Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual.

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Comprehensive comparative assessment of the Arabidopsis thaliana MLO2-CALMODULIN2 interaction by various in vitro and in vivo protein-protein interaction assays.

Mildew resistance locus o (MLO) proteins are heptahelical integral membrane proteins of which some isoforms act as susceptibility factors for the powdery mildew pathogen. In many angiosperm plant species, loss-of-function mlo mutants confer durable broad-spectrum resistance against the fungal disease. Barley Mlo is known to interact via a cytosolic carboxyl-terminal domain with the intracellular calcium sensor calmodulin (CAM) in a calcium-dependent manner. Site-directed mutagenesis has revealed key amino acid residues in the barley Mlo calmodulin-binding domain (CAMBD) that, when mutated, affect the MLO-CAM association. We here tested the respective interaction between Arabidopsis thaliana MLO2 and CAM2 using seven different types of in vitro and in vivo protein-protein interaction assays. In each assay, we deployed a wild-type version of either the MLO2 carboxyl terminus (MLO2CT), harboring the CAMBD, or the MLO2 full-length protein and corresponding mutant variants in which two key residues within the CAMBD were substituted by non-functional amino acids. We focused in particular on the substitution of two hydrophobic amino acids (LW/RR mutant) and found in most protein-protein interaction experiments reduced binding of CAM2 to the corresponding MLO2/MLO2CT-LW/RR mutant variants in comparison with the respective wild-type versions. However, the Ura3-based yeast split-ubiquitin system and in planta bimolecular fluorescence complementation (BiFC) assays failed to indicate reduced CAM2 binding to the mutated CAMBD. Our data shed further light on the interaction of MLO and CAM proteins and provide a comprehensive comparative assessment of different types of protein-protein interaction assays with wild-type and mutant versions of an integral membrane protein.

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks.

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein-protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

Assessment of community efforts to advance network-based prediction of protein-protein interactions.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

A rapid, efficient, and low-cost BiFC protocol and its application in studying in vivo interaction of seed-specific transcription factors, RISBZ and RPBF.

Proteins regulate cellular and biological processes in all living organisms. More than 80% of the proteins interact with one another to perform their respective functions; therefore, studying the protein-protein-interaction has gained attention in functional characterization studies. Bimolecular fluorescence complement (BiFC) assay is widely adopted to determine the physical interaction of two proteins in vivo. Here, we developed a simple, yet effective BiFC assay for protein-protein-interaction using transient Agrobacterium-mediated-transformation of onion epidermal cells by taking case study of Rice-P-box-Binding-Factor (RPBF) and rice-seed-specific-bZIP (RISBZ) in vivo interaction. Our result revealed that both the proteins, i.e., RISBZ and RPBF, interacted in the nucleus and cytosol. These two transcription factors are known for their coordinate/synergistic regulation of seed-protein content via concurrent binding to the promoter region of the seed storage protein (SSP) encoding genes. We further validated our results with BiFC assay in Nicotiana by agroinfiltration method, which exhibited similar results as Agrobacterium-mediated-transformation of onion epidermal cells. We also examined the subcellular localization of RISBZ and RPBF to assess the efficacy of the protocol. The subcellular localization and BiFC assay presented here is quite easy-to-follow, reliable, and reproducible, which can be completed within 2-3 days without using costly instruments and technologies that demand a high skill set.

Assessment of protein-protein interfaces in cryo-EM derived assemblies.

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein-protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.

Systems pharmacology-based study of Tanreqing injection in airway mucus hypersecretion.

ETHNOPHARMACOLOGICAL RELEVANCE: Mucus hypersecretion (MH) is recognized as a key pathophysiological and clinical feature of many airway inflammatory diseases. MUC5AC is a major component of airway mucus. Tanreqing injection (TRQ) is a widely used herbal formula for the treatment of respiratory inflammations for years in China. However, a holistic network pharmacology approach to understanding its therapeutic mechanisms against MH has not been pursued. AIM OF THE STUDY: This study aimed to explore the systems-level potential active compounds and therapeutic mechanisms of TRQ in the treatment of MH. MATERIALS AND METHODS: We established systems pharmacology-based strategies comprising compound screenings, target predictions, and pathway identifications to speculate the potential active compounds and therapeutic targets of TRQ. We also applied compound-target and target-disease network analyses to evaluate the possible action mechanisms of TRQ. Then, lipopolysaccharide (LPS)-induced Sprague-Dawley (SD) rat model was constructed to assess the effect of TRQ in the treatment of MH and to validate the possible molecular mechanisms as predicted in systems pharmacology approach. RESULTS: The comprehensive compound collection successfully generated 55 compound candidates from TRQ. Among them, 11 compounds with high relevance to the potential targets were defined as representative and potential active ingredients in TRQ formula. Target identification revealed 172 potential targets, including pro-inflammatory cytokines of tumor necrosis factor α (TNF-α), interleukin (IL)-6, and IL-8. Pathway analyses uncovered the possible action of TRQ in the regulation of IL-17 signaling pathway and its downstream protein MUC5AC. Then in vivo experiment indicated that TRQ could significantly inhibit LPS stimulated MUC5AC over-production as well as the expression of TNF-α, IL-6, IL-8, and IL-17A, in both protein and mRNA levels. CONCLUSIONS: Based on the systems pharmacology method and in vivo experiment, our work provided a general knowledge on the potential active compounds and possible therapeutic targets of TRQ formula in its anti-MH process. This work might suggest directions for further research on TRQ and provide more insight into better understanding the chemical and pharmacological mechanisms of complex herbal prescriptions in a network perspective.

LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules.

BACKGROUND: Cross-species analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved interaction patterns. Identifying such conserved substructures between PPI networks of different species increases our understanding of the principles deriving evolution of cellular organizations and their functions in a system level. In recent years, network alignment techniques have been applied to genome-scale PPI networks to predict evolutionary conserved modules. Although a wide variety of network alignment algorithms have been introduced, developing a scalable local network alignment algorithm with high accuracy is still challenging. RESULTS: We present a novel pairwise local network alignment algorithm, called LePrimAlign, to predict conserved modules between PPI networks of three different species. The proposed algorithm exploits the results of a pairwise global alignment algorithm with many-to-many node mapping. It also applies the concept of graph entropy to detect initial cluster pairs from two networks. Finally, the initial clusters are expanded to increase the local alignment score that is formulated by a combination of intra-network and inter-network scores. The performance comparison with state-of-the-art approaches demonstrates that the proposed algorithm outperforms in terms of accuracy of identified protein complexes and quality of alignments. CONCLUSION: The proposed method produces local network alignment of higher accuracy in predicting conserved modules even with large biological networks at a reduced computational cost.

Detection of dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach.

The accessibility of a huge amount of protein-protein interaction (PPI) data has allowed to do research on biological networks that reveal the structure of a protein complex, pathways and its cellular organization. A key demand in computational biology is to recognize the modular structure of such biological networks. The detection of protein complexes from the PPI network, is one of the most challenging and significant problems in the post-genomic era. In Bioinformatics, the frequently employed approach for clustering the networks is Markov Clustering (MCL). Many of the researches for protein complex detection were done on the static PPI network, which suffers from a few drawbacks. To resolve this problem, this paper proposes an approach to detect the dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach (DMCL-EHO). Initially, the proposed method divides the PPI network into a set of dynamic subnetworks under various time points by combining the gene expression data and secondly, it employs the clustering analysis on every subnetwork using the MCL along with Elephant Herd Optimization approach. The experimental analysis was employed on different PPI network datasets and the proposed method surpasses various existing approaches in terms of accuracy measures. This paper identifies the common protein complexes that are expressively enriched in gold-standard datasets and also the pathway annotations of the detected protein complexes using the KEGG database.

Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields.

The understanding of subcellular localization (SCL) of proteins and proteome variation in the different tissues and organs of the human body are two crucial aspects for increasing our knowledge of the dynamic rules of proteins, the cell biology, and the mechanism of diseases. Although there have been tremendous contributions to these two fields independently, the lack of knowledge of the variation of spatial distribution of proteins in the different tissues still exists. Here, we proposed an approach that allows predicting protein SCL on tissue specificity through the use of tissue-specific functional associations and physical protein-protein interactions (PPIs). We applied our previously developed Bayesian collective Markov random fields (BCMRFs) on tissue-specific protein-protein interaction network (PPI network) for nine types of tissues focusing on eight high-level SCL. The evaluated results demonstrate the strength of our approach in predicting tissue-specific SCL. We identified 1,314 proteins that their SCL were previously proven cell line dependent. We predicted 549 novel tissue-specific localized candidate proteins while some of them were validated via text-mining.

Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods.

The identification of plant-pathogen protein-protein interactions (PPIs) is an attractive and challenging research topic for deciphering the complex molecular mechanism of plant immunity and pathogen infection. Considering that the experimental identification of plant-pathogen PPIs is time-consuming and labor-intensive, computational methods are emerging as an important strategy to complement the experimental methods. In this work, we first evaluated the performance of traditional computational methods such as interolog, domain-domain interaction and domain-motif interaction in predicting known plant-pathogen PPIs. Owing to the low sensitivity of the traditional methods, we utilized Random Forest to build an inter-species PPI prediction model based on multiple sequence encodings and novel network attributes in the established plant PPI network. Critical assessment of the features demonstrated that the integration of sequence information and network attributes resulted in significant and robust performance improvement. Additionally, we also discussed the influence of Gene Ontology and gene expression information on the prediction performance. The Web server implementing the integrated prediction method, named InterSPPI, has been made freely available at http://systbio.cau.edu.cn/intersppi/index.php. InterSPPI could achieve a reasonably high accuracy with a precision of 73.8% and a recall of 76.6% in the independent test. To examine the applicability of InterSPPI, we also conducted cross-species and proteome-wide plant-pathogen PPI prediction tests. Taken together, we hope this work can provide a comprehensive understanding of the current status of plant-pathogen PPI predictions, and the proposed InterSPPI can become a useful tool to accelerate the exploration of plant-pathogen interactions.

Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment.

Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein⁻protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein⁻protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.

PrimAlign: PageRank-inspired Markovian alignment for large biological networks.

Motivation: Cross-species analysis of large-scale protein-protein interaction (PPI) networks has played a significant role in understanding the principles deriving evolution of cellular organizations and functions. Recently, network alignment algorithms have been proposed to predict conserved interactions and functions of proteins. These approaches are based on the notion that orthologous proteins across species are sequentially similar and that topology of PPIs between orthologs is often conserved. However, high accuracy and scalability of network alignment are still a challenge. Results: We propose a novel pairwise global network alignment algorithm, called PrimAlign, which is modeled as a Markov chain and iteratively transited until convergence. The proposed algorithm also incorporates the principles of PageRank. This approach is evaluated on tasks with human, yeast and fruit fly PPI networks. The experimental results demonstrate that PrimAlign outperforms several prevalent methods with statistically significant differences in multiple evaluation measures. PrimAlign, which is multi-platform, achieves superior performance in runtime with its linear asymptotic time complexity. Further evaluation is done with synthetic networks and results suggest that popular topological measures do not reflect real precision of alignments. Availability and implementation: The source code is available at http://web.ecs.baylor.edu/faculty/cho/PrimAlign. Supplementary information: Supplementary data are available at Bioinformatics online.

Identification of key genes in non­alcoholic fatty liver disease progression based on bioinformatics analysis.

Due to economic development and lifestyle changes, the incidence of non­alcoholic fatty liver disease (NAFLD) has gradually increased in recent years. However, the pathogenesis of NAFLD is not yet fully understood. To identify candidate genes that contribute to the development and progression of NAFLD, two microarray datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified and functional enrichment analyses were performed. A protein­protein interaction network was constructed and modules were extracted using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. The enriched functions and pathways of the DEGs included 'cellular macromolecule biosynthetic process', 'cellular response to chemical stimulus', 'extracellular matrix organization', 'metabolic pathways', 'insulin resistance' and 'forkhead box protein O1 signaling pathway'. The DEGs, including type­1 angiotensin II receptor, formin­binding protein 1­like, RNA­binding protein with serine­rich domain 1, Ras­related C3 botulinum toxin substrate 1 and polyubiquitin­C, were identified using multiple bioinformatics methods and validated in vitro with reverse transcription­quantitative polymerase chain reaction analysis. In conclusion, five hub genes were identified in the present study, and they may aid in understanding of the molecular mechanisms underlying the development and progression of NAFLD.

A nonparametric significance test for sampled networks.

Motivation: Our work is motivated by an interest in constructing a protein-protein interaction network that captures key features associated with Parkinson's disease. While there is an abundance of subnetwork construction methods available, it is often far from obvious which subnetwork is the most suitable starting point for further investigation. Results: We provide a method to assess whether a subnetwork constructed from a seed list (a list of nodes known to be important in the area of interest) differs significantly from a randomly generated subnetwork. The proposed method uses a Monte Carlo approach. As different seed lists can give rise to the same subnetwork, we control for redundancy by constructing a minimal seed list as the starting point for the significance test. The null model is based on random seed lists of the same length as a minimum seed list that generates the subnetwork; in this random seed list the nodes have (approximately) the same degree distribution as the nodes in the minimum seed list. We use this null model to select subnetworks which deviate significantly from random on an appropriate set of statistics and might capture useful information for a real world protein-protein interaction network. Availability and implementation: The software used in this paper are available for download at https://sites.google.com/site/elliottande/. The software is written in Python and uses the NetworkX library. Contact: ande.elliott@gmail.com or felix.reed-tsochas@sbs.ox.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree.

Nowadays a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. In the present work, we propose a method for predicting protein interactions making full use of physicochemical characteristics of amino acids. A protein sequence is encoded at multi-scale by seven properties, including their qualitative and quantitative descriptions, of amino acids. Five kinds of protein descriptors, frequency, composition, transformation, distribution and auto covariance, are extracted from these encodings for representing each protein sequence. The new formed feature representation consisted of 347 dimensions is able to capture not only the compositional and positional information but also their statistical significance of amino acids in the sequence. Based on such a feature representation, the gradient boosting decision tree algorithm is introduced to predict protein interaction class. When the proposed method is tested with the PPI data of S.cerevisiae, it achieves a prediction accuracy of 95.28% at the Matthew's correlation coefficient of 90.68%. Compared with the state-of-the-art works on H.pylori and Human, the accuracies can be raised to 89.27% and 98.00% respectively. Extensive experiments are performed for a crossover protein-protein interactions network and the prediction accuracies are also very promising. Because of learning capabilities of the gradient boosting decision tree and the mutil-scale feature representation scheme, the proposed method might be a useful tool for future proteomics studies.

Quantitative Assessment of the Effects of Trypsin Digestion Methods on Affinity Purification-Mass Spectrometry-based Protein-Protein Interaction Analysis.

Affinity purification-mass spectrometry (AP-MS) has become the method of choice for discovering protein-protein interactions (PPIs) under native conditions. The success of AP-MS depends on the efficiency of trypsin digestion and the recovery of the tryptic peptides for MS analysis. Several different protocols have been used for trypsin digestion of protein complexes in AP-MS studies, but no systematic studies have been conducted on the impact of trypsin digestion conditions on the identification of PPIs. Here, we used NFκB/RelA and Bromodomain-containing protein 4 (BRD4) as baits and test five distinct trypsin digestion methods (two using "on-beads," three using "elution-digestion" protocols). Although the performance of the trypsin digestion protocols change slightly depending on the different baits, antibodies and cell lines used, we found that elution-digestion methods consistently outperformed on-beads digestion methods. The high-abundance interactors can be identified universally by all five methods, but the identification of low-abundance RelA interactors is significantly affected by the choice of trypsin digestion method. We also found that different digestion protocols influence the selected reaction monitoring (SRM)-MS quantification of PPIs, suggesting that optimization of trypsin digestion conditions may be required for robust targeted analysis of PPIs.

Advances in the development of human protein microarrays.

INTRODUCTION: High-content protein microarrays in principle enable the functional interrogation of the human proteome in a broad range of applications, including biomarker discovery, profiling of immune responses, identification of enzyme substrates, and quantifying protein-small molecule, protein-protein and protein-DNA/RNA interactions. As with other microarrays, the underlying proteomic platforms are under active technological development and a range of different protein microarrays are now commercially available. However, deciphering the differences between these platforms to identify the most suitable protein microarray for the specific research question is not always straightforward. Areas covered: This review provides an overview of the technological basis, applications and limitations of some of the most commonly used full-length, recombinant protein and protein fragment microarray platforms, including ProtoArray Human Protein Microarrays, HuProt Human Proteome Microarrays, Human Protein Atlas Protein Fragment Arrays, Nucleic Acid Programmable Arrays and Immunome Protein Arrays. Expert commentary: The choice of appropriate protein microarray platform depends on the specific biological application in hand, with both more focused, lower density and higher density arrays having distinct advantages. Full-length protein arrays offer advantages in biomarker discovery profiling applications, although care is required in ensuring that the protein production and array fabrication methodology is compatible with the required downstream functionality.

SEQUOIA: significance enhanced network querying through context-sensitive random walk and minimization of network conductance.

BACKGROUND: Network querying algorithms provide computational means to identify conserved network modules in large-scale biological networks that are similar to known functional modules, such as pathways or molecular complexes. Two main challenges for network querying algorithms are the high computational complexity of detecting potential isomorphism between the query and the target graphs and ensuring the biological significance of the query results. RESULTS: In this paper, we propose SEQUOIA, a novel network querying algorithm that effectively addresses these issues by utilizing a context-sensitive random walk (CSRW) model for network comparison and minimizing the network conductance of potential matches in the target network. The CSRW model, inspired by the pair hidden Markov model (pair-HMM) that has been widely used for sequence comparison and alignment, can accurately assess the node-to-node correspondence between different graphs by accounting for node insertions and deletions. The proposed algorithm identifies high-scoring network regions based on the CSRW scores, which are subsequently extended by maximally reducing the network conductance of the identified subnetworks. CONCLUSIONS: Performance assessment based on real PPI networks and known molecular complexes show that SEQUOIA outperforms existing methods and clearly enhances the biological significance of the query results. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/SEQUOIA .

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.

Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scoring step is one of the bottlenecks in the performance of many state-of-the-art protocols. The performance of scoring functions depends on the quality of the generated structures and its coupling to the sampling algorithm. A tool kit, GRADSCOPT (GRid Accelerated Directly SCoring OPTimizing), was designed to allow rapid development and optimization of different knowledge-based scoring potentials for specific objectives in protein-protein docking. Different atomistic and coarse-grained potentials can be created by a grid-accelerated directly scoring dependent Monte-Carlo annealing or by a linear regression optimization. We demonstrate that the scoring functions generated by our approach are similar to or even outperform state-of-the-art scoring functions for predicting near-native solutions. Of additional importance, we find that potentials specifically trained to identify the native bound complex perform rather poorly on identifying acceptable or medium quality (near-native) solutions. In contrast, atomistic long-range contact potentials can increase the average fraction of near-native poses by up to a factor 2.5 in the best scored 1% decoys (compared to existing scoring), emphasizing the need of specific docking potentials for different steps in the docking protocol.