AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms

Casearia sylvestris var. lingua (Càmbess.) Eichler leaves aqueous extract improves colon inflammation through mucogenic, antioxidant and anti-inflammatory actions in TNBS- induced IBD rats

ETHNOPHARMACOLOGICAL RELEVANCE

Casearia sylvestris var. lingua (Cambess.) Eichler, a member of the Salicaceae family, holds a prominent place in traditional medicine across various cultures due to its versatile therapeutic properties. Historically, indigenous communities have utilized different parts of the plant, including leaves, bark, and roots, to address a wide array of health conditions. Traditional uses of C. sylvestris var. lingua encompasses the treatment of gastrointestinal disorders, respiratory infections, wound healing, inflammation, and stomach ulcers. Pharmacological studies have demonstrated the plant's antimicrobial, anti-inflammatory, antioxidant, analgesic, gastroprotective, and immunomodulatory effects. This signifies the first scientific validation report for C. sylvestris var. lingua regarding its effectiveness against ulcerative colitis. The report aims to affirm the traditional use of this plant through pre-clinical experiments.

AIM OF THE RESEARCH

This work uses an aqueous extract from C. sylvestris var. lingua leaves (AECs) to evaluate the acute anti-ulcerative colitis efficacy in rat and HT-29 (human colorectal cancer cell line) models.

METHODS

To determine the secondary metabolites of AECs, liquid chromatography with a diode array detector (LC-DAD) study was carried out. 2,4,6-trinitrobenzenesulfonic acid (TNBS, 30 mg/0.25 mL EtOH 30% v/v) was used as an enema to cause acute colitis. Three days were spent giving the C. sylvestris var. lingua extract orally by gavage at dosages of 3, 30, and 300 mg/kg. The same route was used to deliver distilled water to the vehicle and naïve groups. After the animals were sacrificed on the fourth day, intestinal tissues were taken for histological examination and evaluation of biochemical tests such as those measuring superoxide dismutase (SOD), reduced glutathione (GSH), catalase (CAT), malondialdehyde (MDA), nitrite/nitrate, myeloperoxidase (MPO) activity. Additionally, interleukin 1 beta (IL-1β), tumor necrosis factor alpha (TNF-α), and interleukin 10 (IL-10), were conducted on the intestinal tissues. Additionally, an MTT assay was used to evaluate the effect of AECs on the viability of HT-29 cells. Additionally, a molecular docking study was carried out to compare some potential target proteins with identified chemicals found in AECs.

RESULTS

LC-DAD analysis identified five compounds (caffeic acid, ellagic acid, ferulic acid, gallic acid, and quercetin) in AECs. Pre-administration of AECs (3; 30; 300 mg/kg) and mesalazine (500 mg/kg) reduced macroscopic scores (55%, 47%, 45%, and 52%, p < 0.001) and ulcerated areas (70.3%, 70.5%, 57%, and 56%, p < 0.001), respectively. It also increased SOD, GSH, and CAT activities (p < 0.01), while decreasing MDA (p < 0.001), nitrite/nitrate (p < 0.05), and MPO (p < 0.001) activities compared to the colitis group. Concerning inflammatory markers, significant modulations were observed: AECs (3, 30, and 300 mg/kg) lowered levels of IL-1β and TNF-α (p < 0.001) and increased IL-10 levels (p < 0.001) compared to the colitis groups. The viability of HT-29 cells was suppressed by AECs with an IC50 of 195.90 ± 0.01 μg/mL (48 h). During the molecular docking analysis, quercetin, gallic acid, ferulic acid, caffeic acid, and ellagic acid demonstrated consistent binding affinities, forming stable interactions with the 3w3l (TLR8) and the 3ds6 (MAPK14) complexes.

CONCLUSION

These results imply that the intestinal mucogenic, anti-inflammatory, and antioxidant properties of the C. sylvestris var. lingua leaf extract may be involved in its therapeutic actions for ulcerative colitis. The results of the in silico study point to the possibility of quercetin and ellagic acid interacting with P38 and TLR8, respectively, in a beneficial way.

Predicting therapeutic and side effects from drug binding affinities to human proteome structures

Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.

Novel Coumarins Derivatives for A. baumannii Lung Infection Developed by High-Throughput Screening and Reinforcement Learning

With the increasing resistance of Acinetobacter baumannii (A. baumannii) to antibiotics, researchers have turned their attention to the development of new antimicrobial agents. Among them, coumarin-based heterocycles have attracted much attention due to their unique biological activities, especially in the field of antibacterial infection. In this study, a series of coumarin derivatives were synthesized and screened for their bactericidal activities (Ren et al. 2018; Salehian et al. 2021). The inhibitory activities of these compounds on bacterial strains were evaluated, and the related mechanism of the new compounds was explored. Firstly, the MIC values and bacterial growth curves were measured after compound treatment to evaluate the antibacterial activity in vitro. Then, the in vivo antibacterial activities of the new compounds were assessed on A. baumannii-infected mice by determining the mice survival rates, counting bacterial CFU numbers, measuring inflammatory cytokine levels, and histopathology analysis. In addition, the ROS levels in the bacterial cells were measured with DCFH-DA detection kit. Furthermore, the potential target and detailed mechanism of the new compounds during infection disease therapy were predicted and evidenced with molecular docking. After that, ADMET characteristic prediction was completed, and novel, synthesizable, drug-effective molecules were optimized with reinforcement learning study based on the probed compound as a training template. The interaction between the selected structures and target proteins was further evidenced with molecular docking. This series of innovative studies provides important theoretical and experimental data for the development of new anti-A. baumannii infection drugs.

Targeting SHP2 Cryptic Allosteric Sites for Effective Cancer Therapy

SHP2, a pivotal component downstream of both receptor and non-receptor tyrosine kinases, has been underscored in the progression of various human cancers and neurodevelopmental disorders. Allosteric inhibitors have been proposed to regulate its autoinhibition. However, oncogenic mutations, such as E76K, convert SHP2 into its open state, wherein the catalytic cleft becomes fully exposed to its ligands. This study elucidates the dynamic properties of SHP2 structures across different states, with a focus on the effects of oncogenic mutation on two known binding sites of allosteric inhibitors. Through extensive modeling and simulations, we further identified an alternative allosteric binding pocket in solution structures. Additional analysis provides insights into the dynamics and stability of the potential site. In addition, multi-tier screening was deployed to identify potential binders targeting the potential site. Our efforts to identify a new allosteric site contribute to community-wide initiatives developing therapies using multiple allosteric inhibitors to target distinct pockets on SHP2, in the hope of potentially inhibiting or slowing tumor growth associated with SHP2.

Predicted mechanistic impacts of human protein missense variants

Genome sequencing efforts have led to the discovery of tens of millions of protein missense variants found in the human population with the majority of these having no annotated role and some likely contributing to trait variation and disease. Sequence-based artificial intelligence approaches have become highly accurate at predicting variants that are detrimental to the function of proteins but they do not inform on mechanisms of disruption. Here we combined sequence and structure-based methods to perform proteome-wide prediction of deleterious variants with information on their impact on protein stability, protein-protein interactions and small-molecule binding pockets. AlphaFold2 structures were used to predict approximately 100,000 small-molecule binding pockets and stability changes for over 200 million variants. To inform on protein-protein interfaces we used AlphaFold2 to predict structures for nearly 500,000 protein complexes. We illustrate the value of mechanism-aware variant effect predictions to study the relation between protein stability and abundance and the structural properties of interfaces underlying trans protein quantitative trait loci (pQTLs). We characterised the distribution of mechanistic impacts of protein variants found in patients and experimentally studied example disease linked variants in FGFR1.

ProtVar: mapping and contextualizing human missense variation

Genomic variation can impact normal biological function in complex ways and so understanding variant effects requires a broad range of data to be coherently assimilated. Whilst the volume of human variant data and relevant annotations has increased, the corresponding increase in the breadth of participating fields, standards and versioning mean that moving between genomic, coding, protein and structure positions is increasingly complex. In turn this makes investigating variants in diverse formats and assimilating annotations from different resources challenging. ProtVar addresses these issues to facilitate the contextualization and interpretation of human missense variation with unparalleled flexibility and ease of accessibility for use by the broadest range of researchers. By precalculating all possible variants in the human proteome it offers near instantaneous mapping between all relevant data types. It also combines data and analyses from a plethora of resources to bring together genomic, protein sequence and function annotations as well as structural insights and predictions to better understand the likely effect of missense variation in humans. It is offered as an intuitive web server https://www.ebi.ac.uk/protvar where data can be explored and downloaded, and can be accessed programmatically via an API.

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

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

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.

A novel class of inhibitors that disrupts the stability of integrin heterodimers identified by CRISPR-tiling-instructed genetic screens

The plasma membrane is enriched for receptors and signaling proteins that are accessible from the extracellular space for pharmacological intervention. Here we conducted a series of CRISPR screens using human cell surface proteome and integrin family libraries in multiple cancer models. Our results identified ITGAV (integrin αV) and its heterodimer partner ITGB5 (integrin β5) as the essential integrin α/β pair for cancer cell expansion. High-density CRISPR gene tiling further pinpointed the integral pocket within the β-propeller domain of ITGAV for integrin αVβ5 dimerization. Combined with in silico compound docking, we developed a CRISPR-Tiling-Instructed Computer-Aided (CRISPR-TICA) pipeline for drug discovery and identified Cpd_AV2 as a lead inhibitor targeting the β-propeller central pocket of ITGAV. Cpd_AV2 treatment led to rapid uncoupling of integrin αVβ5 and cellular apoptosis, providing a unique class of therapeutic action that eliminates the integrin signaling via heterodimer dissociation. We also foresee the CRISPR-TICA approach to be an accessible method for future drug discovery studies.

It is theoretically possible to avoid misfolding into non-covalent lasso entanglements using small molecule drugs

A novel class of protein misfolding characterized by either the formation of non-native noncovalent lasso entanglements in the misfolded structure or loss of native entanglements has been predicted to exist and found circumstantial support through biochemical assays and limited-proteolysis mass spectrometry data. Here, we examine whether it is possible to design small molecule compounds that can bind to specific folding intermediates and thereby avoid these misfolded states in computer simulations under idealized conditions (perfect drug-binding specificity, zero promiscuity, and a smooth energy landscape). Studying two proteins, type III chloramphenicol acetyltransferase (CAT-III) and D-alanyl-D-alanine ligase B (DDLB), that were previously suggested to form soluble misfolded states through a mechanism involving a failure-to-form of native entanglements, we explore two different drug design strategies using coarse-grained structure-based models. The first strategy, in which the native entanglement is stabilized by drug binding, failed to decrease misfolding because it formed an alternative entanglement at a nearby region. The second strategy, in which a small molecule was designed to bind to a non-native tertiary structure and thereby destabilize the native entanglement, succeeded in decreasing misfolding and increasing the native state population. This strategy worked because destabilizing the entanglement loop provided more time for the threading segment to position itself correctly to be wrapped by the loop to form the native entanglement. Further, we computationally identified several FDA-approved drugs with the potential to bind these intermediate states and rescue misfolding in these proteins. This study suggests it is possible for small molecule drugs to prevent protein misfolding of this type.

Encoding Molecular Docking for Quantum Computers

Molecular docking is important in drug discovery but is burdensome for classical computers. Here, we introduce Grid Point Matching (GPM) and Feature Atom Matching (FAM) to accelerate pose sampling in molecular docking by encoding the problem into quadratic unconstrained binary optimization (QUBO) models so that it could be solved by quantum computers like the coherent Ising machine (CIM). As a result, GPM shows a sampling power close to that of Glide SP, a method performing an extensive search. Moreover, it is estimated to be 1000 times faster on the CIM than on classical computers. Our methods could boost virtual drug screening of small molecules and peptides in future.

Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites

The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effective antiviral drug target remains a time-consuming challenge. Computational methods offer a promising solution by efficiently reducing the number of candidates. In this study, we propose a structure- and deep learning-based approach that identifies vulnerable regions in viral proteins corresponding to drug binding sites. Our approach takes into account the protein dynamics, accessibility and mutability of the binding site and the putative mechanism of action of the drug. We applied this technique to validate drug targeting toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein S. Our findings reveal a conformation- and oligomer-specific glycan-free binding site proximal to the receptor binding domain. This site comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with candidate drug molecules bound to the potential binding sites indicate an equilibrium shifted toward the inactive conformation compared with drug-free simulations. Small molecules targeting this binding site have the potential to prevent the closed-to-open conformational transition of Spike, thereby allosterically inhibiting its interaction with human angiotensin-converting enzyme 2 receptor. Using a pseudotyped virus-based assay with a SARS-CoV-2 neutralizing antibody, we identified a set of hit compounds that exhibited inhibition at micromolar concentrations.

Clustering predicted structures at the scale of the known protein universe

Proteins are key to all cellular processes and their structure is important in understanding their function and evolution. Sequence-based predictions of protein structures have increased in accuracy1, and over 214 million predicted structures are available in the AlphaFold database2. However, studying protein structures at this scale requires highly efficient methods. Here, we developed a structural-alignment-based clustering algorithm-Foldseek cluster-that can cluster hundreds of millions of structures. Using this method, we have clustered all of the structures in the AlphaFold database, identifying 2.30 million non-singleton structural clusters, of which 31% lack annotations representing probable previously undescribed structures. Clusters without annotation tend to have few representatives covering only 4% of all proteins in the AlphaFold database. Evolutionary analysis suggests that most clusters are ancient in origin but 4% seem to be species specific, representing lower-quality predictions or examples of de novo gene birth. We also show how structural comparisons can be used to predict domain families and their relationships, identifying examples of remote structural similarity. On the basis of these analyses, we identify several examples of human immune-related proteins with putative remote homology in prokaryotic species, illustrating the value of this resource for studying protein function and evolution across the tree of life.

RefinePocket: An Attention-Enhanced and Mask-Guided Deep Learning Approach for Protein Binding Site Prediction

Protein binding site prediction is an important prerequisite task of drug discovery and design. While binding sites are very small, irregular and varied in shape, making the prediction very challenging. Standard 3D U-Net has been adopted to predict binding sites but got stuck with unsatisfactory prediction results, incomplete, out-of-bounds, or even failed. The reason is that this scheme is less capable of extracting the chemical interactions of the entire region and hardly takes into account the difficulty of segmenting complex shapes. In this paper, we propose a refined U-Net architecture, called RefinePocket, consisting of an attention-enhanced encoder and a mask-guided decoder. During encoding, taking binding site proposal as input, we employ Dual Attention Block (DAB) hierarchically to capture rich global information, exploring residue relationship and chemical correlations in spatial and channel dimensions respectively. Then, based on the enhanced representation extracted by the encoder, we devise Refine Block (RB) in the decoder to enable self-guided refinement of uncertain regions gradually, resulting in more precise segmentation. Experiments show that DAB and RB complement and promote each other, making RefinePocket has an average improvement of 10.02% on DCC and 4.26% on DVO compared with the state-of-the-art method on four test sets.

Structure-Activity Relationship Study of Cannabidiol-Based Analogs as Negative Allosteric Modulators of the μ-Opioid Receptor

The US faces an unprecedented surge in fatal drug overdoses. Naloxone, the only antidote for opiate overdose, competes at the mu opioid receptor (μOR) orthosteric site. Naloxone struggles against fentanyl-class synthetic opioids that now cause ∼80% of deaths. Negative allosteric modulators (NAMs) targeting secondary sites may noncompetitively downregulate μOR activation. (-)-Cannabidiol ((-)-CBD) is a candidate μOR NAM. To explore its therapeutic potential, we evaluated the structure-activity relationships among CBD analogs to identify NAMs with increased potency. Using a cyclic AMP assay, we characterize reversal of μOR activation by 15 CBD analogs, several of which proved more potent than (-)-CBD. Comparative docking investigations suggest that potent compounds interact with a putative allosteric pocket to stabilize the inactive μOR conformation. Finally, these compounds enhance naloxone displacement of fentanyl from the orthosteric site. Our results suggest that CBD analogs offer considerable potential for the development of next-generation antidotes for opioid overdose.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

Binding Site Detection Remastered: Enabling Fast, Robust, and Reliable Binding Site Detection and Descriptor Calculation with DoGSite3

Binding site prediction on protein structures is a crucial step in early phase drug discovery whenever experimental or predicted structure models are involved. DoGSite belongs to the widely used tools for this task. It is a grid-based method that uses a Difference-of-Gaussian filter to detect cavities on the protein surface. We recently reimplemented the first version of this method, released in 2010, focusing on improved binding site detection in the presence of ligands and optimized parameters for more robust, reliable, and fast predictions and binding site descriptor calculations. Here, we introduce the new version, DoGSite3, compare it to its predecessor, and re-evaluate DoGSite on published data sets for a large-scale comparative performance evaluation.

In Silico Study on the Interactions, Molecular Docking, Dynamics and Simulation of Potential Compounds from Withania somnifera (L.) Dunal Root against Cancer by Targeting KAT6A

Cancer is characterized by the abnormal development of cells that divide in an uncontrolled manner and further take over the body and destroy the normal cells of the body. Although several therapies are practiced, the demand and need for new therapeutic agents are ever-increasing because of issues with the safety, efficacy and efficiency of old drugs. Several plant-based therapeutics are being used for treatment, either as conjugates with existing drugs or as standalone formulations. Withania somnifera (L.) Dunal is a highly studied medicinal plant which is known to possess immunomodulatory activity as well as anticancer properties. The pivotal role of KAT6A in major cellular pathways and its oncogenic nature make it an important target in cancer treatment. Based on the literature and curated datasets, twenty-six compounds from the root of W. somnifera and a standard inhibitor were docked with the target KAT6A using Autodock vina. The compounds and the inhibitor complexes were subjected to molecular dynamics simulation (50 ns) using Desmond to understand the stability and interactions. The top compounds (based on the docking score of less than -8.5 kcal/mol) were evaluated in comparison to the inhibitor. Based on interactions at ARG655, LEU686, GLN760, ARG660, LEU689 and LYS763 amino acids with the inhibitor WM-8014, the compounds from W. somnifera were evaluated. Withanolide D, Withasomniferol C, Withanolide E, 27-Hydroxywithanone, Withanolide G, Withasomniferol B and Sitoindoside IX showed high stability with the residues of interest. The cell viability of human breast cancer MCF-7 cells was evaluated by treating them with W. Somnifera root extract using an MTT assay, which showed inhibitory activity with an IC50 value of 45 µg/mL. The data from the study support the traditional practice of W. somnifera as an anticancer herb.

Targeting RNA:protein interactions with an integrative approach leads to the identification of potent YBX1 inhibitors

RNA-protein interactions (RPIs) are promising targets for developing new molecules of therapeutic interest. Nevertheless, challenges arise from the lack of methods and feedback between computational and experimental techniques during the drug discovery process. Here, we tackle these challenges by developing a drug screening approach that integrates chemical, structural and cellular data from both advanced computational techniques and a method to score RPIs in cells for the development of small RPI inhibitors; and we demonstrate its robustness by targeting Y-box binding protein 1 (YB-1), a messenger RNA-binding protein involved in cancer progression and resistance to chemotherapy. This approach led to the identification of 22 hits validated by molecular dynamics (MD) simulations and nuclear magnetic resonance (NMR) spectroscopy of which 11 were found to significantly interfere with the binding of messenger RNA (mRNA) to YB-1 in cells. One of our leads is an FDA-approved poly(ADP-ribose) polymerase 1 (PARP-1) inhibitor. This work shows the potential of our integrative approach and paves the way for the rational development of RPI inhibitors.

Towards Effective Consensus Scoring in Structure-Based Virtual Screening

Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.

Rational design of the zonulin inhibitor AT1001 derivatives as potential anti SARS-CoV-2

Although vaccines are greatly mitigating the worldwide pandemic diffusion of SARS-Cov-2, therapeutics should provide many distinct advantages as complementary approach to control the viral spreading. Here, we report the development of new tripeptide derivatives of AT1001 against SARS-CoV-2 M^pro. By molecular modeling, a small compound library was rationally designed and filtered for enzymatic inhibition through FRET assay, leading to the identification of compound 4. X-ray crystallography studies provide insights into its binding mode and confirm the formation of a covalent bond with M^pro C145. In vitro antiviral tests indicate the improvement of biological activity of 4 respect to AT1001. In silico and X-ray crystallography analysis led to 58, showing a promising activity against three SARS-CoV-2 variants and a valuable safety in Vero cells and human embryonic lung fibroblasts. The drug tolerance was also confirmed by in vivo studies, along with pharmacokinetics evaluation. In summary, 58 could pave the way to develop a clinical candidate for intranasal administration.

A structural biology community assessment of AlphaFold2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.

PDBspheres: a method for finding 3D similarities in local regions in proteins

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions ('spheres') adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. PDBspheres uses the LGA (Local-Global Alignment) structure alignment algorithm as the main engine for detecting structural similarities between the protein of interest and template spheres from the library, which currently contains >2 million spheres. To assess confidence in structural matches, an all-atom-based similarity metric takes side chain placement into account. Here, we describe the PDBspheres method, demonstrate its ability to detect and characterize binding sites in protein structures, show how PDBspheres-a strictly structure-based method-performs on a curated dataset of 2528 ligand-bound and ligand-free crystal structures, and use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of 4876 structures in the 'refined set' of the PDBbind 2019 dataset.

Zinc Oxide Nanoparticle-Loaded Electrospun Polyvinylidene Fluoride Nanofibers as a Potential Face Protector against Respiratory Viral Infections

ZnO-NPs loaded polyvinylidene fluoride (PVDF) composite nanofibers were fabricated by electrospinning and optimized using different concentrations (0, 2, and 5 wt %) of ZnO-NPs. Characterization techniques, for example, FTIR, SEM, XRD, and tensile strength analysis were performed to analyze the composite nanofibers. Molecular docking calculations were performed to evaluate the binding affinity of PVDF and ZnO@PVDF against the hexon protein of adenovirus (PDB ID: 6CGV). The cytotoxicity of tested materials was evaluated using MTT assay, and nontoxic doses subjected to antiviral evaluation against human adenovirus type-5 as a human respiratory model were analyzed using quantitative polymerase chain reaction assay. IC₅₀ values were obtained at concentrations of 0, 2, and 5% of ZnO-loaded PVDF; however, no cytotoxic effect was detected for the nanofibers. In 5% ZnO-loaded PVDF nanofibers, both the viral entry and its replication were inhibited in both the adsorption and virucidal antiviral mechanisms, making it a potent antiviral filter/mask. Therefore, ZnO-loaded PVDF nanofiber is a potentially prototyped filter embedded in a commercial face mask for use as an antiviral mask with a pronounced potential to reduce the spreading of infectious respiratory diseases, for example, COVID-19 and its analogues.

Systemic evolutionary chemical space exploration for drug discovery

Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

Entry-inhibitory role of catechins against SARS-CoV-2 and its UK variant

BACKGROUND

The global pandemic caused by a RNA virus capable of infecting humans and animals, has resulted in millions of deaths worldwide. Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infects the lungs, and the gastrointestinal tract to some extent. Rapid structural mutations have increased the virulence and infectivity of the virus drastically. One such mutated strain known as the UK variant has caused many deaths in the United Kingdom.

HYPOTHESIS

Among several indigenous natural ingredients used for prevention and cure of many diseases, the catechins have been reported for their antiviral activity, even against SARS-CoV-2. Characteristic mutations present on the spike protein have presented the newer strain its enhanced infectivity. The spike protein helps the virus bind to ACE2 receptor of the host cell and hence is a drug target. Catechins have been reported for their entry-inhibitory activity against several viruses.

METHOD

In this study, we performed molecular docking of different catechins with the wild and mutant variants of the spike protein of SARS-CoV-2. The stability of the best docked complexes was validated using molecular dynamics simulation.

RESULTS

The in-silico studies show that the catechins form favourable interactions with the spike protein and can potentially impair its function. Epigallocatechin gallate (EGCG) showed the best binding among the catechins against both the strains. Both the protein-ligand complexes were stable throughout the simulation time frame.

CONCLUSION

The outcomes should encourage further exploration of the antiviral activity of EGCG against SARS-CoV-2 and its variants.

CHARMM-GUI LBS Finder & Refiner for Ligand Binding Site Prediction and Refinement

A protein performs its task by binding a variety of ligands in its local region that is also known as the ligand-binding-site (LBS). Therefore, accurate prediction, characterization, and refinement of LBS can facilitate protein functional annotations and structure-based drug design. In this work, we present CHARMM-GUI LBS Finder & Refiner (https://www.charmm-gui.org/input/lbsfinder) that predicts potential LBS, offers interactive features for local LBS structure analysis, and prepares various molecular dynamics (MD) systems and inputs by setting up distance restraint potentials for LBS structure refinement. LBS Finder & Refiner supports 5 different commonly used simulation programs, such as NAMD, AMBER, GROMACS, GENESIS, and OpenMM, for LBS structure refinement together with hydrogen mass repartitioning. The capability of LBS Finder & Refiner is illustrated through LBS structure predictions and refinements of 48 modeled and 20 apo benchmark target proteins. Overall, successful LBS structure predictions and refinements are seen in our benchmark tests. We hope that LBS Finder & Refiner is useful to predict, characterize, and refine potential LBS on any given protein of interest.

DynaBiS: A hierarchical sampling algorithm to identify flexible binding sites for large ligands and peptides

Knowing the ligand or peptide binding site in proteins is highly important to guide drug discovery, but experimental elucidation of the binding site is difficult. Therefore, various computational approaches have been developed to identify potential binding sites in protein structures. However, protein and ligand flexibility are often neglected in these methods due to efficiency considerations despite the recognition that protein-ligand interactions can be strongly affected by mutual structural adaptations. This is particularly true if the binding site is unknown, as the screening will typically be performed based on an unbound protein structure. Herein we present DynaBiS, a hierarchical sampling algorithm to identify flexible binding sites for a target ligand with explicit consideration of protein and ligand flexibility, inspired by our previously presented flexible docking algorithm DynaDock. DynaBiS applies soft-core potentials between the ligand and the protein, thereby allowing a certain protein-ligand overlap resulting in efficient sampling of conformational adaptation effects. We evaluated DynaBiS and other commonly used binding site identification algorithms against a diverse evaluation set consisting of 26 proteins featuring peptide as well as small ligand binding sites. We show that DynaBiS outperforms the other evaluated methods for the identification of protein binding sites for large and highly flexible ligands such as peptides, both with a holo or apo structure used as input.

Phosphate binding sites prediction in phosphorylation-dependent protein-protein interactions

MOTIVATION

Phosphate binding plays an important role in modulating protein-protein interactions, which are ubiquitous in various biological processes. Accurate prediction of phosphate binding sites is an important but challenging task. Small size and diversity of phosphate binding sites lead to a substantial challenge for developing accurate prediction methods.

RESULTS

Here we present the phosphate binding site predictor (PBSP), a novel and accurate approach to identifying phosphate binding sites from protein structures. PBSP combines an energy-based ligand-binding sites identification method with reverse focused docking using a phosphate probe. We show that PBSP outperforms not only general ligand binding sites predictors but also other existing phospholigand-specific binding sites predictors. It achieves ∼95% success rate for top 10 predicted sites with an average Matthews correlation coefficient (MCC) value of 0.84 for successful predictions. PBSP can accurately predict phosphate binding modes, with average position error of 1.4 Å and 2.4 Å in bound and unbound datasets, respectively. Lastly, visual inspection of the predictions is conducted. Reasons for failed predictions are further analyzed and possible ways to improve the performance are provided. These results demonstrate a novel and accurate approach to phosphate binding sites identification in protein structures.

AVAILABILITY

The software and benchmark datasets are freely available at http://web.pkusz.edu.cn/wu/PBSP/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Improving Docking Power for Short Peptides Using Random Forest

In recent years, therapeutic peptides have gained a lot interest as demonstrated by the 60 peptides approved as drugs in major markets and 150+ peptides currently in clinical trials. However, while small molecule docking is routinely used in rational drug design efforts, docking peptides has proven challenging partly because docking scoring functions, developed and calibrated for small molecules, perform poorly for these molecules. Here, we present random forest classifiers trained to discriminate correctly docked peptides. We show that, for a testing set of 47 protein-peptide complexes, structurally dissimilar from the training set and previously used to benchmark AutoDock Vina's ability to dock short peptides, these random forest classifiers improve docking power from ∼25% for AutoDock scoring functions to an average of ∼70%. These results pave the way for peptide-docking success rates comparable to those of small molecule docking. To develop these classifiers, we compiled the ProptPep37_2021 data set, a curated, high-quality set of 322 crystallographic protein-peptides complexes annotated with structural similarity information. The data set also provides a collection of high-quality putative poses with a range of deviations from the crystallographic pose, providing correct and incorrect poses (i.e., decoys) of the peptide for each entry. The ProptPep37_2021 data set as well as the classifiers presented here are freely available.

3D U-Net: A voxel-based method in binding site prediction of protein structure

Binding site prediction for new proteins is important in structure-based drug design. The identified binding sites may be helpful in the development of treatments for new viral outbreaks in the world when there is no information available about their pockets with COVID-19 being a case in point. Identification of the pockets using computational methods, as an alternative method, has recently attracted much interest. In this study, the binding site prediction is viewed as a semantic segmentation problem. An improved 3D version of the U-Net model based on the dice loss function is utilized to predict the binding sites accurately. The performance of the proposed model on the independent test datasets and SARS-COV-2 shows the segmentation model could predict the binding sites with a more accurate shape than the recently published deep learning model, i.e. DeepSite. Therefore, the model may help predict the binding sites of proteins and could be used in drug design for novel proteins.

DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins

MOTIVATION

The knowledge of potentially druggable binding sites on proteins is an important preliminary step towards the discovery of novel drugs. The computational prediction of such areas can be boosted by following the recent major advances in the deep learning field and by exploiting the increasing availability of proper data.

RESULTS

In this paper, a novel computational method for the prediction of potential binding sites is proposed, called DeepSurf. DeepSurf combines a surface-based representation, where a number of 3 D voxelized grids are placed on the protein's surface, with state-of-the-art deep learning architectures. After being trained on the large database of scPDB, DeepSurf demonstrates superior results on three diverse testing datasets, by surpassing all its main deep learning-based competitors, while attaining competitive performance to a set of traditional non-data-driven approaches.

AVAILABILITY

The source code of the method along with trained models are freely available at https://github.com/stemylonas/DeepSurf.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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

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

A Strain-Specific Inhibitor of Receptor-Bound HIV-1 Targets a Pocket near the Fusion Peptide

Disruption of viral fusion represents a viable, albeit under-explored, target for HIV therapeutics. Here, while studying the receptor-bound envelope glycoprotein conformation by cryoelectron microscopy (cryo-EM), we identify a pocket near the base of the trimer containing a bound detergent molecule and perform in silico drug screening by using a library of drug-like and commercially available molecules. After down-selection, we solve cryo-EM structures that validate the binding of two small molecule hits in very similar manners to the predicted binding poses, including interactions with aromatic residues within the fusion peptide. One of the molecules demonstrates low micromolar inhibition of the autologous virus by using a very rare phenylalanine in the fusion peptide and stabilizing the surrounding region. This work demonstrates that small molecules can target the fusion process, providing an additional target for anti-HIV therapeutics, and highlights the need to explore how fusion peptide sequence variations affect receptor-mediated conformational states across diverse HIV strains.

Predicting binding sites from unbound versus bound protein structures

We present the application of seven binding-site prediction algorithms to a meticulously curated dataset of ligand-bound and ligand-free crystal structures for 304 unique protein sequences (2528 crystal structures). We probe the influence of starting protein structures on the results of binding-site prediction, so the dataset contains a minimum of two ligand-bound and two ligand-free structures for each protein. We use this dataset in a brief survey of five geometry-based, one energy-based, and one machine-learning-based methods: Surfnet, Ghecom, LIGSITE_csc, Fpocket, Depth, AutoSite, and Kalasanty. Distributions of the F scores and Matthew’s correlation coefficients for ligand-bound versus ligand-free structure performance show no statistically significant difference in structure type versus performance for most methods. Only Fpocket showed a statistically significant but low magnitude enhancement in performance for holo structures. Lastly, we found that most methods will succeed on some crystal structures and fail on others within the same protein family, despite all structures being relatively high-quality structures with low structural variation. We expected better consistency across varying protein conformations of the same sequence. Interestingly, the success or failure of a given structure cannot be predicted by quality metrics such as resolution, Cruickshank Diffraction Precision index, or unresolved residues. Cryptic sites were also examined.

The AutoDock suite at 30

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.

The Sialoside-Binding Pocket of SARS-CoV-2 Spike Glycoprotein Structurally Resembles MERS-CoV

COVID-19 novel coronavirus (CoV) disease caused by severe acquired respiratory syndrome (SARS)-CoV-2 manifests severe lethal respiratory illness in humans and has recently developed into a worldwide pandemic. The lack of effective treatment strategy and vaccines against the SARS-CoV-2 poses a threat to human health. An extremely high infection rate and multi-organ secondary infection within a short period of time makes this virus more deadly and challenging for therapeutic interventions. Despite high sequence similarity and utilization of common host-cell receptor, human angiotensin-converting enzyme-2 (ACE2) for virus entry, SARS-CoV-2 is much more infectious than SARS-CoV. Structure-based sequence comparison of the N-terminal domain (NTD) of the spike protein of Middle East respiratory syndrome (MERS)-CoV, SARS-CoV, and SARS-CoV-2 illustrate three divergent loop regions in SARS-CoV-2, which is reminiscent of MERS-CoV sialoside binding pockets. Comparative binding analysis with host sialosides revealed conformational flexibility of SARS-CoV-2 divergent loop regions to accommodate diverse glycan-rich sialosides. These key differences with SARS-CoV and similarity with MERS-CoV suggest an evolutionary adaptation of SARS-CoV-2 spike glycoprotein reciprocal interaction with host surface sialosides to infect host cells with wide tissue tropism.

An Activity-Guided Map of Electrophile-Cysteine Interactions in Primary Human T Cells

Electrophilic compounds originating from nature or chemical synthesis have profound effects on immune cells. These compounds are thought to act by cysteine modification to alter the functions of immune-relevant proteins; however, our understanding of electrophile-sensitive cysteines in the human immune proteome remains limited. Here, we present a global map of cysteines in primary human T cells that are susceptible to covalent modification by electrophilic small molecules. More than 3,000 covalently liganded cysteines were found on functionally and structurally diverse proteins, including many that play fundamental roles in immunology. We further show that electrophilic compounds can impair T cell activation by distinct mechanisms involving the direct functional perturbation and/or degradation of proteins. Our findings reveal a rich content of ligandable cysteines in human T cells and point to electrophilic small molecules as a fertile source for chemical probes and ultimately therapeutics that modulate immunological processes and their associated disorders.

AutoDock CrankPep: combining folding and docking to predict protein-peptide complexes

MOTIVATION

Protein-peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs.

RESULTS

Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein-peptide complex. We show that it outperforms leading peptide docking methods on two protein-peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein-peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein-protein interactions and interactions with disordered proteins.

AVAILABILITY AND IMPLEMENTATION

ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters

Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.

AutoGridFR: Improvements on AutoDock Affinity Maps and Associated Software Tools

Precomputed affinity maps are used by AutoDock to efficiently describe rigid biomolecules called receptors in automated docking. These maps greatly speed up the docking process and allow users to experiment with the forcefield. Here, we present AutoGridFR (AGFR): a software tool facilitating the calculation of these maps. We describe a new version of the AutoSite algorithm that improves the description of binding pockets automatically detected on receptors, and an algorithm for adding affinity gradients which help search methods optimize solution using fewer evaluations of the scoring functions. AGFR supports the calculation of maps for various advanced docking techniques such as covalent docking, hydrated docking, and docking with flexible receptor sidechains. Maps are stored in a single file along with metadata supporting data provenance, reproducibility, and facilitating their management. Finally, maps can be calculated from the command line or through a modern graphical user interface which also supports their visualization. © 2019 Wiley Periodicals, Inc.

Alterations in plasma membrane ion channel structures stimulate NLRP3 inflammasome activation in APOL1 risk milieu

We evaluated alterations in the structural configurations of channels and activation of nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3 (NLRP3) inflammasome formation in apolipoprotein L1 (APOL1) risk and nonrisk milieus. APOL1G1- and APOL1G2-expressing podocytes (PD) displayed enhanced K⁺ efflux, induction of pyroptosis, and escalated transcription of interleukin (IL)-1β and IL-18. APOL1G1- and APOL1G2-expressing PD promoted the transcription as well as translation of proteins involved in the formation of inflammasomes. Since glyburide (a specific inhibitor of K⁺ efflux channels) inhibited the transcription of NLRP3, IL-1β, and IL-18, the role of K⁺ efflux in the activation of inflammasomes in APOL1 risk milieu was implicated. To evaluate the role of structural alterations in K⁺ channels in plasma membranes, bioinformatics studies, including molecular dynamic simulation, were carried out. Superimposition of bioinformatics reconstructions of APOL1G0, G1, and G2 showed several aligned regions. The analysis of pore-lining residues revealed that Ser342 and Tyr389 are involved in APOL1G0 pore formation and the altered conformations resulting from the Ser342Gly and Ile384Met mutation in the case of APOLG1 and deletion of the Tyr389 residue in the case of APOL1G2 are expected to alter pore characteristics, including K⁺ ion selectivity. Analysis of multiple membrane (lipid bilayer) models of interaction with the peripheral protein, integral membrane protein, and multimer protein revealed that for an APOL1 multimer model, APOL1G0 is not energetically favorable while the APOL1G1 and APOL1G2 moieties favor the insertion of multiple ion channels into the lipid bilayer. We conclude that altered pore configurations carry the potential to facilitate K⁺ ion transport in APOL1 risk milieu.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies

In the current "genomic era" the number of identified genes is growing exponentially. However, the biological function of a large number of the corresponding proteins is still unknown. Recognition of small molecule ligands (e.g., substrates, inhibitors, allosteric regulators, etc.) is pivotal for protein functions in the vast majority of the cases and knowledge of the region where these processes take place is essential for protein function prediction and drug design. In this regard, computational methods represent essential tools to tackle this problem. A significant number of software tools have been developed in the last few years which exploit either protein sequence information, structure information or both. This review describes the most recent developments in protein function recognition and binding site prediction, in terms of both freely-available and commercial solutions and tools, detailing the main characteristics of the considered tools and providing a comparative analysis of their performance.

Docking Flexible Cyclic Peptides with AutoDock CrankPep

While a new therapeutic cyclic peptide is approved nearly every year, docking large macrocycles has remained challenging. Here, we present a new version of our peptide docking software AutoDock CrankPep (ADCP), extended to dock peptides cyclized through their backbone and/or side chain disulfide bonds. We show that within the top 10 solutions, ADCP identifies the proper interactions for 71% of a data set of 38 complexes, thus making it a useful tool for rational peptide-based drug design.

Resequencing CYP2D6 gene in Indian population: CYP2D6*41 identified as the major reduced function allele

Aim: The CYP2D6 gene is highly polymorphic and harbors population specific alleles that define its predominant metabolizer phenotype. This study aimed to identify polymorphisms in Indian population owing to scarcity of CYP2D6 data in this population. Materials & methods: The CYP2D6 gene was resequenced in 105 south Indians using next generation sequencing technology and haplotypes were reconstructed. Results & conclusion: Four novel missense variants have been designated as CYP2D6*110, *111, *112 and *113. The most common alleles were CYP2D6*1 (42%), *2 (32%), and *41 (12.3%) and diplotypes were CYP2D6*1/*2 (26%), *1/*1 (11%), *2/*41 (10%) and *1/*41 (7%) accounting for high incidence of extensive metabolizers in Indians.

Novel Intersubunit Interaction Critical for HIV-1 Core Assembly Defines a Potentially Targetable Inhibitor Binding Pocket

Precise assembly and disassembly of the HIV-1 capsid core are key to the success of viral replication. The forces that govern capsid core formation and dissociation involve intricate interactions between pentamers and hexamers formed by HIV-1 CA. We identified one particular interaction between E28 of one CA and K30′ of the adjacent CA that appears more frequently in pentamers than in hexamers and that is important for capsid assembly. Targeting the corresponding site could lead to the development of antivirals which disrupt this interaction and affect capsid assembly.

HIV-1 capsid protein (CA) plays critical roles in both early and late stages of the viral replication cycle. Mutagenesis and structural experiments have revealed that capsid core stability significantly affects uncoating and initiation of reverse transcription in host cells. This has led to efforts in developing antivirals targeting CA and its assembly, although none of the currently identified compounds are used in the clinic for treatment of HIV infection. A specific interaction that is primarily present in pentameric interfaces in the HIV-1 capsid core was identified and is reported to be important for CA assembly. This is shown by multidisciplinary characterization of CA site-directed mutants using biochemical analysis of virus-like particle formation, transmission electron microscopy of in vitro assembly, crystallographic studies, and molecular dynamic simulations. The data are consistent with a model where a hydrogen bond between CA residues E28 and K30′ from neighboring N-terminal domains (CA_NTDs) is important for CA pentamer interactions during core assembly. This pentamer-preferred interaction forms part of an N-terminal domain interface (NDI) pocket that is amenable to antiviral targeting.