Exploring the composition of protein-ligand binding sites on a large scale

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Exploring Cannabinoids as Potential Inhibitors of SARS-CoV-2 Papain-like Protease: Insights from Computational Analysis and Molecular Dynamics Simulations

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered a global COVID-19 pandemic, challenging healthcare systems worldwide. Effective therapeutic strategies against this novel coronavirus remain limited, underscoring the urgent need for innovative approaches. The present research investigates the potential of cannabis compounds as therapeutic agents against SARS-CoV-2 through their interaction with the virus's papain-like protease (PLpro) protein, a crucial element in viral replication and immune evasion. Computational methods, including molecular docking and molecular dynamics (MD) simulations, were employed to screen cannabis compounds against PLpro and analyze their binding mechanisms and interaction patterns. The results showed cannabinoids with binding affinities ranging from -6.1 kcal/mol to -4.6 kcal/mol, forming interactions with PLpro. Notably, Cannabigerolic and Cannabidiolic acids exhibited strong binding contacts with critical residues in PLpro's active region, indicating their potential as viral replication inhibitors. MD simulations revealed the dynamic behavior of cannabinoid-PLpro complexes, highlighting stable binding conformations and conformational changes over time. These findings shed light on the mechanisms underlying cannabis interaction with SARS-CoV-2 PLpro, aiding in the rational design of antiviral therapies. Future research will focus on experimental validation, optimizing binding affinity and selectivity, and preclinical assessments to develop effective treatments against COVID-19.

Structural investigation of Trypanosoma cruzi Akt-like kinase as drug target against Chagas disease

According to the World Health Organization, Chagas disease (CD) is the most prevalent poverty-promoting neglected tropical disease. Alarmingly, climate change is accelerating the geographical spreading of CD causative parasite, Trypanosoma cruzi, which additionally increases infection rates. Still, CD treatment remains challenging due to a lack of safe and efficient drugs. In this work, we analyze the viability of T. cruzi Akt-like kinase (TcAkt) as drug target against CD including primary structural and functional information about a parasitic Akt protein. Nuclear Magnetic Resonance derived information in combination with Molecular Dynamics simulations offer detailed insights into structural properties of the pleckstrin homology (PH) domain of TcAkt and its binding to phosphatidylinositol phosphate ligands (PIP). Experimental data combined with Alpha Fold proposes a model for the mechanism of action of TcAkt involving a PIP-induced disruption of the intramolecular interface between the kinase and the PH domain resulting in an open conformation enabling TcAkt kinase activity. Further docking experiments reveal that TcAkt is recognized by human inhibitors PIT-1 and capivasertib, and TcAkt inhibition by UBMC-4 and UBMC-6 is achieved via binding to TcAkt kinase domain. Our in-depth structural analysis of TcAkt reveals potential sites for drug development against CD, located at activity essential regions.

From complex data to clear insights: visualizing molecular dynamics trajectories

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

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

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

Multi-cavity molecular descriptor interconnections: Enhanced protocol for prediction of serum albumin drug binding

The role of human serum albumin (HSA) in the transport of molecules predicates its involvement in the determination of drug distribution and metabolism. Optimization of ADME properties are analogous to HSA binding thus this is imperative to the drug discovery process. Currently, various in silico predictive tools exist to complement the drug discovery process, however, the prediction of possible ligand-binding sites on HSA has posed several challenges. Herein, we present a strong and deeper-than-surface case for the prediction of HSA-ligand binding sites using multi-cavity molecular descriptors by exploiting all experimentally available and crystallized HSA-bound drugs. Unlike previously proposed models found in literature, we established an in-depth correlation between the physicochemical properties of available crystallized HSA-bound drugs and different HSA binding site characteristics to precisely predict the binding sites of investigational molecules. Molecular descriptors such as the number of hydrogen bond donors (nHD), number of heteroatoms (nHet), topological polar surface area (TPSA), molecular weight (MW), and distribution coefficient (LogD) were correlated against HSA binding site characteristics, including hydrophobicity, hydrophilicity, enclosure, exposure, contact, site volume, and donor/acceptor ratio. Molecular descriptors nHD, TPSA, LogD, nHet, and MW were found to possess the most inherent capacities providing baseline information for the prediction of serum albumin binding site. We believe that these associations may form the bedrock for establishing a solid correlation between the physicochemical properties and Albumin binding site architecture. Information presented in this report would serve as critical in provisions of rational drug designing as well as drug delivery, bioavailability, and pharmacokinetics.

SiteFerret: Beyond Simple Pocket Identification in Proteins

We present a novel method for the automatic detection of pockets on protein molecular surfaces. The algorithm is based on an ad hoc hierarchical clustering of virtual probe spheres obtained from the geometrical primitives used by the NanoShaper software to build the solvent-excluded molecular surface. The final ranking of putative pockets is based on the Isolation Forest method, an unsupervised learning approach originally developed for anomaly detection. A detailed importance analysis of pocket features provides insight into which geometrical (clustering) and chemical (amino acidic composition) properties characterize a good binding site. The method also provides a segmentation of pockets into smaller subpockets. We prove that subpockets are a convenient representation to pinpoint the binding site with great precision. SiteFerret is outstanding in its versatility, accurately predicting a wide range of binding sites, from those binding small molecules to those binding peptides, including difficult shallow sites.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Covalent drug discovery using sulfur(VI) fluoride exchange warheads

INTRODUCTION

Covalent drug discovery has traditionally focused on targeting cysteine, but the amino acid is often absent in protein binding sites. This review makes the case to move beyond cysteine labeling using sulfur (VI) fluoride exchange (SuFEx) chemistry to expand the druggable proteome.

AREAS COVERED

Recent advances in SuFEx medicinal chemistry and chemical biology are described, which have enabled the development of covalent chemical probes that site-selectively engage amino acid residues (including tyrosine, lysine, histidine, serine, and threonine) in binding pockets. Areas covered include chemoproteomic mapping of the targetable proteome, structure-based design of covalent inhibitors and molecular glues, metabolic stability profiling, and synthetic methodologies that have expedited the delivery of SuFEx modulators.

EXPERT OPINION

Despite recent innovations in SuFEx medicinal chemistry, focused preclinical research is required to ensure the field moves from early chemical probe discovery to the delivery of transformational covalent drug candidates. The authors believe that covalent drug candidates designed to engage residues beyond cysteine using sulfonyl exchange warheads will likely enter clinical trials in the coming years.

Human Autoantigen Atlas: Searching for the Hallmarks of Autoantigens

Understanding autoimmunity to endogenous proteins is crucial in diagnosing and treating autoimmune diseases. In this work, we developed a user-friendly AAgAtlas portal (http://biokb.ncpsb.org.cn/aagatlas_portal/index.php#), which can be used to search for 8045 non-redundant autoantigens (AAgs) and 47 post-translationally modified AAgs against 1073 human diseases that are prioritized by a credential score developed by multisource evidence. Using AAgAtlas, the immunogenic properties of human AAgs was systematically elucidated according to their genetic, biophysical, cytological, expression profile, and evolutionary characteristics. The results indicated that human AAgs are evolutionally conserved in protein sequence and enriched in three hydrophilic and polar amino acid residues (K, D, and E) that are located at the protein surface. AAgs are enriched in proteins that are involved in nucleic acid binding, transferase, and the cytoskeleton. Genome, transcriptome, and proteome analyses further indicated that AAb production is associated with gene variance and abnormal protein expression related to the pathological activities of different tumors. Collectively, our data outlines the hallmarks of human AAgs that facilitate the understanding of humoral autoimmunity and the identification of biomarkers of human diseases.

Genome-Wide Libraries for Protozoan Pathogen Drug Target Screening Using Yeast Surface Display

The lack of genetic tools to manipulate protozoan pathogens has limited the use of genome-wide approaches to identify drug or vaccine targets and understand these organisms' biology. We have developed an efficient method to construct genome-wide libraries for yeast surface display (YSD) and developed a YSD fitness screen (YSD-FS) to identify drug targets. We show the efficacy of our method by generating genome-wide libraries for Trypanosoma brucei, Trypanosoma cruzi, and Giardia lamblia parasites. Each library has a diversity of ∼105 to 106 clones, representing ∼6- to 30-fold of the parasite's genome. Nanopore sequencing confirmed the libraries' genome coverage with multiple clones for each parasite gene. Western blot and imaging analysis confirmed surface expression of the G. lamblia library proteins in yeast. Using the YSD-FS assay, we identified bonafide interactors of metronidazole, a drug used to treat protozoan and bacterial infections. We also found enrichment in nucleotide-binding domain sequences associated with yeast increased fitness to metronidazole, indicating that this drug might target multiple enzymes containing nucleotide-binding domains. The libraries are valuable biological resources for discovering drug or vaccine targets, ligand receptors, protein-protein interactions, and pathogen-host interactions. The library assembly approach can be applied to other organisms or expression systems, and the YSD-FS assay might help identify new drug targets in protozoan pathogens.

Structural characterization of a novel luciferase-like-monooxygenase fromPseudomonas meliae– an in-silico approach.. [DOI: 10.1101/2023.03.27.534437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background

Luciferase is a well-known oxidative enzyme that produces bioluminescence. ThePseudomonas meliaeis a plant pathogen that causes wood rot on nectarine and peach and possesses a luciferase-like monooxygenase. After activation, it produces bioluminescence, and the pathogen’s bioluminescence is a visual indicator of diseased plants.

Methods

The present study aims to model and characterize the luciferase-like monooxygenase protein inP. meliaefor its similarity to well-established luciferase. In this study, the luciferase-like monooxygenase fromP. meliaeinfects chinaberry plants has been modeled first and then studied by comparing it with existing known luciferase. Also, the similarities between uncharacterized luciferase fromP. meliaeand template fromGeobacillus thermodenitrificanswere analyzed to find the novelty ofP. meliae.

Results

The results suggest that the absence of bioluminescence inP. meliaecould be due to the evolutionary mutation in positions 138 and 311. The active site remains identical except for two amino acids;P. meliaeTyr138 instead of His138 and Leu311 instead of His311. Therefore, theP. meliaewill have a potential future application, and mutation of the residues 138 and 311 can be restored luciferase light-emitting ability.

Conclusions

This study will help further improve, activate, and repurpose the luciferase fromP. meliaeas a reporter for gene expression.

Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools

Binding MOAD is a database of protein-ligand complexes and their affinities with many structured relationships across the dataset. The project has been in development for over 20 years, but now, the time has come to bring it to a close. Currently, the database contains 41,409 structures with affinity coverage for 15,223 (37%) complexes. The website BindingMOAD.org provides numerous tools for polypharmacology exploration. Current relationships include links for structures with sequence similarity, 2D ligand similarity, and binding-site similarity. In this last update, we have added 3D ligand similarity using ROCS to identify ligands which may not necessarily be similar in two dimensions but can occupy the same three-dimensional space. For the 20,387 different ligands present in the database, a total of 1,320,511 3D-shape matches between the ligands were added. Examples of the utility of 3D-shape matching in polypharmacology are presented. Finally, plans for future access to the project data are outlined.

Cereblon target validation using a covalent inhibitor of neosubstrate recruitment

Small molecule ligands of cereblon (CRBN), a component of an E3 ubiquitin ligase complex, such as immunomodulatory drugs (IMiDs) or proteolysis targeting chimeras (PROTACs), induce new interactions between the E3 and a target protein that is subsequently polyubiquitinated and proteasomally degraded. The development of new degraders requires validation of CRBN-dependence and existing methods include the use of engineered CRBN knockout cell lines, or PROTACs directed to CRBN itself. Technical limitations of these approaches necessitate a simple and rapid pharmacological method of CRBN inhibition. We developed a sulfonyl fluoride covalent CRBN ligand based on the IMiD EM12 called EM12-SO₂F that was designed to engage His353 on the surface of the IMiD binding site. EM12-SO₂F does not act as a molecular glue degrader like other IMiDs, and instead serves as an inhibitor of such function by blocking the degrader binding site. We demonstrate utility of EM12-SO₂F by inhibiting the degradation of the zinc-finger transcription factor and CRBN neosubstrate IKZF1 by the molecular glue degrader lenalidomide. Increasingly, libraries of degrader molecules are being screened phenotypically to identify starting points for hit elaboration, that simultaneously reveals new therapeutic targets amenable to degradation. Indeed, targeted protein degradation has become an exciting new therapeutic modality and EM12-SO₂F augments the chemical biology toolbox that will advance this area of drug discovery research.

Structural evolution of Delta lineage of SARS-CoV-2

One of the main obstacles in prevention and treatment of COVID-19 is the rapid evolution of the SARS-CoV-2 Spike protein. Given that Spike is the main target of common treatments of COVID-19, mutations occurring at this virulent factor can affect the effectiveness of treatments. The B.1.617.2 lineage of SARS-CoV-2, being characterized by many Spike mutations inside and outside of its receptor-binding domain (RBD), shows high infectivity and relative resistance to existing cures. Here, utilizing a wide range of computational biology approaches, such as immunoinformatics, molecular dynamics (MD), analysis of intrinsically disordered regions (IDRs), protein-protein interaction analyses, residue scanning, and free energy calculations, we examine the structural and biological attributes of the B.1.617.2 Spike protein. Furthermore, the antibody design protocol of Rosetta was implemented for evaluation the stability and affinity improvement of the Bamlanivimab (LY-CoV55) antibody, which is not capable of interactions with the B.1.617.2 Spike. We observed that the detected mutations in the Spike of the B1.617.2 variant of concern can cause extensive structural changes compatible with the described variation in immunogenicity, secondary and tertiary structure, oligomerization potency, Furin cleavability, and drug targetability. Compared to the Spike of Wuhan lineage, the B.1.617.2 Spike is more stable and binds to the Angiotensin-converting enzyme 2 (ACE2) with higher affinity.

MlaC belongs to a unique class of non-canonical substrate-binding proteins and follows a novel phospholipid-binding mechanism

The outer membrane (OM) of Gram-negative bacteria acts as a formidable barrier against a plethora of detrimental compounds owing to its asymmetric nature. This is because the OM possesses lipopolysaccharides (LPSs) in the outer leaflet and phospholipids (PLs) in the inner leaflet. The maintenance of lipid asymmetry (Mla) system is involved in preserving the distribution of PLs in OM. The periplasmic component of the system MlaC serves as the substrate-binding protein (SBP) that shuttles PLs between the inner and outer membranes. However, an in-depth report highlighting its mechanism of ligand binding is still lacking. This study reports the crystal structure of MlaC from Escherichia coli (EcMlaC) at a resolution of 2.5 Å in a quasi-open state, complexed with PL. The structural analysis reveals that EcMlaC and orthologs comprise two major domains, viz. nuclear transport factor 2-like (NTF2-like) and phospholipid-binding protein (PBP). Each domain can be further divided into two subdomains arranged in a discontinuous fashion. This study further reveals that EcMlaC is polyspecific in nature and follows a reverse mechanism of the opening of the substrate-binding site during the ligand binding. Furthermore, MlaC can bind two PLs by forming subsites in the binding pocket. These findings, altogether, have led to the proposition of the unique "segmented domain movement" mechanism of PL binding, not reported for any known SBP to date. Further, unlike typical SBPs, MlaC has originated from a cystatin-like fold. Overall, this study establishes MlaC to be a non-canonical SBP with a unique ligand-binding mechanism.

Holo Protein Conformation Generation from Apo Structures by Ligand Binding Site Refinement

An important part in structure-based drug design is the selection of an appropriate protein structure. It has been revealed that a holo protein structure that contains a well-defined binding site is a much better choice than an apo structure in structure-based drug discovery. Therefore, it is valuable to obtain a holo-like protein conformation from apo structures in the case where no holo structure is available. Meeting the need, we present a robust approach to generate reliable holo-like structures from apo structures by ligand binding site refinement with restraints derived from holo templates with low homology. Our method was tested on a test set of 32 proteins from the DUD-E data set and compared with other approaches. It was shown that our method successfully refined the apo structures toward the corresponding holo conformations for 23 of 32 proteins, reducing the average all-heavy-atom RMSD of binding site residues by 0.48 Å. In addition, when evaluated against all the holo structures in the protein data bank, our method can improve the binding site RMSD for 14 of 19 cases that experience significant conformational changes. Furthermore, our refined structures also demonstrate their advantages over the apo structures in ligand binding mode predictions by both rigid docking and flexible docking and in virtual screening on the database of active and decoy ligands from the DUD-E. These results indicate that our method is effective in recovering holo-like conformations and will be valuable in structure-based drug discovery.

Covalent drugs targeting histidine - an unexploited opportunity?

Covalent drugs and chemical probes often possess pharmacological advantages over reversible binding ligands, such as enhanced potency and pharmacodynamic duration. The highly nucleophilic cysteine thiol is commonly targeted using acrylamide electrophiles, but the amino acid is rarely present in protein binding sites. Sulfonyl exchange chemistry has expanded the covalent drug discovery toolkit by enabling the rational design of irreversible inhibitors targeting tyrosine, lysine, serine and threonine. Probes containing the sulfonyl fluoride warhead have also been shown to serendipitously label histidine residues in proteins. Histidine targeting is an attractive prospect because the residue is frequently proximal to protein small molecule ligands and the imidazole side chain possesses desirable nucleophilicity. We recently reported the design of cereblon molecular glues to site-selectively modify a histidine in the thalidomide binding site using sulfonyl exchange chemistry. We believe that histidine targeting holds great promise for future covalent drug development and this Opinion highlights these opportunities.

Mechanistic Insight on General Protein-Binding Ability of ATP and the Impacts of Arginine Residues

Recent experiments suggested that adenosine triphosphate (ATP) can regulate liquid-liquid phase separation (LLPS) of various proteins and inhibit protein aggregations at its physiological concentration, which is highly correlated with the nonspecific interactions of ATP to a wide variety of proteins. However, the mechanism underlying the general binding capability of ATP largely remains unclear. In this work, we used molecular dynamics simulation to study the binding of ATPs to three proteins with distinct net charges: TDP-43 NTD (-7 e), TAF15-RRM (0 e), HWEL (+8 e). Negatively charged ATP exhibits a strong trend to accumulate around all of these proteins. While only a fraction of the accumulated ATPs directly binds to the limited regions of the protein surface, additional ATPs indirectly bind to proteins by aggregating into ATP clusters. Hence, the proportion of the directly bound ATPs in the clusters as well as their binding regions can be adjusted in response to different proteins, which makes ATP well adapted to a variety of proteins. Moreover, our results suggest that ATP tightly binds to Arg with high affinity, and Arg dominates the direct binding of ATP. Meanwhile, Arg also affects the self-association of accumulated ATPs. The size of the ATP cluster is effectively regulated by the distribution of Arg. Considering the ubiquity of Arg in proteins, our findings are helpful to understand the general binding capability of ATP.

Identification and characterisation of blue light photoreceptor gene family and their expression in tomato (Solanum lycopersicum) under cold stress

The Arabidopsis thaliana L. photoreceptor genes homologues in tomato (Solanum lycopersicum L.) genome were analysed using bioinformatic tools. The expression pattern of these genes under cold stress was also evaluated. Transcriptome analysis of the tomato sequence revealed that the photoreceptor gene family is involved in abiotic stress tolerance. They participate in various pathways and controlling multiple metabolic processes. They are structurally related to PAS, LIGHT-OXYGEN-VOLTAGE-SENSING (LOV), DNA photolyase, 5,10-methenyl tetrahydrofolate (MTHF), flavin-binding kelch F-box, GAF, PHY, Seven-bladed β-propeller and C27 domains. They also interact with flavin adenine dinucleotide (FAD), (5S)-5-methyl-2-(methylsulfanyl)-5-phenyl-3-(phenylamino)-3,5-dihydro-4H-imidazol-4-one (FNM) and Phytochromobilin (PϕB) ligands. These interactions help to create a cascade of protein phosphorylation involving in cell defence transcription or stress-regulated genes. They localisation of these gene families on tomato chromosomes appeared to be uneven. Phylogenetic tree of tomato and Arabidopsis photoreceptor gene family were classified into eight subgroups, indicating gene expression diversity. Morphological and physiological assessment revealed no dead plant after 4h of cold treatment. All the plants were found to be alive, but there were some variations in the data across different parameters. Cold stress significantly reduced the rate of photosynthesis from 10.06 to 3.16μmolm-2 s-1 , transpiration from 4.6 to 1.3mmolm-2 s-1 , and stomatal conductance from 94.6 to 25.6mmolm-2 s-1 . The cold stressed plants also had reduced height, root/shoot length, and fresh/dry biomass weight than the control plants. Relative expression analysis under cold stress revealed that after 4h, light stimulates the transcript level of Cry2 from 1.9 to 5.7 and PhyB from 0.98 to 6.9 compared to other photoreceptor genes.

Cereblon covalent modulation through structure-based design of histidine targeting chemical probes

Synthetic re-engineering of a surface histidine residue on cereblon using sulfonyl exchange chemistry yielded potent irreversible modulators of the E3 ubiquitin ligase complex, including a molecular glue degrader of the novel neosubstrate NTAQ1.

Epigenetic Modulators as Treatment Alternative to Diverse Types of Cancer

DNA is packaged in rolls in an octamer of histones forming a complex of DNA and proteins called chromatin. Chromatin as a structural matrix of a chromosome and its modifications are nowadays considered relevant aspects for regulating gene expression, which has become of high interest in understanding genetic mechanisms regulating various diseases, including cancer. In various types of cancer, the main modifications are found to be DNA methylation in the CpG dinucleotide as a silencing mechanism in transcription, post-translational histone modifications such as acetylation, methylation and others that affect the chromatin structure, the ATP-dependent chromatin remodeling and miRNA-mediated gene silencing. In this review we analyze the main alterations in gene expression, the epigenetic modification patterns that cancer cells present, as well as the main modulators and inhibitors of each epigenetic mechanism and the molecular evolution of the most representative inhibitors, which have opened a promising future in the study of HAT, HDAC, non-glycoside DNMT inhibitors and domain inhibitors.

New perspectives in cancer drug development: computational advances with an eye to design

Computational chemistry has come of age in drug discovery. Indeed, most pharmaceutical development programs rely on computer-based data and results at some point. Herein, we discuss recent applications of advanced simulation techniques to difficult challenges in drug discovery. These entail the characterization of allosteric mechanisms and the identification of allosteric sites or cryptic pockets determined by protein motions, which are not immediately evident in the experimental structure of the target; the study of ligand binding mechanisms and their kinetic profiles; and the evaluation of drug-target affinities. We analyze different approaches to tackle challenging and emerging biological targets. Finally, we discuss the possible perspectives of future application of computation in drug discovery.

A network pharmacology analysis on drug-like compounds from Ganoderma lucidum for alleviation of atherosclerosis

Ganoderma lucidum (GL) is known as a potent alleviator against chronic inflammatory disease like atherosclerosis (AS), but its mechanisms against AS have not been unveiled. This research aimed to identify the key compounds(s) and mechanism(s) of GL against AS through network pharmacology. The compounds from GL were identified by gas chromatography-mass spectrum (GC-MS), and SwissADME screened their physicochemical properties. Then, the target(s) associated with the screened compound(s) or AS related targets were identified by public databases, and we selected the overlapping targets using a Venn diagram. The networks between overlapping targets and compounds were visualized, constructed, and analyzed by RStudio. Finally, we performed a molecular docking test (MDT) to explore key target(s), compound(s), on AutoDockVina. A total of 35 compounds in GL were detected via GC-MS, and 34 compounds (accepted by Lipinski's rule) were selected as drug-like compounds (DLCs). A total of 34 compounds were connected to the number of 785 targets, and DisGeNET and Online Mendelian Inheritance in Man (OMIM) identified 2,606 AS-related targets. The final 98 overlapping targets were extracted between the compounds-targets and AS-related targets. On Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, the number of 27 signaling pathways were sorted out, and a hub signaling pathway (MAPK signaling pathway), a core gene (PRKCA), and a key compound (Benzamide, 4-acetyl-N-[2,6-dimethylphenyl]) were selected among the 27 signaling pathways via MDT. Overall, we found that the identified 3 DLCs from GL have potent anti-inflammatory efficacy, improving AS by inactivating the MAPK signaling pathway. PRACTICAL APPLICATIONS: Ganoderma lucidum (GL) has been used as a medicinal or edible mushroom for chronic inflammatory patients: diabetes mellitus and dyslipidemia, especially atherosclerosis (AS). Until now, the majority of mushroom research has been implemented regarding β-glucan derivatives with very hydrophilic physicochemical properties. It implies that β-glucan or its derivatives have poor bioavailability. Hence, we have involved GC-MS in identifying lipophilic compounds from GL, which filtered them in silico to sort drug-like compounds (DLCs). Then, we retrieved targets associated with the DLCs, and identified a key signaling pathway, key targets, and key compounds against AS. In this paper, we utilized bioinformatics and network pharmacology theory to understand the uncovered pharmacological mechanism of GL on AS. To sum things up, our analysis elucidates the relationships between signaling pathways, targets, and compounds in GL. Ultimately, this work provides biochemical evidence to identify the therapeutic effect of GL on AS, and a scientific basis for deciphering the key mechanism on DLCs of GL against AS.

Ligand-Binding-Site Refinement to Generate Reliable Holo Protein Structure Conformations from Apo Structures

The first important step in a structure-based virtual screening is the judicious selection of a receptor protein. In cases where the holo protein receptor structure is unavailable, significant reduction in virtual screening performance has been reported. In this work, we present a robust method to generate reliable holo protein structure conformations from apo structures using molecular dynamics (MD) simulation with restraints derived from holo structure binding-site templates. We perform benchmark tests on two different datasets: 40 structures from a directory of useful decoy-enhanced (DUD-E) and 84 structures from the Gunasekaran dataset. Our results show successful refinement of apo binding-site structures toward holo conformations in 82% of the test cases. In addition, virtual screening performance of 40 DUD-E structures is significantly improved using our MD-refined structures as receptors with an average enrichment factor (EF), an EF_1% value of 6.2 compared to apo structures with 3.5. Docking of native ligands to the refined structures shows an average ligand root mean square deviation (RMSD) of 1.97 Å (DUD-E dataset and Gunasekaran dataset) relative to ligands in the holo crystal structures, which is comparable to the self-docking (i.e., docking of the native ligand back to its crystal structure receptor) average, 1.34 Å (DUD-E dataset) and 1.36 Å (Gunasekaran dataset). On the other hand, docking to the apo structures yields an average ligand RMSD of 3.65 Å (DUD-E) and 2.90 Å (Gunasekaran). These results indicate that our method is robust and can be useful to improve virtual screening performance of apo structures.

Sensing of Proteins by ICD Response of Iron(II) Clathrochelates Functionalized by Carboxyalkylsulfide Groups

Recognition of elements of protein tertiary structure is crucial for biotechnological and biomedical tasks; this makes the development of optical sensors for certain protein surface elements important. Herein, we demonstrated the ability of iron(II) clathrochelates (1-3) functionalized with mono-, di- and hexa-carboxyalkylsulfide to induce selective circular dichroism (CD) response upon binding to globular proteins. Thus, inherently CD-silent clathrochelates revealed selective inducing of CD spectra when binding to human serum albumin (HSA) (1, 2), beta-lactoglobuline (2) and bovine serum albumin (BSA) (3). Hence, functionalization of iron(II) clathrochelates with the carboxyalkylsulfide group appears to be a promising tool for the design of CD-probes sensitive to certain surface elements of proteins tertiary structure. Additionally, interaction of 1-3 with proteins was also studied by isothermal titration calorimetry, protein fluorescence quenching, electrospray ionization mass spectrometry (ESI-MS) and computer simulations. Formation of both 1:1 and 1:2 assemblies of HSA with 1-3 was evidenced by ESI-MS. A protein fluorescence quenching study suggests that 3 binds with both BSA and HSA via the sites close to Trp residues. Molecular docking calculations indicate that for both BSA and HSA, binding of 3 to Site I and to an "additional site" is more favorable energetically than binding to Site II.

Cannabis Constituents and Acetylcholinesterase Interaction: Molecular Docking, In Vitro Studies and Association with CNR1 rs806368 and ACHE rs17228602

The study documented here was aimed to find the molecular interactions of some of the cannabinoid constituents of cannabis with acetylcholinesterase (AChE). Molecular docking and LogP determination were performed to predict the AChE inhibitory effect and lipophilicity. AChE enzyme activity was measured in the blood of cannabis addicted human subjects. Further, genetic predisposition to cannabis addiction was investigated by association analysis of cannabinoid receptor 1 (CNR1) single nucleotide polymorphism (SNP) rs806368 and ACHE rs17228602 using restriction fragment length polymorphism (RFLP) method. All the understudied cannabis constituents showed promising binding affinities with AChE and are lipophilic in nature. The AChE activity was observed to be indifferent in cannabis addicted and non-addicted healthy controls. There was no significant association with CNR1 SNP rs806368 and ACHE rs17228602. The study concludes that in silico prediction for individual biomolecules of cannabis is different from in vivo physiological action in human subjects when all are present together. However, for a deeper mechanistic insight into these interactions and association, multi-population studies are suggested. Further studies to explore the inhibitory potential of different cannabis constituents for intended AChE inhibitor-based drug are warranted.

BionoiNet: ligand-binding site classification with off-the-shelf deep neural network

MOTIVATION

Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods.

RESULTS

We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures.

AVAILABILITY AND IMPLEMENTATION

BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters

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

Structure-based design and analysis of SuFEx chemical probes

The discerning reactivity of sulfur(vi)-fluoride exchange (SuFEx) chemistry has enabled the context-specific labeling of protein binding sites by chemical probes that incorporate these versatile warheads. Emerging information from protein-probe structures and proteomic mapping experiments is helping advance our understanding of the protein microenvironment that dictates the reactivity of targetable amino acid residues. This review explores these new findings that should influence the future rational design of SuFEx probes for a multitude of applications in chemical biology and drug discovery.

Ligand-Binding-Site Structure Refinement Using Molecular Dynamics with Restraints Derived from Predicted Binding Site Templates

Accurate modeling of ligand-binding-site structures plays a critical role in structure-based virtual screening. However, the structures of the ligand-binding site in most predicted protein models are generally of low quality and need refinements. In this work, we present a ligand-binding-site structure refinement protocol using molecular dynamics simulation with restraints derived from predicted binding site templates. Our benchmark validation shows great performance for 40 diverse sets of proteins from the Astex list. The ligand-binding sites on modeled protein structures are consistently refined using our method with an average Cα RMSD improvement of 0.90 Å. Comparison of ligand binding modes from ligand docking to initial unrefined and refined structures shows an average of 1.97 Å RMSD improvement in the refined structures. These results demonstrate a promising new method of structure refinement for protein ligand-binding-site structures.

Calculation of the Global and Local Conceptual DFT Indices for the Prediction of the Chemical Reactivity Properties of Papuamides A-F Marine Drugs

A well-behaved model chemistry previously validated for the study of the chemical reactivity of peptides was considered for the calculation of the molecular properties and structures of the Papuamide family of marine peptides. A methodology based on Conceptual Density Functional Theory (CDFT) was chosen for the determination of the reactivity descriptors. The molecular active sites were associated with the active regions of the molecules related to the nucleophilic and electrophilic Parr functions. Finally, the drug-likenesses and the bioactivity scores for the Papuamide peptides were predicted through a homology methodology relating them with the calculated reactivity descriptors, while other properties such as the pKas were determined following a methodology developed by our group.

ImmtorLig_DB: repertoire of virtually screened small molecules against immune receptors to bolster host immunity

Host directed therapies to boost immunity against infection are gaining considerable impetus following the observation that use of antibiotics has become a continuous source for the emergence of drug resistant strains of pathogens. Receptors expressed by the cells of immune system play a cardinal role in initiating sequence of events necessary to ameliorate many morbid conditions. Although, ligands for the immune receptors are available; but their use is limited due to complex structure, synthesis and cost-effectiveness. Virtual screening (VS) is an integral part of chemoinformatics and computer-aided drug design (CADD) and aims to streamline the process of drug discovery. ImmtorLig_DB is a repertoire of 5000 novel small molecules, screened from ZINC database and ranked using structure based virtual screening (SBVS) against 25 immune receptors which play a pivotal role in defending and initiating the activation of immune system. Consequently, in the current study, small molecules were screened by docking on the essential domains present on the receptors expressed by cells of immune system. The screened molecules exhibited efficacious binding to immune receptors, and indicated a possibility of discovering novel small molecules. Other features of ImmtorLig_DB include information about availability, clustering analysis, and estimation of absorption, distribution, metabolism, and excretion (ADME) properties of the screened small molecules. Structural comparisons indicate that predicted small molecules may be considered novel. Further, this repertoire is available via a searchable graphical user interface (GUI) through http://bioinfo.imtech.res.in/bvs/immtor/ .

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.

Sulfur as an Acceptor to Bromine in Biomolecular Halogen Bonds

The halogen bond (X-bond) has become an important design element in chemistry, including medicinal chemistry and biomolecular engineering. Although oxygen is the most prevalent and best characterized X-bond acceptor in biomolecules, the interaction is seen with nitrogen, sulfur, and aromatic systems as well. In this study, we characterize the structure and thermodynamics of a Br···S X-bond between a 5-bromouracil base and a phosphorothioate in a model DNA junction. The single-crystal structure of the junction shows the geometry of the Br···S to be variable, while calorimetric studies show that the anionic S acceptor is comparable to or slightly more stable than the analogous O acceptor, with a -3.5 kcal/mol difference in ΔΔH^25°C and -0.4 kcal/mol ΔΔG^25°C (including an entropic penalty ΔΔS^25°C of -10 cal/(mol K)). Thus sulfur is shown to be a favorable acceptor for bromine X-bonds, extending the application of this interaction for the design of inhibitors and biological materials.

Protein-Protein Interaction Modulators for Epigenetic Therapies

Targeting protein-protein interactions (PPIs) is becoming an attractive approach for drug discovery. This is particularly true for difficult or emerging targets, such as epitargets that may be elusive to drugs that fall into the traditional chemical space. The chemical nature of the PPIs makes attractive the use of peptides or peptidomimetics to selectively modulate such interactions. Despite the fact peptide-based drug discovery has been challenging, the use of peptides as leads compounds for drug discovery is still a valid strategy. This chapter discusses the current status of PPIs in epigenetic drug discovery. A special emphasis is made on peptides and peptide-like compounds as potential drug candidates.

Deciphering Cryptic Binding Sites on Proteins by Mixed-Solvent Molecular Dynamics

In recent years, molecular dynamics simulations of proteins in explicit mixed solvents have been applied to various problems in protein biophysics and drug discovery, including protein folding, protein surface characterization, fragment screening, allostery, and druggability assessment. In this study, we perform a systematic study on how mixtures of organic solvent probes in water can reveal cryptic ligand binding pockets that are not evident in crystal structures of apo proteins. We examine a diverse set of eight PDB proteins that show pocket opening induced by ligand binding and investigate whether solvent MD simulations on the apo structures can induce the binding site observed in the holo structures. The cosolvent simulations were found to induce conformational changes on the protein surface, which were characterized and compared with the holo structures. Analyses of the biological systems, choice of probes and concentrations, druggability of the resulting induced pockets, and application to drug discovery are discussed here.

Peptides, Peptidomimetics, and Polypeptides from Marine Sources: A Wealth of Natural Sources for Pharmaceutical Applications

Nature provides a variety of peptides that are expressed in most living species. Evolutionary pressure and natural selection have created and optimized these peptides to bind to receptors with high affinity. Hence, natural resources provide an abundant chemical space to be explored in peptide-based drug discovery. Marine peptides can be extracted by simple solvent extraction techniques. The advancement of analytical techniques has made it possible to obtain pure peptides from natural resources. Extracted peptides have been evaluated as possible therapeutic agents for a wide range of diseases, including antibacterial, antifungal, antidiabetic and anticancer activity as well as cardiovascular and neurotoxin activity. Although marine resources provide thousands of possible peptides, only a few peptides derived from marine sources have reached the pharmaceutical market. This review focuses on some of the peptides derived from marine sources in the past ten years and gives a brief review of those that are currently in clinical trials or on the market.

Physics-Based Visual Characterization of Molecular Interaction Forces

Molecular simulations are used in many areas of biotechnology, such as drug design and enzyme engineering. Despite the development of automatic computational protocols, analysis of molecular interactions is still a major aspect where human comprehension and intuition are key to accelerate, analyze, and propose modifications to the molecule of interest. Most visualization algorithms help the users by providing an accurate depiction of the spatial arrangement: the atoms involved in inter-molecular contacts. There are few tools that provide visual information on the forces governing molecular docking. However, these tools, commonly restricted to close interaction between atoms, do not consider whole simulation paths, long-range distances and, importantly, do not provide visual cues for a quick and intuitive comprehension of the energy functions (modeling intermolecular interactions) involved. In this paper, we propose visualizations designed to enable the characterization of interaction forces by taking into account several relevant variables such as molecule-ligand distance and the energy function, which is essential to understand binding affinities. We put emphasis on mapping molecular docking paths obtained from Molecular Dynamics or Monte Carlo simulations, and provide time-dependent visualizations for different energy components and particle resolutions: atoms, groups or residues. The presented visualizations have the potential to support domain experts in a more efficient drug or enzyme design process.

Quantification of Small Molecule-Protein Interactions using FRET between Tryptophan and the Pacific Blue Fluorophore

We report a new method to quantify the affinity of small molecules for proteins. This method is based on Förster resonance energy transfer (FRET) between endogenous tryptophan (Trp) residues and the coumarin-derived fluorophore Pacific Blue (PB). Tryptophan residues are frequently found in proteins near ligand-binding sites, making this approach potentially applicable to a wide range of systems. To improve access to PB, we developed a scalable multigram synthesis of this fluorophore, starting with inexpensive 2,3,4,5-tetrafluorobenzoic acid. This route was used to synthesize fluorescent derivatives of biotin, as well as lower affinity thiobiotin, iminobiotin, and imidazolidinethione analogues that bind the protein streptavidin. Compared with previously published FRET acceptors for tryptophan, PB proved to be superior in both sensitivity and efficiency. These unique properties of PB enabled direct quantification of dissociation constants (K_d) as well as competitive inhibition constants (K_i) in the micromolar to nanomolar range. In comparison to analogous binding studies using fluorescence polarization, fluorescence quenching, or fluorescence enhancement, affinities determined using Trp-FRET were more precise and accurate as validated using independent isothermal titration calorimetry studies. FRET between tryptophan and PB represents a new tool for the characterization of protein-ligand complexes.

Comparing atom-based with residue-based descriptors in predicting binding site similarity: do backbone atoms matter?

AIM

We question the level of detail required in protein 3D-representation to detect site similarity which is relevant for polypharmacology prediction.

RESULTS

We modified the in-house program SiteAlign to replace generic pharmacophoric descriptors of cavity-lining amino acids by descriptors accounting for solvent exposure. Benchmarking the novel, atom-based, method (SiteAlign2) revealed no global improvement of performance. However, in the rare cases of no sequence or global structure similarities between the compared proteins, SiteAlign2 was more successful if backbone atoms are key determinants of ligand binding.

CONCLUSION

SiteAlign suits the comparison of binding sites for close or distant homologs. SiteAlign2 provides a better insight into the physical model of site similarity between nonhomologs, but at the expense of an increased sensitivity to atomic coordinates.

Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites.

Small-molecule binding sites to explore protein-protein interactions in the cancer proteome

The Cancer Genome Atlas (TCGA) offers an unprecedented opportunity to identify small-molecule binding sites on proteins with overexpressed mRNA levels that correlate with poor survival. Here, we analyze RNA-seq and clinical data for 10 tumor types to identify genes that are both overexpressed and correlate with patient survival. Protein products of these genes were scanned for binding sites that possess shape and physicochemical properties that can accommodate small-molecule probes or therapeutic agents (druggable). These binding sites were classified as enzyme active sites (ENZ), protein-protein interaction sites (PPI), or other sites whose function is unknown (OTH). Interestingly, the overwhelming majority of binding sites were classified as OTH. We find that ENZ, PPI, and OTH binding sites often occurred on the same structure suggesting that many of these OTH cavities can be used for allosteric modulation of enzyme activity or protein-protein interactions with small molecules. We discovered several ENZ (PYCR1, QPRT, and HSPA6) and PPI (CASC5, ZBTB32, and CSAD) binding sites on proteins that have been seldom explored in cancer. We also found proteins that have been extensively studied in cancer that have not been previously explored with small molecules that harbor ENZ (PKMYT1, STEAP3, and NNMT) and PPI (HNF4A, MEF2B, and CBX2) binding sites. All binding sites were classified by the signaling pathways to which the protein that harbors them belongs using KEGG. In addition, binding sites were mapped onto structural protein-protein interaction networks to identify promising sites for drug discovery. Finally, we identify pockets that harbor missense mutations previously identified from analysis of TCGA data. The occurrence of mutations in these binding sites provides new opportunities to develop small-molecule probes to explore their function in cancer.

Real-Time Molecular Visualization Supporting Diffuse Interreflections and Ambient Occlusion

Today molecular simulations produce complex data sets capturing the interactions of molecules in detail. Due to the complexity of this time-varying data, advanced visualization techniques are required to support its visual analysis. Current molecular visualization techniques utilize ambient occlusion as a global illumination approximation to improve spatial comprehension. Besides these shadow-like effects, interreflections are also known to improve the spatial comprehension of complex geometric structures. Unfortunately, the inherent computational complexity of interreflections would forbid interactive exploration, which is mandatory in many scenarios dealing with static and time-varying data. In this paper, we introduce a novel analytic approach for capturing interreflections of molecular structures in real-time. By exploiting the knowledge of the underlying space filling representations, we are able to reduce the required parameters and can thus apply symbolic regression to obtain an analytic expression for interreflections. We show how to obtain the data required for the symbolic regression analysis, and how to exploit our analytic solution to enhance interactive molecular visualizations.

PaPI: pseudo amino acid composition to score human protein-coding variants

Background

High throughput sequencing technologies are able to identify the whole genomic variation of an individual. Gene-targeted and whole-exome experiments are mainly focused on coding sequence variants related to a single or multiple nucleotides. The analysis of the biological significance of this multitude of genomic variant is challenging and computational demanding.

Results

We present PaPI, a new machine-learning approach to classify and score human coding variants by estimating the probability to damage their protein-related function. The novelty of this approach consists in using pseudo amino acid composition through which wild and mutated protein sequences are represented in a discrete model. A machine learning classifier has been trained on a set of known deleterious and benign coding variants with the aim to score unobserved variants by taking into account hidden sequence patterns in human genome potentially leading to diseases. We show how the combination of amphiphilic pseudo amino acid composition, evolutionary conservation and homologous proteins based methods outperforms several prediction algorithms and it is also able to score complex variants such as deletions, insertions and indels.

Conclusions

This paper describes a machine-learning approach to predict the deleteriousness of human coding variants. A freely available web application (http://papi.unipv.it) has been developed with the presented method, able to score up to thousands variants in a single run.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0554-8) contains supplementary material, which is available to authorized users.

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.