Dissolution dynamic nuclear polarization (DNP) provides several orders of magnitude of NMR signal enhancement by converting the much larger electron spin polarization to nuclear spin polarization. Polarization occurs at low temperature (1.4K) and is followed by quickly dissolving the sample for room temperature NMR detection. DNP is generally applicable to almost any small molecules and can polarize various nuclei including 1H, 19F and 13C. The large signal from DNP enhancement reduces the limit of detection to micromolar or sub-micromolar concentration in a single scan. Since DNP enhancement often provides the only source for the observable signal, it enables tracking of the polarization flow. Therefore, DNP is ideal for studying chemical processes. Here, quantitative tools are developed to separate kinetics and spin relaxation, as well as to obtain structural information from these measurements. Techniques needed for analyzing DNP polarized sample are different from those used in conventional NMR because a large, yet non-renewable hyperpolarization is available. Using small flip angle pulse excitation, the hyperpolarization can still be divided into multiple scans. Based on this principle, a scheme is presented that allows reconstruction of indirect spectral dimensions similarly to conventional 2D NMR. Additionally, small flip angle pulses can be used to obtain a succession of scans separated in time. A model describing the combined effects of the evolution of a chemical process and of spin-lattice relaxation is shown. Applied to a Diels-Alder reaction, it permitted measuring kinetics along with the effects of auto- and cross-relaxation. DNP polarization of small molecules also shows significant promise for studying protein-ligand interaction. The binding of fluorinated ligands to the protease trypsin was studied through the observation of various NMR parameter changes, such as line width, signal intensity and chemical shift of the ligands. Intermolecular polarization transfer from hyperpolarized ligand to protein can further provide information about the binding pocket of the protein. As an alternative to direct observation of protein signal, a model is presented to describe a two-step intermolecular polarization transfer between competitively binding ligands mediated through the common binding pocket of the protein. The solutions of this model relate the evolution of signal intensities to the intermolecular cross relaxation rates, which depend on individual distances in the binding epitope. In summary, DNP provides incomparable sensitivity, speed and selectivity to NMR. Quantitative models such as those discussed here enable taking full advantage of these benefits for the study of chemical processes.

Protein-ligand and protein-protein interactions are critical to cellular function. Most cellular metabolic and signal tranduction pathways are influenced by these interactions, consequently molecular level understanding of these associations is an important area of biochemical research. We have examined the thermodynamics of several protein-protein associations and the protein-ligand interactions that mediate them. Using Fluorescence Correlation Spectroscopy, we have examined the putative interaction between pig heart malate dehydrogenase (MDH) and citrate synthase (CTS). We demonstrate a specific, low-affinity interaction between these enzymes. The association is highly polyethylene glycol (PEG)-dependent, and at high concentrations of NaCl or PEG, non-specific aggregates are formed. We demonstrate that oxaloacetate, the intermediate common to both CTS and MDH, induces the association at concentrations below the Km of CTS, suggesting that the open conformation of CTS is involved in the association. Using several biophysical techniques, we have examined the subunit associations of B. stearothermophilus phosphofructokinase (PFK). We demonstrate that the inhibitor bound conformation of the enzyme has reduced subunit affinity. The kinetics and thermodynamics of the phosphoenolpyrvuate (PEP)-induced dissociation of PFK have been quantified. Binding substrate, fructose-6-phosphate (F6P), stabilizes the enzyme to inhibitor-induced dissociation by 132-fold. These data suggest that subunit associations may play a role in the allosteric inhibition of PFK by PEP. The thermodynamics of the protein-ligand associations and allosteric inhibition of E. coli phosphofructokinase have been examined using intrinsic fluorescence and hydrostatic pressure. Both ligand-binding affinity and PEP inhibition are diminished by pressure, whereas substrate-binding affinity for inhibitor-bound enzyme is pressure-insensitive. Larger entropic than enthalpic changes with pressure lead to the overall reduction in free energies. Using a fluorescence-based assay, we have developed a series of baroresistant buffer mixtures. By combining a buffer with acid dissociation of negative volume with a buffer of positive volume, a pressure-resistant mixture is produced. Alteration of the molar ratio of the two component buffers yields mixtures that are pressure-insensitive at pH values around neutrality.

Because bimolecular interactions in water are poorly understood, three tactics commonly used to improve binding affinity in ligand design were investigated: (1) increasing apolar surface area, (2) introducing a conformational constraint, and (3) targeting a cation-[pi] interaction. Thermodynamic parameters of binding ligands to the Grb2 SH2 domain were determined by isothermal titration calorimetry (ITC), and structural data was obtained by X-ray crystallography. The apolar surface area of the pTyr+1 residue in Ac-pTyr-Acnc-Asn-NH₂ was varied by incrementally increasing the size of the cyclic Acnc residue from a 3-membered to a 7-membered ring. Increasing apolar surface area resulted in an increase in Ka due to a more favorable [delta]H⁰ that was dominated a less favorable [delta]S⁰. Structural analyses showed that all ligands bound in a similar mode, so differences in binding thermodynamics were attributed to the pTyr+1 residue. The thermodynamics of binding tripeptides wherein pTyr+1 was an n-alkyl group were studied. Ka increased when Ala was mutated to Abu, but additional methylene groups had no effect on Ka due to strong entropy-enthalpy compensation. While [delta]H⁰ was weakly correlated with buried surface area, there was no change in [delta]H⁰ between one methylene and two methylene groups, presumably because an enthalpic penalty is associated with a gauche interaction between C-[beta] and C-[gamma] of the Xaa side chain that was noted in the crystal structure. An olefin was installed in an attempt to alleviate the energetic penalty incurred from the gauche interaction, but the introduction of the constraint resulted in equipotent ligands. A putative cation-[pi] interaction between Arg67 and various aromatic groups was probed by varying the [pi]-donating capability of groups attached to a tripeptide scaffold. Although crystal structures demonstrated that three of the aryl groups were close enough to Arg67 to form a cation-[pi] interaction, only a modest increase in Ka was observed relative to analogues having only an N-acetyl group. Furthermore, a simple cyclohexyl group in place of aryl groups resulted in ligands that were equipotent with indolyl- and phenyl- derived analogues, so any cation-[pi] interaction is not significant. / text

The development and approval of new drugs is an expensive process. The total cost for the approval of a new compound is on average 1.0 - 1.2 billion dollars and the entire process lasts about 12 - 15 years. The main difficulties are related to poor pharmacokinetics, lack of efficacy and unwanted side effects. These problems have naturally led to the question if new and alternative methodologies can be developed to find reliable and low cost alternatives to existing practices. Nowadays, computer-assisted tools are used to support the decision process along the early stages of the drug discovery path leading from the identification of a suitable biomolecular target to the design/optimization of drug-like molecules. This process includes assessments about target druggability, screening of molecular libraries and the optimization of lead compounds where new drug-like molecules able to bind with sufficiently affinity and specificity to a disease-involved protein are designed. Existing computational methods used by the pharmaceutical industry are usually focused on the screening of library compounds such as docking, chemoinformatics and other ligand-based methods to predict and improve binding affinities, but their reliable application requires improvements in accuracy. New quantitative methods based on molecular simulations of drug binding to a protein could greatly improve prospects for the reliable in-silico design of new potent drug candidates. A common parameter used by medicinal chemists to quantify the affinity between candidate ligands and a target protein is represented by the free energy of binding. However, despite the increased amount of structural information, predicting binding free energy is still a challenge and this technique has found limited use beyond academia. A major reason for limited adoption in the industry is that reliable computer models of drug binding to a protein must reproduce the change in molecular conformations of the drug and protein upon complex formation and this includes the correct modelling of weak non-covalent interactions such as hydrogen bonds, burials of hydrophobic surface areas, Van der Waals interactions, fixations of molecular degrees of freedom solvation/desolvation of polar groups and different entropy contributions related to the solvent and protein interactions. For several classes of proteins these phenomena are not easy to model and often require extremely computationally intensive simulations. The main goal of the thesis was to explore efficient ways of computing binding affinities by using molecular simulations. With this aim, novel software to compute relative binding free energies has been developed. The implementation is based on alchemical transformations and it extended a preexisted piece of software Sire, a molecular modeling framework, by using the OpenMM APIs to run fast molecular dynamics simulations on the latest GPGPU technology. This new piece of software has equipped the scientific community with a flexible and fast tool, not only to predict relative binding affinities, but also a starting point to develop new sampling methods for instance hybrid molecular dynamics and Monte Carlo. The implementation has been validated on the prediction of relative hydration free energy of small molecules, showing good agreement with experimental data. In addition, non-additive effects to binding affinities in series of congeneric Thrombin inhibitors were investigated. Although excellent agreement between predicted and experimental relative binding affinities was achieved, it was not possible to accurately predict the non-additivity levels in most of the examined inhibitors, thus suggesting that improved force fields are required to further advance the state-of-the art of the field.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第16886号 / 工博第3607号 / 新制||工||1545(附属図書館) / 29561 / 京都大学大学院工学研究科合成・生物化学専攻 / (主査)教授 濵地 格, 教授 森 泰生, 教授 白川 昌宏 / 学位規則第4条第1項該当

Accurate prediction of binding free energies of protein-ligand system has long been a focus area for theoretical and computational studies; with important implications in fields like pharmaceuticals, enzyme-redesign, etc. The aim of this project was to develop such a predictive model for calculating binding free energies of protein-ligand systems based on the LIE-SASA methods. Many models have been successfully fit to experimental data, but a general predictive model, not reliant on experimental values, would make LIE-SASA a more powerful and widely applicable method. The model was developed such that There is no significant increase in computational time No increase in complexity of system setup No increase in the number of empirical parameters. The method was tested on a small number of protein-ligand systems, selected with certain constraints. This was our training set, from which we obtain the complete expression for binding free energy. Expectedly, there was good agreement with experimental values for the training set On applying our model to a similar sized validation set, with the same selection constraints as for the training set, we achieved even better agreement with experimental results, with lower standard errors. Finally, the model was tested by applying it to a set of systems without such selection constraints, and again found good agreement with experimental values. In terms of accuracy, the model was comparable to a system specific empirical fit that was performed on this set. These encouraging results could be an indicator of generality.

Conformationally constrained ligands and their flexible analogues were prepared as inhibitors of the Grb2 SH2 domain in order to study the structural and energetic effects of ligand preorganization in protein-ligand interactions. The compounds were prepared by using trans-cyclopropane-containing amino acid mimics, macrocyclization, or [alpha,alpha]-disubstituted amino acid residues. All trans-cyclopropane containing peptides were more potent than their corresponding succinate containing analogues due to an enthalpic advantage. Surprisingly, the binding of constrained peptides to the domain was entropically disfavored relative to their flexible controls. Effects of proton transfer and desolvation as being the source of the unprecedented entropic penalty for the constrained ligands relative to their respective controls were precluded, and X-ray crystallographic studies revealed that the binding conformations for the respective cyclopropane and succinate containing ligands were similar. This led us to believe that differential changes in protein dynamics may occur upon binding of the constrained and flexible ligands, which could contribute to the observed binding energetics. Two 23-membered macrocyclic ligands were slightly more potent than their corresponding linear controls. The amino acids used to link the N- and C-termini of the linear peptides to form the macrocycles were found to affect the energetics of binding. In one case, the 23-membered macrocycle was more potent than its control due to an entropic advantage, whereas the other 23-membered macrocycle was more potent than its control because it benefited from an enthalpic advantage. [alpha,alpha]-Disubstituted and [alpha]-monosubstituted residues that varied in hydrophobic character were incorporated into Grb2 SH2 domain binding tripeptides, and binding became more favorable as nonpolar surface area increased only for the set of tripeptides possessing cyclic [alpha,alpha]-disubstituted residues. The increase in affinity was due to an increasing enthalplic term, whereas the entropy of binding became less favorable. A total synthesis of (±)-lycopladine A was achieved in five steps from known compounds. The tricyclic core of the natural product was prepared utilizing a novel two-step sequence comprising a conjugate addition of a metalated picoline derivative followed by an intramolecular enolate arylation. It was demonstrated that the natural product existed in a solvent dependent equilibrium with its isomeric lactol. / text

Accurate, timely replication of a DNA molecule is a pivotal moment in the life cycle of every living organism. Any temporal or spatial defect putting the fine-tuned replication machinery off balance causes the so-called replication stress. As the replication machinery consists mainly of enzymes and other proteins, it is not surprising that many of the obstacles most severely blocking the replication machinery progress are of protein origin. Therefore, specialized proteases responsible for relieving replication stress matured during evolution. However, neither the full repertoire of proteolytic enzymes and their particular substrates taking place in countering the DNA replication stress nor detailed molecular mechanisms involved remain unknown. This thesis describes how conserved putative aspartic proteases of the Ddi1-like family engage in countering DNA replication stress via a proteolysis dependent mechanism. We structurally and biophysically characterized yeast and human members of the Ddi1-like family, explored their interactions with ubiquitin and polyubiquitin chains, and identified hypersensitivity to DNA replication inhibitor hydroxyurea in a yeast strain double deleted for DDI1 gene together with a DNA dependent metalloprotease WSS1. Detailed analysis of the DDI1 role in hydroxyurea...

BIOLOGICAL processes are governed through specific interactions of macromolecules. The three-dimensional structural information of the macromolecules is necessary to understand the basis of molecular recognition. A large number of protein structures have been determined at a high resolution using various experimental techniques such as X-ray crystallography, NMR, electron microscopy and made publicly available through the Protein Data Bank. In the recent years, comprehending function by studying a large number of related proteins is proving to be very fruitful for understanding their biological role and gaining mechanistic insights into molecular recognition. Availability of large-scale structural data has indeed made this task of predicting the protein function from three-dimensional structure, feasible. Structural bioinformatics, a branch of bioinformatics, has evolved into a separate discipline to rationalize and classify the information present in three-dimensional structures and derive meaningful biological insights. This has provided a better understanding of biological processes at a higher resolution in several cases. Most of the structural bioinformatics approaches so far, have focused on fold-level analysis of proteins and their relationship to sequences. It has long been recognized that sequence-fold or fold-function relationships are highly complex. Information on one aspect cannot be readily extrapolated to the other. To a significant extent, this can be overcome by understanding similarities in proteins by comparing their binding site structures. In this thesis, the primary focus is on analyzing the small-molecule ligand binding sites in protein structures, as most of the biological processes ranging from enzyme catalysis to complex signaling cascades are mediated through protein-ligand interactions. Moreover, given that the precise geometry and the chemical properties of the residues at the ligand binding sites dictate the molecular recognition capabilities, focusing on these sites at the structural level, is likely to yield more direct insights on protein function. The study of binding sites at the structural level poses several problems mainly because the residues at the site may be sequentially discontinuous but spatially proximal. Further, the order of the binding site residues in primary sequence, in most of cases has no significance for ligand binding. Compounding these difficulties are additional factors such as, non-uniform contribution to binding from different residues, and size-variations in binding sites even across closely related proteins. As a result, methods available to study ligand-binding sites in proteins, especially on a large-scale are limited, warranting exploration of new approaches. In the present work, new methods and tools have been developed to address some of these challenges in binding site analysis. First, a novel tool for site-based function annotation of protein structures, called PocketAnnotate was developed ( http://proline.biochem.iisc.ernet. in/pocketannotate/). PocketAnnotate, detects the putative binding sites from a given protein structure and compares them to known binding sites in PDB to derive functional annotation in terms of ligand association. Since the tool derives functional annotation at the level of binding sites, it has an advantage over other methods that solely utilize fold or sequence information. This becomes even more important for cases where there is no detectable homology with entries in existing databases, as Pocket Annotate does not depend on evolutionary based information for annotation. Second, a web-accessible tool for in silico almandine scanning mutations of binding site residues called ABS-Scan has been developed ( http://proline.biochem.iisc.ernet.in/abscan/). This tool helps in assessing the contribution of the individual residues of binding sites in the protein towards ligand recognition. All residues, one at a time, in a binding site are mutated systematically to an alanine and the ability of the corresponding mutant to bind a given ligand is analyzed. The contribution of each residue towards ligand binding is calculated through a G value derived by comparing the binding affinity to the wild-type protein-ligand complex. Third, a database called Protein-Ligand Interaction Clusters (PLIC) has been developed to identify and analyze the information of similarity across binding sites in PDB, which has been provided in the form of a web-accessible database ( http://proline.biochem.iisc.ernet/ PLIC). Protein-ligand interactions are primarily explored using three different computational approaches - (i) binding site characteristics including pocket shape, nature of residues and interaction profiles with different kinds of chemical probes, (ii) atomic contacts between protein and ligands (iii) binding energetics involved in interactions derived from scoring functions developed for docking. The information on variations in these features derived from different computational tools is also included in the database for enabling the characterization of the binding sites. As a case study to demonstrate the usefulness of these tools, they have been applied to decipher the complexity of S-adenosyl methionine interactions with the protein. Around 1,213 binding sites of SAM or SAM-like compounds could be extracted from the PLIC database. The SAM or SAM-like compounds were observed to interact with ∼18 different protein-fold types. The variations in different protein-ligand contacts across fold types were analyzed. The fold-specific interaction properties and contribution of individual residues towards SAM binding are identified. The tools developed and example analyses using them are described in Chapter 2. Chapter 3 describes a large-scale pocketome analysis from structural complexes in PDB, in an effort to characterize the known pocket space of protein-ligand interactions. Tools devel-opted as described in Chapter 2 are used for this. A set of 84,846 binding sites compiled from PDB, have been comprehensively analyzed with an objective of obtaining (a) classification of binding sites, (b) sequence-fold-site relationships among proteins, (c) a minimal set of physicochemical attributes sufficient to explain ligand recognition specificity and (d) site-type specific signatures in terms of physicochemical features. A new method to describe binding sites was developed in the form of BScIds such that the structural fold information is well captured. Binding sites and similarities among them were abstracted in the form of networks where each node represents a binding site and an edge between two nodes represents significant similarity between the sites at the structural level. Pocketome networks were constructed from the large-scale information on protein-ligand interactions in the PLIC database. The large pocketome network was then studied to derive relationships between protein folds and chemical entities they interact with. A classification of the binding pockets was achieved by analyzing the pocketome network using graph theoretical approaches combined with clustering methods. 10,858 clusters were identified from the network, each indicating a site-type. Thus, it can be said that there are about 10,858 site-types. Classification of ligand associations into specific site-types helps greatly in resolving the complex relationships by yielding specific site-type ligand associations. The observed classification was further probed to understand the basis of ligand recognition by representing the pockets through feature vectors. These features capture a wide range of physicochemical properties that can be used to derive site-type specific signatures and explore the pocket-space of protein-ligand interactions. A principal component analysis of these features reveals that binding site feature space is continuous in the entire PDB and minor changes in specific features can give rise to significant differences in ligand specificity, consequently defining their distinct functional roles. The weights were also derived for these features through the use of different information theoretic approaches to explain the multiple-specificity of protein-ligand interactions. Analysis of binding sites arising from contribution of residues from different protein fold-types revealed increasing diversity of physicochemical properties at the site, supporting the hypothesis that combination of folds could give rise to new binding sites. Given that a finer appreciation of the molecular mechanisms within the cell is possible only with the structural information, the next objective was to explore if a structural view of an entire proteome can be obtained and if a pocketome could be constructed and analyzed. With this in mind, the causative agent of tuberculosis - Mycobacterium tuberculosis (Mtb) was chosen. Mtb is also being studied in the laboratory from a systems biology perspective, which enabled exploration of how systems and the structural perspectives could be combined and applied for drug discovery. Chapters 4 to 6 describe this effort. The genome sequence of Mycobacterium tuberculosis (Mtb) H37Rv, indicates the presence of ∼4,000 protein coding genes, of which experimentally determined structures are available for ∼300 proteins. Further, advances in homology modeling methods have made it feasible to obtain structural models for many more proteins in the proteome. Chapter 4 describes the efforts for obtaining the Mtb structural proteome, through which the three-dimensional struc-tures were derived for ∼70% of the proteins in the genome. Functional annotation of each protein was derived based on fold-based functional assignments, binding-site comparisons and consequent ligand associations. PocketAnnotate, a site-based function annotation pipeline was utilized for this purpose and is described in Chapter 2. Besides these, the annotation covers detection of various sequence and sub-structural motifs and quaternary structure predictions based on the corresponding templates. The study provides a unique opportunity to obtain a global perspective of the fold distribution in the genome. The annotation indicates that cellular metabolism can be achieved with only 219 unique folds. New insights about the folds that predominate in the genome, as well as the fold-combinations that make up multi-domain proteins are also obtained. 1,728 binding pockets have been associated with ligands through binding site identification and sub-structure similarity analyses, yielding a list of ligands that can participate in various biochemical events in the mycobacterial cell. A web-accessible database MtbStructuralproteome has been developed to make the data and the analyses available to the community, ( http://proline.physics.iisc.ernet.in/Tbstructuralannotation). The resource, being one of the first to be based on structure-based functional annotations at a genome scale, is expected to be useful for better understanding of tuberculosis and for application in drug discovery. The reported annotation pipeline is fairly generic and can be applied to other genomes as well. Chapter 5 describes the characterization of the Mtb pocketome. For the structural models of the Mtb proteome described in chapter 4, a genome-scale binding site prediction exercise was carried out using three different computational methods and subsequently obtaining consensus predictions. The three methods were independent and were based on considering geometry, inter-molecular energies with probes and sequence conservations in evolutionarily related proteins respectively. In all, 13,858 consensus binding pockets were predicted in 2,877 proteins. The pocket space within Mtb was then explored through systematic all-pair comparisons of binding sites. The number of site-types within Mtb was found to be 6,584, as compared to the ∼400 structural folds and 1,831 unique sequence families. This reveals that the pocket space is larger than the sequence or fold-space, suggesting that variations at the site-level contribute significantly to functional repertoire of the organism. By comparing the pockets with the PDB sites enclosing known ligands, around 6906 binding sites were observed to exhibit significant similarity in the entire pockets to some or the other known binding site in PDB. 1,213 metabolites could be mapped onto 665 enzymes covering most of the metabolic pathways. The identified ligands serve as a predicted metabolome for unit abundances of the proteins. A list of proteins containing unique pockets is also identified. The binding pockets, similarities they share within Mtb and the ligands mapped onto them are all made available in a web-accessible database at http://proline.biochem.iisc.ernet.in/mtbpocketome/. The availability of structural information of the pocketome at a genome-scale opens up several opportunities in drug discovery. They can be directly applied for understanding mechanism of drug action, predicting adverse effects and pharmacodynamics of a drug. Moreover, it enables exploration of new ideas in drug discovery. Polypharmacology is a new concept that aims at modulating multiple drug targets through a single chemical entity. Currently, there are no established approaches to either select appropriate target sets or design polypharmacological drugs. In this study, a structural-proteomics approach is explored to first characterize the pocketome and then utilize it to identify similar binding sites. The knowledge of similarity relationships between the binding sites within the genome can be used in identifying possible polypharmacological drug targets. A pocket similarity based clustering of binding site residues resulted in identification of binding site sets, each having a theoretical potential to interact with a common ligand. A polypharmacological index was formulated to rank targets by incorporating a measure of drug ability and similarity to other pockets within the proteome. By comparing with known drug binding sites from databases such as the Drug Bank, the study has yielded a ready shortlist that includes sets of promising drug targets with polypharmacological possibilities and at the same time has identified possible drug candidates either directly for repurposing or at the least as significant lead clues that can be used to design new drug molecules against the entire group of proteins in each set. This analysis presents a rational approach to identify targets with polypharmacological potential, clues about lead compounds and a list of candidates for drug repurposing. This thesis demonstrates the feasibility of utilizing the structural bioinformatics approaches at a genome-scale. The tools developed for analyzing large-scale data on protein-ligand inter-actions could be applied to characterize the pocket-space of protein-ligand interactions. The network theory approaches applied in this work, make large-scale data tractable and enable binding-site typing. The binding site analysis at a genome-scale for Mtb is first of its kind and has provided novel insights into the pocket space. The binding site analysis performed on a genome-scale for Mtb provided an opportunity to rationalize the polypharmacological target selection and explore drugs for repurposing in TB. In the larger context, structural modelling of a proteome, mapping the small-molecule binding space in it and understanding the determinants of small-molecule recognition forms a major step in defining a proteome at higher resolution. This in turn will serve as a valuable input towards the emerging field of structural-systems biology, which seeks to understand the biological models at a systems level without compromising on the resolution of the study.

Cancer is a leading cause of death worldwide. Development of new cancer drugs is increasingly costly and time-consuming. By exploiting massive amounts of biological data, computational repositioning proposes new uses for old drugs to reduce these development hurdles. A promising approach is the systematic analysis of structural data for identification of shared binding pockets and modes of action. In this thesis, I developed the Protein-Ligand Interaction Profiler (PLIP), which characterizes and indexes protein-ligand interactions to enable comparative analyses and searching in all available structures. Following, I applied PLIP to identify new treatment options in cancer: the heat shock protein Hsp27 confers resistance to drugs in cancer cells and is therefore an attractive target with a postulated drug binding site. Starting from Hsp27, I used PLIP to define an interaction profile to screen all structures from the Protein Data Bank (PDB). The top prediction was experimentally validated in vitro. It inhibits Hsp27 and significantly reduces resistance of multiple myeloma cells against the chemotherapeutic agent bortezomib. Besides computational repositioning, PLIP is used in docking, binding mode analysis, quantification of interactions and many other applications as evidenced by over 12,000 users so far. PLIP is provided to the community online and as open source.