Developing a comparative docking protocol for the prediction of peptide selectivity profiles: investigation of potassium channel toxins

Interactions of the Kv1.1 Channel with Peptide Pore Blockers: A Fluorescent Analysis on Mammalian Cells

The voltage-gated potassium channel Kv1.1, which is abundant in the CNS and peripheral nervous system, controls neuronal excitability and neuromuscular transmission and mediates a number of physiological functions in non-excitable cells. The development of some diseases is accompanied by changes in the expression level and/or activity of the channels in particular types of cells. To meet the requirements of studies related to the expression and localization of the Kv1.1 channels, we report on the subnanomolar affinity of hongotoxin 1 N-terminally labeled with Atto 488 fluorophore (A-HgTx) for the Kv1.1 channel and its applicability for fluorescent imaging of the channel in living cells. Taking into consideration the pharmacological potential of the Kv1.1 channel, a fluorescence-based analytical system was developed for the study of peptide ligands that block the ion conductivity of Kv1.1 and are potentially able to correct abnormal activity of the channel. The system is based on analysis of the competitive binding of the studied compounds and A-HgTx to the mKate2-tagged human Kv1.1 (S369T) channel, expressed in the plasma membrane of Neuro2a cells. The system was validated by measuring the affinities of the known Kv1.1-channel peptide blockers, such as agitoxin 2, kaliotoxin 1, hongotoxin 1, and margatoxin. Peptide pore blocker Ce1, from the venom of the scorpion Centruroides elegans, was shown to possess a nanomolar affinity for the Kv1.1 channel. It is reported that interactions of the Kv1.1 channel with the studied peptide blockers are not affected by the transition of the channel from the closed to open state. The conclusion is made that the structural rearrangements accompanying the channel transition into the open state do not change the conformation of the P-loop (including the selectivity filter) involved in the formation of the binding site of the peptide pore blockers.

Examining the interactions scorpion venom peptides (HP1090, Meucin-13, and Meucin-18) with the receptor binding domain of the coronavirus spike protein to design a mutated therapeutic peptide

The spike protein of SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) interacts with the ACE2 receptor in human cells and starts the infection of COVID-19 disease. Given the importance of spike protein's interaction with ACE2 receptor, we selected some antiviral peptides of venom scorpion such as HP1090, meucin-13, and meucin-18 and performed docking and molecular docking analysis of them with the RBD domain of spike protein. The results showed that meucin-18 (FFGHLFKLATKIIPSLFQ) had better interaction with the RBD domain of spike protein than other peptides. We also designed some mutations in meucin-18 and investigated their interactions with the RBD domain. The results revealed that the A9T mutation had more effective interaction with the RBD domain than the meucin-18 and was able to inhibit spike protein's interaction with ACE2 receptor. Hence, peptide “FFGHLFKLTTKIIPSLFQ” can be considered as the potential drug for the treatment of COVID-19 disease.

Structural Determinants Mediating Tertiapin Block of Neuronal Kir3.2 Channels

Tertiapin (TPN) is a 21 amino acid venom peptide from Apis mellifera that inhibits certain members of the inward rectifier potassium (Kir) channel family at a nanomolar affinity with limited specificity. Structure-based computational simulations predict that TPN behaves as a pore blocker; however, the molecular determinants mediating block of neuronal Kir3 channels have been inconclusive and unvalidated. Here, using molecular docking and molecular dynamics (MD) simulations with 'potential of mean force' (PMF) calculations, we investigated the energetically most favored interaction of TPN with several Kir3.x channel structures. The resulting binding model for Kir3.2-TPN complexes was then tested by targeted mutagenesis of the predicted contact sites, and their impact on the functional channel block was measured electrophysiologically. Together, our findings indicate that a high-affinity TPN block of Kir3.2 channels involves a pore-inserting lysine side chain requiring (1) hydrophobic interactions at a phenylalanine ring surrounding the channel pore and (2) electrostatic interactions with two adjacent Kir3.2 turret regions. Together, these interactions collectively stabilize high-affinity toxin binding to the Kir3.2 outer vestibule, which orients the ε-amino group of TPN-K21 to occupy the outermost K⁺ binding site of the selectivity filter. The structural determinants for the TPN block described here also revealed a favored subunit arrangement for assembled Kir3.x heteromeric channels, in addition to a multimodal binding capacity of TPN variants consistent with the functional dyad model for polybasic peptide pore blockers. These novel findings will aid efforts in re-engineering the TPN pharmacophore to develop peptide variants having unique and distinct Kir channel blocking properties.

Snails In Silico: A Review of Computational Studies on the Conopeptides

Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.

Computational study of aggregation mechanism in human lysozyme[D67H]

Aggregation of proteins is an undesired phenomena that affects both human health and bioengineered products such as therapeutic proteins. Finding preventative measures could be facilitated by a molecular-level understanding of dimer formation, which is the first step in aggregation. Here we present a molecular dynamics (MD) study of dimer formation propensity in human lysozyme and its D67H variant. Because the latter protein aggregates while the former does not, they offer an ideal system for testing the feasibility of the proposed MD approach which comprises three stages: i) partially unfolded conformers involved in dimer formation are generated via high-temperature MD simulations, ii) potential dimer structures are searched using docking and refined with MD, iii) free energy calculations are performed to find the most stable dimer structure. Our results provide a detailed explanation for how a single mutation (D67H) turns human lysozyme from non-aggregating to an aggregating protein. Conversely, the proposed method can be used to identify the residues causing aggregation in a protein, which can be mutated to prevent it.

Molecular Simulations of Disulfide-Rich Venom Peptides with Ion Channels and Membranes

Disulfide-rich peptides isolated from the venom of arthropods and marine animals are a rich source of potent and selective modulators of ion channels. This makes these peptides valuable lead molecules for the development of new drugs to treat neurological disorders. Consequently, much effort goes into understanding their mechanism of action. This paper presents an overview of how molecular simulations have been used to study the interactions of disulfide-rich venom peptides with ion channels and membranes. The review is focused on the use of docking, molecular dynamics simulations, and free energy calculations to (i) predict the structure of peptide-channel complexes; (ii) calculate binding free energies including the effect of peptide modifications; and (iii) study the membrane-binding properties of disulfide-rich venom peptides. The review concludes with a summary and outlook.

Biobetters From an Integrated Computational/Experimental Approach

Biobetters are new drugs designed from existing peptide or protein-based therapeutics by improving their properties such as affinity and selectivity for the target epitope, and stability against degradation. Computational methods can play a key role in such design problems—by predicting the changes that are most likely to succeed, they can drastically reduce the number of experiments to be performed. Here we discuss the computational and experimental methods commonly used in drug design problems, focusing on the inverse relationship between the two, namely, the more accurate the computational predictions means the less experimental effort is needed for testing. Examples discussed include efforts to design selective analogs from toxin peptides targeting ion channels for treatment of autoimmune diseases and monoclonal antibodies which are the fastest growing class of therapeutic agents particularly for cancers and autoimmune diseases.

Complexes of Peptide Blockers with Kv1.6 Pore Domain: Molecular Modeling and Studies with KcsA-Kv1.6 Channel

Potassium voltage-gated Kv1.6 channel, which is distributed primarily in neurons of central and peripheral nervous systems, is of significant physiological importance. To date, several high-affinity Kv1.6-channel blockers are known, but the lack of selective ones among them hampers the studies of tissue localization and functioning of Kv1.6 channels. Here we present an approach to advanced understanding of interactions of peptide toxin blockers with a Kv1.6 pore. It combines molecular modeling studies and an application of a new bioengineering system based on a KcsA-Kv1.6 hybrid channel for the quantitative fluorescent analysis of blocker-channel interactions. Using this system we demonstrate that peptide toxins agitoxin 2, kaliotoxin1 and OSK1 have similar high affinity to the extracellular vestibule of the K⁺-conducting pore of Kv1.6, hetlaxin is a low-affinity ligand, whereas margatoxin and scyllatoxin do not bind to Kv1.6 pore. Binding of toxins to Kv1.6 pore has considerable inverse dependence on the ionic strength. Model structures of KcsA-Kv1.6 and Kv1.6 complexes with agitoxin 2, kaliotoxin 1 and OSK1 were obtained using homology modeling and molecular dynamics simulation. Interaction interfaces, which are formed by 15-19 toxin residues and 10 channel residues, are described and compared. Specific sites of Kv1.6 pore recognition are identified for targeting of peptide blockers. Analysis of interactions between agitoxin 2 derivatives with point mutations (S7K, S11G, L19S, R31G) and KcsA-Kv1.6 confirms reliability of the calculated complex structure.

Modeling of the Binding of Peptide Blockers to Voltage-Gated Potassium Channels: Approaches and Evidence

Modeling of the structure of voltage-gated potassium (KV) channels bound to peptide blockers aims to identify the key amino acid residues dictating affinity and provide insights into the toxin-channel interface. Computational approaches open up possibilities for in silico rational design of selective blockers, new molecular tools to study the cellular distribution and functional roles of potassium channels. It is anticipated that optimized blockers will advance the development of drugs that reduce over activation of potassium channels and attenuate the associated malfunction. Starting with an overview of the recent advances in computational simulation strategies to predict the bound state orientations of peptide pore blockers relative to KV-channels, we go on to review algorithms for the analysis of intermolecular interactions, and then take a look at the results of their application.

Computational Study of Binding of μ-Conotoxin GIIIA to Bacterial Sodium Channels NaVAb and NaVRh

Structures of several voltage-gated sodium (NaV) channels from bacteria have been determined recently, but the same feat might not be achieved for the mammalian counterparts in the near future. Thus, at present, computational studies of the mammalian NaV channels have to be performed using homology models based on the bacterial crystal structures. A successful homology model for the mammalian NaV1.4 channel was recently constructed using the extensive mutation data for binding of μ-conotoxin GIIIA to NaV1.4, which was further validated through studies of binding of other μ-conotoxins and ion permeation. Understanding the similarities and differences between the bacterial and mammalian NaV channels is an important issue, and the NaV1.4-GIIIA system provides a good opportunity for such a comparison. To this end, we study the binding of GIIIA to the bacterial channels NaVAb and NaVRh. The complex structures are obtained using docking and molecular dynamics simulations, and the dissociation of GIIIA is studied through umbrella sampling simulations. The results are compared to those obtained from the NaV1.4-GIIIA system, and the differences in the binding modes arising from the changes in the selectivity filters are highlighted.

Combination of Ambiguous and Unambiguous Data in the Restraint-driven Docking of Flexible Peptides with HADDOCK: The Binding of the Spider Toxin PcTx1 to the Acid Sensing Ion Channel (ASIC) 1a

Peptides that bind to ion channels have attracted much interest as potential lead molecules for the development of new drugs and insecticides. However, the structure determination of large peptide-channel complexes using experimental methods is challenging. Thus structural models are often derived from combining experimental information with restraint-driven docking approaches. Using the complex formed by the venom peptide PcTx1 and the acid sensing ion channel (ASIC) 1a as a case study, we have examined the effect of different combinations of restraints and input structures on the statistical likelihood of (a) correctly predicting the structure of the binding interface and (b) the ability to predict which residues are involved in specific pairwise peptide-channel interactions. For this, we have analyzed over 200,000 water-refined docked structures obtained with various amounts and types of restraints of the peptide-channel complex predicted using the docking program HADDOCK. We found that increasing the number of restraints or even the use of pairwise interaction data resulted in only a modest improvement in the likelihood of finding a structure within a given accuracy. This suggests that shape complementarity and the force field make a large contribution to the accuracy of the predicted structure. The results also showed that there are large variations in the accuracy of the predicted structure depending on the precise combination of residues used as restraints. Finally, we reflect on the limitations of relying on geometric criteria such as root-mean square deviations to assess the accuracy of docking procedures. We propose that in addition to currently used measures, the likelihood of finding a structure within a given level of accuracy should be also used to evaluate docking methods.

Computational approaches for designing potent and selective analogs of peptide toxins as novel therapeutics

Peptide toxins provide valuable therapeutic leads for many diseases. As they bind to their targets with high affinity, potency is usually ensured. However, toxins also bind to off-target receptors, causing potential side effects. Thus, a major challenge in generating drugs from peptide toxins is ensuring their specificity for their intended targets. Computational methods can play an important role in solving such design problems through construction of accurate models of receptor-toxin complexes and calculation of binding free energies. Here we review the computational methods used for this purpose and their application to toxins targeting ion channels. We describe ShK and HsTX1 toxins, high-affinity blockers of the voltage-gated potassium channel Kv1.3, which could be developed as therapeutic agents for autoimmune diseases.

Systematic study of binding of μ-conotoxins to the sodium channel NaV1.4

Voltage-gated sodium channels (NaV) are fundamental components of the nervous system. Their dysfunction is implicated in a number of neurological disorders, such as chronic pain, making them potential targets for the treatment of such disorders. The prominence of the NaV channels in the nervous system has been exploited by venomous animals for preying purposes, which have developed toxins that can block the NaV channels, thereby disabling their function. Because of their potency, such toxins could provide drug leads for the treatment of neurological disorders associated with NaV channels. However, most toxins lack selectivity for a given target NaV channel, and improving their selectivity profile among the NaV1 isoforms is essential for their development as drug leads. Computational methods will be very useful in the solution of such design problems, provided accurate models of the protein-ligand complex can be constructed. Using docking and molecular dynamics simulations, we have recently constructed a model for the NaV1.4-μ-conotoxin-GIIIA complex and validated it with the ample mutational data available for this complex. Here, we use the validated NaV1.4 model in a systematic study of binding other μ-conotoxins (PIIIA, KIIIA and BuIIIB) to NaV1.4. The binding mode obtained for each complex is shown to be consistent with the available mutation data and binding constants. We compare the binding modes of PIIIA, KIIIA and BuIIIB to that of GIIIA and point out the similarities and differences among them. The detailed information about NaV1.4-μ-conotoxin interactions provided here will be useful in the design of new NaV channel blocking peptides.

Molecular dynamics study of binding of µ-conotoxin GIIIA to the voltage-gated sodium channel Na(v)1.4

Homology models of mammalian voltage-gated sodium (NaV) channels based on the crystal structures of the bacterial counterparts are needed to interpret the functional data on sodium channels and understand how they operate. Such models would also be invaluable in structure-based design of therapeutics for diseases involving sodium channels such as chronic pain and heart diseases. Here we construct a homology model for the pore domain of the NaV1.4 channel and use the functional data for the binding of µ-conotoxin GIIIA to NaV1.4 to validate the model. The initial poses for the NaV1.4-GIIIA complex are obtained using the HADDOCK protein docking program, which are then refined in molecular dynamics simulations. The binding mode for the final complex is shown to be in broad agreement with the available mutagenesis data. The standard binding free energy, determined from the potential of mean force calculations, is also in good agreement with the experimental value. Because the pore domains of NaV1 channels are highly homologous, the model constructed for NaV1.4 will provide an excellent template for other NaV1 channels.

NMR investigation of disulfide containing peptides and proteins

Peptides and proteins with disulfide bonds are abundant in all kingdoms and play essential role in many biological events. Because small disulfide-rich peptides (proteins) are usually difficult to crystallize, nuclear magnetic resonance (NMR) is by far one of the most powerful techniques for the determination of their solution structure. Besides the “static” three-dimensional structure, NMR has unique opportunities to acquire additional information about molecular dynamics and folding at atomic resolution. Nowadays it is becoming increasingly evident, that “excited”, “disordered” or “fuzzy” protein states may exhibit biological function and disulfide proteins are also promising targets for such studies. In this short two-three years overview those disulfide peptides and proteins were cited from the literature that were studied by NMR. Though we may have missed some, their structural diversity and complexity as well as their wide repertoire of biological functions is impressive. We emphasised especially antimicrobial peptides and peptide based toxins in addition to some biologically important other structures. Besides the general NMR methods we reviewed some contemporary techniques suitable for disclosing the peculiar properties of disulfide bonds. Interesting dynamics and folding studies of disulfide proteins were also mentioned. It is important to disclose the essential structure, dynamics, function aspects of disulfide proteins since this aids the design of new compounds with improved activity and reduced toxicity. Undoubtedly, NMR has the potential to accelerate the development of new disulfide peptides/proteins with pharmacological activity.

Complex structures between the N-type calcium channel (CaV2.2) and ω-conotoxin GVIA predicted via molecular dynamics

The N-type voltage-gated Ca(2+) channel CaV2.2 is one of the important targets for pain management. ω-Conotoxins isolated from venoms of cone snails, which specifically inhibit CaV2.2, are promising scaffolds for novel analgesics. The inhibitory action of ω-conotoxins on CaV2.2 has been examined experimentally, but the modes of binding of the toxins to this and other related subfamilies of Ca(2+) channels are not understood in detail. Here molecular dynamics simulations are used to construct models of ω-conotoxin GVIA in complex with a homology model of the pore domain of CaV2.2. Three different binding modes in which the side chain of Lys2, Arg17, or Lys24 from the toxin protrudes into the selectivity filter of CaV2.2 are considered. In all the modes, the toxin forms a salt bridge with an aspartate residue of subunit II just above the EEEE ring of the selectivity filter. Using the umbrella sampling technique and potential of mean force calculations, the half-maximal inhibitory concentration (IC50) values are calculated to be 1.5 and 0.7 nM for the modes in which Lys2 and Arg17 occlude the ion conduction pathway, respectively. Both IC50 values compare favorably with the values of 0.04-1.0 nM determined experimentally. The similar IC50 values calculated for the different binding modes demonstrate that GVIA can inhibit CaV2.2 with alternative binding modes. Such a multiple-binding mode mechanism may be common for ω-conotoxins.

Computational methods of studying the binding of toxins from venomous animals to biological ion channels: theory and applications

The discovery of new drugs that selectively block or modulate ion channels has great potential to provide new treatments for a host of conditions. One promising avenue revolves around modifying or mimicking certain naturally occurring ion channel modulator toxins. This strategy appears to offer the prospect of designing drugs that are both potent and specific. The use of computational modeling is crucial to this endeavor, as it has the potential to provide lower cost alternatives for exploring the effects of new compounds on ion channels. In addition, computational modeling can provide structural information and theoretical understanding that is not easily derivable from experimental results. In this review, we look at the theory and computational methods that are applicable to the study of ion channel modulators. The first section provides an introduction to various theoretical concepts, including force-fields and the statistical mechanics of binding. We then look at various computational techniques available to the researcher, including molecular dynamics, brownian dynamics, and molecular docking systems. The latter section of the review explores applications of these techniques, concentrating on pore blocker and gating modifier toxins of potassium and sodium channels. After first discussing the structural features of these channels, and their modes of block, we provide an in-depth review of past computational work that has been carried out. Finally, we discuss prospects for future developments in the field.

Computational studies of marine toxins targeting ion channels

Toxins from marine animals offer novel drug leads for treatment of diseases involving ion channels. Computational methods could be very helpful in this endeavour in several ways, e.g., (i) constructing accurate models of the channel-toxin complexes using docking and molecular dynamics (MD) simulations; (ii) determining the binding free energies of toxins from umbrella sampling MD simulations; (iii) predicting the effect of mutations from free energy MD simulations. Using these methods, one can design new analogs of toxins with improved affinity and selectivity properties. Here we present a review of the computational methods and discuss their applications to marine toxins targeting potassium and sodium channels. Detailed examples from the potassium channel toxins—ShK from sea anemone and κ-conotoxin PVIIA—are provided to demonstrate capabilities of the computational methods to give accurate descriptions of the channel-toxin complexes and the energetics of their binding. An example is also given from sodium channel toxins (µ-conotoxin GIIIA) to illustrate the differences between the toxin binding modes in potassium and sodium channels.

Why the Drosophila Shaker K+ channel is not a good model for ligand binding to voltage-gated Kv1 channels

The Drosophila Shaker K(+) channel is the first cloned voltage-gated potassium channel and has, therefore, played an important role in structural and functional studies of those channels. While such a role is well justified for ion permeation, it is not clear whether this also extends to ligand binding. Despite the high degree of homology among Shaker and Kv1 channels, κ-conotoxin PVIIA (κ-PVIIA) binds to Shaker with high affinity but not to Kv1 channels. Here we address this issue by studying binding of κ-PVIIA to Shaker and Kv1 channels using molecular dynamics (MD) simulations. The structures of the channel-toxin complexes are constructed via docking and refinement with MD. The binding mode of each complex is characterized and compared to available mutagenesis data to validate the complex models. The potential of mean force for dissociation of the Shaker-κ-PVIIA complex is calculated from umbrella sampling MD simulations, and the corresponding binding free energy is determined, which provides further validation of the complex structure. Comparison of the Shaker and Kv1 complex models shows that a few mutations in the turret and extended regions are sufficient to abolish the observed sensitivity of Shaker to κ-PVIIA. This study demonstrates that Shaker is not always a good model for Kv1 channels for ligand binding. It also provides insights into the binding of the toxin to potassium channels that will be useful for improving affinity and selectivity properties of Kv1 channels.

Structural basis of the selective block of Kv1.2 by maurotoxin from computer simulations

The 34-residue polypeptide maurotoxin (MTx) isolated from scorpion venoms selectively inhibits the current of the voltage-gated potassium channel Kv1.2 by occluding the ion conduction pathway. Here using molecular dynamics simulation as a docking method, the binding modes of MTx to three closely related channels (Kv1.1, Kv1.2 and Kv1.3) are examined. We show that MTx forms more favorable electrostatic interactions with the outer vestibule of Kv1.2 compared to Kv1.1 and Kv1.3, consistent with the selectivity of MTx for Kv1.2 over Kv1.1 and Kv1.3 observed experimentally. One salt bridge in the bound complex of MTx-Kv1.2 forms and breaks in a simulation period of 20ns, suggesting the dynamic nature of toxin-channel interactions. The toxin selectivity likely arises from the differences in the shape of the channel outer vestibule, giving rise to distinct orientations of MTx on block. Potential of mean force calculations show that MTx blocks Kv1.1, Kv1.2 and Kv1.3 with an IC₅₀ value of 6 µM, 0.6nM and 18 µM, respectively.

Structure, function, and chemical synthesis of Vaejovis mexicanus peptide 24: a novel potent blocker of Kv1.3 potassium channels of human T lymphocytes

Animal venoms are rich sources of ligands for studying ion channels and other pharmacological targets. Proteomic analyses of the soluble venom from the Mexican scorpion Vaejovis mexicanus smithi showed that it contains more than 200 different components. Among them, a 36-residue peptide with a molecular mass of 3864 Da (named Vm24) was shown to be a potent blocker of Kv1.3 of human lymphocytes (K(d) ∼ 3 pM). The three-dimensional solution structure of Vm24 was determined by nuclear magnetic resonance, showing the peptide folds into a distorted cystine-stabilized α/β motif consisting of a single-turn α-helix and a three-stranded antiparallel β-sheet, stabilized by four disulfide bridges. The disulfide pairs are formed between Cys6 and Cys26, Cys12 and Cys31, Cys16 and Cys33, and Cys21 and Cys36. Sequence analyses identified Vm24 as the first example of a new subfamily of α-type K(+) channel blockers (systematic number α-KTx 23.1). Comparison with other Kv1.3 blockers isolated from scorpions suggests a number of structural features that could explain the remarkable affinity and specificity of Vm24 toward Kv1.3 channels of lymphocytes.

Affinity and selectivity of ShK toxin for the Kv1 potassium channels from free energy simulations

The voltage-gated potassium channel Kv1.3 is an attractive target for treatment of autoimmune diseases. ShK toxin from sea anemone is one of the most potent blockers of Kv1.3, and therefore ShK and its analogues have been proposed as therapeutic leads for such diseases. Increasing the selectivity of the proposed leads for Kv1.3 over other Kv1 channels is a major issue in this endeavor. Here we study binding of ShK toxin to Kv1 channels using free energy simulation methods. Homology models for Kv1.1 and Kv1.3 channels are constructed using the crystal structure of Kv1.2. The initial poses for the Kv1.x-ShK complexes are obtained using HADDOCK, which are then refined via molecular dynamics simulations. The binding mode in each complex is characterized by identifying the strongly interacting residues, which compare well with available mutagenesis studies. For each complex, the potential of mean force is calculated from umbrella sampling simulations, and the corresponding absolute binding free energy is determined. The computed binding free energies are in good agreement with the experimental data, which increases the confidence on the model complexes. The insights gained on Kv1.x-ShK binding modes will be valuable in the development of new ShK analogues with better selectivity properties.