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

Design and development of novel self-assembled catechol-modified bile acid conjugates as pH-responsive apical sodium-dependent bile acid transporter targeting nanoparticles

Catechol-based biomaterials demonstrate biocompatibility, making them suitable for a wide range of therapeutic applications when integrated into various molecular frameworks. However, the development of orally available catechol-based biomaterials has been hindered by significant pH variations and complex interactions in the gastrointestinal (GI) tract. In this study, we introduce a novel catechol-modified bile acid (CMBA), which is synthesized by anchoring the FDA-approved drug, ursodeoxycholic acid to the neurotransmitter dopamine. This modification could form a new apical sodium-dependent bile acid transporter (ASBT) inhibitor (ASBTi) due to the bile acid moiety. The computational analysis using the TRAnsient Pockets in Proteins (TRAPP) module, coupled with MD simulations, revealed that CMBA exhibits a strong binding affinity at residues 51-55 of ASBT with a low inhibitory constant (Ki) value. Notably, in slightly alkaline biological conditions, CMBA molecules self-assemble into carrier-free nanoparticles with an average size of 240.2 ± 44.2 nm, while maintaining their ability to bind with ASBT. When administered orally, CMBA accumulates in the ileum and liver over 24 h, exhibiting significant therapeutic effects on bile acid (BA) metabolism in a high-fat diet (HFD)-fed mouse model. This study underscores the therapeutic potential of the newly developed catechol-based, pH-responsive ASBT-inhibiting nanoparticles presenting a promising avenue for advancing therapy.

Automated Flow Peptide Synthesis Enables Engineering of Proteins with Stabilized Transient Binding Pockets

Engineering at the amino acid level is key to enhancing the properties of existing proteins in a desired manner. So far, protein engineering has been dominated by genetic approaches, which have been extremely powerful but only allow for minimal variations beyond the canonical amino acids. Chemical peptide synthesis allows the unrestricted incorporation of a vast set of unnatural amino acids with much broader functionalities, including the incorporation of post-translational modifications or labels. Here we demonstrate the potential of chemical synthesis to generate proteins in a specific conformation, which would have been unattainable by recombinant protein expression. We use recently established rapid automated flow peptide synthesis combined with solid-phase late-stage modifications to rapidly generate a set of FK506-binding protein 51 constructs bearing defined intramolecular lactam bridges. This trapped an otherwise rarely populated transient pocket-as confirmed by crystal structures-which led to an up to 39-fold improved binding affinity for conformation-selective ligands and represents a unique system for the development of ligands for this rare conformation. Overall, our results show how rapid automated flow peptide synthesis can be applied to precision protein engineering.

Structure and dynamics of pteridine reductase 1: the key phenomena relevant to enzyme function and drug design

Pteridine reductase 1 (PTR1) is a folate and pterin pathway enzyme unique for pathogenic trypanosomatids. As a validated drug target, PTR1 has been the focus of recent research efforts aimed at finding more effective treatments against human parasitic diseases such as leishmaniasis or sleeping sickness. Previous PTR1-centered structural studies highlighted the enzyme characteristics, such as flexible regions around the active site, highly conserved structural waters, and species-specific differences in pocket properties and dynamics, which likely impacts the binding of natural substrates and inhibitors. Furthermore, several aspects of the PTR1 function, such as the substrate inhibition phenomenon and the level of ligand binding cooperativity in the enzyme homotetramer, likely related to the global enzyme dynamics, are poorly known at the molecular level. We postulate that future drug design efforts could greatly benefit from a better understanding of these phenomena through studying both the local and global PTR1 dynamics. This review highlights the key aspects of the PTR1 structure and dynamics relevant to structure-based drug design that could be effectively investigated by modeling approaches. Particular emphasis is given to the perspective of molecular dynamics, what has been accomplished in this area to date, and how modeling could impact the PTR1-targeted drug design in the future.

Grasping cryptic binding sites to neutralize drug resistance in the field of anticancer

Drug resistance is a significant obstacle to successful cancer treatment. The utilization and development of cryptic binding sites (CBSs) in proteins involved in cancer-related drug-resistance (CRDR) could help to overcome that drug resistance. However, there is no comprehensive review of the successful use of CBSs in addressing CRDR. Here, we have systematically summarized and analyzed the opportunities and challenges of using CBSs in addressing CRDR and revealed the key role that CBSs have in targeting CRDR. First, we have identified the CRDR targets and the corresponding CBSs. Second, we discuss the mechanisms by which CBSs can overcome CRDR. Finally, we have provided examples of successful CBS applications in addressing CRDR. We hope that this approach will provide guidance to biologists and chemists in effectively utilizing CBSs for the development of new drugs to alleviate CRDR.

Molecular Dynamics Simulations of the Human Ecto-5'-Nucleotidase (h-ecto-5'-NT, CD73): Insights into Protein Flexibility and Binding Site Dynamics

Human ecto-5'-nucleotidase (h-ecto-5'-NT, CD73) is a homodimeric Zn2+-binding metallophosphoesterase that hydrolyzes adenosine 5'-monophosphate (5'-AMP) to adenosine and phosphate. h-Ecto-5'-NT is a key enzyme in purinergic signaling pathways and has been recognized as a promising biological target for several diseases, including cancer and inflammatory, infectious, and autoimmune diseases. Despite its importance as a biological target, little is known about h-ecto-5'-NT dynamics, which poses a considerable challenge to the design of inhibitors of this target enzyme. Here, to explore h-ecto-5'-NT flexibility, all-atom unbiased molecular dynamics (MD) simulations were performed. Remarkable differences in the dynamics of the open (catalytically inactive) and closed (catalytically active) conformations of the apo-h-ecto-5'-NT were observed during the simulations, and the nucleotide analogue inhibitor AMPCP was shown to stabilize the protein structure in the closed conformation. Our results suggest that the large and complex domain motion that enables the h-ecto-5'-NT open/closed conformational switch is slow, and therefore, it could not be completely captured within the time scale of our simulations. Nonetheless, we were able to explore the faster dynamics of the h-ecto-5'-NT substrate binding site, which is mainly located at the C-terminal domain and well conserved among the protein's open and closed conformations. Using the TRAPP ("Transient Pockets in Proteins") approach, we identified transient subpockets close to the substrate binding site. Finally, conformational states of the substrate binding site with higher druggability scores than the crystal structure were identified. In summary, our study provides valuable insights into h-ecto-5'-NT structural flexibility, which can guide the structure-based design of novel h-ecto-5'-NT inhibitors.

Structural and dynamic determinants for highly selective RET kinase inhibition reveal cryptic druggability

INTRODUCTION

The structural and dynamic determinants that confer highly selective RET kinase inhibition are poorly understood.

OBJECTIVES

To explore the druggability landscape of the RET active site in order to uncover structural and dynamic vulnerabilities that can be therapeutically exploited.

METHODS

We apply an integrated structural, computational and biochemical approach in order to explore the druggability landscape of the RET active site.

RESULTS

We demonstrate that the that the druggability landscape of the RET active site is determined by the conformational setting of the ATP-binding (P-) loop and its coordination with the αC helix. Open and intermediate P-loop structures display additional druggable vulnerabilities within the active site that were not exploited by first generation RET inhibitors. We identify a cryptic pocket adjacent to the catalytic lysine formed by K758, L760, E768 and L772, that we name the post-lysine pocket, with higher druggability potential than the adenine-binding site and with important implications in the regulation of the phospho-tyrosine kinase activity. Crystal structure and simulation data show that the binding mode of highly-selective RET kinase inhibitors LOXO-292 and BLU-667 is controlled by a synchronous open P-loop and αC-in configuration that allows accessibility to the post-lysine pocket. Molecular dynamics simulations show that these inhibitors efficiently occupy the post-lysine pocket with high stability through the simulation time-scale (300 ns), with both inhibitors forming hydrophobic contacts further stabilized by pi-cation interactions with the catalytic K758. Engineered mutants targeting the post-lysine pocket impact on inhibitor binding and sensitivity, as well as RET tyrosine kinase activity.

CONCLUSIONS

The identification of the post-lysine pocket as a new druggable vulnerability in the RET kinase and its exploitation by second generation RET inhibitors have important implications for future drug design and the development of personalized therapies for patients with RET-driven cancers.

CADD, AI and ML in drug discovery: A comprehensive review

Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.

Molecular insight into Aspergillus oryzae β-mannanase interacting with mannotriose revealed by molecular dynamic simulation study

Fungal β-mannanases hydrolyze β-1, 4-glycosidic bonds of mannans and find application in the generation of mannose and prebiotic mannooligosaccharides (MOS). Previously, a MOS generating β-mannanase from Aspergillus oryzae MTCC 1846 (βManAo) was characterized and its structural and functional properties were unraveled through homology modeling and molecular dynamics in this study. The βManAo model was validated with 92.9% and 6.5% of the residues found to be distributed in the most favorable and allowed regions of the Ramachandran plot. Glu244 was found to play a key role in the interaction with mannotriose, indicating conserved amino acids for the catalytic reaction. A detailed metadynamic analysis of the principal components revealed the presence of an α₈-helix in the C-terminus which was very flexible in nature and energy landscapes suggested high conformation sub-states and the complex dynamic behavior of the protein. The binding of the M3 substrate stabilized the β-mannanase and resulted in a reduction in the intermediate conformational sub-states evident from the free energy landscapes. The active site of the β-mannanase is mostly hydrophilic in nature which is accordance with our results, where the major contribution in the binding energy of the substrate with the active site is from electrostatic interactions. Define Secondary Structure of Proteins (DSSP) analysis revealed a major transition of the protein from helix to β-turn for binding with the mannotriose. The molecular dynamics of the βManAo–mannotriose model, and the role and interactions of catalytic residues with ligand were also described. The substrate binding pocket of βManAo was found to be highly dynamic and showed large, concerted movements. The outcomes of the present study can be exploited in further understanding the structural properties and functional dynamics of βManAo.

Anti-trypanosomatid structure-based drug design - lessons learned from targeting the folate pathway

INTRODUCTION

Trypanosomatidic parasitic infections of humans and animals caused by Trypanosoma brucei, Trypanosoma cruzi, and Leishmania species pose a significant health and economic burden in developing countries. There are few effective and accessible treatments for these diseases, and the existing therapies suffer from problems such as parasite resistance and side effects. Structure-based drug design (SBDD) is one of the strategies that has been applied to discover new compounds targeting trypanosomatid-borne diseases.

AREAS COVERED

We review the current literature (mostly over the last 5 years, searched in PubMed database on Nov 11^th 2021) on the application of structure-based drug design approaches to identify new anti-trypanosomatidic compounds that interfere with a validated target biochemical pathway, the trypanosomatid folate pathway.

EXPERT OPINION

The application of structure-based drug design approaches to perturb the trypanosomatid folate pathway has successfully provided many new inhibitors with good selectivity profiles, most of which are natural products or their derivatives or have scaffolds of known drugs. However, the inhibitory effect against the target protein(s) often does not translate to anti-parasitic activity. Further progress is hampered by our incomplete understanding of parasite biology and biochemistry, which is necessary to complement SBDD in a multiparameter optimization approach to discovering selective anti-parasitic drugs.

Fast approximative methods for study of ligand transport and rational design of improved enzymes for biotechnologies

Acceleration of chemical reactions by the enzymes optimized using protein engineering represents one of the key pillars of the contribution of biotechnology towards sustainability. Tunnels and channels of enzymes with buried active sites enable the exchange of ligands, ions, and water molecules between the outer environment and active site pockets. The efficient exchange of ligands is a fundamental process of biocatalysis. Therefore, enzymes have evolved a wide range of mechanisms for repetitive conformational changes that enable periodic opening and closing. Protein-ligand interactions are traditionally studied by molecular docking, whereas molecular dynamics is the method of choice for studying conformational changes and ligand transport. However, computational demands make molecular dynamics impractical for screening purposes. Thus, several approximative methods have been recently developed to study interactions between a protein and ligand during the ligand transport process. Apart from identifying the best binding modes, these methods also provide information on the energetics of the transport and identify problematic regions limiting the ligand passage. These methods use approximations to simulate binding or unbinding events rapidly (calculation times from minutes to hours) and provide energy profiles that can be used to rank ligands or pathways. Here we provide a critical comparison of available methods, showcase their results on sample systems, discuss their practical applications in molecular biotechnologies and outline possible future developments.

Expanding the Accessible Chemical Space of SIRT2 Inhibitors through Exploration of Binding Pocket Dynamics

Considerations of binding pocket dynamics are one of the crucial aspects of the rational design of binders. Identification of alternative conformational states or cryptic subpockets could lead to the discovery of completely novel groups of the ligands. However, experimental characterization of pocket dynamics, besides being expensive, may not be able to elucidate all of the conformational states relevant for drug discovery projects. In this study, we propose the protocol for computational simulations of sirtuin 2 (SIRT2) binding pocket dynamics and its integration into the structure-based virtual screening (SBVS) pipeline. Initially, unbiased molecular dynamics simulations of SIRT2:inhibitor complexes were performed using optimized force field parameters of SIRT2 inhibitors. Time-lagged independent component analysis (tICA) was used to design pocket-related collective variables (CVs) for enhanced sampling of SIRT2 pocket dynamics. Metadynamics simulations in the tICA eigenvector space revealed alternative conformational states of the SIRT2 binding pocket and the existence of a cryptic subpocket. Newly identified SIRT2 conformational states outperformed experimentally resolved states in retrospective SBVS validation. After performing prospective SBVS, compounds from the under-represented portions of the SIRT2 inhibitor chemical space were selected for in vitro evaluation. Two compounds, NDJ18 and NDJ85, were identified as potent and selective SIRT2 inhibitors, which validated the in silico protocol and opened up the possibility for generalization and broadening of its application. The anticancer effects of the most potent compound NDJ18 were examined on the triple-negative breast cancer cell line. Results indicated that NDJ18 represents a promising structure suitable for further evaluation.

Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals

To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO₂ or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.

A Blueprint for High Affinity SARS-CoV-2 Mpro Inhibitors from Activity-Based Compound Library Screening Guided by Analysis of Protein Dynamics

The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ∼30 000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ∼200 virtual screenings of compound libraries on selected protein structures, we redefine the protein's druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

Finding Druggable Sites in Proteins Using TACTICS

Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (trajectory-based analysis of conformations to identify cryptic sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis.

A 'deep dive' into the SARS-Cov-2 polymerase assembly: identifying novel allosteric sites and analyzing the hydrogen bond networks and correlated dynamics

Replication of the SARS-CoV-2 genome is a fundamental step in the virus life cycle and inhibiting the SARS-CoV2 replicase machinery has been proven recently as a promising approach in combating the virus. Despite this recent success, there are still several aspects related to the structure, function and dynamics of the CoV-2 polymerase that still need to be addressed. This includes understanding the dynamicity of the various polymerase subdomains, analyzing the hydrogen bond networks at the active site and at the template entry in the presence of water, studying the binding modes of the nucleotides at the active site, highlighting positions for acceptable nucleotides' substitutions that can be tolerated at different positions within the nascent RNA strand, identifying possible allosteric sites within the polymerase structure and studying their correlated dynamics relative to the catalytic site. Here, we combined various cutting-edge modelling tools with the recently resolved SARS-CoV-2 cryo-EM polymerase structures to fill this gap in knowledge. Our findings provide a detailed analysis of the hydrogen bond networks at various parts of the polymerase structure and suggest possible nucleotides' substitutions that can be tolerated by the polymerase complex. We also report here three 'druggable' allosteric sites within the NSP12 RdRp that can be targeted by small molecule inhibitors. Our correlated motion analysis shows that the dynamics within one of the newly identified sites are linked to the active site, indicating that targeting this site can significantly impact the catalytic activity of the SARS-CoV-2 polymerase.Communicated by Ramaswamy H. Sarma.

Identification of Cryptic Binding Sites Using MixMD with Standard and Accelerated Molecular Dynamics

Protein dynamics play an important role in small molecule binding and can pose a significant challenge in the identification of potential binding sites. Cryptic binding sites have been defined as sites which require significant rearrangement of the protein structure to become physically accessible to a ligand. Mixed-solvent MD (MixMD) is a computational protocol which maps the surface of the protein using molecular dynamics (MD) of the unbound protein solvated in a 5% box of probe molecules with explicit water. This method has successfully identified known active and allosteric sites which did not require reorganization. In this study, we apply the MixMD protocol to identify known cryptic sites of 12 proteins characterized by a wide range of conformational changes. Of these 12 proteins, three require reorganization of side chains, five require loop movements, and four require movement of more significant structures such as whole helices. In five cases, we find that standard MixMD simulations are able to map the cryptic binding sites with at least one probe type. In two cases (guanylate kinase and TIE-2), accelerated MD, which increases sampling of torsional angles, was necessary to achieve mapping of portions of the cryptic binding site missed by standard MixMD. For more complex systems where movement of a helix or domain is necessary, MixMD was unable to map the binding site even with accelerated dynamics, possibly due to the limited timescale (100 ns for individual simulations). In general, similar conformational dynamics are observed in water-only simulations and those with probe molecules. This could imply that the probes are not driving opening events but rather take advantage of mapping sites that spontaneously open as part of their inherent conformational behavior. Finally, we show that docking to an ensemble of conformations from the standard MixMD simulations performs better than docking the apo crystal structure in nine cases and even better than half of the bound crystal structures. Poorer performance was seen in docking to ensembles of conformations from the accelerated MixMD simulations.

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Kinetically selective and potent inhibitors of HDAC8

Histone deacetylase 8 (HDAC8) is an established and validated target for T-cell lymphoma and childhood neuroblastoma. The active site binding pocket of HDAC8 is highly conserved among all zinc-containing representatives of the histone deacetylase (HDAC) family. This explains that most HDACs are unselectively recognized by similar inhibitors featuring a zinc binding group (ZBG), a hydrophobic linker and a head group. In the light of this difficulty, the creation of isoenzyme-selectivity is one of the major challenges in the development of HDAC inhibitors. In a series of trifluoromethylketone inhibitors of HDAC8 compound 10 shows a distinct binding mechanism and a dramatically increased residence time (RT) providing kinetic selectivity against HDAC4. Combining the binding kinetics results with computational docking and binding site flexibility analysis suggests that 10 occupies the conserved catalytic site as well as an adjacent transient sub-pocket of HDAC8.

LARMD: integration of bioinformatic resources to profile ligand-driven protein dynamics with a case on the activation of estrogen receptor

Protein dynamics is central to all biological processes, including signal transduction, cellular regulation and biological catalysis. Among them, in-depth exploration of ligand-driven protein dynamics contributes to an optimal understanding of protein function, which is particularly relevant to drug discovery. Hence, a wide range of computational tools have been designed to investigate the important dynamic information in proteins. However, performing and analyzing protein dynamics is still challenging due to the complicated operation steps, giving rise to great difficulty, especially for nonexperts. Moreover, there is a lack of web protocol to provide online facility to investigate and visualize ligand-driven protein dynamics. To this end, in this study, we integrated several bioinformatic tools to develop a protocol, named Ligand and Receptor Molecular Dynamics (LARMD, http://chemyang.ccnu.edu.cn/ccb/server/LARMD/ and http://agroda.gzu.edu.cn:9999/ccb/server/LARMD/), for profiling ligand-driven protein dynamics. To be specific, estrogen receptor (ER) was used as a case to reveal ERβ-selective mechanism, which plays a vital role in the treatment of inflammatory diseases and many types of cancers in clinical practice. Two different residues (Ile373/Met421 and Met336/Leu384) in the pocket of ERβ/ERα were the significant determinants for selectivity, especially Met336 of ERβ. The helix H8, helix H11 and H7-H8 loop influenced the migration of selective agonist (WAY-244). These computational results were consistent with the experimental results. Therefore, LARMD provides a user-friendly online protocol to study the dynamic property of protein and to design new ligand or site-directed mutagenesis.

High-Performance Analysis of Biomolecular Containers to Measure Small-Molecule Transport, Transbilayer Lipid Diffusion, and Protein Cavities

Compartmentalization is a central theme in biology. Cells are composed of numerous membrane-enclosed structures, evolved to facilitate specific biochemical processes; viruses act as containers of genetic material, optimized to drive infection. Molecular dynamics simulations provide a mechanism to study biomolecular containers and the influence they exert on their environments; however, trajectory analysis software generally lacks knowledge of container interior versus exterior. Further, many relevant container analyses involve large-scale particle tracking endeavors, which may become computationally prohibitive with increasing system size. Here, a novel method based on 3-D ray casting is presented, which rapidly classifies the space surrounding biomolecular containers of arbitrary shape, enabling fast determination of the identities and counts of particles (e.g., solvent molecules) found inside and outside. The method is broadly applicable to the study of containers and enables high-performance characterization of properties such as solvent density, small-molecule transport, transbilayer lipid diffusion, and topology of protein cavities. The method is implemented in VMD, a widely used simulation analysis tool that supports personal computers, clouds, and parallel supercomputers, including ORNL's Summit and Titan and NCSA's Blue Waters, where the method can be employed to efficiently analyze trajectories encompassing millions of particles. The ability to rapidly characterize the spatial relationships of particles relative to a biomolecular container over many trajectory frames, irrespective of large particle counts, enables analysis of containers on a scale that was previously unfeasible, at a level of accuracy that was previously unattainable.

Identification of new allosteric sites and modulators of AChE through computational and experimental tools

Allosteric sites on proteins are targeted for designing more selective inhibitors of enzyme activity and to discover new functions. Acetylcholinesterase (AChE), which is most widely known for the hydrolysis of the neurotransmitter acetylcholine, has a peripheral allosteric subsite responsible for amyloidosis in Alzheimer's disease through interaction with amyloid β-peptide. However, AChE plays other non-hydrolytic functions. Here, we identify and characterise using computational tools two new allosteric sites in AChE, which have allowed us to identify allosteric inhibitors by virtual screening guided by structure-based and fragment hotspot strategies. The identified compounds were also screened for in vitro inhibition of AChE and three were observed to be active. Further experimental (kinetic) and computational (molecular dynamics) studies have been performed to verify the allosteric activity. These new compounds may be valuable pharmacological tools in the study of non-cholinergic functions of AChE.

Protein structure and computational drug discovery

The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand–protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a well-established technique that has produced several marketed drugs. We present here an overview of the various methodologies being used for structure-based computer-aided drug discovery and comment on possible future developments in the field.

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.

Protein Fluctuations and Cavity Changes Relationship

Protein cavities and tunnels are critical for function. Ligand recognition and binding, transport, and enzyme catalysis require cavities rearrangements. Therefore, the flexibility of cavities should be guaranteed by protein vibrational dynamics. Molecular dynamics simulations provide a framework to explore conformational plasticity of protein cavities. Herein, we present a novel procedure to characterize the dynamics of protein cavities in terms of their volume gradient vector. For this purpose, we make use of algorithms for calculation of the cavity volume that result robust for numerical differentiations. Volume gradient vector is expressed in terms of principal component analysis obtained from equilibrated molecular dynamics simulations. We analyze contributions of principal component modes to the volume gradient vector according to their frequency and degree of delocalization. In all our test cases, we find that low frequency modes play a critical role together with minor contributions of high frequency modes. These modes involve concerted motions of significant fractions of the total residues lining the cavities. We make use of variations of the potential energy of a protein in the direction of the volume gradient vector as a measure of flexibility of the cavity. We show that proteins whose collective low frequency fluctuations contribute the most to changes of cavity volume exhibit more flexible cavities.

Rational Drug Design Using Integrative Structural Biology

Modern drug discovery and design approaches rely heavily on high-throughput methods and state-of-the-art infrastructures with robotic facilities and sophisticated platforms. However, the anticipated research output that would eventually lead to new drugs with minimal or no side effects to the market has not been achieved. Despite the vast amount of information generated, very little is converted to knowledge and even less is capitalized for cross-discipline research actions. Therefore, the need for re-launching rational approaches has become apparent. Here we present an overview of the new trends in rational drug design using integrative structural biology with emphasis on X-ray protein crystallography and small molecules as ligands. With the aim to increase researchers' awareness on the available possibilities to perform front line research, we also underline the benefits and enhanced prospects offered to the scientific community, through access to research infrastructures.