Binding interface prediction by combining protein–protein docking results

We developed a method called residue contact frequency (RCF), which uses the complex structures generated by the protein–protein docking algorithm ZDOCK to predict interface residues. Unlike interface prediction algorithms that are based on monomers alone, RCF is binding partner specific. We evaluated the performance of RCF using the area under the precision‐recall (PR) curve (AUC) on a large protein docking Benchmark. RCF (AUC = 0.44) performed as well as meta‐PPISP (AUC = 0.43), which is one of the best monomer‐based interface prediction methods. In addition, we test a support vector machine (SVM) to combine RCF with meta‐PPISP and another monomer‐based interface prediction algorithm Evolutionary Trace to further improve the performance. We found that the SVM that combined RCF and meta‐PPISP achieved the best performance (AUC = 0.47). We used RCF to predict the binding interfaces of proteins that can bind to multiple partners and RCF was able to correctly predict interface residues that are unique for the respective binding partners. Furthermore, we found that residues that contributed greatly to binding affinity (hotspot residues) had significantly higher RCF than other residues. Proteins 2014; 82:57–66. © 2013 Wiley Periodicals, Inc.     

iATTRACT: Simultaneous global and local interface optimization for protein–protein docking refinement

Protein‐protein interactions are abundant in the cell but to date structural data for a large number of complexes is lacking. Computational docking methods can complement experiments by providing structural models of complexes based on structures of the individual partners. A major caveat for docking success is accounting for protein flexibility. Especially, interface residues undergo significant conformational changes upon binding. This limits the performance of docking methods that keep partner structures rigid or allow limited flexibility. A new docking refinement approach, iATTRACT, has been developed which combines simultaneous full interface flexibility and rigid body optimizations during docking energy minimization. It employs an atomistic molecular mechanics force field for intermolecular interface interactions and a structure‐based force field for intramolecular contributions. The approach was systematically evaluated on a large protein‐protein docking benchmark, starting from an enriched decoy set of rigidly docked protein–protein complexes deviating by up to 15 Å from the native structure at the interface. Large improvements in sampling and slight but significant improvements in scoring/discrimination of near native docking solutions were observed. Complexes with initial deviations at the interface of up to 5.5 Å were refined to significantly better agreement with the native structure. Improvements in the fraction of native contacts were especially favorable, yielding increases of up to 70%. Proteins 2015; 83:248–258. © 2014 Wiley Periodicals, Inc.     

PIE—Efficient filters and coarse grained potentials for unbound protein–protein docking

Identifying correct binding modes in a large set of models is an important step in protein–protein docking. We identified protein docking filter based on overlap area that significantly reduces the number of candidate structures that require detailed examination. We also developed potentials based on residue contacts and overlap areas using a comprehensive learning set of 640 two‐chain protein complexes with mathematical programming. Our potential showed substantially better recognition capacity compared to other publicly accessible protein docking potentials in discriminating between native and nonnative binding modes on a large test set of 84 complexes independent of our training set. We were able to rank a near‐native model on the top in 43 cases and within top 10 in 51 cases. We also report an atomic potential that ranks a near‐native model on the top in 46 cases and within top 10 in 58 cases. Our filter+potential is well suited for selecting a small set of models to be refined to atomic resolution. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

DockRank: Ranking docked conformations using partner‐specific sequence homology‐based protein interface prediction

Selecting near‐native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. DockRank uses interface residues predicted by partner‐specific sequence homology‐based protein–protein interface predictor (PS‐HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state‐of‐the‐art docking scoring functions using Success Rate (the percentage of cases that have at least one near‐native conformation among the top m conformations) and Hit Rate (the percentage of near‐native conformations that are included among the top m conformations). In cases where it is possible to obtain partner‐specific (PS) interface predictions from PS‐HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state‐of‐the‐art energy‐based scoring functions (improving Success Rate by up to 4‐fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39‐fold). The latter result underscores the importance of using partner‐specific interface residues in scoring docked conformations. We show that DockRank, when used to re‐rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/ . Proteins 2014; 82:250–267. © 2013 Wiley Periodicals, Inc.     

CyClus: A fast,comprehensive cylindrical interface approximation clustering/reranking method for rigid‐body protein–protein docking decoys

We propose a fast clustering and reranking method, CyClus, for protein–protein docking decoys. This method enables comprehensive clustering of whole decoys generated by rigid‐body docking using cylindrical approximation of the protein–proteininterface and hierarchical clustering procedures. We demonstrate the clustering and reranking of 54,000 decoy structures generated by ZDOCK for each complex within a few minutes. After parameter tuning for the test set in ZDOCK benchmark 2.0 with the ZDOCK and ZRANK scoring functions, blind tests for the incremental data in ZDOCK benchmark 3.0 and 4.0 were conducted. CyClus successfully generated smaller subsets of decoys containing near‐native decoys. For example, the number of decoys required to create subsets containing near‐native decoys with 80% probability was reduced from 22% to 50% of the number required in the original ZDOCK. Although specific ZDOCK and ZRANK results were demonstrated, the CyClus algorithm was designed to be more general and can be applied to a wide range of decoys and scoring functions by adjusting just two parameters, p and T. CyClus results were also compared to those from ClusPro. Proteins 2013; © 2012 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.     

Hot‐spot analysis to dissect the functional protein–protein interface of a tRNA‐modifying enzyme

Interference with protein–protein interactions of interfaces larger than 1500 Ų by small drug‐like molecules is notoriously difficult, particularly if targeting homodimers. The tRNA modifying enzyme Tgt is only functionally active as a homodimer. Thus, blocking Tgt dimerization is a promising strategy for drug therapy as this protein is key to the development of Shigellosis. Our goal was to identify hot‐spot residues which, upon mutation, result in a predominantly monomeric state of Tgt. The detailed understanding of the spatial location and stability contribution of the individual interaction hot‐spot residues and the plasticity of motifs involved in the interface formation is a crucial prerequisite for the rational identification of drug‐like inhibitors addressing the respective dimerization interface. Using computational analyses, we identified hot‐spot residues that contribute particularly to dimer stability: a cluster of hydrophobic and aromatic residues as well as several salt bridges. This in silico prediction led to the identification of a promising double mutant, which was validated experimentally. Native nano‐ESI mass spectrometry showed that the dimerization of the suggested mutant is largely prevented resulting in a predominantly monomeric state. Crystal structure analysis and enzyme kinetics of the mutant variant further support the evidence for enhanced monomerization and provide first insights into the structural consequences of the dimer destabilization. Proteins 2014; 82:2713–2732. © 2014 Wiley Periodicals, Inc.     

Different combinations of atomic interactions predict protein‐small molecule and protein‐DNA/RNA affinities with similar accuracy

Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein?ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein?protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

Peptidic modulators of protein‐protein interactions: Progress and challenges in computational design

With the decline in productivity of drug‐development efforts, novel approaches to rational drug design are being introduced and developed. Naturally occurring and synthetic peptides are emerging as novel promising compounds that can specifically and efficiently modulate signaling pathways in vitro and in vivo. We describe sequence‐based approaches that use peptides to mimic proteins in order to inhibit the interaction of the mimicked protein with its partners. We then discuss a structure‐based approach, in which protein‐peptide complex structures are used to rationally design and optimize peptidic inhibitors. We survey flexible peptide docking techniques and discuss current challenges and future directions in the rational design of peptidic inhibitors. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 505–513, 2009. This article was originally published online as an accepted preprint. The “Published Online”date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com     

Imidazo[1,2‐c]pyrimidin‐5(6H)‐one as a novel core of cyclin‐dependent kinase 2 inhibitors: Synthesis,activity measurement,docking, and quantum mechanical scoring

We report on the synthesis, activity testing, docking, and quantum mechanical scoring of novel imidazo[1,2‐c]pyrimidin‐5(6H)‐one scaffold for cyclin‐dependent kinase 2 (CDK2) inhibition. A series of 26 compounds substituted with aromatic moieties at position 8 has been tested in in vitro enzyme assays and shown to inhibit CDK2. 2D structure‐activity relationships have ascertained that small substituents at position 8 (up to the size of naphtyl or methoxyphenyl) generally lead to single‐digit micromolar IC₅₀ values, whereas bigger substituents (substituted biphenyls) decreased the compounds' activities. The binding modes of the compounds obtained using Glide docking have exhibited up to 2 hinge‐region hydrogen bonds to CDK2 and differed in the orientation of the inhibitor core and the placement of the 8‐substituents. Semiempirical quantum mechanics‐based scoring identified probable favourable binding modes, which will serve for future structure‐based design and synthetic optimization of substituents of the heterocyclic core. In summary, we have identified a novel core for CDK2 inhibition and will explore it further to increase the potencies of the compounds and also monitor selectivities against other protein kinases.     

Spectroscopic characterization,DFT studies,molecular docking and cytotoxic evaluation of 4‐nitro‐indole‐3‐carboxaldehyde: A potent lung cancer agent

The 4‐nitro‐1H‐indole‐carboxaldehyde (NICA) molecule was characterized experimentally using FT‐IR, FT‐Raman and UV‐Vis spectra, and it was studied theoretically using DFT calculations. The optimized structure of the NICA molecule was determined by DFT calculations using B3LYP functional with cc‐pVTZ basis set. The electron localization function (ELF) and local orbital localizer (LOL) studies were performed to visualize the electron delocalization in the molecule. The experimental and theoretical wavenumbers of the title molecule were assigned using VEDA 4.0 program. The charge delocalization and stability of the title molecule were investigated using natural bond orbital (NBO) analysis. Frontier molecular orbitals (FMOs) and related molecular properties were calculated. UV‐Vis spectrum was calculated theoretically and validated experimentally. The reactive sites of the molecule were studied from the MEP surface and Fukui function analysis. The molecular docking analysis reveals that the NICA ligand shows better inhibitory activity against RAS, which causes lung cancer. The in vitro cytotoxic activity of the molecule against human lung cancer cell lines (A549) was determined by MTT assay. Thus, the NICA molecule can be used as a potential candidate for the development of the drug against lung cancer.     

Surface shapes and surrounding environment analysis of single‐ and double‐stranded DNA‐binding proteins in protein‐DNA interface

Protein‐DNA bindings are critical to many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the residues shape (peak, flat, or valley) and the surrounding environment of double‐stranded DNA‐binding proteins (DSBs) and single‐stranded DNA‐binding proteins (SSBs) in protein‐DNA interfaces. In the results, we found that the interface shapes, hydrogen bonds, and the surrounding environment present significant differences between the two kinds of proteins. Built on the investigation results, we constructed a random forest (RF) classifier to distinguish DSBs and SSBs with satisfying performance. In conclusion, we present a novel methodology to characterize protein interfaces, which will deepen our understanding of the specificity of proteins binding to ssDNA (single‐stranded DNA) or dsDNA (double‐stranded DNA). Proteins 2016; 84:979–989. © 2016 Wiley Periodicals, Inc.     

Accurate prediction of interfacial residues in two‐domain proteins using evolutionary information: Implications for three‐dimensional modeling

With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure–function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two‐domain proteins. We also use information from the three‐dimensional structures of individual domains of two‐domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (～85%) and specific (～95%) to the domain–domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain–domain interaction, rigid‐body docking was able to provide us with accurate full‐length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. Proteins 2014; 82:1219–1234. © 2013 Wiley Periodicals, Inc.     

Loss of STI1‐mediated neuronal survival and differentiation in disease‐associated mutations of prion protein

Cellular prion protein (PrP^C ) is widely expressed and displays a variety of well‐described functions in the central nervous system (CNS ). Mutations of the PRNP gene are known to promote genetic human spongiform encephalopathies, but the components of gain‐ or loss‐of‐function mutations to PrP^C remain a matter for debate. Among the proteins described to interact with PrP^C is Stress‐inducible protein 1 (STI 1), a co‐chaperonin that is secreted from astrocytes and triggers neuroprotection and neuritogenesis through its interaction with PrP^C . In this work, we evaluated the impact of different PrP^C pathogenic point mutations on signaling pathways induced by the STI 1‐PrP^C interaction. We found that some of the pathogenic mutations evaluated herein induce partial or total disruption of neuritogenesis and neuroprotection mediated by mitogen‐activated protein kinase (MAPK )/extracellular signal‐regulated kinases 1 and 2 (ERK 1/2) and protein kinase A (PKA ) signaling triggered by STI 1‐PrP^C engagement. A pathogenic mutant PrP^C that lacked both neuroprotection and neuritogenesis activities fail to promote negative dominance upon wild‐type PrP^C . Also, a STI 1‐α7‐nicotinic acetylcholine receptor‐dependent cellular signaling was present in a PrP^C mutant that maintained both neuroprotection and neuritogenesis activities similar to what has been previously observed by wild‐type PrP^C . These results point to a loss‐of‐function mechanism underlying the pathogenicity of PrP^C mutations.

     

Molecular recognition and binding of beta‐lactamase II from Bacillus cereus with penicillin V and sulbactam by spectroscopic analysis in combination with docking simulation

The molecular recognition and binding interaction of beta‐lactamase II from Bacillus cereus (Bc II) with penicillin V (PV) and sulbactam (Sul) at 277 K were studied by spectroscopic analysis and molecular docking. The results showed that a non‐fluorescence static complex was separately formed between Bc II and two ligands, the molecular ratio of Bc II to PV or Sul was both 1:1 in the binding and the binding constants were 2.00 × 10⁶ and 3.98 × 10⁵ (L/mol), respectively. The negative free energy changes and apparent activation energies indicated that both the binding processes were spontaneous. Molecular docking showed that in the binding process, the whole Sul molecule entered into the binding pocket of Bc II while only part of the whole PV molecule entered into the pocket due to a long side chain, and electrostatic interactions were the major contribution to the binding processes. In addition, a weak conformational change of Bc II was also observed in the molecular recognition and binding process of Bc II with PV or Sul. This study may provide some valuable information for exploring the recognition and binding of proteins with ligands in the binding process and for the design of novel super‐antibiotics.     

Role of tyrosine hot‐spot residues at the interface of colicin E9 and immunity protein 9: A comparative free energy simulation study

The endonuclease activity of the bacterial colicin 9 enzyme is controlled by the specific and high‐affinity binding of immunity protein 9 (Im9). Molecular dynamics simulation studies in explicit solvent were used to investigate the free energy change associated with the mutation of two hot‐spot interface residues [tyrosine (Tyr): Tyr54 and Tyr55] of Im9 to Ala. In addition, the effect of several other mutations (Leu33Ala, Leu52Ala, Val34Ala, Val37Ala, Ser48Ala, and Ile53Ala) with smaller influence on binding affinity was also studied. Good qualitative agreement of calculated free energy changes and experimental data on binding affinity of the mutations was observed. The simulation studies can help to elucidate the molecular details on how the mutations influence protein–protein binding affinity. The role of solvent and conformational flexibility of the partner proteins was studied by comparing the results in the presence or absence of solvent and with or without positional restraints. Restriction of the conformational mobility of protein partners resulted in significant changes of the calculated free energies but of similar magnitude for isolated Im9 and for the complex and therefore in only modest changes of binding free energy differences. Although the overall binding free energy change was similar for the two Tyr–Ala mutations, the physical origin appeared to be different with solvation changes contributing significantly to the Tyr55Ala mutation and to a loss of direct protein–protein interactions dominating the free energy change due to the Tyr54Ala mutation. Proteins 2013. © 2012 Wiley Periodicals, Inc.