Structure-based Computational Methods Research Articles

Molecular Docking: Principles, Advances, and Its Applications in Drug Discovery

Abstract: Molecular docking is a structure-based computational method that generates the binding pose and affinity between ligands and targets. There are many powerful docking programs. However, there is no single program that is suitable for every system. Hence, an appropriate program is chosen based on availability, need, and computer capacity. Molecular docking has clear steps that should be followed carefully to get a good result. : Molecular docking has many applications at various stages in drug discovery. Although it has various application areas, it is commonly applied in virtual screening and drug repurposing. As a result, it is playing a substantial role in the endeavor to discover a potent drug against COVID-19. There are also approved drugs in the pharmaceutical market that are developed through the use of molecular docking. As the accessible data is increasing and the method is advancing with the contribution of the latest computational developments, its use in drug discovery is also increasing. : Molecular docking has played a crucial role in making drug discovery faster, cheaper, and more effective. More advances in docking algorithms, integration with other computational methods, and the introduction of new approaches are expected. Thus, more applications that will make drug discovery easier are expected.

In Silico Screening and Molecular Dynamics Simulations against Tyrosine-protein Kinase Fyn Reveal Potential Novel Therapeutic Candidates for Bovine Papillomatosis.

Decreased beef productivity due to papillomatosis has led to the development and identification of novel targets and molecules to treat the disease. Protein kinases are promising targets for the design of numerous chemotherapy drugs. This study aimed to screen and design new inhibitors of bovine Fyn, a protein kinase, using structure-based computational methods, such as molecular docking and molecular dynamics simulation (MDS). To carry out the molecular docking analysis, five ligands obtained through structural similarity between active compounds along with the cross-inhibition function between the ChEMBL and Drugbank databases were used. Molecular modeling was performed, and the generated models were validated using PROCHECK and Verify 3D. Molecular docking was performed using Autodock Vina. The complexes formed between Fyn and the three best ligands had their stability assessed by MDS. In these simulations, the complexes were stabilized for 100 ns in relation to a pressure of 1 atm, with an average temperature of 300 k and a potential energy of 1,145,336 kJ/m converged in 997 steps. Docking analyses showed that all selected ligands had a high binding affinity with Fyn and presented hydrogen bonds at important active sites. MDS results support the docking results, as the ligand showed similar and stable interactions with amino acids present at the binding site of the protein. In all simulations, sorafenib obtained the best results of interaction with the bovine Fyn. The results highlight the identification of possible bovine Fyn inhibitors; however, further studies are important to confirm these results experimentally.

Computational Epitope Prediction and Design for Antibody Development and Detection.

The design of optimized protein antigens is a fundamental step in the development of new vaccine candidates and in the detection of therapeutic antibodies. A fundamental prerequisite is the identification of antigenic regions that are most prone to interact with antibodies, namely, B-cell epitopes. Here, we describe an efficient structure-based computational method for epitope prediction, called MLCE. In this approach, all that is required is the 3D structure of the antigen of interest. MLCE can be applied to glycosylated proteins, facilitating the identification of immunoreactive versus immune-shielding carbohydrates.

Combining Network-based and Matrix Factorization to Predict Novel Drug-target Interactions: A Case Study Using the Brazilian Natural Chemical Database

Background: Chemogenomic techniques use mathematical calculations to predict new DrugTarget Interactions (DTIs) based on drugs' chemical and biological information and pharmacological targets. Compared to other structure-based computational methods, they are faster and less expensive. Network analysis and matrix factorization are two practical chemogenomic approaches for predicting DTIs from many drugs and targets. However, despite the extensive literature introducing various chemogenomic techniques and methodologies, there is no consensus for predicting interactions using a drug or a target, a set of drugs, and a dataset of known interactions Methods: This study predicted novel DTIs from a limited collection of drugs using a heterogeneous ensemble based on network and matrix factorization techniques. We examined three network-based approaches and two matrix factorization-based methods on benchmark datasets. Then, we used one network approach and one matrix factorization technique on a small collection of Brazilian plant-derived pharmaceuticals. Results: We have discovered two novel DTIs and compared them to the Therapeutic Target Database to detect linked disorders, such as breast cancer, prostate cancer, and Cushing syndrome, with two drugs (Quercetin and Luteolin) originating from Brazilian plants. Conclusion: The suggested approach allows assessing the performance of approaches only based on their sensitivity, independent of their unfavorable interactions. Findings imply that integrating network and matrix factorization results might be a helpful technique in bioinformatics investigations involving the development of novel medicines from a limited range of drugs.

Calcium Ion Binding to the Mutants of Calmodulin: A Structure-Based Computational Predictive Model of Binding Affinity Using a Charge Scaling Approach in Molecular Dynamics Simulation.

The binding of calcium ions (Ca2+) to the calcium-binding proteins (CBPs) controls a plethora of regulatory processes. Among the roles played by CBPs in several diseases, the onset and progress of some cardiovascular diseases are caused by mutations in calmodulin (CaM), an important member of CBPs. Rationalization and prediction of the binding affinity of Ca2+ ions to the CaM can play important roles in understanding the origin of cardiovascular diseases. However, there is no robust structure-based computational method for predicting the binding affinity of Ca2+ ions to the different forms of CBPs in general and CaM in particular. In the current work, we have devised a fast yet accurate computational technique to accurately calculate the binding affinity of Ca2+ to the different forms of CaM. This method combines the well-known molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method and a charge scaling approach developed by previous authors that takes care of the polarization of CaM and Ca2+ ions. Our detailed analysis of the different components of binding free energy shows that subtle changes in electrostatics and van der Waals contribute to the difference in the binding affinity of mutants from that of the wild type (WT), and the charge scaling approach is superior in calculating these subtle changes in electrostatics as compared to the nonpolarizable force field used in this work. A statistically significant regression model made from our binding free energy calculations gives a correlation coefficient close to 0.8 to the experimental results. This structure-based predictive model can open up a new strategy to understand and predict the binding of Ca2+ to the mutants of CBPs, in general.

Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) Simulation: A Tool for Structure-Based Drug Design and Discovery.

Quantum Mechanics (QM) is the physics-based theory that explains the physical properties of nature at the level of atoms and sub-atoms. Molecular mechanics (MM) construct molecular systems through the use of classical mechanics. So, when combined, hybrid quantum mechanics and molecular mechanics (QM/MM) can act as computer-based methods that can be used to calculate the structure and property data of molecular structures. Hybrid QM/MM combines the strengths of QM with accuracy and MM with speed. QM/MM simulation can also be applied for the study of chemical processes in solutions, as well as in the proteins, and has a great scope in structure-based drug design (SBDD) and discovery. Hybrid QM/MM can also be applied to HTS to derive QSAR models. Due to the availability of many protein crystal structures, it has a great role in computational chemistry, especially in structure- and fragment-based drug design. Fused QM/MM simulations have been developed as a widespread method to explore chemical reactions in condensed phases. In QM/MM simulations, the quantum chemistry theory is used to treat the space in which the chemical reactions occur; however, the rest is defined through the molecular mechanics force field (MMFF). In this review, we have extensively reviewed recent literature pertaining to the use and applications of hybrid QM/MM simulations for ligand and structure-based computational methods for the design and discovery of therapeutic agents.

Photopharmacology of Ion Channels through the Light of the Computational Microscope.

The optical control and investigation of neuronal activity can be achieved and carried out with photoswitchable ligands. Such compounds are designed in a modular fashion, combining a known ligand of the target protein and a photochromic group, as well as an additional electrophilic group for tethered ligands. Such a design strategy can be optimized by including structural data. In addition to experimental structures, computational methods (such as homology modeling, molecular docking, molecular dynamics and enhanced sampling techniques) can provide structural insights to guide photoswitch design and to understand the observed light-regulated effects. This review discusses the application of such structure-based computational methods to photoswitchable ligands targeting voltage- and ligand-gated ion channels. Structural mapping may help identify residues near the ligand binding pocket amenable for mutagenesis and covalent attachment. Modeling of the target protein in a complex with the photoswitchable ligand can shed light on the different activities of the two photoswitch isomers and the effect of site-directed mutations on photoswitch binding, as well as ion channel subtype selectivity. The examples presented here show how the integration of computational modeling with experimental data can greatly facilitate photoswitchable ligand design and optimization. Recent advances in structural biology, both experimental and computational, are expected to further strengthen this rational photopharmacology approach.

Computational Design of Structured and Functional Peptide Macrocycles.

Structure-based computational design methods have been developed to create proteins in silico with diverse shapes and sizes that accurately fold in vitro, from 7-residue macrocycles to megadalton-scale self-assembling nanomaterials. Precise control over protein shape has further enabled design and optimization of functional therapeutic proteins, including agonists, antagonists, enzymes, and vaccines. Computational design of functional peptides of smaller size presents a persistent challenge, with few successful examples to date. Herein we describe validated general methods for computational design of peptides using the Rosetta molecular modeling suite and discuss outstanding challenges and future directions.

Potency-Enhancing Mutations of Gating Modifier Toxins for the Voltage-Gated Sodium Channel NaV1.7 Can Be Predicted Using Accurate Free-Energy Calculations.

Gating modifier toxins (GMTs) isolated from venomous organisms such as Protoxin-II (ProTx-II) and Huwentoxin-IV (HwTx-IV) that inhibit the voltage-gated sodium channel NaV1.7 by binding to its voltage-sensing domain II (VSDII) have been extensively investigated as non-opioid analgesics. However, reliably predicting how a mutation to a GMT will affect its potency for NaV1.7 has been challenging. Here, we hypothesize that structure-based computational methods can be used to predict such changes. We employ free-energy perturbation (FEP), a physics-based simulation method for predicting the relative binding free energy (RBFE) between molecules, and the cryo electron microscopy (cryo-EM) structures of ProTx-II and HwTx-IV bound to VSDII of NaV1.7 to re-predict the relative potencies of forty-seven point mutants of these GMTs for NaV1.7. First, FEP predicted these relative potencies with an overall root mean square error (RMSE) of 1.0 ± 0.1 kcal/mol and an R2 value of 0.66, equivalent to experimental uncertainty and an improvement over the widely used molecular-mechanics/generalized born-surface area (MM-GB/SA) RBFE method that had an RMSE of 3.9 ± 0.8 kcal/mol. Second, inclusion of an explicit membrane model was needed for the GMTs to maintain stable binding poses during the FEP simulations. Third, MM-GB/SA and FEP were used to identify fifteen non-standard tryptophan mutants at ProTx-II[W24] predicted in silico to have a at least a 1 kcal/mol gain in potency. These predicted potency gains are likely due to the displacement of high-energy waters as identified by the WaterMap algorithm for calculating the positions and thermodynamic properties of water molecules in protein binding sites. Our results expand the domain of applicability of FEP and set the stage for its prospective use in biologics drug discovery programs involving GMTs and NaV1.7.

Rationalizing PROTAC-Mediated Ternary Complex Formation Using Rosetta.

Proteolysis-targeting chimaeras (PROTACs) are molecules that combine a target-binding warhead with an E3 ligase-recruiting moiety; by drawing the target protein into a ternary complex with the E3 ligase, PROTACs induce target protein degradation. While PROTACs hold exciting potential as chemical probes and as therapeutic agents, development of a PROTAC typically requires synthesis of numerous analogs to thoroughly explore variations on the chemical linker; without extensive trial and error, it is unclear how to link the two protein-recruiting moieties to promote formation of a productive ternary complex. Here, we describe a structure-based computational method for evaluating the suitability of a given linker for ternary complex formation. Our method uses Rosetta to dock the protein components and then builds the PROTAC from its component fragments into each binding mode; complete models of the ternary complex are then refined. We apply this approach to retrospectively evaluate multiple PROTACs from the literature, spanning diverse target proteins. We find that modeling ternary complex formation is sufficient to explain both activity and selectivity reported for these PROTACs, implying that other cellular factors are not key determinants of activity in these cases. We further find that interpreting PROTAC activity is best approached using an ensemble of structures of the ternary complex rather than a single static conformation and that members of a structurally conserved protein family can be recruited by the same PROTAC through vastly different binding modes. To encourage adoption of these methods and promote further analyses, we disseminate both the computational methods and the models of ternary complexes.

Integrating DNA-encoded chemical libraries with virtual combinatorial library screening: Optimizing a PARP10 inhibitor

Two critical steps in drug development are 1) the discovery of molecules that have the desired effects on a target, and 2) the optimization of such molecules into lead compounds with the required potency and pharmacokinetic properties for translation. DNA-encoded chemical libraries (DECLs) can nowadays yield hits with unprecedented ease, and lead-optimization is becoming the limiting step. Here we integrate DECL screening with structure-based computational methods to streamline the development of lead compounds. The presented workflow consists of enumerating a virtual combinatorial library (VCL) derived from a DECL screening hit and using computational binding prediction to identify molecules with enhanced properties relative to the original DECL hit. As proof-of-concept demonstration, we applied this approach to identify an inhibitor of PARP10 that is more potent and druglike than the original DECL screening hit.

Studies of structural determinants of substrate binding in the Creatine Transporter (CreaT, SLC6A8) using molecular models

Creatine is a crucial metabolite that plays a fundamental role in ATP homeostasis in tissues with high-energy demands. The creatine transporter (CreaT, SLC6A8) belongs to the solute carrier 6 (SLC6) transporters family, and more particularly to the GABA transporters (GATs) subfamily. Understanding the molecular determinants of specificity within the SLC6 transporters in general, and the GATs in particular is very challenging due to the high similarity of these proteins. In the study presented here, our efforts focused on finding key structural features involved in binding selectivity for CreaT using structure-based computational methods. Due to the lack of three-dimensional structures of SLC6A8, our approach was based on the realization of two reliable homology models of CreaT using the structures of two templates, i.e. the human serotonin transporter (hSERT) and the prokaryotic leucine transporter (LeuT). Our models reveal that an optimal complementarity between the shape of the binding site and the size of the ligands is necessary for transport. These findings provide a framework for a deeper understanding of substrate selectivity of the SLC6 family and other LeuT fold transporters.

A Novel Structural Model of the Creatine Transporter Rationalizes its Structural Determinants of Binding

Creatine is a crucial metabolite that plays a fundamental role in ATP homeostasis in tissues with high-energy demands. The creatine transporter (CreaT, SLC6A8) belongs to the solute carrier 6 (SLC6) transporters family, and more particularly to the GABA transporters (GATs) subfamily. Understanding the molecular determinants of specificity within the SLC6 transporters in general, and the GATs in particular is very challenging due to the high similarity of these proteins. In the study presented here, our efforts focused on finding key structural features involved in binding selectivity for CreaT using structure-based computational methods. Due to the lack of tridimensional structures of SLC6A8, our approach was based on the realization of a homology model combining the structures of two templates, i.e. the human serotonine transporter and the prokaryotic leucine transporter. During this meticulous process, particular attention was given to the transmembrane helix 10, in which previous studies identify a π-helix among the GATs subfamily. Understanding the role of the π-helix and its function in the binding site of CreaT is of outmost importance for the realization of a reliable homology model and thus our primary concern. Our model reveals that an optimal complementarity between the shape of the binding site and the size of the ligands is necessary for transport. These findings provide a framework for a deeper understanding of substrate selectivity of the SLC6 family and other LeuT fold transporters.

Computational investigation of peptide binding stabilities of HLA-B*27 and HLA-B*44 alleles

Major Histocompatibility Complex (MHC) is a cell surface glycoprotein that binds to foreign antigens and presents them to T lymphocyte cells on the surface of Antigen Presenting Cells (APCs) for appropriate immune recognition. Recently, studies focusing on peptide-based vaccine design have allowed a better understanding of peptide immunogenicity mechanisms, which is defined as the ability of a peptide to stimulate CTL-mediated immune response. Peptide immunogenicity is also known to be related to the stability of peptide-loaded MHC (pMHC) complex. In this study, ENCoM server was used for structure-based estimation of the impact of single point mutations on pMHC complex stabilities. For this purpose, two human MHC molecules from the HLA-B*27 group (HLA-B*27:05 and HLA-B*27:09) in complex with four different peptides (GRFAAAIAK, RRKWRRWHL, RRRWRRLTV and IRAAPPPLF) and three HLA-B*44 molecules (HLA-B*44:02, HLA-B*44:03 and HLA-B*44:05) in complex with two different peptides (EEYLQAFTY and EEYLKAWTF) were analyzed. We found that the stability of pMHC complexes is dependent on both peptide sequence and MHC allele. Furthermore, we demonstrate that allele-specific peptide-binding preferences can be accurately revealed using structure-based computational methods predicting the effect of mutations on protein stability.

Identification of Aethina tumida Kir Channels as Putative Targets of the Bee Venom Peptide Tertiapin Using Structure-Based Virtual Screening Methods.

Venoms are comprised of diverse mixtures of proteins, peptides, and small molecules. Identifying individual venom components and their target(s) with mechanism of action is now attainable to understand comprehensively the effectiveness of venom cocktails and how they collectively function in the defense and predation of an organism. Here, structure-based computational methods were used with bioinformatics tools to screen and identify potential biological targets of tertiapin (TPN), a venom peptide from Apis mellifera (European honey bee). The small hive beetle (Aethina tumida (A. tumida)) is a natural predator of the honey bee colony and was found to possess multiple inwardly rectifying K+ (Kir) channel subunit genes from a genomic BLAST search analysis. Structure-based virtual screening of homology modelled A. tumida Kir (atKir) channels found TPN to interact with a docking profile and interface “footprint” equivalent to known TPN-sensitive mammalian Kir channels. The results support the hypothesis that atKir channels, and perhaps other insect Kir channels, are natural biological targets of TPN that help defend the bee colony from infestations by blocking K+ transport via atKir channels. From these in silico findings, this hypothesis can now be subsequently tested in vitro by validating atKir channel block as well as in vivo TPN toxicity towards A. tumida. This study highlights the utility and potential benefits of screening in virtual space for venom peptide interactions and their biological targets, which otherwise would not be feasible.

Rational Design of Peptide Ligands Based on Knob–Socket Protein Packing Model Using CD13 as a Prototype Receptor

Structure-based computational peptide design methods have gained significant interest in recent years owing to the availability of structural insights into protein–protein interactions obtained from the crystal structures. The majority of these approaches design new peptide ligands by connecting the crucial amino acid residues from the protein interface and are generally not based on any predicted receptor–ligand interaction. In this work, a peptide design method based on the Knob–Socket model was used to identify the specific ligand residues packed into the receptor interface. This method enables peptide ligands to be designed rationally by predicting amino acid residues that will fit best at the binding site of the receptor protein. In this, specific peptide ligands were designed for the model receptor CD13, overexpression of which has been observed in several cancer types. From the initial library of designed peptides, three potential candidates were selected based on simulated energies in the CD13 bin...

Computational affinity maturation of camelid single-domain intrabodies against the nonamyloid component of alpha-synuclein

Improving the affinity of protein-protein interactions is a challenging problem that is particularly important in the development of antibodies for diagnostic and clinical use. Here, we used structure-based computational methods to optimize the binding affinity of VHNAC1, a single-domain intracellular antibody (intrabody) from the camelid family that was selected for its specific binding to the nonamyloid component (NAC) of human α-synuclein (α-syn), a natively disordered protein, implicated in the pathogenesis of Parkinson’s disease (PD) and related neurological disorders. Specifically, we performed ab initio modeling that revealed several possible modes of VHNAC1 binding to the NAC region of α-syn as well as mutations that potentially enhance the affinity between these interacting proteins. While our initial design strategy did not lead to improved affinity, it ultimately guided us towards a model that aligned more closely with experimental observations, revealing a key residue on the paratope and the participation of H4 loop residues in binding, as well as confirming the importance of electrostatic interactions. The binding activity of the best intrabody mutant, which involved just a single amino acid mutation compared to parental VHNAC1, was significantly enhanced primarily through a large increase in association rate. Our results indicate that structure-based computational design can be used to successfully improve the affinity of antibodies against natively disordered and weakly immunogenic antigens such as α-syn, even in cases such as ours where crystal structures are unavailable.

Repurposing approach identifies new treatment options for invasive fungal disease

Drug repositioning is the process of discovery, validation and marketing of previously approved drugs for new indications. Our aim was drug repositioning, using ligand-based and structure-based computational methods, of compounds that are similar to two hit compounds previously selected by our group that show promising antifungal activity. Through the ligand-based method, 100 compounds from each of three databases (MDDR, DrugBank and TargetMol) were selected by the Tanimoto coefficient, as similar to LMM5 or LMM11. These compounds were analyzed by the scaffold trees, and up to 10 compounds from each database were selected. The structure-based method (molecular docking) using thioredoxin reductase as the target drug was performed as a complementary approach, resulting in six compounds that were tested in an in vitro assay. All compounds, particularly raltegravir, showed antifungal activity against the genus Paracoccidioides. Raltegravir, an antiviral drug, showed promising antifungal activity against the experimental murine paracoccidioidomycosis, with significant reduction of the fungal burden and decreased alterations in the lung structure of mice treated with 1 mg/kg of raltegravir. In conclusion, the combination of two in silico methods for drug repositioning was able to select an antiviral drug with promising antifungal activity for treatment of paracoccidioidomycosis.

Chemistry‐driven Hit‐to‐lead Optimization Guided by Structure‐based Approaches

For several decades, hit identification for drug discovery has been facilitated by developments in both fragment-based and high-throughput screening technologies. However, a major bottleneck in drug discovery projects continues to be the optimization of primary hits from screening campaigns in order to derive lead compounds. Computational chemistry or molecular modeling can play an important role during this hit-to-lead (H2L) stage by both suggesting putative optimizations and decreasing the number of compounds to be experimentally synthesized and evaluated. However, it is also crucial to consider the feasibility of organically synthesizing these virtually designed compounds. Furthermore, the generated molecules should have reasonable physicochemical properties and be medicinally relevant. This review focuses on chemistry-driven and structure-based computational methods that can be used to tackle the difficult problem of H2L optimization, with emphasis being placed on the strategy developed in our laboratory.

Using a Machine-Learning Approach to Predict Discontinuous Antibody-Specific B-Cell Epitopes

Background: Predicting B-cell epitopes is important for understanding disease pathogenesis, identifying potential autoantigens, and designing vaccines and immune-based cancer therapies. The experimental approaches used for detecting B-cell epitopes are often laborious and resource-intensive. Thus, several computational methods have been developed for predicting the epitopes of a given antigen. However, most of these methods are coarse binary classifications of antigen regions within epitopes or non-epitopes and do not specify antibodies. Therefore, we aim to solve this antibodyspecified epitope prediction problem using a developed structure-based computational machine-learning method, Epitopia, to reflect this biological reality accurately. Result: We selected 60 non-redundant antibody-antigen protein complexes to train and applied the leave-one-out cross-validation method to test the accuracy of our proposed methods; we compared the results with the Epitopia, Discotope 2.0,PEASE and the state-of-the-art tool SEPPA 2.0. We considered the role of both complementarity determining region residues and antigen surface residues in antigenantibody interactions and assigned a score for each antigen surface residue. If we considered a prediction to be successful if the average score of “epitope residues” exceeded the average score over all surface residues. Then the success rates of our proposed methods were all higher than Epitopia and the best one was 83.3%, which is 7% higher than the rate obtained with Epitopia (76%). The results show that antibody-specific methods are competitive with, and sometimes even better than, Epitopia, which only considers the antigen residues. Conclusion: Our antibody-specific methods provide sufficient accuracy in locating epitopes because whether an antigen surface residue is an epitope depends on whether the antibody recognizes it. This approach is a new method for identifying B-cell epitopes. Based on our results, we believe that the prediction of antibody-specific B cell epitopes is more meaningful and efficient than those which are only considering antigen residues. Keywords: Antibody-specific B-cell epitope, paratope-epitope residue pair, machine-learning, bioinformatics, antigen, epitopia.