A Pilot Study of All-Computational Drug Design Protocol-From Structure Prediction to Interaction Analysis

Antiangiogenic potential of phytochemicals from Clerodendrum inerme (L.) Gaertn investigated through in silico and quantum computational methods

Suppressing vascular endothelial growth factor (VEGF), its receptor (VEGFR2), and the VEGF/VEGFR2 signaling cascade system to inhibit angiogenesis has emerged as a possible cancer therapeutic target. The present work was designed to discover and evaluate bioactive phytochemicals from the Clerodendrum inerme (L.) Gaertn plant for their anti-angiogenic potential. Molecular docking of twenty-one phytochemicals against the VEGFR-2 (PDB ID: 3VHE) protein was performed, followed by ADMET profiling and molecular docking simulations. These investigations unveiled two hit compounds, cirsimaritin (- 12.29 kcal/mol) and salvigenin (- 12.14 kcal/mol), with the highest binding energy values when compared to the reference drug, Sorafenib (- 15.14 kcal/mol). Furthermore, only nine phytochemicals (cirsimaritin and salvigenin included) obeyed Lipinski's rule of five and passed ADMET filters. Molecular dynamics simulations run over 100 ns revealed that the protein-ligand complexes remained stable with minimal backbone fluctuations. The binding free energy values of cirsimaritin (- 52.35 kcal/mol) and salvigenin (- 55.89 kcal/mol), deciphered by MM-GBSA analyses, further corroborated the docking interactions. The HOMO-LUMO band energy gap (ΔE) was calculated using density-functional theory (DFT) and substantiated using density of state (DOS) spectra. The chemical reactivity analyses revealed that salvigenin exhibited the highest chemical softness value (6.384 eV), the lowest hardness value (0.07831 eV), and the lowest ΔE value (0.1566 eV), which implies salvigenin was less stable and chemically more reactive than cirsimaritin and sorafenib. These findings provide further evidence that cirsimaritin and salvigenin have the ability to prevent angiogenesis and the development of cancer. Nevertheless, more in vitro and in vivo confirmation is necessary.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

A combined in silico approaches of 2D-QSAR, molecular docking, molecular dynamics and ADMET prediction of anti-cancer inhibitor activity for actinonin derivatives

Inhibition of human mitochondrial peptide deformylase (HsPDF) plays a major role in reducing growth, proliferation, and cellular cancer survival. In this work, a series of 32 actinonin derivatives for HsPDF (PDB: 3G5K) inhibitor's anticancer activity was computationally analyzed for the first time, using an in silico study considering 2D-QSAR modeling, and molecular docking studies, and validated by molecular dynamics and ADMET properties. The results of multilinear regression (MLR) and artificial neural networks (ANN) statistical analysis reveal a good correlation between pIC50 activity and the seven (7) descriptors. The developed models were highly significant with cross-validation, the Y-randomization test and their applicability range. In addition, all considered data sets show that the AC30 compound, exhibits the best binding affinity (docking score = -212.074 kcal/mol and H-bonding energy = -15.879 kcal/mol). Furthermore, molecular dynamics simulations were performed at 500 ns, confirming the stability of the studied complexes under physiological conditions and validating the molecular docking results. Five selected actinonin derivatives (AC1, AC8, AC15, AC18 and AC30), exhibiting best docking score, were rationalized as potential leads for HsPDF inhibition, in well agreement with experimental outcomes. Furthermore, based on the in silico study, new six molecules (AC32, AC33, AC34, AC35, AC36 and AC37) were suggested as HsPDF inhibition candidates, which would be combined with in-vitro and in-vivo studies to perspective validation of their anticancer activity. Indeed, the ADMET predictions indicate that these six new ligands have demonstrated a fairly good drug-likeness profile.

Computational Methods for Molecular Understanding of the Antibiotic-Aminoacyl tRNA Synthetase Interaction

Developing an understanding of the interactions between an antibiotic and its binding site in a pathogen cell is the key to antibiotic design-an important cost-saving methodology compared to the costly and time-consuming random trial-and-error approach. The rapid development of antibiotic resistance provides an impetus for such studies. Recent years have witnessed the beginning of the use of combined computational techniques, including computer simulations and quantum mechanical computations, to understand how antibiotics bind at the active site of aminoacyl tRNA synthetases (aaRSs) from pathogens. Such computational protocols assist the knowledge-based design of antibiotics targeting aaRSs, which are their validated targets. After the ideas behind the protocols and their strategic planning are discussed, the protocols are described along with their major outcomes. This is followed by an integration of results from the different basic protocols. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Analysis of active-site residues from primary sequence of synthetase and transfer RNAs Basic Protocol 2: Molecular dynamics simulation-based protocol to study the structure and dynamics of the aaRS active site:antibiotic complex Basic Protocol 3: Quantum mechanical method-based protocol to study the structure and dynamics of the aaRS active site:antibiotic complex.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Targeting cryptic-orthosteric site of PD-L1 for inhibitor identification using structure-guided approach

Approved mAbs that block the protein-protein interaction (PPI) interface of the PD-1/PD-L1 immune checkpoint axis have led to significant improvements in cancer treatment. Despite having drawbacks of mAbs only few a compounds are reported till date against this axis. Inhibiting PPIs using small molecules has emerged as a significant therapeutic opportunity, demanding for the identification of drug-like molecules at an accelerated pace under the hit-to-lead campaigns. Due to the PD-L1's cross-talk with PD-1/CD80 and its overexpression on cancer cells, as well as the availability of its crystal structures with small molecules, it is an enticing therapeutic target for structure-assisted small molecule design. Furthermore, the selection of chemical databases enriched with focused designing for PPI interfaces is crucial. Therefore, in this study we have utilized the Asinex signature library for structure-assisted virtual screening to find the potential PD-L1 inhibitors by targeting the cryptic PD-L1 interface, followed by induced fit docking for pose refinements in the pocket. The obtained hits were then subjected to interaction fingerprinting and ligand-based drug-likeness investigations in order to evaluate and analyze their drug-like qualities (ADME). Twelve compounds qualified for molecular dynamics simulations, followed by thermodynamic calculations for evaluation of their stability and energetics inside the pocket. Two novel compounds with different chemical moieties have been identified that are consistent throughout the simulation, mimicking the interactions and binding energies with BMS-1166. These compounds appear as potential therapeutic candidates to be explored experimentally, thereby paving the way for the development of novel leads as immunomodulators.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Genetic Algorithms Applied to Thermodynamic Rational Design of Mimetic Antibodies Based on the GB1 Domain of Streptococcal Protein G: An Atomistic Simulation Study

The development of mimetic antibodies (MA) capable of combining the high affinity and selectivity of antibodies with the small size of the peptides has enormous potential for applications in current biotechnology. In this work, we demonstrate that in silico MA design is possible through genetic algorithms (GA) developed from shell scripts capable of combining software commonly used for atomistic simulation. Our results demonstrate that, using the GB1 domain of the streptococcal G protein as a model, it is possible to optimize the molecular recognition capacity of a large MA population in a few generations. In the first case, GA was able to generate 10 MA with binding free energy (BFE) less than the vascular endothelial cell growth factor conjugated with the fms-type tyrosine kinase receptor. In the second case, it generated 13 MA with BFE less than that of the hepatitis C-E2 viral envelope conjugate with the antibody. Through the GA developed in this work, we demonstrate the use of a new protocol, capable of guiding experimental methods for the design of bioactive peptides that can assist in the development of new therapeutic molecules.

Computer-Assisted Design of Macrocyclic Chelators for Actinium-225 Radiotherapeutics

Actinium-225 (²²⁵Ac) is an excellent candidate for targeted radiotherapeutic applications for treating cancer, because of its 10-day half-life and emission of four high-energy α²⁺ particles. To harness and direct the energetic potential of actinium, strongly binding chelators that remain stable in vivo during biological targeting must be developed. Unfortunately, controlling chelation for actinium remains challenging. Actinium is the largest +3 cation on the periodic table and has a 6d⁰5f⁰ electronic configuration, and its chemistry is relatively unexplored. Herein, we present theoretical work focused on improving the understanding of actinium bonding with macrocyclic chelating agents as a function of (1) macrocycle ring size, (2) the number and identity of metal binding functional groups, and (3) the length of the tether linking the metal binding functional group to the macrocyclic backbone. Actinium binding by these chelators is presented within the context of complexation with DOTA^4-, the most relevant Ac³⁺ binding agent for contemporary radiopharmaceutical applications. The results enabled us to develop a new strategy for actinium chelator design. The approach is rooted in our identification that Ac³⁺-chelation chemistry is dominated by ionic bonding interactions and relies on (1) maximizing electrostatic interactions between the metal binding functional group and the Ac³⁺ cation and (2) minimizing electronic repulsion between negatively charged actinium binding functional groups. This insight will provide a foundation for future innovation in developing the next generation of multifunctional actinium chelators.

In silico identification of drug candidates against COVID-19

The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M pro ), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 M pro . Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of M pro . Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for M pro . Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design.