Computational engineering of water-soluble human potassium ion channels through qty transformation

ABSTRACT Transmembrane potassium ion channels are crucial for ion transport, metabolism, and signaling, and serve as promising targets for anti-cancer therapies. However, their hydrophobic

transmembrane nature requires detergents, posing a major bottleneck for experimental handling. In this paper, we present a structural bioinformatics study of six experimentally determined

and twelve modeled potassium channel structures, in which hydrophobic amino acids (L, I/V, and F) were systematically replaced with neutral hydrophilic ones (Q, T, and Y), making the

proteins more water-soluble. QTY (computationally predicted) and native (experimental and repredicted) variants show remarkable structural similarity (RMSD: ~0.50 Å – ~2.14 Å) despite

significant sequence differences. QTY variants, both rigid and refined with MD simulations, maintain comparable to native variants stability, solvent-accessible surface area (SASA), and

ionic, aromatic, and van der Waals interactions but differ in the grand average of hydropathy (GRAVY), solubility, and hydrophobic contacts. Overall, our study presents a computational

approach for designing hydrophilic potassium ion channels while maintaining the native global structure that could potentially simplify their practical use by eliminating the need for

detergents. SIMILAR CONTENT BEING VIEWED BY OTHERS CONSTRUCTING ION CHANNELS FROM WATER-SOLUBLE Α-HELICAL BARRELS Article 10 May 2021 DESIGN OF A WATER-SOLUBLE TRANSMEMBRANE RECEPTOR KINASE

WITH INTACT MOLECULAR FUNCTION BY QTY CODE Article Open access 10 June 2024 STRUCTURAL INFORMATIC STUDY OF DETERMINED AND ALPHAFOLD2 PREDICTED MOLECULAR STRUCTURES OF 13 HUMAN SOLUTE CARRIER

TRANSPORTERS AND THEIR WATER-SOLUBLE QTY VARIANTS Article Open access 22 November 2022 INTRODUCTION Potassium channels, including voltage-gated (VGKC, Kv), calcium-activated (KCa),

inward-rectifier (Kir, IRK), and tandem pore domain (K2P) types, are integral transmembrane (TM) proteins with 2TM, 4TM, and 6TM alpha-helices. They are one of the most diverse and widely

distributed classes of ion channels in virtually all organisms and cells. Potassium ion channels switch between closed and opened conformations and regulate the K+ flow across cell

membranes. This ability to selectively pass potassium ions through the pore is crucial for their function. For example, potassium channel blockers can physically obstruct the pathway from

the intracellular solution into the pore, altering the function of the channel1. Hence, the pore plays an important role in both normal physiological functions and pathophysiological

processes2,3. They are crucial for muscle contraction, nerve impulse propagation, cellular activation, and the secretion of biologically active molecules. Additionally, potassium ion

channels serve as therapeutic targets for various brain disorders, including brain and spinal cord ischemia, epilepsy, migraine, multiple sclerosis, pain, stroke, Alzheimer’s disease,

Parkinson’s disease, and schizophrenia2,3. In the past two decades, the involvement of potassium ion channels in cancer metabolism, growth, and metastasis has become evident especially for

KCNA1, KCNA3, KCNA5, KCNC4, KCND1, KCNH2, KCNH5, KCNJ3, KCNJ8, KCNJ10, KCNJ11, KCNJ12, KCNK2, KCNK5, KCNK9, KCNMA1, KCNN3, and KCNN4 (Table 1)4,5,6,7,8,9. Some potassium ion channels have

been found to be involved in various cancer-related processes, for example, cell cycle events, cell proliferation, evasion of apoptosis, sustained angiogenesis, and metastasis7,8,9,10,11.

Others are mainly involved either in cell proliferation12,13,14, or cell migration15,16,17,18,19,20. Recently, potassium ion channels have emerged as promising targets for the treatment of

different types of cancer21,22,23,24,25,26,27. Thus, it is possible to combine all recently available knowledge of potassium ion channels to formulate medical cocktails to attack cancer

cells from many angles in cancer therapies. Structural bioinformatics studies of these potassium channels may yield significant clinical benefits for cancer patients. Several molecular

structures of human potassium ion channels have been experimentally (X-ray or Cryo-EM) determined, including KCNN4 (protein Kca3.1) (PDB: 6CNM)28, KCNK2 (K2P2.1) (PDB: 4TWK), KCNA3 (Kv1.3)

(PDB: 7EJ1)29, KCNMA1 (Kca1.1) (PDB: 6V22)30, KCNH2 (Kv11.1) (PDB: 5VA2)31, KCNJ11 (Kir6.2) (PDB: 6C3O)32. However, structures of other potassium channels haven’t yet been determined. Since

these potassium channels form 2TM, 4TM and 6TM helices, the structural determination of these transporters requires systematic detergent screens before protein purification can be carried

out, making the process quite challenging33. AlphaFold234,35 and RoseTTAFold36,37 were introduced in July 2021 as revolutionary artificial intelligence (AI) computational tools for the

accurate protein structure predictions. Since then, both tools have already made a significant impact on our understanding of the molecular structure of numerous proteins that were

previously inaccessible. Now we are able to get structural models to perform computational analysis and design of potassium ion channels. This way we can computationally engineer the native

channels to make them more suitable for the experimental verification and therapeutic purposes, by, for example, transforming them into hydrophilic analogies (QTY variants). We previously

applied the QTY (Glutamine, Threonine, Tyrosine) code that replaces TM hydrophobic amino acids with structurally similar hydrophilic amino acids (L → Q, I/V → T, F → Y) to design several

detergent-free transmembrane (TM) protein chemokine receptors and cytokine receptors for various uses 38,39,40,41. The purified QTY variant proteins exhibited predicted characteristics,

stable structures and retained ligand-binding activity 38,39,40,41. Later we prepared QTY variant protein structure predictions using AlphaFold2, achieving results in hours 42 instead of ~ 5

 weeks for each molecular simulation using GOMoDo, AMBER and YASARA programs 38,39,40. We already used AlphaFold2 to design water-soluble QTY variants of the 14 human glucose transporters43

and 13 human solute carrier transporters44, and now move to potassium ion channels. Here we report a structural bioinformatics study of 6 Cryo-EM experimentally determined and 12

AlphaFold2-predicted human potassium ion channels and their water-soluble QTY variants. Native proteins and their water-soluble QTY variants share remarkable structural similarities

notwithstanding significant protein sequence differences. Furthermore, different variants have similar characteristics that are responsible for protein stability, thereby preserving the

essential sequence and structural parameters associated with protein function. These water-soluble QTY variants of potassium ion channels may be useful as antigens for the discovery and

development of therapeutic monoclonal antibodies45. RESULTS AND DISCUSSIONS THE RATIONALE OF THE QTY CODE The QTY code specifically selects three neutrally polar amino acids, namely

glutamine (Q), threonine (T), and tyrosine (Y), to replace the hydrophobic amino acids leucine (L), isoleucine (I), valine (V), and phenylalanine (F). According to the electron density maps,

we observed significant structural similarities between the original hydrophobic amino acids (L, I, V, F) and their respective replacements (Q, T, Y)38,39. The hydrophobic amino acids

within the transmembrane alpha-helices are replaced by Q, T, and Y, resulting in the loss of hydrophobic characteristics in these alpha-helical segments (Fig. 1, Fig. 2, Fig. 5, Fig. 6, Fig.

 7, Fig. 8). PROTEIN SEQUENCE ALIGNMENTS AND OTHER CHARACTERISTICS We aligned the native potassium ion channels with their QTY variants. The QTY variants exhibit a significant proportion of

hydrophobic residues replaced by QTY counterparts (L, I/V, F → Q, T, Y transformation) in the potassium ion channels, both overall (~ 4.8%–15.7%) and particularly in the transmembrane

domains (~ 48.4% → 60.7%). For example, the transmembrane domain exhibits differences ranging from 48.4% to > 60.7%, while the overall potassium ion channel proteins differ from their QTY

variants by 5.5% to 14.3%, depending on the number of transmembrane alpha-helices present in the proteins (Fig. 1, Fig. 2, Table 2). Molecular weight is one of the factors to consider in

comparing the native and QTY variant proteins, as QTY transformation should not cause drastic global structural transfiguration and significant changes in general parameters to let mutated

proteins to keep their function. The molecular weight of QTY variants is slightly higher than that of native proteins (Fig. 1, Fig. 2, Table 2). This can be attributed to the fact that

nitrogen (14 Daltons) and oxygen (16 Daltons) that are more frequent in hydrophilic amino acids have a higher molecular weight compared to carbon (12 Daltons). Additionally, QTY variants

possess water-soluble side chains. The glutamine (Q) side chains form four hydrogen bonds with water: two donors through -NH2 and two acceptors through oxygen on -C = O. Similarly, the side

chains -OH of threonine (T) and tyrosine (Y) each form three hydrogen bonds with water: one donor from hydrogen (H) and two acceptors from oxygen (O). Hydrogen bonds directly reflect

structural stability, so drastic changes in hydrogen bonds can influence protein stability and integrity. The isoelectric point (pI) of the potassium ion channels vary, with some falling

within the acidic range and others in the basic range. For instance, 7 potassium ion channels, including KCNA1, KCNA3, KCNA5, KCNC4, KCNJ12, KCNK5, and KCNMA1, have acidic pIs ranging from

5.07 to approximately 6.60. KCNH5 has pI close to neutral, around 7.5. On the other hand, 9 potassium ion channels, namely KCNH2, KCND1, KCNJ3, KCNJ8, KCNJ10, KCNJ11, KCNK2, KCNN3, and

KCNN4, have basic pIs ranging from pI 8.2 to pI 9.38 (Table 2, Fig. 1, Fig. 2). Notably, despite significant QTY sequence replacements, the pIs remain identical for 7 native channels and

their QTY variants, including KCNA1, KCNA3, KCNA5, KCNC4, KCNJ12, KCNK5, and KCNMA1. The pIs in the QTY variants show minimal changes, and in some cases, there are no changes at all. This is

because the neutral amino acids glutamine (Q), threonine (T), and tyrosine (Y) do not carry any charges at neutral pH. Consequently, the introduction of these amino acids does not

significantly alter the pI of the proteins. This is important as changes in pI can lead to non-specific interactions. Changes in pI can influence protein-solvent and protein–protein

interactions, as the protonation state of amino acids depends on the pI values. It can lead to local effects on the stabilization of the protein and even global effects if the number of

local disruptions is high enough. From a thermodynamic point of view, when the pI variation increases, more amino acids change their protonation states, the initial interatomic interactions

rearrange, leading to higher disorder and an increase in entropy, resulting in positive free energy. SUPERPOSITION OF NATIVE AND WATER-SOLUBLE QTY VARIANTS We used experimental Cryo-EM

structures that are available for six native potassium ion channels to assess the accuracy of the AlphaFold2 predictions for the native potassium channels. The comparisons revealed RMSD

values ranging from ~ 0.5 Å to 2.14 Å (Table 2, Fig. 3), indicating that the models can accurately represent the protein structures. As shown in Fig. 3, these structures superposed well,

particularly in the transmembrane alpha-helices (except for some unstructured loops) where the experimentally determined structures (magenta color) and the AlphaFold2-predicted water-soluble

QTY variants (cyan color) closely overlap. These observations indicate that these structures share highly similar folds, despite significant QTY amino acid replacements (up to 60% QTY

substitutions) in the transmembrane alpha-helices of the water-soluble QTY variants. It is important to note that while AlphaFold2 predicts the global protein structure with high accuracy,

there may still be some deviations between the predicted structures and the actual structures, particularly in the loops. This is because loops are inherently flexible and challenging to

predict. The lower accuracy in loop regions can result in higher RMSD deviations, which can introduce some bias in the arrangement of the alpha-helical regions, even if the overall fold of

the protein is correctly predicted. However, it is reasonable to assume that in dynamic environments, these models will undergo relaxation and loop repacking, leading to adjustments in the

transmembrane bundle and ultimately reducing the RMSD. The AlphaFold2-predicted native structures (green color) and experimentally-determined Cryo-EM native structures (magenta color) are

similar. The RMSD for the pairwise structures are between ~ 0.40 Å to 1.92 Å, and the seven proteins fall below 1.5 Å (Table 2, Fig. 4). As shown in Fig. 4, these structures superposed well,

particularly in the transmembrane alpha-helices where the experimentally determined structures and the AlphaFold2-predicted water-soluble QTY variants closely overlap. These observations

indicate that these structures share highly similar folds. The close superposition of these structures confirms the high accuracy of AlphaFold2’s predictions, as the predicted native

structures directly superpose with Cryo-EM structures. These results also suggest that the native potassium ion channels and their water-soluble QTY variants share remarkable structural

similarity. Based on the accurate superpositions of Cryo-EM and predicted native structures, we then used AlphaFold2 to predict the structures of the 12 native potassium channels and 12 QTY

variants without Cryo-EM structures (Fig. 5). We anticipate a good resemblance between the native potassium channels and their QTY variants because of the high similarity of 1.5 Å electron

density maps between the hydrophobic amino acids L, V/I, F and Q, T, Y hydrophilic amino acids38,39. ANALYSIS OF THE HYDROPHOBIC SURFACE OF NATIVE AND QTY PROTEINS The native potassium

channels are highly hydrophobic, especially in the 2TM, 4TM or 6TM alpha-helical domains. The 2TM, 4TM, and 6TM domains are directly embedded within the hydrophobic lipid bilayer, allowing

the hydrophobic side chains of amino acids leucine (L), isoleucine (I), valine (V), and phenylalanine (F) to interact directly with lipid molecules while excluding water (Fig. 6).

Consequently, these domains display highly hydrophobic surfaces. As a result, potassium ion channels are inherently insoluble in water and require detergents to maintain the structure after

they are removed from lipid bilayer membranes. In the absence of appropriate detergents, they readily aggregate, precipitate, and lose their biological functionality. Through the QTY

conversion, the hydrophobic surfaces observed in the 2TM, 4TM, and 6TM alpha helices are significantly reduced (Fig. 5, Fig. 6). This transformation from hydrophobic to hydrophilic

alpha-helices does not lead to significant alterations in the AlphaFold2 predicted structures. We believe that the structures between the native and QTY variants remains similar, based on

our previous biochemical experiments38,39,40,41,48. These experiments demonstrated that QTY-designed chemokine receptors and cytokine receptors, which share general similarities with

potassium channels as transmembrane alpha-helical proteins, retained their structural integrity, stability, and ligand-binding activities38,39,40,41,48. COMPARISON OF PROTEIN SEQUENCE

CHARACTERISTICS BETWEEN NATIVE, CRYO-EM, AND QTY VARIANT STRUCTURES We carried out systematic bioinformatic analyses and compared five characteristics of the predicted native, Cryo-EM, and

QTY variant structures of all 18 potassium ion channels. They include: I) stability, II) grand average of hydropathy (GRAVY), III) flexibility, IV) solvent-accessible surface area (SASA), V)

and solubility. Furthermore, we also compared seven molecular interactions including: I) hydrogen bonds (HBO), II) polar contacts (PLR), III) ionic interactions (ION), IV) aromatic contacts

(ARO), V) hydrophobic contacts (HDP), VI) van der Waals interactions (VdW), VII) and van der Waals clashes (VCL). Various sequence and structure parameters can be used to assess the

difference in solubility between hydrophilic and hydrophobic proteins. GRAVY’s negative and positive values indicate the relative hydrophilicity and hydrophobicity of amino acids

respectively. A GRAVY value < 0 implies a higher degree of hydrophilicity of a protein, whereas a GRAVY value > 0 indicates a hydrophobic nature. In the context of protein flexibility,

higher values indicate greater flexibility. Solubility is defined using a threshold of 0.5: a protein is considered insoluble if its value is below 0.5, while values above 0.5 indicate

solubility. By conducting a Kruskal–Wallis H-test, we found that the differences in all sequence parameters were insignificant between the native and Cryo-EM proteins. We utilized this as a

baseline to demonstrate the similarity of sequences obtained from experimental structures and AlphaFold2 models. However, we observed significant differences in GRAVY, flexibility, and

solubility between the native and QTY structures, as well as the Cryo-EM and QTY structures. These results indicate that QTY sequences have lower GRAVY values, as they are more

water-soluble, and higher flexibility and solubility values, which are expected during the transformation of hydrophobic proteins into their hydrophilic counter parts. On the other hand,

stability and SASA showed insignificant variations in the same comparisons (Fig. 7). Regarding protein interatomic interactions, the comparison between the AlphaFold2-predicted native models

and Cryo-EM structures revealed no significant differences according to the Kruskal–Wallis H-test, similar to the analysis of sequence characteristics. Only the number of HDP exhibits

significant variation between the native and QTY variant structures, as well as the Cryo-EM and QTY variant structures, while all other interactions have little changes (Fig. 8). MOLECULAR

DYNAMICS SIMULATIONS Molecular dynamics (MD) simulations performed on KCNJ11 and KCNN4 yield results similar but not identical to the previous rigid investigations. We used only the

converged part of the trajectories (last 100 ns and 1000 frames) to calculate molecular interactions, as previously done for the rigid structures. All KCNN4 converged around 150 ns in both

membrane and water (frame #1500), while KCNJ11 converged at the same time in water but required more time for stabilization in membrane (300 ns). Even though KCNJ11 doesn’t show strong

convergence even at the simulation’s end, we still decided to keep this system since, after visual inspection, the protein structures looked reliable (Fig. 9). We separated QTY systems into

2 categories: QTY in membrane and QTY in water. Similar to the rigid analysis, HDP shows significant differences in comparison between both I) native _vs_. QTY (in membrane and in water),

and II) Cryo-EM _vs_. QTY (in membrane and in water) systems. However, MD shows that PLR, ARO, and VCL vary between native and QTY in membrane, and HBO, PLR, ARO, and VCL between Cryo-EM and

QTY in membrane (in the rigid case only HDP were different). We can observe the same trends in the box- and bar-plots (Fig. 10). The increase of PLR and decrease of ARO in QTY variants

additionally confirm that QTY has become more water-soluble. The increase in VCL can be the direct consequence of the QTY amino acid replacements, while the decrease in HBO can imply lower

stability. However, the significance of HBO is at the limit of the sensitivity of the test, so it cannot be said with certainty that it is really significant. Moreover, during the visual

evaluation of the systems through MD, we can suggest that both HBO and VCL don’t disrupt the global structure of the QTY proteins (Fig. 9). QTY in water behaves similarly to QTY in PLR, HDP,

and VCL contacts compared to native systems, but it adds one more significantly different parameter (ARO). Compared to Cryo-EM systems, in addition to PLR, HDP, and VCL, which are

significant in QTY in membrane, ION and ARO have also become significant in QTY in water. Higher amounts of ARO in QTY in water can suggest partial rearrangement of surface residues to form

more hydrophobic interactions within the protein. An increase in ION can reflect a higher amount of interaction between the remaining surface residues (those not oriented inward to form more

ARO) and the surrounding water, maximizing interaction between the residues both within the protein and with the outside environment. In the absence of a membrane, such rearrangements can

support protein stability and a thermodynamically favorable state in water. QTY in water has more ARO and ION and less HDP with both proteins compared to QTY in membrane. Additionally, KCNN4

QTY in membrane has less HBO than QTY in water. The amount of HBO and PLR aren’t significantly different between QTY in membrane and QTY in water. It implies that QTY might be able to exist

in both conditions (membrane and water) and keep its hydrophilic properties. Interestingly, the global amount of ARO, ION, and HBO are higher in QTY in water similarly to Cryo-EM and native

systems, but differently than QTY in membrane. All inter-atomic interactions remain similar between native and Cryo-EM systems based on the Kruskal–Wallis H-test. Structural overview of the

protein conformation before and after MD shows that all proteins keep their global structure throughout the MD (Fig. 11). The main conformational changes include alpha-helical shifts and

loop fluctuations. When we made a pairwise comparison between each pair of systems (10 conformations per protein), we noticed RMSD values up to ~ 9 Å for KCNN4 and up to ~ 8 Å for KCNJ11

(Fig. 11). Considering that the comparisons were based on the backbone of all converged conformations between each pair of systems, such fluctuations are expected due to the dynamic nature

of the proteins and their environment and don’t affect the overall arrangement of the proteins. Comparison between converged conformations for each pair of systems (QTY in water _vs_. QTY in

membrane, QTY in water _vs_. Cryo-EM, QTY in water _vs_. native, QTY in membrane _vs_. Cryo-EM, QTY in membrane _vs_. native, Cryo-EM _vs_. native) also shows no significant changes in

global protein folds (Fig. 11). All of these results suggest the maintenance of the native protein structure after incorporation of QTY mutations. STRUCTURAL ANALYSIS OF OLIGOMER INTERFACES

FORMED BY QTY VARIANTS Potassium ion channels form oligomers to perform their functions (Fig. 12). Due to the limitations of the currently available in silico tools, our study focused on the

monomeric forms of these proteins. To check if the QTY forms will still form functional biological assemblies, we placed the last MD frames (400 ns, frame #4,000 for proteins in membrane

and 300 ns, frame #3,000 for proteins in water) of the mutated monomers into the available for KCNN4 (PDB: 6CNM)28 and KCNJ11 (PDB: 6C3O)32 Cryo-EM structures of oligomers to see how QTY

mutations affect the interface between the domains. We aligned all the in silico monomer models (KCNN4Cryo-EM, KCNN4Native, KCNN4QTY in membrane, KCNN4QTY in water, KCNJ11Cryo-EM,

KCNJ11Native, KCNJ11QTY in membrane, and KCNJ11QTY in water) to the corresponding domains of the Cryo-EM structures. The superposition has the same RMSD values as stated in Fig. 11 and Table

2 as the monomeric version of the KCNN4Cryo-EM and KCNJ11Cryo-EM were obtained from the same PDB structures by removing other domains. First, we checked how many Cryo-EM and QTY monomer

residues are in the interface with other domains in the oligomer structures. We define as the interface those monomer residues that have at least one atom from other domains in the oligomer

within 5 Å distance of any monomer atom. In the oligomer state 28.8% (77 out of 267 residues) of KCNN4 and 82.0% (91 out of 111 residues) of KCNJ11 residues in Cryo-EM monomer structures

form the interface between domains. When it comes to QTY variants, KCNN4 has 22.8% (61 out of 267 residues) and 27.3% (73 out of 267 residues) and KCNJ11 has 71.2% (79 out of 111 residues)

and 77.5% (86 out of 111 residues) interface residues in water and membrane systems correspondingly. Next, we looked into how many contacts that the original Cryo-EM monomers have remain the

same (unmutated) at the QTY variants. In KCNN4 9 out of 77 residues of the original Cryo-EM interface were mutated to QTY (11.7% out of Cryo-EM interface and 15.0% out of all QTY

mutations), while in KCNJ11 it is 7 out of 91 residues (7.7% out of Cryo-EM interface and 21.2% out of all QTY mutations). Finally, we inspected how many mutations are at the interface in

the QTY structures. KCNN4 systems have 5 out of 60 residues in both QTY in membrane (12.2% out of QTY interface) and QTY in water cases (9.4% out of QTY interface) making it 8.3% out of all

QTY mutations. KCNJ11QTY in membrane has 5 QTY residues (8.3% out of QTY interface and 15.2% out of all QTY mutations) while KCNJ11QTY in water has 6 QTY residues (8.45% out of QTY interface

and 18.18% out of all QTY mutations) both out of 33 interface residues in total. These numbers highlight a minor interference on the interface that is unlikely to disrupt the oligomer state

in the biological assemblies, making it possible for QTY variants to be functional in real life. Our findings confirm, at the level of molecular models, the feasaibility of transforming

hydrophobic proteins into hydrophilic analogues while preserving their original functional structure and properties. CONCLUSION Nature has evolved three distinct types of alpha-helices: I)

hydrophilic alpha-helices found in water-soluble enzymes including hemoglobin and dehydrogenases, as well as circulating proteins such as growth factors, cytokines, and hormones; II)

hydrophobic alpha-helices found in integral transmembrane proteins such as G protein-coupled receptors, transporters, and various ion channels, including photosynthesis systems; and III)

amphiphilic alpha-helices, which contain both hydrophilic and hydrophobic amino acid residues. Despite their differences in hydrophobicity and hydrophilicity, these three types of

alpha-helices share nearly identical molecular structures 39,61,62,63. This is the structural basis of the QTY code. Applying the simple QTY code, our study presents a straightforward

approach to systematically convert hydrophobic alpha-helices in potassium ion channels into their water-soluble variants. After successfully evaluating the AlphaFold2 capability to predict

potassium ion channels of 6 Cryo-EM structures, we proceeded to predict the structures of 12 potassium ion channels without the experimentally determined structures. We structurally- and

bioinformatically analyzed 42 sequences and structures of transmembrane potassium ion channels including 6 Cryo-EM, 18 native and 18 QTY variants. The structures of QTY variants show a

global similarity to native proteins, thus suggesting that the QTY variant proteins are likely to retain their original functions. To validate this assumption, we employed various

bioinformatics tools to calculate sequence and structure characteristics associated with protein stability and water-solubility. Our results revealed that the QTY variants maintained

comparable structural stability to the native proteins, while displaying water-solubility-related parameters. Furthermore, we observe reduced surface hydrophobicity patches in QTY variants

compared to native potassium ion channels. Our findings further support the QTY code as a promising method for modeling water-soluble alpha-helical integral membrane proteins. Nonetheless,

it can be complicated to apply QTY code in the oligomer setting. Though our systems showed little QTY mutations on the interface between domains, it might not be the case in other systems.

Considering that oligomers are mostly formed by hydrophobic interaction between monomers the mutations in the interface between monomers can disrupt the stability of the complex or even

prevent its formation at all. Therefore, while our results provide insights into the potential effects of QTY mutations on individual monomers, they may not fully capture the consequences of

these mutations in the full oligomeric assemblies where such hydrophobic interactions are integral. It is worth noting that keeping native residues at the interface between monomers in the

oligomers will increase the probability of proper oligomer complex formation while QTY mutations at other parts will keep the proteins soluble. We believe that the water-soluble potassium

ion channels hold potential for applications including use as soluble antigens to generate therapeutic monoclonal antibodies (mAbs). Currently, there are no such approved therapeutic mAbs to

treat a wide range of diseases including cancers. The water-soluble QTY membrane receptors have already been used to design an ultra-sensitive sensing device on dual layer S-layer protein

and graphene-based conducting surfaces64. These results underline the practical use of QTY code in therapeutic and biotechnological purposes.  METHODS PROTEIN SEQUENCE CHARACTERISTICS We use

the term “Cryo-EM” for wild-type proteins with experimentally determined structures, while the other two variants (native and QTY) lack experimental structures. Hence, “native” is applied

to unmodified sequences (wild type) and “QTY” to modified ones (non-wild type). The “Cryo-EM” structures were obtained from the Protein Data Bank (PDB), while both “native” and “QTY”

structures were predicted based on wild-type and mutated sequences, respectively. The native protein sequences for potassium ion channels are obtained from Uniprot

(https://www.uniprot.org)46 including KCNA1 (Q09470), KCNA3 (P22001), KCNA5 (P22460), KCNC4 (Q03721), KCND1 (Q9NSA2), KCNH2 (Q12809), KCNH5 (Q8NCM2), KCNJ3 (P48549), KCNJ8 (Q15842), KCNJ10

(P78508), KCNJ11 (Q14654), KCNJ12 (Q14500), KCNK2 (O95069), KCNK5 (O95279), KCNK9 (Q9NPC2), KCNMA1 (Q12791), KCNN3 (Q9UGI6), and KCNN4 (O15554). Native and QTY sequences47 were aligned to

obtain the secondary structure corresponding to the sequences38,39,40,42,43,44,48. For further analysis we used sequences extracted from either predicted or available structures by Biopython

(1.81)51. We ensured that all sequences have the same length by aligning them according to the Cryo-EM structures in cases where experimental structures are present. In other cases (when

only native and QTY sequences are available), the sequences have the same length from the beginning. We used PDBsum49 generate tool

(http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/Generate.html) to visualize the secondary structure corresponding to the sequences. The website Expasy

(https://web.expasy.org/compute_pi)50 was used to calculate the molecular weights (MW) and pI values of the proteins. The Python library Biopython (1.81)51 was used to compute stability52,

grand average of hydropathy (GRAVY)53, and flexibility54 based on sequence information. Protein solubility was predicted by DeepSoluE (http://lab.malab.cn/~wangchao/softs/DeepSoluE/)55.

ALPHAFOLD2 STRUCTURE PREDICTION We used AlphaFold234,35 as AlphaFold2_advanced.ipynb notebook from ColabFold (https://github.com/sokrypton/ColabFold)56 for the structure predictions of the

native and QTY variants following the instructions at the website on 2 × 20 Intel Xeon Gold 6248 cores, 384 GB RAM, and a Nvidia Volta V100 GPU. Predicted models were relaxed using

CplabFold’s relax_amber.ipynb (https://github.com/sokrypton/ColabFold) 56. The initial sequence data was taken from the Uniprot website (https://www.uniprot.org)46 that contains information

about each protein ID, entry name, description, and FASTA sequence. The QTY code web server (https://pss.sjtu.edu.cn/) 47 converted the FASTA protein sequences into their water-soluble

versions. These steps were optimized using Python libraries for web applications such as requests and splinter. We cleaned the models by adjusting their lengths to match those of the

available Cryo-EM structures or, in the case of unavailable experimental structures, the lengths of the native models. We also removed any unstructured portions. SUPERPOSED STRUCTURES We

performed superposition of native, QTY, and available Cryo-EM structures using PyMOL (https://pymol.org/2/). Experimental structures of potassium ion channels analyzed in this study include

KCNN4 (protein Kca3.1) (PDB: 6CNM)28, KCNK2 (K2P2.1) (PDB: 4TWK), KCNA3 (Kv1.3) (PDB: 7EJ1)29, KCNMA1 (Kca1.1) (PDB: 6V22)30, KCNH2 (Kv11.1) (PDB: 5VA2)31, KCNJ11 (Kir6.2) (PDB: 6C3O)32.

These structures were obtained from the Protein Data Bank (https://www.rcsb.org)57 in PDB format and processed by removing all atoms considered as HETATM. In cases where experimental

structures were unavailable (QTY variants and other native proteins not mentioned above), we used AlphaFold2 models34,35. PROTEIN INTER-ATOMIC INTERACTIONS Hydrogens were added to all

native, QTY, and Cryo-EM structures by Reduce (3.24.130724) software57,58. After that we converted PDB files into CIF using BioPython (1.79)50. We calculated interatomic contacts by Arpeggio

(1.4.1)59 using CIF files as an input. STRUCTURE VISUALIZATION Two key programs were used for structure visualization: PyMOL (https://pymol.org/2/) and UCSF Chimera

(https://www.rbvi.ucsf.edu/chimera/)60. PyMOL was used for the superposed models, while hydrophobicity patches were visualized using Chimera. MOLECULAR DYNAMICS SIMULATIONS We chose 2

proteins (KCNJ11 and KCNN4) that performed the best in the rigid protein inter-atomic interaction analysis for classical molecular dynamics (MD) simulations. To thoroughly investigate the

behavior of all variants (Cryo-EM, native, and QTY) in dynamics we arranged 8 systems: KCNN4Cryo-EM, KCNN4Native, KCNN4QTY in membrane, KCNN4QTY in water, KCNJ11Cryo-EM, KCNJ11Native,

KCNJ11QTY in membrane and KCNJ11QTY in water. We started from the protonated structures obtained on the previous steps of this study. Considering 2 different simulation environments (water

and membrane) we varied the steps prior to the product MD simulations. For the systems with proteins in membrane, terminus (NTER and CTER) were added to both ends of each protein to make

them uncharged. We constructed the bilipid membrane and incorporated the protein into it using CHARM-GUI (https://charmm-gui.org/)65,66,67. All simulation boxes have a rectangular shape. We

chose POPC as the main type of membrane lipids based on the previous studies68. All proteins were oriented in the membrane by aligning the first principal axis along Z. We included 0.15 M

KCl ion concentration and neutralized the systems’ charge making the conditions close to physiological. Prior to the production MD simulation, we performed energy minimization (EM) and

several rounds of NVT and NPT simulations. The initial equilibration was conducted through a 6-step process, utilizing the default scripts from the CHARMM-GUI webserver. Additionally, we

performed 2 more NPT equilibration steps (1 ns and 10 ns long respectively) to further stabilize the systems. Systems without a membrane contain the proteins (KCNJ11 and KCNN4), solvent

(TIP3P69 water molecules), and ions (0.15 M KCl) and undergo only 1 round of EM, NVT, and NPT. EM was identical to the same with proteins in a membrane. NVT runs 50,000 steps (100 ps), while

NPT runs 5,000,000 steps (10 ns). In both water and membrane systems, CHARMM36m69 force field was used for the 400 ns (300 ns for proteins in water) production MD runs with 2 fs integration

time step. Energy-related information saved every 10,000 steps and the trajectory information every 50,000 steps, yielding 4,000 frames (3,000 frames for proteins in water) in total. The

LINCS70 algorithm was applied to hydrogen bonds with a maximum of one iteration and fourth-order interpolation. Temperature coupling was achieved using the V-rescale algorithm with separate

temperature groups for solvent, membrane, and solute at 320 K. Pressure coupling was applied using the Parrinello-Rahman barostat in a semi-isotropic mode. All simulations were made in

GROMACS 2023.371. Details about the used parameters can be found on the Zenodo repository in mdp-files. Convergence and RMSD values data was calculated and plotted using Python’s library

MDAnalysis72,73. OLIGOMER ANALYSIS KCNN4 (PDB: 6CNM)28 and KCNJ11 (PDB: 6C3O)32 oligomers were obtained from PDB. The interface between monomers in the oligomers and their visualizations

were made using PyMOL (https://pymol.org/2/). Further analysis of interacting residues on the interface was performed in Python using custom scripts. DATA AVAILABILITY European

Bioinformatics Institute (https://alphafold.ebi.ac.uk) is the deposit site for all AlphaFold2 predicted > 220 million protein structures74,75. All structures, scripts, and datasets used

in this study are stored in the GitHub repository: https://github.com/eva-smorodina/kcn. Files and data related to MD simulations are uploaded on Zenodo:

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ACKNOWLEDGEMENTS We sincerely thank Jan Christiansen, Founder and Chairman of Baltic-Asiatic Holdings A/S, Copenhagen, Denmark, for providing Eva Smorodina a high-performance computer that

was used to carry out the molecular dynamic simulations for Figure 9 and Figure 10 and Figure 11. We gratefully acknowledge the partial funding provided by PT Metiska Farma for this work. We

would also like to express our appreciation to Dorrie Langsley for her valuable help with English editing. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Laboratory for Computational and

Systems Immunology, Department of Immunology, University of Oslo, Oslo University Hospital, Oslo, Norway Eva Smorodina * Laboratory of Food Microbial Technology, State Key Laboratory of

Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, 200240, China Fei Tao & Rui Qing * PT Metiska Farma, Daerah Khusus Ibukota,

Jakarta, 12220, Indonesia Steve Yang * Laboratory of Molecular Architecture, Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA Shuguang

Zhang Authors * Eva Smorodina View author publications You can also search for this author inPubMed Google Scholar * Fei Tao View author publications You can also search for this author

inPubMed Google Scholar * Rui Qing View author publications You can also search for this author inPubMed Google Scholar * Steve Yang View author publications You can also search for this

author inPubMed Google Scholar * Shuguang Zhang View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS E.S. Performed structural bioinformatic

experiments, molecular dynamic simulations, made Figs. 3, 4, 5, 6, 7, 8, 9 and 10; made the Table 2, responsible for the data curation, reviewed and wrote first draft of the manuscript. F.T.

Made Fig. 1 and Fig. 2, reviewed and wrote the manuscript. R.Q. Reviewed and wrote the manuscript. S.Y. Reviewed and wrote the manuscript, provided financial support to cover the

publication cost. S.Z. Conceived the ideas in this manuscript, supervised the research project, selected potassium ion channel protein examples, made the Table 1, wrote the first draft

manuscript, reviewed and wrote the manuscript. CORRESPONDING AUTHOR Correspondence to Shuguang Zhang. ETHICS DECLARATIONS COMPETING INTERESTS Massachusetts Institute of Technology (MIT) has

filed several patent applications for the QTY code for GPCRs, and OH2Laboratories has obtained a license from MIT to develop water-soluble GPCR variants. However, this article does not focus

on GPCRs. One of the authors, S.Z., is an inventor of the QTY code and holds a minor equity position in OH2Laboratories. S.Z. has also founded a startup called 511 Therapeutics, which aims

to develop therapeutic monoclonal antibodies targeting solute carrier transporters for the treatment of pancreatic cancer. S.Z. holds a majority equity position in 511 Therapeutics. PT

Metiska Farma partially sponsored the study but had no influence or interference in the study design, data collection, analysis, interpretation of data, manuscript writing, or decision to

publish the results. All other authors declare no competing interests. ETHICAL APPROVAL It is a purely digital structural biology study utilizing publicly accessible in silico programs. We

state that: I) all methods were conducted in compliance with applicable guidelines and regulations; II) all experimental protocols received approval from an institutional and licensing

committee; and III) no human biological samples or human subjects were involved in this study. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to

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http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Smorodina, E., Tao, F., Qing, R. _et al._ Computational engineering of

water-soluble human potassium ion channels through QTY transformation. _Sci Rep_ 14, 28159 (2024). https://doi.org/10.1038/s41598-024-76603-7 Download citation * Received: 10 July 2023 *

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KEYWORDS * Convert hydrophobic to hydrophilic alpha-helix * Membrane protein design * Protein structural prediction * QTY code * Water-soluble membrane proteins