 + −
Ala 1
||
uau uac uaa uag cau cac caa cag aau aac aaa aag gau gac gaa gag
Tyr 6 + − stop stop His 1 + − Gln 2 + − Asn 1 + − Lys 3 + + Asp 1 − − Glu 2 − −
g ugu ugc uga ugg cgu cgc cga cgg agu agc aga agg ggu ggc gga ggg
Cys 0 + − uc a stop Trp 7 + − g u c Arg 2 + + a g Ser 0 + − u c Arg 2 + + ag u c Gly 0 + − a g
Third Position
First Position
u
uuu uuc uua uug cuu cuc cua cug auu auc aua aug guu guc gua gug
c
First digit after residue name is amount of wrapping. Second indicator is polarity; | |: nonpolar, + −: polar, − −: negatively charged, + +: positively charged. Digital Biology 2016
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Determining dehydrons without structure
Can use disorder scores as proxy for dehydrons. Dehydrons correspond to changes in score. Digital Biology 2016
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Dehydron regions identified by sequence alone
Changes in disorder score relate to (a) protein binding and (b) catalytic regions (dehydrons facilitate catalysis) [5, 6].
Technology used to find peptides for treating heart disease [9]. ´ Ariel Fernandez and L. Ridgway Scott. Drug leads for interactive protein targets with unknown structure. Drug Discovery Today, October 2015. Digital Biology 2016
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Peptides for treating heart disease
U.S. Patent No. 9,051,387 B2, 9 June 2015 Treating Heart Failure by Inhibiting Myosin Interaction with a Regulatory Myosin Binding Protein [MyBP-C] INVENTORS - Richard Moss, Ariel Fernandez Inhibitor peptide:
FSSLLKKRDAFRRDAK
“The Wisconsin Alumni Research Foundation (WARF) is seeking commercial partners interested in developing and using peptides that promote cardiac muscle contraction by disrupting the binding of MyBP-C to myosin.” Digital Biology 2016
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Oppositely charged residues without a counter ion?
Lysines become deprotonated by nearby dehydrons [10]? Digital Biology 2016
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Conclusions
Data mining in the Protein Data Bank ...and other protein data bases can yield insights regarding a “mental picture of the constitution of living matter” Jacques Loeb, Science (1897). Yields technologies that can aid • understanding of disease, • drug design, and • fundamental protein biophysics. Digital Biology 2016
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Thanks
This talk is based on joint work with (among others) ´ Ariel Ferndandez (Argentine Institute of Mathematics ´ and Collegium Basilea, “Alberto Pedro Calderon” Institute for Advanced Study, Basel, Switzerland), Chris Fraser, Bioanalytical Computing Inc., Harold Scheraga (Cornell), and Kristina Rogale Plazonic (Princeton); and at U. Chicago: Steve Berry, Tatiana Orlova. We acknowledge partial support from Institute for Biophysical Dynamics at the University of Chicago and NSF grant DMS-1226019. We thank the MBI at Ohio State for support during fall 2015. Digital Biology 2016
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References [1] Kevin Cahill and V. Adrian Parsegian. Computational consequences of neglected first-order van der Waals forces. arXiv preprint q-bio/0312005, 2003. [2] Yugong Cheng, Tanguy LeGall, Christopher J. Oldfield, James P. Mueller, Ya-Yue J. Van, Pedro Romero, Marc S. Cortese, Vladimir N. Uversky, and A. Keith Dunker. Rational drug design via intrinsically disordered protein. Trends in Biotechnology, 24(10):435 – 442, 2006. [3] T. C. Choy. van der Waals interaction of the hydrogen molecule: An exact implicit energy density functional. Phys. Rev. A, 62:012506, Jun 2000. [4] Alfonso De Simone, Guy G. Dodson, Chandra S. Verma, Adriana Zagari, and Franca Fraternali. Prion and water: Tight and dynamical hydration sites have a key role in structural stability. Proceedings of the National Academy of Sciences, USA, 102:7535–7540, 2005. ´ [5] Ariel Fernandez. Packing defects functionalize soluble proteins. FEBS letters, 589(9):967–973, 2015. ´ [6] Ariel Fernandez. Quantum theory of interfacial tension quantitatively predicts spontaneous charging of nonpolar aqueous interfaces. Physics Letters A, 2015. ´ [7] Ariel Fernandez and L. Ridgway Scott. Dehydron: a structurally encoded signal for protein interaction. Biophysical Journal, 85:1914–1928, 2003. ´ [8] Ariel Fernandez and L. Ridgway Scott. Modulating drug impact by wrapping target proteins. Expert Opinion on Drug Discovery, 2:249–259, 2007. ´ [9] Richard Moss and Ariel Fernandez. Inhibition of mybp-c binding to myosin as a treatment for heart failure, June 2015. ´ [10] L. Ridgway Scott and Ariel Fernandez Stigliano. Mismatched ions indicate quantum effects in proteins. Research Report UC/CS TR-2015-10, Dept. Comp. Sci., Univ. Chicago, 2015.
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Datamining and Drug Design L. Ridgway Scott Departments of Computer Science and Mathematics, Computation Institute, and Institute for Biophysical Dynamics, University of Chicago ´ This talk is based on joint work with Ariel Ferndandez (Argentine Institute ´ and Collegium Basilea, Institute of Mathematics “Alberto Pedro Calderon” for Advanced Study, Basel, Switzerland), Chris Fraser, Bioanalytical Computing Inc. (formerly UChicago), Harold Scheraga (Cornell), and Kristina Rogale Plazonic (Princeton); and at U. Chicago: Steve Berry, Tatiana Orlova. Digital Biology 2016
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Computational Models
100 nanometers 10 nanometers
continuum
nanometer quantum mechanics
Angstrom
molecular dynamics
electrostatics
10 picometers attosec femtosec picosec nanosec microsec msec Figure 1: Computational models at multiple scales
Nonbonded interactions involved in all scales. Digital Biology 2016
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Failure of drug designs in clinical trials [2]
Figure 2: FDA approvals of new molecule entities (NME), numbers of phase III clinical trials completed and pharmaceutical R&D spending from 1990 to 2005. NME approvals reached a peak in the period from 1996 to 1999 and are now dropping in spite of continually increasing R&D spending and phase III clinical trials. Digital Biology 2016
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Aligned backbones for two paralog kinases
Dehydrons for Chk1 marked in green and those for Pdk1 in red [8].
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HIV-1 protease with ‘dehydron wrapper’ inhibitor
Detail of the protease cavity, pattern of packing defects, and inhibitor positioned as dehydron wrapper. Digital Biology 2016
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Desolvation spheres for flap
Desolvation spheres for flap Gly-49–Gly-52 dehydron containing nonpolar groups of the wrapping inhibitor. Digital Biology 2016
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Body composition
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Proteins as digital components
Proteins are the essential components of life: • used to build complexes, e.g., viruses (bricks and mortar) • involved in signalling (information transmission) • enzymes essential in catalysis (chemical machines)
In all these cases, protein-ligand interaction is essential. These interactions are deterministic (always the same). Proteins function as discrete components not as analog devices. Digital Biology 2016
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The hydrophobic effect
Hydrophobic effect crucial in protein-ligand association. Water is essential to life as we know it, but hostile to proteins. The role of water in protein biophysics: to modulate electric forces via the dielectric effect. Hydrophobicity fosters water removal and supports protein-ligand interaction, but it also modulates the dielectric effect. Water is a strong dielectric, and protein sidechains are a complex mix of charged, polar, and hydrophobic parts. But the hydrophobic effect is non-specific in action. What makes proteins interact in a repeatable way? Digital Biology 2016
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What sidechains are found at interfaces?
By examining interfaces in PDB structures, we can see which residues are most likely to be found at interfaces. Asparagine Threonine Glycine Serine CH2
H
C
C CH3
OH
H H
C
Aspartic acid Alanine Cysteine CH2 SH
C
H
❅ ❅ ❅ ❅
NH2
CH3
OH CH2
❅ ❅ ❅ ❅
O
O−
O
Sidechains most likely to be involved in interactions, ordered from the left (asparagine), are not hydrophobic. Digital Biology 2016
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Electronic forces
The only force of significance in biochemistry is the electric force. — But often modulated by indirection or induction. In terrestrial biology, water plays a significant role as a dielectric which mediates non-covalent interactions (hydrogen bonds, salt bridges, cation-pi interactions). But the dielectric effect of water is modulated by hydrophobic components of proteins. Moreover, a ligand can change the hydrophobic environment upon binding. In protein-ligand interactions, this makes intramolecular bonds as important as intermolecular interactions. Digital Biology 2016
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Our technology
Interaction between physical chemistry and data mining in biophysical data bases. • Data mining can lead to new results in physical chemistry that are significant in biology. • Using physical chemistry to look at data provides insights regarding function. We review recent results regarding protein-ligand interaction based on insights about hydrophobic effects. We show that sidechain configurations modulate dielectric effect. We discuss how these can be used to understand a novel factor that supports protein-ligand binding. Digital Biology 2016
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Competing effects: why this is so hard
Protein sidechains have large electrostatic gradients Water is a strong dielectric Hydrophobic groups modify the water structure Large electrostatic gradients
Screening by dielectric effect
Modulation of dielectric strength by hydrophobic effect Figure 3: Three competing effects that determine protein behavior. These conspire to weaken interactive forces, making biological relationships more tenuous and amenable to mutation.
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Protein basics
Proteins are sequences of amino acids which are covalently bonded along a “backbone.” Proteins of biological significance fold into a three-dimensional structure by adding hydrogen bonds between carbonyl and amide groups on the backbone of different amino acids. In addition, other bonds, such as a salt bridge or a disulfide bond can form between particular amino acids (Cysteine has sulfur atoms in its sidechain). But the hydrogen bond is the primary mode of structure formation in proteins. Digital Biology 2016
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Chains of amino acid residues
Proteins are chains of amino acid residues whose basic unit is the peptide group. ✧✧
H ❜ ❜
Cαi+1
+
N Ri
✧ ❅ ❅ ✧ i
Cα
(a)
C
i+1 C ✟ α
✟
❅ ❅ i+1 R
Ri+1 Ri
❜❜
O−
❜❜
✧ ❅ ❅ ✧ i
Cα
N+ C
✧ H ✧
❜❜
O−
(b)
Figure 4: The rigid state of the peptide bond: (a) trans form, (b) cis form. The double bond between the central carbon and nitrogen keeps the peptide bond planar. Digital Biology 2016
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Linear (primary) structure of proteins R R
R
R
R
R
Figure 5: Cartoon of peptide sequence where all peptides are in trans form (cf. Figure 4). Small boxes represent C-alpha carbons, arrow heads represent amide groups NH, arrow tails represent carbonyl groups CO, and thin rectangular boxes are double bond between backbone C and N. The different residues are indicated by R’s. The numbering scheme is increasing from left to right, so that the arrow formed by the carbonyl-amide pair points in the direction of increasing residue number. The three-dimensional nature of the protein is left to the imagination. Digital Biology 2016
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Hydrogen bonds and secondary structure Proteins have a hierarchy of structure, the next being secondary structure consisting of two primary types: alpha-helices and beta-sheets (a.k.a., α-helices and β-sheets). Alpha helices are helical arrangements of the subsequent peptide complexes with a distinctive hydrogen bond arrangement between the amide (NH) and carbonyl (OC) groups in peptides separated by k steps in the sequence, where primarily k = 4 but with k = 3 and k = 5 also occurring less frequently:
(a) Digital Biology 2016
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Beta sheets Beta sheets represent different hydrogen bond arrangements: (b) is the anti-parallel arrangement and (c) is the parallel.
(b)
(c)
Both structures are essentially flat, in contrast to the helical structure in (a).
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Dehydrons in human hemoglobin
Dehydrons in human hemoglobin From PNAS 100: 6446-6451 (2003) Ariel Fernandez, Jozsef Kardos, L. Ridgway Scott, Yuji Goto, and R. Stephen Berry. Structural defects and the diagnosis of amyloidogenic propensity.
Well-wrapped hydrogen bonds are grey, and dehydrons are green. The standard ribbon model of “structure” lacks indicators of electrostatic environment. Digital Biology 2016
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A quote
from Nature’s Robots ....
“The exact and definite determination of life phenomena which are common to plants and animals is only one side of the physiological problem of today. The other side is the construction of a mental picture of the constitution of living matter from these general qualities. In this portion of our work we need the aid of physical chemistry.” Jacques Loeb, The biological problems of today: physiology. Science 7, 154-156 (1897).
so our theme is not so new .... Digital Biology 2016
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Data mining definition
WHATIS.COM: Data mining is sorting through data to identify patterns and establish relationships. Data mining parameters include: • Association - looking for patterns where one event is connected to another event • Sequence or path analysis - looking for patterns where one event leads to another later event • Classification - looking for new patterns (May result in a change in the way the data is organized but that’s ok) • Clustering - finding and visually documenting groups of facts not previously known Conclusion: Data mining involves looking at data. Digital Biology 2016
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Data mining lens
If data mining is looking at data then ☛
✟
What type of lens do we use? ✡ ✠
• All of these have chemical representations, e.g., C400 H620 N100 O120 P1 S1 • Alphabetic sequences describe much of biology: DNA, RNA, proteins. • All of these have three-dimensional structure. • But structure alone does not explain how they function. Physical chemistry clarifies the picture and allows function to be more easily interpreted. Digital Biology 2016
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Sequences can tell a story
Protein sequences aardvarkateatavisticallyacademicianaccelerative acetylglycineachievementacidimetricallyacridity actressadamantadhesivenessadministrativelyadmit afflictiveafterdinneragrypniaaimlessnessairlift and DNA sequences actcatatactagagtacttagacttatactagagcattacttagat can be studied using automatically determined lexicons. Joint work with John Goldsmith, Terry Clark, Jing Liu.
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Sequences can tell a story
Protein sequences (a linguistic lens) aardvarkateatavisticallyacademicianaccelerative acetylglycineachievementacidimetricallyacridity actressadamantadhesivenessadministrativelyadmit afflictiveafterdinneragrypniaaimlessnessairlift and DNA sequences actcatatactagagtacttagacttatactagagcattacttagat can be studied using automatically determined lexicons. Joint work with John Goldsmith, Terry Clark, Jing Liu.
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What sidechains are found at interfaces?
By examining interfaces in PDB structures, we can see which residues are most likely to be found at interfaces. Asparagine Threonine Glycine Serine CH2
H
C
C CH3
OH
H H
C
Aspartic acid Alanine Cysteine OH CH2 C
H
❅ ❅ ❅ ❅
NH2
CH3
CH2 SH
❅ ❅ ❅ ❅
O
O−
O
Sidechains most likely to be involved in interactions, ordered from the left (asparagine), are not hydrophobic.
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Amino acid sidechains have different properties
Carbonaceous groups on sidechains are hydrophobic: Valine
Leucine
CH2
CH2
Isoleucine H
C
❅ ❅
CH2
Proline
CH3 CH2
CH2
CH ❅ ❅
CH3
CH3
CH3
CH2
CH2
❅ ❅
CH2
Phenylalanine
CH2
✟❍ ✟ ✟✟ ❍ ❍❍ ✟ ❍ ❍✟
Amino acid residues (sidechains only shown) having only carbonaceous groups.
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Charges in a dielectric
Charges in a dielectric are like lights in a fog.
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Wrapping modifies dielectric effect
Hydrophobic (CHn ) groups remove water locally. This causes a reduction in ε locally. (Resulting increase in φ makes dehydrons sticky.) This can be quantified and used to predict binding sites. The placement of hydrophobic groups near an electrostatic bond is called wrapping. Like putting insulation on an electrical wire. We can see this effect on a single hydrogen bond. Digital Biology 2016
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Unit of hydrophobicity
A single carbonaceous group CHn can enhance the strength and stability of a hydrogen bond. Consider the effect of such a group in • methyl alchohol versus ethyl alchohol • ethylene glycol versus propylene glycol • (deadly versus drinkable) Can we see a molecular-level effect analogous to the change in dielectric permittivity? What can a simple model of dielectric modulation predict?
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Wrapping protects hydrogen bond from water
CHn CHn C
CHn H
O
N
CHn
CHn
CHn
CHn H
C
O O
H
H Well wrapped hydrogen bond Digital Biology 2016
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H
O
H
N
O
H O
H
H
H Underwrapped hydrogen bond 30/64
Extent of wrapping changes nature of hydrogen bond Hydrogen bonds (B) not protected from water do not persist.
From De Simone, et al., PNAS 102 no 21 7535-7540 (2005) Digital Biology 2016
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Dynamics of hydrogen bonds and wrapping
900 800 700 600 500 400 300 200 100 0 0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Figure 6: Distribution of bond lengths for two hydrogen bonds formed in a structure of the sheep prion [4]. Horizontal axis measured in nanometers, vertical axis represents numbers of occurrences taken from a simulation ˚ with 20, 000 data points with bin widths of 0.1 Angstrom. Distribution for the well-wrapped hydrogen bond (H3) has smaller mean value but a longer (exponential) tail, whereas distribution for the underwrapped hydrogen bond (H1) has larger mean but Gaussian tail. Digital Biology 2016
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Ligand binding removes water
CHn
CHn CHn
CHn C
O
H
11111 00000 00000 11111 00000 11111 00000 N 11111 00000 11111 00000 11111
CHn C
O
CHn H
N
O
H O
H
H
L IG A N D
H
Binding of ligand changes underprotected hydrogen bond (high dielectric) to strong bond (low dielectric)
No intermolecular bonds needed! Digital Biology 2016
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Inter- versus intra-molecular
Intermolecular bonds are like the power cord on my computer.
Figure 7: Wireless Charging (from Technology Review).
Intramolecular bonds are like the charger on electric toothbrush. Digital Biology 2016
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Intermolecular versus intramolecular H bonds
CHn
CHn CHn
CHn
H
C
O
CHn N
C
O
CHn H
N
L IG A N D
Energetic contribution to binding comparable but can be better for intramolecular. Digital Biology 2016
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Dehydrons in human hemoglobin
Dehydrons in human hemoglobin From PNAS 100: 6446-6451 (2003) Ariel Fernandez, Jozsef Kardos, L. Ridgway Scott, Yuji Goto, and R. Stephen Berry. Structural defects and the diagnosis of amyloidogenic propensity.
Well-wrapped hydrogen bonds are grey, and dehydrons are green. The standard ribbon model of “structure” lacks indicators of electrostatic environment. Digital Biology 2016
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Wrapping made quantitative
Wrapping made quantitative by counting carbonaceous groups in the neighborhood of a hydrogen bond.
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Distribution of wrapping
Distribution of wrapping for an antibody complex. PDB file 1P2C: Light chain, A, dotted line; Heavy chain, B, dashed line; HEL, C, solid line 12 line 2 line 3 line 4
frequency of occurrence
10
8
6
4
2
0 0
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5 10 15 20 25 30 35 number of noncarbonaceous groups in each desolvation sphere: radius=6.0 Angstroms
40
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Stickiness of dehydrons
Attractive force of dehydrons predicted and measured in Ariel Fernandez and L. Ridgway Scott. Adherence of packing defects in soluble proteins. Phys. Rev. Lett. 2003 91:18102(4)
by considering rates of adhesion to phospholipid (DLPC) bilayer. Deformation of phospholipid bilayer by dehydrons measured in Ariel Fernandez and L. Ridgway Scott. Under-wrapped soluble proteins as signals triggering membrane morphology. Journal of Chemical Physics 119(13), 6911-6915 (2003).
Single molecule measurement of dehydronic force in Ariel Fernandez. Direct nanoscale dehydration of hydrogen bonds. Journal of Physics D: Applied Physics 38, 2928-2932, 2005. Fine print: careful definition of dehydron requires assessing modification of dielectric enviroment by test hydrophobe. That is, geometry of carbon groups matters, although counting gets it right ≈ 90% of the time [7].
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Charge-force relationship
Here’s the math.... Charges ρ induce an electric field e = ∇φ given by ∇· (ε∇φ) = ∇· (εe) = ρ, where ε is permittivity of medium. Energy =
(1) R
ρφ dx.
In vacuum, ε is permittivity of free space, ε0 . In other media (e.g., water) the value of ε is much larger. ε measures strength of dielectric enviroment. Water removal decreases ε in (1), and increases φ. Hydrophilic groups contribute to right-hand side ρ in (1). Digital Biology 2016
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HIV protease dehydron
The HIV protease has a dehydron at an antibody binding site.
When the antibody binds at the dehydron, it wraps it with hydrophobic groups.
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A model for protein-protein interaction
Foot-and-mouth disease virus assembly from small proteins. Digital Biology 2016
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Dehydrons guide binding
Dehydrons guide binding of component proteins VP1, VP2 and VP3 of foot-and-mouth disease virus. Digital Biology 2016
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Extreme interaction: amyloid formation
Standard application of bioinformatics: look at distribution tails. If some is good, more may be better, but too many may be bad. Too many dehydrons signals trouble: the human prion.
From PNAS 100: 6446-6451 (2003) Ariel Fernandez, Jozsef Kardos, L. Ridgway Scott, Yuji Goto, and R. Stephen Berry. Structural defects and the diagnosis of amyloidogenic propensity. Digital Biology 2016
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Dehydrons as indicators of protein interactivity
If dehydrons provide mechanism for proteins to interact, then more interactive proteins should have more dehydrons, and vice versa. We only expect a correlation since there are (presumably) other ways for proteins to interact. The DIP database collects information about protein interactions, based on individual protein domains: can measure interactivity of different regions of a given protein.
Result: Interactivity of proteins correlates strongly with number of dehydrons. PNAS 101(9):2823-7 (2004) The nonconserved wrapping of conserved protein folds reveals a trend toward increasing connectivity in proteomic networks. ´ Ariel Fernandez, L. R. Scott and R. Steve Berry Digital Biology 2016
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Dehydron counts correlate with interactivity
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Dehydron variation over different species Species (common name) Aplysia limacina (mollusc) Chironomus thummi thummi (insect) Thunnus albacares (tuna) Caretta caretta (sea turtle) Physeter catodon (whale) Sus scrofa (pig) Equus caballus (horse) Elephas maximus (Asian elephant) Phoca vitulina (seal) H. sapiens (human)
peptides 146 136 146 153 153 153 152 153 153 146
H bonds 106 101 110 110 113 113 112 115 109 102
dehydrons 0 3 8 11 11 12 14 15 16 16
Number of dehydrons in Myoglobin of different species
1LHT (sea turtle) [11]
1MBA (mollusc) [0] 1ECA (insect) [4] 1MYT (yellow−fin tuna) [8]
1MBS (seal) [16] 1BZ6 (sperm whale) [11] 1DRW (horse) [14] 1MWC (wild boar) [12] 2MM1 (human) [16] Digital Biology 2016
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Anecdotal evidence
Anecdotal evidence: basic structure is similar, dehydron number increases. SH3 domains are from nematode C. elegans (a) H. sapiens (b); ubiquitin from E. coli (c) and H. sapiens (d); hemoglobin from Paramecium (e) and H. sapiens-subunit (f). Digital Biology 2016
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Wrapping technology in drug design Synopsis of “Modulating drug impact by wrapping target proteins” by Ariel ´ Fernandez and L. Ridgway Scott, Expert Opinion on Drug Discovery 2007.
Drug ligands bind to proteins near dehydrons, enhancing wrapping upon attachment. Drug side effects often caused by binding to proteins with structure similar to target. We can exploit the differences in dehydron patterns in homologous proteins to make drugs more specific. Digital Biology 2016
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Drug ligand non-polar groups Drug ligand provides additional non-polar carbonaceous group(s) in desolvation domain, enhancing wrapping of hydrogen bond.
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HIV-1 protease with ‘dehydron wrapper’ inhibitor
Detail of the protease cavity, pattern of packing defects, and inhibitor positioned as dehydron wrapper. Digital Biology 2016
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Desolvation spheres for flap
Desolvation spheres for flap Gly-49–Gly-52 dehydron containing nonpolar groups of the wrapping inhibitor. Digital Biology 2016
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Drug specificity
Tyrosine kinases: a family of proteins with very similar structure. • Called paralogous because they are similar proteins within a given species. • Presumed to have evolved from a common source. • Crucial target of cancer drug therapy. Gleevec targets particular tyrosine kinases and has been one of the most successful cancer drugs. However, it also targets similar proteins and can cause unwanted side effects (it is cardiotoxic). Differences between the dehydron patters in similar proteins can be used to differentiate them and guide the re-design of drug ligands. Digital Biology 2016
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Aligned paralog kinases
Aligned backbones for two paralog kinases; dehydrons for Chk1 are marked in green and those for Pdk1 are in red.
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Dehydron in C-Kit
Dehydron Cys673-Gly676 in C-Kit not conserved in paralogs Bcr-Abl, Lck, Chk1 and Pdk1. By methylating Gleevec at para position (1), inhibitor becomes selective wrapper of C-Kit dehydron. Digital Biology 2016
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Wrapping technology: WBZ drugs
Two variants of imatinib, WBZ-4 and WBZ-7, were developed using wrapping technology: “WBZ-4, unlike imatinib, targets C-Kit but not Bcr-Abl.... potential for cardiotoxicity is even lower, researchers found in laboratory, animal, and computer testing. WBZ-4 appears to be as effective against GIST as imatinib” “Gleevec is far more effective against a drug-resistant strain of cancer when the drug wraps the target .... modified version of the drug, WBZ-7, ... seals out water from a critical area.” Digital Biology 2016
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Genetic code Genetic code minimizes changes of polarity due to single-letter codon mutations, but it facilitates changes in wrapping due to single-letter codon mutations. Second Position
u
c a g
Phe 7
||
Leu 4
||
Leu 4 Ile
4
|| ||
Met 1 + − Val 3
||
ucu ucc uca ucg ccu ccc cca ccg acu acc aca acg gcu gcc gca gcg
a
Ser 0 + −
Pro 2
||
Thr 1 + −
Ala 1
||
uau uac uaa uag cau cac caa cag aau aac aaa aag gau gac gaa gag
Tyr 6 + − stop stop His 1 + − Gln 2 + − Asn 1 + − Lys 3 + + Asp 1 − − Glu 2 − −
g ugu ugc uga ugg cgu cgc cga cgg agu agc aga agg ggu ggc gga ggg
Cys 0 + − uc a stop Trp 7 + − g u c Arg 2 + + a g Ser 0 + − u c Arg 2 + + ag u c Gly 0 + − a g
Third Position
First Position
u
uuu uuc uua uug cuu cuc cua cug auu auc aua aug guu guc gua gug
c
First digit after residue name is amount of wrapping. Second indicator is polarity; | |: nonpolar, + −: polar, − −: negatively charged, + +: positively charged. Digital Biology 2016
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Determining dehydrons without structure
Can use disorder scores as proxy for dehydrons. Dehydrons correspond to changes in score. Digital Biology 2016
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Dehydron regions identified by sequence alone
Changes in disorder score relate to (a) protein binding and (b) catalytic regions (dehydrons facilitate catalysis) [5, 6].
Technology used to find peptides for treating heart disease [9]. ´ Ariel Fernandez and L. Ridgway Scott. Drug leads for interactive protein targets with unknown structure. Drug Discovery Today, October 2015. Digital Biology 2016
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Peptides for treating heart disease
U.S. Patent No. 9,051,387 B2, 9 June 2015 Treating Heart Failure by Inhibiting Myosin Interaction with a Regulatory Myosin Binding Protein [MyBP-C] INVENTORS - Richard Moss, Ariel Fernandez Inhibitor peptide:
FSSLLKKRDAFRRDAK
“The Wisconsin Alumni Research Foundation (WARF) is seeking commercial partners interested in developing and using peptides that promote cardiac muscle contraction by disrupting the binding of MyBP-C to myosin.” Digital Biology 2016
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Oppositely charged residues without a counter ion?
Lysines become deprotonated by nearby dehydrons [10]? Digital Biology 2016
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Conclusions
Data mining in the Protein Data Bank ...and other protein data bases can yield insights regarding a “mental picture of the constitution of living matter” Jacques Loeb, Science (1897). Yields technologies that can aid • understanding of disease, • drug design, and • fundamental protein biophysics. Digital Biology 2016
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Thanks
This talk is based on joint work with (among others) ´ Ariel Ferndandez (Argentine Institute of Mathematics ´ and Collegium Basilea, “Alberto Pedro Calderon” Institute for Advanced Study, Basel, Switzerland), Chris Fraser, Bioanalytical Computing Inc., Harold Scheraga (Cornell), and Kristina Rogale Plazonic (Princeton); and at U. Chicago: Steve Berry, Tatiana Orlova. We acknowledge partial support from Institute for Biophysical Dynamics at the University of Chicago and NSF grant DMS-1226019. We thank the MBI at Ohio State for support during fall 2015. Digital Biology 2016
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References [1] Kevin Cahill and V. Adrian Parsegian. Computational consequences of neglected first-order van der Waals forces. arXiv preprint q-bio/0312005, 2003. [2] Yugong Cheng, Tanguy LeGall, Christopher J. Oldfield, James P. Mueller, Ya-Yue J. Van, Pedro Romero, Marc S. Cortese, Vladimir N. Uversky, and A. Keith Dunker. Rational drug design via intrinsically disordered protein. Trends in Biotechnology, 24(10):435 – 442, 2006. [3] T. C. Choy. van der Waals interaction of the hydrogen molecule: An exact implicit energy density functional. Phys. Rev. A, 62:012506, Jun 2000. [4] Alfonso De Simone, Guy G. Dodson, Chandra S. Verma, Adriana Zagari, and Franca Fraternali. Prion and water: Tight and dynamical hydration sites have a key role in structural stability. Proceedings of the National Academy of Sciences, USA, 102:7535–7540, 2005. ´ [5] Ariel Fernandez. Packing defects functionalize soluble proteins. FEBS letters, 589(9):967–973, 2015. ´ [6] Ariel Fernandez. Quantum theory of interfacial tension quantitatively predicts spontaneous charging of nonpolar aqueous interfaces. Physics Letters A, 2015. ´ [7] Ariel Fernandez and L. Ridgway Scott. Dehydron: a structurally encoded signal for protein interaction. Biophysical Journal, 85:1914–1928, 2003. ´ [8] Ariel Fernandez and L. Ridgway Scott. Modulating drug impact by wrapping target proteins. Expert Opinion on Drug Discovery, 2:249–259, 2007. ´ [9] Richard Moss and Ariel Fernandez. Inhibition of mybp-c binding to myosin as a treatment for heart failure, June 2015. ´ [10] L. Ridgway Scott and Ariel Fernandez Stigliano. Mismatched ions indicate quantum effects in proteins. Research Report UC/CS TR-2015-10, Dept. Comp. Sci., Univ. Chicago, 2015.
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