Electrostatic Network Research Articles

Rationalizing Diverse Binding Mechanisms to the Same Protein Fold: Insights for Ligand Recognition and Biosensor Design.

The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem, which could enable many practical applications of protein biosensors. In this work, we analyzed two engineered biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free-energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.

Computational biophysical characterization of the effect of gatekeeper mutations on the binding of ponatinib to the FGFR kinase

Fibroblast Growth Factor Receptor (FGFR) is connected to numerous downstream signalling cascades regulating cellular behavior. Any dysregulation leads to a plethora of illnesses, including cancer. Therapeutics are available, but drug resistance driven by gatekeeper mutation impedes the treatment. Ponatinib is an FDA-approved drug against BCR-ABL kinase and has shown effective results against FGFR-mediated carcinogenesis. Herein, we undertake molecular dynamics simulation-based analysis on ponatinib against all the FGFR isoforms having Val to Met gatekeeper mutations. The results suggest that ponatinib is a potent and selective inhibitor for FGFR1, FGFR2, and FGFR4 gatekeeper mutations. The extensive electrostatic and van der Waals interaction network accounts for its high potency. The FGFR3_VM mutation has shown resistance towards ponatinib, which is supported by their lesser binding affinity than wild-type complexes. The disengaged molecular brake and engaged hydrophobic spine were believed to be the driving factors for weak protein-ligand interaction. Taken together, the inhibitory and structural characteristics exhibited by ponatinib may aid in thwarting resistance based on Val-to-Met gatekeeper mutations at an earlier stage of treatment and advance the design and development of other inhibitors targeted at FGFRs harboring gatekeeper mutations.

A low-cost machine learning process for gait measurement using an electrostatic sensors network

Continuous in-house measurement of gait of elderly People is relevant for health professionals. To be adopted by most, the system must be low-cost and non-intrusive. In this paper we present a solution for measuring the walking velocity based on a network of 4 electric potential sensors. In our experiments, we also add PIR sensors used in our previous work for comparative purposes. A temporary Depth camera is used for training the model on walking velocity. The first results presented are obtained without machine learning. Then a machine learning regression method is tested to reduce the uncertainty of the sensors. The results show that the electric potential sensors are suitable for the in-house measurement of walking speed of elderly people. The uncertainty is lower than the target of 0.15 m s-1 known as the upper limit for detecting a reduction in speed due to illness. As for the PIR sensors, electric potential sensors consume very little energy, they are inexpensive, they can be embedded and hidden in the home which makes them less -intrusive and furthermore have better accuracy.

Structural effects of charge destabilization and amino acid substitutions in amyloid fragments of CsgA

The most studied functional amyloid is the CsgA, major curli subunit protein, which is produced by numerous strains of Enterobacteriaceae. Although CsgA sequences are highly conserved, they exhibit species diversity, which reflects the specific evolutionary and functional adaptability of the major curli subunit. Herein, we performed bioinformatics analyses to uncover the differences in the amyloidogenic properties of the R4 fragments in Escherichia coli and Salmonella enterica and proposed four mutants for more detailed studies: M1, M2, M3, and M4. The mutated sequences were characterized by various experimental techniques, such as circular dichroism, ATR-FTIR, FT-Raman, thioflavin T, transmission electron microscopy and confocal microscopy. Additionally, molecular dynamics simulations were performed to determine the role of buffer ions in the aggregation process. Our results demonstrated that the aggregation kinetics, fibril morphology, and overall structure of the peptide were significantly affected by the positions of charged amino acids within the repeat sequences of CsgA. Notably, substituting glycine with lysine resulted in the formation of distinctive spherically packed globular aggregates. The differences in morphology observed are attributed to the influence of phosphate ions, which disrupt the local electrostatic interaction network of the polypeptide chains. This study provides knowledge on the preferential formation of amyloid fibrils based on charge states within the polypeptide chain.

Charge-Optimized Electrostatic Interaction Atom-Centered Neural Network Algorithm.

Machine-learning algorithms have been proposed to capture electrostatic interactions by using effective partial charges. These algorithms often rely on a pretrained model for partial charge prediction using density functional theory-calculated partial charges as references, which introduces complexity to the force field model. The accuracy of the trained model also depends on the reliability of charge partition methods, which can be dependent on the specific system and methodology employed. In this study, we propose an atom-centered neural network (ANN) algorithm that eliminates the need for reference charges. Our algorithm requires only a single NN model for each element to obtain both atomic energy and charges. These atomic charges are then employed to compute electrostatic energies using the Ewald summation algorithm. Subsequently, the force field model is trained on total energy and forces, with the inclusion of electrostatic energy. To evaluate the performance of our algorithm, we conducted tests on three benchmark systems, including a Ge slab with an O adatom system, a TiO2 crystalline system, and a Pd-O nanoparticle system. Our results demonstrate reasonably accurate predictions of partial charges and electrostatic interactions. This algorithm provides a self-consistent charge prediction strategy and possibilities for robust and reliable modeling of electrostatic interactions in machine-learning potentials.

Chitosan-alginate sponge with multiple cross-linking networks for adsorption of anionic dyes: Preparation, property evaluation, mechanism exploration, and application

A chitosan-alginate sponge (CAS) with multiple cross-linking networks was developed using chitosan, sodium alginate, polyvinyl alcohol, and glutaraldehyde to adsorb and enrich the anionic dyes form the food samples. The multiple networks in CAS refer to the electrostatic cross-linking network, hydrogen bonding cross-linking network, and covalent cross-linking network. Compared with pure chitosan and alginate sponges, the CAS showed better three-dimensional network structure, mechanical behavior, and stability, which is benefit by multiple cross-linking networks. The physical and chemical properties of CAS were systematically studied by a series of characterizations. The adsorption performance of CAS on anionic dyes was inspected with different dye concentration, time, temperature, and pH conditions. CAS exhibited a good and stable adsorption property to amaranth, carmine, and sunset yellow with the saturation adsorption capacity of 94.34, 111.5, and 80.05 mg∙g–1, respectively. Furthermore, CAS performed outstanding selectivity to anionic dyes with the selectivity factor up to 16.99. Through electrostatic potential analysis, it is inferred that CAS mainly adsorbs anionic dyes through electrostatic interactions. CAS showed satisfactory reusability, maintaining 97 %–99 % of adsorption performance after six cycles of recycling. Finally, CAS was combined with high-performance liquid chromatography for the enrichment and detection of anionic dyes in candy and cocktail samples, achieving the enrichment factor up to 84.77.

Construction of a multifunctional dual-network chitosan composite aerogel with enhanced tunability

Typically, the tailorable versatility of biomass aerogels is attributed to the tunable internal molecular structure, providing broad application prospects. Herein, a simple and novel preparation strategy for developing multifunctional dual-network chitosan/itaconic acid (CSI) aerogel with tunability by using freeze-drying and vacuum heat treatment techniques. By regulating the temperature and duration of amidation reaction, electrostatic interactions between chitosan (CS) and itaconic acid (IA) was abstemiously converted into amide bond in frozen aerogel, with IA acting as an efficient in-situ cross-linking agent, which yielded CSI aerogels with different electrostatic/covalent cross-linking ratios. Heat treatment and tuning of the covalent cross-linking degree of CSI aerogel changed their microstructure and density, which led to enhanced performance. For example, the specific modulus of CSI1.5–160 °C-5 h (71.69 ± 2.55 MPa·cm3·g−1) increased by 119 % compared to that of CSI1.5 (32.73 ± 0.718 MPa·cm3·g−1), converting the material from superhydrophilic to hydrophobic (124° ± 3.6°), exhibiting favorable stability and heat transfer performance. In addition, part of -NH3+ of CS was retained in the electrostatic cross-linked network, endowing the aerogel with antibacterial properties. The findings of this study provide insights and a reliable strategy for fabricating biomass aerogel with good comprehensive performance via ingenious structural design and simple regulation methods.

Crystal structure of Bak bound to the BH3 domain of Bnip5, a noncanonical BH3 domain-containing protein.

The activation or inactivation of B-cell lymphoma-2 (Bcl-2) antagonist/killer (Bak) is critical for controlling mitochondrial outer membrane permeabilization-dependent apoptosis. Its pro-apoptotic activity is controlled by intermolecular interactions with the Bcl-2 homology 3 (BH3) domain, which is accommodated in the hydrophobic pocket of Bak. Bcl-2-interacting protein 5 (Bnip5) is a noncanonical BH3 domain-containing protein that interacts with Bak. Bnip5 is characterized by its controversial effects on the regulation of the pro-apoptotic activity of Bak. In the present study, we determined the crystal structure of Bak bound to Bnip5 BH3. The intermolecular association appeared to be typical at first glance, but we found that it is maintained by tight hydrophobic interactions together with hydrogen/ionic bonds, which accounts for their high binding affinity with a dissociation constant of 775 nM. Structural analysis of the complex showed that Bnip5 interacts with Bak in a manner similar to that of the Bak-activating pro-apoptotic factor peroxisomal testis-enriched protein 1, particularly in the destabilization of the intramolecular electrostatic network of Bak. Our structure is considered to reflect the initial point of drastic and consecutive conformational and stoichiometric changes in Bak induced by Bnip5 BH3, which helps in explaining the effects of Bnip5 in regulating Bak-mediated apoptosis.

Regenerable carbon honeycomb monoliths directly prepared from brown coal: A novel carbon product

A novel, straightforward and efficient method for preparing electrically regenerable carbon honeycomb monoliths (CHMs) directly from brown coal is described. Soft brown coal, which inherently contains relatively high oxygen functional group concentrations and high moisture contents, was kneaded with NaOH or KOH to achieve homogenised and smooth dough with plasticine consistency. This method of alkali salt addition leads to a high level of ion-exchange. The exchanged cations form a strong electrostatic network, strengthening the coal structure such that the dough can be directly extruded into high cell density CHMs (e.g., 227 cells/in2) which maintain their structural integrity throughout drying¸ carbonisation and activation processes.It was found that a modest temperature increase (to above 50 °C) enabled by the kneading, played an important role in developing the dough quality via an apparent irreversible, exothermic cross-linking reaction.The fabricated CHMs are structurally robust with high compressive strength (e.g., 40 MPa). They exhibit good electrical conductivity (e.g. 150 S/m) and high surface areas (e.g., 1175 m2/g), which can both be tailored for specific applications. The used CHM was fully regenerated by in-situ resistive heating after liquid adsorption in a short time (30 min) under atmospheric air, thanks to its optimal electrical conductivity and stable structure. These characteristics along with its low material and production cost make this CHM a promising material for a variety of applications, such as methane storage and phenol adsorption.

Modulation of Functional Phosphorylation Sites by Basic Residues in the Unique Domain of c-Src.

In contrast to the well-studied canonical regulatory mechanisms, the way by which the recently discovered Src N-terminal regulatory element (SNRE) modulates Src activity is not yet well understood. Phosphorylation of serine and threonine residues modulates the charge distribution along the disordered region of the SNRE and may affect a fuzzy complex with the SH3 domain that is believed to act as an information transduction element. The pre-existing positively charged sites can interact with the newly introduced phosphate groups by modulating their acidity, introducing local conformational restrictions, or by coupling various phosphosites into a functional unit. In this paper, we use pH-dependent NMR measurements combined with single point mutations to identify the interactions of basic residues with physiologically important phosphorylated residues and to characterize the effect of these interactions in neighbor residues, thus providing insight into the electrostatic network in the isolated disordered regions and in the entire SNRE. From a methodological point of view, the linear relationships observed between the mutation-induced pKa changes of the phosphate groups of phosphoserine and phosphothreonine and the pH-induced chemical shifts of the NH groups of these residues provide a very convenient alternative to identify interacting phosphate groups without the need to introduce point mutations on specific basic residues.

Modeling the role of charged residues in thermophilic proteins by rotamer and dynamic cross correlation analysis.

Discerning the determinants of protein thermostability is very important both from the theoretical and applied perspective. Different lines of evidence seem to indicate that a dynamical network of salt bridges/charged residues plays a fundamental role in the thermostability of enzymes. In this work, we applied measures of dynamic variance, like the Gini coefficients, Kullback-Leibler (KL) divergence and dynamic cross correlation (DCC) coefficients to compare the behavior of 3 pairs of homologous proteins from the thermophilic bacterium Thermus thermophilus and mesophilic Escherichia coli. Molecular dynamic (MD) simulations of these proteins were performed at 303K and 363K. In the characterization of their side chain rotamer distributions, the corresponding Gini coefficients and KL-divergence both revealed significant correlations with temperature. Similarly, a DCC analysis revealed a higher trend to de-correlate the movement of charged residues at higher temperatures in the thermophilic proteins, when compared with their mesophilic homologues. These results highlight the importance of dynamic electrostatic network interactions for the thermostability of enzymes.

Effects of Electrostatic Force and Network Structure on the Stability of Proton-Exchange Membrane Fuel Cell Catalyst Ink

The stability of the catalyst slurry of a proton-exchange membrane fuel cell (PEMFC) is of great significance to its large-scale production and commercialization. In this study, three kinds of slurries with different stabilities were prepared using different probe ultrasonic powers. The influence of electrostatic force and network structure on slurry stability was also studied. In addition, the catalyst layer (CL) and membrane electrode assembly (MEA) were further tested to determine the relationship between slurry stability, CL, and MEA performance. The results showed that the slurry prepared with 600 W dispersion power had the least agglomeration on day 12, which is due to the clusters in the slurry having the smallest average particle size and the largest surface area, thereby allowing them to absorb the most Nafion and have the largest electrostatic force to inhibit agglomeration. However, the slurry with 1200 W dispersion power had the least sedimentation after 9.4 days because the strength of the network structure in the slurry strengthened the most, resulting in a significant increase in viscosity and inhibition of sedimentation. Electrochemical tests showed that the MEA gradually exhibited worse electrical performance and higher impedance due to the agglomeration of catalyst particles caused by the standing process. Altogether, this study provides insights to better understand and regulate the stability of catalyst slurries.

Surface patches induce nonspecific binding and phase separation of antibodies

Nonspecific interactions are a key challenge in the successful development of therapeutic antibodies. The tendency for nonspecific binding of antibodies is often difficult to reduce by rational design, and instead, it is necessary to rely on comprehensive screening campaigns. To address this issue, we performed a systematic analysis of the impact of surface patch properties on antibody nonspecificity using a designer antibody library as a model system and single-stranded DNA as a nonspecificity ligand. Using an in-solution microfluidic approach, we find that the antibodies tested bind to single-stranded DNA with affinities as high as KD = 1 µM. We show that DNA binding is driven primarily by a hydrophobic patch in the complementarity-determining regions. By quantifying the surface patches across the library, the nonspecific binding affinity is shown to correlate with a trade-off between the hydrophobic and total charged patch areas. Moreover, we show that a change in formulation conditions at low ionic strengths leads to DNA-induced antibody phase separation as a manifestation of nonspecific binding at low micromolar antibody concentrations. We highlight that phase separation is driven by a cooperative electrostatic network assembly mechanism of antibodies with DNA, which correlates with a balance between positive and negative charged patches. Importantly, our study demonstrates that both nonspecific binding and phase separation are controlled by the size of the surface patches. Taken together, these findings highlight the importance of surface patches and their role in conferring antibody nonspecificity and its macroscopic manifestation in phase separation.

Electrostatic networks for characterization of allosteric pathways: Allosteric paths in Cas9 apo, DNA- and RNA-bound forms.

Allostery is a fundamental process by which biological macromolecules transmit the effect of a local perturbation at one site to a distal, functional site, allowing for regulation of activity. The long-range coupling between residues that gives rise to allostery in a protein is built up from short-range electrostatic and hydrophobic interactions. These are arguably the largest determinants of protein structure and are essential regulators of protein function. We introduce an effective coulombic electrostatic coupling network obtained from the analysis of molecular dynamics simulations of Cas9 in its apo, DNA- and RNA-bound forms. We characterize key electrostatic events that determine its functional activity and targeting precision. We demonstrate the locality of the electrostatic-interaction network over other connectivity matrices as validated through direct comparisons to NMR measurements. We define an electrostatic-based centrality metric that allows us to pinpoint relevant donor-acceptor pairs that promote charge displacements that modulate the cross-interaction between the PAM-interacting region and catalytic domains. We determine key amino acid residues central to the network, allowing us to identify a circular allosteric pathway that channels perturbations from the PAM-interacting domain to the HNH and RuvCII domains, and then back to the PAM-contacting region. The connectivity around HNH is important for controlling the directionality of signal transfer from and towards the PAM-interacting domain. The effective coulombic electrostatic coupling network makes it possible to elucidate allosteric pathways and provides valuable interpretations of experimental measurements.

Stimuli responsive hierarchical assembly of supramolecular conductive fibers from coiled-coil building blocks.

Proteins and peptides provide inspiration for synthetic stimuli-responsive nanomaterials. In this work, the assembly mechanism of a de novo designed peptide that forms α-helical coiled-coil hexamer fibers was probed through point mutations of the amino acid sequence and varying assembly solution pH. Buffer pH affects multi-scale aspects of supramolecular assembly, including peptide monomer secondary structure and interactions between α-helical coiled-coil hexamer building blocks. These higher order assembly interfaces are formed by electrostatic networks between solvent-exposed polarizable side chains, as observed by cryo-electron microscopy. One of the resulting hierarchical structures was a novel assembly of α-helical cross coiled-coils that exhibits electrical conductivity. Here, we show that pH-gated assembly can be achieved with only one amino acid mutation. This work opens materials design for more stimuli-responsive and functional materials with simple pH-gating structural motifs.

Computation of conformational transitions and activation of Src tyrosine kinase.

A protein molecule can adapt different structural forms depending on solution conditions or ligation state. The stable forms can be defined experimentally, but the transition pathways and metastable states between them cannot. A computational method to explore equilibrium conformational transition pathways is the adaptive bias path optimization (ABPO) algorithm. ABPO, implemented in the CHARMM program, differs from other path methods by using enhanced sampling along a path between two known states without restraining the system to the reduced coordinates of the evolving path. ABPO converges to a physically reasonable path even in the case of a highly rugged energy landscape and is amenable to assessing the power of the reduced variables chosen to explore the transition. ABPO was applied to three cases where a conformational transition is localized to one part of the polypeptide chain. With proper selection of the reduced coordinate set, sampling without restraints converged to a physically reasonable path. We also applied ABPO to a more complex transition of Src tyrosine kinase catalytic domain that undergoes a more complex transition between enzymatically down‑regulated and activated conformations. The results using coarse-grained and all‑atom models identified a switched electrostatic network (SEN) that could confer an entropic advantage to reduce energetic barriers by limiting the search space of the activation loop. The richness in atomic detail provided by the ABPO path reveals how the SEN couples the dynamics of the key αC‑helix and the activation loop, which provided a new rationale for the highly conserved HRD sequence of kinases. Further, the results related to energetics of the rotation of the αC‑helix provide a rationale for the interactions observed in a number of kinase regulatory complexes, some for which the basis for regulation from inspection of the structure along is puzzling.

Revisiting the Burden Borne by Fumarase: Enzymatic Hydration of an Olefin.

Fumarate hydratase (FH) is a remarkable catalyst that decreases the free energy of the catalyzed reaction by 30 kcal mol-1, much larger than most exceptional enzymes with extraordinary catalytic rates. Two classes of FH are observed in nature: class-I and class-II, which have different folds, yet catalyze the same reversible hydration/dehydration reaction of the dicarboxylic acids fumarate/malate, with equal efficiencies. Using class-I FH from the hyperthermophilic archaeon Methanocaldococcus jannaschii (Mj) as a model along with comparative analysis with the only other available class-I FH structure from Leishmania major (Lm), we provide insights into the molecular mechanism of catalysis in this class of enzymes. The structure of MjFH apo-protein has been determined, revealing that large intersubunit rearrangements occur across apo- and holo-protein forms, with a largely preorganized active site for substrate binding. Site-directed mutagenesis of active site residues, kinetic analysis, and computational studies, including density functional theory (DFT) and natural population analysis, together show that residues interacting with the carboxylate group of the substrate play a pivotal role in catalysis. Our study establishes that an electrostatic network at the active site of class-I FH polarizes the substrate fumarate through interactions with its carboxylate groups, thereby permitting an easier addition of a water molecule across the olefinic bond. We propose a mechanism of catalysis in FH that occurs through transition-state stabilization involving the distortion of the electronic structure of the substrate olefinic bond mediated by the charge polarization of the bound substrate at the enzyme active site.

Mutagenesis studies of TRPV1 subunit interfaces informed by genomic variant analysis

Protein structures and mutagenesis studies have been instrumental in elucidating molecular mechanisms of ion channel function, but making informed choices about which residues to target for mutagenesis can be challenging. Therefore, we investigated the potential for using human population genomic data to further refine our selection of mutagenesis sites in TRPV1. Single nucleotide polymorphism data of TRPV1 from gnomAD 2.1.1 revealed a lower number of missense variants within buried residues of the ankyrin repeat domain and an increased number of variants between secondary structure elements of the transmembrane segments. We hypothesized that residues critical to interactions at interfaces between subunits or domains in the channel would exhibit a similar reduction in variants. We identified in the structure of ground squirrel TRPV1 (PDB: 7LQY) a possible electrostatic network between K155 and K160 in the N-terminal ankyrin repeat domain and E761 and D762 in the C-terminus (K-KED). Consistent with our hypothesis for residues at key interface sites, none of the four residues have any variants reported in gnomAD 2.1.1. Ca2+ imaging of TRPV1 K-KED mutants confirmed significant roles for these residues, but we found that the electrostatic interaction is not essential since channel function is still observed in total charge reversals on the C-terminal side of the interface (E761K/D762K). Interestingly, Ca2+ imaging responses for a charge swap experiment with K155D/D762K showed partially restored wild-type responses. Using electrophysiology, we found that charge reversals on either K155 or D762 increased the baseline currents of TRPV1, and the charge swapped double mutant, K155D/D762K, partially restored baseline currents to wild-type levels. We interpret these results to mean that contacts across residues in the K-KED interface shift the equilibria of conformations to closed pore states. Our study demonstrates the utility and applicability of a combined missense variant and structure targeted investigation of residues at TRPV1 subunit interfaces.

Sodium Phytate-Incorporated Gelatin-Silicate Nanoplatelet Composites for Enhanced Cohesion and Hemostatic Function of Shear-Thinning Biomaterials.

Shear-thinning biomaterials (STBs) based on gelatin-silicate nanoplatelets (SNs) are emerging as an alternative to conventional coiling and clipping techniques in the treatment of vascular anomalies. Improvements in the cohesion of STB hydrogels pave the way toward their translational application in minimally invasive therapies such as endovascular embolization repair. In the present study, sodium phytate (Phyt) additives are used to tune the electrostatic network of SNs-gelatin STBs, thereby promoting their mechanical integrity and facilitating injectability through standard catheters. We show that an optimized amount of Phyt enhances storage modulus by approximately one order of magnitude and reduces injection force by ≈58% without compromising biocompatibility and hydrogel wet stability. The Phyt additives are found to decrease the immune responses induced by SNs. In vitro embolization experiments suggest a significantly lower rate of failure in Phyt-incorporated STBs than in control groups. Furthermore, the addition of Phyt leads to accelerated blood coagulation (reduces clotting time by ≈45% compared to controls) due to the contributions of negatively charged phosphate groups, which aid in the prolonged durability of STB in coagulopathic patients. Therefore, the proposed approach is an effective method for the design of robust and injectable STBs for minimally invasive treatment of vascular malformations.

Electrostatics in Computational Biophysics and Its Implications for Disease Effects.

This review outlines the role of electrostatics in computational molecular biophysics and its implication in altering wild-type characteristics of biological macromolecules, and thus the contribution of electrostatics to disease mechanisms. The work is not intended to review existing computational approaches or to propose further developments. Instead, it summarizes the outcomes of relevant studies and provides a generalized classification of major mechanisms that involve electrostatic effects in both wild-type and mutant biological macromolecules. It emphasizes the complex role of electrostatics in molecular biophysics, such that the long range of electrostatic interactions causes them to dominate all other forces at distances larger than several Angstroms, while at the same time, the alteration of short-range wild-type electrostatic pairwise interactions can have pronounced effects as well. Because of this dual nature of electrostatic interactions, being dominant at long-range and being very specific at short-range, their implications for wild-type structure and function are quite pronounced. Therefore, any disruption of the complex electrostatic network of interactions may abolish wild-type functionality and could be the dominant factor contributing to pathogenicity. However, we also outline that due to the plasticity of biological macromolecules, the effect of amino acid mutation may be reduced, and thus a charge deletion or insertion may not necessarily be deleterious.