Genome-wide screening for genetic variants in polyadenylation signal (PAS) sites in mouse selection lines for fatness and leanness.

Alternative polyadenylation (APA) determines mRNA stability, localisation, translation and protein function. Several diseases, including obesity, have been linked to APA. Studies have shown that single nucleotide polymorphisms in polyadenylation signals (PAS-SNPs) can influence APA and affect phenotype and disease susceptibility. However, these studies focussed on associations between single PAS-SNP alleles with very large effects and phenotype. Therefore, we performed a genome-wide screening for PAS-SNPs in the polygenic mouse selection lines for fatness and leanness by whole-genome sequencing. The genetic variants identified in the two lines were overlapped with locations of PAS sites obtained from the PolyASite 2.0 database. Expression data for selected genes were extracted from the microarray expression experiment performed on multiple tissue samples. In total, 682 PAS-SNPs were identified within 583 genes involved in various biological processes, including transport, protein modifications and degradation, cell adhesion and immune response. Moreover, 63 of the 583 orthologous genes in human have been previously associated with human diseases, such as nervous system and physical disorders, and immune, endocrine, and metabolic diseases. In both lines, PAS-SNPs have also been identified in genes broadly involved in APA, such as Polr2c, Eif3e and Ints11. Five PAS-SNPs within 5 genes (Car, Col4a1, Itga7, Lat, Nmnat1) were prioritised as potential functional variants and could contribute to the phenotypic disparity between the two selection lines. The developed PAS-SNPs catalogue presents a key resource for planning functional studies to uncover the role of PAS-SNPs in APA, disease susceptibility and fat deposition.

CaNDis: a web server for investigation of causal relationships between diseases, drugs and drug targets.

MOTIVATION: Causal biological interaction networks represent cellular regulatory pathways. Their fusion with other biological data enables insights into disease mechanisms and novel opportunities for drug discovery. RESULTS: We developed Causal Network of Diseases (CaNDis), a web server for the exploration of a human causal interaction network, which we expanded with data on diseases and FDA-approved drugs, on the basis of which we constructed a disease-disease network in which the links represent the similarity between diseases. We show how CaNDis can be used to identify candidate genes with known and novel roles in disease co-occurrence and drug-drug interactions. AVAILABILITYAND IMPLEMENTATION: CaNDis is freely available to academic users at http://candis.ijs.si and http://candis.insilab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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

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

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

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

Genetic variants of the hypoxia-inducible factor 3 alpha subunit (Hif3a) gene in the Fat and Lean mouse selection lines.

BACKGROUND: Adipose tissue hypoxia and members of the hypoxia-inducible factor alpha (HIFA) are involved in development of obesity. However, the mechanism and functions of HIF3A, one of three HIFA paralogs, in fat deposition have not been sufficiently studied. METHODS AND RESULTS: In the present study, we investigated whether Hif3a sequence variants are associated with divergent fat deposition in mouse selection lines for fatness and leanness. Sequencing and RFLP were used to analyse sequence variants within Hif3a. To identify candidate regulatory variants, we performed literature screening and used databases and bioinformatics tools like Ensembl, MethPrimer, TargetScanMouse, miRDB, PolyAsite, RISE, LncRRIsearch, RNAfold, PredictProtein, CAIcal, and switches.ELM Resource. There are 90 sequence variants in Hif3a between the two mouse lines. While most Fat line variants locate within intronic regions, Lean line variants are mainly in 3' UTR. We constructed a map of Hif3a potential regulatory regions and identified 39 regulatory variants by integrating data on constrained and regulatory elements, CpGs, and miRNAs and lncRNAs binding sites. Moreover, 3' UTR and two exonic variants may influence mRNA stability, translation rate and protein functionality. We propose as priority candidates for further functional studies a missense (rs37398126) and synonymous (rs37739792) variants, and intronic (rs47471302) variant that overlap conserved element in promoter region and predicted lncRNAs binding site. CONCLUSION: The results indicate a potential involvement of Hif3a in fat deposition. Additionally, approach used in the present study may serve as a general guideline for constructing an integrative gene map for prioritizing candidate gene variants with phenotypic effects.

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

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

Loop Grafting between Similar Local Environments for Fc-Silent Antibodies.

Reduction of the affinity of the fragment crystallizable (Fc) region with immune receptors by substitution of one or a few amino acids, known as Fc-silencing, is an established approach to reduce the immune effector functions of monoclonal antibody therapeutics. This approach to Fc-silencing, however, is problematic as it can lead to instability and immunogenicity of the developed antibodies. We evaluated loop grafting as a novel approach to Fc-silencing in which the Fc loops responsible for immune receptor binding were replaced by loops of up to 20 amino acids from similar local environments in other human and mouse antibodies. Molecular dynamics simulations of the designed variants of an Fc region in a complex with the immune receptor FcγIIIa confirmed that loop grafting potentially leads to a significant reduction in the binding of the antibody variants to the receptor, while retaining their stability. In comparison, standard variants with less than eight substituted amino acids showed possible instability and a lower degree of Fc-silencing due to the occurrence of compensatory interactions. The presented approach to Fc-silencing is general and could be used to modulate undesirable side effects of other antibody therapeutics without affecting their stability or increasing their immunogenicity.

CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials.

Small-molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations such as improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g., cofactors, metal ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm, that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions, and cofactor interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind, Astex, and PINC proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions such that the statistical score of the best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best-docked pose with biological activity. CANDOCK along with all structures and scripts used for benchmarking is available at https://github.com/chopralab/candock_benchmark.

Discovery of Novel Potential Human Targets of Resveratrol by Inverse Molecular Docking.

Resveratrol is a polyphenol known for its antioxidant and anti-inflammatory properties, which support its use as a treatment for variety of diseases. There are already known connections of resveratrol to chemoprevention of cancer because of its ability to prevent tumor initiation and inhibit tumor promotion and progression. Resveratrol is also believed to be important in cardiovascular diseases and neurological disorders, such as Alzheimer's disease. Using an inverse molecular docking approach, we sought to find new potential targets of resveratrol. Docking of resveratrol into each ProBiS predicted binding site of >38 000 protein structures from the Protein Data Bank was examined, and a number of novel potential targets into which resveratrol was docked successfully were found. These explain known actions or predict new effects of resveratrol. The results included three human proteins that are already known to bind resveratrol. A majority of proteins discovered however have no already described connections with resveratrol. We report new potential target human proteins and proteins connected with different organisms into which resveratrol can dock. Our results reveal previously unknown potential target human proteins, whose connection with cardiovascular and neurological disorders could lead to new potential treatments for variety of diseases. We believe that our research could help in future experimental studies on revestratol bioactivity in humans.

GenProBiS: web server for mapping of sequence variants to protein binding sites.

Discovery of potentially deleterious sequence variants is important and has wide implications for research and generation of new hypotheses in human and veterinary medicine, and drug discovery. The GenProBiS web server maps sequence variants to protein structures from the Protein Data Bank (PDB), and further to protein-protein, protein-nucleic acid, protein-compound, and protein-metal ion binding sites. The concept of a protein-compound binding site is understood in the broadest sense, which includes glycosylation and other post-translational modification sites. Binding sites were defined by local structural comparisons of whole protein structures using the Protein Binding Sites (ProBiS) algorithm and transposition of ligands from the similar binding sites found to the query protein using the ProBiS-ligands approach with new improvements introduced in GenProBiS. Binding site surfaces were generated as three-dimensional grids encompassing the space occupied by predicted ligands. The server allows intuitive visual exploration of comprehensively mapped variants, such as human somatic mis-sense mutations related to cancer and non-synonymous single nucleotide polymorphisms from 21 species, within the predicted binding sites regions for about 80 000 PDB protein structures using fast WebGL graphics. The GenProBiS web server is open and free to all users at http://genprobis.insilab.org.

Inverse Molecular Docking as a Novel Approach to Study Anticarcinogenic and Anti-Neuroinflammatory Effects of Curcumin.

Research efforts are placing an ever increasing emphasis on identifying signal transduction pathways related to the chemopreventive activity of curcumin. Its anticarcinogenic effects are presumably mediated by the regulation of signaling cascades, including nuclear factor κB (NF-κB), activator protein 1 (AP-1), and mitogen-activated protein kinases (MAPK). By modulating signal transduction pathways, curcumin induces apoptosis in malignant cells, thus inhibiting cancer development and progression. Due to the lack of mechanistic insight in the scientific literature, we developed a novel inverse molecular docking protocol based on the CANDOCK algorithm. For the first time, we performed inverse molecular docking of curcumin into a collection of 13,553 available human protein structures from the Protein Data Bank resulting in prioritized target proteins of curcumin. Our predictions were in agreement with the scientific literature and confirmed that curcumin binds to folate receptor ß, DNA (cytosine-5)-methyltransferase 3A, metalloproteinase-2, mitogen-activated protein kinase 9, epidermal growth factor receptor and apoptosis-inducing factor 1. We also identified new potential protein targets of curcumin, namely deoxycytidine kinase, NAD-dependent protein deacetylase sirtuin-1 and -2, ecto-5'-nucleotidase, core histone macro-H2A.1, tyrosine-protein phosphatase non-receptor type 11, macrophage colony-stimulating factor 1 receptor, GTPase HRas, aflatoxin B1 aldehyde reductase member 3, aldo-keto reductase family 1 member C3, amiloride-sensitive amine oxidase, death-associated protein kinase 2 and tryptophan-tRNA ligase, that may all play a crucial role in its observed anticancer effects. Moreover, our inverse docking results showed that curcumin potentially binds also to the proteins cAMP-specific 3',5'-cyclic phosphodiesterase 4D and 17-ß-hydroxysteroid dehydrogenase type 10, which provides a new explanation for its efficiency in the treatment of Alzheimer's disease. We firmly believe that our computational results will complement and direct future experimental studies on curcumin's anticancer activity as well as on its therapeutic effects against Alzheimer's disease.

Discovery of new MurA inhibitors using induced-fit simulation and docking.

We report on the successful application of ProBiS-CHARMMing web server in the discovery of new inhibitors of MurA, an enzyme that catalyzes the first committed cytoplasmic step of bacterial peptidoglycan synthesis. The available crystal structures of Escherichia coli MurA in the Protein Data Bank have binding sites whose small volume does not permit the docking of drug-like molecules. To prepare the binding site for docking, the ProBiS-CHARMMing web server was used to simulate the induced-fit effect upon ligand binding to MurA, resulting in a larger, more holo-like binding site. The docking of a filtered ZINC compound library to this enlarged binding site was then performed and resulted in three compounds with promising inhibitory potencies against MurA. Compound 1 displayed significant inhibitory potency with IC50 value of 1µM. All three compounds have novel chemical structures, which could be used for further optimization of small-molecule MurA inhibitors.

Identification of Conserved Water Sites in Protein Structures for Drug Design.

Identification of conserved waters in protein structures is a challenging task with applications in molecular docking and protein stability prediction. As an alternative to computationally demanding simulations of proteins in water, experimental cocrystallized waters in the Protein Data Bank (PDB) in combination with a local structure alignment algorithm can be used for reliable prediction of conserved water sites. We developed the ProBiS H2O approach based on the previously developed ProBiS algorithm, which enables identification of conserved water sites in proteins using experimental protein structures from the PDB or a set of custom protein structures available to the user. With a protein structure, a binding site, or an individual water molecule as a query, ProBiS H2O collects similar proteins from the PDB and performs local or binding site-specific superimpositions of the query structure with similar proteins using the ProBiS algorithm. It collects the experimental water molecules from the similar proteins and transposes them to the query protein. Transposed waters are clustered by their mutual proximity, which enables identification of discrete sites in the query protein with high water conservation. ProBiS H2O is a robust and fast new approach that uses existing experimental structural data to identify conserved water sites on the interfaces of protein complexes, for example protein-small molecule interfaces, and elsewhere on the protein structures. It has been successfully validated in several reported proteins in which conserved water molecules were found to play an important role in ligand binding with applications in drug design.

ProBiS-ligands: a web server for prediction of ligands by examination of protein binding sites.

The ProBiS-ligands web server predicts binding of ligands to a protein structure. Starting with a protein structure or binding site, ProBiS-ligands first identifies template proteins in the Protein Data Bank that share similar binding sites. Based on the superimpositions of the query protein and the similar binding sites found, the server then transposes the ligand structures from those sites to the query protein. Such ligand prediction supports many activities, e.g. drug repurposing. The ProBiS-ligands web server, an extension of the ProBiS web server, is open and free to all users at http://probis.cmm.ki.si/ligands.

Nonpeptidic Selective Inhibitors of the Chymotrypsin-Like (ß5 i) Subunit of the Immunoproteasome.

Elevated expression of the immunoproteasome has been associated with autoimmune diseases, inflammatory diseases, and various types of cancer. Selective inhibitors of the immunoproteasome are not only scarce, but also almost entirely restricted to peptide-based compounds. Herein, we describe nonpeptidic reversible inhibitors that selectively block the chymotrypsin-like (ß5i) subunit of the human immunoproteasome in the low micromolar range. The most potent of the reversibly acting compounds were then converted into covalent, irreversible, nonpeptidic inhibitors that retained selectivity for the ß5i subunit. In addition, these inhibitors discriminate between the immunoproteasome and the constitutive proteasome in cell-based assays. Along with their lack of cytotoxicity, these data point to these nonpeptidic compounds being suitable for further investigation as ß5i-selective probes for possible application in noncancer diseases related to the immunoproteasome.

ProBiS-CHARMMing: Web Interface for Prediction and Optimization of Ligands in Protein Binding Sites.

Proteins often exist only as apo structures (unligated) in the Protein Data Bank, with their corresponding holo structures (with ligands) unavailable. However, apoproteins may not represent the amino-acid residue arrangement upon ligand binding well, which is especially problematic for molecular docking. We developed the ProBiS-CHARMMing web interface by connecting the ProBiS ( http://probis.cmm.ki.si ) and CHARMMing ( http://www.charmming.org ) web servers into one functional unit that enables prediction of protein-ligand complexes and allows for their geometry optimization and interaction energy calculation. The ProBiS web server predicts ligands (small compounds, proteins, nucleic acids, and single-atom ligands) that may bind to a query protein. This is achieved by comparing its surface structure against a nonredundant database of protein structures and finding those that have binding sites similar to that of the query protein. Existing ligands found in the similar binding sites are then transposed to the query according to predictions from ProBiS. The CHARMMing web server enables, among other things, minimization and potential energy calculation for a wide variety of biomolecular systems, and it is used here to optimize the geometry of the predicted protein-ligand complex structures using the CHARMM force field and to calculate their interaction energies with the corresponding query proteins. We show how ProBiS-CHARMMing can be used to predict ligands and their poses for a particular binding site, and minimize the predicted protein-ligand complexes to obtain representations of holoproteins. The ProBiS-CHARMMing web interface is freely available for academic users at http://probis.nih.gov.

LiSiCA: A Software for Ligand-Based Virtual Screening and Its Application for the Discovery of Butyrylcholinesterase Inhibitors.

We developed LiSiCA (ligand similarity using clique algorithm)--ligand-based virtual screening software that uses a fast maximum clique algorithm to find two- and three-dimensional similarities between pairs of molecules and applied it to the discovery of novel potent butyrylcholinesterase inhibitors. LiSiCA, which runs in parallel on multiple processor cores, was successfully tested on the Database of Useful Decoys-Enhanced, to evaluate its ability to discriminate active molecules from decoys. We then applied LiSiCA for the discovery of novel inhibitors of human butyrylcholinesterase, a promising anti-Alzheimer target, using a known inhibitor as the reference compound. We demonstrated that LiSiCA is capable of finding potent nanomolar inhibitors, whose scaffolds differed from the reference compound, thus proving its ability for scaffold hopping and usefulness in drug discovery.

Molecular dynamics to enhance structure-based virtual screening on cathepsin B.

Molecular dynamics (MD) and molecular docking are commonly used to study molecular interactions in drug discovery. Most docking approaches consider proteins as rigid, which can decrease the accuracy of predicted docked poses. Therefore MD simulations can be used prior to docking to add flexibility to proteins. We evaluated the contribution of using MD together with docking in a docking study on human cathepsin B, a well-studied protein involved in numerous pathological processes. Using CHARMM biomolecular simulation program and AutoDock Vina molecular docking program, we found, that short MD simulations significantly improved molecular docking. Our results, expressed with the area under the receiver operating characteristic curves, show an increase in discriminatory power i.e. the ability to discriminate active from inactive compounds of molecular docking, when docking is performed to selected snapshots from MD simulations.

Structure-based function prediction of uncharacterized protein using binding sites comparison.

A challenge in structural genomics is prediction of the function of uncharacterized proteins. When proteins cannot be related to other proteins of known activity, identification of function based on sequence or structural homology is impossible and in such cases it would be useful to assess structurally conserved binding sites in connection with the protein's function. In this paper, we propose the function of a protein of unknown activity, the Tm1631 protein from Thermotoga maritima, by comparing its predicted binding site to a library containing thousands of candidate structures. The comparison revealed numerous similarities with nucleotide binding sites including specifically, a DNA-binding site of endonuclease IV. We constructed a model of this Tm1631 protein with a DNA-ligand from the newly found similar binding site using ProBiS, and validated this model by molecular dynamics. The interactions predicted by the Tm1631-DNA model corresponded to those known to be important in endonuclease IV-DNA complex model and the corresponding binding free energies, calculated from these models were in close agreement. We thus propose that Tm1631 is a DNA binding enzyme with endonuclease activity that recognizes DNA lesions in which at least two consecutive nucleotides are unpaired. Our approach is general, and can be applied to any protein of unknown function. It might also be useful to guide experimental determination of function of uncharacterized proteins.

ProBiS-2012: web server and web services for detection of structurally similar binding sites in proteins.

The ProBiS web server is a web server for detection of structurally similar binding sites in the PDB and for local pairwise alignment of protein structures. In this article, we present a new version of the ProBiS web server that is 10 times faster than earlier versions, due to the efficient parallelization of the ProBiS algorithm, which now allows significantly faster comparison of a protein query against the PDB and reduces the calculation time for scanning the entire PDB from hours to minutes. It also features new web services, and an improved user interface. In addition, the new web server is united with the ProBiS-Database and thus provides instant access to pre-calculated protein similarity profiles for over 29 000 non-redundant protein structures. The ProBiS web server is particularly adept at detection of secondary binding sites in proteins. It is freely available at http://probis.cmm.ki.si/old-version, and the new ProBiS web server is at http://probis.cmm.ki.si.