High-Affinity Antibodies Designing of SARS-CoV-2 Based on Molecular Dynamics Simulations.

SARS-CoV-2 has led to a global pandemic of new crown pneumonia, which has had a tremendous impact on human society. Antibody drug therapy is one of the most effective way of combating SARS-CoV-2. In order to design potential antibody drugs with high affinity, we used antibody S309 from patients with SARS-CoV as the target antibody and RBD of S protein as the target antigen. Systems with RBD glycosylated and non-glycosylated were constructed to study the influence of glycosylation. From the results of molecular dynamics simulations, the steric effects of glycans on the surface of RBD plays a role of "wedge", which makes the L335-E340 region of RBD close to the CDR3 region of the heavy chain of antibody and increases the contact area between antigen and antibody. By mutating the key residues of antibody at the interaction interface, we found that the binding affinities of antibody mutants G103A, P28W and Y100W were all stronger than that of the wild-type, especially for the G103A mutant. G103A significantly reduces the distance between the binding region of L335-K356 in the antigen and P28-Y32 of heavy chain in the antibody through structural transition. Taken together, the antibody design method described in this work can provide theoretical guidance and a time-saving method for antibody drug design.

Protein decoys may battle COVID-19 and more.

Drugs designed to resemble viruses' cellular targets move into clinical trials.

Surely you are joking, Mr Docking!

In the wake of recent COVID-19 pandemics scientists around the world rushed to deliver numerous CADD (Computer-Aided Drug Discovery) methods and tools that could be reliably used to discover novel drug candidates against the SARS-CoV-2 virus. With that, there emerged a trend of a significant democratization of CADD that contributed to the rapid development of various COVID-19 drug candidates currently undergoing different stages of validation. On the other hand, this democratization also inadvertently led to the surge rapidly performed molecular docking studies to nominate multiple scores of novel drug candidates supported by computational arguments only. Albeit driven by best intentions, most of such studies also did not follow best practices in the field that require experience and expertise learned through multiple rigorously designed benchmarking studies and rigorous experimental validation. In this Viewpoint we reflect on recent disbalance between small number of rigorous and comprehensive studies and the proliferation of purely computational studies enabled by the ease of docking software availability. We further elaborate on the hyped oversale of CADD methods' ability to rapidly yield viable drug candidates and reiterate the critical importance of rigor and adherence to the best practices of CADD in view of recent emergence of AI and Big Data in the field.

Computer-aided drug design for the pain-like protease (PLpro) inhibitors against SARS-CoV-2.

A new coronavirus, known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a highly contagious virus and has caused a massive worldwide health crisis. While large-scale vaccination efforts are underway, the management of population health, economic impact and asof-yet unknown long-term effects on physical and mental health will be a key challenge for the next decade. The papain-like protease (PLpro) of SARS-CoV-2 is a promising target for antiviral drugs. This report used pharmacophore-based drug design technology to identify potential compounds as PLpro inhibitors against SARS-CoV-2. The optimal pharmacophore model was fully validated using different strategies and then was employed to virtually screen out 10 compounds with inhibitory. Molecular docking and non-bonding interactions between the targeted protein PLpro and compounds showed that UKR1129266 was the best compound. These results provided a theoretical foundation for future studies of PLpro inhibitors against SARS-CoV-2.

Protein decoys may battle COVID-19 and more.

Drugs designed to resemble viruses' cellular targets move into clinical trials.

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein-peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

Interfering with nucleotide excision by the coronavirus 3'-to-5' exoribonuclease.

Some of the most efficacious antiviral therapeutics are ribonucleos(t)ide analogs. The presence of a 3'-to-5' proofreading exoribonuclease (ExoN) in coronaviruses diminishes the potency of many ribonucleotide analogs. The ability to interfere with ExoN activity will create new possibilities for control of SARS-CoV-2 infection. ExoN is formed by a 1:1 complex of nsp14 and nsp10 proteins. We have purified and characterized ExoN using a robust, quantitative system that reveals determinants of specificity and efficiency of hydrolysis. Double-stranded RNA is preferred over single-stranded RNA. Nucleotide excision is distributive, with only one or two nucleotides hydrolyzed in a single binding event. The composition of the terminal basepair modulates excision. A stalled SARS-CoV-2 replicase in complex with either correctly or incorrectly terminated products prevents excision, suggesting that a mispaired end is insufficient to displace the replicase. Finally, we have discovered several modifications to the 3'-RNA terminus that interfere with or block ExoN-catalyzed excision. While a 3'-OH facilitates hydrolysis of a nucleotide with a normal ribose configuration, this substituent is not required for a nucleotide with a planar ribose configuration such as that present in the antiviral nucleotide produced by viperin. Design of ExoN-resistant, antiviral ribonucleotides should be feasible.

Surely you are joking, Mr Docking!

In the wake of recent COVID-19 pandemics scientists around the world rushed to deliver numerous CADD (Computer-Aided Drug Discovery) methods and tools that could be reliably used to discover novel drug candidates against the SARS-CoV-2 virus. With that, there emerged a trend of a significant democratization of CADD that contributed to the rapid development of various COVID-19 drug candidates currently undergoing different stages of validation. On the other hand, this democratization also inadvertently led to the surge rapidly performed molecular docking studies to nominate multiple scores of novel drug candidates supported by computational arguments only. Albeit driven by best intentions, most of such studies also did not follow best practices in the field that require experience and expertise learned through multiple rigorously designed benchmarking studies and rigorous experimental validation. In this Viewpoint we reflect on recent disbalance between small number of rigorous and comprehensive studies and the proliferation of purely computational studies enabled by the ease of docking software availability. We further elaborate on the hyped oversale of CADD methods' ability to rapidly yield viable drug candidates and reiterate the critical importance of rigor and adherence to the best practices of CADD in view of recent emergence of AI and Big Data in the field.

Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling.

In silico identification of potent protein inhibitors commonly requires prediction of a ligand binding free energy (BFE). Thermodynamics integration (TI) based on molecular dynamics (MD) simulations is a BFE calculation method capable of acquiring accurate BFE, but it is computationally expensive and time-consuming. In this work, we have developed an efficient automated workflow for identifying compounds with the lowest BFE among thousands of congeneric ligands, which requires only hundreds of TI calculations. Automated machine learning (AutoML) orchestrated by active learning (AL) in an AL-AutoML workflow allows unbiased and efficient search for a small set of best-performing molecules. We have applied this workflow to select inhibitors of the SARS-CoV-2 papain-like protease and were able to find 133 compounds with improved binding affinity, including 16 compounds with better than 100-fold binding affinity improvement. We obtained a hit rate that outperforms that expected of traditional expert medicinal chemist-guided campaigns. Thus, we demonstrate that the combination of AL and AutoML with free energy simulations provides at least 20× speedup relative to the naïve brute force approaches.

SARS-CoV-2 Main Protease Drug Design, Assay Development, and Drug Resistance Studies.

SARS-CoV-2 is the etiological pathogen of the COVID-19 pandemic, which led to more than 6.5 million deaths since the beginning of the outbreak in December 2019. The unprecedented disruption of social life and public health caused by COVID-19 calls for fast-track development of diagnostic kits, vaccines, and antiviral drugs. Small molecule antivirals are essential complements of vaccines and can be used for the treatment of SARS-CoV-2 infections. Currently, there are three FDA-approved antiviral drugs, remdesivir, molnupiravir, and paxlovid. Given the moderate clinical efficacy of remdesivir and molnupiravir, the drug-drug interaction of paxlovid, and the emergence of SARS-CoV-2 variants with potential drug-resistant mutations, there is a pressing need for additional antivirals to combat current and future coronavirus outbreaks.In this Account, we describe our efforts in developing covalent and noncovalent main protease (Mpro) inhibitors and the identification of nirmatrelvir-resistant mutants. We initially discovered GC376, calpain inhibitors II and XII, and boceprevir as dual inhibitors of Mpro and host cathepsin L from a screening of a protease inhibitor library. Given the controversy of targeting cathepsin L, we subsequently shifted the focus to designing Mpro-specific inhibitors. Specifically, guided by the X-ray crystal structures of these initial hits, we designed noncovalent Mpro inhibitors such as Jun8-76-3R that are highly selective toward Mpro over host cathepsin L. Using the same scaffold, we also designed covalent Mpro inhibitors with novel cysteine reactive warheads containing di- and trihaloacetamides, which similarly had high target specificity. In parallel to our drug discovery efforts, we developed the cell-based FlipGFP Mpro assay to characterize the cellular target engagement of our rationally designed Mpro inhibitors. The FlipGFP assay was also applied to validate the structurally disparate Mpro inhibitors reported in the literature. Lastly, we introduce recent progress in identifying naturally occurring Mpro mutants that are resistant to nirmatrelvir from genome mining of the nsp5 sequences deposited in the GISAID database. Collectively, the covalent and noncovalent Mpro inhibitors and the nirmatrelvir-resistant hot spot residues from our studies provide insightful guidance for future work aimed at developing orally bioavailable Mpro inhibitors that do not have overlapping resistance profile with nirmatrelvir.

Discovery of inhibitors against SARS-CoV-2 main protease using fragment-based drug design.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent of coronavirus disease 2019 (COVID-19), in which the main protease (Mpro) plays an important role in the virus's life cycle. In this work, two representative peptide inhibitors (11a and PF-07321332) were selected, and their interaction mechanisms of non-covalently bound with Mpro were firstly investigated by means of molecular dynamical simulation. Then, using the fragment-based drug design method, some fragments from the existing SARS-CoV and SARS-CoV-2 inhibitors were selected to replace the original P2 and P3 fragments, resulting in some new molecules. Among them, two molecules (O-74 and N-98) were confirmed by molecular docking and molecular dynamics simulation, and ADMET properties prediction was employed for further verification. The results shown that they presented excellent activity and physicochemical properties, and had the potential to be new inhibitors for SARS-CoV-2 main protease.

Insights from incorporating quantum computing into drug design workflows.

MOTIVATION: While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS: We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION: Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling studies on the role of vitamins B1 (thiamin), B3 (nicotinamide), B6 (pyridoxamine), and caffeine as potential leads for the drug design against COVID-19.

In response to the COVID-19 pandemic, and the lack of effective and safe antivirals against it, we adopted a new approach in which food supplements with vital antiviral characteristics, low toxicity, and fast excretion have been targeted. The structures and chemical properties of the food supplements were compared to the promising antivirals against SARS-COV-2. Our goal was to exploit the food supplements to mimic the topical antivirals' functions but circumventing their severe side effects, which has limited the necessary dosage needed to exhibit the desired antiviral activity. On this line, after a comparative structural analysis of the chemicals mentioned above, and investigation of their potential mechanisms of action, we selected caffeine and some compounds of the vitamin B family and further applied molecular modeling techniques to evaluate their interactions with the RDB domain of the Spike protein of SARS-CoV-2 (SC2Spike) and its corresponding binding site on human ACE-2 (HssACE2). Our results pointed to vitamins B1 and B6 in the neutral form as potential binders to the HssACE2 RDB binding pocket that might be able to impair the SARS-CoV-2 mechanism of cell invasion, qualifying as potential leads for experimental investigation against COVID-19.

Comprehensive Fragment Screening of the SARS-CoV-2 Proteome Explores Novel Chemical Space for Drug Development.

SARS-CoV-2 (SCoV2) and its variants of concern pose serious challenges to the public health. The variants increased challenges to vaccines, thus necessitating for development of new intervention strategies including anti-virals. Within the international Covid19-NMR consortium, we have identified binders targeting the RNA genome of SCoV2. We established protocols for the production and NMR characterization of more than 80 % of all SCoV2 proteins. Here, we performed an NMR screening using a fragment library for binding to 25 SCoV2 proteins and identified hits also against previously unexplored SCoV2 proteins. Computational mapping was used to predict binding sites and identify functional moieties (chemotypes) of the ligands occupying these pockets. Striking consensus was observed between NMR-detected binding sites of the main protease and the computational procedure. Our investigation provides novel structural and chemical space for structure-based drug design against the SCoV2 proteome.

In silico investigation and potential therapeutic approaches of natural products for COVID-19: Computer-aided drug design perspective.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a substantial number of deaths around the world, making it a serious and pressing public health hazard. Phytochemicals could thus provide a rich source of potent and safer anti-SARS-CoV-2 drugs. The absence of approved treatments or vaccinations continues to be an issue, forcing the creation of new medicines. Computer-aided drug design has helped to speed up the drug research and development process by decreasing costs and time. Natural compounds like terpenoids, alkaloids, polyphenols, and flavonoid derivatives have a perfect impact against viral replication and facilitate future studies in novel drug discovery. This would be more effective if collaboration took place between governments, researchers, clinicians, and traditional medicine practitioners' safe and effective therapeutic research. Through a computational approach, this study aims to contribute to the development of effective treatment methods by examining the mechanisms relating to the binding and subsequent inhibition of SARS-CoV-2 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp). The in silico method has also been employed to determine the most effective drug among the mentioned compound and their aquatic, nonaquatic, and pharmacokinetics' data have been analyzed. The highest binding energy has been reported -11.4 kcal/mol against SARS-CoV-2 main protease (7MBG) in L05. Besides, all the ligands are non-carcinogenic, excluding L04, and have good water solubility and no AMES toxicity. The discovery of preclinical drug candidate molecules and the structural elucidation of pharmacological therapeutic targets have expedited both structure-based and ligand-based drug design. This review article will assist physicians and researchers in realizing the enormous potential of computer-aided drug design in the design and discovery of therapeutic molecules, and hence in the treatment of deadly diseases.

A molecular evolution algorithm for ligand design in DOCK.

As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single-molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein-ligand systems. Ensemble-based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS-CoV-2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run-time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.

A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery.

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.

Computational Design of Miniprotein Inhibitors Targeting SARS-CoV-2 Spike Protein.

The ongoing pandemic of COVID-19 caused by SARS-CoV-2 has become a global health problem. There is an urgent need to develop therapeutic drugs, effective therapies, and vaccines to prevent the spread of the virus. The virus first enters the host cell through the interaction between the receptor binding domain (RBD) of spike protein and the peptidase domain (PD) of the angiotensin-converting enzyme 2 (ACE2). Therefore, blocking the binding of RBD and ACE2 is a promising strategy to inhibit the invasion and infection of the virus in the host cell. In the study, we designed several miniprotein inhibitors against SARS-CoV-2 by single/double/triple-point mutant, based on the initial inhibitor LCB3. Molecular dynamics (MD) simulations and trajectory analysis were performed for an in-depth analysis of the structural stability, essential protein motions, and per-residue energy decomposition involved in the interaction of inhibitors with the RBD. The results showed that the inhibitors have adapted the protein RBD in the binding interface, thereby forming stable complexes. These inhibitors display low binding free energy in the MM/PBSA calculations, substantiating their strong interaction with RBD. Moreover, the binding affinity of the best miniprotein inhibitor, H6Y-M7L-L17F mutant, to RBD was ∼45 980 times (ΔG = RT ln Ki) higher than that of the initial inhibitor LCB3. Following H6Y-M7L-L17F mutant, the inhibitors with strong binding activity are successively H6Y-L17F, L17F, H6Y, and F30Y mutants. Our research proves that the miniprotein inhibitors can maintain their secondary structure and have a highly stable blocking (binding) effect on SARS-CoV-2. This study proposes novel miniprotein mutant inhibitors with enhanced binding to spike protein and provides potential guidance for the rational design of new SARS-CoV-2 spike protein inhibitors.