Dynamic undocking and the quasi-bound state as tools for drug discovery

Identification and Validation of New DNA-PKcs Inhibitors through High-Throughput Virtual Screening and Experimental Verification

DNA-PKcs is a crucial protein target involved in DNA repair and response pathways, with its abnormal activity closely associated with the occurrence and progression of various cancers. In this study, we employed a deep learning-based screening and molecular dynamics (MD) simulation-based pipeline, identifying eight candidates for DNA-PKcs targets. Subsequent experiments revealed the effective inhibition of DNA-PKcs-mediated cell proliferation by three small molecules (5025-0002, M769-1095, and V008-1080). These molecules exhibited anticancer activity with IC50 (inhibitory concentration at 50%) values of 152.6 μM, 30.71 μM, and 74.84 μM, respectively. Notably, V008-1080 enhanced homology-directed repair (HDR) mediated by CRISPR/Cas9 while inhibiting non-homologous end joining (NHEJ) efficiency. Further investigations into the structure-activity relationships unveiled the binding sites and critical interactions between these small molecules and DNA-PKcs. This is the first application of DeepBindGCN_RG in a real drug screening task, and the successful discovery of a novel DNA-PKcs inhibitor demonstrates its efficiency as a core component in the screening pipeline. Moreover, this study provides important insights for exploring novel anticancer therapeutics and advancing the development of gene editing techniques by targeting DNA-PKcs.

SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein-ligand binding affinity predictions in minutes

Accurate estimation of protein-ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization.

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.

Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations

The initial phases of drug discovery - in silico drug design - could benefit from first principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, yet many applications are currently limited by the short time scales that this approach can cover. Developing scalable first principle QM/MM MD interfaces fully exploiting current exascale machines - so far an unmet and crucial goal - will help overcome this problem, opening the way to the study of the thermodynamics and kinetics of ligand binding to protein with first principle accuracy. Here, taking two relevant case studies involving the interactions of ligands with rather large enzymes, we showcase the use of our recently developed massively scalable Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework (currently using DFT to describe the QM region) to investigate reactions and ligand binding in enzymes of pharmacological relevance. We also demonstrate for the first time strong scaling of MiMiC-QM/MM MD simulations with parallel efficiency of ∼70% up to >80,000 cores. Thus, among many others, the MiMiC interface represents a promising candidate toward exascale applications by combining machine learning with statistical mechanics based algorithms tailored for exascale supercomputers.

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction

The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical-chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.

Predicting Protein-Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges

The prediction of drug-target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.

Lenalidomide Stabilizes Protein-Protein Complexes by Turning Labile Intermolecular H-Bonds into Robust Interactions

Targeted protein degradation is a promising therapeutic strategy, spearheaded by the anti-myeloma drugs lenalidomide and pomalidomide. These drugs stabilize very efficiently the complex between the E3 ligase Cereblon (CRBN) and several non-native client proteins (neo-substrates), including the transcription factors Ikaros and Aiolos and the enzyme Caseine Kinase 1α (CK1α,), resulting in their degradation. Although the structures for these complexes have been determined, there are no evident interactions that can account for the high efficiency of formation of the ternary complex. We show that lenalidomide's stabilization of the CRBN-CK1α complex is largely due to hydrophobic shielding of intermolecular hydrogen bonds. We also find a quantitative relationship between hydrogen bond robustness and binding affinities of the ternary complexes. These results pave the way to further understand cooperativity effects in drug-induced protein-protein complexes and could help in the design of improved molecular glues and more efficient protein degraders.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein-Ligand Binding Poses

Predicting the correct pose of a ligand binding to a protein and its associated binding affinity is of great importance in computer-aided drug discovery. A number of approaches have been developed to these ends, ranging from the widely used fast molecular docking to the computationally expensive enhanced sampling molecular simulations. In this context, methods such as coarse-grained metadynamics and binding pose metadynamics (BPMD) use simulations with metadynamics biasing to probe the binding affinity without trying to fully converge the binding free energy landscape in order to decrease the computational cost. In BPMD, the metadynamics bias perturbs the ligand away from the initial pose. The resistance of the ligand to this bias is used to calculate a stability score. The method has been shown to be useful in reranking predicted binding poses from docking. Here, we present OpenBPMD, an open-source Python reimplementation and reinterpretation of BPMD. OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. We also investigated the role of accurate water positioning on the performance of the algorithm and showed how the combination with a grand-canonical Monte Carlo algorithm improves the accuracy of the predictions.

4-Chloro-1,2-phenylenediamine induced structural perturbation and genotoxic aggregation in human serum albumin

4-Chloro-1,2-phenylenediamine (4-Cl-OPD) is a halogenated aromatic diamine used as a precursor in permanent hair color production. Despite its well-documented mutagenic and carcinogenic effects in various in vitro and in vivo models, its role in fibrillar aggregate formation and their genotoxic effect in therapeutic proteins has received less attention. The significance of human serum albumin (HSA) arises from its involvement in bio-regulatory and transport processes. HSA misfolding and aggregation are responsible for some of the most frequent neurodegenerative disorders. We used various complementary approaches to track the formation of amyloid fibrils and their genotoxic effect. Molecular dynamics study demonstrated the complex stability. The impact of 4-Cl-OPD on the structural dynamics of HSA was confirmed by Raman spectroscopy, X-ray diffraction, HPLC and SDS-PAGE. Fibrilllar aggregates were investigated using Congo red assay, DLS, and SEM. The genotoxic nature of 4-Cl-OPD was confirmed using plasmid nicking assay and DAPI staining, which revealed DNA damage and cell apoptosis. 4-Cl-OPD provides a model system for studying fibrillar aggregation and their genotoxic potential in the current investigation. Future studies should investigate the inhibition of the aggregation/fibrillation process, which may yield valuable clinical insights.

An Efficient Modern Strategy to Screen Drug Candidates Targeting RdRp of SARS-CoV-2 With Potentially High Selectivity and Specificity

Desired drug candidates should have both a high potential binding chance and high specificity. Recently, many drug screening strategies have been developed to screen compounds with high possible binding chances or high binding affinity. However, there is still no good solution to detect whether those selected compounds possess high specificity. Here, we developed a reverse DFCNN (Dense Fully Connected Neural Network) and a reverse docking protocol to check a given compound’s ability to bind diversified targets and estimate its specificity with homemade formulas. We used the RNA-dependent RNA polymerase (RdRp) target as a proof-of-concept example to identify drug candidates with high selectivity and high specificity. We first used a previously developed hybrid screening method to find drug candidates from an 8888-size compound database. The hybrid screening method takes advantage of the deep learning-based method, traditional molecular docking, molecular dynamics simulation, and binding free energy calculated by metadynamics, which should be powerful in selecting high binding affinity candidates. Also, we integrated the reverse DFCNN and reversed docking against a diversified 102 proteins to the pipeline for assessing the specificity of those selected candidates, and finally got compounds that have both predicted selectivity and specificity. Among the eight selected candidates, Platycodin D and Tubeimoside III were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC₅₀ values of 619.5 and 265.5 nM, respectively. Our study discovered that Tubeimoside III could inhibit SARS-CoV-2 replication potently for the first time. Furthermore, the underlying mechanisms of Platycodin D and Tubeimoside III inhibiting SARS-CoV-2 are highly possible by blocking the RdRp cavity according to our screening procedure. In addition, the careful analysis predicted common critical residues involved in the binding with active inhibitors Platycodin D and Tubeimoside III, Azithromycin, and Pralatrexate, which hopefully promote the development of non-covalent binding inhibitors against RdRp.

Cosolvent Sites-Based Discovery of Mycobacterium Tuberculosis Protein Kinase G Inhibitors

Computer-aided drug discovery methods play a major role in the development of therapeutically important small molecules, but their performance needs to be improved. Molecular dynamics simulations in mixed solvents are useful in understanding protein–ligand recognition and improving molecular docking predictions. In this work, we used ethanol as a cosolvent to find relevant interactions for ligands toward protein kinase G, an essential protein of Mycobacterium tuberculosis (Mtb). We validated the hot spots by screening a database of fragment-like compounds and another one of known kinase inhibitors. Next, we performed a pharmacophore-guided docking simulation and found three low micromolar inhibitors, including one with a novel chemical scaffold that we expanded to four derivative compounds. Binding affinities were characterized by intrinsic fluorescence quenching assays, isothermal titration calorimetry, and the analysis of melting curves. The predicted binding mode was confirmed by X-ray crystallography. Finally, the compounds significantly inhibited the viability of Mtb in infected THP-1 macrophages.

Development of an Automatic Pipeline for Participation in the CELPP Challenge

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining—whenever possible—empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein–ligand complexes, which will be addressed in future versions of the pipeline.

Computational Design of Inhibitors Targeting the Catalytic β Subunit of Escherichia coli FOF1-ATP Synthase

With the uncontrolled growth of multidrug-resistant bacteria, there is an urgent need to search for new therapeutic targets, to develop drugs with novel modes of bactericidal action. FoF1-ATP synthase plays a crucial role in bacterial bioenergetic processes, and it has emerged as an attractive antimicrobial target, validated by the pharmaceutical approval of an inhibitor to treat multidrug-resistant tuberculosis. In this work, we aimed to design, through two types of in silico strategies, new allosteric inhibitors of the ATP synthase, by targeting the catalytic β subunit, a centerpiece in communication between rotor subunits and catalytic sites, to drive the rotary mechanism. As a model system, we used the F1 sector of Escherichia coli, a bacterium included in the priority list of multidrug-resistant pathogens. Drug-like molecules and an IF1-derived peptide, designed through molecular dynamics simulations and sequence mining approaches, respectively, exhibited in vitro micromolar inhibitor potency against F1. An analysis of bacterial and Mammalia sequences of the key structural helix-turn-turn motif of the C-terminal domain of the β subunit revealed highly and moderately conserved positions that could be exploited for the development of new species-specific allosteric inhibitors. To our knowledge, these inhibitors are the first binders computationally designed against the catalytic subunit of FOF1-ATP synthase.

SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction

A novel crystallographic fragment screening data set was generated and used in the SAMPL7 challenge for protein-ligands. The SAMPL challenges prospectively assess the predictive power of methods involved in computer-aided drug design. Application of various methods to fragment molecules are now widely used in the search for new drugs. However, there is little in the way of systematic validation specifically for fragment-based approaches. We have performed a large crystallographic high-throughput fragment screen against the therapeutically relevant second bromodomain of the Pleckstrin-homology domain interacting protein (PHIP2) that revealed 52 different fragments bound across 4 distinct sites, 47 of which were bound to the pharmacologically relevant acetylated lysine (Kac) binding site. These data were used to assess computational screening, binding pose prediction and follow-up enumeration. All submissions performed randomly for screening. Pose prediction success rates (defined as less than 2 Å root mean squared deviation against heavy atom crystal positions) ranged between 0 and 25% and only a very few follow-up compounds were deemed viable candidates from a medicinal-chemistry perspective based on a common molecular descriptors analysis. The tight deadlines imposed during the challenge led to a small number of submissions suggesting that the accuracy of rapidly responsive workflows remains limited. In addition, the application of these methods to reproduce crystallographic fragment data still appears to be very challenging. The results show that there is room for improvement in the development of computational tools particularly when applied to fragment-based drug design.

Contribution of Hydrophobic Interactions to Protein Mechanical Stability

The role of hydrophobic and polar interactions in providing thermodynamic stability to folded proteins has been intensively studied, but the relative contribution of these interactions to the mechanical stability is less explored. We used steered molecular dynamics simulations with constant-velocity pulling to generate force-extension curves of selected protein domains and monitor hydrophobic surface unravelling upon extension. Hydrophobic contribution was found to vary between one fifth and one third of the total force while the rest of the contribution is attributed primarily to hydrogen bonds. Moreover, hydrophobic force peaks were shifted towards larger protein extensions with respect to the force peaks attributed to hydrogen bonds. The higher importance of hydrogen bonds compared to hydrophobic interactions in providing mechanical resistance is in contrast with the relative importance of the hydrophobic interactions in providing thermodynamic stability of proteins. The different contributions of these interactions to the mechanical stability are explained by the steeper free energy dependence of hydrogen bonds compared to hydrophobic interactions on the relative positions of interacting atoms. Comparative analyses for several protein domains revealed that the variation of hydrophobic forces is modest, while the contribution of hydrogen bonds to the force peaks becomes increasingly important for mechanically resistant protein domains.

Extracting Atomic Contributions to Binding Free Energy Using Molecular Dynamics Simulations with Mixed Solvents (MDmix)

BACKGROUND

Mixed solvents MD (MDmix) simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery. One of the assumptions behind the methodology is the transferability of free energy values from the simulated cosolvent molecules to larger drug-like molecules. However, the binding free energy maps (ΔGbind) calculated for the different moieties of the cosolvent molecules (e.g. a hydroxyl map for the ethanol) are largely influenced by the rest of the solvent molecule and do not reflect the intrinsic affinity of the moiety in question. As such, they are hardly transferable to different molecules.

METHOD

To achieve transferable energies, we present here a method for decomposing the molecular binding free energy into accurate atomic contributions.

RESULT

We demonstrate with two qualitative visual examples how the corrected energy maps better match known binding hotspots and how they can reveal hidden hotspots with actual drug design potential.

CONCLUSION

Atomic decomposition of binding free energies derived from MDmix simulations provides transferable and quantitative binding free energy maps.

Discovery and In Vivo Proof of Concept of a Highly Potent Dual Inhibitor of Soluble Epoxide Hydrolase and Acetylcholinesterase for the Treatment of Alzheimer's Disease

With innumerable clinical failures of target-specific drug candidates for multifactorial diseases, such as Alzheimer's disease (AD), which remains inefficiently treated, the advent of multitarget drug discovery has brought a new breath of hope. Here, we disclose a class of 6-chlorotacrine (huprine)-TPPU hybrids as dual inhibitors of the enzymes soluble epoxide hydrolase (sEH) and acetylcholinesterase (AChE), a multitarget profile to provide cumulative effects against neuroinflammation and memory impairment. Computational studies confirmed the gorge-wide occupancy of both enzymes, from the main site to a secondary site, including a so far non-described AChE cryptic pocket. The lead compound displayed in vitro dual nanomolar potencies, adequate brain permeability, aqueous solubility, human microsomal stability, lack of neurotoxicity, and it rescued memory, synaptic plasticity, and neuroinflammation in an AD mouse model, after low dose chronic oral administration.

Drug Discovery of Plausible Lead Natural Compounds That Target the Insulin Signaling Pathway: Bioinformatics Approaches

The growing smooth talk in the field of natural compounds is due to the ancient and current interest in herbal medicine and their potentially positive effects on health. Dozens of antidiabetic natural compounds were reported and tested in vivo, in silico, and in vitro. The role of these natural compounds, their actions on the insulin signaling pathway, and the stimulation of the glucose transporter-4 (GLUT4) insulin-responsive translocation to the plasma membrane (PM) are all crucial in the treatment of diabetes and insulin resistance. In this review, we collected and summarized a group of available in vivo and in vitro studies which targeted isolated phytochemicals with possible antidiabetic activity. Moreover, the in silico docking of natural compounds with some of the insulin signaling cascade key proteins is also summarized based on the current literature. In this review, hundreds of recent studies on pure natural compounds that alleviate type II diabetes mellitus (type II DM) were revised. We focused on natural compounds that could potentially regulate blood glucose and stimulate GLUT4 translocation through the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway. On attempt to point out potential new natural antidiabetic compounds, this review also focuses on natural ingredients that were shown to interact with proteins in the insulin signaling pathway in silico, regardless of their in vitro/in vivo antidiabetic activity. We invite interested researchers to test these compounds as potential novel type II DM drugs and explore their therapeutic mechanisms.

Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins

Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family.

PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks

Deep learning has been successfully applied to structure-based protein–ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented K_DEEP, a convolutional neural network that predicted the binding affinity of a given protein–ligand complex while reaching state-of-the-art performance. However, it was unclear what this model was learning. In this work, we present a new application to visualize the contribution of each input atom to the prediction made by the convolutional neural network, aiding in the interpretability of such predictions. The results suggest that K_DEEP is able to learn meaningful chemistry signals from the data, but it has also exposed the inaccuracies of the current model, serving as a guideline for further optimization of our prediction tools.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

Discovery of Novel BRD4 Ligand Scaffolds by Automated Navigation of the Fragment Chemical Space

Fragment-based drug discovery (FBDD) is a very effective hit identification method. However, the evolution of fragment hits into suitable leads remains challenging and largely artisanal. Fragment evolution is often scaffold-centric, meaning that its outcome depends crucially on the chemical structure of the starting fragment. Considering that fragment screening libraries cover only a small proportion of the corresponding chemical space, hits should be seen as probes highlighting privileged areas of the chemical space rather than actual starting points. We have developed an automated computational pipeline to mine the chemical space around any specific fragment hit, rapidly finding analogues that share a common interaction motif but are structurally novel and diverse. On a prospective application on the bromodomain-containing protein 4 (BRD4), starting from a known fragment, the platform yields active molecules with nonobvious scaffold changes. The procedure is fast and inexpensive and has the potential to uncover many hidden opportunities in FBDD.

Exploring the binding mechanism of positive allosteric modulators in human metabotropic glutamate receptor 2 using molecular dynamics simulations

Positive allosteric modulators (PAMs) of human metabotropic glutamate receptor 2 (hmGlu₂) are well-known in the treatment of psychiatric disorders for their higher selectivity and lower tolerance risk. A variety of PAMs have been reported over the last decade and two compounds were in Phase II clinical trials for schizophrenia and anxiety. These trials were discontinued on account of the unsatisfactory therapeutic efficacy, but PAMs were explored as novel treatments for addiction and epilepsy. Thus, it is still important to explore novel hmGlu₂ PAMs in the near future. Nowadays, the challenges in optimizing drug potency and improving scaffold diversity for PAMs are the noncomprehensive character analyses of multiple scaffolds; the exploration of the binding modes of PAMs in the allosteric binding site have been proposed to reduce this difficulty. However, there has been no comprehensive research about the binding profiles of PAMs in the hmGlu₂ receptor. To address this issue, this work explores the binding characters of eight PAMs representing five chemical series by multiple computational methods. As a result, the shared binding modes of the eight studied PAMs interacting with 15 residues in the allosteric binding site were defined. In addition, the reduced hydrophobicity with low electronegativity of R₁, increased hydrophobicity with low negative electron density of R₂ and the electronegativity of the linker were identified as indicators that regulate the affinity of PAMs. This finding agrees well with the physicochemical properties of reported multiple series PAMs. This comprehensive work sheds additional light on the binding mechanism and physicochemical regularity underlining PAMs affinity and could be further utilized as a structural and energetic blueprint for discovering and assessing novel PAMs for hmGlu₂.

Alkylammonium Cation Affinities of Nitrogenated Organobases: The Roles of Hydrogen Bonding and Proton Transfer

Alkylammonium cation affinities of 64 nitrogen-containing organobases, as well as the respective proton transfer processes from the alkylammonium cations to the base, have been computed in the gas phase by using DFT methods. The guanidine bases show the highest proton transfer values (191.9-233 kJ mol^-1 ) whereas the cis-2,2'-biimidazole presents the largest affinity towards the alkylammonium cations (>200 kJ mol^-1 ) values. The resulting data have been compared with the experimentally reported proton affinities of the studied nitrogen-containing organobases revealing that the propensity of an organobase for the proton transfer process increases linearly with its proton affinity. This work can provide a tool for designing senors for bioactive compounds containing amino groups that are protonated at physiological pH.

Fragment-based drug design facilitates selective kinase inhibitor discovery

Protein kinases (PKs) are important drug targets, but kinases selectivity poses a challenge to protein kinase inhibitors (PKIs) design. Fragment-based drug discovery (FBDD) has achieved great success in the discovery of highly specific PKIs. It makes full use of kinase-fragment interaction in target kinase subpockets to obtain promising selectivity. However, it's difficult to understand the complicated kinase-fragment interaction space, and systemic discussion of these interactions is still lacking. Herein, we introduce the advantages of the FBDD strategy in PKIs design. Key features of the selectivity of kinase-fragment interactions are summarized and analyzed. Some promising PKIs are introduced as case studies to help understand the fragment-to-lead (F2L) optimization process. Novel strategies and technologies for FBDD in PKIs discovery are also outlooked.

The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding

The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.

Discovery of RTA ricin subunit inhibitors: a computational study using PM7 quantum chemical method and steered molecular dynamics

Ricin is a potent toxin derived from the castor bean plant and comprises two subunits, RTA and RTB. Because of its cytotoxicity, ricin has alarmed world authorities for its potential use as a chemical weapon. Ricin also affects castor bean agribusiness, given the risk of animal and human poisoning. Over the years, many groups attempted to propose small-molecules that bind to the RTA active site, the catalytic chain. Despite such efforts, there is still no effective countermeasure against ricin poisoning. The computational study carried out in the present work renews the discussion about small-molecules that may inhibit this toxin. Here, a structure-based virtual screening protocol capable of discerning active RTA inhibitors from inactive ones was performed to screen over 2 million compounds from the ZINC database to find novel scaffolds that strongly bind into the active site of the RTA. Besides, a novel score method based on ligand undocking force profiles and semi-empirical quantum chemical calculations provided insights into the rescore of docking poses. Summing up, the filtering steps pointed out seven main compounds, with the SCF00-451 as a promising candidate to inhibit the killing activity of such potent phytotoxin.

A novel virtual screening procedure identifies Pralatrexate as inhibitor of SARS-CoV-2 RdRp and it reduces viral replication in vitro

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus poses serious threats to the global public health and leads to worldwide crisis. No effective drug or vaccine is readily available. The viral RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. A hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected market available drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008μM and 9.453 μM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of fast and accurate anti-viral drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors

Targeted covalent inhibitors are currently showing great promise for systems that are normally difficult to target with small molecule therapies. This renewed interest has spurred the refinement of existing computational methods as well as the designof new ones, expanding the toolbox for discovery and optimization of selectiveand effective covalent inhibitors. Commonly applied approaches are covalentdocking methods that predict the conformation of the covalent complex with known residues. More recently, a new predictive method, reactive docking, was developed, building on the growing corpus of data generated by large proteomics experiments. This method was successfully used in several 'inverse drug discovery' programs that use high-throughput techniques to isolate effective compounds based on screening of entire compound libraries based on desired phenotypes.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Metadynamics as a Postprocessing Method for Virtual Screening with Application to the Pseudokinase Domain of JAK2

With standard scoring methods, top-ranked compounds from virtual screening by docking often turn out to be inactive. For this reason, metadynamics, a method used to sample rare events, was studied to further evaluate docking poses with the aim of reducing false positives. Specifically, virtual screening was performed with Glide SP to seek potential molecules to bind to the ATP site in the pseudokinase domain of JAK2 kinase, and promising compounds were selected from the top-ranked 1000 based on visualization. Rescoring with Glide XP, GOLD, and MM/GBSA was unable to differentiate well between active and inactive compounds. Metadynamics was then used to gauge the relative binding affinity from the required time or the potential of mean force needed to dissociate the ligand from the bound complex. With consideration of previously known binders of varying affinities, metadynamics was able to differentiate between the most active compounds and inactive or weakly active ones, and it could identify correctly most of the selected virtual screening compounds as false positives. Thus, metadynamics has the potential to be a viable postprocessing method for virtual screening, minimizing the expense of buying or synthesizing inactive compounds.

The rise of molecular simulations in fragment-based drug design (FBDD): an overview

Fragment-based drug discovery (FBDD) is an innovative approach, progressively more applied in the academic and industrial context, to enhance hit identification for previously considered undruggable biological targets. In particular, FBDD discovers low-molecular-weight (LMW) ligands (<300Da) able to bind to therapeutically relevant macromolecules in an affinity range from the micromolar (μM) to millimolar (mM). X-ray crystallography (XRC) and nuclear magnetic resonance (NMR) spectroscopy are commonly the methods of choice to obtain 3D information about the bound ligand-protein complex, but this can occasionally be problematic, mainly for early, low-affinity fragments. The recent development of computational fragment-based approaches provides a further strategy for improving the identification of fragment hits. In this review, we summarize the state of the art of molecular dynamics simulations approaches used in FBDD, and discuss limitations and future perspectives for these approaches.

Identification of 14 Known Drugs as Inhibitors of the Main Protease of SARS-CoV-2

A consensus virtual screening protocol has been applied to ca. 2000 approved drugs to seek inhibitors of the main protease (M ^pro ) of SARS-CoV-2, the virus responsible for COVID-19. 42 drugs emerged as top candidates, and after visual analyses of the predicted structures of their complexes with M ^pro , 17 were chosen for evaluation in a kinetic assay for M ^pro inhibition. Remarkably 14 of the compounds at 100-μM concentration were found to reduce the enzymatic activity and 5 provided IC ₅₀ values below 40 μM: manidipine (4.8 μM), boceprevir (5.4 μM), lercanidipine (16.2 μM), bedaquiline (18.7 μM), and efonidipine (38.5 μM). Structural analyses reveal a common cloverleaf pattern for the binding of the active compounds to the P1, P1', and P2 pockets of M ^pro . Further study of the most active compounds in the context of COVID-19 therapy is warranted, while all of the active compounds may provide a foundation for lead optimization to deliver valuable chemotherapeutics to combat the pandemic.

Distinct binding of cetirizine enantiomers to human serum albumin and the human histamine receptor H1

Cetirizine, a major metabolite of hydroxyzine, became a marketed second-generation H₁ antihistamine that is orally active and has a rapid onset of action, long duration of effects and a very good safety record at recommended doses. The approved drug is a racemic mixture of (S)-cetirizine and (R)-cetirizine, the latter being the levorotary enantiomer that also exists in the market as a third-generation, non-sedating and highly selective antihistamine. Both enantiomers bind tightly to the human histamine H₁ receptor (hH₁R) and behave as inverse agonists but the affinity and residence time of (R)-cetirizine are greater than those of (S)-cetirizine. In blood plasma, cetirizine exists in the zwitterionic form and more than 90% of the circulating drug is bound to human serum albumin (HSA), which acts as an inactive reservoir. Independent X-ray crystallographic work has solved the structure of the hH₁R:doxepin complex and has identified two drug-binding sites for cetirizine on equine serum albumin (ESA). Given this background, we decided to model a membrane-embedded hH₁R in complex with either (R)- or (S)-cetirizine and also the complexes of both ESA and HSA with these two enantiomeric drugs to analyze possible differences in binding modes between enantiomers and also among targets. The ensuing molecular dynamics simulations in explicit solvent and additional computational chemistry calculations provided structural and energetic information about all of these complexes that is normally beyond current experimental possibilities. Overall, we found very good agreement between our binding energy estimates and extant biochemical and pharmacological evidence. A much higher degree of solvent exposure in the cetirizine-binding site(s) of HSA and ESA relative to the more occluded orthosteric binding site in hH₁R is translated into larger positional fluctuations and considerably lower affinities for these two nonspecific targets. Whereas it is demonstrated that the two known pockets in ESA provide enough stability for cetirizine binding, only one such site does so in HSA due to a number of amino acid replacements. At the histamine-binding site in hH₁R, the distinct interactions established between the phenyl and chlorophenyl moieties of the two enantiomers with the amino acids lining up the pocket and between their free carboxylates and Lys179 in the second extracellular loop account for the improved pharmacological profile of (R)-cetirizine.

Discovery of a novel kinase hinge binder fragment by dynamic undocking

A virtual screening workflow for fragment-sized kinase inhibitors is presented, along with a newly identified and validated hinge binder fragment.

One of the key motifs of type I kinase inhibitors is their interactions with the hinge region of ATP binding sites. These interactions contribute significantly to the potency of the inhibitors; however, only a tiny fraction of the available chemical space has been explored with kinase inhibitors reported in the last twenty years. This paper describes a workflow utilizing docking with rDock and dynamic undocking (DUck) for the virtual screening of fragment libraries in order to identify fragments that bind to the kinase hinge region. We have identified 8-amino-2H-isoquinolin-1-one (MR1), a novel and potent hinge binding fragment, which was experimentally tested on a diverse set of kinases, and is hereby suggested for future fragment growing or merging efforts against various kinases, particularly MELK. Direct binding of MR1 to MELK was confirmed by STD-NMR, and its binding to the ATP-pocket was confirmed by a new competitive binding assay based on microscale thermophoresis.

Combined strategies in structure-based virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

An investigation of structural stability in protein-ligand complexes reveals the balance between order and disorder

The predominant view in structure-based drug design is that small-molecule ligands, once bound to their target structures, display a well-defined binding mode. However, structural stability (robustness) is not necessary for thermodynamic stability (binding affinity). In fact, it entails an entropic penalty that counters complex formation. Surprisingly, little is known about the causes, consequences and real degree of robustness of protein-ligand complexes. Since hydrogen bonds have been described as essential for structural stability, here we investigate 469 such interactions across two diverse structure sets, comprising of 79 drug-like and 27 fragment ligands, respectively. Completely constricted protein-ligand complexes are rare and may fulfill a functional role. Most complexes balance order and disorder by combining a single anchoring point with looser regions. 25% do not contain any robust hydrogen bond and may form loose structures. Structural stability analysis reveals a hidden layer of complexity in protein-ligand complexes that should be considered in ligand design.

DUckCov: a Dynamic Undocking-Based Virtual Screening Protocol for Covalent Binders

Thanks to recent guidelines, the design of safe and effective covalent drugs has gained significant interest. Other than targeting non‐conserved nucleophilic residues, optimizing the noncovalent binding framework is important to improve potency and selectivity of covalent binders toward the desired target. Significant efforts have been made in extending the computational toolkits to include a covalent mechanism of protein targeting, like in the development of covalent docking methods for binding mode prediction. To highlight the value of the noncovalent complex in the covalent binding process, here we describe a new protocol using tethered and constrained docking in combination with Dynamic Undocking (DUck) as a tool to privilege strong protein binders for the identification of novel covalent inhibitors. At the end of the protocol, dedicated covalent docking methods were used to rank and select the virtual hits based on the predicted binding mode. By validating the method on JAK3 and KRas, we demonstrate how this fast iterative protocol can be applied to explore a wide chemical space and identify potent targeted covalent inhibitors.

Exploring the binding mechanism between human profilin (PFN1) and polyproline-10 through binding mode screening

The large magnitude of protein-protein interaction (PPI) pairs within the human interactome necessitates the development of predictive models and screening tools to better understand this fundamental molecular communication. However, despite enormous efforts from various groups to develop predictive techniques in the last decade, PPI complex structures are in general still very challenging to predict due to the large number of degrees of freedom. In this study, we use the binding complex of human profilin (PFN1) and polyproline-10 (P10) as a model system to examine various approaches, with the aim of going beyond normal protein docking for PPI prediction and evaluation. The potential of mean force (PMF) was first obtained from the time-consuming umbrella sampling, which confirmed that the most stable binding structure identified by the maximal PMF difference is indeed the crystallographic binding structure. Moreover, crucial residues previously identified in experimental studies, W3, H133, and S137 of PFN1, were found to form favorable hydrogen bonds with P10, suggesting a zipping process during the binding between PFN1 and P10. We then explored both regular molecular dynamics (MD) and steered molecular dynamics (SMD) simulations, seeking for better criteria of ranking the PPI prediction. Despite valuable information obtained from conventional MD simulations, neither the commonly used interaction energy between the two binding parties nor the long-term root mean square displacement correlates well with the PMF results. On the other hand, with a sizable collection of trajectories, we demonstrated that the average and minimal rupture works calculated from SMD simulations correlate fairly well with the PMFs (R ² = 0.67), making this a promising PPI screening method.

Solvents to Fragments to Drugs: MD Applications in Drug Design

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.

Binding Affinity via Docking: Fact and Fiction

In 1982, Kuntz et al. published an article with the title “A Geometric Approach to Macromolecule-Ligand Interactions”, where they described a method “to explore geometrically feasible alignment of ligands and receptors of known structure”. Since then, small molecule docking has been employed as a fast way to estimate the binding pose of a given compound within a specific target protein and also to predict binding affinity. Remarkably, the first docking method suggested by Kuntz and colleagues aimed to predict binding poses but very little was specified about binding affinity. This raises the question as to whether docking is the right tool to estimate binding affinity. The short answer is no, and this has been concluded in several comprehensive analyses. However, in this opinion paper we discuss several critical aspects that need to be reconsidered before a reliable binding affinity prediction through docking is realistic. These are not the only issues that need to be considered, but they are perhaps the most critical ones. We also consider that in spite of the huge efforts to enhance scoring functions, the accuracy of binding affinity predictions is perhaps only as good as it was 10–20 years ago. There are several underlying reasons for this poor performance and these are analyzed. In particular, we focus on the role of the solvent (water), the poor description of H-bonding and the lack of the systems’ true dynamics. We hope to provide readers with potential insights and tools to overcome the challenging issues related to binding affinity prediction via docking.