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

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.

Determination of Multidirectional Pathways for Ligand Release from the Receptor: A New Approach Based on Differential Evolution

Steered molecular dynamics (SMD) simulation is a powerful method in computer-aided drug design as it can be used to access the relative binding affinity with high precision but with low computational cost. The success of SMD depends on the choice of the direction along which the ligand is pulled from the receptor-binding site. In most simulations, the unidirectional pathway was used, but in some cases, this choice resulted in the ligand colliding with the complex surface of the exit tunnel. To overcome this difficulty, several variants of SMD with multidirectional pulling have been proposed, but they are not completely devoid of disadvantages. Here, we have proposed to determine the direction of pulling with a simple scoring function that minimizes the receptor–ligand interaction, and an optimization algorithm called differential evolution is used for energy minimization. The effectiveness of our protocol was demonstrated by finding expulsion pathways of Huperzine A and camphor from the binding site of Torpedo California acetylcholinesterase and P450cam proteins, respectively, and comparing them with the previous results obtained using memetic sampling and random acceleration molecular dynamics. In addition, by applying this protocol to a set of ligands bound with LSD1 (lysine specific demethylase 1), we obtained a much higher correlation between the work of pulling force and experimental data on the inhibition constant IC50 compared to that obtained using the unidirectional approach based on minimal steric hindrance.

Long-range electrostatic interactions significantly modulate the affinity of dynein for microtubules

The dynein family of microtubule minus-end-directed motor proteins drives diverse functions in eukaryotic cells, including cell division, intracellular transport, and flagellar beating. Motor protein processivity, which characterizes how far a motor walks before detaching from its filament, depends on the interaction between its microtubule-binding domain (MTBD) and the microtubule. Dynein's MTBD switches between high- and low-binding affinity states as it steps. Significant structural and functional data show that specific salt bridges within the MTBD and between the MTBD and the microtubule govern these affinity state shifts. However, recent computational work suggests that nonspecific, long-range electrostatic interactions between the MTBD and the microtubule may also play an important role in the processivity of dynein. To investigate this hypothesis, we mutated negatively charged amino acids remote from the dynein MTBD-microtubule-binding interface to neutral residues and measured the binding affinity using microscale thermophoresis and optical tweezers. We found a significant increase in the binding affinity of the mutated MTBDs for microtubules. Furthermore, we found that charge screening by free ions in solution differentially affected the binding and unbinding rates of MTBDs to microtubules. Together, these results demonstrate a significant role for long-range electrostatic interactions in regulating dynein-microtubule affinity. Moreover, these results provide insight into the principles that potentially underlie the biophysical differences between molecular motors with various processivities and protein-protein interactions more generally.

Screening Balanites aegyptiaca for inhibitors against putative drug targets in Microsporum gypseum - Subtractive proteome, docking and simulation approach

Microsporum gypseum is a keratinophilic fungi grouped under dermatophytes infecting skin, hair and nail portions in human and animals causing tinea corporis, tinea facei and tinea capitis. As both human and fungi are eukaryotes, the available drugs for treating dermatophytes produce some side effects due to drug interaction with human also. Apart from this, the gut microbiota has a very big role in the health of human which should not be affected by the drugs. Hence this study focused on finding a target which is unique and essential to M. gypseum and non-homologous to human and gut microbiota, non-homologous to human domain architecture, highly interacting with other proteins, sub-cellular localization of proteins and non-druggability analysis of the targets using subtractive proteomics approach which resulted with 3 novel drug targets from M. gypseum which were modeled using I-TASSER, refined by ModRefiner and validated by PROCHECK. Further these targets were docked with compounds identified through LC-MS of fractioned methanol extract of B. aegyptiaca fruit pulp using Glide module and the stability of the docked complex was analyzed by molecular dynamics simulation using Desmond module of Schrodinger. Cyanidin-3-O-rhamnoside had better interaction with all the targets and Taurocholic acid had better result with ECCP which suggests the multi-targeting potency of these two compounds against M. gypseum which has to be confirmed by in vitro and in vivo studies.

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.

Structural And Computational Perspectives of Selectively Targeting Mutant Proteins

Diseases are often caused by mutant proteins. Many drugs have limited effectiveness and/or toxic side effects because of a failure to selectively target the disease-causing mutant variant, rather than the functional wild type protein. Otherwise, the drugs may even target different proteins with similar structural features. Designing drugs that successfully target mutant proteins selectively represents a major challenge. Decades of cancer research have led to an abundance of potential therapeutic targets, often touted to be "master regulators". For many of these proteins, there are no FDA-approved drugs available; for others, off-target effects result in dose-limiting toxicity. Cancer-related proteins are an excellent medium to carry the story of mutant-specific targeting, as the disease is both initiated and sustained by mutant proteins; furthermore, current chemotherapies generally fail at adequate selective distinction. This review discusses some of the challenges associated with selective targeting from a structural biology perspective, as well as some of the developments in algorithm approach and computational workflow that can be applied to address those issues. One of the most widely researched proteins in cancer biology is p53, a tumor suppressor. Here, p53 is discussed as a specific example of a challenging target, with contemporary drugs and methodologies used as examples of burgeoning successes. The oncogene KRAS, which has been described as "undruggable", is another extensively investigated protein in cancer biology. This review also examines KRAS to exemplify progress made towards selective targeting of diseasecausing mutant proteins. Finally, possible future directions relevant to the topic are discussed.