Discovery of binding proteins for a protein target using protein–protein docking‐based virtual screening

Target structure‐based virtual screening, which employs protein‐small molecule docking to identify potential ligands, has been widely used in small‐molecule drug discovery. In the present study, we used a protein–protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all‐to‐all protein–protein docking run on a large dataset was performed. The three‐dimensional rigid docking program SDOCK was used to examine protein–protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z‐score, and convergency of the low‐score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all‐to‐all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor‐α (TNFα), which is a well‐known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top‐ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein–protein docking for the discovery of novel binding proteins for specific protein targets. Proteins 2014; 82:2472–2482. © 2014 Wiley Periodicals, Inc.     

A distance‐dependent atomic knowledge‐based potential and force for discrimination of native structures from decoys

The purpose of this article is to introduce a novel model for discriminating correctly folded proteins from well designed decoy structures using mechanical interatomic forces. In our model, we consider a protein as a collection of springs and the force imposed to each atom is calculated. A potential function is obtained from statistical contact preferences within known protein structures. Combining this function with the spring equation, the interatomic forces are calculated. Finally, we consider a structure and define a score function on the 3D structure of a protein. We compare the force imposed to each atom of a protein with the corresponding atom in the other structures. We then assign larger scores to those atoms with lower forces. The total score is the sum of partial scores of atoms. The optimal structure is assumed to be the one with the highest score in the data set. To evaluate the performance of our model, we apply it on several decoy sets. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Improving the orientation‐dependent statistical potential using a reference state

Statistical potentials are frequently engaged in the protein structural prediction and protein folding for conformational evaluation. Theoretically, to describe the many‐body effect, pairwise interaction between two atom groups should be corrected by their relative geometric orientation. The potential functions developed by this means are called orientation‐dependent statistical potentials and have exhibited substantially improved performance. However, none of the currently available orientation‐dependent statistical potentials use any reference state, which has been proven to greatly enhance the power of distance‐dependent statistical potentials in numerous previous studies. In this work, we designed a reasonable reference state for the orientation‐dependent statistical potentials: using the average geometric relationship between atom pairs in known structures by neglecting their residue identities. The statistical potential developed using this reference state (called ORDER_AVE) prevails most available rival potentials in a series of tests on the decoy sets, although the information of side chain atoms (except the β‐carbon) is absent in its construction. Proteins 2014; 82:2383–2393. © 2014 Wiley Periodicals, Inc.     

VoroMQA: Assessment of protein structure quality using interatomic contact areas

In the absence of experimentally determined protein structure many biological questions can be addressed using computational structural models. However, the utility of protein structural models depends on their quality. Therefore, the estimation of the quality of predicted structures is an important problem. One of the approaches to this problem is the use of knowledge‐based statistical potentials. Such methods typically rely on the statistics of distances and angles of residue‐residue or atom‐atom interactions collected from experimentally determined structures. Here, we present VoroMQA (Voronoi tessellation‐based Model Quality Assessment), a new method for the estimation of protein structure quality. Our method combines the idea of statistical potentials with the use of interatomic contact areas instead of distances. Contact areas, derived using Voronoi tessellation of protein structure, are used to describe and seamlessly integrate both explicit interactions between protein atoms and implicit interactions of protein atoms with solvent. VoroMQA produces scores at atomic, residue, and global levels, all in the fixed range from 0 to 1. The method was tested on the CASP data and compared to several other single‐model quality assessment methods. VoroMQA showed strong performance in the recognition of the native structure and in the structural model selection tests, thus demonstrating the efficacy of interatomic contact areas in estimating protein structure quality. The software implementation of VoroMQA is freely available as a standalone application and as a web server at http://bioinformatics.lt/software/voromqa . Proteins 2017; 85:1131–1145. © 2017 Wiley Periodicals, Inc.     

Discovery of multiple interacting partners of gankyrin,a proteasomal chaperone and an oncoprotein—Evidence for a common hot spot site at the interface and its functional relevance

Gankyrin, a non‐ATPase component of the proteasome and a chaperone of proteasome assembly, is also an oncoprotein. Gankyrin regulates a variety of oncogenic signaling pathways in cancer cells and accelerates degradation of tumor suppressor proteins p53 and Rb. Therefore gankyrin may be a unique hub integrating signaling networks with the degradation pathway. To identify new interactions that may be crucial in consolidating its role as an oncogenic hub, crystal structure of gankyrin‐proteasome ATPase complex was used to predict novel interacting partners. EEVD, a four amino acid linear sequence seems a hot spot site at this interface. By searching for EEVD in exposed regions of human proteins in PDB database, we predicted 34 novel interactions. Eight proteins were tested and seven of them were found to interact with gankyrin. Affinity of four interactions is high enough for endogenous detection. Others require gankyrin overexpression in HEK 293 cells or occur endogenously in breast cancer cell line‐ MDA‐MB‐435, reflecting lower affinity or presence of a deregulated network. Mutagenesis and peptide inhibition confirm that EEVD is the common hot spot site at these interfaces and therefore a potential polypharmacological drug target. In MDA‐MB‐231 cells in which the endogenous CLIC1 is silenced, trans‐expression of Wt protein (CLIC1_EEVD) and not the hot spot site mutant (CLIC1_AAVA) resulted in significant rescue of the migratory potential. Our approach can be extended to identify novel functionally relevant protein‐protein interactions, in expansion of oncogenic networks and in identifying potential therapeutic targets. Proteins 2014; 82:1283–1300. © 2013 Wiley Periodicals, Inc.     

DynaDock: A new molecular dynamics‐based algorithm for protein–peptide docking including receptor flexibility

Molecular docking programs play an important role in drug development and many well‐established methods exist. However, there are two situations for which the performance of most approaches is still not satisfactory, namely inclusion of receptor flexibility and docking of large, flexible ligands like peptides. In this publication a new approach is presented for docking peptides into flexible receptors. For this purpose a two step procedure was developed: first, the protein–peptide conformational space is scanned and approximate ligand poses are identified and second, the identified ligand poses are refined by a new molecular dynamics‐based method, optimized potential molecular dynamics (OPMD). The OPMD approach uses soft‐core potentials for the protein–peptide interactions and applies a new optimization scheme to the soft‐core potential. Comparison with refinement results obtained by conventional molecular dynamics and a soft‐core scaling approach shows significant improvements in the sampling capability for the OPMD method. Thus, the number of starting poses needed for successful refinement is much lower than for the other methods. The algorithm was evaluated on 15 protein–peptide complexes with 2–16mer peptides. Docking poses with peptide RMSD values <2.10 Å from the equilibrated experimental structures were obtained in all cases. For four systems docking into the unbound receptor structures was performed, leading to peptide RMSD values <2.12 Å. Using a specifically fitted scoring function in 11 of 15 cases the best scoring poses featured a peptide RMSD ≤2.10 Å. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Validating and improving elastic network models with molecular dynamics simulations

Elastic network models (ENMs) are a class of simple models intended to represent the collective motions of proteins. In contrast to all‐atom molecular dynamics simulations, the low computational investment required to use an ENM makes them ideal for speculative hypothesis‐testing situations. Historically, ENMs have been validated via comparison to crystallographic B‐factors, but this comparison is relatively low‐resolution and only tests the predictions of relative flexibility. In this work, we systematically validate and optimize a number of ENM‐type models by quantitatively comparing their predictions to microsecond‐scale all‐atom simulations of three different G protein coupled receptors. We show that, despite their apparent simplicity, well‐optimized ENMs perform remarkably well, reproducing the protein fluctuations with an accuracy comparable to what one would expect from all‐atom simulations run for several hundred nanoseconds. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Glucose‐regulated protein 78: A new partner of p53 in trophoblast

Although wild‐type p53 protein is overexpressed in first trimester trophoblast, it is inactive towards its target genes Metalloproteinase 2 and 9. This seems to be due to a complex mechanism of inactivation and stabilization of p53 relying on the formation of protein complexes involving the N‐terminus of p53. To detect the proteins associated with this sequence, we incubated biotinylated p53 N‐terminal peptide in cytotrophoblastic cell medium 24 h before lysis of cells. We purified the proteins retained on biotinylated peptide using a neutravidin affinity column. Proteins were then identified by peptide mass finger printing followed or not by peptide fragmentation sequencing. Among these proteins, we identified glucose‐regulated protein 78 (GRP78) and verified its interaction with p53 in trophoblastic cells by immunoprecipitation and Western blot analysis. Moreover, the decreased expression of GRP78 induced by GRP78siRNA or versipelostatin decreased the formation of high molecular weight p53 complexes and p53 monomer and increased trophoblastic invasion. These results suggest that GRP78 is involved in inactivation and stabilization of p53 and in the regulation of trophoblastic invasion.     

Optimized distance‐dependent atom‐pair‐based potential DOOP for protein structure prediction

The DOcking decoy‐based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance‐dependent atom‐pair interactions. To optimize the atom‐pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand–receptor systems (or just pairs). Thus, a total of 8609 ligand–receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand–receptor systems, 1000 evenly sampled docking decoys with 0–10 Å interface root‐mean‐square‐deviation (iRMSD) were generated with a method used before for protein–protein docking. A neural network‐based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel‐like energy landscape for the interaction between these hypothetical ligand–receptor systems. Thus, our method hierarchically models the overall funnel‐like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom‐pair‐based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation‐dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand–receptor systems and their decoys are freely available at http://agknapp.chemie.fu‐berlin.de/doop/ . Proteins 2015; 83:881–890. © 2015 Wiley Periodicals, Inc.     

Delineation of key XRCC4/Ligase IV interfaces for targeted disruption of non‐homologous end joining DNA repair

Efficient DNA repair mechanisms frequently limit the effectiveness of chemotherapeutic agents that act through DNA damaging mechanisms. Consequently, proteins involved in DNA repair have increasingly become attractive targets of high‐throughput screening initiatives to identify modulators of these pathways. Disruption of the XRCC4‐Ligase IV interaction provides a novel means to efficiently halt repair of mammalian DNA double strand break repair; however; the extreme affinity of these proteins presents a major obstacle for drug discovery. A better understanding of the interaction surfaces is needed to provide a more specific target for inhibitor studies. To clearly define key interface(s) of Ligase IV necessary for interaction with XRCC4, we developed a competitive displacement assay using ESI‐MS/MS and determined the minimal inhibitory fragment of the XRCC4‐interacting region (XIR) capable of disrupting a complex of XRCC4/XIR. Disruption of a single helix (helix 2) within the helix‐loop‐helix clamp of Ligase IV was sufficient to displace XIR from a preformed complex. Dose‐dependent response curves for the disruption of the complex by either helix 2 or helix‐loop‐helix fragments revealed that potency of inhibition was greater for the larger helix‐loop‐helix peptide. Our results suggest a susceptibility to inhibition at the interface of helix 2 and future studies would benefit from targeting this surface of Ligase IV to identify modulators that disrupt its interaction with XRCC4. Furthermore, helix 1 and loop regions of the helix‐loop‐helix clamp provide secondary target surfaces to identify adjuvant compounds that could be used in combination to more efficiently inhibit XRCC4/Ligase IV complex formation and DNA repair. Proteins 2014; 82:187–194. © 2013 Wiley Periodicals, Inc.     

Analysis on long‐range residue–residue communication using molecular dynamics

We investigated the possibility of inter‐residue communication of side chains in barstar, an 89 residue protein, using mutual information theory. The normalized mutual information (NMI) of the dihedral angles of the side chains was obtained from all‐atom molecular dynamics simulations. The accumulated NMI from an explicit solvent equilibrated trajectory (600 ns) with free backbone exhibits a parabola‐shaped distribution over the inter‐residue distances (0–36 Å): smaller at the end regimes but larger in the middle regime. This analysis, plus several other measures, does not find unusual long‐range communication for free backbone in explicit solvent simulations. Proteins 2014; 82:2896–2901. © 2014 Wiley Periodicals, Inc.     

Interaction of an ionic complementary peptide with a hydrophobic graphite surface

Protein adsorption on a surface plays an important role in biomaterial science and medicine. It is strongly related to the interaction between the protein residues and the surface. Here we report all‐atom molecular dynamics simulations of the adsorption of an ionic complementary peptide, EAK16‐II, to the hydrophobic highly ordered pyrolytic graphite surface. We find that, the hydrophobic interaction is the main force to govern the adsorption, and the peptide interchain electrostatic interaction affects the adsorption rate. Under neutral pH condition, the interchain electrostatic attraction facilitates the adsorption, whereas under acidic and basic conditions, because of the protonation and deprotonation of glutamic acid and lysine residues, respectively, the resulting electrostatic repulsion slows down the adsorption. We also found that under basic condition, during the adsorption peptide Chain II will be up against a choice to adsorb to the surface through the hydrophobic interaction or to form a temporary hydrophobic core with the deposited peptide Chain I. These results provide a basis for understanding some of the fundamental interactions governing peptide adsorption on the surface, which can shed new light on novel applications, such as the design of implant devices and drug delivery materials.     

Selective refinement and selection of near‐native models in protein structure prediction

In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near‐native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target‐specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all‐atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best‐refined models are selected with SMDR by testing their relative stability against gradual heating through all‐atom MD simulations. Through extensive testing we have found that Mufold‐MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions. Proteins 2015; 83:1823–1835. © 2015 Wiley Periodicals, Inc.     

DNA Modeller: an interactive program for modelling stacks of DNA base pairs on a microcomputer

DNA Modeller is a microcomputer program for interactively manipulatingup to 20 bp in a DNA double helical arrangement. It calculatesthe van der Waals and electrostatic energies of base-base interactionsusing the AMBER potential, minimizes the energy with respectto the pair (buckle, propeller, opening, shear, stretch, stagger)and step (tilt, roll, twist, shift, slide, rise) parameters,calculates lengths of the canonical hydrogen bonds between thecomplementary bases, and calculates interatomic distances betweenthe successive base pairs. Input/output files are simple listsof the step and pair parameters or lists of the atom specifications(N1, C2, etc.) and their Cartesian coordinates (compatible withthe Desktop Molecular Modeller ^*.mol files). The program issupplied with a readbrk utility which transforms PDB/NDB tothe^*.mol format readable by DNA Modeller. The DNA crystal structuresdeposited in the PDB or NDB databases can thus be analyzed,and their bases visualized and interactively manipulated. Inaddition, DNA Modeller can calculate the base pair and stepgeometrical parameters and interaction energies. A plotter utilitycreates wire mono or stereo pictures of the bases. This programis designed for IBM-compatible computers working under DOS orcan run as a DOS application under MS Windows 3.x or Merge (SCOUnix DOS emulator).     

Computational identification of post‐translational modification‐based nuclear import regulations by characterizing nuclear localization signal‐import receptor interaction

The binding affinity between a nuclear localization signal (NLS) and its import receptor is closely related to corresponding nuclear import activity. PTM‐based modulation of the NLS binding affinity to the import receptor is one of the most understood mechanisms to regulate nuclear import of proteins. However, identification of such regulation mechanisms is challenging due to the difficulty of assessing the impact of PTM on corresponding nuclear import activities. In this study we proposed NIpredict, an effective algorithm to predict nuclear import activity given its NLS, in which molecular interaction energy components (MIECs) were used to characterize the NLS‐import receptor interaction, and the support vector regression machine (SVR) was used to learn the relationship between the characterized NLS‐import receptor interaction and the corresponding nuclear import activity. Our experiments showed that nuclear import activity change due to NLS change could be accurately predicted by the NIpredict algorithm. Based on NIpredict, we developed a systematic framework to identify potential PTM‐based nuclear import regulations for human and yeast nuclear proteins. Application of this approach has identified the potential nuclear import regulation mechanisms by phosphorylation of two nuclear proteins including SF1 and ORC6. Proteins 2014; 82:2783–2796. © 2014 Wiley Periodicals, Inc.