A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

ProteinsPlus: a publicly available resource for protein structure mining

The openly accessible ProteinsPlus web server at https://proteins.plus is a unique resource enabling protein structure mining and modeling, focussing on protein-ligand interactions. Since its launch in 2017, the number of tools is steadily increasing. Currently, the server comprises six methods for protein structure analysis, four tools for mining the Protein Data Bank (PDB), and five prediction approaches regarding protein-ligand complex modeling. Users can use experimental structures from the PDB or computationally predicted structures from the AlphaFold Protein Structure Database as starting points. Alternatively, they can upload individual structure files. Recent updates include novel methods for detecting binding sites and predicting solvent channels in crystallographic structures, as well as updates of tools for protein-ligand interaction depiction in 2D and binding site mining. Given these updates, we present a real-life application scenario that underpins the novelties and applicability of the web server's tools for modern structure-based design projects. It also highlights the next steps for the web server, which will be redesigned using a different technology stack to improve the inter-usability of the tools, ease maintainability, and make it future-proof.

TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor

Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if a deviation in local structural features influences the function. Here, we present TopEC, a 3D graph neural network based on a localized 3D descriptor to learn chemical reactions of enzymes from enzyme structures and predict Enzyme Commission (EC) classes. Using message-passing frameworks, we include distance and angle information to significantly improve the predictive performance for EC classification (F-score: 0.72) compared to regular 2D graph neural networks. We trained networks without fold bias that can classify enzyme structures for a vast functional space (>800 ECs). Our model is robust to uncertainties in binding site locations and similar functions in distinct binding sites. We observe that TopEC networks learn from an interplay between biochemical features and local shape-dependent features. TopEC is available as a repository on GitHub: https://github.com/IBG4-CBCLab/TopEC and https://doi.org/10.25838/d5p-66 .

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

Structure-Based Drug Design of ADRA2A Antagonists Derived from Yohimbine

Yohimbine, a natural indole alkaloid and a nonselective adrenoceptor antagonist, possesses potential benefits in treating inflammatory disorders and sepsis. Nevertheless, its broader clinical use faces challenges due to its low receptor selectivity. A structure-activity relationship study of novel yohimbine analogues identified amino esters of yohimbic acid as potent and selective ADRA2A antagonists. Specifically, amino ester 4n, in comparison to yohimbine, showed a 6-fold higher ADRA1A/ADRA2A selectivity index (SI > 556 for 4n) and a 25-fold higher ADRA2B/ADRA2A selectivity index. Compound 4n also demonstrated high plasma and microsomal stability, moderate-to-low membrane permeability determining its limited ability to cross the blood-brain barrier, and negligible toxicity on nontumor normal human dermal fibroblasts. Compound 4n represents an important complementary pharmacological tool to study the involvement of adrenoceptor subtypes in pathophysiologic conditions such as inflammation and sepsis and a novel candidate for further preclinical development to treat ADRA2A-mediated pathologies.

SiteMine: Large-scale binding site similarity searching in protein structure databases

Drug discovery and design challenges, such as drug repurposing, analyzing protein-ligand and protein-protein complexes, ligand promiscuity studies, or function prediction, can be addressed by protein binding site similarity analysis. Although numerous tools exist, they all have individual strengths and drawbacks with regard to run time, provision of structure superpositions, and applicability to diverse application domains. Here, we introduce SiteMine, an all-in-one database-driven, alignment-providing binding site similarity search tool to tackle the most pressing challenges of binding site comparison. The performance of SiteMine is evaluated on the ProSPECCTs benchmark, showing a promising performance on most of the data sets. The method performs convincingly regarding all quality criteria for reliable binding site comparison, offering a novel state-of-the-art approach for structure-based molecular design based on binding site comparisons. In a SiteMine showcase, we discuss the high structural similarity between cathepsin L and calpain 1 binding sites and give an outlook on the impact of this finding on structure-based drug design. SiteMine is available at https://uhh.de/naomi.

VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

An Intriguing Purview on the Design of Macrocyclic Inhibitors for Unexplored Protein Kinases through Their Binding Site Comparison

Kinases play an important role in regulating various intracellular signaling pathways that control cell proliferation, differentiation, survival, and other cellular processes, and their deregulation causes more than 400 diseases. Consequently, macrocyclization can be considered a noteworthy approach to developing new therapeutic agents for human diseases. Macrocyclization has emerged as an effective drug discovery strategy over the past decade to improve target selectivity and potency of small molecules. Small compounds with linear structures upon macrocyclization can lead to changes in their physicochemical and biological properties by firmly reducing conformational flexibility. A number of distinct protein kinases exhibit similar binding sites. Comparison of protein binding sites provides crucial insights for drug discovery and development. Binding site similarities are helpful in understanding polypharmacology, identifying potential off-targets, and repurposing known drugs. In this review, we focused on comparing the binding sites of those kinases for which macrocyclic inhibitors are available/studied so far. Furthermore, we calculated the volume of the binding site pocket for each targeted kinase and then compared it with the binding site pocket of the kinase for which only acyclic inhibitors were designed to date. Our review and analysis of several explored kinases might be useful in targeting new protein kinases for macrocyclic drug discovery.

CavityPlus 2022 Update: An Integrated Platform for Comprehensive Protein Cavity Detection and Property Analyses with User-friendly Tools and Cavity Databases

Ligand binding sites provide essential information for uncovering protein functions and structure-based drug discovery. To facilitate cavity detection and property analysis process, we developed a comprehensive web server, CavityPlus in 2018. CavityPlus applies the CAVITY program to detect potential binding sites in a given protein structure. The CavPharmer, CorrSite, and CovCys tools can then be applied to generate receptor-based pharmacophore models, identify potential allosteric sites, or detect druggable cysteine residues for covalent drug design. While CavityPlus has been widely used, the constantly evolving knowledge and methods make it necessary to improve and extend its functions. This study presents a new version of CavityPlus, CavityPlus 2022 through a series of upgrades. We upgraded the CAVITY tool to greatly speed up cavity detection calculation. We optimized the CavPharmer tool for fast speed and more accurate results. We integrated the newly developed CorrSite2.0 into the CavityPlus 2022 web server for its improved performance of allosteric site prediction. We also added a new CavityMatch module for drug repurposing and protein function studies by searching similar cavities to a given cavity from pre-constructed cavity databases. The new version of CavityPlus is freely available at http://pkumdl.cn:8000/cavityplus/.

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.

PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol^-1 on an independent test set with an R² value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

TWN-RENCOD: A novel method for protein binding site comparison

Several diverse proteins possess similar binding sites. Protein binding site comparison provides valuable insights for the drug discovery and development. Binding site similarities are useful in understanding polypharmacology, identifying potential off-targets and repurposing of known drugs. Many binding site analysis and comparison methods are available today, however, these methods may not be adequate to explain variation in the activity of a drug or a small molecule against a number of similar proteins. Water molecules surrounding the protein surface contribute to structure and function of proteins. Water molecules form diverse types of hydrogen-bonded cyclic water-ring networks known as topological water networks (TWNs). Analysis of TWNs in binding site of proteins may improve understanding of the characteristics of binding sites. We propose TWN-based residue encoding (TWN-RENCOD), a novel binding site comparison method which compares the aqueous environment in binding sites of similar proteins. As compared to other existing methods, results obtained using our method correlated better with differences in wide range of activity of a known drug (Sunitinib) against nine different protein kinases (KIT, PDGFRA, VEGFR2, PHKG2, ITK, HPK1, MST3, PAK6 and CDK2).

Protein binding sites for drug design

Drug development is a lengthy and challenging process that can be accelerated at early stages by new mathematical approaches and modern computers. To address this important issue, we are developing new mathematical solutions for the detection and characterization of protein binding sites that are important for new drug development. In this review, we present algorithms based on graph theory combined with molecular dynamics simulations that we have developed for studying biological target proteins to provide important data for optimizing the early stages of new drug development. A particular focus is the development of new protein binding site prediction algorithms (ProBiS) and new web tools for modeling pharmaceutically interesting molecules-ProBiS Tools (algorithm, database, web server), which have evolved into a full-fledged graphical tool for studying proteins in the proteome. ProBiS differs from other structural algorithms in that it can align proteins with different folds without prior knowledge of the binding sites. It allows detection of similar binding sites and can predict molecular ligands of various types of pharmaceutical interest that could be advanced to drugs to treat a disease, based on the entire Protein Data Bank (PDB) and AlphaFold database, including proteins not yet in the PDB. All ProBiS Tools are freely available to the academic community at http://insilab.org and https://probis.nih.gov.

Compounds in Indonesian Ginger Rhizome Extracts and Their Potential for Anti-Skin Aging Based on Molecular Docking

Skin aging is a condition caused by reactive oxygen species (ROS) and advanced glycation end products (AGEs). Indonesian gingers (Zingiber officinale), which consists of Gajah (GG), Red (MM), and Emprit (EE) ginger, are thought to produce anti-skin aging compounds through enzyme inhibition. The enzymes used in the molecular docking study were collagenase, hyaluronidase, elastase, and tyrosinase. This study aimed to determine the compounds contained in Indonesian ginger rhizome ethanolic extracts using liquid chromatography–mass spectrometry/mass spectrometry to differentiate metabolites contained in the different Indonesian ginger rhizome extracts. A principal component analysis (PCA) and a heat map analysis were used in order to determine which compounds and extracts contained potential anti-skin aging properties based on a molecular docking study. Ascorbic acid was used as a control ligand in the molecular docking study. Ninety-eight compounds were identified in three different ginger rhizomes extracts and were grouped into three separate quadrants. The most potent compound for anti-skin aging in the Indonesian ginger rhizome extracts was octinoxate. Octinoxate showed a high abundance in the EE ginger rhizome extract. Therefore, the EE ginger extract was the Indonesian ginger rhizome extract with the greatest potential for anti-skin aging.

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network

The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.

ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites

ProBiS (Protein Binding Sites), a local structure-based comparison algorithm, is used in the new ProBiS-Fold web server to annotate human structures from the AlphaFold database without a corresponding structure in the Protein Data Bank (PDB) to discover new druggable binding sites. The ProBiS algorithm is used to compare each query protein structure predicted by the AlphaFold approach with the protein structures from the PDB to identify similarities between known binding sites found in the PDB and yet unknown binding sites in the AlphaFold database. Ligands bound in these identified similar PDB sites are then transferred to each query protein from the AlphaFold database, and binding sites are identified as ligand clusters on an AlphaFold protein. Small molecule binding sites are assigned druggability scores based on the similarity of their ligands to known drugs, allowing them to be ranked according to their perceived and actual potential for drug development. ProBiS-Fold provides interactive and downloadable binding sites for the entire human structural proteome, including more than 3000 new druggable binding sites that have no corresponding structure in the PDB, taking into account AlphaFold's model quality, to enable protein structure-function relationship studies and pharmaceutical drug discovery research. The web server is freely accessible to academic users at http://probis-fold.insilab.org.

Structural fractal analysis of the active sites of acetylcholinesterase from various organisms

Acetylcholinesterase (AChE) is the object of many studies due to the fact that it plays an important role in the vital activity of organisms. In particular, when new AChE inhibitors are developed, much attention is paid to the specificity of their action. One of the approaches used to study the specificity is to compare AChE taken from various organisms. In this work, crystallographic data are used to investigate the active sites of AChE (ASAs) in the free (uncomplexed) state for the following five organisms: Homo sapiens (HS), Mus musculus (MM), Torpedo californica (TC), Electrophorus electricus (EE), and Drosophila melanogaster (DM). The structural fractal analysis (SFA) proposed by us earlier is used as a research method. This method is based on the calculation and comparison of the fractal dimensions of molecular structures. SFA demonstrates that there are no significant structural differences between the active sites of human AChE and other AChEs. However, differences are found for the MM/EE pair. Further analysis of individual AARs has revealed two different areas of active sites. Ser203, Trp236, Phe338, and Tyr341 are found to belong to a variable region, and the remaining AARs belong to a conservative region of the ASAs. The fraction of "variability" is low, 0.8%.

Estimating the Similarity between Protein Pockets

With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.

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.

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer's disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning

Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein-ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/csm_potential.

A New Strategy for Multitarget Drug Discovery/Repositioning Through the Identification of Similar 3D Amino Acid Patterns Among Proteins Structures: The Case of Tafluprost and its Effects on Cardiac Ion Channels

The identification of similar three-dimensional (3D) amino acid patterns among different proteins might be helpful to explain the polypharmacological profile of many currently used drugs. Also, it would be a reasonable first step for the design of novel multitarget compounds. Most of the current computational tools employed for this aim are limited to the comparisons among known binding sites, and do not consider several additional important 3D patterns such as allosteric sites or other conserved motifs. In the present work, we introduce Geomfinder2.0, which is a new and improved version of our previously described algorithm for the deep exploration and discovery of similar and druggable 3D patterns. As compared with the original version, substantial improvements that have been incorporated to our software allow: (i) to compare quaternary structures, (ii) to deal with a list of pairs of structures, (iii) to know how druggable is the zone where similar 3D patterns are detected and (iv) to significantly reduce the execution time. Thus, the new algorithm achieves up to 353x speedup as compared to the previous sequential version, allowing the exploration of a significant number of quaternary structures in a reasonable time. In order to illustrate the potential of the updated Geomfinder version, we show a case of use in which similar 3D patterns were detected in the cardiac ions channels NaV1.5 and TASK-1. These channels are quite different in terms of structure, sequence and function and both have been regarded as important targets for drugs aimed at treating atrial fibrillation. Finally, we describe the in vitro effects of tafluprost (a drug currently used to treat glaucoma, which was identified as a novel putative ligand of NaV1.5 and TASK-1) upon both ion channels’ activity and discuss its possible repositioning as a novel antiarrhythmic drug.

ProBiS-Dock: A Hybrid Multitemplate Homology Flexible Docking Algorithm Enabled by Protein Binding Site Comparison

The protein data bank (PDB) is a rich source of protein ligand structures, but ligands are not explicitly used in current docking algorithms. We have developed ProBiS-Dock, a docking algorithm complementary to the ProBiS-Dock Database (J. Chem. Inf. Model. 2021, 61, 4097-4107) that treats small molecules and proteins as fully flexible entities and allows conformational changes in both after ligand binding. A new scoring function is described that consists of a binding site-specific scoring function (ProBiS-Score) and a general statistical scoring function. ProBiS-Dock enables rapid docking of small molecules to proteins and has been successfully validated in silico against standard benchmarks. It enables rapid search for new active ligands by leveraging existing knowledge in the PDB. The potential of the software for drug development has been confirmed in vitro by the discovery of new inhibitors of human indoleamine 2,3-dioxygenase 1, an enzyme that is an attractive target for cancer therapy and catalyzes the first rate-determining step of l-tryptophan metabolism via the kynurenine pathway. The software is freely available to academic users at http://insilab.org/probisdock.

MeCOM: A Method for Comparing Three-Dimensional Metalloenzyme Active Sites

Since metalloenzymes are a large collection of metal ion(s) dependent enzymes, comparison analyses of metalloenzyme active sites are critical for metalloenzyme de novo design, function investigation, and inhibitor development. Here, we report a method named MeCOM for comparing metalloenzyme active sites. It is characterized by metal ion(s) centric active site recognition and three-dimensional superimposition using α-carbon or pharmacophore features. The test results revealed that for the given metalloenzymes, MeCOM could effectively recognize the active sites, extract active site features, and superimpose the active sites; it also could correctly identify similar active sites, differentiate dissimilar active sites, and evaluate the similarity degree. Moreover, MeCOM showed potential to establish new associations between structurally distinct metalloenzymes by active site comparison. MeCOM is freely available at https://mecom.ddtmlab.org.

Virtual Screening in Search for a Chemical Probe for Angiotensin-Converting Enzyme 2 (ACE2)

We elaborate new models for ACE and ACE2 receptors with an excellent prediction power compared to previous models. We propose promising workflows for working with huge compound collections, thereby enabling us to discover optimized protocols for virtual screening management. The efficacy of elaborated roadmaps is demonstrated through the cost-effective molecular docking of 1.4 billion compounds. Savings of up to 10-fold in CPU time are demonstrated. These developments allowed us to evaluate ACE2/ACE selectivity in silico, which is a crucial checkpoint for developing chemical probes for ACE2.

Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges

Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.

Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor binding sites revealed by computer vision

Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-α) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF-α trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets.

Connecting chemistry and biology through molecular descriptors

Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.

MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances

The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances, and active-site boundaries are used for the analysis of active sites. The algorithms derived provide model equations that can predict whether changes in distances, such as contraction or expansion, will result in improved binding affinity. The algorithm is confirmed using kinetic studies of dihydrofolate reductase (DHFR), together with two DHFR-TS crystal structures. Empirical analyses of 881 crystal structures involving 180 ligands are used to interpret protein-ligand binding affinities. MANORAA links to major biological databases for web-based analysis of drug design. The frequency of atoms inside the main protease structures, including those from SARS-CoV-2, shows how the rigid part of the ligand can be used as a probe for molecular design (http://manoraa.org).

Structural Fractal Analysis of the Active Site of Acetylcholinesterase in Complexes with Huperzine A, Galantamine, and Donepezil

The fractal dimension (D) of the active site of hAChE in the unliganded state and as part of complexes with hyperzine A, galantamine, and donepezil is calculated using molecular interatomic-distance histograms. Fractal matrices of structural changes (FMSCs) are formed by pairwise comparison of the values of D and by revealing the significance of their differences. FMSCs are found to be used to quantitatively estimate the changes in the structures of the molecules in various states. When analyzing FMSCs, we found that the most significant structural changes are related to the Glu202 amino acid residue. No structural perturbations are revealed in the case of Trp86, Gly122, Ala204, Phe338, Tyr341, Gly448, and Ile451.

ProBiS-Dock Database: A Web Server and Interactive Web Repository of Small Ligand-Protein Binding Sites for Drug Design

We have developed a new system, ProBiS-Dock, which can be used to determine the different types of protein binding sites for small ligands. The binding sites identified this way are then used to construct a new binding site database, the ProBiS-Dock Database, that allows for the ranking of binding sites according to their utility for drug development. The newly constructed database currently has more than 1.4 million binding sites and offers the possibility to investigate potential drug targets originating from different biological species. The interactive ProBiS-Dock Database, a web server and repository that consists of all small-molecule ligand binding sites in all of the protein structures in the Protein Data Bank, is freely available at http://probis-dock-database.insilab.org. The ProBiS-Dock Database will be regularly updated to keep pace with the growth of the Protein Data Bank, and our anticipation is that it will be useful in drug discovery.

Discovery of an Unexpected Similarity in Ligand Binding between BRD4 and PPARγ

Knowledge about interrelationships between different proteins is crucial in fundamental research for the elucidation of protein networks and pathways. Furthermore, it is especially critical in chemical biology to identify further key regulators of a disease and to take advantage of polypharmacology effects. Here, we present a new concept that combines a scaffold-based analysis of bioactivity data with a subsequent screening to identify novel inhibitors for a protein target of interest. The initial scaffold-based analysis revealed a flavone-like scaffold that can be found in ligands of different unrelated proteins indicating a similarity in ligand binding. This similarity was further investigated by testing compounds on bromodomain-containing protein 4 (BRD4) that were similar to known ligands of the other identified protein targets. Several new BRD4 inhibitors were identified and proven to be validated hits based on orthogonal assays and X-ray crystallography. The most important discovery was an unexpected relationship between BRD4 and peroxisome-proliferator activated receptor gamma (PPARγ). Both proteins share binding site similarities near a common hydrophobic subpocket which should allow the design of a polypharmacology-based ligand targeting both proteins. Such dual-BRD4-PPARγ modulators open up new therapeutic opportunities, because both are important drug targets for cancer therapy and many more important diseases. Thereon, a complex structure of sulfasalazine was obtained that involves two bromodomains and could be a potential starting point for the design of a bivalent BRD4 inhibitor.

De Novo Molecule Design Through the Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites

De novo molecule design through the molecular generative model has gained increasing attention in recent years. Here, a novel generative model was proposed by integrating the three-dimensional (3D) structural information of the protein binding pocket into the conditional RNN (cRNN) model to control the generation of drug-like molecules. In this model, the composition of the protein binding pocket is effectively characterized through a coarse-grain strategy and the 3D information of the pocket can be represented by the sorted eigenvalues of the Coulomb matrix (EGCM) of the coarse-grained atoms composing the binding pocket. In current work, we used our EGCM method and a previously reported binding pocket descriptor, DeeplyTough, to train cRNN models and evaluated their performance. It has been shown that the model trained with the constraint of protein environment information has a clear tendency on generating compounds with higher similarity to the original X-ray-bound ligand than the normal RNN model and also better docking scores. Our results demonstrate the potential application of the controlled generative model for the targeted molecule generation and guided exploration on the drug-like chemical space.

Site2Vec: a reference frame invariant algorithm for vector embedding of protein–ligand binding sites

Protein–ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Binding sites would also determine ADMET properties of a drug molecule. Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. To this end, methods for computing similarities between binding sites are still evolving and is an active area of research even today. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites. In this regard, deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame. We report here a novel algorithm, Site2Vec, that derives reference frame invariant vector embedding of a protein–ligand binding site. The method is based on pairwise distances between representative points and chemical compositions in terms of constituent amino acids of a site. The vector embedding serves as a locality sensitive hash function for proximity queries and determining similar sites. The method has been the top performer with more than 95% quality scores in extensive benchmarking studies carried over 10 data sets and against 23 other site comparison methods in the field. The algorithm serves for high throughput processing and has been evaluated for stability with respect to reference frame shifts, coordinate perturbations and residue mutations. We also provide the method as a standalone executable and a web service hosted at (http://services.iittp.ac.in/bioinfo/home).

Cannabinoid Receptor Subtype 2 (CB2R) in a Multitarget Approach: Perspective of an Innovative Strategy in Cancer and Neurodegeneration

The cannabinoid receptor subtype 2 (CB2R) represents an interesting and new therapeutic target for its involvement in the first steps of neurodegeneration as well as in cancer onset and progression. Several studies, focused on different types of tumors, report a promising anticancer activity induced by CB2R agonists due to their ability to reduce inflammation and cell proliferation. Moreover, in neuroinflammation, the stimulation of CB2R, overexpressed in microglial cells, exerts beneficial effects in neurodegenerative disorders. With the aim to overcome current treatment limitations, new drugs can be developed by specifically modulating, together with CB2R, other targets involved in such multifactorial disorders. Building on successful case studies of already developed multitarget strategies involving CB2R, in this Perspective we aim at prompting the scientific community to consider new promising target associations involving HDACs (histone deacetylases) and σ receptors by employing modern approaches based on molecular hybridization, computational polypharmacology, and machine learning algorithms.

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.

Binding site characterization - similarity, promiscuity, and druggability

The elucidation of non-obvious binding site similarities has provided useful indications for the establishment of polypharmacology, the identification of potential off-targets, or the repurposing of known drugs. The concept underlying all of these approaches is promiscuous binding which can be analyzed from a ligand-based or a binding site-based perspective. Herein, we applied methods for the automated analysis and comparison of protein binding sites to study promiscuous binding on a novel dataset of sites in complex with ligands sharing common shape and physicochemical properties. We show the suitability of this dataset for the benchmarking of novel binding site comparison methods. Our investigations also reveal promising directions for further in-depth analyses of promiscuity and druggability in a pocket-centered manner. Drawbacks concerning binding site similarity assessment and druggability prediction are outlined, enabling researchers to avoid the typical pitfalls of binding site analyses.

Binding site comparisons for target-centered drug discovery

INTRODUCTION

The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.