DelPhiForce, a tool for electrostatic force calculations: Applications to macromolecular binding

Electrostatic interactions in nucleosome and higher-order structures are regulated by protonation state of histone ionizable residue

The nucleosome serves as the fundamental unit of chromatin organization, with electrostatic interactions acting as the driving forces in the folding of nucleosomes into chromatin. Perturbations around physiological pH conditions can lead to changes in the protonation states of titratable histone residues, impacting nucleosome surface electrostatic potentials and interactions. However, the effects of proton uptake or release of histone ionizable groups on nucleosome-partner protein interactions and higher-order chromatin structures remain largely unexplored. Here, we conducted comprehensive analyses of histone titratable residue pKa values in various nucleosome contexts, utilizing 96 experimentally determined complex structures. We revealed that pH-induced changes in histone residue protonation states modulated nucleosome surface electrostatic potentials and significantly influenced nucleosome-partner protein interactions. Furthermore, we observed that proton uptake or release often accompanied nucleosome-partner protein interactions, facilitating their binding processes. In addition, our findings suggest that alterations in histone protonation can also regulate nucleosome self-association, thereby modulating the organization and dynamics of higher-order chromatin structure. This study advances our understanding of nucleosome-chromatin factor interactions and how chromatin organization is regulated at the molecular level.

Elucidating the interactions between Kinesin-5/BimC and the microtubule: insights from TIRF microscopy and molecular dynamics simulations

Kinesin-5 s are bipolar motor proteins that contribute to cell division by crosslinking and sliding apart antiparallel microtubules inside the mitotic spindle. However, the mechanism underlying the interactions between kinesin-5 and the microtubule remains poorly understood. In this study, we investigated the binding of BimC, a kinesin-5 motor from Aspergillus nidulans, to the microtubule using a combination of total internal reflection fluorescence (TIRF) microscopy and molecular dynamics (MD) simulations. TIRF microscopy experiments revealed that increasing the concentration of KCl in the motility buffer from 0 mM to 150 mM completely abolishes the ability of BimC to bind to the microtubule. Consistent with this experimental finding, MD simulations demonstrated a significant reduction in the strength of electrostatic interactions between BimC and microtubules at 150 mM KCl compared to 0 mM KCl. Furthermore, we identified several salt bridges at the BimC-microtubule interface, with positively charged residues on BimC interacting with negatively charged residues on the tubulin heterodimer. These results provide mechanistic insights into the role of electrostatic interactions in kinesin-5-microtubule binding, advancing our understanding of the molecular underpinnings of kinesin-5 motility.

Modeling Viral Capsid Assembly: A Review of Computational Strategies and Applications

Viral capsid assembly is a complex and critical process, essential for understanding viral behavior, evolution, and the development of antiviral treatments, vaccines, and nanotechnology. Significant progress in studying viral capsid assembly has been achieved through various computational approaches, including molecular dynamics (MD) simulations, stochastic dynamics simulations, coarse-grained (CG) models, electrostatic analyses, lattice models, hybrid techniques, machine learning methods, and kinetic models. Each of these techniques offers unique advantages, and by integrating these diverse computational strategies, researchers can more accurately model the dynamic behaviors and structural features of viral capsids, deepening our understanding of the assembly process. This review provides a comprehensive overview of studies on viral capsid assembly, emphasizing their critical role in advancing our knowledge. It examines the contributions, strengths, and limitations of different computational methods, presents key computational works in the field, and analyzes milestone studies that have shaped current research.

Bound ion effects: Using machine learning method to study the kinesin Ncd's binding with microtubule

Drosophila Ncd proteins are motor proteins that play important roles in spindle organization. Ncd and the tubulin dimer are highly charged. Thus, it is crucial to investigate Ncd-tubulin dimer interactions in the presence of ions, especially ions that are bound or restricted at the Ncd-tubulin dimer binding interfaces. To consider the ion effects, widely used implicit solvent models treat ions implicitly in the continuous solvent environment without focusing on the individual ions' effects. But highly charged biomolecules such as the Ncd and tubulin dimer may capture some ions at highly charged regions as bound ions. Such bound ions are restricted to their binding sites; thus, they can be treated as part of the biomolecules. By applying multiscale computational methods, including the machine-learning-based Hybridizing Ions Treatment-2 program, molecular dynamics simulations, DelPhi, and DelPhiForce, we studied the interaction between the Ncd motor domain and the tubulin dimer using a hybrid solvent model, which considers the bound ions explicitly and the other ions implicitly in the solvent environment. To identify the importance of treating bound ions explicitly, we also performed calculations using the implicit solvent model without considering the individual bound ions. We found that the calculations of the electrostatic features differ significantly between those of the hybrid solvent model and the pure implicit solvent model. The analyses show that treating bound ions at highly charged regions explicitly is crucial for electrostatic calculations. This work proposes a machine-learning-based approach to handle the bound ions using the hybrid solvent model. Such an approach is not only capable of handling kinesin-tubulin complexes but is also appropriate for other highly charged biomolecules, such as DNA/RNA, viral capsid proteins, etc.

Antigen specific VNAR screening in whitespotted bamboo shark (Chiloscyllium plagiosum) with next generation sequencing

IgNAR exhibits significant promise in the fields of cancer and anti-virus biotherapies. Notably, the variable regions of IgNAR (VNAR) possess comparable antigen binding affinity with much smaller molecular weight (∼12 kDa) compared to IgNAR. Antigen specific VNAR screening is a changeling work, which limits its application in medicine and therapy fields. Though phage display is a powerful tool for VNAR screening, it has a lot of drawbacks, such as small library coverage, low expression levels, unstable target protein, complicating and time-consuming procedures. Here we report VANR screening with next generation sequencing (NGS) could effectively overcome the limitations of phage display, and we successfully identified approximately 3000 BAFF-specific VNARs in Chiloscyllium plagiosum vaccinated with the BAFF antigen. The results of modelling and molecular dynamics simulation and ELISA assay demonstrated that one out of the top five abundant specific VNARs exhibited higher binding affinity to the BAFF antigen than those obtained through phage display screening. Our data indicates NGS would be an alternative way for VNAR screening with plenty of advantages.

How does the ion concentration affect the functions of kinesin BimC

BimC family proteins are bipolar motor proteins belonging to the kinesin superfamily which promote mitosis by crosslinking and sliding apart antiparallel microtubules. Understanding the binding mechanism between the kinesin and the microtubule is crucial for researchers to make advances in the treatment of cancer and other malignancies. Experimental research has shown that the ion concentration affects the function of BimC significantly. But the insights of the ion-dependent function of BimC remain unclear. By combining molecular dynamics (MD) simulations with a series of computational approaches, we studied the electrostatic interactions at the binding interfaces of BimC and the microtubule under different KCl concentrations. We found the electrostatic interaction between BimC and microtubule is stronger at 0 mM KCl compared to 150 mM KCl, which is consistent with experimental conclusions. Furthermore, important salt bridges and residues at the binding interfaces of the complex were identified, which illustrates the details of the BimC-microtubule interactions. Molecular dynamics analyses of salt bridges identified that the important residues on the binding interface of BimC are positively charged, while those residues on the binding interface of the tubulin heterodimer are negatively charged. The finding in this work reveals some important mechanisms of kinesin-microtubule binding, which helps the future drug design for cancer therapy.

A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape

The family of Janus Kinases (JAKs) associated with the JAK-signal transducers and activators of transcription signaling pathway plays a vital role in the regulation of various cellular processes. The conformational change of JAKs is the fundamental steps for activation, affecting multiple intracellular signaling pathways. However, the transitional process from inactive to active kinase is still a mystery. This study is aimed at investigating the electrostatic properties and transitional states of JAK1 to a fully activation to a catalytically active enzyme. To achieve this goal, structures of the inhibited/activated full-length JAK1 were modelled and the energies of JAK1 with Tyrosine Kinase (TK) domain at different positions were calculated, and Dijkstra's method was applied to find the energetically smoothest path. Through a comparison of the energetically smoothest paths of kinase inactivating P733L and S703I mutations, an evaluation of the reasons why these mutations lead to negative or positive regulation of JAK1 are provided. Our energy analysis suggests that activation of JAK1 is thermodynamically spontaneous, with the inhibition resulting from an energy barrier at the initial steps of activation, specifically the release of the TK domain from the inhibited Four-point-one, Ezrin, Radixin, Moesin-PK cavity. Overall, this work provides insights into the potential pathway for TK translocation and the activation mechanism of JAK1.

Redox driven B12-ligand switch drives CarH photoresponse

CarH is a coenzyme B12-dependent photoreceptor involved in regulating carotenoid biosynthesis. How light-triggered cleavage of the B12 Co-C bond culminates in CarH tetramer dissociation to initiate transcription remains unclear. Here, a series of crystal structures of the CarH B12-binding domain after illumination suggest formation of unforeseen intermediate states prior to tetramer dissociation. Unexpectedly, in the absence of oxygen, Co-C bond cleavage is followed by reorientation of the corrin ring and a switch from a lower to upper histidine-Co ligation, corresponding to a pentacoordinate state. Under aerobic conditions, rapid flash-cooling of crystals prior to deterioration upon illumination confirm a similar B12-ligand switch occurs. Removal of the upper His-ligating residue prevents monomer formation upon illumination. Combined with detailed solution spectroscopy and computational studies, these data demonstrate the CarH photoresponse integrates B12 photo- and redox-chemistry to drive large-scale conformational changes through stepwise Co-ligation changes.

Accurate Boundary Integral Formulations for the Calculation of Electrostatic Forces with an Implicit-Solvent Model

An accurate force calculation with the Poisson-Boltzmann equation is challenging, as it requires the electric field on the molecular surface. Here we present a calculation of the electric field on the solute-solvent interface that is exact for piecewise linear variations of the potential and analyze four different alternatives to compute the force using a boundary element method. We performed a verification exercise for two cases: the isolated and two interacting molecules. Our results suggest that the boundary element method outperforms the finite difference method, as the latter needs a much finer mesh than in solvation energy calculations to get acceptable accuracy in the force, whereas the same surface mesh as in a standard energy calculation is appropriate for the boundary element method. Among the four evaluated alternatives of force calculation, we saw that the most accurate one is based on the Maxwell stress tensor. However, for a realistic application, like the barnase-barstar complex, the approach based on variations of the energy functional, which is less accurate, gives equivalent results. This analysis is useful toward using the Poisson-Boltzmann equation for force calculations in applications where high accuracy is key, for example, to feed molecular dynamics models or to enable the study of the interaction between large molecular structures, like viruses adsorbed onto substrates.

Phosphorylation of Tyrosine 841 Plays a Significant Role in JAK3 Activation

Janus Kinase 3 (JAK3) plays a key role in the development, proliferation, and differentiation of various immune cells. It regulates gene expression by phosphorylation of Signal Transducers and Activators of Transcriptions (STATs) via the JAK/STAT pathway. Recently, we found a new JAK3 phosphorylation site, tyrosine 841 (Y841). The results showed that pY841 helps the kinase domain flip around the pseudo kinase domain, which may cause JAK3 conformational changes. It also reduces the size of the cleft between the N-lobe and the C-lobe of the JAK3 kinase domain. However, pY841 was found to enlarge the cleft when ATP/ADP was bound to the kinase. The increase in the cleft size suggested that pY841 enhanced the elasticity of the kinase domain. For unphosphorylated JAK3 (JAK3-Y841), the binding forces between the kinase domain and ATP or ADP were similar. After phosphorylation of Y841, JAK3-pY841 exhibited more salt bridges and hydrogen bonds between ATP and the kinase than between ADP and the kinase. Consequently, the electrostatic binding force between ATP and the kinase was higher than that between ADP and the kinase. The result was that compared to ADP, ATP was more attractive to JAK3 when Y841 was phosphorylated. Therefore, JAK3-pY841 tended to bind ATP rather than ADP. This work provides new insights into the role of phosphorylation in kinase activation and ATP hydrolysis and sheds light on the importance of understanding the molecular mechanisms that regulate the kinase function.

A Comprehensive Study on the Electrostatic Properties of Tubulin-Tubulin Complexes in Microtubules

Microtubules are key players in several stages of the cell cycle and are also involved in the transportation of cellular organelles. Microtubules are polymerized by α/β tubulin dimers with a highly dynamic feature, especially at the plus ends of the microtubules. Therefore, understanding the interactions among tubulins is crucial for characterizing microtubule dynamics. Studying microtubule dynamics can help researchers make advances in the treatment of neurodegenerative diseases and cancer. In this study, we utilize a series of computational approaches to study the electrostatic interactions at the binding interfaces of tubulin monomers. Our study revealed that among all the four types of tubulin-tubulin binding modes, the electrostatic attractive interactions in the α/β tubulin binding are the strongest while the interactions of α/α tubulin binding in the longitudinal direction are the weakest. Our calculations explained that due to the electrostatic interactions, the tubulins always preferred to form α/β tubulin dimers. The interactions between two protofilaments are the weakest. Thus, the protofilaments are easily separated from each other. Furthermore, the important residues involved in the salt bridges at the binding interfaces of the tubulins are identified, which illustrates the details of the interactions in the microtubule. This study elucidates some mechanistic details of microtubule dynamics and also identifies important residues at the binding interfaces as potential drug targets for the inhibition of cancer cells.

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.

Using a comprehensive approach to investigate the interaction between Kinesin-5/Eg5 and the microtubule

Kinesins are microtubule-based motor proteins that play important roles ranging from intracellular transport to cell division. Human Kinesin-5 (Eg5) is essential for mitotic spindle assembly during cell division. By combining molecular dynamics (MD) simulations with other multi-scale computational approaches, we systematically studied the interaction between Eg5 and the microtubule. We find the electrostatic feature on the motor domains of Eg5 provides attractive interactions to the microtubule. Additionally, the folding and binding energy analysis reveals that the Eg5 motor domain performs its functions best when in a weak acidic environment. Molecular dynamics analyses of hydrogen bonds and salt bridges demonstrate that, on the binding interfaces of Eg5 and the tubulin heterodimer, salt bridges play the most significant role in holding the complex. The salt bridge residues on the binding interface of Eg5 are mostly positive, while salt bridge residues on the binding interface of tubulin heterodimer are mostly negative. Such salt bridge residue distribution is consistent with electrostatic potential calculations. In contrast, the interface between α and β-tubulins is dominated by hydrogen bonds rather than salt bridges. Compared to the Eg5/α-tubulin interface, the Eg5/β-tubulin interface has a greater number of salt bridges and higher occupancy for salt bridges. This asymmetric salt bridge distribution may play a significant role in Eg5's directionality. The residues involved in hydrogen bonds and salt bridges are identified in this work and may be helpful for anticancer drug design.

HIT web server: a hybrid method to improve electrostatic calculations for biomolecules

The electrostatic features of highly charged biomolecules are crucial and challenging tasks in computational biophysics. The electrostatic calculations by traditional implicit solvent methods are efficient but have difficulties on highly charged biomolecules. We have developed a Hybridizing Ion Treatment (HIT) tool, which successfully hybridizes the explicit ions and implicit solvation model to accurately calculate the electrostatic potential for highly charged biomolecules. Here we implemented the HIT tool into a web server. In this study, a training set was prepared to optimize the number of frames for the HIT web server. The results on tubulins, DNAs, and RNAs, reveal the mechanisms for the motor proteins, DNA of HIV, and tRNA. This HIT web server can be widely used to study highly charged biomolecules, including DNAs, RNAs, molecular motors, and other highly charged biomolecules.

Computational Study on E-Hooks of Tubulins in the Binding Process with Kinesin

Cargo transport within cells is essential to healthy cells, which requires microtubules-based motors, including kinesin. The C-terminal tails (E-hooks) of alpha and beta tubulins of microtubules have been proven to play important roles in interactions between the kinesins and tubulins. Here, we implemented multi-scale computational methods in E-hook-related analyses, including flexibility investigations of E-hooks, binding force calculations at binding interfaces between kinesin and tubulins, electrostatic potential calculations on the surface of kinesin and tubulins. Our results show that E-hooks have several functions during the binding process: E-hooks utilize their own high flexibilities to increase the chances of reaching a kinesin; E-hooks help tubulins to be more attractive to kinesin. Besides, we also observed the differences between alpha and beta tubulins: beta tubulin shows a higher flexibility than alpha tubulin; beta tubulin generates stronger attractive forces (about twice the strengths) to kinesin at different distances, no matter with E-hooks in the structure or not. Those facts may indicate that compared to alpha tubulin, beta tubulin contributes more to attracting and catching a kinesin to microtubule. Overall, this work sheds the light on microtubule studies, which will also benefit the treatments of neurodegenerative diseases, cancer treatments, and preventions in the future.

Molecular mechanisms of cardiac actomyosin transforming from rigor state to post-rigor state

Sudden cardiac death contributed to half of all deaths from cardiovascular diseases. The mechanism of the kinetic cycle of cardiac myosin is crucial for heart protection and drug development. The state change in the myosin kinetic cycle from the rigor state to the post-rigor state is fundamental to explain binding and dissociation. Here, we used β-cardiac myosin in the rigor and post-rigor states to model the actomyosin complexes. Molecular dynamics simulations, electrostatic analysis, and energetic analysis of actomyosin complexes were performed in this work. The results showed that there are fewer interactions and lower electrostatic binding strength in the post-rigor state than in the rigor state. In the post-rigor state, there were higher free binding energy, fewer salt bridges, and fewer hydrogen bonds. The results showed a lower binding affinity in the post-rigor state than in the rigor state. The decrease in the binding affinity provided important conditions for dissociation of the myosin from the actin filament. Although previous studies focused mostly on the binding process, this study provides evidence of dissociation, which is even more important in the myosin kinetic cycle. This research on the mechanism of myosin kinetic cycles provides a novel direction for future genetic disease studies.

Computational Study on the Function of Palmitoylation on the Envelope Protein in SARS-CoV-2

SARS-CoV-2 that caused COVID-19 has spread since the end of 2019. Its major effects resulted in over four million deaths around the whole world by August 2021. Therefore, understanding virulence mechanisms is important to prevent future outbreaks and for COVID-19 drug development. The envelope (E) protein is an important structural protein, affecting virus assembly and budding. The E protein pentamer is a viroporin, serving as an ion transferring channel in cells. In this work, we applied molecular dynamic simulations and topological and electrostatic analyses to study the effects of palmitoylation on the E protein pentamer. The results indicate that the cation transferring direction is more from the lumen to the cytosol. The structure of the palmitoylated E protein pentamer is more stable while the loss of palmitoylation caused the pore radius to reduce and even collapse. The electrostatic forces on the two sides of the palmitoylated E protein pentamer are more beneficial to attract cations in the lumen and to release cations into the cytosol. The results indicate the importance of palmitoylation, which can help the drug design for the treatment of COVID-19.

Computational Study on DNA Repair: The Roles of Electrostatic Interactions Between Uracil-DNA Glycosylase (UDG) and DNA

Uracil-DNA glycosylase (UDG) is one of the most important base excision repair (BER) enzymes involved in the repair of uracil-induced DNA lesion by removing uracil from the damaged DNA. Uracil in DNA may occur due to cytosine deamination or deoxy uridine monophosphate (dUMP) residue misincorporation during DNA synthesis. Medical evidences show that an abnormal expression of UDG is related to different types of cancer, including colorectal cancer, lung cancer, and liver cancer. Therefore, the research of UDG is crucial in cancer treatment and prevention as well as other clinical activities. Here we applied multiple computational methods to study UDG in several perspectives: Understanding the stability of the UDG enzyme in different pH conditions; studying the differences in charge distribution between the pocket side and non-pocket side of UDG; analyzing the field line distribution at the interfacial area between UDG and DNA; and performing electrostatic binding force analyses of the special region of UDG (pocket area) and the target DNA base (uracil) as well as investigating the charged residues on the UDG binding pocket and binding interface. Our results show that the whole UDG binding interface, and not the UDG binding pocket area alone, provides the binding attractive force to the damaged DNA at the uracil base.

In silico studies on stilbenolignan analogues as SARS-CoV-2 Mpro inhibitors

COVID-19, a new strain of coronavirus family, was identified at the end of 2019 in China. The COVID-19 virus spread rapidly all over the world. Scientists strive to find virus-specific antivirals for the treatment of COVID-19. The present study reports a molecular docking study of the stilbenolignans and SARS-CoV-2 main protease (SARS-CoV-2 Mpro) inhibitors. The detailed interactions between the stilbenolignan analogues and SARS-CoV-2 Mpro inhibitors were determined as hydrophobic bonds, hydrogen bonds and electronic bonds, inhibition activity, ligand efficiency, bonding type and distance and etc. The binding energies of the stilbenolignan analogues were obtained from the molecular docking of SARS-CoV-2 Mpro. Lehmbachol D, Maackolin, Gnetucleistol, Gnetifolin F, Gnetofuran A and Aiphanol were found to be -7.7, -8.2, -7.3, -8.5, -8.0 and -7.3 kcal/mol, respectively. Osirus, Molinspiration and SwissADME chemoinformatic tools were used to examine ADMET properties, pharmacokinetic parameters and toxicological characteristics of the stilbenolignan analogues. All analogues obey the Lipinski's rule of five. Furthermore, stilbenolignan analogues were studied to predict their binding affinities against SARS-CoV-2 Mpro using molecular modeling and simulation techniques, and the binding free energy calculations of all complexes were calculated using the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) method. With the data presented here it has been observed that these analogues may be a good candidate for SARS-CoV-2 Mpro in vivo studies, so more research can be done on stilbenolignan analogues.

Electrostatic features for nucleocapsid proteins of SARS-CoV and SARS-CoV-2

COVID-19 is increasingly affecting human health and global economy. Understanding the fundamental mechanisms of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is highly demanded to develop treatments for COVID-19. SARS-CoV and SARS-CoV-2 share 92.06% identity in their N protein RBDs' sequences, which results in very similar structures. However, the SARS-CoV-2 is more easily to spread. Utilizing multi-scale computational approaches, this work studied the fundamental mechanisms of the nucleocapsid (N) proteins of SARS-CoV and SARS-CoV-2, including their stabilities and binding strengths with RNAs at different pH values. Electrostatic potential on the surfaces of N proteins show that both the N proteins of SARS-CoV and SARS-CoV-2 have dominantly positive potential to attract RNAs. The binding forces between SARS-CoV N protein and RNAs at different distances are similar to that of SARS-CoV-2, both in directions and magnitudes. The electric filed lines between N proteins and RNAs are also similar for both SARS-CoV and SARS-CoV-2. The folding energy and binding energy dependence on pH revealed that the best environment for N proteins to perform their functions with RNAs is the weak acidic environment.

Hybrid method for representing ions in implicit solvation calculations

Fast and accurate calculations of the electrostatic features of highly charged biomolecules such as DNA, RNA, and highly charged proteins are crucial and challenging tasks. Traditional implicit solvent methods calculate the electrostatic features quickly, but these methods are not able to balance the high net biomolecular charges effectively. Explicit solvent methods add unbalanced ions to neutralize the highly charged biomolecules in molecular dynamic simulations, which require more expensive computing resources. Here we report developing a novel method, Hybridizing Ions Treatment (HIT), which hybridizes the implicit solvent method with an explicit method to realistically calculate the electrostatic potential for highly charged biomolecules. HIT utilizes the ionic distribution from an explicit method to predict the bound ions. The bound ions are then added in the implicit solvent method to perform the electrostatic potential calculations. In this study, two training sets were developed to optimize parameters for HIT. The performance on the testing set demonstrates that HIT significantly improves the electrostatic calculations. Results on molecular motors myosin and kinesin reveal some mechanisms and explain some previous experimental findings. HIT can be widely used to study highly charged biomolecules, including DNA, RNA, molecular motors, and other highly charged biomolecules. The HIT package is available at http://compbio.utep.edu/static/downloads/download_hit.zip.

StructureMan: A Structure Manipulation Tool to Study Large Scale Biomolecular Interactions

Studying biomolecular interactions is a crucial but challenging task. Due to their large scales, many biomolecular interactions are difficult to be simulated via all atom models. An effective approach to investigate the biomolecular interactions is highly demanded in many areas. Here we introduce a Structure Manipulation (StructureMan) program to operate the structures when studying the large-scale biomolecular interactions. This novel StructureMan tool provides comprehensive operations which can be utilized to study the interactions in various large biological systems. Combining with electrostatic calculation programs such as DelPhi and DelPhiForce, StructureMan was implemented to reveal the detailed electrostatic features in two large biological examples, the viral capsid and molecular motor-microtubule complexes. Applications on these two examples revealed interesting binding mechanisms in the viral capsid and molecular motor. Such applications demonstrated that the StructureMan can be widely used when studying the biomolecular interactions in large scale biological problems. This novel tool provides an alternative approach to efficiently study the biomolecular interactions, especially for large scale biology systems. The StructureMan tool is available at our website: http://compbio.utep.edu/static/downloads/script-for-munipulation2.zip.

Spike Proteins of SARS-CoV and SARS-CoV-2 Utilize Different Mechanisms to Bind With Human ACE2

The ongoing outbreak of COVID-19 has been a serious threat to human health worldwide. The virus SARS-CoV-2 initiates its infection to the human body via the interaction of its spike (S) protein with the human Angiotensin-Converting Enzyme 2 (ACE2) of the host cells. Therefore, understanding the fundamental mechanisms of how SARS-CoV-2 S protein receptor binding domain (RBD) binds to ACE2 is highly demanded for developing treatments for COVID-19. Here we implemented multi-scale computational approaches to study the binding mechanisms of human ACE2 and S proteins of both SARS-CoV and SARS-CoV-2. Electrostatic features, including electrostatic potential, electric field lines, and electrostatic forces of SARS-CoV and SARS-CoV-2 were calculated and compared in detail. The results demonstrate that SARS-CoV and SARS-CoV-2 S proteins are both attractive to ACE2 by electrostatic forces even at different distances. However, the residues contributing to the electrostatic features are quite different due to the mutations between SARS-CoV S protein and SARS-CoV-2 S protein. Such differences are analyzed comprehensively. Compared to SARS-CoV, the SARS-CoV-2 binds with ACE2 using a more robust strategy: The electric field line related residues are distributed quite differently, which results in a more robust binding strategy of SARS-CoV-2. Also, SARS-CoV-2 has a higher electric field line density than that of SARS-CoV, which indicates stronger interaction between SARS-CoV-2 and ACE2, compared to that of SARS-CoV. Key residues involved in salt bridges and hydrogen bonds are identified in this study, which may help the future drug design against COVID-19.

Ab-initio binding of barnase–barstar with DelPhiForce steered Molecular Dynamics (DFMD) approach

Receptor–ligand interactions are involved in various biological processes, therefore understanding the binding mechanism and ability to predict the binding mode are essential for many biological investigations. While many computational methods exist to predict the 3D structure of the corresponding complex provided the knowledge of the monomers, here we use the newly developed DelPhiForce steered Molecular Dynamics (DFMD) approach to model the binding of barstar to barnase to demonstrate that first-principles methods are also capable of modeling the binding. Essential component of DFMD approach is enhancing the role of long-range electrostatic interactions to provide guiding force of the monomers toward their correct binding orientation and position. Thus, it is demonstrated that the DFMD can successfully dock barstar to barnase even if the initial positions and orientations of both are completely different from the correct ones. Thus, the electrostatics provides orientational guidance along with pulling force to deliver the ligand in close proximity to the receptor.

A computational model of ESAT-6 complex in membrane

One quarter of the world's population are infected by Mycobacterium tuberculosis (Mtb), which is a leading death-causing bacterial pathogen. Recent evidence has demonstrated that two virulence factors, ESAT-6 and CFP-10, play crucial roles in Mtb's cytosolic translocation. Many efforts have been made to study the ESAT-6 and CFP-10 proteins. Some studies have shown that ESAT-6 has an essential role in rupturing phagosome. However, the mechanisms of how ESAT-6 interacts with the membrane have not yet been fully understood. Recent studies indicate that the ESAT-6 disassociates with CFP-10 upon their interaction with phagosome membrane, forming a membrane-spanning pore. Based on these observations, as well as the available structure of ESAT-6, ESAT-6 is hypothesized to form an oligomer for membrane insertion as well as rupturing. Such an ESAT-6 oligomer may play a significant role in the tuberculosis infection. Therefore, deeper understanding of the oligomerization of ESAT-6 will establish new directions for tuberculosis treatment. However, the structure of the oligomer of ESAT-6 is not known. Here, we proposed a comprehensive approach to model the complex structures of ESAT-6 oligomer inside a membrane. Several computational tools, including MD simulation, symmetrical docking, MM/PBSA, are used to obtain and characterize such a complex structure. Results from our studies lead to a well-supported hypothesis of the ESAT-6 oligomerization as well as the identification of essential residues in stabilizing the ESAT-6 oligomer which provide useful insights for future drug design targeting tuberculosis. The approach in this research can also be used to model and study other cross-membrane complex structures.

Electrostatics of Prokaryotic Ribosome and Its Biological Implication

Ribosomes are essential machines for protein synthesis in cells. Their structures are very complex but conserved in different species. Because most parts of a ribosome are composed of negatively charged RNAs, its electrostatics should play a fundamental role in the realization of its functions. However, a complete picture of the electrostatics of ribosomes is still absent at present. Here, assisted by the latest version of DelPhi (version 8.4), we illustrate a picture of the electrostatics of a prokaryotic ribosome as well as its molecular chaperones. The revealed electrostatics features are consistent with available experimental data as well as the functions of the ribosome and its molecular chaperones and provides a basis for further studying the mechanism underlying these functions.

Using computational approaches to study dengue virus capsid assembly

Dengue viral capsid plays a significant role in viral life cycle of dengue, especially in vial genome protection and virus-cell fusion. Revealing mechanisms of the viral capsid protein assembly may lead to the discovery of anti-viral drugs that inhibit the assembly of the viral capsid. The E and M-proteins are arranged into heterotetramers, which consists of two copies of E and M-protein. The heterotetramers are assembled into a highly ordered capsid. While many investigations of the interactions between E and M-proteins have been performed, there are very few studies on the interactions between the heterotetramers and their roles in capsid assembly. Utilizing a series of computational approaches, this study focuses on the assembly mechanism of the heterotetramers. Our electrostatic analyses lead to the identification of four binding modes between each two dengue heterotetramers that repeat periodically throughout the virus capsid. Among these four binding modes, heterotetramers in binding modes I, II and IV are attractive. But in the binding mode III the heterotetramers repel each other, making mode III a suitable target for drug design. Furthermore, MD simulations were performed following by salt bridges analysis. This study demonstrates that using computational approaches is a promising direction to study the dengue virus.

DelPhi Suite: New Developments and Review of Functionalities

Electrostatic potential, energies, and forces affect virtually any process in molecular biology, however, computing these quantities is a difficult task due to irregularly shaped macromolecules and the presence of water. Here, we report a new edition of the popular software package DelPhi along with describing its functionalities. The new DelPhi is a C++ object-oriented package supporting various levels of multiprocessing and memory distribution. It is demonstrated that multiprocessing results in significant improvement of computational time. Furthermore, for computations requiring large grid size (large macromolecular assemblages), the approach of memory distribution is shown to reduce the requirement of RAM and thus permitting large-scale modeling to be done on Linux clusters with moderate architecture. The new release comes with new features, whose functionalities and applications are described as well. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

Computational chemistry methods to investigate the effects caused by DNA variants linked with disease

Computational chemistry offers variety of tools to study properties of biological macromolecules. These tools vary in terms of levels of details from quantum mechanical treatment to numerous macroscopic approaches. Here, we provide a review of computational chemistry algorithms and tools for modeling the effects of genetic variations and their association with diseases. Particular emphasis is given on modeling the effects of missense mutations on stability, conformational dynamics, binding, hydrogen bond network, salt bridges, and pH-dependent properties of the corresponding macromolecules. It is outlined that the disease may be caused by alteration of one or several of above-mentioned biophysical characteristics, and a successful prediction of pathogenicity requires detailed analysis of how the alterations affect the function of involved macromolecules. The review provides a short list of most commonly used algorithms to predict the molecular effects of mutations as well.

Modeling Electrostatic Force in Protein-Protein Recognition

Electrostatic interactions are important for understanding molecular interactions, since they are long-range interactions and can guide binding partners to their correct binding positions. To investigate the role of electrostatic forces in molecular recognition, we calculated electrostatic forces between binding partners separated at various distances. The investigation was done on a large set of 275 protein complexes using recently developed DelPhiForce tool and in parallel, evaluating the total electrostatic force via electrostatic association energy. To accomplish the goal, we developed a method to find an appropriate direction to move one chain of protein complex away from its bound position and then calculate the corresponding electrostatic force as a function of separation distance. It is demonstrated that at large distances between the partners, the electrostatic force (magnitude and direction) is consistent among the protocols used and the main factors contributing to it are the net charge of the partners and their interfaces. However, at short distances, where partners form specific pair-wise interactions or de-solvation penalty becomes significant, the outcome depends on the precise balance of these factors. Based on the electrostatic force profile (force as a function of distance), we group the cases into four distinctive categories, among which the most intriguing is the case termed "soft landing." In this case, the electrostatic force at large distances is favorable assisting the partners to come together, while at short distance it opposes binding, and thus slows down the approach of the partners toward their physical binding.

DFMD: Fast and Effective DelPhiForce Steered Molecular Dynamics Approach to Model Ligand Approach Toward a Receptor: Application to Spermine Synthase Enzyme

Here we report a novel approach, the DelPhiForce Molecular Dynamics (DFMD) method, for steered molecular dynamics simulations to model receptor-ligand association involving charged species. The main purpose of developing DFMD is to simulate ligand's trajectory toward the receptor and thus to predict the "entrance" of the binding pocket and conformational changes associated with the binding. We demonstrate that the DFMD is superior compared with molecular dynamics simulations applying standard cut-offs, provides correct binding forces, allows for modeling the ligand approach at long distances and thus guides the ligand toward the correct binding spot, and it is very fast (frequently the binding is completed in <1 ns). The DFMD is applied to model the binding of two ligands to a receptor (spermine synthase) and it is demonstrated that it guides the ligands toward the corresponding pockets despite of the initial ligand's position with respect to the receptor. Predicted conformational changes and the order of ligand binding are experimentally verified.

The Roles of Electrostatic Interactions in Capsid Assembly Mechanisms of Giant Viruses

In the last three decades, many giant DNA viruses have been discovered. Giant viruses present a unique and essential research frontier for studies of self-assembly and regulation of supramolecular assemblies. The question on how these giant DNA viruses assemble thousands of proteins so accurately to form their protein shells, the capsids, remains largely unanswered. Revealing the mechanisms of giant virus assembly will help to discover the mysteries of many self-assembly biology problems. Paramecium bursaria Chlorella virus-1 (PBCV-1) is one of the most intensively studied giant viruses. Here, we implemented a multi-scale approach to investigate the interactions among PBCV-1 capsid building units called capsomers. Three binding modes with different strengths are found between capsomers around the relatively flat area of the virion surface at the icosahedral 2-fold axis. Furthermore, a capsomer structure manipulation package is developed to simulate the capsid assembly process. Using these tools, binding forces among capsomers were investigated and binding funnels were observed that were consistent with the final assembled capsid. In addition, total binding free energies of each binding mode were calculated. The results helped to explain previous experimental observations. Results and tools generated in this work established an initial computational approach to answer current unresolved questions regarding giant virus assembly mechanisms. Results will pave the way for studying more complicated process in other biomolecular structures.

E-hooks provide guidance and a soft landing for the microtubule binding domain of dynein

Macromolecular binding is a complex process that involves sensing and approaching the binding partner, adopting the proper orientation, and performing the physical binding. We computationally investigated the role of E-hooks, which are intrinsically disordered regions (IDRs) at the C-terminus of tubulin, on dynein microtubule binding domain (MTBD) binding to the microtubule as a function of the distance between the MTBD and its binding site on the microtubule. Our results demonstrated that the contacts between E-hooks and the MTBD are dynamical; multiple negatively charted patches of amino acids on the E-hooks grab and release the same positively charged patches on the MTBD as it approaches the microtubule. Even when the distance between the MTBD and the microtubule was greater than the E-hook length, the E-hooks sensed and guided MTBD via long-range electrostatic interactions in our simulations. Moreover, we found that E-hooks exerted electrostatic forces on the MTBD that were distance dependent; the force pulls the MTBD toward the microtubule at long distances but opposes binding at short distances. This mechanism provides a "soft-landing" for the MTBD as it binds to the microtubule. Finally, our analysis of the conformational states of E-hooks in presence and absence of the MTBD indicates that the binding process is a mixture of the induced-fit and lock-and-key macromolecular binding hypotheses. Overall, this novel binding mechanism is termed "guided-soft-binding" and could have broad-reaching impacts on the understanding of how IDRs dock to structured proteins.

DelPhiForce web server: electrostatic forces and energy calculations and visualization

Summary

Electrostatic force is an essential component of the total force acting between atoms and macromolecules. Therefore, accurate calculations of electrostatic forces are crucial for revealing the mechanisms of many biological processes. We developed a DelPhiForce web server to calculate and visualize the electrostatic forces at molecular level. DelPhiForce web server enables modeling of electrostatic forces on individual atoms, residues, domains and molecules, and generates an output that can be visualized by VMD software. Here we demonstrate the usage of the server for various biological problems including protein-cofactor, domain-domain, protein-protein, protein-DNA and protein-RNA interactions.

Availability and implementation

The DelPhiForce web server is available at: http://compbio.clemson.edu/delphi-force.

Contact

delphi@clemson.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Computational Approaches to Prioritize Cancer Driver Missense Mutations

Cancer is a complex disease that is driven by genetic alterations. There has been a rapid development of genome-wide techniques during the last decade along with a significant lowering of the cost of gene sequencing, which has generated widely available cancer genomic data. However, the interpretation of genomic data and the prediction of the association of genetic variations with cancer and disease phenotypes still requires significant improvement. Missense mutations, which can render proteins non-functional and provide a selective growth advantage to cancer cells, are frequently detected in cancer. Effects caused by missense mutations can be pinpointed by in silico modeling, which makes it more feasible to find a treatment and reverse the effect. Specific human phenotypes are largely determined by stability, activity, and interactions between proteins and other biomolecules that work together to execute specific cellular functions. Therefore, analysis of missense mutations’ effects on proteins and their complexes would provide important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of cancer progression and facilitating treatment and prevention. Herein, we summarize the major computational approaches and tools that provide not only the classification of missense mutations as cancer drivers or passengers but also the molecular mechanisms induced by driver mutations. This review focuses on the discussion of annotation and prediction methods based on structural and biophysical data, analysis of somatic cancer missense mutations in 3D structures of proteins and their complexes, predictions of the effects of missense mutations on protein stability, protein-protein and protein-nucleic acid interactions, and assessment of conformational changes in protein conformations induced by mutations.

The capricious electrostatic force: Revealing the signaling pathway in integrin α2-I domain

Integrins are cellular adhesion proteins located on cell surface. They are known to have open and closed conformations that correspond to high and low binding affinity to ligands, respectively. Integrin [Formula: see text]2 binds to the ligands via the ligand binding domain, [Formula: see text]2-I domain, which also has open and closed conformations. Experimentally, the closed to open conformation change is shown to be triggered by pulling the C-terminal away from the ligand binding site, but how the signal propagates from the distant C-terminal to the binding site is unknown. To explain the mechanisms of the conformation change, we built models of the [Formula: see text]2-I domain open and closed conformations in ligand free and ligand bound states, respectively. We found that the signaling pathway consists of F313-I280-V252 residues that connect the C-terminal and the ligand binding site. The pathway is highly conserved as revealed by a protein sequence analysis among 55 species. Furthermore, MM/PBSA energy calculations on the stabilities and ligand binding affinities of the closed and open conformations are consistent with experimental measurements. The open conformation is more favorable for ligand binding, and the closed conformation is more stable in unbound state. Energy analysis also revealed the “hot spots” for ligand binding, and most residues that contribute strongly to ligand binding free energy are highly conserved in evolution. In addition, the electrostatic analysis showed that the closed conformation has stronger long-range electrostatic attraction to the ligand compared with the open conformation. The difference is caused by the rearrangement of several charged residues during the binding. These observations make us suggest that the integrin [Formula: see text]2-I domain binding process involves the two-step “dock-lock” mechanism. The closed conformation first attracts the ligand from a long distance and afterwards, the open conformation locks the ligand at the binding site with high binding affinity.

Electrostatic recognition in substrate binding to serine proteases

Serine proteases of the Chymotrypsin family are structurally very similar but have very different substrate preferences. This study investigates a set of 9 different proteases of this family comprising proteases that prefer substrates containing positively charged amino acids, negatively charged amino acids, and uncharged amino acids with varying degree of specificity. Here, we show that differences in electrostatic substrate preferences can be predicted reliably by electrostatic molecular interaction fields employing customized GRID probes. Thus, we are able to directly link protease structures to their electrostatic substrate preferences. Additionally, we present a new metric that measures similarities in substrate preferences focusing only on electrostatics. It efficiently compares these electrostatic substrate preferences between different proteases. This new metric can be interpreted as the electrostatic part of our previously developed substrate similarity metric. Consequently, we suggest, that substrate recognition in terms of electrostatics and shape complementarity are rather orthogonal aspects of substrate recognition. This is in line with a 2‐step mechanism of protein‐protein recognition suggested in the literature.

Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents

Conventional modeling techniques to model macromolecular solvation and its effect on binding in the framework of Poisson-Boltzmann based implicit solvent models make use of a geometrically defined surface to depict the separation of macromolecular interior (low dielectric constant) from the solvent phase (high dielectric constant). Though this simplification saves time and computational resources without significantly compromising the accuracy of free energy calculations, it bypasses some of the key physio-chemical properties of the solute-solvent interface, e.g., the altered flexibility of water molecules and that of side chains at the interface, which results in dielectric properties different from both bulk water and macromolecular interior, respectively. Here we present a Gaussian-based smooth dielectric model, an inhomogeneous dielectric distribution model that mimics the effect of macromolecular flexibility and captures the altered properties of surface bound water molecules. Thus, the model delivers a smooth transition of dielectric properties from the macromolecular interior to the solvent phase, eliminating any unphysical surface separating the two phases. Using various examples of macromolecular binding, we demonstrate its utility and illustrate the comparison with the conventional 2-dielectric model. We also showcase some additional abilities of this model, viz. to account for the effect of electrolytes in the solution and to render the distribution profile of water across a lipid membrane.

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

Forces and Disease: Electrostatic force differences caused by mutations in kinesin motor domains can distinguish between disease-causing and non-disease-causing mutations

The ability to predict if a given mutation is disease-causing or not has enormous potential to impact human health. Typically, these predictions are made by assessing the effects of mutation on macromolecular stability and amino acid conservation. Here we report a novel feature: the electrostatic component of the force acting between a kinesin motor domain and tubulin. We demonstrate that changes in the electrostatic component of the binding force are able to discriminate between disease-causing and non-disease-causing mutations found in human kinesin motor domains using the receiver operating characteristic (ROC). Because diseases may originate from multiple effects not related to kinesin-microtubule binding, the prediction rate of 0.843 area under the ROC plot due to the change in magnitude of the electrostatic force alone is remarkable. These results reflect the dependence of kinesin’s function on motility along the microtubule, which suggests a precise balance of microtubule binding forces is required.