Molecular Docking and Molecular Dynamics Simulations in Related to Leishmania donovani: An Update and Literature Review

Abstract

Leishmaniasis, a disease caused by Leishmania parasites and transmitted via sandflies, presents in two main forms: cutaneous and visceral, the latter being more severe. With 0.7 to 1 million new cases each year, primarily in Brazil, diagnosing remains challenging due to diverse disease manifestations. Traditionally, the identification of Leishmania species is inferred from clinical and epidemiological data. Advances in disease management depend on technological progress and the improvement of parasite identification programs. Current treatments, despite the high incidence, show limited efficacy due to factors like cost, toxicity, and lengthy regimens causing poor adherence and resistance development. Diagnostic techniques have improved but a significant gap remains between scientific progress and application in endemic areas. Complete genomic sequence knowledge of Leishmania allows for the identification of therapeutic targets. With the aid of computational tools, testing, searching, and detecting affinity in molecular docking are optimized, and strategies that assess advantages among different options are developed. The review focuses on the use of molecular docking and molecular dynamics (MD) simulation for drug development. It also discusses the limitations and advancements of current treatments, emphasizing the importance of new techniques in improving disease management.

1. Introduction

Leishmaniasis is a disease caused by different species of parasites of the genus Leishmania, with female sandflies as the transmission vector [1]. Various species of Leishmania produce different clinical manifestations, with two clinically notable forms: cutaneous and visceral. The visceral form is characterized by systemic involvement and greater severity [2].

Encompassing over 60 countries where it is considered an endemic disease, it is estimated that there are between 0.7 and 1 million new cases of leishmaniasis per year, with a global incidence of visceral leishmaniasis (VL) ranging from 50,000 to 90,000 reported in 2017. Brazil has the highest number of reported cases in Latin America, accounting for over 99% of the cases [3].

The clinical diagnosis of the disease is challenging due to the various manifestations and lesions that may appear depending on the spectrum and type of the disease [4]. Therefore, currently, the identification of different Leishmania species is mostly inferred from the clinical and epidemiological background [5].

Progress in the diagnosis and treatment of Leishmaniosis relies directly on the development of new technological tools and advancements that enable the creation and improvement of parasite identification and typing programs [6]. Despite the millions of cases detected each year, current treatments do not demonstrate sufficient efficacy to combat the disease.

A crucial point in laboratory diagnosis is the detection of the etiological agent and the identification of the species to which it belongs [7]. Despite the significant advances in diagnostic techniques, there are still significant differences between scientific advancements and diagnostic processes in Leishmania infections in endemic areas [4,8]. Therefore, the great challenge for researchers and clinicians is to bridge this gap and facilitate access to and understanding of this knowledge in the most affected areas [9].

Since the invention of techniques such as DNA amplification via PCR, there has been continuous improvement in the speed and effectiveness of Leishmania identification in biological samples [10].

Consequently, research and the search for other versatile and cost-effective methods for detecting pathogenic Leishmania species should be encouraged, allowing for a better understanding of genomic structure, genetic activity, protein expression, and metabolic changes that translate into improved diagnostic and therapeutic processes.

Thus, for example, knowledge of the complete genomic sequence of Leishmania increases the possibilities of identifying therapeutic targets [11].

The use of new computational tools allows for the identification of therapeutic targets and the development of strategies that assess advantages among the different available options, thus reducing both time and costs [12].

Different techniques such as virtual screening or molecular dynamics focus on optimizing processes for testing, searching, and detecting affinity in molecular docking; simulating biological systems; and even analyzing internal motions of proteins [13].

Molecular dynamics simulation is a computational method that provides information about the behavior of any molecular system by integrating Newton’s laws of motion, thus refining the protein structure of binding sites and providing relevant and useful information for understanding and comparing different interactions [13].

The use of techniques such as molecular docking and molecular dynamics simulation allows the development of more effective therapeutic strategies with greater advantages against Leishmania infection. Molecular dynamics simulations provide knowledge about the binding modes of inhibitors, identifying better strategies and more suitable drugs with fewer disadvantages and side effects for the treatment of leishmaniasis [13].

The goal of this review is to describe the foundations, key features, and significance of computational tools in the field of leishmaniasis. By initially providing a description of protein structure and function, it allows for an understanding of the development and applicability of techniques and algorithms, highlighting recent advancements, use cases, and limitations.

Our review primarily focuses on describing the necessary concepts of molecular docking and molecular dynamics (MD) simulation, highlighting their importance in the development of new drugs, as well as the potential and applicability of these techniques in current clinical settings in the field of leishmaniasis. We emphasize the different techniques and algorithms used for this purpose.

Furthermore, we also highlight the fact that it has become a significant health problem, discussing the advances and limitations of current treatment and the development of new techniques such as molecular docking and molecular dynamics (MD) simulation, which have become fundamental tools for a disease whose diagnosis has traditionally relied on parasite isolation, visualization, and cultivation in infected tissue.

2. Basic Concepts

2.1. Introduction to Protein Structure and Function

The fundamental macromolecules known as proteins are necessary for many biological functions [14]. For researchers to fully understand the molecular pathways underlying diseases like leishmaniasis, which is caused by protozoan parasites such as Leishmania donovani, a thorough understanding of their structure and function is essential. In this paper, we give a brief overview of protein structure and function in simulations of molecular dynamics and docking [15].

Amino acids are the building blocks of proteins, and peptide bonds bind them together. The primary structure of a protein is determined by its linear amino acid sequence [16]. The secondary, tertiary, and quaternary structures of the polypeptide chain are formed during folding, which also gives rise to the chain’s three-dimensional structure [17].

The polypeptide chain’s regional folding patterns, which largely involve α-helices and β-sheets, are called the secondary structure. Between the atoms in the backbone, hydrogen bonds hold these structural motifs in place [18]. The tertiary structure depicts the protein’s overall three-dimensional arrangement, including the spatial organization of its folds, loops, and secondary structure components. The stability of the tertiary structure is influenced by a number of non-covalent interactions, including van der Waals contacts, electrostatic forces, and hydrophobic interactions [19].

When proteins are made up of many subunits, the quaternary structure is important. Functional complexes are produced as a result of interactions between these subunits. Protein function and structure are closely related [20]. In addition to enzyme catalysis, signal transduction, molecular recognition, and structural support, proteins also perform a wide variety of other tasks [21].

The investigation of protein–ligand interactions and dynamic behavior makes use of the potent computational tools of molecular docking and molecular dynamics simulations. The binding style and affinities of a ligand to a protein are predicted by molecular docking [22]. It entails looking for energetically advantageous ligand conformations within the binding site of the protein. In order to rank candidate ligands and determine the binding free energy, various scoring functions are applied [23].

On the other hand, molecular dynamics simulations use conventional Newtonian dynamics methods to model the mobility and behavior of atoms over time. Investigating the dynamic behavior of proteins and their interactions with ligands involves solving the equations of motion [24]. Insights into protein flexibility, conformational changes, and stability, which are essential for comprehending their function and developing therapeutic interventions, can be obtained through simulations.

In the case of Leishmania donovani, protein–ligand interactions crucial to the parasite’s survival and growth have been examined using molecular docking and molecular dynamics simulations [25]. These computational techniques have contributed to the discovery of possible therapeutic targets, the creation of fresh inhibitors, and the enhancement of currently available medications. They have also provided important information for drug discovery efforts by shedding light on the dynamics and structural changes of crucial proteins [26].

2.2. Description of Molecular Docking

A computer method that is essential in the development of new drugs is molecular docking. Through an examination of their potential conformations and interactions, it entails predicting the optimal orientation and binding affinity of a tiny molecule (ligand) to a target protein (receptor) [27]. The parasite Leishmania donovani causes the neglected tropical disease VL, and molecular docking has been proven to be a useful technique for locating prospective therapeutic targets, creating new medications, and improving already-effective therapies [28].

2.2.1. Primary Ideas

The fundamental concepts of molecular recognition and binding interactions serve as the foundation for molecular docking. Predicting a ligand’s binding mechanism in a receptor’s active region and calculating the binding affinity are the main goals. Important ideas include the following:

• Molecular Representation: Ligands and receptors are frequently depicted as three-dimensional structures with atomic characteristics, such as charge and atom kinds, that are connected to them [29].

• Search Algorithms: To explore the conformational space of the ligand and receptor and find the best binding mode, docking algorithms use a variety of search methodologies. To assess the energy or fitness of each conformation, these algorithms make use of scoring functions [30].

• Scoring Functions: By taking into account variables including van der Waals interactions, electrostatic interactions, hydrogen bonds, and solvation effects, scoring functions calculate the binding affinity between a ligand and receptor [31]. The scoring function assigns a numerical value to binding affinity and rates various ligand postures.

2.2.2. Used Techniques and Algorithms

For molecular docking, numerous techniques and algorithms have been developed. Several of the commonly employed methods include the following:

• Structure-based Docking: This technique makes use of the ligand and receptor’s three-dimensional structures [30]. Using search algorithms like Genetic Algorithms, Monte Carlo techniques, or molecular dynamics simulations, it investigates the conformational space of the ligand and receptor to identify the most advantageous binding mode [22].

• Ligand-based Docking: When the receptor structure is unknown, ligand-based docking techniques are applied. These techniques are dependent on understanding the known ligands that bind to the receptor [32]. To find candidate ligands with related features, similarity-based methods like virtual screening and pharmacophore modeling are applied [33].

2.2.3. Applications in the Field of Leishmaniasis

Molecular docking has significantly aided efforts to find new treatments for leishmaniasis. A few noteworthy applications are as follows:

• Target Identification: In order to discover possible therapeutic targets in Leishmania donovani, docking experiments have been used. Researchers can find important proteins involved in the parasite’s survival and growth by examining the interactions between known ligands and target proteins.

• Virtual Screening: Docking-based virtual screening makes it possible to quickly screen sizable chemical libraries for prospective Leishmania donovani therapeutic candidates. Researchers might give higher priority to substances with strong binding affinity for additional experimental validation by docking small molecules against target proteins.

• Drug Design and Optimization: Docking is essential to the logical development and refinement of medications to combat Leishmania donovani. To increase the potency, selectivity, and pharmacokinetic features of lead drugs, researchers can change and enhance the binding relationships between ligands and target proteins.

3. Molecular Docking Applications in Leishmaniasis

3.1. Selection of Therapeutic Targets

Molecular docking assists in the selection of suitable therapeutic targets in the Leishmania parasite. By analyzing the protein structures and their interactions with ligands, docking simulations can identify essential proteins involved in the parasite’s survival and virulence. This information aids in the prioritization of potential targets for drug intervention.

Molecular docking has played a crucial role in the identification and selection of therapeutic targets for Leishmaniasis [34]. This approach enables researchers to predict the binding affinity and mode of interaction between small molecules and specific target proteins within the Leishmania parasite. By understanding the molecular interactions, researchers can prioritize and select potential therapeutic targets for further investigation [35]. The selection of therapeutic targets in Leishmaniasis often focuses on proteins that are vital for the parasite’s survival, growth, or virulence. These proteins may include enzymes involved in metabolic pathways, transporters, surface receptors, or signaling proteins [36]. By targeting these essential proteins, it becomes possible to disrupt crucial biological processes in the parasite, leading to its inhibition or elimination. Molecular docking studies aid in the identification of potential target proteins by screening large databases of protein structures. Computational tools can analyze the binding sites of these proteins and predict their suitability as drug targets based on factors such as druggability, accessibility, and essentiality for the parasite’s survival. The docking simulations can evaluate the binding affinity and specificity of small molecules toward these target proteins [37]. Additionally, molecular docking helps prioritize potential drug candidates by assessing their binding affinities and interactions with the selected target proteins. Compounds with favorable docking scores and strong binding interactions can be considered promising leads for further experimental validation [38].

It is worth noting that the selection of therapeutic targets in Leishmaniasis is a complex process that requires a comprehensive understanding of the parasite’s biology and pathogenesis. The integration of experimental data, bioinformatics analysis, and structural biology techniques can complement molecular docking studies and provide a more holistic approach to target selection. In conclusion, molecular docking plays a significant role in the selection of therapeutic targets for Leishmaniasis. By identifying and prioritizing proteins that are crucial for the parasite’s survival, researchers can focus their efforts on developing effective treatments to combat this disease [39].

3.2. Identification of Inhibitors and Candidate Molecules

Molecular docking enables the screening and identification of inhibitors and candidate molecules that can interact with the selected targets [40]. Virtual docking experiments simulate the binding interactions between small molecule compounds or potential drugs and the target proteins. By analyzing the binding affinities and interaction patterns, researchers can identify compounds with high binding affinity and favorable drug-like properties. These identified molecules can serve as starting points for further development and optimization into potential drugs for leishmaniasis treatment [41].

One of the primary goals of molecular docking in Leishmaniasis research is to identify small molecules that can act as inhibitors of essential proteins or enzymes involved in the parasite’s survival and replication [41]. By targeting these proteins, researchers aim to disrupt critical biological processes and inhibit the growth and proliferation of the parasite. In the initial stage of the molecular docking process, researchers build a library of small molecules or obtain one from existing databases [42]. These compounds can be natural products, synthetic chemicals, or repurposed drugs. The compounds are virtually screened against a selected target protein, and docking simulations are performed to predict their binding affinity and mode of interaction. The docking scores obtained from these simulations help prioritize and rank the potential inhibitors. Compounds with favorable docking scores and strong interactions with the target protein are considered promising candidates for further evaluation. These candidate molecules can then be subjected to experimental validation, such as in vitro assays or animal models, to assess their efficacy and specificity against Leishmania parasites [43].

Moreover, molecular docking can aid in the optimization of lead compounds by exploring modifications and predicting their binding affinities. This process, known as structure-based drug design, allows researchers to make chemical modifications to the identified hit compounds, enhancing their potency, selectivity, and pharmacokinetic properties. Iterative docking simulations can guide the design and synthesis of analogs or derivatives with improved inhibitory activity [44].

In recent years, advances in computational methods, increased availability of protein structures, and compound libraries have contributed to the identification of potential inhibitors and candidate molecules against Leishmaniasis. Molecular docking has been proven to be a valuable approach for the initial screening and prioritization of compounds, accelerating the drug discovery process for this disease [44].

3.3. Evaluation of the Activity of Existing Drugs

Molecular docking also plays a vital role in evaluating the activity of existing drugs against leishmaniasis. By docking approved drugs or known compounds against specific target proteins, researchers can assess their binding affinities and predict their efficacy in inhibiting the target’s function [45]. This approach provides valuable insights into repurposing existing drugs for leishmaniasis treatment, potentially accelerating the drug discovery process by identifying candidates that can be readily tested in preclinical and clinical studies [46].

The evaluation of existing drugs in Leishmaniasis through molecular docking involves the following steps:

• Selection of Target Proteins: Specific proteins that play crucial roles in the Leishmania parasite’s lifecycle or pathogenesis are identified as potential targets. These proteins may include enzymes, transporters, receptors, or other key molecules involved in essential biological processes [47].

• Building Compound Libraries: A library of known drugs is compiled, including approved drugs, experimental compounds, or compounds from existing databases. This diverse collection of molecules serves as a resource for virtual screening and docking simulations [47].

• Virtual Screening: Virtual screening involves the computational docking of the compounds from the library onto the target proteins. The docking algorithms predict the binding orientations and affinities of the drugs within the protein’s active site, allowing for the identification of potential drug–protein interactions [47].

• Binding Affinity Analysis: The docking scores obtained from the simulations provide a measure of the binding affinity between the drugs and the target proteins. Compounds with high docking scores are considered to have strong binding potential and are further investigated for their activity against Leishmania parasites [47].

• Interaction Analysis: The docking results are analyzed to understand the specific interactions between the drugs and the target proteins. This analysis helps to identify key molecular interactions, such as hydrogen bonding, hydrophobic interactions, or electrostatic interactions, that contribute to the binding and potential inhibitory activity [47].

• Experimental Validation: Promising drug candidates identified through molecular docking are subjected to experimental validation to assess their activity against Leishmania parasites. In vitro assays, such as enzyme inhibition assays or parasite growth inhibition assays, help determine the efficacy and selectivity of the drugs. Animal models may also be used to evaluate the in vivo activity and toxicity profiles of the drug candidates [47].

• Optimization and Lead Refinement: Based on the results of molecular docking and experimental validation, lead compounds can be optimized through structure-based drug design approaches. Iterative docking simulations and computational chemistry techniques assist in modifying the chemical structures of the drug candidates to improve their potency, selectivity, and pharmacokinetic properties [47].

By employing molecular docking in the evaluation of existing drugs, researchers can expedite the drug discovery process for Leishmaniasis. This approach offers the advantage of repurposing known drugs, which have already undergone safety and toxicity assessments, potentially reducing the time and cost required for drug development [48].

Overall, molecular docking is a valuable tool in the field of leishmaniasis research and drug discovery. It aids in the selection of therapeutic targets, the identification of inhibitors and candidate molecules, and the evaluation of existing drugs. By leveraging the computational power of molecular docking, researchers can expedite the search for effective treatments against leishmaniasis, potentially leading to the development of novel therapeutics and improved outcomes for patients affected by this global health problem [48].

4. Applications of Molecular Dynamics Simulations in Leishmaniasis

Molecular dynamics (MD) simulations are computational techniques used to study the behavior and dynamics of biological molecules at an atomic level [49]. In the context of leishmaniasis, MD simulations have several applications that aid in understanding protein–ligand interactions, stability, conformational studies, and the analysis of conformational and dynamic changes in proteins. Below is an elaboration on these applications.

4.1. Characterization of Protein–Ligand Interactions

MD simulations provide insights into the binding mechanisms and dynamics of protein–ligand interactions in leishmaniasis. By simulating the movement of the protein and ligand over time, researchers can observe the formation and breaking of hydrogen bonds, van der Waals interactions, and other non-covalent interactions that contribute to binding [50]. This information helps in understanding the strength and stability of the protein–ligand complex and guides the design of more potent and selective inhibitors.

4.2. Stability and Conformational Studies

MD simulations are valuable for investigating the stability and conformational changes of proteins involved in leishmaniasis. By subjecting the protein to a simulated environment, researchers can observe its behavior, folding, and unfolding over time [51]. This allows the assessment of stability, identification of key regions responsible for structural changes, and exploration of the effects of mutations or ligand binding on protein dynamics. Such insights are crucial for understanding the functional properties of proteins and can aid in drug design and optimization [52,53].

4.3. Analysis of Conformational and Dynamic Changes in Proteins

MD simulations provide a detailed view of the conformational and dynamic changes that occur in proteins relevant to leishmaniasis. By analyzing trajectories generated from MD simulations, researchers can identify important structural transitions, such as loop movements, domain motions, or protein unfolding [53,54]. This knowledge can uncover key functional regions or reveal allosteric sites that can be targeted for drug intervention. Additionally, comparing MD simulations of different protein states can shed light on the conformational changes associated with protein function and provide insights into potential drug-binding sites [55].

5. Recent Advances and Case Studies

5.1. Review of Recent Studies Using Molecular Docking and Molecular Dynamics Simulations in Leishmaniasis

In recent years, there have been notable advancements in the application of molecular docking and molecular dynamics (MD) simulations in the field of leishmaniasis research. Several studies have utilized these computational techniques to investigate protein–ligand interactions, protein dynamics, and drug discovery [56]. These studies have contributed valuable insights into the molecular mechanisms underlying leishmaniasis and have aided in the identification of potential therapeutic targets and drug candidates.

One recent study utilized molecular docking to screen a library of natural compounds against leishmanial proteins [57]. The results identified several promising compounds with high binding affinities, suggesting their potential as antileishmanial agents. Another study employed MD simulations to investigate the stability and dynamics of drug-resistant forms of leishmanial proteins. The simulations revealed conformational changes associated with resistance, providing crucial information for designing new inhibitors to overcome drug resistance [58].

Computational research is currently underway to identify potential small molecule inhibitors targeting Leishmania pteridine reductase 1 (PTR1), a crucial enzyme in parasite DNA synthesis [59]. The discovery of inhibitors for this enzyme is an active area of research. A recent study has revealed the existence of several compounds with a high binding affinity for PTR1, surpassing even the enzyme’s natural substrate. These interactions occur with specific residues in PTR1 that play an important role in its catalytic activity. Moreover, remarkable stability in the complexes formed between these compounds and PTR1 has been observed during computational simulations [60].

Another study used a computational approach to model the three-dimensional structure of ornithine decarboxylase from Leishmania donovani (Ld ODC), and high-throughput virtual screening was performed with 8630 ligands from the ZINC database [2]. Of these, 45 ligands were selected based on their high binding score and validated by molecular oxygen simulations. Two major molecules, ceftaroline fosamyl and rimegepant, were identified as the most promising and were subjected to molecular dynamics simulations, density functional theory, and generalized natal surface area analysis of molecular mechanics, and their results revealed that ceftaroline fosamyl and rimegepant exhibited binding affinities of −10,719 and −10,159 kcal/mol, respectively [2]. These in silico findings suggest that both ceftaroline fosamyl and rimegepant may be promising options for the development of treatments against leishmaniasis. However, further studies are required to evaluate and confirm their efficacy and safety [2].

In addition, recently, research has been conducted using molecular polymer techniques to predict protein–ligand interactions of both natural and human-made substances [61]. In particular, it has been found that Withania somnifera, an important medicinal plant in India, can be used in combination with effective doses of antileishmanial drugs as a promising alternative to treat VL, thereby enhancing the host immune response [62]. On this basis, recent research using the molecular antibody technique to predict protein–small molecule interactions has identified N-myristoyltransferase (NMT) as a promising therapeutic target for the development of new drugs and observed that natural compounds from Withania somnifera, such as calycopteretin-3-ruthinoside and withanoside IX, have been found to have a high affinity for Leishmania NMT and could be used as potential drugs or to design more effective inhibitors [41].

5.2. Relevant Results and Conclusions Drawn

The application of molecular docking and MD simulations in leishmaniasis research has yielded significant results and drawn important conclusions. These studies have identified potential therapeutic targets, such as essential leishmanial proteins involved in key biological processes, allowing researchers to design specific inhibitors to disrupt the parasite’s survival and proliferation [63].

Moreover, through molecular docking and MD simulations, researchers have identified lead compounds with high binding affinities to these targets, serving as potential candidates for drug development [64]. The computational techniques have also provided insights into the structural dynamics and stability of leishmanial proteins, aiding in the design of more effective drugs by targeting specific regions or allosteric sites.

Additionally, these studies have deepened our understanding of the interaction between leishmanial proteins and the host immune system. By elucidating the mechanisms of immune evasion, researchers can develop strategies to boost the host immune response and enhance the efficacy of immunotherapies [65].

Computational tools, such as molecular docking and molecular dynamics simulations, have emerged as indispensable techniques in the field of drug discovery for Leishmaniasis. These methods enable the investigation of protein–ligand interactions and provide valuable insights into the molecular mechanisms underlying the disease [66]. Through molecular docking, researchers can predict the binding affinity and orientation of potential drug candidates to target proteins involved in Leishmania infection. This information aids in the identification of novel therapeutic targets and the design of optimized drugs with enhanced efficacy and specificity [67].

Molecular dynamics simulations have proven instrumental in understanding the dynamic behavior of proteins and their interaction with ligands. By employing Newtonian dynamics methods, researchers can explore the conformational changes, flexibility, and stability of proteins associated with Leishmaniasis. This knowledge enhances our understanding of the disease mechanisms and assists in the rational design of targeted therapeutics [48].

The integration of molecular docking and molecular dynamics simulations has yielded significant advancements in Leishmaniasis research. These computational approaches have contributed to the identification of potential drug targets, the discovery of new inhibitors, and the optimization of existing drugs. By elucidating the structural and functional properties of protein targets, computational tools enable researchers to develop more effective treatments for Leishmaniasis [46].

The use of computational techniques not only accelerates the drug discovery process but also offers cost-effective alternatives to traditional experimental methods [68]. By reducing the reliance on laborious and expensive laboratory experiments, computational approaches provide a valuable avenue for screening large compound libraries and prioritizing potential drug candidates for further evaluation [46].

In summary, the integration of molecular docking and molecular dynamics simulations has revolutionized Leishmaniasis research by facilitating the identification and optimization of potential therapeutic targets. These computational tools have enhanced our understanding of the disease mechanisms and accelerated the development of effective treatments for this neglected tropical disease.

6. Limitations and Challenges

6.1. Description of Current Limitations in the Use of Molecular Docking and Molecular Dynamics Simulations in Leishmaniasis

While molecular docking and molecular dynamics (MD) simulations have become valuable tools in leishmaniasis research, there are certain limitations that need to be addressed. Some of the current limitations are described below.

Lack of accurate protein structures: Accurate and experimentally determined protein structures are essential for reliable molecular docking and MD simulations. However, the availability of crystal structures or high-quality protein models for leishmanial proteins can be limited, posing a challenge in accurately predicting protein–ligand interactions.

Challenges in simulating large protein systems: Leishmania proteins, especially those involved in complex biological processes, can be large and exhibit conformational flexibility. Simulating such large protein systems using MD simulations can be computationally demanding and time-consuming, limiting the scale and scope of the simulations.

Representation of water and solvation effects: Accurately representing the water molecules and solvation effects in molecular docking and MD simulations is crucial for capturing realistic protein–ligand interactions. However, the treatment of water molecules in these simulations often involves approximations, which can affect the accuracy of the results.

Force field limitations: Force fields, which describe the interactions between atoms and molecules in simulations, have inherent limitations. Force fields used in MD simulations may not fully capture the complexity of protein–ligand interactions, leading to inaccuracies in predicting binding affinities and protein dynamics.

6.2. Future Challenges and Promising Areas of Research

Despite the current limitations, there are promising areas of research and future challenges that can advance the use of molecular docking and MD simulations in leishmaniasis:

Improved accuracy in protein structure determination: Advances in experimental techniques, such as X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance, will contribute to a more comprehensive understanding of leishmanial protein structures. This will enhance the accuracy of molecular docking and MD simulations [69].

Development of better force fields: Ongoing efforts to refine force fields and develop specialized parameters for leishmanial proteins will improve the accuracy of simulations and enable more accurate predictions of protein–ligand interactions.

Integration of multi-scale modeling: Combining molecular docking and MD simulations with other computational techniques, such as quantum mechanics or coarse-grained modeling, can provide a more comprehensive understanding of the dynamic behavior of leishmanial proteins and their interactions with ligands [69].

Incorporation of machine learning and artificial intelligence: The integration of machine learning and artificial intelligence algorithms can enhance the efficiency and accuracy of molecular docking and MD simulations [69]. These approaches can assist in predicting binding affinities, analyzing large datasets, and guiding the design of novel inhibitors.

7. Conclusions

In conclusion, while molecular docking and MD simulations have limitations in their current applications in leishmaniasis research, ongoing research efforts hold promise for addressing these challenges. Advances in protein structure determination, force field development, multi-scale modeling, and the incorporation of machine learning can improve the accuracy and applicability of these computational techniques, leading to a deeper understanding of leishmaniasis and the development of more effective treatments.

Author Contributions

Data collection team included M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; Conceptualization: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; methodology: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M., and J.J.B.; software: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; validation: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; formal analysis: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; investigation: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; resources: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; data curation: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; writing—original draft preparation: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; writing—review and editing: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; visualization: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; supervision: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; project administration: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B.; funding acquisition: M.R.C.-M., E.T.-A., A.E.-B., D.A.L.-F., R.S., A.J.R.-M. and J.J.B. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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References

1. Ebrahimi, S.; Bordbar, A.; Rastaghi, A.R.E.; Parvizi, P. Spatial distribution of sand fly species (Psychodidae: Phlebtominae), ecological niche, and climatic regionalization in zoonotic foci of cutaneous leishmaniasis, southwest of Iran. J. Vector Ecol.; 2016; 41, pp. 103-109. [DOI: https://dx.doi.org/10.1111/jvec.12200] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27232131]

2. Sheikh, S.Y.; Ansari, W.A.; Hassan, F.; Faruqui, T.; Khan, M.F.; Akhter, Y.; Khan, A.R.; Siddiqui, M.A.; Al-Khedhairy, A.A.; Nasibullah, M. Drug repositioning to discover novel ornithine decarboxylase inhibitors against visceral leishmaniasis. J. Mol. Recognit.; 2023; 36, e3021. [DOI: https://dx.doi.org/10.1002/jmr.3021] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37092713]

3. Burza, S.; Croft, S.L.; Boelaert, M. Leishmaniasis; Lancet: London, UK, 2018; Volume 392, pp. 951-970.

4. Akhoundi, M.; Downing, T.; Votýpka, J.; Kuhls, K.; Lukeš, J.; Cannet, A.; Ravel, C.; Marty, P.; Delaunay, P.; Kasbari, M. et al. Leishmania infections: Molecular targets and diagnosis. Mol. Asp. Med.; 2017; 57, pp. 1-29. [DOI: https://dx.doi.org/10.1016/j.mam.2016.11.012]

5. Lee, H.; Baek, K.H.; Phan, T.N.; Park, I.S.; Lee, S.; Kim, J.; No, J.H. Discovery of Leishmania donovani topoisomerase IB selective inhibitors by targeting protein-protein interactions between the large and small subunits. Biochem. Biophys. Res. Commun.; 2021; 569, pp. 193-198. [DOI: https://dx.doi.org/10.1016/j.bbrc.2021.07.019]

6. Alvar, J.; Vélez, I.D.; Bern, C.; Herrero, M.; Desjeux, P.; Cano, J.; Jannin, J.; Boer, M.D. WHO Leishmaniasis Control Team. Leishmaniasis worldwide and global estimates of its incidence. PLoS ONE; 2012; 7, e35671. [DOI: https://dx.doi.org/10.1371/journal.pone.0035671]

7. Reithinger, R.; Dujardin, J.-C.; Louzir, H.; Pirmez, C.; Alexander, B.; Brooker, S. Cutaneous leishmaniasis. Lancet Infect. Dis.; 2007; 7, pp. 581-596. [DOI: https://dx.doi.org/10.1016/S1473-3099(07)70209-8]

8. de Vries, H.J.C.; Reedijk, S.H.; Schallig, H.D.F.H. Cutaneous leishmaniasis: Recent developments in diagnosis and management. Am. J. Clin. Dermatol.; 2015; 16, pp. 99-109. [DOI: https://dx.doi.org/10.1007/s40257-015-0114-z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25687688]

9. Marfurt, J.; Nasereddin, A.; Niederwieser, I.; Jaffe, C.L.; Beck, H.-P.; Felger, I. Identification and differentiation of Leishmania species in clinical samples by PCR amplification of the miniexon sequence and subsequent restriction fragment length polymorphism analysis. J. Clin. Microbiol.; 2003; 41, pp. 3147-3153. [DOI: https://dx.doi.org/10.1128/JCM.41.7.3147-3153.2003] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12843055]

10. Deborggraeve, S.; Laurent, T.; Espinosa, D.; Van der Auwera, G.; Mbuchi, M.; Wasunna, M.; El-Safi, S.; Al-Basheer, A.A.; Arévalo, J.; Miranda-Verástegui, C. et al. A simplified and standardized polymerase chain reaction format for the diagnosis of leishmaniasis. J. Infect. Dis.; 2008; 198, pp. 1565-1572. [DOI: https://dx.doi.org/10.1086/592509]

11. Vadloori, B.; Sharath, A.K.; Prabhu, N.P.; Maurya, R. Homology modelling, molecular docking, and molecular dynamics simulations reveal the inhibition of Leishmania donovani dihydrofolate reductase-thymidylate synthase enzyme by Withaferin-A. BMC Res. Notes; 2018; 11, 246. [DOI: https://dx.doi.org/10.1186/s13104-018-3354-1]

12. Kashif, M.; Tabrez, S.; Husein, A.; Arish, M.; Kalaiarasan, P.; Manna, P.P.; Subbarao, N.; Akhter, Y.; Rub, A. Identification of novel inhibitors against UDP-galactopyranose mutase to combat leishmaniasis. J. Cell Biochem.; 2018; 119, pp. 2653-2665. [DOI: https://dx.doi.org/10.1002/jcb.26433]

13. Mandlik, V.; Singh, S. Molecular docking and molecular dynamics simulation study of inositol phosphorylceramide synthase-inhibitor complex in leishmaniasis: Insight into the structure based drug design. F1000Research; 2016; 5, 1610. [DOI: https://dx.doi.org/10.12688/f1000research.9151.1]

14. Boominathan, V.P.; Ferreira, T. Predicting tbx22 Zebrafish Protein Structure Using Multi-Level Prediction Tools and Demonstration of Conserved Structural Domains in Relation to Orthologous tbx22 Proteins in Humans. J. Biosci. Med.; 2016; 4, pp. 79-92. [DOI: https://dx.doi.org/10.4236/jbm.2016.43014]

15. Routaray, C.B.; Bhor, R.; Bai, S.; Kadam, N.S.; Jagtap, S.; Doshi, P.J.; Sundar, S.; Sawant, S.; Kulkarni, M.J.; Pai, K. SWATH-MS based quantitative proteomics analysis to evaluate the antileishmanial effect of Commiphora wightii “Guggul” and Amphotericin B on a clinical isolate of Leishmania donovani. J. Proteom.; 2020; 223, 103800. [DOI: https://dx.doi.org/10.1016/j.jprot.2020.103800] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32380292]

16. Zhou, P.; Wang, J.; Wang, M.; Hou, J.; Lu, J.R.; Xu, H. Amino acid conformations control the morphological and chiral features of the self-assembled peptide nanostructures: Young investigators perspective. J. Colloid Interface Sci.; 2019; 548, pp. 244-254. [DOI: https://dx.doi.org/10.1016/j.jcis.2019.04.019] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31004957]

17. Banach, M.; Fabian, P.; Stapor, K.; Konieczny, L.; Roterman, A.I. Structure of the Hydrophobic Core Determines the 3D Protein Structure-Verification by Single Mutation Proteins. Biomolecules; 2020; 10, 767. [DOI: https://dx.doi.org/10.3390/biom10050767] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32423068]

18. Kozak, J.J.; Gray, H.B. Stereochemistry of residues in turning regions of helical proteins. JBIC J. Biol. Inorg. Chem.; 2019; 24, pp. 879-888. [DOI: https://dx.doi.org/10.1007/s00775-019-01696-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31511993]

19. Fukuda, H.; Tomii, K. DeepECA: An end-to-end learning framework for protein contact prediction from a multiple sequence alignment. BMC Bioinform.; 2020; 21, 10. [DOI: https://dx.doi.org/10.1186/s12859-019-3190-x]

20. Kaur, H.; Bari, N.K.; Garg, A.; Sinha, S. Protein morphology drives the structure and catalytic activity of bio-inorganic hybrids. Int. J. Biol. Macromol.; 2021; 176, pp. 106-116. [DOI: https://dx.doi.org/10.1016/j.ijbiomac.2021.01.217]

21. Han, Y.; Lin, Z.; Zhou, J.; Yun, G.; Guo, R.; Richardson, J.J.; Caruso, F. Polyphenol-Mediated Assembly of Proteins for Engineering Functional Materials. Angew. Chem. Int. Ed.; 2020; 59, pp. 15618-15625. [DOI: https://dx.doi.org/10.1002/anie.202002089]

22. Bekker, G.-J.; Araki, M.; Oshima, K.; Okuno, Y.; Kamiya, N. Exhaustive search of the configurational space of heat-shock protein 90 with its inhibitor by multicanonical molecular dynamics based dynamic docking. J. Comput. Chem.; 2020; 41, pp. 1606-1615. [DOI: https://dx.doi.org/10.1002/jcc.26203] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32267975]

23. Nguyen, D.D.; Wei, G.-W. AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening. J. Chem. Inf. Model.; 2019; 59, pp. 3291-3304. [DOI: https://dx.doi.org/10.1021/acs.jcim.9b00334] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31257871]

24. Zhang, J.; Ma, W. Data-driven discovery of governing equations for fluid dynamics based on molecular simulation. J. Fluid. Mech.; 2020; 892, A5.Available online: https://www.cambridge.org/core/article/datadriven-discovery-of-governing-equations-for-fluid-dynamics-based-on-molecular-simulation/0525E75B0C42BF9B1E8D9B12A8ED58CD (accessed on 31 March 2020). [DOI: https://dx.doi.org/10.1017/jfm.2020.184]

25. Giri, S.; Shaha, C. Leishmania donovani parasite requires Atg8 protein for infectivity and survival under stress. Cell Death Dis.; 2019; 10, 808. [DOI: https://dx.doi.org/10.1038/s41419-019-2038-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31649242]

26. Suh, D.; Lee, J.W.; Choi, S.; Lee, Y. Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction. Int. J. Mol. Sci.; 2021; 22, 6032. [DOI: https://dx.doi.org/10.3390/ijms22116032] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34199677]

27. Yadav, R.; Pandey, A.; Awasthi, N.; Shukla, A. Molecular Docking Studies of Enzyme Binding Drugs on Family of Cytochrome P450. Adv. Sci. Eng. Med.; 2020; 12, pp. 83-87. [DOI: https://dx.doi.org/10.1166/asem.2020.2520]

28. Rashidi, S.; Fernández-Rubio, C.; Manzano-Román, R.; Mansouri, R.; Shafiei, R.; Ali-Hassanzadeh, M.; Barazesh, A.; Karimazar, M.; Hatam, G.; Nguewa, P. Potential therapeutic targets shared between leishmaniasis and cancer. Parasitology; 2021; 148, pp. 655-671. [DOI: https://dx.doi.org/10.1017/S0031182021000160]

29. Lee, H.S.; Im, W. Stalis: A Computational Method for Template-Based Ab Initio Ligand Design. J. Comput. Chem.; 2019; 40, pp. 1622-1632. [DOI: https://dx.doi.org/10.1002/jcc.25813]

30. Wang, A.; Zhang, Y.; Chu, H.; Liao, C.; Zhang, Z.; Li, G. Higher Accuracy Achieved for Protein-Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking. J. Chem. Inf. Model.; 2020; 60, pp. 2939-2950. [DOI: https://dx.doi.org/10.1021/acs.jcim.9b01168]

31. Lopes, S.P.; Castillo, Y.P.; Monteiro, M.L.; Menezes, R.R.D.; Almeida, R.N.; Martins, A.M.; Sousa, D.P.D. Trypanocidal Mechanism of Action and in silico Studies of p-Coumaric Acid Derivatives. Int. J. Mol. Sci.; 2019; 20, 5916. [DOI: https://dx.doi.org/10.3390/ijms20235916]

32. Santos, K.B.; Guedes, I.A.; Karl, A.L.M.; Dardenne, L.E. Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein–Peptide Data Set. J. Chem. Inf. Model.; 2020; 60, pp. 667-683. [DOI: https://dx.doi.org/10.1021/acs.jcim.9b00905] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31922754]

33. Ballante, F.; Kooistra, A.J.; Kampen, S.; de Graaf, C.; Carlsson, J. Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?. Pharmacol. Rev.; 2021; 73, pp. 527-565. [DOI: https://dx.doi.org/10.1124/pharmrev.120.000246] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34907092]

34. Hendrickx, S.; Boulet, G.; Mondelaers, A.; Dujardin, J.C.; Rijal, S.; Lachaud, L.; Cos, P.; Delputte, P.; Maes, L. Experimental selection of paromomycin and miltefosine resistance in intracellular amastigotes of Leishmania donovani and L. infantum. Parasitol. Res.; 2014; 113, pp. 1875-1881. [DOI: https://dx.doi.org/10.1007/s00436-014-3835-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24615359]

35. Singh, S.; Vijaya Prabhu, S.; Suryanarayanan, V.; Bhardwaj, R.; Singh, S.K.; Dubey, V.K. Molecular docking and structure-based virtual screening studies of potential drug target, CAAX prenyl proteases, of Leishmania donovani. J. Biomol. Struct. Dyn.; 2016; 34, pp. 2367-2386. [DOI: https://dx.doi.org/10.1080/07391102.2015.1116411]

36. Vivarini, Á.C.; Calegari-Silva, T.C.; Saliba, A.M.; Boaventura, V.S.; França-Costa, J.; Khouri, R.; Dierckx, T.; Dias-Teixeira, K.L.; Fasel, N.; Barral, A.M.P. et al. Systems Approach Reveals Nuclear Factor Erythroid 2-Related Factor 2/Protein Kinase R Crosstalk in Human Cutaneous Leishmaniasis. Front. Immunol.; 2017; 8, 1127. [DOI: https://dx.doi.org/10.3389/fimmu.2017.01127]

37. Keval, R.; Tejas, G. Basics, types and applications of molecular docking: A review. IP Int. J. Compr. Adv. Pharmacol.; 2022; 7, 16019.

38. Prabhu, S.V.; Tiwari, K.; Suryanarayanan, V.; Dubey, V.K.; Singh, S.K. Exploration of New and Potent Lead Molecules Against CAAX Prenyl Protease I of Leishmania donovani Through Pharmacophore Based Virtual Screening Approach. Comb. Chem. High Throughput Screen; 2017; 20, pp. 255-271. [DOI: https://dx.doi.org/10.2174/1386207320666170120164515]

39. VM, V.; Dubey, V.K.; Ponnuraj, K. Identification of two natural compound inhibitors of Leishmania donovani Spermidine Synthase (SpdS) through molecular docking and dynamic studies. J. Biomol. Struct. Dyn.; 2018; 36, pp. 2678-2693.

40. Pinzi, L.; Rastelli, G. Molecular Docking: Shifting Paradigms in Drug Discovery. Int. J. Mol. Sci.; 2019; 20, 4331. [DOI: https://dx.doi.org/10.3390/ijms20184331]

41. Orabi, M.A.A.; Alshahrani, M.M.; Sayed, A.M.; Abouelela, M.E.; Shaaban, K.A.; Abdel-Sattar, E.S. Identification of Potential Leishmania N-Myristoyltransferase Inhibitors from Withania somnifera (L.) Dunal: A Molecular Docking and Molecular Dynamics Investigation. Metabolites; 2023; 13, 93. [DOI: https://dx.doi.org/10.3390/metabo13010093]

42. Batista, F.A.; Ramos, S.L., Jr.; Tassone, G.; Leitão, A.; Montanari, C.A.; Botta, M.; Mori, M.; Borges, J.C. Discovery of small molecule inhibitors of Leishmania braziliensis Hsp90 chaperone. J. Enzyme Inhib. Med. Chem.; 2020; 35, pp. 639-649. [DOI: https://dx.doi.org/10.1080/14756366.2020.1726342] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32048531]

43. Bhardwaj, R.; Das, M.; Singh, S.; Chiranjivi, A.K.; Prabhu, S.V.; Singh, S.K.; Dubey, V.K. Evaluation of CAAX prenyl protease II of Leishmania donovani as potential drug target: Infectivity and growth of the parasite is significantly lowered after the gene knockout. Eur. J. Pharm. Sci.; 2017; 102, pp. 156-160. [DOI: https://dx.doi.org/10.1016/j.ejps.2017.03.005] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28279761]

44. Das, S.; Banerjee, A.; Kamran, M.; Ejazi, S.A.; Asad, M.; Ali, N.; Chakrabarti, S. A chemical inhibitor of heat shock protein 78 (HSP78) from Leishmania donovani represents a potential antileishmanial drug candidate. J. Biol. Chem.; 2020; 295, pp. 9934-9947. [DOI: https://dx.doi.org/10.1074/jbc.RA120.014587]

45. Pandey, R.K.; Sharma, D.; Bhatt, T.K.; Sundar, S.; Prajapati, V.K. Developing imidazole analogues as potential inhibitor for Leishmania donovani trypanothione reductase: Virtual screening, molecular docking, dynamics and ADMET approach. J. Biomol. Struct. Dyn.; 2015; 33, pp. 2541-2553. [DOI: https://dx.doi.org/10.1080/07391102.2015.1085904] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26305585]

46. Bhowmik, D.; Jagadeesan, R.; Rai, P.; Nandi, R.; Gugan, K.; Kumar, D. Evaluation of potential drugs against leishmaniasis targeting catalytic subunit of Leishmania donovani nuclear DNA primase using ligand based virtual screening, docking and molecular dynamics approaches. J. Biomol. Struct. Dyn.; 2021; 39, pp. 1838-1852. [DOI: https://dx.doi.org/10.1080/07391102.2020.1739557] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32141397]

47. Sakyi, P.O.; Broni, E.; Amewu, R.K.; Miller WA 3rd Wilson, M.D.; Kwofie, S.K. Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase. Front. Cell Infect. Microbiol.; 2022; 12, 859981. [DOI: https://dx.doi.org/10.3389/fcimb.2022.859981]

48. Pandey, R.K.; Kumbhar, B.V.; Sundar, S.; Kunwar, A.; Prajapati, V.K. Structure-based virtual screening, molecular docking, ADMET and molecular simulations to develop benzoxaborole analogs as potential inhibitor against Leishmania donovani trypanothione reductase. J. Recept. Signal. Transduct. Res.; 2017; 37, pp. 60-70. [DOI: https://dx.doi.org/10.3109/10799893.2016.1171344]

49. Borhani, D.W.; Shaw, D.E. The future of molecular dynamics simulations in drug discovery. J. Comput. Aided Mol. Des.; 2012; 26, pp. 15-26. [DOI: https://dx.doi.org/10.1007/s10822-011-9517-y]

50. Chen, K.; Kurgan, L. Investigation of atomic level patterns in protein-small ligand interactions. PLoS ONE; 2009; 4, e4473. [DOI: https://dx.doi.org/10.1371/journal.pone.0004473]

51. Psachoulia, E.; Bond, P.J.; Sansom, M.S.P. MD simulations of Mistic: Conformational stability in detergent micelles and water. Biochemistry; 2006; 45, pp. 9053-9058. [DOI: https://dx.doi.org/10.1021/bi0608818]

52. Skjaerven, L.; Reuter, N.; Martinez, A. Dynamics, flexibility and ligand-induced conformational changes in biological macromolecules: A computational approach. Future Med. Chem.; 2011; 3, pp. 2079-2100. [DOI: https://dx.doi.org/10.4155/fmc.11.159] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22098354]

53. Frezza, E.; Lavery, R. Internal Coordinate Normal Mode Analysis: A Strategy To Predict Protein Conformational Transitions. J. Phys. Chem. B; 2019; 123, pp. 1294-1301. [DOI: https://dx.doi.org/10.1021/acs.jpcb.8b11913] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30665293]

54. Ilizaliturri-Flores, I.; Correa-Basurto, J.; Benítez-Cardoza, C.G.; Zamorano-Carrillo, A. A study of the structural properties and thermal stability of human Bcl-2 by molecular dynamics simulations. J. Biomol. Struct. Dyn.; 2014; 32, pp. 1707-1719. [DOI: https://dx.doi.org/10.1080/07391102.2013.833858] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24028527]

55. Pricer, R.; Gestwicki, J.E.; Mapp, A.K. From Fuzzy to Function: The New Frontier of Protein-Protein Interactions. Acc. Chem. Res.; 2017; 50, pp. 584-589. [DOI: https://dx.doi.org/10.1021/acs.accounts.6b00565] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28945413]

56. Morris, C.J.; Corte, D.D. Using molecular docking and molecular dynamics to investigate protein-ligand interactions. Mod. Phys. Lett. B; 2021; Available online: https://www.nstl.gov.cn/paper_detail.html?id=9368c2d81762376c94f06498e4f1631a (accessed on 2 September 2023).

57. Nabi, S.A.; Ramzan, F.; Lone, M.S.; Beg, M.A.; Hamid, A.; Nainwal, L.M.; Samim, M.; Shafi, S.; Bano, S.; Javed, K. Synthesis, crystallographic study, molecular docking, ADMET, DFT and biological evaluation of new series of aurone derivatives as anti-leishmanial agents. J. Mol. Struct.; 2022; 1256, 132528. [DOI: https://dx.doi.org/10.1016/j.molstruc.2022.132528]

58. Ciemny, M.P.; Debinski, A.; Paczkowska, M.; Kolinski, A.; Kurcinski, M.; Kmiecik, S. Protein-peptide molecular docking with large-scale conformational changes: The p53-MDM2 interaction. Sci. Rep.; 2016; 6, 37532. [DOI: https://dx.doi.org/10.1038/srep37532] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27905468]

59. Vickers, T.J.; Beverley, S.M. Folate metabolic pathways in Leishmania. Essays Biochem.; 2011; 51, pp. 63-80.

60. Boakye, A.; Gasu, E.N.; Mensah, J.O.; Borquaye, L.S. Computational studies on potential small molecule inhibitors of Leishmania pteridine reductase 1. J. Biomol. Struct. Dyn.; 2023; pp. 1-14. [DOI: https://dx.doi.org/10.1080/07391102.2023.2166119]

61. Cheung, L.S.L.; Kanwar, M.; Ostermeier, M.; Konstantopoulos, K. A Hot-Spot Motif Characterizes the Interface between a Designed Ankyrin-Repeat Protein and Its Target Ligand. Biophys. J.; 2012; 102, pp. 407-416. [DOI: https://dx.doi.org/10.1016/j.bpj.2012.01.004]

62. Tripathi, C.D.P.; Kushawaha, P.K.; Sangwan, R.S.; Mandal, C.; Misra-Bhattacharya, S.; Dube, A. Withania somnifera chemotype NMITLI 101R significantly increases the efficacy of antileishmanial drugs by generating strong IFN-γ and IL-12 mediated immune responses in Leishmania donovani infected hamsters. Phytomedicine Int. J. Phytother. Phytopharm.; 2017; 24, pp. 87-95. [DOI: https://dx.doi.org/10.1016/j.phymed.2016.11.012]

63. Gupta, O.; Pradhan, T.; Bhatia, R.; Monga, V. Recent advancements in anti-leishmanial research: Synthetic strategies and structural activity relationships. Eur. J. Med. Chem.; 2021; 223, 113606. [DOI: https://dx.doi.org/10.1016/j.ejmech.2021.113606] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34171661]

64. Elhady, S.S.; Abdelhameed, R.F.; Malatani, R.T.; Alahdal, A.M.; Bogari, H.A.; Almalki, A.J.; Mohammad, K.A.; Ahmed, S.A.; Khedr, A.I.; Darwish, K.M. Molecular Docking and Dynamics Simulation Study of Hyrtios erectus Isolated Scalarane Sesterterpenes as Potential SARS-CoV-2 Dual Target Inhibitors. Biology; 2021; 10, 389. [DOI: https://dx.doi.org/10.3390/biology10050389] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34062724]

65. Gupta, A.K.; Das, S.; Kamran, M.; Ejazi, S.A.; Ali, N. The pathogenicity and virulence of Leishmania-interplay of virulence factors with host defenses. Virulence; 2022; 13, pp. 903-935. [DOI: https://dx.doi.org/10.1080/21505594.2022.2074130]

66. Bertazzo, M.; Bernetti, M.; Recanatini, M.; Masetti, M.; Cavalli, A. Fully Flexible Docking via Reaction-Coordinate-Independent Molecular Dynamics Simulations. J. Chem. Inf. Model.; 2018; 58, pp. 490-500. [DOI: https://dx.doi.org/10.1021/acs.jcim.7b00674] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29378136]

67. de Almeida Machado, P.; Carneiro, M.P.D.; de Jesus Sousa-Batista, A.; Pereira Lopes, F.J.; de Araujo Lima, A.P.C.; Chaves, S.P.; Sodero, A.C.R.; de Matos Guedes, H.L. Leishmanicidal therapy targeted to parasite proteases. Life Sci.; 2019; 219, pp. 163-181. [DOI: https://dx.doi.org/10.1016/j.lfs.2019.01.015]

68. Hernandez, A.V.; Phan, M.T.; Rocco, J.; Pasupuleti, V.; Barboza, J.J.; Piscoya, A.; Roman, Y.M.; White, C.M. Efficacy and Safety of Hydroxychloroquine for Hospitalized COVID-19 Patients: A Systematic Review and Meta-Analysis. J. Clin. Med.; 2021; 10, 2503. [DOI: https://dx.doi.org/10.3390/jcm10112503]

69. Akash, S.; Hossain, A.; Mukerjee, N.; Sarker, M.M.R.; Khan, M.F.; Hossain, M.J.; Rashid, M.A.; Kumer, A.; Ghosh, A.; León-Figueroa, D.A. et al. Modified coptisine derivatives as an inhibitor against pathogenic Rhizomucor miehei, Mycolicibacterium smegmatis (Black Fungus), Monkeypox, and Marburg virus by molecular docking and molecular dynamics simulation-based drug design approach. Front. Pharmacol.; 2023; 14, 1140494. [DOI: https://dx.doi.org/10.3389/fphar.2023.1140494]