Search

The global connectivities in very large protein similarity networks contain traces of evolution among the proteins for detecting protein remote evolutionary relations or structural similarities. To investigate how well a protein network captures the evolutionary information, a key limitation is the intensive computation of pairwise sequence similarities needed to construct very large protein networks. In this article, we introduce label propagation on low-rank kernel approximation (LP-LOKA) for searching massively large protein networks. LP-LOKA propagates initial protein similarities in a low-rank graph by Nyström approximation without computing all pairwise similarities. With scalable parallel implementations based on distributed-memory using message-passing interface and Apache-Hadoop/Spark on cloud, LP-LOKA can search protein networks with one million proteins or more. In the experiments on Swiss-Prot/ADDA/CASP data, LP-LOKA significantly improved protein ranking over the widely used HMM-HMM or profile-sequence alignment methods utilizing large protein networks. It was observed that the larger the protein similarity network, the better the performance, especially on relatively small protein superfamilies and folds. The results suggest that computing massively large protein network is necessary to meet the growing need of annotating proteins from newly sequenced species and LP-LOKA is both scalable and accurate for searching massively large protein networks.

Protein similarity comparisons may be made on a local or global basis and may consider sequence information or differing levels of structural information. We present a local three-dimensional method that compares protein binding site surfaces in full atomic detail. The approach is based on the morphological similarity method which has been widely applied for global comparison of small molecules. We apply the method to all-by-all comparisons two sets of human protein kinases, a very diverse set of ATP-bound proteins from multiple species, and three heterogeneous benchmark protein binding site data sets. Cases of disagreement between sequence-based similarity and binding site similarity yield informative examples. Where sequence similarity is very low, high pocket similarity can reliably identify important binding motifs. Where sequence similarity is very high, significant differences in pocket similarity are related to ligand binding specificity and similarity. Local protein binding pocket similarity provides qualitatively complementary information to other approaches, and it can yield quantitative information in support of functional annotation. Proteins 2011; © 2011 Wiley-Liss, Inc.

A statistical method to predict protein pK(a) has been developed by using the 3D structure of a protein and a database of 434 experimental protein pK(a) values. Each pK(a) in the database is associated with a fingerprint that describes the chemical environment around an ionizable residue. A computational tool, MoKaBio, has been developed to identify automatically ionizable residues in a protein, generate fingerprints that describe the chemical environment around such residues, and predict pK(a) from the experimental pK(a) values in the database by using a similarity metric. The method, which retrieved the pK(a) of 429 of the 434 ionizable sites in the database correctly, was crossvalidated by leave-one-out and yielded root mean square error (RMSE) = 0.95, a result that is superior to that obtained by using the Null Model (RMSE 1.07) and other well-established protein pK(a) prediction tools. This novel approach is suitable to rationalize protein pK(a) by comparing the region around the ionizable site with similar regions whose ionizable site pK(a) is known. The pK(a) of residues that have a unique environment not represented in the training set cannot be predicted accurately, however, the method offers the advantage of being trainable to increase its predictive power.

Perhaps the most widely used applications of bioinformatics are tools such as psi-blast for searching sequence databases. We describe a recently developed protein database search algorithm called rankprop. rankprop relies upon a precomputed network of pairwise protein similarities. The algorithm performs a diffusion operation from a specified query protein across the protein similarity network. The resulting activation scores, assigned to each database protein, encode information about the global structure of the protein similarity network. This type of algorithm has a rich history in associationist psychology, artificial intelligence and web search. We describe the rankprop algorithm and its relatives, and we provide evidence that the algorithm successfully improves upon the rankings produced by psi-blast.

We present the assessment of the CASP4 fold recognition category. The tasks we had to execute include the splitting of multidomain targets into single domains, the classification of target domains in terms of prediction categories, the numerical evaluation of predictions, the mapping of numerical scores to quality indices, the ranking of predictors, the selection of top-performing groups, and the analysis and critical discussion of the state of the art in this field. The 125 fold recognition groups were assessed by a total score that summarizes their performance over all targets and a quality score reflecting the average quality of the submitted models. Most of the top-performing groups achieved respectable results on both scores simultaneously. Several groups submitted models that were much closer to the respective target structures than any of the known folds in the Protein Data Bank. The CASP4 assessment included the automated servers of the parallel CAFASP experiment. For the total score, the highest rank achieved by a fully automated server is 12. Two thirds of the predictors have rather low scores.

A Personal Foreword. Preface. List of Contributors. I Introduction. 1 The Basic Principles of GRID (Peter Goodford). 1.1 Introduction. 1.2 Philosophy and Objectives. 1.3 Priorities. 1.4 The GRID Method. 1.5 The GRID Force Field. 1.6 Nomenclature. 1.7 Calibrating the GRID Force Field. 1.8 The Output from GRID. 1.9 Conclusions. 2 Calculation and Application of Molecular Interaction Fields (Rebecca C. Wade). 2.1 Introduction. 2.2 Calculation of MIFs. 2.3 Selected Applications of MIFs. 2.4 Concluding Remarks and Outlook. II Pharmacodynamics. 3 Protein Selectivity Studies Using GRID-MIFs (Thomas Fox). 3.1 Introduction. 3.2 GRID Calculations and Chemometric Analysis. 3.3 Applications. 3.4 Discussion and Conclusion. 4 FLAP: 4-Point Pharmacophore Fingerprints from GRID (Francesca Perruccio, Jonathan S. Mason, Simone Sciabola, and Massimo Baroni). 4.1 Introduction. 4.2 FLAP Theory. 4.3 Docking. 4.4 Structure Based Virtual Screening (SBVS). 4.5 Ligand Based Virtual Screening (LBVS). 4.6 Protein Similarity. 4.7 TOPP (Triplets of Pharmacophoric Points). 4.8 Conclusions. 5 The Complexity of Molecular Interaction: Molecular Shape Fingerprints by the PathFinder Approach (Iain McLay, Mike Hann, Emanuele Carosati, Gabriele Cruciani, and Massimo Baroni). 5.1 Introduction. 5.2 Background. 5.3 The PathFinder Approach. 5.4 Examples. 5.5 Conclusions. 6 Alignment-independent Descriptors from Molecular Interaction Fields (Manuel Pastor). 6.1 Introduction. 6.2 GRIND. 6.3 How to Interpret a GRIND-based 3D QSAR Model. 6.4 GRIND Limitations and Problems. 6.5 Recent and Future Developments. 6.6 Conclusions. 7 3D-QSAR Using the GRID/GOLPE Approach (Wolfgang Sippl). 7.1 Introduction. 7.2 3D-QSAR Using the GRID/GOLPE Approach. 7.3 GRID/GOLPE Application Examples. 7.4 Conclusion. III Pharmacokinetics. 8 Use of MIF-based VolSurf Descriptors in Physicochemical and Pharmacokinetic Studies (Raimund Mannhold, Giuliano Berellini, Emanuele Carosati, and Paolo Benedetti). 8.1 ADME Properties and Their Prediction. 8.2 VolSurf Descriptors. 8.3 Application Examples. 8.4 Conclusion. 9 Molecular Interaction Fields in ADME and Safety (Giovanni Cianchetta, Yi Li, Robert Singleton, Meng Zhang, Marianne Wildgoose, David Rampe, Jiesheng Kang, and Roy J. Vaz). 9.1 Introduction. 9.2 GRID and MIFs. 9.3 Role of Pgp Efflux in the Absorption. 9.4 HERG Inhibition. 9.5 CYP 3A4 Inhibition. 9.6 Conclusions. 10 Progress in ADME Prediction Using GRID-Molecular Interaction Fields (Ismael Zamora, Marianne Ridderstr.m, Anna-Lena Ungell, Tommy Andersson, and Lovisa Afzelius). 10.1 Introduction: ADME Field in the Drug Discovery Process. 10.2 Absorption. 10.3 Distribution. 10.4 Metabolism. 10.5 Conclusions. 11 Rapid ADME Filters for Lead Discovery (Tudor I. Oprea, Paolo Benedetti, Giuliano Berellini, Marius Olah, Kim Fejgin, and Scott Boyer). 11.1 Introduction. 11.2 The Rule of Five (Ro5) as ADME Filter. 11.3 Molecular Interaction Fields (MIFs): VolSurf. 11.4 MIF-based ADME Models. 11.5 Clinical Pharmacokinetics (PK) and Toxicological (Tox) Datasets. 11.6 VolSurf in Clinical PK Data Modeling. 11.7 ChemGPS-VolSurf (GPSVS) in Clinical PK Property Modeling. 11.8 ADME Filters: GPSVS vs. Ro5. 11.9 PENGUINS: Ultrafast ADME Filter. 11.10 Integrated ADME and Binding Affinity Predictions. 11.11 Conclusions. 12 GRID-Derived Molecular Interaction Fields for Predicting the Site of Metabolism in Human Cytochromes (Gabriele Cruciani, Yasmin Aristei, Riccardo Vianello, and Massimo Baroni). 12.1 Introduction. 11.2 The Human Cytochromes P450. 12.3 CYPs Characterization using GRID Molecular Interaction Fields. 12.4 Description of the Method. 12.5 An Overview of the Most Significant Results. 12.6 Conclusions. 12.7 Software Package. Index. CD-ROM Information.

Preface. A Personal Foreword. List of Contributors. Introduction (G. Muller & H. Kubinyi). I: GENERAL ASPECTS. 1. Target Family-directed Masterkeys in Chemogenomics (G. Muller). 2. Drug Discovery from Side Effects (H. Kubinyi). 3. The Value of Chemical Genetics in Drug Discovery (K. Russell & W. Michne). 4. Structural Aspects of Binding Site Similarity: A 3D Upgrade for Chemogenomics (A. Bergner & J. Gunther). II: TARGET FAMILIES. 5. The Contribution of Molecular Informatics to Chemogenomics: Knowledge-based Discovery of Biological Targets and Chemical Lead Compounds (E. Jacoby, et al.). 6. Chemical Kinomics (B. Klebl, et al.). 7. Structural Aspects of Kinases and Their Inhibitors (R. Buijsman). 8. A Chemical Genomics Approach for Ion Channel Modulators (K. Baringhaus & G. Hessler). 9. Phosphodiesterase Inhibitors: A Chemogenomic View (M. Hendrix & C. Kallus). 10. Proteochemometrics: A Tool for Modeling the Molecular Interaction Space (J. Wikberg, et al.). III: CHEMICAL LIBRARIES. 11. Some Principles Related to Chemogenomics in Compound Library and Template Design for GPCRs (T. Webb). 12. Computational Filters in Lead Generation: Targeting Drug-like Chemotypes (W. Guba & O. Roche). 13. Navigation in Chemical Space: Ligand-based Design of Focused Compound Libraries (G. Schneider & P. Schneider). 14. Natural Product-derived Combinatorial Libraries and Protein Structure Similarity as Guiding Principles for the Discovery of Drug Candidates (M. Koch & H. Waldmann). 15. Combinatorial Chemistry in the Age of Chemical Genomics (R. Joseph & P. Arya). Index.