Protein clefts in molecular recognition and function

ANN based prediction of ligand binding sites outside deep cavities to facilitate drug designing

The ever-changing environmental conditions and pollution are the prime reasons for the onset of several emerging and re-merging diseases. This demands the faster designing of new drugs to curb the deadly diseases in less waiting time to cure the animals and humans. Drug molecules interact with only protein surface on specific locations termed as ligand binding sites (LBS). Therefore, the knowledge of LBS is required for rational drug designing. Existing geometrical LBS prediction methods rely on search of cavities based on the fact that 83% of the LBS found in deep cavities, however, these methods usually fail where LBS localize outside deep cavities. To overcome this challenge, the present work provides an artificial neural network (ANN) based method to predict LBS outside deep cavities in animal proteins including human to facilitate drug designing. In the present work a feed-forward backpropagation neural network was trained by utilizing 38 structural, atomic, physiochemical, and evolutionary discriminant features of LBS and non-LBS residues localized in the extracted roughest patch on protein surface. The performance of this ANN based prediction method was found 76% better for those proteins where cavity subspace (extracted by MetaPocket 2.0, a consensus method) failed to predict LBS due to their localization outside the deep cavities. The prediction of LBS outside deep cavities will facilitate in drug designing for the proteins where it is not possible due to lack of LBS information as the geometrical LBS prediction methods rely on extraction of deep cavities.

Colabind: A Cloud-Based Approach for Prediction of Binding Sites Using Coarse-Grained Simulations with Molecular Probes

Binding site prediction is a crucial step in understanding protein-ligand and protein-protein interactions (PPIs) with broad implications in drug discovery and bioinformatics. This study introduces Colabind, a robust, versatile, and user-friendly cloud-based approach that employs coarse-grained molecular dynamics simulations in the presence of molecular probes, mimicking fragments of drug-like compounds. Our method has demonstrated high effectiveness when validated across a diverse range of biological targets spanning various protein classes, successfully identifying orthosteric binding sites, as well as known druggable allosteric or PPI sites, in both experimentally determined and AI-predicted protein structures, consistently placing them among the top-ranked sites. Furthermore, we suggest that careful inspection of the identified regions with a high affinity for specific probes can provide valuable insights for the development of pharmacophore hypotheses. The approach is available at https://github.com/porekhov/CG_probeMD.

AP-2δ Is the Most Relevant Target of AP-2 Family-Focused Cancer Therapy and Affects Genome Organization

Formerly hailed as "undruggable" proteins, transcription factors (TFs) are now under investigation for targeted therapy. In cancer, this may alter, inter alia, immune evasion or replicative immortality, which are implicated in genome organization, a process that accompanies multi-step tumorigenesis and which frequently develops in a non-random manner. Still, targeting-related research on some TFs is scarce, e.g., among AP-2 proteins, which are known for their altered functionality in cancer and prognostic importance. Using public repositories, bioinformatics tools, and RNA-seq data, the present study examined the ligandability of all AP-2 members, selecting the best one, which was investigated in terms of mutations, targets, co-activators, correlated genes, and impact on genome organization. AP-2 proteins were found to have the conserved "TF_AP-2" domain, but manifested different binding characteristics and evolution. Among them, AP-2δ has not only the highest number of post-translational modifications and extended strands but also contains a specific histidine-rich region and cleft that can receive a ligand. Uterine, colon, lung, and stomach tumors are most susceptible to AP-2δ mutations, which also co-depend with cancer hallmark genes and drug targets. Considering AP-2δ targets, some of them were located proximally in the spatial genome or served as co-factors of the genes regulated by AP-2δ. Correlation and functional analyses suggested that AP-2δ affects various processes, including genome organization, via its targets; this has been eventually verified in lung adenocarcinoma using expression and immunohistochemistry data of chromosomal conformation-related genes. In conclusion, AP-2δ affects chromosomal conformation and is the most appropriate target for cancer therapy focused on the AP-2 family.

Towards rational computational peptide design

Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available-and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.

Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase

The therapeutic challenges pertaining to leishmaniasis due to reported chemoresistance and toxicity necessitate the need to explore novel pathways to identify plausible inhibitory molecules. Leishmania donovani 24-sterol methyltransferase (LdSMT) is vital for the synthesis of ergosterols, the main constituents of Leishmania cellular membranes. So far, mammals have not been shown to possess SMT or ergosterols, making the pathway a prime candidate for drug discovery. The structural model of LdSMT was elucidated using homology modeling to identify potential novel 24-SMT inhibitors via virtual screening, scaffold hopping, and de-novo fragment-based design. Altogether, six potential novel inhibitors were identified with binding energies ranging from −7.0 to −8.4 kcal/mol with e-LEA3D using 22,26-azasterol and S1–S4 obtained from scaffold hopping via the ChEMBL, DrugBank, PubChem, ChemSpider, and ZINC15 databases. These ligands showed comparable binding energy to 22,26-azasterol (−7.6 kcal/mol), the main inhibitor of LdSMT. Moreover, all the compounds had plausible ligand efficiency-dependent lipophilicity (LELP) scores above 3. The binding mechanism identified Tyr92 to be critical for binding, and this was corroborated via molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) calculations. The ligand A1 was predicted to possess antileishmanial properties with a probability of activity (Pa) of 0.362 and a probability of inactivity (Pi) of 0.066, while A5 and A6 possessed dermatological properties with Pa values of 0.205 and 0.249 and Pi values of 0.162 and 0.120, respectively. Structural similarity search via DrugBank identified vabicaserin, daledalin, zanapezil, imipramine, and cefradine with antileishmanial properties suggesting that the de-novo compounds could be explored as potential antileishmanial agents.

Structural analysis of M1AP variants associated with severely impaired spermatogenesis causing male infertility

Background

Impaired meiosis can result in absence of sperm in the seminal fluid. This condition, namely non-obstructive azoospermia (NOA), is one of the reasons of male infertility. Despite the low number of studies on meiosis 1-associated protein (M1AP) in the literature, M1AP is known to be crucial for spermatogenesis. Recently, seven variants (five missense, one frameshift, one splice-site) have been reported in the M1AP gene as associated with NOA, cryptozoospermia and oligozoospermia in two separate studies. However, all missense variants were evaluated as variant of uncertain significance by these studies. Therefore, we aimed to analyze their structural impacts on the M1AP protein that could lead to NOA.

Methods

We firstly performed an evolutionary conservation analysis for the variant positions. Afterwards, a comprehensive molecular modelling study was performed for the M1AP structure. By utilizing this model, protein dynamics were sampled for the wild-type and variants by performing molecular dynamics (MD) simulations.

Results

All variant positions are highly conserved, indicating that they are potentially important for function. In MD simulations, none of the variants led to a general misfolding or loss of stability in the protein structure, but they did cause severe modifications in the conformational dynamics of M1AP, particularly through changes in local interactions affecting flexibility, hinge and secondary structure.

Conclusions

Due to critical perturbations in protein dynamics, we propose that these variants may cause NOA by affecting important interactions regulating meiosis, particularly in wild-type M1AP deficiency since the variants are reported to be homozygous or bi-allelic in the infertile individuals. Our results provided reasonable insights about the M1AP structure and the effects of the variants to the structure and dynamics, which should be further investigated by experimental studies to validate.

Identification of potent inhibitors of ATP synthase subunit c (AtpE) from Mycobacterium tuberculosis using in silico approach

ATP synthase subunit c (AtpE) is an enzyme that catalyzes the production of ATP from ADP in the presence of sodium or proton gradient from Mycobacterium tuberculosis (MTB). This enzyme considered an essential target for drug design and shares the same pathway with the target of Isoniazid. Thus, this enzyme would serve as an alternative target of the Isoniazid. The three dimensional (3D) model structure of the AtpE was constructed based on the principle of homology modeling using the Modeller9.16. The developed model was subjected to energy minimization and refinement using molecular dynamic (MD) simulation. The minimized model structure was searched against Zinc and PubChem database to determine ligands that bind to the enzyme with minimum binding energy using RASPD and PyRx tool. A total of 4776 compounds capable of bindings to AtpE with minimum binding energy were selected. These compounds further screened for physicochemical properties (Lipinski rule of five). All the compounds that possessed the desirable property selected and used for molecular docking analysis. Five (5) compounds with minimum binding energies ranged between ─8.69, and ─8.44 kcal/mol, less than the free binding energy of ATP were selected. These compounds further screened for the absorption, distribution, metabolism, excretion, and toxicity (ADME and toxicity) properties. Of the five compounds, three (ZINC14732869, ZINC14742188, and ZINC12205447) fitted all the ADME and toxicity properties and subjected to MD simulation and Molecular Mechanics Generalized Born and Surface Area (MM-GBSA) analyses. The results indicated that the ligands formed relatively stable complexes and had free binding energies, less than the binding energy of the ATP. Therefore, these ligands considered as prospective inhibitors of MTB after successful experimental validation.

Which Properties Allow Ligands to Open and Bind to the Transient Binding Pocket of Human Aldose Reductase?

The transient specificity pocket of aldose reductase only opens in response to specific ligands. This pocket may offer an advantage for the development of novel, more selective ligands for proteins with similar topology that lack such an adaptive pocket. Our aim was to elucidate which properties allow an inhibitor to bind in the specificity pocket. A series of inhibitors that share the same parent scaffold but differ in their attached aromatic substituents were screened using ITC and X-ray crystallography for their ability to occupy the pocket. Additionally, we investigated the electrostatic potentials and charge distribution across the attached terminal aromatic groups with respect to their potential to bind to the transient pocket of the enzyme using ESP calculations. These methods allowed us to confirm the previously established hypothesis that an electron-deficient aromatic group is an important prerequisite for opening and occupying the specificity pocket. We also demonstrated from our crystal structures that a pH shift between 5 and 8 does not affect the binding position of the ligand in the specificity pocket. This allows for a comparison between thermodynamic and crystallographic data collected at different pH values.

Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.

Cryo-EM structures of inhibitory antibodies complexed with arginase 1 provide insight into mechanism of action

Human Arginase 1 (hArg1) is a metalloenzyme that catalyzes the hydrolysis of L-arginine to L-ornithine and urea, and modulates T-cell-mediated immune response. Arginase-targeted therapies have been pursued across several disease areas including immunology, oncology, nervous system dysfunction, and cardiovascular dysfunction and diseases. Currently, all published hArg1 inhibitors are small molecules usually less than 350 Da in size. Here we report the cryo-electron microscopy structures of potent and inhibitory anti-hArg antibodies bound to hArg1 which form distinct macromolecular complexes that are greater than 650 kDa. With local resolutions of 3.5 Å or better we unambiguously mapped epitopes and paratopes for all five antibodies and determined that the antibodies act through orthosteric and allosteric mechanisms. These hArg1:antibody complexes present an alternative mechanism to inhibit hArg1 activity and highlight the ability to utilize antibodies as probes in the discovery and development of peptide and small molecule inhibitors for enzymes in general.

Identification of Small-Molecule Positive Modulators of Calcitonin-like Receptor-Based Receptors

Class B G protein-coupled receptors are highly therapeutically relevant but challenges remain in identifying suitable small-molecule drugs. The calcitonin-like receptor (CLR) in particular is linked to conditions such as migraine, cardiovascular disease, and inflammatory bowel disease. The CLR cannot act as a cell-surface receptor alone but rather must couple to one of three receptor activity-modifying proteins (RAMPs), forming heterodimeric receptors for the peptides adrenomedullin and calcitonin gene-related peptide. These peptides have extended binding sites across their receptors. This is one reason why there are few small-molecule ligands that can modulate these receptors. Here we describe small molecules that are able to positively modulate the signaling of the CLR with all three RAMPs but are not active at the related calcitonin receptor. These compounds were selected from a β-arrestin recruitment screen, coupled with rounds of medicinal chemistry to improve their activity. Translational potential is shown as the compounds can positively modulate cAMP signaling in a vascular cell line model. Binding experiments do not support an extracellular domain binding site; however, molecular modeling reveals potential allosteric binding sites in multiple receptor regions. These are the first small-molecule positive modulators described for the CLR:RAMP complexes.

Structure and function prediction of arsenate reductase from Deinococcus indicus DR1

Arsenic prevalence in the environment impelled many organisms to develop resistance over the course of evolution. Tolerance to arsenic, either as the pentavalent [As(V)] form or the trivalent form [As(III)], by bacteria has been well studied in prokaryotes, and the mechanism of action is well defined. However, in the rod-shaped arsenic tolerant Deinococcus indicus DR1, the key enzyme, arsenate reductase (ArsC) has not been well studied. ArsC of D. indicus belongs to the Grx-linked prokaryotic arsenate reductase family. While it shares homology with the well-studied ArsC of Escherichia coli having a catalytic cysteine (Cys 12) and arginine triad (Arg 60, 94, and 107), the active site of D.indicus ArsC contains four residues Glu 9, Asp 53, Arg 86, and Glu 100, and with complete absence of structurally equivalent residue for crucial Cys 12. Here, we report that the mechanism of action of ArsC of D. indicus is different as a result of convergent evolution and most likely able to detoxify As(V) using a mix of positively- and negatively-charged residues in its active site, unlike the residues of E. coli. This suggests toward the possibility of an alternative mechanism of As (V) degradation in bacteria.

Large-scale docking predicts that sORF-encoded peptides may function through protein-peptide interactions in Arabidopsis thaliana

Several recent studies indicate that small Open Reading Frames (sORFs) embedded within multiple eukaryotic non-coding RNAs can be translated into bioactive peptides of up to 100 amino acids in size. However, the functional roles of the 607 Stress Induced Peptides (SIPs) previously identified from 189 Transcriptionally Active Regions (TARs) in Arabidopsis thaliana remain unclear. To provide a starting point for functional annotation of these plant-derived peptides, we performed a large-scale prediction of peptide binding sites on protein surfaces using coarse-grained peptide docking. The docked models were subjected to further atomistic refinement and binding energy calculations. A total of 530 peptide-protein pairs were successfully docked. In cases where a peptide encoded by a TAR is predicted to bind at a known ligand or cofactor-binding site within the protein, it can be assumed that the peptide modulates the ligand or cofactor-binding. Moreover, we predict that several peptides bind at protein-protein interfaces, which could therefore regulate the formation of the respective complexes. Protein-peptide binding analysis further revealed that peptides employ both their backbone and side chain atoms when binding to the protein, forming predominantly hydrophobic interactions and hydrogen bonds. In this study, we have generated novel predictions on the potential protein-peptide interactions in A. thaliana, which will help in further experimental validation.

Enhanced activity of Withania somnifera family-1 glycosyltransferase (UGT73A16) via mutagenesis

This work used an approach of enzyme engineering towards the improved production of baicalin as well as alteration of acceptor and donor substrate preferences in UGT73A16. The 3D model of Withania somnifera family-1 glycosyltransferase (UGT73A16) was constructed based on the known crystal structures of plant UGTs. Structural and functional properties of UGT73A16 were investigated using docking and mutagenesis. The docking studies were performed to understand the key residues involved in substrate recognition. In the molecular model of UGT73A16, substrates binding pockets are located between N- and C-terminal domains. Modeled UGT73A16 was docked with UDP-glucose, UDP-glucuronic acid (UDPGA), kaempferol, isorhamnetin, 3-hydroxy flavones, naringenin, genistein and baicalein. The protein-ligand interactions showed that His 16, Asp 246, Lys 255, Ala 337, Gln 339, Val 340, Asn 358 and Glu 362 amino acid residues may be important for catalytic activity. The kinetic parameters indicated that mutants A337C and Q339A exhibited 2-3 fold and 6-7 fold more catalytic efficiency, respectively than wild type, and shifted the sugar donor specificity from UDP-glucose to UDPGA. The mutant Q379H displayed large loss of activity with UDP-glucose and UDPGA strongly suggested that last amino acid residue of PSPG box is important for glucuronosylation and glucosylation and highly specific to sugar binding sites. The information obtained from docking and mutational studies could be beneficial in future to engineer this biocatalyst for development of better ones.

Cryptic binding sites on proteins: definition, detection, and druggability

Many proteins in their unbound structures lack surface pockets appropriately sized for drug binding. Hence, a variety of experimental and computational tools have been developed for the identification of cryptic sites that are not evident in the unbound protein but form upon ligand binding, and can provide tractable drug target sites. The goal of this review is to discuss the definition, detection, and druggability of such sites, and their potential value for drug discovery. Novel methods based on molecular dynamics simulations are particularly promising and yield a large number of transient pockets, but it has been shown that only a minority of such sites are generally capable of binding ligands with substantial affinity. Based on recent studies, current methodology can be improved by combining molecular dynamics with fragment docking and machine learning approaches.

In Silico Characterization and Structural Modeling of Dermacentor andersoni p36 Immunosuppressive Protein

Ticks cause approximately $17–19 billion economic losses to the livestock industry globally. Development of recombinant antitick vaccine is greatly hindered by insufficient knowledge and understanding of proteins expressed by ticks. Ticks secrete immunosuppressant proteins that modulate the host's immune system during blood feeding; these molecules could be a target for antivector vaccine development. Recombinant p36, a 36 kDa immunosuppressor from the saliva of female Dermacentor andersoni, suppresses T-lymphocytes proliferation in vitro. To identify potential unique structural and dynamic properties responsible for the immunosuppressive function of p36 proteins, this study utilized bioinformatic tool to characterize and model structure of D. andersoni p36 protein. Evaluation of p36 protein family as suitable vaccine antigens predicted a p36 homolog in Rhipicephalus appendiculatus, the tick vector of East Coast fever, with an antigenicity score of 0.7701 that compares well with that of Bm86 (0.7681), the protein antigen that constitute commercial tick vaccine Tickgard™. Ab initio modeling of the D. andersoni p36 protein yielded a 3D structure that predicted conserved antigenic region, which has potential of binding immunomodulating ligands including glycerol and lactose, found located within exposed loop, suggesting a likely role in immunosuppressive function of tick p36 proteins. Laboratory confirmation of these preliminary results is necessary in future studies.

Exploring the structural origins of cryptic sites on proteins

Molecular dynamics (MD) simulations of proteins reveal the existence of many transient surface pockets; however, the factors determining what small subset of these represent druggable or functionally relevant ligand binding sites, called "cryptic sites," are not understood. Here, we examine multiple X-ray structures for a set of proteins with validated cryptic sites, using the computational hot spot identification tool FTMap. The results show that cryptic sites in ligand-free structures generally have a strong binding energy hot spot very close by. As expected, regions around cryptic sites exhibit above-average flexibility, and close to 50% of the proteins studied here have unbound structures that could accommodate the ligand without clashes. Nevertheless, the strong hot spot neighboring each cryptic site is almost always exploited by the bound ligand, suggesting that binding may frequently involve an induced fit component. We additionally evaluated the structural basis for cryptic site formation, by comparing unbound to bound structures. Cryptic sites are most frequently occluded in the unbound structure by intrusion of loops (22.5%), side chains (19.4%), or in some cases entire helices (5.4%), but motions that create sites that are too open can also eliminate pockets (19.4%). The flexibility of cryptic sites frequently leads to missing side chains or loops (12%) that are particularly evident in low resolution crystal structures. An interesting observation is that cryptic sites formed solely by the movement of side chains, or of backbone segments with fewer than five residues, result only in low affinity binding sites with limited use for drug discovery.

Comparative assessment of strategies to identify similar ligand-binding pockets in proteins

Background

Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges.

Results

We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock.

Conclusions

Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5–10%. All data reported in this paper are freely available at https://osf.io/6ngbs/.

Exploring Protein Cavities through Rigidity Analysis

The geometry of cavities in the surfaces of proteins facilitates a variety of biochemical functions. To better understand the biochemical nature of protein cavities, the shape, size, chemical properties, and evolutionary nature of functional and nonfunctional surface cavities have been exhaustively surveyed in protein structures. The rigidity of surface cavities, however, is not immediately available as a characteristic of structure data, and is thus more difficult to examine. Using rigidity analysis for assessing and analyzing molecular rigidity, this paper performs the first survey of the relationships between cavity properties, such as size and residue content, and how they correspond to cavity rigidity. Our survey measured a variety of rigidity metrics on 120,323 cavities from 12,785 sequentially non-redundant protein chains. We used VASP-E, a volume-based algorithm for analyzing cavity geometry. Our results suggest that rigidity properties of protein cavities are dependent on cavity surface area.

PDBsum: Structural summaries of PDB entries

PDBsum is a web server providing structural information on the entries in the Protein Data Bank (PDB). The analyses are primarily image‐based and include protein secondary structure, protein‐ligand and protein‐DNA interactions, PROCHECK analyses of structural quality, and many others. The 3D structures can be viewed interactively in RasMol, PyMOL, and a JavaScript viewer called 3Dmol.js. Users can upload their own PDB files and obtain a set of password‐protected PDBsum analyses for each. The server is freely accessible to all at: http://www.ebi.ac.uk/pdbsum.

Probing protein flexibility reveals a mechanism for selective promiscuity

Many eukaryotic regulatory proteins adopt distinct bound and unbound conformations, and use this structural flexibility to bind specifically to multiple partners. However, we lack an understanding of how an interface can select some ligands, but not others. Here, we present a molecular dynamics approach to identify and quantitatively evaluate the interactions responsible for this selective promiscuity. We apply this approach to the anticancer target PD-1 and its ligands PD-L1 and PD-L2. We discover that while unbound PD-1 exhibits a hard-to-drug hydrophilic interface, conserved specific triggers encoded in the cognate ligands activate a promiscuous binding pathway that reveals a flexible hydrophobic binding cavity. Specificity is then established by additional contacts that stabilize the PD-1 cavity into distinct bound-like modes. Collectively, our studies provide insight into the structural basis and evolution of multiple binding partners, and also suggest a biophysical approach to exploit innate binding pathways to drug seemingly undruggable targets.

DOI:http://dx.doi.org/10.7554/eLife.22889.001

Many proteins need to interact with other proteins to carry out their various tasks in cells. Such interactions are essential for almost all biological processes and are often disrupted in disease. Cells have thousands of different types of proteins and each has a unique shape that determines which other proteins it can bind to.

It was previously thought that two proteins bind to each other in a manner similar to that of a lock and a key, in which the rigid shape of one protein meshes perfectly with the rigid shape of its partner. However, many proteins are flexible and adopt different shapes depending on whether they are attached to their partner, or not. Moreover, an individual protein may also bind to several different partners, each requiring that protein to adopt several different shapes. These observations have challenged the lock and key model and suggest that flexibility in the structure of a protein plays a key role in its binding to other proteins. However, it is not clear how structural flexibility enables a protein to bind to several different partners while being selective enough to prevent the protein from binding to the wrong ones.

A protein called PD-1 is involved in immune responses in humans and is an emerging target for drugs to treat cancer. Pabon and Camacho used computer simulations to model PD-1’s structural flexibility and to find out how this enables the protein to form different shapes when it binds to different partners. The experiments show that the region of PD-1 that binds to other proteins adopts a different shape in the absence and presence of its partners. The binding partners make initial contact with PD-1 via specific features that they share in common. This causes PD-1 to change shape, uncovering a surface of PD-1 that is flexible and is able to accommodate a variety of partners. After this, the binding partners form additional contacts with PD-1 that are specific to each partner.

These findings suggest that the ability of a protein to bind to several different partners is unlocked by certain structures that are present in the binding partners. These structures are found in proteins produced by many different organisms, suggesting that this mechanism is likely to be widespread in nature. This work may open up new avenues for designing drugs to target PD-1 and other proteins that contribute to disease but have so far been impossible to target with drugs.

DOI:http://dx.doi.org/10.7554/eLife.22889.002

The ProFunc Function Prediction Server

The ProFunc web server is a tool for helping identify the function of a given protein whose 3D coordinates have been experimentally determined or homology modeled. It uses a cocktail of both sequence- and structure-based methods to identify matches to other proteins that may, in turn, suggest the query protein's most likely function. The server was originally developed to aid the worldwide structural genomics effort at the start of the millennium. It accepts a file containing the protein's 3D coordinates in PDB format, and, when processing is complete, sends an email containing a link to the password-protected result pages. The results include an at-a-glance summary, as well as separate pages containing more detailed analyses. The server can be found at: http://www.ebi.ac.uk/thornton-srv/databases/profunc .

Computational methods in drug discovery

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

Structures of the Streptococcus sanguinis SrpA Binding Region with Human Sialoglycans Suggest Features of the Physiological Ligand

Streptococcus sanguinis is a leading cause of bacterial infective endocarditis, a life-threatening infection of heart valves. S. sanguinis binds to human platelets with high avidity, and this adherence is likely to enhance virulence. Previous studies suggest that a serine-rich repeat adhesin termed SrpA mediates the binding of S. sanguinis to human platelets via its interaction with sialoglycans on the receptor GPIbα. However, in vitro binding assays with SrpA and defined sialoglycans failed to identify specific high-affinity ligands. To improve our understanding of the interaction between SrpA and human platelets, we determined cocrystal structures of the SrpA sialoglycan binding region (SrpA_BR) with five low-affinity ligands: three sialylated trisaccharides (sialyl-T antigen, 3'-sialyllactose, and 3'-sialyl-N-acetyllactosamine), a sialylated tetrasaccharide (sialyl-Lewis^X), and a sialyl galactose disaccharide component common to these sialoglyans. We then combined structural analysis with mutagenesis to further determine whether our observed interactions between SrpA_BR and glycans are important for binding to platelets and to better map the binding site for the physiological receptor. We found that the sialoglycan binding site of SrpA_BR is significantly larger than the sialoglycans cocrystallized in this study, which suggests that binding of SrpA to platelets either is multivalent or occurs via a larger, disialylated glycan.

Allosteric Inhibitory Molecular Recognition of a Photochromic Dye by a Digestive Enzyme: Dihydroindolizine makes α-chymotrypsin Photo-responsive

The structural-functional regulation of enzymes by the administration of an external stimulus such as light could create photo-switches that exhibit unique biotechnological applications. However, molecular recognition of small ligands is a central phenomenon involved in all biological processes. We demonstrate herein that the molecular recognition of a photochromic ligand, dihydroindolizine (DHI), by serine protease α-chymotrypsin (CHT) leads to the photo-control of enzymatic activity. We synthesized and optically characterized the photochromic DHI. Light-induced reversible pyrroline ring opening and a consequent thermal back reaction via 1,5-electrocyclization are responsible for the photochromic behavior. Furthermore, DHI inhibits the enzymatic activity of CHT in a photo-controlled manner. Simultaneous binding of the well-known inhibitors 4-nitrophenyl anthranilate (NPA) or proflavin (PF) in the presence of DHI displays spectral overlap between the emission of CHT-NPA or CHT-PF with the respective absorption of cis or trans DHI. The results suggest an opportunity to explore the binding site of DHI using Förster resonance energy transfer (FRET). Moreover, to more specifically evaluate the DHI binding interactions, we employed molecular docking calculations, which suggested binding near the hydrophobic site of Cys-1-Cys-122 residues. Variations in the electrostatic interactions of the two conformers of DHI adopt unfavorable conformations, leading to the allosteric inhibition of enzymatic activity.

Disorder transitions and conformational diversity cooperatively modulate biological function in proteins

Structural differences between conformers sustain protein biological function. Here, we studied in a large dataset of 745 intrinsically disordered proteins, how ordered-disordered transitions modulate structural differences between conformers as derived from crystallographic data. We found that almost 50% of the proteins studied show no transitions and have low conformational diversity while the rest show transitions and a higher conformational diversity. In this last subset, 60% of the proteins become more ordered after ligand binding, while 40% more disordered. As protein conformational diversity is inherently connected with protein function our analysis suggests differences in structure-function relationships related to order-disorder transitions.

Molecular Characterization and Computational Modelling of New Delhi Metallo-β-Lactamase-5 from an Escherichia coli Isolate (KOEC3) of Bovine Origin

Emergence of antimicrobial resistance mediated through New Delhi metallo-β-lactamases (NDMs) is a serious therapeutic challenge. Till date, 16 different NDMs have been described. In this study, we report the molecular and structural characteristics of NDM-5 isolated from an Escherichia coli isolate (KOEC3) of bovine origin. Using PCR amplification, cloning and sequencing of full blaNDM gene, we identified the NDM type as NDM-5. Cloning of full gene in E. coli DH5α and subsequent assessment of antibiotic susceptibility of the transformed cells indicated possible role of native promoter in expression blaNDM-5. Translated amino acid sequence had two substitutions (Val88Leu and Met154Leu) compared to NDM-1. Theoretically deduced isoelectric pH of NDM-5 was 5.88 and instability index was 36.99, indicating a stable protein. From the amino acids sequence, a 3D model of the protein was computed. Analysis of the protein structure elucidated zinc coordination and also revealed a large binding cleft and flexible nature of the protein, which might be the reason for broad substrate range. Docking experiments revealed plausible binding poses for five carbapenem drugs in the vicinity of metal ions. In conclusion, results provided possible explanation for wide range of antibiotics catalyzed by NDM-5 and likely interaction modes with five carbapenem drugs.

CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites

Many proteins have small-molecule binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially "druggable" human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite Web server is available at http://salilab.org/cryptosite.

Binding of small molecules at interface of protein-protein complex - A newer approach to rational drug design

Protein–protein interaction is a vital process which drives many important physiological processes in the cell and has also been implicated in several diseases. Though the protein–protein interaction network is quite complex but understanding its interacting partners using both in silico as well as molecular biology techniques can provide better insights for targeting such interactions. Targeting protein–protein interaction with small molecules is a challenging task because of druggability issues. Nevertheless, several studies on the kinetics as well as thermodynamic properties of protein–protein interactions have immensely contributed toward better understanding of the affinity of these complexes. But, more recent studies on hot spots and interface residues have opened up new avenues in the drug discovery process. This approach has been used in the design of hot spot based modulators targeting protein–protein interaction with the objective of normalizing such interactions.

Engineered Autonomous Human Variable Domains

BACKGROUND

The complex multi-chain architecture of antibodies has spurred interest in smaller derivatives that retain specificity but can be more easily produced in bacteria. Domain antibodies consisting of single variable domains are the smallest antibody fragments and have been shown to possess enhanced ability to target epitopes that are difficult to access using multidomain antibodies. However, in contrast to natural camelid antibody domains, human variable domains typically suffer from low stability and high propensity to aggregate.

METHODS

This review summarizes strategies to improve the biophysical properties of heavy chain variable domains from human antibodies with an emphasis on aggregation resistance. Several protein engineering approaches have targeted antibody frameworks and complementarity determining regions to stabilize the native state and prevent aggregation of the denatured state.

CONCLUSION

Recent findings enable the construction of highly diverse libraries enriched in aggregation-resistant variants that are expected to provide binders to diverse antigens. Engineered domain antibodies possess unique advantages in expression, epitope preference and flexibility of formatting over conventional immunoreagents and are a promising class of antibody fragments for biomedical development.

Large-Scale Analysis Exploring Evolution of Catalytic Machineries and Mechanisms in Enzyme Superfamilies

Enzymes, as biological catalysts, form the basis of all forms of life. How these proteins have evolved their functions remains a fundamental question in biology. Over 100 years of detailed biochemistry studies, combined with the large volumes of sequence and protein structural data now available, means that we are able to perform large-scale analyses to address this question. Using a range of computational tools and resources, we have compiled information on all experimentally annotated changes in enzyme function within 379 structurally defined protein domain superfamilies, linking the changes observed in functions during evolution to changes in reaction chemistry. Many superfamilies show changes in function at some level, although one function often dominates one superfamily. We use quantitative measures of changes in reaction chemistry to reveal the various types of chemical changes occurring during evolution and to exemplify these by detailed examples. Additionally, we use structural information of the enzymes active site to examine how different superfamilies have changed their catalytic machinery during evolution. Some superfamilies have changed the reactions they perform without changing catalytic machinery. In others, large changes of enzyme function, in terms of both overall chemistry and substrate specificity, have been brought about by significant changes in catalytic machinery. Interestingly, in some superfamilies, relatives perform similar functions but with different catalytic machineries. This analysis highlights characteristics of functional evolution across a wide range of superfamilies, providing insights that will be useful in predicting the function of uncharacterised sequences and the design of new synthetic enzymes.

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Examining how enzyme function evolves using sequence, structure, and reaction mechanism data.

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Quantifying changes in reaction mechanisms reveals how function has diverged in many superfamilies.

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Homologous domains frequently use different catalytic residues, which sometimes perform the same enzyme chemistry.

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This large-scale analysis has significance in protein function prediction and enzyme design.

Computational Prediction of RNA-Binding Proteins and Binding Sites

Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%–8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein–RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein–RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.

MHJ_0461 is a multifunctional leucine aminopeptidase on the surface of Mycoplasma hyopneumoniae

Aminopeptidases are part of the arsenal of virulence factors produced by bacterial pathogens that inactivate host immune peptides. Mycoplasma hyopneumoniae is a genome-reduced pathogen of swine that lacks the genetic repertoire to synthesize amino acids and relies on the host for availability of amino acids for growth. M. hyopneumoniae recruits plasmin(ogen) onto its cell surface via the P97 and P102 adhesins and the glutamyl aminopeptidase MHJ_0125. Plasmin plays an important role in regulating the inflammatory response in the lungs of pigs infected with M. hyopneumoniae. We show that recombinant MHJ_0461 (rMHJ_0461) functions as a leucine aminopeptidase (LAP) with broad substrate specificity for leucine, alanine, phenylalanine, methionine and arginine and that MHJ_0461 resides on the surface of M. hyopneumoniae. rMHJ_0461 also binds heparin, plasminogen and foreign DNA. Plasminogen bound to rMHJ_0461 was readily converted to plasmin in the presence of tPA. Computational modelling identified putative DNA and heparin-binding motifs on solvent-exposed sites around a large pore on the LAP hexamer. We conclude that MHJ_0461 is a LAP that moonlights as a multifunctional adhesin on the cell surface of M. hyopneumoniae.

NRGsuite: a PyMOL plugin to perform docking simulations in real time using FlexAID

Ligand protein docking simulations play a fundamental role in understanding molecular recognition. Herein we introduce the NRGsuite, a PyMOL plugin that permits the detection of surface cavities in proteins, their refinements, calculation of volume and use, individually or jointly, as target binding-sites for docking simulations with FlexAID. The NRGsuite offers the users control over a large number of important parameters in docking simulations including the assignment of flexible side-chains and definition of geometric constraints. Furthermore, the NRGsuite permits the visualization of the docking simulation in real time. The NRGsuite give access to powerful docking simulations that can be used in structure-guided drug design as well as an educational tool. The NRGsuite is implemented in Python and C/C++ with an easy to use package installer. The NRGsuite is available for Windows, Linux and MacOS.

Availability and implementation: http://bcb.med.usherbrooke.ca/flexaid.

Contact:rafael.najmanovich@usherbroke.ca

Supplementary information:Supplementary data are available at Bioinformatics online.

Optimal ligand descriptor for pocket recognition based on the Beta-shape

Structure-based virtual screening is one of the most important and common computational methods for the identification of predicted hit at the beginning of drug discovery. Pocket recognition and definition is frequently a prerequisite of structure-based virtual screening, reducing the search space of the predicted protein-ligand complex. In this paper, we present an optimal ligand shape descriptor for a pocket recognition algorithm based on the beta-shape, which is a derivative structure of the Voronoi diagram of atoms. We investigate six candidates for a shape descriptor for a ligand using statistical analysis: the minimum enclosing sphere, three measures from the principal component analysis of atoms, the van der Waals volume, and the beta-shape volume. Among them, the van der Waals volume of a ligand is the optimal shape descriptor for pocket recognition and best tunes the pocket recognition algorithm based on the beta-shape for efficient virtual screening. The performance of the proposed algorithm is verified by a benchmark test.

Molecular dynamics study of naturally existing cavity couplings in proteins

Couplings between protein sub-structures are a common property of protein dynamics. Some of these couplings are especially interesting since they relate to function and its regulation. In this article we have studied the case of cavity couplings because cavities can host functional sites, allosteric sites, and are the locus of interactions with the cell milieu. We have divided this problem into two parts. In the first part, we have explored the presence of cavity couplings in the natural dynamics of 75 proteins, using 20 ns molecular dynamics simulations. For each of these proteins, we have obtained two trajectories around their native state. After applying a stringent filtering procedure, we found significant cavity correlations in 60% of the proteins. We analyze and discuss the structure origins of these correlations, including neighbourhood, cavity distance, etc. In the second part of our study, we have used longer simulations (≥100 ns) from the MoDEL project, to obtain a broader view of cavity couplings, particularly about their dependence on time. Using moving window computations we explored the fluctuations of cavity couplings along time, finding that these couplings could fluctuate substantially during the trajectory, reaching in several cases correlations above 0.25/0.5. In summary, we describe the structural origin and the variations with time of cavity couplings. We complete our work with a brief discussion of the biological implications of these results.

Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis

Background

Identifying druggable cavities on a protein surface is a crucial step in structure based drug design. The cavities have to present suitable size and shape, as well as appropriate chemical complementarity with ligands.

Results

We present a novel cavity prediction method that analyzes results of virtual screening of specific ligands or fragment libraries by means of Self-Organizing Maps. We demonstrate the method with two thoroughly studied proteins where it successfully identified their active sites (AS) and relevant secondary binding sites (BS). Moreover, known active ligands mapped the AS better than inactive ones. Interestingly, docking a naive fragment library brought even more insight. We then systematically applied the method to the 102 targets from the DUD-E database, where it showed a 90% identification rate of the AS among the first three consensual clusters of the SOM, and in 82% of the cases as the first one. Further analysis by chemical decomposition of the fragments improved BS prediction. Chemical substructures that are representative of the active ligands preferentially mapped in the AS.

Conclusion

The new approach provides valuable information both on relevant BSs and on chemical features promoting bioactivity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0518-z) contains supplementary material, which is available to authorized users.

A novel alkyl hydroperoxidase (AhpD) of Anabaena PCC7120 confers abiotic stress tolerance in Escherichia coli

In silico analysis together with cloning, molecular characterization and heterologous expression reports that the hypothetical protein All5371 of Anabaena sp. PCC7120 is a novel hydroperoxide scavenging protein similar to AhpD of bacteria. The presence of E(X)11CX HC(X)3H motif in All5371 confers peroxidase activity and closeness to bacterial AhpD which is also reflected by its highest 3D structure homology with Rhodospirillum rubrum AhpD. Heterologous expression of all5371 complimented for ahpC and conferred resistance in MJF178 strain (ahpCF::Km) of Escherichia coli. All5371 reduced the organic peroxide more efficiently than inorganic peroxide and the recombinant E. coli strain following exposure to H2O2, CdCl2, CuCl2, heat, UV-B and carbofuron registered increased growth over wild-type and mutant E. coli transformed with empty vector. Appreciable expression of all5371 in Anabaena sp. PCC7120 as measured by qRT-PCR under selected stresses and their tolerance against H2O2, tBOOH, CuOOH and menadione attested its role in stress tolerance. In view of the above, All5371 of Anabaena PCC7120 emerged as a new hydroperoxide detoxifying protein.

Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions

Systematic interrogation of mutation or protein modification data is important to identify sites with functional consequences and to deduce global consequences from large data sets. Mechismo (mechismo.russellab.org) enables simultaneous consideration of thousands of 3D structures and biomolecular interactions to predict rapidly mechanistic consequences for mutations and modifications. As useful functional information often only comes from homologous proteins, we benchmarked the accuracy of predictions as a function of protein/structure sequence similarity, which permits the use of relatively weak sequence similarities with an appropriate confidence measure. For protein–protein, protein–nucleic acid and a subset of protein–chemical interactions, we also developed and benchmarked a measure of whether modifications are likely to enhance or diminish the interactions, which can assist the detection of modifications with specific effects. Analysis of high-throughput sequencing data shows that the approach can identify interesting differences between cancers, and application to proteomics data finds potential mechanistic insights for how post-translational modifications can alter biomolecular interactions.

Dissecting the binding mode of low affinity phage display peptide ligands to protein targets by hydrogen/deuterium exchange coupled to mass spectrometry

Phage display (PD) is frequently used to discover peptides capable of binding to biological protein targets. The structural characterization of peptide–protein complexes is often challenging due to their low binding affinities and high structural flexibility. Here, we investigate the use of hydrogen/deuterium exchange mass spectrometry (HDX-MS) to characterize interactions of low affinity peptides with their cognate protein targets. The HDX-MS workflow was optimized to accurately detect low-affinity peptide–protein interactions by use of ion mobility, electron transfer dissociation, nonbinding control peptides, and statistical analysis of replicate data. We show that HDX-MS can identify regions in the two epigenetic regulator proteins KDM4C and KDM1A that are perturbed through weak interactions with PD-identified peptides. Two peptides cause reduced HDX on opposite sides of the active site of KDM4C, indicating distinct binding modes. In contrast, the perturbation site of another PD-selected peptide inhibiting the function of KDM1A maps to a GST-tag. Our results demonstrate that HDX-MS can validate and map weak peptide–protein interactions and pave the way for understanding and optimizing the binding of peptide scaffolds identified through PD and similar ligand discovery approaches.

Identification of single-stranded and double-stranded DNA binding proteins based on protein structure

Background

Protein-DNA interactions are essential for many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. DNA binding proteins can be classified into double-stranded DNA binding proteins (DSBs) and single-stranded DNA binding proteins (SSBs), and they take part in different biological functions. DSBs usually act as transcriptional factors to regulate the genes' expressions, while SSBs usually play roles in DNA replication, recombination, and repair, etc. Understanding the binding specificity of a DNA binding protein is helpful for the research of protein functions.

Results

In this paper, we investigated the differences between DSBs and SSBs on surface tunnels as well as the OB-fold domain information. We detected the largest clefts on the protein surfaces, to obtain several features to be used for distinguishing the potential interfaces between SSBs and DSBs, and compared its structure with each of the six OB-fold protein templates, and use the maximal alignment score TM-score as the OB-fold feature of the protein, based on which, we constructed the support vector machine (SVM) classification model to automatically distinguish these two kinds of proteins, with prediction accuracy of 87%,83% and 83% for HOLO-set, APO-set and Mixed-set respectively.

Conclusions

We found that they have different ranges of tunnel lengths and tunnel curvatures; moreover, the alignment results with OB-fold templates have also found to be the discriminative feature of SSBs and DSBs. Experimental results on 10-fold cross validation indicate that the new feature set are effective to describe DNA binding proteins. The evaluation results on both bound (DNA-bound) and non-bound (DNA-free) proteins have shown the satisfactory performance of our method.

A curated C. difficile strain 630 metabolic network: prediction of essential targets and inhibitors

BACKGROUND

Clostridium difficile is the leading cause of hospital-borne infections occurring when the natural intestinal flora is depleted following antibiotic treatment. Current treatments for Clostridium difficile infections present high relapse rates and new hyper-virulent and multi-resistant strains are emerging, making the study of this nosocomial pathogen necessary to find novel therapeutic targets.

RESULTS

We present iMLTC806cdf, an extensively curated reconstructed metabolic network for the C. difficile pathogenic strain 630. iMLTC806cdf contains 806 genes, 703 metabolites and 769 metabolic, 117 exchange and 145 transport reactions. iMLTC806cdf is the most complete and accurate metabolic reconstruction of a gram-positive anaerobic bacteria to date. We validate the model with simulated growth assays in different media and carbon sources and use it to predict essential genes. We obtain 89.2% accuracy in the prediction of gene essentiality when compared to experimental data for B. subtilis homologs (the closest organism for which such data exists). We predict the existence of 76 essential genes and 39 essential gene pairs, a number of which are unique to C. difficile and have non-existing or predicted non-essential human homologs. For 29 of these potential therapeutic targets, we find 125 inhibitors of homologous proteins including approved drugs with the potential for drug repositioning, that when validated experimentally could serve as starting points in the development of new antibiotics.

CONCLUSIONS

We created a highly curated metabolic network model of C. difficile strain 630 and used it to predict essential genes as potential new therapeutic targets in the fight against Clostridium difficile infections.

On the role of physics and evolution in dictating protein structure and function

How many of the structural and functional properties of proteins are inherent? Computer simulations provide a powerful tool to address this question. A series of studies on QS, quasi-spherical, compact polypeptides which lack any secondary structure; ART, artificial, proteins comprised of compact homopolypeptides with protein-like secondary structure; and PDB, native, single domain proteins shows that essentially all native global folds, pockets and protein-protein interfaces are in the ART library. This suggests that many protein properties are inherent and that evolution is involved in fine-tuning. The completeness of the space of ligand binding pockets and protein-protein interfaces suggests that promiscuous interactions are intrinsic to proteins and that the capacity to perform the biochemistry of life at low level does not require evolution. If so, this has profound consequences for the origin of life.

Knowledge-based fragment binding prediction

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.

Molecular dynamics simulation on the conformational transition of the mad2 protein from the open to the closed state

The Mad2 protein, with two distinct conformations of open- and closed-states, is a key player in the spindle checkpoint. The closed Mad2 state is more active than the open one. We carried out conventional and targeted molecular dynamics simulations for the two stable Mad2 states and their conformational transition to address the dynamical transition mechanism from the open to the closed state. The intermediate structure in the transition process shows exposure of the β6 strand and an increase of space around the binding sites of β6 strand due to the unfolding of the β7/8 sheet and movement of the β6/4/5 sheet close to the αC helix. Therefore, Mad2 binding to the Cdc20 protein in the spindle checkpoint is made possible. The interconversion between these two states might facilitate the functional activity of the Mad2 protein. Motion correlation analysis revealed the allosteric network between the β1 strand and β7/8 sheet via communication of the β5-αC loop and the β6/4/5 sheet in this transition process.

Interaction of classical platinum agents with the monomeric and dimeric Atox1 proteins: a molecular dynamics simulation study

We carried out molecular dynamics simulations and free energy calculations for a series of binary and ternary models of the cisplatin, transplatin and oxaliplatin agents binding to a monomeric Atox1 protein and a dimeric Atox1 protein to investigate their interaction mechanisms. All three platinum agents could respectively combine with the monomeric Atox1 protein and the dimeric Atox1 protein to form a stable binary and ternary complex due to the covalent interaction of the platinum center with the Atox1 protein. The results suggested that the extra interaction from the oxaliplatin ligand-Atox1 protein interface increases its affinity only for the OxaliPt + Atox1 model. The binding of the oxaliplatin agent to the Atox1 protein might cause larger deformation of the protein than those of the cisplatin and transplatin agents due to the larger size of the oxaliplatin ligand. However, the extra interactions to facilitate the stabilities of the ternary CisPt + 2Atox1 and OxaliPt + 2Atox1 models come from the α1 helices and α2-β4 loops of the Atox1 protein-Atox1 protein interface due to the cis conformation of the platinum agents. The combinations of two Atox1 proteins in an asymmetric way in the three ternary models were analyzed. These investigations might provide detailed information for understanding the interaction mechanism of the platinum agents binding to the Atox1 protein in the cytoplasm.

Association of a common TLR-6 polymorphism with coronary artery disease - implications for healthy ageing?

BACKGROUND

The pro-inflammatory status of the elderly triggers most of the age-related diseases such as cancer and atherosclerosis. Atherosclerosis, the leading cause world wide of morbidity and death, is an inflammatory disease influenced by life-style and genetic host factors. Stimuli such as oxLDL or microbial ligands have been proposed to trigger inflammation leading to atherosclerosis. It has recently been shown that oxLDL activates immune cells via the Toll-like receptor (TLR) 4/6 complex. Several common single nucleotide polymorphisms (SNPs) of the TLR system have been associated with atherosclerosis. To investigate the role of TLR-6 we analyzed the association of the TLR-6 SNP Pro249Ser with atherogenesis.

RESULTS

Genotyping of two independent groups with CAD, as well as of healthy controls revealed a significant association of the homozygous genotype with a reduced risk for atherosclerosis (odds ratio: 0.69, 95% CI 0.51-0.95, P = 0.02). In addition, we found a trend towards an association with the risk of restenosis after transluminal coronary angioplasty (odds ratio: 0.53, 95% CI 0.24-1.16, P = 0.12). In addition, first evidence is presented that the frequency of this protective genotype increases in a healthy population with age. Taken together, our results define a role for TLR-6 and its genetic variations in modulating the inflammatory response leading to atherosclerosis.

CONCLUSIONS

These results may lead to a better risk stratification, and potentially to an improved prophylactic treatment of high-risk populations. Furthermore, the protective effect of this polymorphism may lead to an increase of this genotype in the healthy elderly and may therefore be a novel genetic marker for the well-being during aging.

A comprehensive survey of small-molecule binding pockets in proteins

Many biological activities originate from interactions between small-molecule ligands and their protein targets. A detailed structural and physico-chemical characterization of these interactions could significantly deepen our understanding of protein function and facilitate drug design. Here, we present a large-scale study on a non-redundant set of about 20,000 known ligand-binding sites, or pockets, of proteins. We find that the structural space of protein pockets is crowded, likely complete, and may be represented by about 1,000 pocket shapes. Correspondingly, the growth rate of novel pockets deposited in the Protein Data Bank has been decreasing steadily over the recent years. Moreover, many protein pockets are promiscuous and interact with ligands of diverse scaffolds. Conversely, many ligands are promiscuous and interact with structurally different pockets. Through a physico-chemical and structural analysis, we provide insights into understanding both pocket promiscuity and ligand promiscuity. Finally, we discuss the implications of our study for the prediction of protein-ligand interactions based on pocket comparison.

The life of a living cell relies on many distinct proteins to carry out their functions. Most of these functions are rooted in interactions between the proteins and metabolites, small-molecules essential for life. By targeting specific proteins relevant to a disease, drug molecules may provide a cure. A deep understanding of the nature of interactions between proteins and small-molecules (or ligands) through analyzing their structures may help predict protein function or improve drug design. In this contribution, we present a large-scale analysis of a non-redundant set of over 20,000 experimental protein-ligand complex structures available in the current Protein Data Bank. We seek answers to several fundamental questions: How many representative pockets are there that serve as ligand-binding sites in proteins? To what extent can we infer a similar protein-ligand interaction by matching the structures of protein pockets? How different are the ligands found in the same pocket? For a promiscuous protein pocket, how does a pocket maintain favorable interactions with very different ligands? Conversely, how different are those pockets that interact with the same ligand? We find the structural space of protein pocket is small and that both protein promiscuity and ligand promiscuity are very common in Nature.

Prediction and experimental validation of enzyme substrate specificity in protein structures

Structural Genomics aims to elucidate protein structures to identify their functions. Unfortunately, the variation of just a few residues can be enough to alter activity or binding specificity and limit the functional resolution of annotations based on sequence and structure; in enzymes, substrates are especially difficult to predict. Here, large-scale controls and direct experiments show that the local similarity of five or six residues selected because they are evolutionarily important and on the protein surface can suffice to identify an enzyme activity and substrate. A motif of five residues predicted that a previously uncharacterized Silicibacter sp. protein was a carboxylesterase for short fatty acyl chains, similar to hormone-sensitive-lipase-like proteins that share less than 20% sequence identity. Assays and directed mutations confirmed this activity and showed that the motif was essential for catalysis and substrate specificity. We conclude that evolutionary and structural information may be combined on a Structural Genomics scale to create motifs of mixed catalytic and noncatalytic residues that identify enzyme activity and substrate specificity.

Hot spots in a network of functional sites

It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8–58%, C 84–95%, P 5–19% and A 81–92% on unbound structures and S 8–51%, C 97–99%, P 14–50%, A 94–97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation.