Comments on the article "On evaluating molecular-docking methods for pose prediction and enrichment factors"

The recent article "On Evaluating Molecular-Docking Methods for Pose Prediction and Enrichment Factors" (Chen H. et al. J. Chem. Inf. Model. 2006, 46, 401-415) contains a series of comments on a similar study we published in Proteins in 2004 (Perola et al. Proteins 2004, 56, 235-249). We believe that some of these comments are misleading, and we feel that an adequate response is in order.     

Structure-based virtual screening with supervised consensus scoring: evaluation of pose prediction and enrichment factors

Since the evaluation of ligand conformations is a crucial aspect of structure-based virtual screening, scoring functions play significant roles in it. However, it is known that a scoring function does not always work well for all target proteins. When one cannot know which scoring function works best against a target protein a priori, there is no standard scoring method to know it even if 3D structure of a target protein-ligand complex is available. Therefore, development of the method to achieve high enrichments from given scoring functions and 3D structure of protein-ligand complex is a crucial and challenging task. To address this problem, we applied SCS (supervised consensus scoring), which employs a rough linear correlation between the binding free energy and the root-mean-square deviation (rmsd) of a native ligand conformations and incorporates protein-ligand binding process with docked ligand conformations using supervised learning, to virtual screening. We evaluated both the docking poses and enrichments of SCS and five scoring functions (F-Score, G-Score, D-Score, ChemScore, and PMF) for three different target proteins: thymidine kinase (TK), thrombin (thrombin), and peroxisome proliferator-activated receptor gamma (PPARgamma). Our enrichment studies show that SCS is competitive or superior to a best single scoring function at the top ranks of screened database. We found that the enrichments of SCS could be limited by a best scoring function, because SCS is obtained on the basis of the five individual scoring functions. Therefore, it is concluded that SCS works very successfully from our results. Moreover, from docking pose analysis, we revealed the connection between enrichment and average centroid distance of top-scored docking poses. Since SCS requires only one 3D structure of protein-ligand complex, SCS will be useful for identifying new ligands.     

FRED pose prediction and virtual screening accuracy

Results of a previous docking study are reanalyzed and extended to include results from the docking program FRED and a detailed statistical analysis of both structure reproduction and virtual screening results. FRED is run both in a traditional docking mode and in a hybrid mode that makes use of the structure of a bound ligand in addition to the protein structure to screen molecules. This analysis shows that most docking programs are effective overall but highly inconsistent, tending to do well on one system and poorly on the next. Comparing methods, the difference in mean performance on DUD is found to be statistically significant (95% confidence) 61% of the time when using a global enrichment metric (AUC). Early enrichment metrics are found to have relatively poor statistical power, with 0.5% early enrichment only able to distinguish methods to 95% confidence 14% of the time.     

Specific enrichment methods for glycoproteome research

Glycosylation is, by far, one of the most common and important post-translational modifications and becomes a target for proteomic research. A key challenge in glycoproteome research is the development of fast and effective enrichment strategies for high-throughput glycosylation analysis. Different kinds of glycan-capturing anchors have been developed and successfully applied to glyco-specific enrichment in large-scale glycosylation identification in the past few years. In this paper, we highlight several examples on various types of enrichment methods that have been utilized to specifically capture glycopeptides/glycoproteins for subsequent mass spectrometric analysis.     

N-磷酸化修饰蛋白质的富集和鉴定方法

蛋白质磷酸化修饰在细胞的信号转导、代谢、发育等生命过程中发挥着重要作用。除了研究较为透彻的发生在丝氨酸、苏氨酸和酪氨酸侧链羟基的O-磷酸化修饰之外,近年来,发生在组氨酸、精氨酸和赖氨酸侧链氨基的N-磷酸化修饰受到了越来越广泛的关注。然而,由于N-磷酸化修饰具有独特的P-N键结构,导致其化学稳定性差。尤其是在O-磷酸化肽段富集常用的酸性条件下,N-磷酸化极易丢失。因此,目前对N-磷酸化蛋白质的研究仍处于初始阶段。该文针对蛋白质N-磷酸化修饰的特点、富集和鉴定方法进行了综述,并对其发展前景进行了展望。     

Evaluation of DOCK 6 as a pose generation and database enrichment tool

In conjunction with the recent American Chemical Society symposium titled "Docking and Scoring: A Review of Docking Programs" the performance of the DOCK6 program was evaluated through (1) pose reproduction and (2) database enrichment calculations on a common set of organizer-specified systems and datasets (ASTEX, DUD, WOMBAT). Representative baseline grid score results averaged over five docking runs yield a relatively high pose identification success rate of 72.5?% (symmetry corrected rmsd) and sampling rate of 91.9?% for the multi site ASTEX set (N?=?147) using organizer-supplied structures. Numerous additional docking experiments showed that ligand starting conditions, symmetry, multiple binding sites, clustering, and receptor preparation protocols all affect success. Encouragingly, in some cases, use of more sophisticated scoring and sampling methods yielded results which were comparable (Amber score ligand movable protocol) or exceeded (LMOD score) analogous baseline grid-score results. The analysis highlights the potential benefit and challenges associated with including receptor flexibility and indicates that different scoring functions have system dependent strengths and weaknesses. Enrichment studies with the DUD database prepared using the SB2010 preparation protocol and native ligand pairings yielded individual area under the curve (AUC) values derived from receiver operating characteristic curve analysis ranging from 0.29 (bad enrichment) to 0.96 (good enrichment) with an average value of 0.60 (27/38 have AUC?≥?0.5). Strong early enrichment was also observed in the critically important 1.0-2.0?% region. Somewhat surprisingly, an alternative receptor preparation protocol yielded comparable results. As expected, semi-random pairings yielded poorer enrichments, in particular, for unrelated receptors. Overall, the breadth and number of experiments performed provide a useful snapshot of current capabilities of DOCK6 as well as starting points to guide future development efforts to further improve sampling and scoring.     

Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016

We describe the performance of multiple pose prediction methods for the D3R 2016 Grand Challenge. The pose prediction challenge includes 36 ligands, which represent 4 chemotypes and some miscellaneous structures against the FXR ligand binding domain. In this study we use a mix of fully automated methods as well as human-guided methods with considerations of both the challenge data and publicly available data. The methods include ensemble docking, colony entropy pose prediction, target selection by molecular similarity, molecular dynamics guided pose refinement, and pose selection by visual inspection. We evaluated the success of our predictions by method, chemotype, and relevance of publicly available data. For the overall data set, ensemble docking, visual inspection, and molecular dynamics guided pose prediction performed the best with overall mean RMSDs of 2.4, 2.2, and 2.2 Å respectively. For several individual challenge molecules, the best performing method is evaluated in light of that particular ligand. We also describe the protein, ligand, and public information data preparations that are typical of our binding mode prediction workflow.     

生物表面活性剂及其评价方法

生物表面活性剂是由微生物分泌的天然产物,它无毒,可以生物降解,对环境影响很小,具有较高的表面活性,因此是合成表面活性剂的理想替代品.本文主要阐述了生物表面活性剂的类别、特性、及其表面活性的定性、定量分析及评价方法.     

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.     

Thermal methods for evaluating polymorphic transitions in nifedipine

The thermal behaviour of nifedipine was studied with the view to understand the various phase transitions between its polymorphs. The focus was on polymorph identification, accompanying morphological changes during crystallization and the nature of the phase transformations. These features were compared to the complexity of the crystallization mechanisms, studied by dynamic differential scanning calorimetry (DSC) heating techniques. DSC, thermogravimetry (TG) established the temperature limits for preparation of amorphous nifedipine from the melt. DSC studies identified that metastable form B, melting point ∼163 °C, was enantiotropically related to a third modification, form C, which existed at lower temperatures. Form C converted endothermically to form B at ∼56 °C on heating and was shown by hot stage microscopy (HSM) to be accompanied by morphological changes. Modulated temperature differential scanning calorimetry (MTDSC) showed discontinuities in the reversing heat flow signal during crystallization of amorphous nifedipine (from ∼92 °C) to form B, which suggested that a number of polymorphs may nucleate from the melt prior to form B formation. Identification of the number of nifedipine polymorphs included the use of combined DSC-powder X-ray diffraction (PXRD) and variable temperature powder X-ray diffraction (VTPXRD). The crystallization kinetics studied by dynamic DSC heating techniques followed by analysis using the Friedman isoconversion method where values of activation energy (E) and frequency factor (A) were estimated as a function of alpha or extent of conversion (α). The variations in E with α, from 0.05 to 0.9, for the amorphous to form B conversion could indicate the formation of intermediate polymorphs prior to form B. The form B to form A conversion showed a constancy in E on kinetic analysis from α 0.05 to 0.9, which suggested that a constant crystallization mechanism operated during formation of the thermodynamically stable form A.     

The ensemble performance index: an improved measure for assessing ensemble pose prediction performance

We present a theoretical study on the performance of ensemble docking methodologies considering multiple protein structures. We perform a theoretical analysis of pose prediction experiments which is completely unbiased, as we make no assumptions about specific scoring functions, search paradigms, protein structures, or ligand data sets. We introduce a novel interpretable measure, the ensemble performance index (EPI), for the assessment of scoring performance in ensemble docking, which will be applied to simulated and real data sets.     

A pose prediction approach based on ligand 3D shape similarity

Molecular docking predicts the best pose of a ligand in the target protein binding site by sampling and scoring numerous conformations and orientations of the ligand. Failures in pose prediction are often due to either insufficient sampling or scoring function errors. To improve the accuracy of pose prediction by tackling the sampling problem, we have developed a method of pose prediction using shape similarity. It first places a ligand conformation of the highest 3D shape similarity with known crystal structure ligands into protein binding site and then refines the pose by repacking the side-chains and performing energy minimization with a Monte Carlo algorithm. We have assessed our method utilizing CSARdock 2012 and 2014 benchmark exercise datasets consisting of co-crystal structures from eight proteins. Our results revealed that ligand 3D shape similarity could substitute conformational and orientational sampling if at least one suitable co-crystal structure is available. Our method identified poses within 2 Å RMSD as the top-ranking pose for 85.7 % of the test cases. The median RMSD for our pose prediction method was found to be 0.81 Å and was better than methods performing extensive conformational and orientational sampling within target protein binding sites. Furthermore, our method was better than similar methods utilizing ligand 3D shape similarity for pose prediction.     

An overview on enrichment methods for cell surface proteome profiling

Cell surface proteins are essential for many important biological processes, including cell–cell interactions, signal transduction, and molecular transportation. With the characteristics of low abundance, high hydrophobicity, and high heterogeneity, it is difficult to get a comprehensive view of cell surface proteome by direct analysis. Thus, it is important to selectively enrich the cell surface proteins before liquid chromatography with mass spectrometry analysis. In recent years, a variety of enrichment methods have been developed. Based on the separation mechanism, these methods could be mainly classified into three types. The first type is based on their difference in the physicochemical property, such as size, density, charge, and hydrophobicity. The second one is based on the bimolecular affinity interaction with lectin or antibody. And the third type is based on the chemical covalent coupling to free side groups of surface‐exposed proteins or carbohydrate chains, such as primary amines, carboxyl groups, glycan side chains. In addition, metabolic labeling and enzymatic reaction‐based methods have also been employed to selectively isolate cell surface proteins. In this review, we will provide a comprehensive overview of the enrichment methods for cell surface proteome profiling.     

Multivariate statistical methods for evaluating biodegradation of mineral oil

Two methods were developed for evaluating natural attenuation and bioremediation of mineral oil after environmental spills and during in vitro experiments. Gas chromatography-mass spectrometry (GC-MS) in selected ion monitoring (SIM) mode was used to obtain compound-specific data. The chromatographic data were then preprocessed either by calculating the first derivative, retention time alignment and normalization or by peak identification, quantification and calculation of diagnostic ratios within homologue series of polycyclic aromatic compounds (PACs). Finally, principal component analysis (PCA) was applied to the preprocessed chromatograms or diagnostic ratios to study the fate of the oil. The methods were applied to data from an in vitro biodegradation experiment with a North Sea crude oil exposed to three mixtures of bacterial strains: R (alkane degraders and surfactant producers), U (PAC degraders) and M (mixture of R- and U-strains) over a 1-year-period with five sampling times. Assessment of variation in degradability within isomer groups of methylfluorenes (m/z 180), methylphenanthrenes (m/z 192) and methyldibenzothiophenes (m/z 198) was used to evaluate the effects of microbial degradation on the composition of the oil. The two evaluation methods gave comparable results. In the objective pattern matching approach, principal component 1 (PC1) described the general changes in the isomer abundances, whereas M samples were separated from U and R samples along PC2. Furthermore, in the diagnostic ratio approach, a third component (PC3) could be extracted; although minor, it separated R samples from U and M samples. These results demonstrated that the two methods were able to differentiate between the effects due to the different bacterial activities, and that bacterial strain mixtures affected the PAC isomer patterns in different ways in accordance with their different metabolic capabilities.     

Industrial need for accurate thermophysical data and for reliable prediction methods

Intelligent design of chemical-process equipment requires accurate thermophysical property values for pure components and mixtures including solutions. Databases used by practicing engineers should include the best numbers available and estimated uncertainties of these numbers. Many important property values have not been measured and must be estimated. Care should be exercised in selecting the estimation method. Property values that are a function of temperature, pressure, and/or composition can be correlated using appropriate equations. Such equations and the number of adjustable parameters in these equations should be selected with care. Examples of determining uncertainties, estimation techniques used, and correlating equations are given.