Structure-based drug design: aiming for a perfect fit

The molecular determinants of calcium ATPase inhibition by curcuminoids

The natural product curcumin and some of its analogs are known inhibitors of the transmembrane enzyme sarco/endoplasmic reticulum calcium ATPase (SERCA). Despite their widespread use, the curcuminoids' binding site in SERCA and their relevant interactions with the enzyme remain elusive. This lack of knowledge has prevented the development of curcuminoids into valuable experimental tools or into agents of therapeutic value. We used the crystal structures of SERCA in its E1 conformation in conjunction with computational tools such as docking and surface screens to determine the most likely curcumin binding site, along with key enzyme/inhibitor interactions. Additionally, we determined the inhibitory potencies and binding affinities for a small set of curcumin analogs. The predicted curcumin binding site is a narrow cleft in the transmembrane section of SERCA, close to the transmembrane/cytosol interface. In addition to pronounced complementarity in shape and hydrophobicity profiles between curcumin and the binding pocket, several hydrogen bonds were observed that were spread over the entire curcumin scaffold, involving residues on several transmembrane helices. Docking-predicted interactions were compatible with experimental observations for inhibitory potencies and binding affinities. Based on these findings, we propose an inhibition mechanism that assumes that the presence of a curcuminoid in the binding site arrests the catalytic cycle of SERCA by preventing it from converting from the E1 to the E2 conformation. This blockage of conformational change is accomplished by a combination of steric hinderance and hydrogen-bond-based cross-linking of transmembrane helices that require flexibility throughout the catalytic cycle.

GTalign: spatial index-driven protein structure alignment, superposition, and search

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search-an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign's high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function.

Protein engineering enables a soakable crystal form of human CDK7 primed for high-throughput crystallography and structure-based drug design

Cyclin dependent kinase 7 (CDK7) is an important therapeutic kinase best known for its dual role in cell cycle regulation and gene transcription. Here, we describe the application of protein engineering to generate constructs leading to high resolution crystal structures of human CDK7 in both active and inactive conformations. The active state of the kinase was crystallized by incorporation of an additional surface residue mutation (W132R) onto the double phosphomimetic mutant background (S164D and T170E) that yielded the inactive kinase structure. A novel back-soaking approach was developed to determine crystal structures of several clinical and pre-clinical inhibitors of this kinase, demonstrating the potential utility of the crystal system for structure-based drug design (SBDD). The crystal structures help to rationalize the mode of inhibition and the ligand selectivity profiles versus key anti-targets. The protein engineering approach described here illustrates a generally applicable strategy for structural enablement of challenging molecular targets.

ChemoDOTS: a web server to design chemistry-driven focused libraries

In drug discovery, the successful optimization of an initial hit compound into a lead molecule requires multiple cycles of chemical modification. Consequently, there is a need to efficiently generate synthesizable chemical libraries to navigate the chemical space surrounding the primary hit. To address this need, we introduce ChemoDOTS, an easy-to-use web server for hit-to-lead chemical optimization freely available at https://chemodots.marseille.inserm.fr/. With this tool, users enter an activated form of the initial hit molecule then choose from automatically detected reactive functions. The server proposes compatible chemical transformations via an ensemble of encoded chemical reactions widely used in the pharmaceutical industry during hit-to-lead optimization. After selection of the desired reactions, all compatible chemical building blocks are automatically coupled to the initial hit to generate a raw chemical library. Post-processing filters can be applied to extract a subset of compounds with specific physicochemical properties. Finally, explicit stereoisomers and tautomers are computed, and a 3D conformer is generated for each molecule. The resulting virtual library is compatible with most docking software for virtual screening campaigns. ChemoDOTS rapidly generates synthetically feasible, hit-focused, large, diverse chemical libraries with finely-tuned physicochemical properties via a user-friendly interface providing a powerful resource for researchers engaged in hit-to-lead optimization.

ERNEST COST action overview on the (patho)physiology of GPCRs and orphan GPCRs in the nervous system

G protein-coupled receptors (GPCRs) are a large family of cell surface receptors that play a critical role in nervous system function by transmitting signals between cells and their environment. They are involved in many, if not all, nervous system processes, and their dysfunction has been linked to various neurological disorders representing important drug targets. This overview emphasises the GPCRs of the nervous system, which are the research focus of the members of ERNEST COST action (CA18133) working group 'Biological roles of signal transduction'. First, the (patho)physiological role of the nervous system GPCRs in the modulation of synapse function is discussed. We then debate the (patho)physiology and pharmacology of opioid, acetylcholine, chemokine, melatonin and adhesion GPCRs in the nervous system. Finally, we address the orphan GPCRs, their implication in the nervous system function and disease, and the challenges that need to be addressed to deorphanize them.

Rational Design of Drugs Targeting G-Protein-Coupled Receptors: A Structural Biology Perspective

G protein-coupled receptors (GPCRs) play a key role in the transduction of extracellular signals to cells and regulation of many biological processes, which makes these membrane proteins one of the most important targets for pharmacological agents. A significant increase in the number of resolved atomic structures of GPCRs has opened the possibility of developing pharmaceuticals targeting these receptors via structure-based drug design (SBDD). SBDD employs information on the structure of receptor-ligand complexes to search for selective ligands without the need for an extensive high-throughput experimental ligand screening and can significantly expand the chemical space for ligand search. In this review, we describe the process of deciphering GPCR structures using X-ray diffraction analysis and cryoelectron microscopy as an important stage in the rational design of drugs targeting this receptor class. Our main goal was to present modern developments and key features of experimental methods used in SBDD of GPCR-targeting agents to a wide range of specialists.

Regulating Protein-RNA Interactions: Advances in Targeting the LIN28/Let-7 Pathway

Originally discovered in C. elegans, LIN28 is an evolutionarily conserved zinc finger RNA-binding protein (RBP) that post-transcriptionally regulates genes involved in developmental timing, stem cell programming, and oncogenesis. LIN28 acts via two distinct mechanisms. It blocks the biogenesis of the lethal-7 (let-7) microRNA (miRNA) family, and also directly binds messenger RNA (mRNA) targets, such as IGF-2 mRNA, and alters downstream splicing and translation events. This review focuses on the molecular mechanism of LIN28 repression of let-7 and current strategies to overcome this blockade for the purpose of cancer therapy. We highlight the value of the LIN28/let-7 pathway as a drug target, as multiple oncogenic proteins that the pathway regulates are considered undruggable due to their inaccessible cellular location and lack of cavities for small molecule binding.

Unlocking translational machinery for antitubercular drug development

Targeting translational factor proteins (TFPs) presents significant promise for the development of innovative antitubercular drugs. Previous insights from antibiotic binding mechanisms and recently solved 3D crystal structures of Mycobacterium tuberculosis (Mtb) elongation factor thermo unstable-GDP (EF-Tu-GDP), elongation factor thermo stable-EF-Tu (EF-Ts-EF-Tu), and elongation factor G-GDP (EF-G-GDP) have opened up new avenues for the design and development of potent antituberculosis (anti-TB) therapies.

Nigella sativa and its chemical constituents: pre-clinical and clinical evidence for their potential anti-SARS-CoV-2 effects

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over 500 million reported cases of COVID-19 worldwide with relatively high morbidity and mortality. Although global vaccination drive has helped control the pandemic, the newer variant of the virus still holds the world in ransom. Several medicinal herbs with antiviral properties have been reported, and one such promising herb is Nigella sativa (NS). Recent molecular docking, pre-clinical, and clinical studies have shown that NS extracts may have the potential to prevent the entry of coronaviruses into the host cell as well as to treat and manage COVID-19 symptoms. Several active compounds from NS, such as nigelledine, α-hederin, dithymoquinone (DTQ), and thymoquinone (TQ), have been proposed as excellent ligands to target angiotensin-converting enzyme 2 (ACE2 receptors) and other targets on host cells as well as the spike protein (S protein) on SARS-CoV-2. By binding to these target proteins, these ligands could potentially prevent the binding between ACE2 and S protein. Though several articles have been published on the promising therapeutic role of NS and its constituents against SARS-CoV-2 infection, in this review, we consolidate the published information on NS and SARS-CoV-2, focusing on pre-clinical in silico studies as well as clinical trials reported between 2012 and 2023.

Mechanistic Insights into S-Depalmitolyse Activity of Cln5 Protein Linked to Neurodegeneration and Batten Disease: A QM/MM Study

Ceroid lipofuscinosis neuronal protein 5 (Cln5) is encoded by the CLN5 gene. The genetic variants of this gene are associated with the CLN5 form of Batten disease. Recently, the first crystal structure of Cln5 was reported. Cln5 shows cysteine palmitoyl thioesterase S-depalmitoylation activity, which was explored via fluorescent emission spectroscopy utilizing the fluorescent probe DDP-5. In this work, the mechanism of the reaction between Cln5 and DDP-5 was studied computationally by applying a QM/MM methodology at the ωB97X-D/6-31G(d,p):AMBER level. The results of our study clearly demonstrate the critical role of the catalytic triad Cys280-His166-Glu183 in S-depalmitoylation activity. This is evidenced through a comparison of the pathways catalyzed by the Cys280-His166-Glu183 triad and those with only Cys280 involved. The computed reaction barriers are in agreement with the catalytic efficiency. The calculated Gibb's free-energy profile suggests that S-depalmitoylation is a rate-limiting step compared to the preceding S-palmitoylation, with barriers of 26.1 and 25.3 kcal/mol, respectively. The energetics were complemented by monitoring the fluctuations in the electron density distribution through NBO charges and bond strength alterations via local mode stretching force constants during the catalytic pathways. This comprehensive protocol led to a more holistic picture of the reaction mechanism at the atomic level. It forms the foundation for future studies on the effects of gene mutations on both the S-palmitoylation and S-depalmitoylation steps, providing valuable data for the further development of enzyme replacement therapy, which is currently the only FDA-approved therapy for childhood neurodegenerative diseases, including Batten disease.

Design, Synthesis, and Antifungal Activity of N-(alkoxy)-Diphenyl Ether Carboxamide Derivates as Novel Succinate Dehydrogenase Inhibitors

Succinate dehydrogenase (SDH, EC 1.3.5.1) is one of the most promising targets for fungicide development and has attracted great attention worldwide. However, existing commercial fungicides targeting SDH have led to the increasingly prominent problem of pathogen resistance, so it is necessary to develop new fungicides. Herein, we used a structure-based molecular design strategy to design and synthesize a series of novel SDHI fungicides containing an N-(alkoxy)diphenyl ether carboxamide skeleton. The mycelial growth inhibition experiment showed that compound M15 exhibited a very good control effect against four plant pathogens, with inhibition rates of more than 60% at a dose of 50 μg/mL. A structure-activity relationship study found that N-O-benzyl-substituted derivatives showed better antifungal activity than others, especially the introduction of a halogen on the benzyl. Furthermore, the molecular docking results suggested that π-π interactions with Trp35 and hydrogen bonds with Tyr33 and Trp173 were crucial interaction sites when inhibitors bound to SDH. Morphological observation of mycelium revealed that M15 could inhibit the growth of mycelia. Moreover, in vivo and in vitro tests showed that M15 not only inhibited the enzyme activity of SDH but also effectively protected rice from damage due to R. solani infection, with a result close to that of the control at a concentration of 200 μg/mL. Thus, the N-(alkoxy)diphenyl ether carboxamide skeleton is a new starting point for the discovery of new SDH inhibitors and is worthy of further investigation.

A sequence-based evolutionary distance method for Phylogenetic analysis of highly divergent proteins

Because of the limited effectiveness of prevailing phylogenetic methods when applied to highly divergent protein sequences, the phylogenetic analysis problem remains challenging. Here, we propose a sequence-based evolutionary distance algorithm termed sequence distance (SD), which innovatively incorporates site-to-site correlation within protein sequences into the distance estimation. In protein superfamilies, SD can effectively distinguish evolutionary relationships both within and between protein families, producing phylogenetic trees that closely align with those based on structural information, even with sequence identity less than 20%. SD is highly correlated with the similarity of the protein structure, and can calculate evolutionary distances for thousands of protein pairs within seconds using a single CPU, which is significantly faster than most protein structure prediction methods that demand high computational resources and long run times. The development of SD will significantly advance phylogenetics, providing researchers with a more accurate and reliable tool for exploring evolutionary relationships.

A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling

Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors.

Recent Advances in Chemotherapeutics for Leishmaniasis: Importance of the Cellular Biochemistry of the Parasite and Its Molecular Interaction with the Host

Leishmaniasis, a category 1 neglected protozoan disease caused by a kinetoplastid pathogen called Leishmania, is transmitted through dipteran insect vectors (phlebotomine, sand flies) in three main clinical forms: fatal visceral leishmaniasis, self-healing cutaneous leishmaniasis, and mucocutaneous leishmaniasis. Generic pentavalent antimonials have long been the drug of choice against leishmaniasis; however, their success is plagued with limitations such as drug resistance and severe side effects, which makes them redundant as frontline therapy for endemic visceral leishmaniasis. Alternative therapeutic regimens based on amphotericin B, miltefosine, and paromomycin have also been approved. Due to the unavailability of human vaccines, first-line chemotherapies such as pentavalent antimonials, pentamidine, and amphotericin B are the only options to treat infected individuals. The higher toxicity, adverse effects, and perceived cost of these pharmaceutics, coupled with the emergence of parasite resistance and disease relapse, makes it urgent to identify new, rationalized drug targets for the improvement in disease management and palliative care for patients. This has become an emergent need and more relevant due to the lack of information on validated molecular resistance markers for the monitoring and surveillance of changes in drug sensitivity and resistance. The present study reviewed the recent advances in chemotherapeutic regimens by targeting novel drugs using several strategies including bioinformatics to gain new insight into leishmaniasis. Leishmania has unique enzymes and biochemical pathways that are distinct from those of its mammalian hosts. In light of the limited number of available antileishmanial drugs, the identification of novel drug targets and studying the molecular and cellular aspects of these drugs in the parasite and its host is critical to design specific inhibitors targeting and controlling the parasite. The biochemical characterization of unique Leishmania-specific enzymes can be used as tools to read through possible drug targets. In this review, we discuss relevant metabolic pathways and novel drugs that are unique, essential, and linked to the survival of the parasite based on bioinformatics and cellular and biochemical analyses.

SiteRadar: Utilizing Graph Machine Learning for Precise Mapping of Protein-Ligand-Binding Sites

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning algorithms, the existing solutions do not provide significant advantages over nonmachine learning approaches and there is still a big room for improvement. The low ability to identify protein-ligand-binding sites makes available approaches inapplicable to automated drug design. Here, we present SiteRadar, a new algorithm for mapping cavities that are likely to bind a small-molecule ligand. SiteRadar shows higher accuracy in binding site identification compared with FPocket and PUResNet. SiteRadar demonstrates an ability to detect up to 74% of true ligand-binding sites according to the top N + 2 metric and usually covers approximately 80% of ligand atoms. Therefore, SiteRadar can be regarded as a promising solution for implementation into algorithms for automated drug design.

Protein Design Strategies for the Structural–Functional Studies of G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are an important family of membrane proteins responsible for many physiological functions in human body. High resolution GPCR structures are required to understand their molecular mechanisms and perform rational drug design, as GPCRs play a crucial role in a variety of diseases. That is difficult to obtain for the wild-type proteins because of their low stability. In this review, we discuss how this problem can be solved by using protein design strategies developed to obtain homogeneous stabilized GPCR samples for crystallization and cryoelectron microscopy.

TNF-α Inhibitors from Natural Compounds: An Overview, CADD Approaches, and their Exploration for Anti-inflammatory Agents

Inflammation is a natural process that occurs in the organism in response to harmful external agents. Despite being considered beneficial, exaggerated cases can cause severe problems for the body. The main inflammatory manifestations are pain, increased temperature, edema, decreased mobility, and quality of life for affected individuals. Diseases such as arthritis, cancer, allergies, infections, arteriosclerosis, neurodegenerative diseases, and metabolic problems are mainly characterized by an exaggerated inflammatory response. Inflammation is related to two categories of substances: pro- and anti-inflammatory mediators. Among the pro-inflammatory mediators is Tumor Necrosis Factor-α (TNF-α). It is associated with immune diseases, cancer, and psychiatric disorders which increase its excretion. Thus, it becomes a target widely used in discovering new antiinflammatory drugs. In this context, secondary metabolites biosynthesized by plants have been used for thousands of years and continue to be one of the primary sources of new drug scaffolds against inflammatory diseases. To decrease costs related to the drug discovery process, Computer-Aided Drug Design (CADD) techniques are broadly explored to increase the chances of success. In this review, the main natural compounds derived from alkaloids, flavonoids, terpene, and polyphenols as promising TNF-α inhibitors will be discussed. Finally, we applied a molecular modeling protocol involving all compounds described here, suggesting that their interactions with Tyr⁵⁹, Tyr¹¹⁹, Tyr¹⁵¹, Leu⁵⁷, and Gly¹²¹ residues are essential for the activity. Such findings can be useful for research groups worldwide to design new anti-inflammatory TNF-α inhibitors.

Repurposing antiparasitic antimonials to noncovalently rescue temperature-sensitive p53 mutations

The tumor suppressor p53 is inactivated by over hundreds of heterogenous mutations in cancer. Here, we purposefully selected phenotypically reversible temperature-sensitive (TS) p53 mutations for pharmacological rescue with thermostability as the compound-screening readout. This rational screening identified antiparasitic drug potassium antimony tartrate (PAT) as an agent that can thermostabilize the representative TS mutant p53-V272M via noncovalent binding. PAT met the three basic criteria for a targeted drug: availability of a co-crystal structure, compatible structure-activity relationship, and intracellular target specificity, consequently exhibiting antitumor activity in a xenograft mouse model. At the antimony dose in clinical antiparasitic therapy, PAT effectively and specifically rescued p53-V272M in patient-derived primary leukemia cells in single-cell RNA sequencing. Further scanning of 815 frequent p53-missense mutations identified 65 potential PAT-treatable mutations, most of which were temperature sensitive. These results lay the groundwork for repurposing noncovalent antiparasitic antimonials for precisely treating cancers with the 65 p53 mutations.

Extended-ensemble docking to probe dynamic variation of ligand binding sites during large-scale structural changes of proteins

Proteins can sample a broad landscape as they undergo conformational transition between different functional states. At the same time, as key players in almost all cellular processes, proteins are important drug targets. Considering the different conformational states of a protein is therefore central for a successful drug-design strategy. Here we introduce a novel docking protocol, termed extended-ensemble docking, pertaining to proteins that undergo large-scale (global) conformational changes during their function. In its application to multidrug ABC-transporter P-glycoprotein (Pgp), extensive non-equilibrium molecular dynamics simulations employing system-specific collective variables are first used to describe the transition cycle of the transporter. An extended set of conformations (extended ensemble) representing the full transition cycle between the inward- and the outward-facing states is then used to seed high-throughput docking calculations of known substrates, non-substrates, and modulators of the transporter. Large differences are predicted in the binding affinities to different conformations, with compounds showing stronger binding affinities to intermediate conformations compared to the starting crystal structure. Hierarchical clustering of the binding modes shows all ligands preferably bind to the large central cavity of the protein, formed at the apex of the transmembrane domain (TMD), whereas only small binding populations are observed in the previously described R and H sites present within the individual TMD leaflets. Based on the results, the central cavity is further divided into two major subsites, first preferably binding smaller substrates and high-affinity inhibitors, whereas the second one shows preference for larger substrates and low-affinity modulators. These central subsites along with the low-affinity interaction sites present within the individual TMD leaflets may respectively correspond to the proposed high- and low-affinity binding sites in Pgp. We propose further an optimization strategy for developing more potent inhibitors of Pgp, based on increasing its specificity to the extended ensemble of the protein, instead of using a single protein structure, as well as its selectivity for the high-affinity binding site. In contrast to earlier in silico studies using single static structures of Pgp, our results show better agreement with experimental studies, pointing to the importance of incorporating the global conformational flexibility of proteins in future drug-discovery endeavors.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

Cheminformatic analysis of natural product-based drugs and chemical probes

Covering: 1981 to 2019Natural products continue to play a major role in drug discovery, with half of new chemical entities based structurally on a natural product. Herein, we report a cheminformatic analysis of the structural and physicochemical properties of natural product-based drugs in comparison to top-selling brand-name synthetic drugs, and a selection of chemical probes recently discovered from diversity-oriented synthesis libraries. In this analysis, natural product-based drugs covered a broad range of chemical space based on size, polarity, and three-dimensional structure. Natural product-based structures were also more prevalent in top-selling drugs of 2018 compared to 2006. Further, the drugs clustered well according to biosynthetic origins, but less so based on therapeutic classes. Macrocycles occupied distinctive and relatively underpopulated regions of chemical space, while chemical probes largely overlapped with synthetic drugs. This analysis highlights the continued opportunities to leverage natural products and their pharmacophores in modern drug discovery.

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.

Repurposing FDA-approved Drugs Targeting SARS-CoV2 3CLpro: a study by applying Virtual Screening, Molecular Dynamics, MM-PBSA Calculations and Covalent Docking

Background:

Since the end of 2019, the etiologic agent SAR-CoV-2 responsible for one of the most significant epidemics in history has caused severe global economic, social, and health damages. The drug repurposing approach and application of Structure-based Drug Discovery (SBDD) using in silico techniques are increasingly frequent, leading to the identification of several molecules that may represent promising potential.

Method:

In this context, here we use in silico methods of virtual screening (VS), pharmacophore modeling (PM), and fragment-based drug design (FBDD), in addition to molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM -PBSA) calculations, and covalent docking (CD) for the identification of potential treatments against SARS-CoV-2. We initially validated the docking protocol followed by VS in 1,613 FDA-approved drugs obtained from the ZINC database. Thus, we identified 15 top hits, of which three of them were selected for further simulations. In parallel, for the compounds with a fit score value ≤ of 30, we performed the FBDD protocol, where we designed 12 compounds

Result:

By applying a PM protocol in the ZINC database, we identified three promising drug candidates. Then, the 9 top hits were evaluated in simulations of MD, MM-PBSA, and CD. Subsequently, MD showed that all identified hits showed stability at the active site without significant changes in the protein's structural integrity, as evidenced by the RMSD, RMSF, Rg, SASA graphics. They also showed interactions with the catalytic dyad (His41 and Cys145) and other essential residues for activity (Glu166 and Gln189) and high affinity for MM-PBSA, with possible covalent inhibition mechanism.

Conclution:

Finally, our protocol helped identify potential compounds wherein ZINC896717 (Zafirlukast), ZINC1546066 (Erlotinib), and ZINC1554274 (Rilpivirine) were more promising and could be explored in vitro, in vivo, and clinical trials to prove their potential as antiviral agents.

Machine learning to estimate the local quality of protein crystal structures

Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, called quality assessment based on an electron density map (QAEmap), which evaluates local protein structures determined by X-ray crystallography and could be applied to correct structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and putative high-resolution experimental electron density map. This correlation could be used as a metric to modify the structure. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.

Assay development in leishmaniasis drug discovery: a comprehensive review

INTRODUCTION

Cutaneous, muco-cutaneous and visceral leishmaniasis occur due to an infection with the protozoan parasite Leishmania. The current therapeutic options are limited mainly due to extensive toxicity, emerging resistance and variation in efficacy based on species and strain of the Leishmania parasite. There exists a high unmet medical need to identify new chemical starting points for drug discovery to tackle the disease.

AREAS COVERED

The authors have highlighted the recent progress, limitations and successes achieved in assay development for leishmaniasis drug discovery.

EXPERT OPINION

It is true that sophisticated and robust phenotypic in vitro assays have been developed during the last decade, however limitations and challenges remain with respect to variation in activity reported between different research groups and success in translating in vitro outcomes in vivo. The variability is not only due to strain and species differences but also a lack of well-defined criteria and assay conditions, e.g. culture media, host cell type, assay formats, parasite form used, multiplicity of infection and incubation periods. Thus, there is an urgent need for more physiologically relevant assays that encompass multi-species phenotypic approaches to identify new chemical starting points for leishmaniasis drug discovery.

Design, synthesis, and biological evaluation of phenyl thiazole-based AR-V7 degraders

Multiple Splice variants of AR have been reported in the past few years. These splice variants are upregulated in most cases of CRPC resulting in poor prognosis. Most of these variants lack the ligand binding domain (LBD) but still bind to DNA resulting in constitutive activation of downstream targets. The AR-V7 splice variant has been characterized extensively and current clinical trials in CRPC are exploring the use of AR-V7 as a biomarker. New therapeutic molecules that selectively target AR-V7 are also being explored. However, there is a dearth of information available on the selectivity, phenotypic responses in AR-V7 dependent cell lines and pharmacokinetic properties of such molecules. Using our proprietary computational algorithms and rational SAR optimization, we have developed a potent and selective AR-V7 degrader from a known AR DNA binding domain (DBD) binder. This molecule effectively degraded AR-V7 in a CRPC cell line and demonstrated good oral bioavailability in mouse PK studies. This tool compound can be used to evaluate the pharmacological effects of AR-V7 degraders. Further exploration of SAR can be pursued to develop more optimized lead compounds.

Molecular Modeling Targeting Transmembrane Serine Protease 2 (TMPRSS2) as an Alternative Drug Target Against Coronaviruses

Since November 2019, the new Coronavirus disease (COVID-19) caused by the etiological agent SARS-CoV-2 has been responsible for several cases worldwide, becoming pandemic in March 2020. Pharmaceutical industries and academics have joined their efforts to discover new therapies to control the disease, since there are no specific drugs to combat this emerging virus. Thus, several targets have been explored, among them the transmembrane protease serine 2 (TMPRSS2) has gained greater interest in the scientific community. In this context, this review will describe the importance of TMPRSS2 protease and the significant advances in virtual screening focused on discovering new inhibitors. In this review, it was observed that molecular modeling methods could be powerful tools in identifying new molecules against SARS-CoV-2. Thus, this review could be used to guide researchers worldwide to explore the biological and clinical potential of compounds that could be promising drug candidates against SARS-CoV-2, acting by inhibition of TMPRSS2 protein.

Targeting protein-protein interactions in the DNA damage response pathways for cancer chemotherapy

Cellular DNA damage response (DDR) is an extensive signaling network that orchestrates DNA damage recognition, repair and avoidance, cell cycle progression and cell death. DDR alteration is a hallmark of cancer, with the deficiency in one DDR capability often compensated by a dependency on alternative pathways endowing cancer cells with survival and growth advantage. Targeting these DDR pathways has provided multiple opportunities for the development of cancer therapies. Traditional drug discovery has mainly focused on catalytic inhibitors that block enzyme active sites, which limits the number of potential drug targets within the DDR pathways. This review article describes the emerging approach to the development of cancer therapeutics targeting essential protein-protein interactions (PPIs) in the DDR network. The overall strategy for the structure-based design of small molecule PPI inhibitors is discussed, followed by an overview of the major DNA damage sensing, DNA repair, and DNA damage tolerance pathways with a specific focus on PPI targets for anti-cancer drug design. The existing small molecule inhibitors of DDR PPIs are summarized that selectively kill cancer cells and/or sensitize cancers to front-line genotoxic therapies, and a range of new PPI targets are proposed that may lead to the development of novel chemotherapeutics.

Binding of tyrosine kinase inhibitor to epidermal growth factor receptor: surface-enhanced infrared absorption microscopy reveals subtle protein secondary structure variations

Surface-Enhanced Infrared Absorption (SEIRA) has been proposed as a valuable tool for protein binding studies, but its performances have been often proven on model proteins undergoing severe secondary structure rearrangements, while ligand binding only marginally involves the protein backbone in the vast majority of the biologically relevant cases. In this study we demonstrate the potential of SEIRA microscopy for highlighting the very subtle secondary structure modifications associated with the binding of Lapatinib, a tyrosine kinase inhibitor (TKI), to epidermal growth factor receptor (EGFR), a well-known driver of tumorigenesis in pathological settings such as lung, breast and brain cancers. By boosting the performances of Mid-IR plasmonic devices based on nanoantennas cross-geometry, accustoming the protein purification protocols, carefully tuning the protein anchoring methodology and optimizing the data analysis, we were able to detect EGFR secondary structure modification associated with few amino acids. A nano-patterned platform with this kind of sensitivity bridges biophysical and structural characterization methods, thus opening new possibilities in studying of proteins of biomedical interest, particularly for drug-screening purposes.

Drugging the "Undruggable" MYCN Oncogenic Transcription Factor: Overcoming Previous Obstacles to Impact Childhood Cancers

Effective treatment of pediatric solid tumors has been hampered by the predominance of currently "undruggable" driver transcription factors. Improving outcomes while decreasing the toxicity of treatment necessitates the development of novel agents that can directly inhibit or degrade these elusive targets. MYCN in pediatric neural-derived tumors, including neuroblastoma and medulloblastoma, is a paradigmatic example of this problem. Attempts to directly and specifically target MYCN have failed due to its similarity to MYC, the unstructured nature of MYC family proteins in their monomeric form, the lack of an understanding of MYCN-interacting proteins and ability to test their relevance in vivo, the inability to obtain structural information on MYCN protein complexes, and the challenges of using traditional small molecules to inhibit protein-protein or protein-DNA interactions. However, there is now promise for directly targeting MYCN based on scientific and technological advances on all of these fronts. Here, we discuss prior challenges and the reasons for renewed optimism in directly targeting this "undruggable" transcription factor, which we hope will lead to improved outcomes for patients with pediatric cancer and create a framework for targeting driver oncoproteins regulating gene transcription.

Drug Design Strategies to Avoid Resistance in Direct-Acting Antivirals and Beyond

Drug resistance is prevalent across many diseases, rendering therapies ineffective with severe financial and health consequences. Rather than accepting resistance after the fact, proactive strategies need to be incorporated into the drug design and development process to minimize the impact of drug resistance. These strategies can be derived from our experience with viral disease targets where multiple generations of drugs had to be developed to combat resistance and avoid antiviral failure. Significant efforts including experimental and computational structural biology, medicinal chemistry, and machine learning have focused on understanding the mechanisms and structural basis of resistance against direct-acting antiviral (DAA) drugs. Integrated methods show promise for being predictive of resistance and potency. In this review, we give an overview of this research for human immunodeficiency virus type 1, hepatitis C virus, and influenza virus and the lessons learned from resistance mechanisms of DAAs. These lessons translate into rational strategies to avoid resistance in drug design, which can be generalized and applied beyond viral targets. While resistance may not be completely avoidable, rational drug design can and should incorporate strategies at the outset of drug development to decrease the prevalence of drug resistance.

Proteolysis targeting chimeras (PROTACs) come of age: entering the third decade of targeted protein degradation

With the discovery of PROteolysis TArgeting Chimeras (PROTACs) twenty years ago, targeted protein degradation (TPD) has changed the landscape of drug development. PROTACs have evolved from cell-impermeable peptide-small molecule chimeras to orally bioavailable clinical candidate drugs that degrade oncogenic proteins in humans. As we move into the third decade of TPD, the pace of discovery will only accelerate. Improved technologies are enabling the development of ligands for "undruggable" proteins and the recruitment of new E3 ligases. Moreover, enhanced computing power will expedite identification of active degraders. Here we discuss the strides made in these areas and what advances we can look forward to as the next decade in this exciting field begins.

Abiraterone and D4, 3-keto Abiraterone binding to CYP17A1, a structural comparison study by molecular dynamic simulation

The importance of computer-aided drug design and development is clear nowadays. These approaches smooth the way of designing some efficient candidates based on drugs in use. At this place, we studied the mechanism of D4-abiraterone (D4A), the active metabolite of Abiraterone (Abi), binding to CYP17A1 compared with Abi. The molecular dynamics simulation results reveal that the metabolite, which lacks the key 3β-OH group, has a varied H-bond forming pattern. The critical H-bond between 3β-OH of Abi with Asn_202 turns to 3 Keto-O of D4A with Arg_239 in the substrate-binding site. This interaction causes a remarkable distance of 0.63 nm between D4A nitrogen and Fe in heme, which reduces its 17,20 lyase selectivity. The D4A keto moiety presents an immense number of H-bond with surrounding solvent molecules compared with the Abi hydroxyl group. As a result, D4A develops a weaker H-bond network with the enzyme. Otherwise, the heterocyclic nature of inhibitors helps for noticeable van der Waals interaction formation with CYP17A1. However, Abi stabilized position in the binding site helps more van der Waals interactions deposition than D4A. These results convinced the importance of the conserved H-bond for acquiring the proper position by the substrate or inhibitor in the binding site.

Exploiting Folding and Degradation Machineries To Target Undruggable Proteins: What Can a Computational Approach Tell Us?

Advances in genomics and proteomics have unveiled an ever-growing number of key proteins and provided mechanistic insights into the genesis of pathologies. This wealth of data showed that changes in expression levels of specific proteins, mutations, and post-translational modifications can result in (often subtle) perturbations of functional protein-protein interaction networks, which ultimately determine disease phenotypes. Although many such validated pathogenic proteins have emerged as ideal drug targets, there are also several that escape traditional pharmacological regulation; these proteins have thus been labeled "undruggable". The challenges posed by undruggable targets call for new sorts of molecular intervention. One fascinating solution is to perturb a pathogenic protein's expression levels, rather than blocking its activities. In this Concept paper, we shall discuss chemical interventions aimed at recruiting undruggable proteins to the ubiquitin proteasome system, or aimed at disrupting protein-protein interactions in the chaperone-mediated cellular folding machinery: both kinds of intervention lead to a decrease in the amount of active pathogenic protein expressed. Specifically, we shall discuss the role of computational strategies in understanding the molecular determinants characterizing the function of synthetic molecules typically designed for either type of intervention. Finally, we shall provide our perspectives and views on the current limitations and possibilities to expand the scope of rational approaches to the design of chemical regulators of protein levels.

Antiviral Drug Targets of Single-Stranded RNA Viruses Causing Chronic Human Diseases

Ribonucleic acid (RNA) viruses associated with chronic diseases in humans are major threats to public health causing high mortality globally. The high mutation rate of RNA viruses helps them to escape the immune response and also is responsible for the development of drug resistance. Chronic infections caused by human immunodeficiency virus (HIV) and hepatitis viruses (HBV and HCV) lead to acquired immunodeficiency syndrome (AIDS) and hepatocellular carcinoma respectively, which are one of the major causes of human deaths. Effective preventative measures to limit chronic and re-emerging viral infections are absolutely necessary. Each class of antiviral agents targets a specific stage in the viral life cycle and inhibits them from its development and proliferation. Most often, antiviral drugs target a specific viral protein, therefore only a few broad-spectrum drugs are available. This review will be focused on the selected viral target proteins of pathogenic viruses containing single-stranded (ss) RNA genome that causes chronic infections in humans (e.g. HIV, HCV, Flaviviruses). In the recent past, an exponential increase in the number of available three-dimensional protein structures (>150000 in Protein Data Bank), allowed us to better understand the molecular mechanism of action of protein targets and antivirals. Advancements in the in silico approaches paved the way to design and develop several novels, highly specific small-molecule inhibitors targeting the viral proteins.

Structure driven compound optimization in targeted protein degradation

Small molecule induced protein degradation has created tremendous excitement in drug discovery within recent years. Not being confined to target inhibition and being able to remove disease-causing protein targets via engagement and subsequent ubiquitination has provided scientists with a powerful tool to expand the druggable space. At the center of this approach sits the ternary complex formed between an E3 ubiquitin ligase, the small molecule degrader, and the target protein. A productive ternary complex is pivotal for a ubiquitin to be transferred to a surface lysine of the target protein resulting in poly-ubiquitination which enables recognition and finally degradation by the proteasome. As understanding the ternary complex means understanding the degradation process, many efforts are put into obtaining structural information of the ternary complex and getting a snapshot of the underlying conformations and molecular contacts. Locking this transient trimeric intermediate in a crystalline state has proven to be very demanding but the obtained results have tremendously improved our understanding of small molecule degraders. This review discusses target protein degradation from a structural perspective and highlights the evolution of certain degraders based on the obtained structural insights.

Identification of Novel Phyto-chemicals from Ocimum basilicum for the Treatment of Parkinson's Disease using In Silico Approach

BACKGROUND

Parkinson's disease is characterized by decreased level of dopaminergic neurotransmitters and this decrease is due to the degradation of dopamine by protein Monoamine Oxidase B (MAO-B). In order to treat Parkinson's disease, MAO-B should be inhibited.

OBJECTIVE

To find out the novel phytochemicals from plant Ocimum basilicum that can inhibit MAO-B by using the in silico methods.

METHODS

The data of chemical constituents from plant Ocimum basilicum was collected and inhibitory activity of these phytochemicals was then predicted by using the Structure-Based (SB) and Ligand-Based Virtual Screening (LBVS) methods. Molecular docking, one of the common Structure-Based Virtual Screening method, has been used during this search. Traditionally, molecular docking is used to predict the orientation and binding affinity of the ligand within the active site of the protein. Molegro Virtual Docker (MVD) software has been used for this purpose. On the other hand, Random Forest Model, one of the LBVS method, has also been used to predict the activity of these chemical constituents of Ocimum basilicum against the MAO-B.

RESULTS

During the docking studies, all the 108 compounds found in Ocimum basilicum were docked within the active site of MAO-B (PDB code: 4A79) out of which, 57 compounds successfully formed the hydrogen bond with tyr 435, a crucial amino acid for the biological activity of the enzyme. Rutin (-182.976 Kcal/mol), Luteolin (-163.171 Kcal/mol), Eriodictyol-7-O-glucoside (- 160.13 Kcal/mol), Rosmarinic acid (-133.484 Kcal/mol) and Isoquercitrin (-131.493 Kcal/mol) are among the top hits with the highest MolDock score along with hydrogen interaction with tyr 435. Using the RF model, ten compounds out of 108 chemical constituent of Ocimum basilicum were predicted to be active, Apigenin (1.0), Eriodictyol (1.0), Orientin (0.876), Kaempferol (0.8536), Luteolin (0.813953) and Rosmarinic-Acid (0.7738095) are predicted to be most active with the highest RF score.

CONCLUSION

The comparison of the two screening methods show that the ten compounds that were predicted to be active by the RF model, are also found in top hits of docking studies with the highest score. The top hits obtained during this study are predicted to be the inhibitor of MAO-B, thus, could be used further for the development of drugs for the treatment of Parkinson's disease (PD).

Current Computational Approaches for the Development of Anti-HIV Inhibitors: An Overview

BACKGROUND

Today, HIV-1 infection has become an extensive problem to public health and a greater challenge to all working researchers throughout the world. Since the beginning of HIV-1 virus, several antiviral therapeutic agents have been developed at various stages to combat HIV-1 infection. But, many of antiviral drugs are on the platform of drug resistance and toxicology issues, needs an urgent constructive investigation for the development of productive and protective therapeutics to make an improvement of individual life suffering with viral infection. As developing a novel agent is very costly, challenging and time taking route in the recent times.

METHODS

The review summarized about the modern approaches of computational aided drug discovery to developing a novel inhibitor within a short period of time and less cost.

RESULTS

The outcome suggests on the premise of reported information that the computational drug discovery is a powerful technology to design a defensive and fruitful therapeutic agents to combat HIV-1 infection and recover the lifespan of suffering one.

CONCLUSION

Based on survey of the reported information, we concluded that the current computational approaches is highly supportive in the progress of drug discovery and controlling the viral infection.

Structure-based discovery and development of metabotropic glutamate receptor 5 negative allosteric modulators

The metabotropic glutamate (mGlu) receptors are a family of eight class C G protein-coupled receptors (GPCRs) which modulate cell signaling and synaptic transmission to the major excitatory neurotransmitter l-glutamate (l-glutamic acid). Due to their role in modulating glutamate response, their widespread distribution in the central nervous system (CNS) and some evidence of dysregulation in disease, the mGlu receptors have become attractive pharmacological targets. As the orthosteric (glutamate) binding site is highly conserved across the eight mGlu receptors, it is difficult not only to generate ligands with subtype selectivity but, due to the nature of the binding site, with suitable drug-like properties to allow oral bioavailability and CNS penetration. Selective pharmacological targeting of a single receptor subtype can be achieved by targeting alternative (allosteric) binding sites. The nature of the allosteric binding pockets allows ligands to be developed that have good physical chemical properties as evidenced by several allosteric modulators of mGlu receptors entering clinical trials. The first negative allosteric modulators of the metabotropic glutamate 5 (mGlu₅) receptor were discovered from high throughput screening activities. An alternative approach to drug discovery is to use structural knowledge to enable structure-based drug design (SBDD), which allows the design of molecules in a more rational, rather than empirical, fashion. Here we will describe the process of SBDD in the discovery of the mGlu₅ negative allosteric modulator HTL0014242 and describe how knowledge of receptor structure can also be used to gain insights into the receptor activation mechanisms.

Targeting the C-Terminal Domain Small Phosphatase 1

The human C-terminal domain small phosphatase 1 (CTDSP1/SCP1) is a protein phosphatase with a conserved catalytic site of DXDXT/V. CTDSP1’s major activity has been identified as dephosphorylation of the 5th Ser residue of the tandem heptad repeat of the RNA polymerase II C-terminal domain (RNAP II CTD). It is also implicated in various pivotal biological activities, such as acting as a driving factor in repressor element 1 (RE-1)-silencing transcription factor (REST) complex, which silences the neuronal genes in non-neuronal cells, G1/S phase transition, and osteoblast differentiation. Recent findings have denoted that negative regulation of CTDSP1 results in suppression of cancer invasion in neuroglioma cells. Several researchers have focused on the development of regulating materials of CTDSP1, due to the significant roles it has in various biological activities. In this review, we focused on this emerging target and explored the biological significance, challenges, and opportunities in targeting CTDSP1 from a drug designing perspective.

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.

Unlocking the Potential of HK2 in Cancer Metabolism and Therapeutics

Glycolysis is a tightly regulated process in which several enzymes, such as Hexokinases (HKs), play crucial roles. Cancer cells are characterized by specific expression levels of several isoenzymes in different metabolic pathways and these features offer possibilities for therapeutic interventions. Overexpression of HKs (mostly of the HK2 isoform) have been consistently reported in numerous types of cancer. Moreover, deletion of HK2 has been shown to decrease cancer cell proliferation without explicit side effects in animal models, which suggests that targeting HK2 is a viable strategy for cancer therapy. HK2 inhibition causes a substantial decrease of glycolysis that affects multiple pathways of central metabolism and also destabilizes the mitochondrial outer membrane, ultimately enhancing cell death. Although glycolysis inhibition has met limited success, partly due to low selectivity for specific isoforms and excessive side effects of the reported HK inhibitors, there is ample ground for progress. The current review is focused on HK2 inhibition, envisaging the development of potent and selective anticancer agents. The information on function, expression, and activity of HKs is presented, along with their structures, known inhibitors, and reported effects of HK2 ablation/inhibition. The structural features of the different isozymes are discussed, aiming to stimulate a more rational approach to the design of selective HK2 inhibitors with appropriate drug-like properties. Particular attention is dedicated to a structural and sequence comparison of the structurally similar HK1 and HK2 isoforms, aiming to unveil differences that could be explored therapeutically. Finally, several additional catalytic- and non-catalytic roles on different pathways and diseases, recently attributed to HK2, are reviewed and their implications briefly discussed.

Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set

Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .

Structure-Based Design of a Macrocyclic PROTAC

Constraining a molecule in its bioactive conformation via macrocyclization represents an attractive strategy to rationally design functional chemical probes. While this approach has been applied to enzyme inhibitors or receptor antagonists, to date it remains unprecedented for bifunctional molecules that bring proteins together, such as PROTAC degraders. Herein, we report the design and synthesis of a macrocyclic PROTAC by adding a cyclizing linker to the BET degrader MZ1. A co-crystal structure of macroPROTAC-1 bound in a ternary complex with VHL and the second bromodomain of Brd4 validated the rational design. Biophysical studies revealed enhanced discrimination between the second and the first bromodomains of BET proteins. Despite a 12-fold loss of binary binding affinity for Brd4, macroPROTAC-1 exhibited cellular activity comparable to MZ1. Our findings support macrocyclization as an advantageous strategy to enhance PROTAC degradation potency and selectivity between homologous targets.

Thera-SAbDab: the Therapeutic Structural Antibody Database

The Therapeutic Structural Antibody Database (Thera-SAbDab; http://opig.stats.ox.ac.uk/webapps/therasabdab) tracks all antibody- and nanobody-related therapeutics recognized by the World Health Organisation (WHO), and identifies any corresponding structures in the Structural Antibody Database (SAbDab) with near-exact or exact variable domain sequence matches. Thera-SAbDab is synchronized with SAbDab to update weekly, reflecting new Protein Data Bank entries and the availability of new sequence data published by the WHO. Each therapeutic summary page lists structural coverage (with links to the appropriate SAbDab entries), alignments showing where any near-matches deviate in sequence, and accompanying metadata, such as intended target and investigated conditions. Thera-SAbDab can be queried by therapeutic name, by a combination of metadata, or by variable domain sequence - returning all therapeutics that are within a specified sequence identity over a specified region of the query. The sequences of all therapeutics listed in Thera-SAbDab (461 unique molecules, as of 5 August 2019) are downloadable as a single file with accompanying metadata.

Modulation of DNA structure formation using small molecules

Genome integrity is essential for proper cell function such that genetic instability can result in cellular dysfunction and disease. Mutations in the human genome are not random, and occur more frequently at "hotspot" regions that often co-localize with sequences that have the capacity to adopt alternative (i.e. non-B) DNA structures. Non-B DNA-forming sequences are mutagenic, can stimulate the formation of DNA double-strand breaks, and are highly enriched at mutation hotspots in human cancer genomes. Thus, small molecules that can modulate the conformations of these structure-forming sequences may prove beneficial in the prevention and/or treatment of genetic diseases. Further, the development of molecular probes to interrogate the roles of non-B DNA structures in modulating DNA function, such as genetic instability in cancer etiology are warranted. Here, we discuss reported non-B DNA stabilizers, destabilizers, and probes, recent assays to identify ligands, and the potential biological applications of these DNA structure-modulating molecules.

Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins

BACKGROUND

Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies.

RESULTS

Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered.

CONCLUSIONS

Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking.

Conformationally Restricted Monosaccharide-Cored Glycoside Amphiphiles: The Effect of Detergent Headgroup Variation on Membrane Protein Stability

Detergents are widely used to isolate membrane proteins from lipid bilayers, but many proteins solubilized in conventional detergents are structurally unstable. Thus, there is major interest in the development of novel amphiphiles to facilitate membrane protein research. In this study, we have designed and synthesized novel amphiphiles with a rigid scyllo-inositol core, designated scyllo-inositol glycosides (SIGs). Varying the headgroup structure allowed the preparation of three sets of SIGs that were evaluated for their effects on membrane protein stability. When tested with a few model membrane proteins, representative SIGs conferred enhanced stability to the membrane proteins compared to a gold standard conventional detergent (DDM). Of the novel amphiphiles, a SIG designated STM-12 was most effective at preserving the stability of the multiple membrane proteins tested here. In addition, a comparative study of the three sets suggests that several factors, including micelle size and alkyl chain length, need to be considered in the development of novel detergents for membrane protein research. Thus, this study not only describes new detergent tools that are potentially useful for membrane protein structural study but also introduces plausible correlations between the chemical properties of detergents and membrane protein stabilization efficacy.