Pocket-based drug design: exploring pocket space

Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review

Recent breakthroughs in deep learning have revolutionized protein sequence and structure prediction. These advancements are built on decades of protein design efforts, and are overcoming traditional time and cost limitations. Diffusion models, at the forefront of these innovations, significantly enhance design efficiency by automating knowledge acquisition. In the field of de novo protein design, the goal is to create entirely novel proteins with predetermined structures. Given the arbitrary positions of proteins in 3-D space, graph representations and their properties are widely used in protein generation studies. A critical requirement in protein modelling is maintaining spatial relationships under transformations (rotations, translations, and reflections). This property, known as equivariance, ensures that predicted protein characteristics adapt seamlessly to changes in orientation or position. Equivariant graph neural networks offer a solution to this challenge. By incorporating equivariant graph neural networks to learn the score of the probability density function in diffusion models, one can generate proteins with robust 3-D structural representations. This review examines the latest deep learning advancements, specifically focusing on frameworks that combine diffusion models with equivariant graph neural networks for protein generation.

The Entamoeba histolytica Vps26 (EhVps26) retromeric protein is involved in phagocytosis: Bioinformatic and experimental approaches

The retromer is a cellular structure that recruits and recycles proteins inside the cell. In mammalian and yeast, the retromer components have been widely studied, but very little in parasites. In yeast, it is formed by a SNX-BAR membrane remodeling heterodimer and the cargo selecting complex (CSC), composed by three proteins. One of them, the Vps26 protein, possesses a flexible and intrinsically disordered region (IDR), that facilitates interactions with other proteins and contributes to the retromer binding to the endosomal membrane. In Entamoeba histolytica, the protozoan parasite responsible for human amoebiasis, the retromer actively participates during the high mobility and phagocytosis of trophozoites, but the molecular details in these events, are almost unknown. Here, we studied the EhVps26 role in phagocytosis. Bioinformatic analyses of EhVps26 revealed a typical arrestin folding structure of the protein, and a long and charged IDR, as described in other systems. EhVps26 molecular dynamics simulations (MDS) allowed us to predict binding pockets for EhVps35, EhSNX3, and a PX domain-containing protein; these pockets were disorganized in a EhVps26 truncated version lacking the IDR. The AlphaFold2 software predicted the interaction of EhVps26 with EhVps35, EhVps29 and EhSNX3, in a model similar to the reported mammalian crystals. By confocal and transmission electron microscopy, EhVps26 was found in the trophozoites plasma membrane, cytosol, endosomes, and Golgi-like apparatus. During phagocytosis, it followed the erythrocytes pathway, probably participating in cargoes selection and recycling. Ehvps26 gene knocking down evidenced that the EhVps26 protein is necessary for efficient phagocytosis.

Protein ligand binding site prediction using graph transformer neural network

Ligand binding site prediction is a crucial initial step in structure-based drug discovery. Although several methods have been proposed previously, including those using geometry based and machine learning techniques, their accuracy is considered to be still insufficient. In this study, we introduce an approach that leverages a graph transformer neural network to rank the results of a geometry-based pocket detection method. We also created a larger training dataset compared to the conventionally used sc-PDB and investigated the correlation between the dataset size and prediction performance. Our findings indicate that utilizing a graph transformer-based method alongside a larger training dataset could enhance the performance of ligand binding site prediction.

Computational methods and key considerations for in silico design of proteolysis targeting chimera (PROTACs)

Proteolysis-targeting chimeras (PROTACs), as heterobifunctional molecules, have garnered significant attention for their ability to target previously undruggable proteins. Due to the challenges in obtaining crystal structures of PROTAC molecules in the ternary complex, a plethora of computational tools have been developed to aid in PROTAC design. These computational tools can be broadly classified into artificial intelligence (AI)-based or non-AI-based methods. This review aims to provide a comprehensive overview of the latest computational methods for the PROTAC design process, covering both AI and non-AI approaches, from protein selection to ternary complex modeling and prediction. Key considerations for in silico PROTAC design are discussed, along with additional considerations for deploying AI-based models. These considerations are intended to guide subsequent model development in the PROTAC design process. Finally, future directions and recommendations are provided.

In silico studies of the open form of human tissue transglutaminase

Human tissue transglutaminase (tTG) is an intriguing multifunctional enzyme involved in various diseases, including celiac disease and neurological disorders. Although a number of tTG inhibitors have been developed, the molecular determinants governing ligand binding remain incomplete due to the lack of high-resolution structural data in the vicinity of its active site. In this study, we obtained the complete high-resolution model of tTG by in silico methods based on available PDB structures. We discovered significant differences in the active site architecture between our and known tTG models, revealing an additional loop which affects the ligand binding affinity. We assembled a library of new potential tTG inhibitors based on the obtained complete model of the enzyme. Our library substantially expands the spectrum of possible drug candidates targeting tTG and encompasses twelve molecular scaffolds, eleven of which are novel and exhibit higher binding affinity then already known ones, according to our in silico studies. The results of this study open new directions for structure-based drug design of tTG inhibitors, offering the complete protein model and suggesting a wide range of new compounds for further experimental validation.

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.

De novo design of cavity-containing proteins with a backbone-centered neural network energy function

The design of small-molecule-binding proteins requires protein backbones that contain cavities. Previous design efforts were based on naturally occurring cavity-containing backbone architectures. Here, we designed diverse cavity-containing backbones without predefined architectures by introducing tailored restraints into the backbone sampling driven by SCUBA (Side Chain-Unknown Backbone Arrangement), a neural network statistical energy function. For 521 out of 5816 designs, the root-mean-square deviations (RMSDs) of the Cα atoms for the AlphaFold2-predicted structures and our designed structures are within 2.0 Å. We experimentally tested 10 designed proteins and determined the crystal structures of two of them. One closely agrees with the designed model, while the other forms a domain-swapped dimer, where the partial structures are in agreement with the designed structures. Our results indicate that data-driven methods such as SCUBA hold great potential for designing de novo proteins with tailored small-molecule-binding function.

Allosteric modulation of SHP2: Quest from known to unknown

Src homology-2 domain-containing protein tyrosine phosphatase-2 (SHP2) is a key regulatory factor in the cell cycle and its activating mutations play an important role in the development of various cancers, making it an important target for antitumor drugs. Due to the highly conserved amino acid sequence and positively charged nature of the active site of SHP2, it is difficult to discover inhibitors with high affinity for the catalytic site of SHP2 and sufficient cell permeability, making it considered an "undruggable" target. However, the discovery of allosteric regulation mechanisms provides new opportunities for transforming undruggable targets into druggable ones. Given the limitations of orthosteric inhibitors, SHP2 allosteric inhibitors have become a more selective and safer research direction. In this review, we elucidate the oncogenic mechanism of SHP2 and summarize the discovery methods of SHP2 allosteric inhibitors, providing new strategies for the design and improvement of SHP2 allosteric inhibitors.

Inhibition of the predicted allosteric site of the SARS-CoV-2 main protease through flavonoids

Since its emergence in 2019, coronavirus infection (COVID-19) has become a global pandemic and killed several million people worldwide. Even though several types of vaccines are available against the COVID-19 virus, SARS-CoV-2, new strains are emerging that pose a constant danger to vaccine effectiveness. In this computational study, we identified and predicted potent allosteric inhibitors of the SARS-CoV-2 main protease (Mpro). Via molecular docking and simulations, more than 100 distinct flavonoids were docked with the allosteric site of Mpro. Docking experiments revealed four top hit compounds (Hesperidin, Schaftoside, Brickellin, and Marein) that bound strongly to the Mpro predicted allosteric site. Simulation analyses further revealed that these continually interacted with the enzyme's allosteric region throughout the simulation time. ADMET and Lipinski drug likenesses were calculated to indicate the therapeutic value of the top four hits: They were non-toxic and exhibited high human intestinal absorption concentrations. These novel allosteric site inhibitors provide a higher chance of drugging SARS-CoV2 Mpro due to the rapid mutation rate of the viral enzyme's active sites. Our findings provide a new avenue for developing novel allosteric inhibitors of SARS-CoV-2 Mpro.Communicated by Ramaswamy H. Sarma.

Applications and prospects of cryo-EM in drug discovery

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time- and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy (cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence (AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of medium-resolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

In silico investigation of binding propensity of hematoxylin derivative and damnacanthal for their potential inhibitory effect on HIV-1 Vpr from different subtypes

HIV-1, the causative agent of AIDS leads to many deaths worldwide though few options are available as therapeutics. To deal with the continuous mutation in the virus genome, requirement of new drugs is always there. Subtype variation plays a crucial role in case of HIV-1 therapeutics development. In this study, we want to investigate some pre examined molecules that can be effective for HIV-1 VPR. Inhibition of several protein-protein interactions with the small molecules will lead to identify some molecules as therapeutics other than the conventional drugs. We retrieved the sequences of different subtypes from the database and representative sequences were identified. Representative structures were modelled and validated using MD simulations. Forty molecules, showing anti Vpr activity in vitro were identified from literature survey and those were docked with each subtype representative structures. Two molecules a stable Hematoxylin Derivative (SHD) and Damnacanthal (D3), these were shown to be bind more effectively for all the subtypes. The stability of the protein and those two small molecule complexes were identified again with MD simulation followed by the binding energy calculation. Thus, these molecules can be thought as any option other than the conventional drug targeting HIV-1 Vpr.Communicated by Ramaswamy H. Sarma.

KinFams: De-Novo Classification of Protein Kinases Using CATH Functional Units

Protein kinases are important targets for treating human disorders, and they are the second most targeted families after G-protein coupled receptors. Several resources provide classification of kinases into evolutionary families (based on sequence homology); however, very few systematically classify functional families (FunFams) comprising evolutionary relatives that share similar functional properties. We have developed the FunFam-MARC (Multidomain ARchitecture-based Clustering) protocol, which uses multi-domain architectures of protein kinases and specificity-determining residues for functional family classification. FunFam-MARC predicts 2210 kinase functional families (KinFams), which have increased functional coherence, in terms of EC annotations, compared to the widely used KinBase classification. Our protocol provides a comprehensive classification for kinase sequences from >10,000 organisms. We associate human KinFams with diseases and drugs and identify 28 druggable human KinFams, i.e., enriched in clinically approved drugs. Since relatives in the same druggable KinFam tend to be structurally conserved, including the drug-binding site, these KinFams may be valuable for shortlisting therapeutic targets. Information on the human KinFams and associated 3D structures from AlphaFold2 are provided via our CATH FTP website and Zenodo. This gives the domain structure representative of each KinFam together with information on any drug compounds available. For 32% of the KinFams, we provide information on highly conserved residue sites that may be associated with specificity.

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

Binding pocket stabilization by high-throughput screening of yeast display libraries

Protein dynamics have a great influence on the binding pockets of some therapeutic targets. Flexible protein binding sites can result in transient binding pocket formation which might have a negative impact on drug screening efforts. Here, we describe a protein engineering strategy with FK506-binding protein 51 (FKBP51) as a model protein, which is a promising target for stress-related disorders. High-throughput screening of yeast display libraries of FKBP51 resulted in the identification of variants exhibiting higher affinity binding of conformation-specific FKBP51 selective inhibitors. The gene libraries of a random mutagenesis and site saturation mutagenesis of the FK1 domain of FKBP51 encoding sequence were used to create a yeast surface display library. Fluorescence-activated cell sorting for FKBP51 variants that bind conformation-specific fluorescently labeled ligands with high affinity allowed for the identification of 15 different protein variants with improved binding to either, or both FKBP51-specific ligands used in the screening, with improved affinities up to 34-fold compared to the wild type. These variants will pave the way to a better understanding of the conformational flexibility of the FKBP51 binding pocket and may enable the isolation of new selective ligands that preferably and selectively bind the active site of the protein in its open conformation state.

In silico comparative structural and compositional analysis of glycoproteins of RSV to study the nature of stability and transmissibility of RSV A

The current scenario of COVID-19 makes us to think about the devastating diseases that kill so many people every year. Analysis of viral proteins contributes many things that are utterly useful in the evolution of therapeutic drugs and vaccines. In this study, sequence and structure of fusion glycoproteins and major surface glycoproteins of respiratory syncytial virus (RSV) were analysed to reveal the stability and transmission rate. RSV A has the highest abundance of aromatic residues. The Kyte-Doolittle scale indicates the hydrophilic nature of RSV A protein which leads to the higher transmission rate of this virus. Intra-protein interactions such as carbonyl interactions, cation-pi, and salt bridges were shown to be greater in RSV A compared to RSV B, which might lead to improved stability. This study discovered the presence of a network aromatic-sulphur interaction in viral proteins. Analysis of ligand binding pocket of RSV proteins indicated that drugs are performing better on RSV B than RSV A. It was also shown that increasing the number of tunnels in RSV A proteins boosts catalytic activity. This study will be helpful in drug discovery and vaccine development.

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.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems

We give a review on the landscape theory of the equilibrium biological systems and landscape-flux theory of the nonequilibrium biological systems as the global driving force. The emergences of the behaviors, the associated thermodynamics in terms of the entropy and free energy and dynamics in terms of the rate and paths have been quantitatively demonstrated. The hierarchical organization structures have been discussed. The biological applications ranging from protein folding, biomolecular recognition, specificity, biomolecular evolution and design for equilibrium systems as well as cell cycle, differentiation and development, cancer, neural networks and brain function, and evolution for nonequilibrium systems, cross-scale studies of genome structural dynamics and experimental quantifications/verifications of the landscape and flux are illustrated. Together, this gives an overall global physical and quantitative picture in terms of the landscape and flux for the behaviors, dynamics and functions of biological systems.

Discovery of Small Molecule NSC290956 as a Therapeutic Agent for KRas Mutant Non-Small-Cell Lung Cancer

HRas-GTP has a transient intermediate state with a “non-signaling open conformation” in GTP hydrolysis and nucleotide exchange. Due to the same hydrolysis process and the structural homology, it can be speculated that the active KRas adopts the same characteristics with the “open conformation.” This implies that agents locking this “open conformation” may theoretically block KRas-dependent signaling. Applying our specificity-affinity drug screening approach, NSC290956 was chosen by high affinity and specificity interaction with the “open conformation” structure HRasG60A-GppNp. In mutant KRas-driven non-small-cell lung cancer (NSCLC) model system, NSC290956 effectively suppresses the KRas-GTP state and gives pharmacological KRas inhibition with concomitant blockages of both the MAPK-ERK and AKT-mTOR pathways. The dual inhibitory effects lead to the metabolic phenotype switching from glycolysis to mitochondrial metabolism, which promotes the cancer cell death. In the xenograft model, NSC290956 significantly reduces H358 tumor growth in nude mice by mechanisms similar to those observed in the cells. Our work indicates that NSC290956 can be a promising agent for the mutant KRas-driven NSCLC therapy.

Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions

New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.

Targeted Protein Degradation: An Important Tool for Drug Discovery for "Undruggable" Tumor Transcription Factors

Conventional small-molecule drugs (SMDs) are compounds characterized by low molecular weight, high cell permeability, and high selectivity. In clinical translation, SMDs are regarded as good candidates for oral drug formulation. SMD inhibitors play an important role in cancer treatment; however, resistance and low effectiveness have been major bottlenecks in clinical application. Generally, only 20% of cell proteins can potentially be targeted and have been developed as SMDs; thus, some types of tumor targets are considered “undruggable.” Among these are transcription factors (TFs), an important class of proteins that regulate the occurrence, formation, and development of tumors. It is difficult for SMDs and macromolecular drugs to identify bioactive sites in TFs and hence for use as pharmacological inhibitors in targeting TF proteins. For this reason, technologies that enable targeted protein degradation, such as proteolysis-targeting chimera or molecular glues, could serve as a potential tool to solve these conundrums.

Marine natural compounds as potents inhibitors against the main protease of SARS-CoV-2-a molecular dynamic study

Sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA (ssRNA) virus, responsible for severe acute respiratory disease (COVID-19). A large number of natural compounds are under trial for screening compounds, possessing potential inhibitory effect against the viral infection. Keeping in view the importance of marine compounds in antiviral activity, we investigated the potency of some marine natural products to target SARS-CoV-2 main protease (Mpro) (PDB ID 6MO3). The crystallographic structure of Mpro in an apo form was retrieved from Protein Data Bank and marine compounds from PubChem. These structures were prepared for docking and the complex with good docking score was subjected to molecular dynamic (MD) simulations for a period of 100 ns. To measure the stability, flexibility, and average distance between the target and compounds, root mean square deviations (RMSD), root mean square fluctuation (RMSF), and the distance matrix were calculated. Among five marine compounds, C-1 (PubChem CID 11170714) exhibited good activity, interacting with the active site and surrounding residues, forming many hydrogen and hydrophobic interactions. The C-1 also attained a stable dynamic behavior, and the average distance between compound and target remains constant. In conclusion, marine natural compounds may be used as a potential inhibitor against SARS-CoV-2 for better management of COVID-19.

Marine natural compounds as potents inhibitors against the main protease of SARS-CoV-2-a molecular dynamic study

Sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA (ssRNA) virus, responsible for severe acute respiratory disease (COVID-19). A large number of natural compounds are under trial for screening compounds, possessing potential inhibitory effect against the viral infection. Keeping in view the importance of marine compounds in antiviral activity, we investigated the potency of some marine natural products to target SARS-CoV-2 main protease (M^pro) (PDB ID 6MO3). The crystallographic structure of M^pro in an apo form was retrieved from Protein Data Bank and marine compounds from PubChem. These structures were prepared for docking and the complex with good docking score was subjected to molecular dynamic (MD) simulations for a period of 100 ns. To measure the stability, flexibility, and average distance between the target and compounds, root mean square deviations (RMSD), root mean square fluctuation (RMSF), and the distance matrix were calculated. Among five marine compounds, C-1 (PubChem CID 11170714) exhibited good activity, interacting with the active site and surrounding residues, forming many hydrogen and hydrophobic interactions. The C-1 also attained a stable dynamic behavior, and the average distance between compound and target remains constant. In conclusion, marine natural compounds may be used as a potential inhibitor against SARS-CoV-2 for better management of COVID-19.

Pillar[5]arene-Derived endo-Functionalized Molecular Tube for Mimicking Protein-Ligand Interactions

Artificial tubular molecular pockets bearing polar functionalities on their inner surface are useful model systems for understanding the mechanisms of protein-ligand interactions in living systems. We herein report a pillar[5]arene-derived molecular tube, [P4-(OH)BPO], whose endo conformational isomer endo-[P4-(OH)BPO] possesses an inwardly pointing hydrogen-bond (H-bond) donor (OH) in its deep cavity and a strong H-bond acceptor (C═O) on its predominantly hydrophobic inner surface, rendering it a perfect protein binding pocket mimetic. A fragment-based drug design model was established using endo-[P4-(OH)BPO] and a library of various shape-complementary fragment ligands (1-38). On the basis of the binding affinity data for "fragment-pocket" complexes G⊂endo-[P4-(OH)BPO] (G = 1-38), two rationally designed "lead molecules" (39 and 40) were identified as being able to enhance binding affinity significantly by forming H-bonds with both the donor and acceptor of endo-[P4-(OH)BPO]. The described work opens new avenues for developing pillar[n]arene-derived protein binding pocket-mimetic systems for studies of protein-ligand interactions and mechanisms of enzymatic reactions.

Into the Fray! A Beginner's Guide to Medicinal Chemistry

Modern medicinal chemistry is a complex, multidimensional discipline that operates at the interface of the chemical and biological sciences. The medicinal chemistry contribution to drug discovery is typically described in the context of the well-recited linear progression of the drug discovery pipeline. However, compound optimization is idiosyncratic to each project, and clear definitions of hit and lead molecules and the subsequent progress along the pipeline becomes easily blurred. In addition, this description lacks insight into the entangled relationship between chemical and pharmacological properties, and thus provides limited guidance on how innovative medicinal chemistry strategies can be applied to solve optimization problems, regardless of the stage in the pipeline. Through discussion and illustrative examples, this article seeks to provide insights into the finesse of medicinal chemistry and the subtlety of balancing chemical properties pharmacology. In so doing, it aims to serve as an accessible and simple-to-digest guide for anyone who wishes to learn about the underlying principles of medicinal chemistry, in a context that has been decoupled from the pipeline description.

CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction

CavityPlus is a web server that offers protein cavity detection and various functional analyses. Using protein three-dimensional structural information as the input, CavityPlus applies CAVITY to detect potential binding sites on the surface of a given protein structure and rank them based on ligandability and druggability scores. These potential binding sites can be further analysed using three submodules, CavPharmer, CorrSite, and CovCys. CavPharmer uses a receptor-based pharmacophore modelling program, Pocket, to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities. CovCys automatically detects druggable cysteine residues, which is especially useful to identify novel binding sites for designing covalent allosteric ligands. Overall, CavityPlus provides an integrated platform for analysing comprehensive properties of protein binding cavities. Such analyses are useful for many aspects of drug design and discovery, including target selection and identification, virtual screening, de novo drug design, and allosteric and covalent-binding drug design. The CavityPlus web server is freely available at http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus.

TRAPP webserver: predicting protein binding site flexibility and detecting transient binding pockets

The TRAnsient Pockets in Proteins (TRAPP) webserver provides an automated workflow that allows users to explore the dynamics of a protein binding site and to detect pockets or sub-pockets that may transiently open due to protein internal motion. These transient or cryptic sub-pockets may be of interest in the design and optimization of small molecular inhibitors for a protein target of interest. The TRAPP workflow consists of the following three modules: (i) TRAPP structure— generation of an ensemble of structures using one or more of four possible molecular simulation methods; (ii) TRAPP analysis—superposition and clustering of the binding site conformations either in an ensemble of structures generated in step (i) or in PDB structures or trajectories uploaded by the user; and (iii) TRAPP pocket—detection, analysis, and visualization of the binding pocket dynamics and characteristics, such as volume, solvent-exposed area or properties of surrounding residues. A standard sequence conservation score per residue or a differential score per residue, for comparing on- and off-targets, can be calculated and displayed on the binding pocket for an uploaded multiple sequence alignment file, and known protein sequence annotations can be displayed simultaneously. The TRAPP webserver is freely available at http://trapp.h-its.org.

New Binding Sites, New Opportunities for GPCR Drug Discovery

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

P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure

Background

Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers.

Results

We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein. We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation.

Conclusions

P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines.

Electronic supplementary material

The online version of this article (10.1186/s13321-018-0285-8) contains supplementary material, which is available to authorized users.

Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.

Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092

Development of new antimicrobials and vaccines for Streptococcus pneumoniae (pneumococcus) are necessary to halt the rapid rise in multiple resistant strains. Carbohydrate substrate binding proteins (SBPs) represent viable targets for the development of protein-based vaccines and new antimicrobials because of their extracellular localization and the centrality of carbohydrate import for pneumococcal metabolism, respectively. Described here is a rationalized integrated protocol to carry out a comprehensive characterization of SP0092, which can be extended to other carbohydrate SBPs from the pneumococcus and other bacteria. This procedure can aid the structure-based design of inhibitors for this class of proteins. Presented in the first part of this manuscript are protocols for biochemical analysis by thermal shift assay, multi angle light scattering (MALS), and size exclusion chromatography (SEC), which optimize the stability and homogeneity of the sample directed to crystallization trials and so enhance the probability of success. The second part of this procedure describes the characterization of the SBP crystals using a tunable wavelength anomalous diffraction synchrotron beamline, and data collection protocols for measuring data that can be used to resolve the crystallized protein structure.

Protein-Protein Interactions (PPIs) as an Alternative to Targeting the ATP Binding Site of Kinase

Most of the developed kinase inhibitor drugs are ATP competitive and suffer from drawbacks such as off-target kinase activity, development of resistance due to mutation in the ATP binding pocket and unfavorable intellectual property situations. Besides the ATP binding pocket, protein kinases have binding sites that are involved in Protein-Protein Interactions (PPIs); these PPIs directly or indirectly regulate the protein kinase activity. Of recent, small molecule inhibitors of PPIs are emerging as an alternative to ATP competitive agents. Rational design of inhibitors for kinase PPIs could be carried out using molecular modeling techniques. In silico tools available for the prediction of hot spot residues and cavities at the PPI sites and the means to utilize this information for the identification of inhibitors are discussed. Moreover, in silico studies to target the Aurora B-INCENP PPI sites are discussed in context. Overall, this chapter provides detailed in silico strategies that are available to the researchers for carrying out structure-based drug design of PPI inhibitors.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites

In the field of medicinal chemistry there is increasing focus on identifying key proteins whose biochemical functions can firmly be linked to serious diseases. Such proteins become targets for drug or inhibitor molecules that could treat or halt the disease through therapeutic action or by blocking the protein function respectively. The protein must be targeted at the relevant biologically active site for drug or inhibitor binding to be effective. As insufficient experimental data is available to confirm the biologically active binding site for novel protein targets, researchers often rely on computational prediction methods to identify binding sites. Presented herein is a short review on structure-based computational methods that (i) predict putative binding sites and (ii) assess the druggability of predicted binding sites on protein targets. This review briefly covers the principles upon which these methods are based, where they can be accessed and their reliability in identifying the correct binding site on a protein target. Based on this review, we believe that these methods are useful in predicting putative binding sites, but as they do not account for the dynamic nature of protein-ligand binding interactions, they cannot definitively identify the correct site from a ranked list of putative sites. To overcome this shortcoming, we strongly recommend using molecular docking to predict the most likely protein-ligand binding site(s) and mode(s), followed by molecular dynamics simulations and binding thermodynamics calculations to validate the docking results. This protocol provides a valuable platform for experimental and computational efforts to design novel drugs and inhibitors that target disease-related proteins.

Improving the performance of the PLB index for ligand-binding site prediction using dihedral angles and the solvent-accessible surface area

Protein ligand-binding site prediction is highly important for protein function determination and structure-based drug design. Over the past twenty years, dozens of computational methods have been developed to address this problem. Soga et al. identified ligand cavities based on the preferences of amino acids for the ligand-binding site (RA) and proposed the propensity for ligand binding (PLB) index to rank the cavities on the protein surface. However, we found that residues exhibit different RAs in response to changes in solvent exposure. Furthermore, previous studies have suggested that some dihedral angles of amino acids in specific regions of the Ramachandran plot are preferred at the functional sites of proteins. Based on these discoveries, the amino acid solvent-accessible surface area and dihedral angles were combined with the RA and PLB to obtain two new indexes, multi-factor RA (MF-RA) and multi-factor PLB (MF-PLB). MF-PLB, PLB and other methods were tested using two benchmark databases and two particular ligand-binding sites. The results show that MF-PLB can improve the success rate of PLB for both ligand-bound and ligand-unbound structures, particularly for top choice prediction.

Identification of protein-ligand binding sites by the level-set variational implicit-solvent approach

Protein–ligand binding is a key biological process at the molecular level. The identification and characterization of small-molecule binding sites on therapeutically relevant proteins have tremendous implications for target evaluation and rational drug design. In this work, we used the recently developed level-set variational implicit-solvent model (VISM) with the Coulomb field approximation (CFA) to locate and characterize potential protein–small-molecule binding sites. We applied our method to a data set of 515 protein–ligand complexes and found that 96.9% of the cocrystallized ligands bind to the VISM-CFA-identified pockets and that 71.8% of the identified pockets are occupied by cocrystallized ligands. For 228 tight-binding protein–ligand complexes (i.e, complexes with experimental pK_d values larger than 6), 99.1% of the cocrystallized ligands are in the VISM-CFA-identified pockets. In addition, it was found that the ligand binding orientations are consistent with the hydrophilic and hydrophobic descriptions provided by VISM. Quantitative characterization of binding pockets with topological and physicochemical parameters was used to assess the “ligandability” of the pockets. The results illustrate the key interactions between ligands and receptors and can be very informative for rational drug design.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Considerations of Protein Subpockets in Fragment-Based Drug Design

While the fragment-based drug design approach continues to gain importance, gaps in the tools and methods available in the identification and accurate utilization of protein subpockets have limited the scope. The importance of these features of small molecule-protein recognition is highlighted with several examples. A generalized solution for the identification of subpockets and corresponding chemical fragments remains elusive, but there are numerous advancements in methods that can be used in combination to address subpockets. Finally, additional examples of approaches that consider the relative importance of small-molecule co-dependence of protein conformations are highlighted to emphasize an increased significance of subpockets, especially at protein interfaces.

Structural basis for Marburg virus neutralization by a cross-reactive human antibody

The filoviruses, including Marburg and Ebola, express a single glycoprotein on their surface, termed GP, which is responsible for attachment and entry of target cells. Filovirus GPs differ by up to 70% in protein sequence, and no antibodies are yet described that cross-react among them. Here, we present the 3.6 Å crystal structure of Marburg virus GP in complex with a cross-reactive antibody from a human survivor, and a lower resolution structure of the antibody bound to Ebola virus GP. The antibody, MR78, recognizes a GP1 epitope conserved across the filovirus family, which likely represents the binding site of their NPC1 receptor. Indeed, MR78 blocks binding of the essential NPC1 domain C. These structures and additional small-angle X-ray scattering of mucin-containing MARV and EBOV GPs suggest why such antibodies were not previously elicited in studies of Ebola virus, and provide critical templates for development of immunotherapeutics and inhibitors of entry.

Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features

BACKGROUND

Protein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function. Embedded within the binding site detection problem is the problem of pocket ranking - how to score and sort candidate pockets so that the best scored predictions correspond to true ligand binding sites. Although there exist multiple pocket detection algorithms, they mostly employ a fairly simple ranking function leading to sub-optimal prediction results.

RESULTS

We have developed a new pocket scoring approach (named PRANK) that prioritizes putative pockets according to their probability to bind a ligand. The method first carefully selects pocket points and labels them by physico-chemical characteristics of their local neighborhood. Random Forests classifier is subsequently applied to assign a ligandability score to each of the selected pocket point. The ligandability scores are finally merged into the resulting pocket score to be used for prioritization of the putative pockets. With the used of multiple datasets the experimental results demonstrate that the application of our method as a post-processing step greatly increases the quality of the prediction of Fpocket and ConCavity, two state of the art protein-ligand binding site prediction algorithms.

CONCLUSIONS

The positive experimental results show that our method can be used to improve the success rate, validity and applicability of existing protein-ligand binding site prediction tools. The method was implemented as a stand-alone program that currently contains support for Fpocket and Concavity out of the box, but is easily extendible to support other tools. PRANK is made freely available at http://siret.ms.mff.cuni.cz/prank.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.