Exploring Protein-Peptide Recognition Pathways Using a Supervised Molecular Dynamics Approach

Accurate prediction of dynamic protein-ligand binding using P-score ranking

Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

Fighting Antimicrobial Resistance: Insights on How the Staphylococcus aureus NorA Efflux Pump Recognizes 2-Phenylquinoline Inhibitors by Supervised Molecular Dynamics (SuMD) and Molecular Docking Simulations

The superbug Staphylococcus aureus (S. aureus) exhibits several resistance mechanisms, including efflux pumps, that strongly contribute to antimicrobial resistance. In particular, the NorA efflux pump activity is associated with S. aureus resistance to fluoroquinolone antibiotics (e.g., ciprofloxacin) by promoting their active extrusion from cells. Thus, since efflux pump inhibitors (EPIs) are able to increase antibiotic concentrations in bacteria as well as restore their susceptibility to these agents, they represent a promising strategy to counteract bacterial resistance. Additionally, the very recent release of two NorA efflux pump cryo-electron microscopy (cryo-EM) structures in complex with synthetic antigen-binding fragments (Fabs) represents a real breakthrough in the study of S. aureus antibiotic resistance. In this scenario, supervised molecular dynamics (SuMD) and molecular docking experiments were combined to investigate for the first time the molecular mechanisms driving the interaction between NorA and efflux pump inhibitors (EPIs), with the ultimate goal of elucidating how the NorA efflux pump recognizes its inhibitors. The findings provide insights into the dynamic NorA-EPI intermolecular interactions and lay the groundwork for future drug discovery efforts aimed at the identification of novel molecules to fight antimicrobial resistance.

Computational Design, Synthesis, and Biological Evaluation of Diimidazole Analogues Endowed with Dual PCSK9/HMG-CoAR-Inhibiting Activity

Proprotein convertase subtilisin/kexin 9 (PCSK9) is responsible for the degradation of the hepatic low-density lipoprotein receptor (LDLR), which regulates circulating cholesterol levels. Consequently, the PCSK9 inhibition is a valuable therapeutic approach for the treatment of hypercholesterolemia and cardiovascular diseases. In our studies, we discovered Rim13, a polyimidazole derivative reducing the protein-protein interaction between PCSK9 and LDLR with an IC₅₀ of 1.6 μM. The computational design led to the optimization of the shape of the PCSK9/ligand complementarity, enabling the discovery of potent diimidazole derivatives. In fact, carrying out biological assays to fully characterize the cholesterol-lowering activity of the new analogues and using both biochemical and cellular techniques, compound Dim16 displayed improved PCSK9 inhibitory activity (IC₅₀ 0.9 nM). Interestingly, similar to other lupin-derived peptides and their synthetic analogues, some compounds in this series showed dual hypocholesterolemic activity since some of them complementarily inhibited the 3-hydroxy-3-methylglutaryl coenzyme A reductase.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Ligand-Guided Investigation of a Series of Formamidine-Based Thiuram Disulfides as Potential Dual-Inhibitors of COX-1and COX-2

A series of thiuram disulfides 1-6 which had been previously synthesized and characterized,^[1] were studied for their potential therapeutic properties. Target-fishing analyses through HitPick and SwissTarget prediction identified COX1 and COX2, which are essential biomolecules in cancer-related inflammations, as the possible targets for compounds 1 and 4 among all the compounds tested. These two proteins have enjoyed interest as targets for treating some neoplastic cancer types such as breast, colorectal, skin, pancreatic, haematological and head cancers. The inhibitory potency of 1 and 4 as lead anticancer drug candidates with dual-target ability against COX1 and COX2 was examined through molecular docking, molecular dynamics simulation and post-MD analyses such as binding energy calculation, RMSD, RMSF, and RoG. The two compounds had better docking scores and binding energies than the known inhibitors of COX1 and COX2. Insights from the RMSD, RMSF, and RoG suggested that both 1 and 4 showed observable influence on the structural stability of these targets throughout the simulation. The reported observations of the effects of 1 and 4 on the structures of COX1 and COX2 indicate their probable inhibitory properties against these target proteins and their potential as lead anticancer drug candidates.

Investigating RNA-protein recognition mechanisms through supervised molecular dynamics (SuMD) simulations

Ribonucleic acid (RNA) plays a key regulatory role within the cell, cooperating with proteins to control the genome expression and several biological processes. Due to its characteristic structural features, this polymer can mold itself into different three-dimensional structures able to recognize target biomolecules with high affinity and specificity, thereby attracting the interest of drug developers and medicinal chemists. One successful example of the exploitation of RNA's structural and functional peculiarities is represented by aptamers, a class of therapeutic and diagnostic tools that can recognize and tightly bind several pharmaceutically relevant targets, ranging from small molecules to proteins, making use of the available structural and conformational freedom to maximize the complementarity with their interacting counterparts. In this scientific work, we present the first application of Supervised Molecular Dynamics (SuMD), an enhanced sampling Molecular Dynamics-based method for the study of receptor-ligand association processes in the nanoseconds timescale, to the study of recognition pathways between RNA aptamers and proteins, elucidating the main advantages and limitations of the technique while discussing its possible role in the rational design of RNA-based therapeutics.

The full activation mechanism of the adenosine A1 receptor revealed by GaMD and Su-GaMD simulations

The full activation process of G protein-coupled receptor (GPCR) plays an important role in cellular signal transduction. However, it remains challenging to simulate the whole process in which the GPCR is recognized and activated by a ligand and then couples to the G protein on a reasonable simulation timescale. Here, we developed a molecular dynamics (MD) approach named supervised (Su) Gaussian accelerated MD (GaMD) by incorporating a tabu-like supervision algorithm into a standard GaMD simulation. By using this Su-GaMD method, from the active and inactive structure of adenosine A₁ receptor (A₁R), we successfully revealed the full activation mechanism of A₁R, including adenosine (Ado)-A₁R recognition, preactivation of A₁R, and A₁R-G protein recognition, in hundreds of nanoseconds of simulations. The binding of Ado to the extracellular side of A₁R initiates conformational changes and the preactivation of A₁R. In turn, the binding of G_i2 to the intracellular side of A₁R causes a decrease in the volume of the extracellular orthosteric site and stabilizes the binding of Ado to A₁R. Su-GaMD could be a useful tool to reconstruct or even predict ligand-protein and protein-protein recognition pathways on a short timescale. The intermediate states revealed in this study could provide more detailed complementary structural characterizations to facilitate the drug design of A₁R in the future.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

Selective activation of Gαob by an adenosine A1 receptor agonist elicits analgesia without cardiorespiratory depression

The development of therapeutic agonists for G protein-coupled receptors (GPCRs) is hampered by the propensity of GPCRs to couple to multiple intracellular signalling pathways. This promiscuous coupling leads to numerous downstream cellular effects, some of which are therapeutically undesirable. This is especially the case for adenosine A1 receptors (A1Rs) whose clinical potential is undermined by the sedation and cardiorespiratory depression caused by conventional agonists. We have discovered that the A1R-selective agonist, benzyloxy-cyclopentyladenosine (BnOCPA), is a potent and powerful analgesic but does not cause sedation, bradycardia, hypotension or respiratory depression. This unprecedented discrimination between native A1Rs arises from BnOCPA's unique and exquisitely selective activation of Gob among the six Gαi/o subtypes, and in the absence of β-arrestin recruitment. BnOCPA thus demonstrates a highly-specific Gα-selective activation of the native A1R, sheds new light on GPCR signalling, and reveals new possibilities for the development of novel therapeutics based on the far-reaching concept of selective Gα agonism.

A plausible contributor to multiple sclerosis; presentation of antigenic myelin protein epitopes by major histocompatibility complexes

BACKGROUND

Multiple sclerosis (MS) can be induced upon successful presentation of myelin antigens by MHC I/II. Antigenic similarity between the myelin and viral proteins may worsen the immunological responses.

METHODOLOGY

Antigenic regions within myelin proteins; PLP1, MBP, MOG, and MAG were analyzed using SVMTrip and EMBOSS. Homology search identified sequence similarity between the predicted host epitopes and viral proteins. NetMHCpan predicted MHC I/II binding followed by peptide-protein docking through the HPEPDOCK server. Thereafter we analyzed conformational flexibility and stability of 15 protein-peptide complexes based on high docking scores. The binding free energy was calculated using conventional (MD) and Gaussian accelerated molecular dynamics simulation.

RESULTS

PLP1, MBP, MAG and MOG contained numerous antigenic epitopes. MBP and MOG epitopes had sequence similarity to HHV-6 BALF5; EBNA1 and CMV glycoprotein M (gM), and EBV LMP2B, gp350/220; HHV-8 ORFs respectively. Many herpes virus proteins like tegument, envelope glycoproteins, and ORFs of EBV, CMV, HHV-6, and HHV-8 demonstrated sequence similarity with MAG and PLP1. Some antigenic peptides were also linear B-cell epitopes and influenced cytokine production by T-cell. MHC I allele HLA-B*57:01 bound to PLP1 peptide and HLA-A*68:02 bound to a MAG peptide strongly. MHC II alleles HLA-DRB1*04:05 and HLA-DR1*01:01 associated with MAG- and MOG-derived peptides, respectively, demonstrating high HPEPDOCK scores. MD simulations established stable binding of certain peptides with the MHC namely HLA-B*51:01-MBP(DYKSAHKGFKGVDAQGTLSKIFKL), HLA-B*57:01-PLP1(PDKFVGITYALTVVWLLVFACSAVPVYIYF), HLA-DR1*01:01-MOG(VEDPFYWVSPGVLVLLAVLPVLLLQITVGLVFLCLQYR) and HLA-DRB1*04:05-MAG(TWVQVSLLHFVPTREA).

CONCLUSIONS

Cross-reactivity between self-antigens and pathogen derived immunodominant epitopes may induce MS. Our study supported the role of specific MHC alleles as a contributing MS risk factor.

Cryo-EM structure of the heptameric calcium homeostasis modulator 1 channel

Calcium homeostasis modulator 1 (CALHM1) is a voltage- and Ca²⁺-gated ATP channel that plays an important role in neuronal signaling. However, as the previously reported CALHM structures are all in the ATP-conducting state, the gating mechanism of ATP permeation is still elusive. Here, we report cryo-EM reconstructions of two Danio rerio CALHM1 heptamers with ordered or flexible long C-terminal helices at resolutions of 3.2 Å and 2.9 Å, respectively, and one D. rerio CALHM1 octamer with flexible long C-terminal helices at a resolution of 3.5 Å. Structural analysis shows that the heptameric CALHM1s are in an ATP-nonconducting state with a central pore diameter of approximately 6.6 Å. Compared with those inside the octameric CALHM1, the N-helix inside the heptameric CALHM1 is in the “down” position to avoid steric clashing with the adjacent TM1 helix. Molecular dynamics simulations show that as the N-helix moves from the “down” position to the “up” position, the pore size of ATP molecule permeation increases significantly. Our results provide important information for elucidating the mechanism of ATP molecule permeation in the CALHM1 channel.

Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives

Biomolecular recognition such as binding of small molecules, nucleic acids, peptides and proteins to their target receptors plays key roles in cellular function and has been targeted for therapeutic drug design. Molecular dynamics (MD) is a computational approach to analyze these binding processes at an atomistic level, which provides valuable understandings of the mechanisms of biomolecular recognition. However, the rather slow biomolecular binding events often present challenges for conventional MD (cMD), due to limited simulation timescales (typically over hundreds of nanoseconds to tens of microseconds). In this regard, enhanced sampling methods, particularly accelerated MD (aMD), have proven useful to bridge the gap and enable all-atom simulations of biomolecular binding events. Here, we will review the recent method developments of Gaussian aMD (GaMD), ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD), which have greatly expanded our capabilities to simulate biomolecular binding processes. Spontaneous binding of various biomolecules to their receptors has been successfully simulated by GaMD. Microsecond LiGaMD and Pep-GaMD simulations have captured repetitive binding and dissociation of small-molecule ligands and highly flexible peptides, and thus enabled ligand/peptide binding thermodynamics and kinetics calculations. We will also present relevant application studies in simulations of important drug targets and future perspectives for rational computer-aided drug design.

HT-SuMD: making molecular dynamics simulations suitable for fragment-based screening. A comparative study with NMR

Fragment-based lead discovery (FBLD) is one of the most efficient methods to develop new drugs. We present here a new computational protocol called High-Throughput Supervised Molecular Dynamics (HT-SuMD), which makes it possible to automatically screen up to thousands of fragments, representing therefore a new valuable resource to prioritise fragments in FBLD campaigns. The protocol was applied to Bcl-XL, an oncological protein target involved in the regulation of apoptosis through protein-protein interactions. Initially, HT-SuMD performances were validated against a robust NMR-based screening, using the same set of 100 fragments. These independent results showed a remarkable agreement between the two methods. Then, a virtual screening on a larger library of additional 300 fragments was carried out and the best hits were validated by NMR. Remarkably, all the in silico selected fragments were confirmed as Bcl-XL binders. This represents, to date, the largest computational fragments screening entirely based on MD.

Insights on peptide topology in the computational design of protein ligands: the example of lysozyme binding peptides

Herein, we compared the ability of linear and cyclic peptides generated in silico to target different protein sites: internal pockets and solvent-exposed sites. We selected human lysozyme (HuL) as a model target protein combined with the computational evolution of linear and cyclic peptides. The sequence evolution of these peptides was based on the PARCE algorithm. The generated peptides were screened based on their aqueous solubility and HuL binding affinity. The latter was evaluated by means of scoring functions and atomistic molecular dynamics (MD) trajectories in water, which allowed prediction of the structural features of the protein-peptide complexes. The computational results demonstrated that cyclic peptides constitute the optimal choice for solvent exposed sites, while both linear and cyclic peptides are capable of targeting the HuL pocket effectively. The most promising binders found in silico were investigated experimentally by surface plasmon resonance (SPR), nuclear magnetic resonance (NMR), and electrospray ionization mass spectrometry (ESI-MS) techniques. All tested peptides displayed dissociation constants in the micromolar range, as assessed by SPR; however, both NMR and ESI-MS suggested multiple binding modes, at least for the pocket binding peptides. A detailed NMR analysis confirmed that both linear and cyclic pocket peptides correctly target the binding site they were designed for.

Shedding Light on the Molecular Recognition of Sub-Kilodalton Macrocyclic Peptides on Thrombin by Supervised Molecular Dynamics

Macrocycles are attractive structures for drug development due to their favorable structural features, potential in binding to targets with flat featureless surfaces, and their ability to disrupt protein–protein interactions. Moreover, large novel highly diverse libraries of low-molecular-weight macrocycles with therapeutically favorable characteristics have been recently established. Considering the mentioned facts, having a validated, fast, and accurate computational protocol for studying the molecular recognition and binding mode of this interesting new class of macrocyclic peptides deemed to be helpful as well as insightful in the quest of accelerating drug discovery. To that end, the ability of the in-house supervised molecular dynamics protocol called SuMD in the reproduction of the X-ray crystallography final binding state of a macrocyclic non-canonical tetrapeptide—from a novel library of 8,988 sub-kilodalton macrocyclic peptides—in the thrombin active site was successfully validated. A comparable binding mode with the minimum root-mean-square deviation (RMSD) of 1.4 Å at simulation time point 71.6 ns was achieved. This method validation study extended the application domain of the SuMD sampling method for computationally cheap, fast but accurate, and insightful macrocycle–protein molecular recognition studies.

Exploring Ligand Binding to Calcitonin Gene-Related Peptide Receptors

Class B1 G protein-coupled receptors (GPCRs) are important targets for many diseases, including cancer, diabetes, and heart disease. All the approved drugs for this receptor family are peptides that mimic the endogenous activating hormones. An understanding of how agonists bind and activate class B1 GPCRs is fundamental for the development of therapeutic small molecules. We combined supervised molecular dynamics (SuMD) and classic molecular dynamics (cMD) simulations to study the binding of the calcitonin gene-related peptide (CGRP) to the CGRP receptor (CGRPR). We also evaluated the association and dissociation of the antagonist telcagepant from the extracellular domain (ECD) of CGRPR and the water network perturbation upon binding. This study, which represents the first example of dynamic docking of a class B1 GPCR peptide, delivers insights on several aspects of ligand binding to CGRPR, expanding understanding of the role of the ECD and the receptor-activity modifying protein 1 (RAMP1) on agonist selectivity.

Targeting SARS-CoV-2 Receptor Binding Domain with Stapled Peptides: An In Silico Study

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved into a pandemic of unprecedented scale. This coronavirus enters cells by the interaction of the receptor binding domain (RBD) with the human angiotensin-converting enzyme 2 receptor (hACE2). In this study, we employed a rational structure-based design to propose 22-mer stapled peptides using the structure of the hACE2 α1 helix as a template. These peptides were designed to retain the α-helical character of the natural structure, to enhance binding affinity, and to display a better solubility profile compared to other designed peptides available in the literature. We employed different docking strategies (PATCHDOCK and ZDOCK) followed by a double-step refinement process (FIBERDOCK) to rank our peptides, followed by stability analysis/evaluation of the interaction profile of the best docking predictions using a 500 ns molecular dynamics (MD) simulation, and a further binding affinity analysis by molecular mechanics with generalized Born and surface area (MM/GBSA) method. Our most promising stapled peptides presented a stable profile and could retain important interactions with the RBD in the presence of the E484K RBD mutation. We predict that these peptides can bind to the viral RBD with similar potency to the control NYBSP-4 (a 30-mer experimentally proven peptide inhibitor). Furthermore, our study provides valuable information for the rational design of double-stapled peptide as inhibitors of SARS-CoV-2 infection.

Peptide Gaussian accelerated molecular dynamics (Pep-GaMD): Enhanced sampling and free energy and kinetics calculations of peptide binding

Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to simulate both binding and unbinding of peptides and calculate peptide binding free energies through conventional molecular dynamics, due to long biological timescales and extremely high flexibility of the peptides. Based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique, we have developed a new computational method "Pep-GaMD," which selectively boosts essential potential energy of the peptide in order to effectively model its high flexibility. In addition, another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Independent 1 µs dual-boost Pep-GaMD simulations have captured repetitive peptide dissociation and binding events, which enable us to calculate peptide binding thermodynamics and kinetics. The calculated binding free energies and kinetic rate constants agreed very well with available experimental data. Furthermore, the all-atom Pep-GaMD simulations have provided important insights into the mechanism of peptide binding to proteins that involves long-range electrostatic interactions and mainly conformational selection. In summary, Pep-GaMD provides a highly efficient, easy-to-use approach for unconstrained enhanced sampling and calculations of peptide binding free energies and kinetics.

Multisite Model of Allosterism for the Adenosine A1 Receptor

Despite being a target for about one-third of approved drugs, G protein-coupled receptors (GPCRs) still represent a tremendous reservoir for therapeutic strategies against diseases. For example, several cardiovascular and central nervous system conditions could benefit from clinical agents that activate the adenosine 1 receptor (A₁R); however, the pursuit of A₁R agonists for clinical use is usually impeded by both on- and off-target side effects. One of the possible strategies to overcome this issue is the development of positive allosteric modulators (PAMs) capable of selectively enhancing the effect of a specific receptor subtype and triggering functional selectivity (a phenomenon also referred to as bias). Intriguingly, besides enforcing the effect of agonists upon binding to an allosteric site, most of the A₁R PAMs display intrinsic partial agonism and orthosteric competition with antagonists. To rationalize this behavior, we simulated the binding of the prototypical PAMs PD81723 and VCP171, the full-agonist NECA, the antagonist 13B, and the bitopic agonist VCP746. We propose that a single PAM can bind several A₁R sites rather than a unique allosteric pocket, reconciling the structure-activity relationship and the mutagenesis results.

Developing novel classes of protein kinase CK1δ inhibitors by fusing [1,2,4]triazole with different bicyclic heteroaromatic systems

Protein kinase CK1δ expression and activity is involved in different pathological situations that include neuroinflammatory and neurodegenerative diseases. For this reason, protein kinase CK1δ has become a possible therapeutic target for these conditions. 5,6-fused bicyclic heteroaromatic systems that resemble adenine of ATP represent optimal scaffolds for the development of a new class of ATP competitive CK1δ inhibitors. In particular, a new series of [1,2,4]triazolo[1,5-c]pyrimidines and [1,2,4]triazolo[1,5-a][1,3,5]triazines was developed. Some crucial interactors have been identified, such as the presence of a free amino group able to interact with the residues of the hinge region at the 5- and 7- positions of the [1,2,4]triazolo[1,5-c]pyrimidine and [1,2,4]triazolo[1,5-a][1,3,5]triazine scaffolds, respectively; or the presence of a 3-hydroxyphenyl or 3,5-dihydroxyphenyl moiety at the 2- position of both nuclei. Molecular modeling studies identified the key interactions involved in the inhibitor-protein recognition process that appropriately fit with the outlined structure-activity relationship. Considering the fact that the CK1 protein kinase is involved in various pathologies in particular of the central nervous system, the interest in the development of new inhibitors permeable to the blood-brain barrier represents today an important goal in the pharmaceutical field. The best potent compound of the series is the 5-(7-amino-5-(benzylamino)-[1,2,4]triazolo[1,5-a][1,3,5]triazin-2-yl)benzen-1,3-diol (compound 51, IC₅₀ = 0.18 μM) that was predicted to have an intermediate ability to cross the membrane in our in vitro assay and represents an optimal starting point to both studies the therapeutic value of protein kinase CK1δ inhibition and to develop new more potent derivatives.

Recent advances in atomic molecular dynamics simulation of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in cellular functions. The inherent structural heterogeneity of IDPs makes the high-resolution experimental characterization of IDPs extremely difficult. Molecular dynamics (MD) simulation could provide the atomic-level description of the structural and dynamic properties of IDPs. This perspective reviews the recent progress in atomic MD simulation studies of IDPs, including the development of force fields and sampling methods, as well as applications in IDP-involved protein-protein interactions. The employment of large-scale simulations and advanced sampling techniques allows more accurate estimation of the thermodynamics and kinetics of IDP-mediated protein interactions, and the holistic landscape of the binding process of IDPs is emerging.

Computational Evolution of Beta-2-Microglobulin Binding Peptides for Nanopatterned Surface Sensors

The bottom-up design of smart nanodevices largely depends on the accuracy by which each of the inherent nanometric components can be functionally designed with predictive methods. Here, we present a rationally designed, self-assembled nanochip capable of capturing a target protein by means of pre-selected binding sites. The sensing elements comprise computationally evolved peptides, designed to target an arbitrarily selected binding site on the surface of beta-2-Microglobulin (β2m), a globular protein that lacks well-defined pockets. The nanopatterned surface was generated by an atomic force microscopy (AFM)-based, tip force-driven nanolithography technique termed nanografting to construct laterally confined self-assembled nanopatches of single stranded (ss)DNA. These were subsequently associated with an ssDNA-peptide conjugate by means of DNA-directed immobilization, therefore allowing control of the peptide's spatial orientation. We characterized the sensitivity of such peptide-containing systems against β2m in solution by means of AFM-based differential topographic imaging and surface plasmon resonance (SPR) spectroscopy. Our results show that the confined peptides are capable of specifically capturing β2m from the surface-liquid interface with micromolar affinity, hence providing a viable proof-of-concept for our approach to peptide design.

Targeting the coronavirus SARS-CoV-2: computational insights into the mechanism of action of the protease inhibitors lopinavir, ritonavir and nelfinavir

Coronavirus SARS-CoV-2 is a recently discovered single-stranded RNA betacoronavirus, responsible for a severe respiratory disease known as coronavirus disease 2019, which is rapidly spreading. Chinese health authorities, as a response to the lack of an effective therapeutic strategy, started to investigate the use of lopinavir and ritonavir, previously optimized for the treatment and prevention of HIV/AIDS viral infection. Despite the clinical use of these two drugs, no information regarding their possible mechanism of action at the molecular level is still known for SARS-CoV-2. Very recently, the crystallographic structure of the SARS-CoV-2 main protease (Mpro), also known as C30 Endopeptidase, was published. Starting from this essential structural information, in the present work we have exploited supervised molecular dynamics, an emerging computational technique that allows investigating at an atomic level the recognition process of a ligand from its unbound to the final bound state. In this research, we provided molecular insight on the whole recognition pathway of Lopinavir, Ritonavir, and Nelfinavir, three potential C30 Endopeptidase inhibitors, with the last one taken into consideration due to the promising in-vitro activity shown against the structurally related SARS-CoV protease.

Comparing Fragment Binding Poses Prediction Using HSP90 as a Key Study: When Bound Water Makes the Difference

Fragment-Based Drug Discovery (FBDD) approaches have gained popularitynot only in industry but also in academic research institutes. However, the computational prediction of the binding mode adopted by fragment-like molecules within a protein binding site is still a very challenging task. One of the most crucial aspects of fragment binding is related to the large amounts of bound waters in the targeted binding pocket. The binding affinity of fragmentsmay not be sufficientto displace the bound water molecules. In the present work, we confirmed the importance of the bound water molecules in the correct prediction of the fragment binding mode. Moreover, we investigate whether the use of methods based on explicit solvent molecular dynamics simulations can improve the accuracy of fragment posing. The protein chosen for this study is HSP-90.

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.

Investigation of noncovalent interactions between peptides with potential intrinsic sequence patterns by mass spectrometry

RATIONALE

The conformation of a protein largely depends on the interactions between peptides. Specific and intrinsic sequence peptide patterns, such as DNA double helix backbones, may be present in proteins. A computational statistical deep learning method has supported this assumption, but it has not been experimentally proven. Mass spectrometry, as a fast and accurate experimental method, could be used to evaluate the interaction of biomolecules. The results would be of great value for further study of the mechanism of protein folding.

METHODS

Several potential intrinsic peptides were chosen by the deep learning method, including seven groups of pentapeptides and five groups of nonapeptides. The noncovalent interactions between mixed polypeptides were investigated by electrospray ionization mass spectrometry (ESI-MS) in full-scan and collision-induced dissociation (CID) modes. Molecular dynamics and molecular mechanics Poisson-Boltzmann surface area (MD-MM/PBSA) analyses were also performed to support the results.

RESULTS

The ESI-MS spectra showed that 11 of the 12 groups of mixed polypeptides formed binary and ternary complexes with relatively high stability. The binding between nonapeptide groups was stronger than that between pentapeptide groups according to the relative intensity. The binding energies calculated by the MM/PBSA binding energy tool also provided strong evidence for the combination of the complexes. Electrostatic interactions, hydrophobic interactions, and van der Waals forces were thought to stabilize the complexes according to the binding models.

CONCLUSIONS

The results implied the formation of stable complexes between polypeptides and identified their noncovalent interactions, proving that specific sequences and combinations with relatively strong binding ability exist in potential intrinsic sequences of peptides in protein structures.

New Insights into Key Determinants for Adenosine 1 Receptor Antagonists Selectivity Using Supervised Molecular Dynamics Simulations

Adenosine receptors (ARs), like many otherGprotein-coupledreceptors (GPCRs), are targets of primary interest indrug design. However, one of the main limits for the development of drugs for this class of GPCRs is the complex selectivity profile usually displayed by ligands. Numerous efforts have been madefor clarifying the selectivity of ARs, leading to the development of many ligand-based models. The structure of the AR subtype A₁ (A₁AR) has been recently solved, providing important structural insights. In the present work, we rationalized the selectivity profile of two selective A₁AR and A_2AAR antagonists, investigating their recognition trajectories obtained by Supervised Molecular Dynamics from an unbound state and monitoring the role of the water molecules in the binding site.

How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs

The early and late development of new anticancer drugs, small molecules or peptides can be slowed down by some issues such as poor selectivity for the target or poor ADME properties. Computer-aided drug design (CADD) and target drug delivery (TDD) techniques, although apparently far from each other, are two research fields that can give a significant contribution to overcome these problems. Their combination may provide mechanistic understanding resulting in a synergy that makes possible the rational design of novel anticancer based therapies. Herein, we aim to discuss selected applications, some also from our research experience, in the fields of anticancer small organic drugs and peptides.

Exploring the RNA-Recognition Mechanism Using Supervised Molecular Dynamics (SuMD) Simulations: Toward a Rational Design for Ribonucleic-Targeting Molecules?

Although proteins have represented the molecular target of choice in the development of new drug candidates, the pharmaceutical importance of ribonucleic acids has gradually been growing. The increasing availability of structural information has brought to light the existence of peculiar three-dimensional RNA arrangements, which can, contrary to initial expectations, be recognized and selectively modulated through small chemical entities or peptides. The application of classical computational methodologies, such as molecular docking, for the rational development of RNA-binding candidates is, however, complicated by the peculiarities characterizing these macromolecules, such as the marked conformational flexibility, the singular charges distribution, and the relevant role of solvent molecules. In this work, we have thus validated and extended the applicability domain of SuMD, an all-atoms molecular dynamics protocol that allows to accelerate the sampling of molecular recognition events on a nanosecond timescale, to ribonucleotide targets of pharmaceutical interest. In particular, we have proven the methodological ability by reproducing the binding mode of viral or prokaryotic ribonucleic complexes, as well as that of artificially engineered aptamers, with an impressive degree of accuracy.

Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations

Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology, and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3–4.8 Å, being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6–2.7 Å, yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions.

Deciphering the Molecular Recognition Mechanism of Multidrug Resistance Staphylococcus aureus NorA Efflux Pump Using a Supervised Molecular Dynamics Approach

The use and misuse of antibiotics has resulted in critical conditions for drug-resistant bacteria emergency, accelerating the development of antimicrobial resistance (AMR). In this context, the co-administration of an antibiotic with a compound able to restore sufficient antibacterial activity may be a successful strategy. In particular, the identification of efflux pump inhibitors (EPIs) holds promise for new antibiotic resistance breakers (ARBs). Indeed, bacterial efflux pumps have a key role in AMR development; for instance, NorA efflux pump contributes to Staphylococcus aureus (S. aureus) resistance against fluoroquinolone antibiotics (e.g., ciprofloxacin) by promoting their active extrusion from the cells. Even though NorA efflux pump is known to be a potential target for EPIs development, the absence of structural information about this protein and the little knowledge available on its mechanism of action have strongly hampered rational drug discovery efforts in this area. In the present work, we investigated at the molecular level the substrate recognition pathway of NorA through a Supervised Molecular Dynamics (SuMD) approach, using a NorA homology model. Specific amino acids were identified as playing a key role in the efflux pump-mediated extrusion of its substrate, paving the way for a deeper understanding of both the mechanisms of action and the inhibition of such efflux pumps.

Revisiting the Allosteric Regulation of Sodium Cation on the Binding of Adenosine at the Human A2A Adenosine Receptor: Insights from Supervised Molecular Dynamics (SuMD) Simulations

One of the most intriguing findings highlighted from G protein-coupled receptor (GPCR) crystallography is the presence, in many members of class A, of a partially hydrated sodium ion in the middle of the seven transmembrane helices (7TM) bundle. In particular, the human adenosine A2A receptor (A2A AR) is the first GPCR in which a monovalent sodium ion was crystallized in a distal site from the canonical orthosteric one, corroborating, from a structural point of view, its role as a negative allosteric modulator. However, the molecular mechanism by which the sodium ion influences the recognition of the A2A AR agonists is not yet fully understood. In this study, the supervised molecular dynamics (SuMD) technique was exploited to analyse the sodium ion recognition mechanism and how its presence influences the binding of the endogenous agonist adenosine. Due to a higher degree of flexibility of the receptor extracellular (EC) vestibule, we propose the sodium-bound A2A AR as less efficient in stabilizing the adenosine during the different steps of binding.

Coupling Supervised Molecular Dynamics (SuMD) with Entropy Estimations To Shine Light on the Stability of Multiple Binding Sites

Exploring at the molecular level, all possible ligand-protein approaching pathways and, consequently, identifying the energetically favorable binding sites is considered crucial to depict a clear picture of the whole scenario of ligand-protein binding. In fact, a ligand can recognize a protein in multiple binding sites, adopting multiple conformations in every single binding site and inducing protein modifications upon binding. In the present work, we would like to present how it is possible to couple a supervised molecular dynamics (SuMD) approach to explore, from an unbound state, the most energetically favorable recognition pathways of the ligand to its protein, with an enthalpic and entropic characterization of the most stable ligand-protein bound states, using the protein kinase CK2α as a prototype study. We identified two accessory binding pockets surrounding the ATP-binding site having a strong enthalpic contribution but a different configurational entropy contribution, suggesting that they play a different role.

Inhibition of PCSK9D374Y/LDLR Protein-Protein Interaction by Computationally Designed T9 Lupin Peptide

The inhibition of the PCSK9/LDLR protein-protein interaction is a promising strategy for developing new hypocholesterolemic agents. Familial hypercholesterolemia is linked to specific PCSK9 mutations: the D374Y is the most potent gain-of-function (GOF) PCSK9 mutation among clinically relevant ones. Recently, a lupin peptide (T9) showed inhibitory effects on this mutant PCSK9 form, being also capable to increase liver uptake of low density lipoprotein cholesterol. In this Letter, aiming to improve the potency of this peptide, the T9 residues mainly responsible for the interaction with PCSK9^D374Y (hot spots) were computationally predicted. Then, the "non-hot" residues were suitably substituted by new amino acids capable to theoretically increase the structural complementarity between T9 and PCSK9^D374Y. The outcomes of this study were confirmed by in vitro biochemical assays and cellular investigations, showing that a new T9 analog is able to increase the LDLR expression on the liver cell surface by 84% at the concentration of 10 μM.

Biological Characterization of Computationally Designed Analogs of peptide TVFTSWEEYLDWV (Pep2-8) with Increased PCSK9 Antagonistic Activity

The inhibition of the PCSK9/LDLR protein-protein interaction (PPI) is a promising strategy for developing new hypocholesterolemic agents. Recently, new antibodies have been approved for therapy, but the high cost and low patients' compliance stimulate the development of alternatives. Starting from the structural information available for the complex between PCSK9 and TVFTSWEEYLDWV (Pep2-8) peptide inhibitor and using computational methods, in this work we identified two Pep2-8 analogs as potential inhibitors of the PCSK9/LDLR PPI. Their biological characterization confirmed the theoretical outcomes. Remarkably, the treatment of HepG2 cells with these peptides increased the LDLR protein level on the cellular membrane, with activities that were 100 and 50 times better than the one of Pep2-8 tested at a 50 μM concentration. Moreover, they were 50 and 5 times more active than Pep2-8 in improving the functional ability of HepG2 cells to uptake extracellular LDL.

New Binding Sites, New Opportunities for GPCR Drug Discovery

Many central biological events rely on protein-ligand interactions. The identification and characterization of protein-binding sites for ligands are crucial for the understanding of functions of both endogenous ligands and synthetic drug molecules. G protein-coupled receptors (GPCRs) typically detect extracellular signal molecules on the cell surface and transfer these chemical signals across the membrane, inducing downstream cellular responses via G proteins or β-arrestin. GPCRs mediate many central physiological processes, making them important targets for modern drug discovery. Here, we focus on the most recent breakthroughs in finding new binding sites and binding modes of GPCRs and their potentials for the development of new medicines.

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.

Identification and In Silico Prediction of Anticoagulant Peptides from the Enzymatic Hydrolysates of Mytilus edulis Proteins

Mytilus edulis is a typical marine bivalve mollusk. Many kinds of bioactive components with nutritional and pharmaceutical activities in Mytilus edulis were reported. In this study, eight different parts of Mytilus edulis tissues, i.e., the foot, byssus, pedal retractor muscle, mantle, gill, adductor muscle, viscera, and other parts, were separated and the proteins from these tissues were prepared. A total of 277 unique peptides from the hydrolysates of different proteins were identified by UPLC-Q-TOF-MS/MS, and the molecular weight distribution of the peptides in different tissues was investigated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The bioactivity of the peptides was predicted through the Peptide Ranker database and molecular docking. Moreover, the peptides from the adductor muscle were chosen to do the active validation of anticoagulant activity. The active mechanism of three peptides from the adductor muscle, VQQELEDAEERADSAEGSLQK, RMEADIAAMQSDLDDALNGQR, and AAFLLGVNSNDLLK, were analyzed by Discovery Studio 2017, which also explained the anticoagulant activity of the hydrolysates of proteins from adductor muscle. This study optimized a screening and identification method of bioactive peptides from enzymatic hydrolysates of different tissues in Mytilus edulis.

Molecular Signature for Receptor Engagement in the Metabolic Peptide Hormone Amylin

The pancreatic peptide hormone, amylin, plays a critical role in the control of appetite, and synergizes with other key metabolic hormones such as glucagon-like peptide 1 (GLP-1). There is opportunity to develop potent and long-acting analogues of amylin or hybrids between these and GLP-1 mimetics for treating obesity. To achieve this, interrogation of how the 37 amino acid amylin peptide engages with its complex receptor system is required. We synthesized an extensive library of peptides to profile the human amylin sequence, determining the role of its disulfide loop, amidated C-terminus and receptor "capture" and "activation" regions in receptor signaling. We profiled four signaling pathways with different ligands at multiple receptor subtypes, in addition to exploring selectivity determinants between related receptors. Distinct roles for peptide subregions in receptor binding and activation were identified, resulting in peptides with greater activity than the native sequence. Enhanced peptide activity was preserved in the brainstem, the major biological target for amylin. Interpretation of our data using full-length active receptor models supported by molecular dynamics, metadynamics, and supervised molecular dynamics simulations guided the synthesis of a potent dual agonist of GLP-1 and amylin receptors. The data offer new insights into the function of peptide amidation, how allostery drives peptide-receptor interactions, and provide a valuable resource for the development of novel amylin agonists for treating diabetes and obesity.

Supervised Molecular Dynamics (SuMD) Approaches in Drug Design

Supervised MD (SuMD) is a computational method that enables the exploration of ligand-receptor recognition pathway in a reduced timescale. The performance speedup is due to the incorporation of a tabu-like supervision algorithm on the ligand-receptor approaching distance into a classic molecular dynamics (MD) simulation. SuMD enables the investigation of ligand-receptor binding events independently from the starting position, chemical structure of the ligand (small molecules or peptides), and also from its receptor-binding affinity. The application of SuMD highlights an appreciable capability of the technique to reproduce the crystallographic structures of several ligand-protein complexes and can provide high-quality protein-ligand models of for which yet experimental confirmation of binding mode is not available.

Comparison of the Human A2A Adenosine Receptor Recognition by Adenosine and Inosine: New Insight from Supervised Molecular Dynamics Simulations

Adenosine deaminase converts adenosine into inosine. In contrast to adenosine, relatively little attention has been paid to the physiological roles of inosine. Nevertheless, recent studies have demonstrated that inosine has neuroprotective, cardioprotective, immunomodulatory, and antidepressive effects. Inosine was recently shown to be a less potent agonist than adenosine at the A_2A adenosine receptor. To better depict the differences in the mechanisms of receptor recognition between adenosine and inosine, we carried out supervised molecular dynamics (SuMD) simulations, and the results are analyzed herein.