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Daberdaku, Sebastian (2016) Protein contour modelling and computation for complementarity detection and docking. [Ph.D. thesis]

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Abstract (english)

The aim of this thesis is the development and application of a model that effectively and efficiently integrates the evaluation of geometric and electrostatic complementarity for the protein-protein docking problem. Proteins perform their biological roles by interacting with other biomolecules and forming macromolecular complexes. The structural characterization of protein complexes is important to understand the underlying biological processes. Unfortunately, there are several limitations to the available experimental techniques, leaving the vast majority of these complexes to be determined by means of computational methods such as protein-protein docking. The ultimate goal of the protein-protein docking problem is the in silico prediction of the three-dimensional structure of complexes of two or more interacting proteins, as occurring in living organisms, which can later be verified in vitro or in vivo. These interactions are highly specific and take place due to the simultaneous formation of multiple weak bonds: the geometric complementarity of the contours of the interacting molecules is a fundamental requirement in order to enable and maintain these interactions. However, shape complementarity alone cannot guarantee highly accurate docking predictions, as there are several physicochemical factors, such as Coulomb potentials, van der Waals forces and hydrophobicity, affecting the formation of protein complexes.

In order to set up correct and efficient methods for the protein-protein docking, it is necessary to provide a unique representation which integrates geometric and physicochemical criteria in the complementarity evaluation. To this end, a novel local surface descriptor, capable of capturing both the shape and electrostatic distribution properties of macromolecular surfaces, has been designed and implemented. The proposed methodology effectively integrates the evaluation of geometrical and electrostatic distribution complementarity of molecular surfaces, while maintaining efficiency in the descriptor comparison phase. The descriptor is based on the 3D Zernike invariants which possess several attractive features, such as a compact representation, rotational and translational invariance and have been shown to adequately capture global and local protein surface shape similarity and naturally represent physicochemical properties on the molecular surface.

Locally, the geometric similarity between two portions of protein surface implies a certain degree of complementarity, but the same cannot be stated about electrostatic distributions. Complementarity in electrostatic distributions is more complex to handle, as charges must be matched with opposite ones even if they do not have the same magnitude. The proposed method overcomes this limitation as follows. From a unique electrostatic distribution function, two separate distribution functions are obtained, one for the positive and one for the negative charges, and both functions are normalised in [0, 1]. Descriptors are computed separately for the positive and negative charge distributions, and complementarity evaluation is then done by cross-comparing descriptors of distributions of charges of opposite signs.

The proposed descriptor uses a discrete voxel-based representation of the Connolly surface on which the corresponding electrostatic potentials have been mapped. Voxelised surface representations have received a lot of interest in several bioinformatics and computational biology applications as a simple and effective way of jointly representing geometric and physicochemical properties of proteins and other biomolecules by mapping auxiliary information in each voxel. Moreover, the voxel grid can be defined at different resolutions, thus giving the means to effectively control the degree of detail in the discrete representation along with the possibility of producing multiple representations of the same molecule at different resolutions.

A specific algorithm has been designed for the efficient computation of voxelised macromolecular surfaces at arbitrary resolutions, starting from experimentally-derived structural data (X-ray crystallography, NMR spectroscopy or cryo-electron microscopy). Fast surface generation is achieved by adapting an approximate Euclidean Distance Transform algorithm in the Connolly surface computation step and by exploiting the geometrical relationship between the latter and the Solvent Accessible surface. This algorithm is at the base of VoxSurf (Voxelised Surface calculation program), a tool which can produce discrete representations of macromolecules at very high resolutions starting from the three-dimensional information of their corresponding PDB files. By employing compact data structures and implementing a spatial slicing protocol, the proposed tool can calculate the three main molecular surfaces at high resolutions with limited memory demands.

To reduce the surface computation time without affecting the accuracy of the representation, two parallel algorithms for the computation of voxelised macromolecular surfaces, based on a spatial slicing procedure, have been introduced. The molecule is sliced in a user-defined number of parts and the portions of the overall surface can be calculated for each slice in parallel. The molecule is sliced with planes perpendicular to the abscissa axis of the Cartesian coordinate system defined in the molecule's PDB entry.

The first algorithms uses an overlapping margin of one probe-sphere radius length among slices in order to guarantee the correctness of the Euclidean Distance Transform. Because of this margin, the Connolly surface can be computed nearly independently for each slice. Communications among processes are necessary only during the pocket identification procedure which ensures that pockets spanning through more than one slice are correctly identified and discriminated from solvent-excluded cavities inside the molecule.

In the second parallel algorithm the size of the overlapping margin between slices has been reduced to a one-voxel length by adapting a multi-step region-growing Euclidean Distance Transform algorithm. At each step, distance values are first calculated independently for every slice, then, a small portion of the borders' information is exchanged between adjacent slices.

The proposed methodologies will serve as a basis for a full-fledged protein-protein docking protocol based on local feature matching. Rigorous benchmark tests have shown that the combined geometric and electrostatic descriptor can effectively identify shape and electrostatic distribution complementarity in the binding sites of protein-protein complexes, by efficiently comparing circular surface patches and significantly decreasing the number of false positives obtained when using a purely-geometric descriptor. In the validation experiments, the contours of the two interacting proteins are divided in circular patches: all possible patch pairs from the two proteins are then evaluated in terms of complementarity and a general ranking is produced. Results show that native patch pairs obtain higher ranks when using the newly proposed descriptor, with respect to the ranks obtained when using the purely-geometric one.

Abstract (italian)

Lo scopo di questa tesi è lo sviluppo e l'applicazione di un modello che integri efficacemente ed efficientemente la valutazione della complementarietà geometrica ed elettrostatica per il problema del docking proteina-proteina. Le proteine svolgono i loro ruoli biologici interagendo con altre biomolecole formando complessi macromolecolari. La caratterizzazione strutturale dei complessi proteici è importante per comprendere i processi biologici che guidano tali interazioni. Gli attuali limiti delle tecniche sperimentali fanno si che la maggior parte dei complessi debba essere risolta tramite tecniche computazionali come il docking proteina-proteina. Il docking proteina-proteina ha come scopo la predizione in silico delle strutture tridimensionali dei complessi formati da due o più proteine interagenti, così come si verificano negli organismi viventi, e che possono essere successivamente verificate in vitro o in vivo. Queste interazioni sono altamente specifiche, ed avvengono grazie all'instaurazione simultanea di molteplici legami deboli: la complementarietà geometrica dei contorni esterni delle molecole interagenti è un requisito fondamentale affinché queste interazioni avvengano e si mantengano nel tempo. La sola complementarietà di forma, però, non basta a garantire predizioni di docking accurate, dato che esistono molti fattori fisico-chimici oltre alla complementarietà di forma, come i potenziali di Coulomb, forze di van der Waals e l'idrofobicità, i quali influiscono nella formazione del complesso proteico.

Al fine di sviluppare metodi corretti ed efficienti per il docking proteina-proteina si rende necessaria una nuova rappresentazione del contorno di proteine che integri criteri geometrici ed elettrostatici nella valutazione della complementarietà. A tal proposito, è stato progettato ed implementato un nuovo descrittore locale del contorno proteico, in grado di catturare entrambe le proprietà di complementarietà geometrica e elettrostatica delle superfici macromolecolari. La metodologia proposta integra efficacemente la valutazione della complementarietà geometrica ed elettrostatica delle superfici molecolari, permettendo la comparazione efficiente tra descrittori. Il descrittore si basa sulle invarianti 3D di Zernike, le quali posseggono diverse proprietà interessanti, come l'invarianza alle rotazioni e alle traslazioni, la capacità di catturare efficacemente la similarità sia locale che globale delle superfici proteiche, e di rappresentarne in modo naturale le proprietà fisico-chimiche.

Localmente, la similarità geometrica tra due porzioni di superficie proteica implica un certo grado di complementarietà. Lo stesso però non vale per i potenziali elettrostatici. La complementarietà dei potenziali elettrostatici è più complessa da rilevare, poiché devono combaciare cariche di segno opposto che non hanno necessariamente la stessa ampiezza. Il metodo proposto supera questa limitazione nel modo seguente. Da un'unica funzione di distribuzione di carica elettrostatica vengono ricavate due funzioni di distribuzione di carica, una per le cariche positive ed una per le cariche negative. Entrambe le funzioni di distribuzione vengono normalizzate in [0, 1]. I descrittori vengono poi calcolati separatamente per le due distribuzioni di carica, e la valutazione della complementarietà viene eseguita confrontando tra loro i descrittori corrispondenti a cariche di segno opposto.

Il descrittore proposto utilizza una rappresentazione discreta a voxel della superficie di Connolly sulla quale sono stati mappati i corrispettivi potenziali elettrostatici. Le rappresentazioni a voxel delle superfici hanno ricevuto un notevole interesse in molte applicazioni bioinformatiche e di biologia computazionale poiché forniscono un metodo semplice ed efficace per rappresentare congiuntamente le proprietà geometriche e fisico-chimiche di proteine ed altre biomolecole, mappando informazioni ausiliarie in ciascun voxel. In più, variando la risoluzione della griglia di voxel si può controllare i grado di dettaglio da rappresentare. Inoltre, si possono ottenere rappresentazioni a grana variabile per una determinata molecola.

È stato progettato e sviluppato un algoritmo specifico per il calcolo efficiente delle superfici a voxel di macromolecole a risoluzioni arbitrarie, a partire da dati sperimentali (cristallografia a raggi X, spettroscopia NMR, microscopia crioelettronica). La generazione efficiente della superficie di Connolly viene effettuata tramite un algoritmo che calcola la Trasformata di Distanza Euclidea approssimata e che sfrutta la relazione geometrica che c'è tra la superficie accessibile al solvente e la superficie di Connolly. Questo algoritmo è alla base di VoxSurf (Voxelised Surface calculation program), uno strumento software in grado di produrre rappresentazioni discrete di macromolecole a risoluzioni molto alte a partire dalle informazioni tridimensionali dei corrispettivi file PDB. Utilizzando strutture dati compatte ed implementando un protocollo di slicing spaziale, il tool proposto può calcolare le tre principali superfici molecolari ad alte risoluzioni con limitati requisiti di memoria.

Due algoritmi paralleli sono stati introdotti per ridurre il tempo di computazione delle superfici, senza però incidere negativamente sulla precisione delle rappresentazioni. Entrambi si basano su di un protocollo di slicing spaziale: la molecola viene "tagliata" in un determinato numero di parti, e le porzioni della superficie vengono calcolate per ciascuna slice in parallelo. La molecola viene tagliata con piani perpendicolari all'asse delle ascisse del sistema di coordinate cartesiane definito nel file PDB della molecola.

Il primo algoritmo utilizza margini sovrapposti tra slice adiacenti, di dimensione pari al raggio della sfera-sonda che rappresenta la molecola di solvente. Il margine garantisce che la superficie di Connolly possa essere calcolata quasi-indipendentemente per ciascuna slice. Le comunicazioni tra processi si rendono necessarie soltanto durante l'identificazione delle tasche, la quale garantisce che vengano identificate correttamente tasche della superficie molecolare che si estendono attraverso più di una slice.

Nel secondo algoritmo parallelo, la dimensione dei margini sovrapposti è stato ridotto in lunchezza ad un solo voxel tramite l'introduzione di un algoritmo per la Trasformata di Distanza Euclidea a più step. Ad ogni step, i valori di distanza vengono dapprima calcolati indipendentemente per ciascuna slice. Poi, i valori di distanza euclidea di un piccolo sottoinsieme di voxel appartenenti al bordo vengono scambiati tra slice adiacenti.

Le metodologie introdotte sono propedeutiche allo sviluppo di un protocollo di docking proteina-proteina basato sul local feature matching. Test su benchmark hanno dimostrato che il descrittore congiunto di geometria ed elettrostaticità è in grado di identificare la complementarietà di forma e di distribuzione di carica nei siti di legame dei complessi proteina-proteina, confrontando efficientemente patch circolari di superficie e diminuendo notevolmente il numero di falsi positivi che altrimenti si avrebbero utilizzando un descrittore puramente geometrico. Negli esperimenti di validazione, i contorni delle proteine interagenti sono stati suddivisi in patch circolari: tutte le possibili coppie di patch dalle due proteine sono state valutate in termini di complementarietà ed è stato stilato un ranking generale. I risultati dimostrano che, quando si utilizza il nuovo descrittore, le coppie di patch native ottengono rank più alti rispetto a quelli ottenuti utilizzando il descrittore puramente geometrico.

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EPrint type:Ph.D. thesis
Tutor:Ferrari, Carlo
Data di deposito della tesi:27 July 2016
Anno di Pubblicazione:27 July 2016
Key Words:protein surface; protein contour; docking; local surface matching; descriptors; 3D Zernike descriptors
Settori scientifico-disciplinari MIUR:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Struttura di riferimento:Dipartimenti > Dipartimento di Ingegneria dell'Informazione
Codice ID:9691
Depositato il:09 Nov 2017 16:53
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Le url contenute in alcuni riferimenti sono raggiungibili cliccando sul link alla fine della citazione (Vai!) e tramite Google (Ricerca con Google). Il risultato dipende dalla formattazione della citazione.

[1] Mathias Rask-Andersen, Markus Sällman Almén, and Helgi B Schiöth. Trends in the exploitation of novel drug targets. Nature reviews Drug discovery, 10(8):579– 590, 2011. Cerca con Google

[2] Bruce Alberts, Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, and Peter Walter. The shape and structure of proteins. Garland Science, 2002. Cerca con Google

[3] CB Anfinsen. The formation and stabilization of protein structure. Biochemical Journal, 128(4):737, 1972. Cerca con Google

[4] Christian B. Anfinsen. Principles that govern the folding of protein chains. Science, 181(4096):223–230, 1973. Cerca con Google

[5] Yigong Shi. A glimpse of structural biology through X-ray crystallography. Cell, 159(5):995–1014, 2014. Cerca con Google

[6] Christoph Göbl, Tobias Madl, Bernd Simon, and Michael Sattler. NMR approaches for structural analysis of multidomain proteins and complexes in solution. Progress in nuclear magnetic resonance spectroscopy, 80:26–63, 2014. Cerca con Google

[7] Ewen Callaway. The revolution will not be crystallized: a new method sweeps through structural biology. Nature, 525(7568):172, 2015. Cerca con Google

[8] Shoshana J Wodak and Joël Janin. Computer analysis of protein–protein interaction. Journal of molecular biology, 124(2):323–342, 1978. Cerca con Google

[9] David W Ritchie. Recent progress and future directions in protein–protein docking. Current Protein and Peptide Science, 9(1):1–15, 2008. Cerca con Google

[10] Sandor Vajda and Dima Kozakov. Convergence and combination of methods in protein–protein docking. Current opinion in structural biology, 19(2):164–170, 2009. Cerca con Google

[11] Sandor Vajda, David R Hall, and Dima Kozakov. Sampling and scoring: A marriage made in heaven. Proteins: Structure, Function, and Bioinformatics, 81(11):1874– 1884, 2013. Cerca con Google

[12] Sheng-You Huang. Search strategies and evaluation in protein–protein docking: principles, advances and challenges. Drug Discovery Today, 19(8):1081 – 1096, 2014. Cerca con Google

[13] Emil Fischer. Einflußder configuration auf die wirkung der enzyme. Berichte der deutschen chemischen Gesellschaft, 27(3):2985–2993, 1894. Cerca con Google

[14] Dina Duhovny, Ruth Nussinov, and Haim J. Wolfson. Efficient unbound docking of rigid molecules. In Roderic Guigó and Dan Gusfield, editors, Algorithms in Bioinformatics: Second International Workshop, WABI 2002 Rome, Italy, September 17–21, 2002 Proceedings, pages 185–200. Springer Berlin Heidelberg, Berlin, Heidelberg, 2002. Cerca con Google

[15] Inbal Halperin, Buyong Ma, Haim Wolfson, and Ruth Nussinov. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins: Structure, Function, and Bioinformatics, 47(4):409–443, 2002. Cerca con Google

[16] Helen M Kent, Mary Shannon Moore, B Booth Quimby, Anne ME Baker, Airlie J McCoy, Gretchen A Murphy, Anita H Corbett, and Murray Stewart. Engineered mutants in the switch ii loop of ran define the contribution made by key residues to the interaction with nuclear transport factor 2 (ntf2) and the role of this interaction in nuclear protein import. Journal of molecular biology, 289(3):565–577, 1999. Cerca con Google

[17] Timothy L Bullock, David W Clarkson, Helen M Kent, and Murray Stewart. The 1.6 å resolution crystal structure of nuclear transport factor 2 (NTF2). Journal of molecular biology, 260(3):422–431, 1996. Cerca con Google

[18] Murray Stewart, Helen M Kent, and Airlie J McCoy. Structural basis for molecular recognition between nuclear transport factor 2 (NTF2) and the GDP-bound form of the Ras-family GTPase Ran. Journal of molecular biology, 277(3):635–646, 1998. Cerca con Google

[19] Masahito Ohue, Takehiro Shimoda, Shuji Suzuki, Yuri Matsuzaki, Takashi Ishida, and Yutaka Akiyama. MEGADOCK 4.0: an ultra–high-performance protein– protein docking software for heterogeneous supercomputers. Bioinformatics, 30(22):3281–3283, 2014. Cerca con Google

[20] Ephraim Katchalski-Katzir, Isaac Shariv, Miriam Eisenstein, Asher A Friesem, Claude Aflalo, and Ilya A Vakser. Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proceedings of the National Academy of Sciences, 89(6):2195–2199, 1992. Cerca con Google

[21] Andrey Tovchigrechko and Ilya A Vakser. GRAMM-X public web server for protein– protein docking. Nucleic acids research, 34(suppl 2):W310–W314, 2006. Cerca con Google

[22] Henry A Gabb, Richard M Jackson, and Michael JE Sternberg. Modelling protein docking using shape complementarity, electrostatics and biochemical information. Journal of molecular biology, 272(1):106–120, 1997. Cerca con Google

[23] Jeffrey G Mandell, Victoria A Roberts, Michael E Pique, Vladimir Kotlovyi, Julie C Mitchell, Erik Nelson, Igor Tsigelny, and Lynn F Ten Eyck. Protein docking using continuum electrostatics and geometric fit. Protein Engineering, 14(2):105–113, 2001. Cerca con Google

[24] Victoria A Roberts, Elaine E Thompson, Michael E Pique, Martin S Perez, and LF Ten Eyck. DOT2: Macromolecular docking with improved biophysical models. Journal of computational chemistry, 34(20):1743–1758, 2013. Cerca con Google

[25] Rong Chen, Li Li, and Zhiping Weng. ZDOCK: an initial-stage protein-docking algorithm. Proteins: Structure, Function, and Bioinformatics, 52(1):80–87, 2003. Cerca con Google

[26] Alexander Heifetz, Ephraim Katchalski-Katzir, and Miriam Eisenstein. Electrostatics in protein–protein docking. Protein Science, 11(3):571–587, 2002. Cerca con Google

[27] Dima Kozakov, Ryan Brenke, Stephen R. Comeau, and Sandor Vajda. PIPER: An FFT-based protein docking program with pairwise potentials. Proteins: Structure, Function, and Bioinformatics, 65(2):392–406, 2006. Cerca con Google

[28] Chandrajit L Bajaj, Rezaul Chowdhury, and Vinay Siddahanavalli. F 2Dock: Fast Fourier Protein–Protein Docking. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 8(1):45–58, 2011. Cerca con Google

[29] Rezaul Chowdhury, Muhibur Rasheed, Donald Keidel, Maysam Moussalem, Arthur Olson, Michel Sanner, and Chandrajit Bajaj. Protein–Protein Docking with F2Dock 2.0 and GB-Rerank. PLoS ONE, 8(3):e51307, 2013. Cerca con Google

[30] Changsheng Zhang and Luhua Lai. SDOCK: A global protein–protein docking program using stepwise force-field potentials. Journal of computational chemistry, 32(12):2598–2612, 2011. Cerca con Google

[31] Lin Li, Dachuan Guo, Yangyu Huang, Shiyong Liu, and Yi Xiao. ASPDock: protein–protein docking algorithm using atomic solvation parameters model. BMC bioinformatics, 12(1):1, 2011. Cerca con Google

[32] Carles Pons, Daniel Jiménez-González, Cecilia González-Álvarez, Harald Servat, Daniel Cabrera-Benítez, Xavier Aguilar, and Juan Fernández-Recio. Cell-dock: high-performance protein–protein docking. Bioinformatics, 28(18):2394–2396, 2012. Cerca con Google

[33] Victor I Lesk and Michael JE Sternberg. 3D-Garden: a system for modelling protein–protein complexes based on conformational refinement of ensembles generated with the marching cubes algorithm. Bioinformatics, 24(9):1137–1144, 2008. Cerca con Google

[34] David W Ritchie and Graham JL Kemp. Protein docking using spherical polar fourier correlations. Proteins: Structure, Function, and Bioinformatics, 39(2):178– 194, 2000. Cerca con Google

[35] José Ignacio Garzon, José Ramón Lopéz-Blanco, Carles Pons, Julio Kovacs, Ruben Abagyan, Juan Fernandez-Recio, and Pablo Chacon. FRODOCK: a new approach for fast rotational protein–protein docking. Bioinformatics, 25(19):2544–2551, 2009. Cerca con Google

[36] Aurelien Grosdidier, Vincent Zoete, and Olivier Michielin. SwissDock, a protein– small molecule docking web service based on EADock DSS. Nucleic acids research, 39(suppl 2):W270–W277, 2011. Cerca con Google

[37] Aurélien Grosdidier, Vincent Zoete, and Olivier Michielin. Fast docking using the CHARMM force field with EADock DSS. Journal of computational chemistry, 32(10):2149–2159, 2011. Cerca con Google

[38] Fan Jiang and Sung-Hou Kim. “soft docking”: Matching of molecular surface cubes. Journal of Molecular Biology, 219(1):79 – 102, 1991. Cerca con Google

[39] Nan Li, Zhonghua Sun, and Fan Jiang. SOFTDOCK application to protein–protein interaction benchmark and CAPRI. Proteins: Structure, Function, and Bioinformatics, 69(4):801–808, 2007. Cerca con Google

[40] Fan Jiang, Wei Lin, and Zihe Rao. SOFTDOCK: understanding of molecular recognition through a systematic docking study. Protein engineering, 15(4):257–263, 2002. Cerca con Google

[41] P Nuno Palma, Ludwig Krippahl, John E Wampler, and José JG Moura. BiGGER: a new (soft) docking algorithm for predicting protein interactions. Proteins: Structure, Function, and Bioinformatics, 39(4):372–384, 2000. Cerca con Google

[42] Genki Terashi, Mayuko Takeda-Shitaka, Kazuhiko Kanou, Mitsuo Iwadate, Daisuke Takaya, and Hideaki Umeyama. The SKE-DOCK server and human teams based on a combined method of shape complementarity and free energy estimation. Proteins: Structure, Function, and Bioinformatics, 69(4):866–872, 2007. Cerca con Google

[43] Irwin D Kuntz, Jeffrey M Blaney, Stuart J Oatley, Robert Langridge, and Thomas E Ferrin. A geometric approach to macromolecule-ligand interactions. Journal of molecular biology, 161(2):269–288, 1982. Cerca con Google

[44] Dina Schneidman-Duhovny, Yuval Inbar, Ruth Nussinov, and Haim J Wolfson. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic acids research, 33(suppl 2):W363–W367, 2005. Cerca con Google

[45] Vishwesh Venkatraman, Yifeng Yang, Lee Sael, and Daisuke Kihara. Protein– protein docking using region-based 3D Zernike descriptors. BMC Bioinform., 10(1):407, 2009. Cerca con Google

[46] Bin Li and Daisuke Kihara. Protein docking prediction using predicted protein– protein interface. BMC Bioinform., 13(1):7, 2012. Cerca con Google

[47] Eleanor J Gardiner, Peter Willett, and Peter J Artymiuk. GAPDOCK: A genetic algorithm approach to protein docking in CAPRI round 1. Proteins: Structure, Function, and Bioinformatics, 52(1):10–14, 2003. Cerca con Google

[48] Juan Esquivel-Rodriguez, Yifeng David Yang, and Daisuke Kihara. Multi-LZerD: Multiple protein docking for asymmetric complexes. Proteins: Struct. Funct. Bioinform., 80(7):1818–1833, 2012. Cerca con Google

[49] Shengyin Gu, Patrice Koehl, Joel Hass, and Nina Amenta. Surface-histogram: A new shape descriptor for protein–protein docking. Proteins: Structure, Function, and Bioinformatics, 80(1):221–238, 2012. Cerca con Google

[50] Zujun Shentu, Mohammad Al Hasan, Christopher Bystroff, and Mohammed J Zaki. Context shapes: Efficient complementary shape matching for protein–protein docking. Proteins: Structure, Function, and Bioinformatics, 70(3):1056–1073, 2008. Cerca con Google

[51] Apostolos Axenopoulos, Petros Daras, Georgios E Papadopoulos, and Elias Houstis. SP-dock: protein–protein docking using shape and physicochemical complementarity. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 10(1):135–150, 2013. Cerca con Google

[52] Jeffrey J Gray, Stewart Moughon, Chu Wang, Ora Schueler-Furman, Brian Kuhlman, Carol A Rohl, and David Baker. Protein–protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. Journal of molecular biology, 331(1):281–299, 2003. Cerca con Google

[53] Juan Fernández-Recio, Maxim Totrov, and Ruben Abagyan. ICM-DISCO docking by global energy optimization with fully flexible side-chains. Proteins: Structure, Function, and Bioinformatics, 52(1):113–117, 2003. Cerca con Google

[54] Sebastian Schneider, Adrien Saladin, Sebastien Fiorucci, Chantal Prevost, and Martin Zacharias. ATTRACT and PTOOLS: Open Source Programs for Protein– Protein Docking. Computational Drug Discovery and Design, pages 221–232, 2012. Cerca con Google

[55] Cyril Dominguez, Rolf Boelens, and Alexandre MJJ Bonvin. HADDOCK: a protein– protein docking approach based on biochemical or biophysical information. Journal of the American Chemical Society, 125(7):1731–1737, 2003. Cerca con Google

[56] Sjoerd J De Vries, Marc van Dijk, and Alexandre MJJ Bonvin. The HADDOCK web server for data-driven biomolecular docking. Nature protocols, 5(5):883–897, 2010. Cerca con Google

[57] Mieczyslaw Torchala, Iain H Moal, Raphael AG Chaleil, Juan Fernandez-Recio, and Paul A Bates. SwarmDock: a server for flexible protein–protein docking. Bioinformatics, 29(6):807–809, 2013. Cerca con Google

[58] Garrett M Morris, Ruth Huey, William Lindstrom, Michel F Sanner, Richard K Belew, David S Goodsell, and Arthur J Olson. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of computational chemistry, 30(16):2785–2791, 2009. Cerca con Google

[59] Stephen R Comeau, David W Gatchell, Sandor Vajda, and Carlos J Camacho. ClusPro: a fully automated algorithm for protein–protein docking. Nucleic acids research, 32(suppl 2):W96–W99, 2004. Cerca con Google

[60] Gidon Moont, Henry A Gabb, and Michael JE Sternberg. Use of pair potentials across protein interfaces in screening predicted docked complexes. Proteins: Structure, Function, and Bioinformatics, 35(3):364–373, 1999. Cerca con Google

[61] Brian Pierce and Zhiping Weng. ZRANK: reranking protein docking predictions with an optimized energy function. Proteins: Structure, Function, and Bioinformatics, 67(4):1078–1086, 2007. Cerca con Google

[62] Tammy Man-Kuang Cheng, Tom L Blundell, and Juan Fernandez-Recio. pyDock: Electrostatics and desolvation for effective scoring of rigid-body protein–protein docking. Proteins: Structure, Function, and Bioinformatics, 68(2):503–515, 2007. Cerca con Google

[63] Xiao Hui Ma, Cun Xin Wang, Chun Hua Li, and Wei Zu Chen. A fast empirical approach to binding free energy calculations based on protein interface information. Protein engineering, 15(8):677–681, 2002. Cerca con Google

[64] Gwo-Yu Chuang, Dima Kozakov, Ryan Brenke, Stephen R Comeau, and Sandor Vajda. DARS (Decoys As the Reference State) potentials for protein–protein docking. Biophysical journal, 95(9):4217–4227, 2008. Cerca con Google

[65] Shiyong Liu and Ilya A Vakser. DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein–protein docking. BMC bioinformatics, 12(1):280, 2011. Cerca con Google

[66] Carles Pons, David Talavera, Xavier de la Cruz, Modesto Orozco, and Juan Fernandez-Recio. Scoring by Intermolecular Pairwise Propensities of Exposed Residues (SIPPER): A New Efficient Potential for Protein–Protein Docking. Journal of chemical information and modeling, 51(2):370–377, 2011. Cerca con Google

[67] DVS Ravikant and Ron Elber. PIE - efficient filters and coarse grained potentials for unbound protein–protein docking. Proteins: Structure, Function, and Bioinformatics, 78(2):400–419, 2010. Cerca con Google

[68] Sheng-You Huang and Xiaoqin Zou. MDockPP: A hierarchical approach for protein– protein docking and its application to CAPRI rounds 15–19. Proteins: Structure, Function, and Bioinformatics, 78(15):3096–3103, 2010. Cerca con Google

[69] Richard M Jackson, Henry A Gabb, and Michael JE Sternberg. Rapid refinement of protein interfaces incorporating solvation: application to the docking problem. Journal of molecular biology, 276(1):265–285, 1998. Cerca con Google

[70] Carlos J Camacho and Sandor Vajda. Protein docking along smooth association pathways. Proceedings of the National Academy of Sciences, 98(19):10636–10641, 2001. Cerca con Google

[71] Sergio Ruiz-Carmona, Daniel Alvarez-Garcia, Nicolas Foloppe, A Beatriz Garmendia-Doval, Szilveszter Juhos, Peter Schmidtke, Xavier Barril, Roderick E Hubbard, and S David Morley. rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Computational Biology, 10(4):e1003571, 2014. Cerca con Google

[72] Nelly Andrusier, Ruth Nussinov, and Haim J Wolfson. FireDock: fast interaction refinement in molecular docking. Proteins: Structure, Function, and Bioinformatics, 69(1):139–159, 2007. Cerca con Google

[73] Efrat Mashiach, Ruth Nussinov, and Haim J Wolfson. FiberDock: Flexible inducedfit backbone refinement in molecular docking. Proteins: Structure, Function, and Bioinformatics, 78(6):1503–1519, 2010. Cerca con Google

[74] Vishwesh Venkatraman and David W Ritchie. Flexible protein docking refinement using pose-dependent normal mode analysis. Proteins: Structure, Function, and Bioinformatics, 80(9):2262–2274, 2012. Cerca con Google

[75] Ioannis Ch Paschalidis, Yang Shen, Pirooz Vakili, and Sandor Vajda. SDU: A semidefinite programming-based underestimation method for stochastic global optimization in protein docking. IEEE transactions on automatic control, 52(4):664– 676, 2007. Cerca con Google

[76] Satoshi Omori and Akio Kitao. CyClus: A fast, comprehensive cylindrical interface approximation clustering/reranking method for rigid-body protein–protein docking decoys. Proteins: Structure, Function, and Bioinformatics, 81(6):1005–1016, 2013. Cerca con Google

[77] Edrisse Chermak, Andrea Petta, Luigi Serra, Anna Vangone, Vittorio Scarano, Luigi Cavallo, and Romina Oliva. CONSRANK: a server for the analysis, comparison and ranking of docking models based on inter-residue contacts. Bioinformatics, page btu837, 2014. Cerca con Google

[78] Iain H Moal, Rocco Moretti, David Baker, and Juan Fernández-Recio. Scoring functions for protein–protein interactions. Current opinion in structural biology, 23(6):862–867, 2013. Cerca con Google

[79] Pravin Ambure and Kunal Roy. Scoring functions in docking experiments. In Methods and Algorithms for Molecular Docking-Based Drug Design and Discovery, pages 54–98. IGI Global, 2016. Cerca con Google

[80] Jie Liu and Renxiao Wang. Classification of current scoring functions. Journal of chemical information and modeling, 55(3):475–482, 2015. Cerca con Google

[81] Marcel L Verdonk, Jason C Cole, Michael J Hartshorn, Christopher W Murray, and Richard D Taylor. Improved protein–ligand docking using GOLD. Proteins: Structure, Function, and Bioinformatics, 52(4):609–623, 2003. Cerca con Google

[82] Elaine C Meng, Brian K Shoichet, and Irwin D Kuntz. Automated docking with grid-based energy evaluation. Journal of computational chemistry, 13(4):505–524, 1992. Cerca con Google

[83] Anthony K Felts, Emilio Gallicchio, Anders Wallqvist, and Ronald M Levy. Distinguishing native conformations of proteins from decoys with an effective free energy estimator based on the opls all-atom force field and the surface generalized Born solvent model. Proteins: Structure, Function, and Bioinformatics, 48(2):404–422, 2002. Cerca con Google

[84] Philipp Kynast, Philippe Derreumaux, and Birgit Strodel. Evaluation of the coarsegrained OPEP force field for protein–protein docking. BMC biophysics, 9(1):1, 2016. Cerca con Google

[85] Thom Vreven, Howook Hwang, and Zhiping Weng. Integrating atom-based and residue-based scoring functions for protein–protein docking. Protein Science, 20(9):1576–1586, 2011. Cerca con Google

[86] Rong Chen and Zhiping Weng. Docking unbound proteins using shape complementarity, desolvation, and electrostatics. Proteins: Structure, Function, and Bioinformatics, 47(3):281–294, 2002. Cerca con Google

[87] Carlos J Camacho and Chao Zhang. FastContact: rapid estimate of contact and binding free energies. Bioinformatics, 21(10):2534–2536, 2005. Cerca con Google

[88] Maciej Blaszczyk, Mateusz Kurcinski, Maksim Kouza, Lukasz Wieteska, Aleksander Debinski, Andrzej Kolinski, and Sebastian Kmiecik. Modeling of protein–peptide interactions using the CABS-dock web server for binding site search and flexible docking. Methods, 93:72–83, 2016. Cerca con Google

[89] Anisah W Ghoorah, Marie-Dominique Devignes, Malika Smaïl-Tabbone, and David W Ritchie. KBDOCK 2013: a spatial classification of 3D protein domain family interactions. Nucleic acids research, page gkt1199, 2013. Cerca con Google

[90] Chao Zhang, George Vasmatzis, James L Cornette, and Charles DeLisi. Determination of atomic desolvation energies from the structures of crystallized proteins. Journal of molecular biology, 267(3):707–726, 1997. Cerca con Google

[91] Dennis M Krüger, José Ignacio Garzón, Pablo Chacón, and Holger Gohlke. DrugScore PPI knowledge-based potentials used as scoring and objective function in protein–protein docking. PloS one, 9(2):e89466, 2014. Cerca con Google

[92] Fabian Glaser, David M Steinberg, Ilya A Vakser, and Nir Ben-Tal. Residue frequencies and pairing preferences at protein–protein interfaces. Proteins: Structure, Function, and Bioinformatics, 43(2):89–102, 2001. Cerca con Google

[93] Sheng-You Huang and Xiaoqin Zou. An iterative knowledge-based scoring function for protein–protein recognition. Proteins: Structure, Function, and Bioinformatics, 72(2):557–579, 2008. Cerca con Google

[94] Sankar Basu and Björn Wallner. Finding correct protein–protein docking models using ProQDock. Bioinformatics, 32(12):i262–i270, 2016. Cerca con Google

[95] Guo-Bo Li, Ling-Ling Yang, Wen-Jing Wang, Lin-Li Li, and Sheng-Yong Yang. ID-Score: a new empirical scoring function based on a comprehensive set of descriptors related to protein–ligand interactions. Journal of chemical information and modeling, 53(3):592–600, 2013. Cerca con Google

[96] David Zilian and Christoph A Sotriffer. SFCscore RF: a random forest-based scoring function for improved affinity prediction of protein–ligand complexes. Journal of chemical information and modeling, 53(8):1923–1933, 2013. Cerca con Google

[97] Pedro J Ballester and John BO Mitchell. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics, 26(9):1169–1175, 2010. Cerca con Google

[98] Michael K Gilson, James A Given, and Martha S Head. A new class of models for computing receptor–ligand binding affinities. Chemistry & biology, 4(2):87–92, 1997. Cerca con Google

[99] Xiaoqin Zou, Yaxiong Sun, and Irwin D Kuntz. Inclusion of solvation in ligand binding free energy calculations using the generalized-born model. Journal of the American Chemical Society, 121(35):8033–8043, 1999. Cerca con Google

[100] Paul S Charifson, Joseph J Corkery, Mark A Murcko, and W Patrick Walters. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. Journal of medicinal chemistry, 42(25):5100–5109, 1999. Cerca con Google

[101] Shide Liang, Samy O Meroueh, Guangce Wang, Chao Qiu, and Yaoqi Zhou. Consensus scoring for enriching near-native structures from protein–protein docking decoys. Proteins: Structure, Function, and Bioinformatics, 75(2):397–403, 2009. Cerca con Google

[102] Romina Oliva, Anna Vangone, and Luigi Cavallo. Ranking multiple docking solutions based on the conservation of inter-residue contacts. Proteins: Structure, Function, and Bioinformatics, 81(9):1571–1584, 2013. Cerca con Google

[103] Jianwen A Feng and Garland R Marshall. SKATE: a docking program that decouples systematic sampling from scoring. Journal of computational chemistry, 31(14):2540–2554, 2010. Cerca con Google

[104] Irina Hashmi, Bahar Akbal-Delibas, Nurit Haspel, and Amarda Shehu. Guiding protein docking with geometric and evolutionary information. Journal of bioinformatics and computational biology, 10(03):1242008, 2012. Cerca con Google

[105] Sheng-You Huang. Exploring the potential of global protein–protein docking: an overview and critical assessment of current programs for automatic ab initio docking. Drug Discov Today, 20(8):969–977, 2015. Cerca con Google

[106] Apostolos Axenopoulos, Petros Daras, Georgios Papadopoulos, and Elias Houstis. A shape descriptor for fast complementarity matching in molecular docking. IEEE/ACM Trans Comput Biol Bioinf, 8(6):1441–1457, November 2011. Cerca con Google

[107] Daisuke Kihara, Lee Sael, Rayan Chikhi, and Juan Esquivel-Rodriguez. Molecular Surface Representation Using 3D Zernike Descriptors for Protein Shape Comparison and Docking. Current Protein and Peptide Science, 12(6):520–533, 2011. Cerca con Google

[108] Jinan Cao, DK Pham, L Tonge, and DV Nicolau. Predicting surface properties of proteins on the Connolly molecular surface. Smart Mater Struct, 11(5):772, 2002. Cerca con Google

[109] Maciej Długosz and Joanna Trylska. Electrostatic similarity of proteins: Application of three dimensional spherical harmonic decomposition. Journal of Chemical Physics, 129(1), 2008. Cerca con Google

[110] Sergio Decherchi, José Colmenares, Chiara Eva Catalano, Michela Spagnuolo, Emil Alexov, and Walter Rocchia. Between algorithm and model: Different molecular surface definitions for the Poisson-Boltzmann based electrostatic characterization of biomolecules in solution. Commun Comput Phys, 13:61, 2013. Cerca con Google

[111] A. Via, F. Ferrè, B. Brannetti, and M. Helmer-Citterich. Protein surface similarities: a survey of methods to describe and compare protein surfaces. Cellular and Molecular Life Sciences, 57(13-14):1970–1977, 2000. Cerca con Google

[112] Stefan Schmitt, Daniel Kuhn, and Gerhard Klebe. A new method to detect related function among proteins independent of sequence and fold homology. Journal of Molecular Biology, 323(2):387–406, 2002. Cerca con Google

[113] Molly B. Schmid. Structural proteomics: the potential of high-throughput structure determination. Trends in Microbiology, 10(10):s27–s31, 2002. Cerca con Google

[114] Kengo Kinoshita and Haruki Nakamura. Identification of protein biochemical functions by similarity search using the molecular surface database eF-site. Protein Science, 12(8):1589–1595, 2003. Cerca con Google

[115] Fabrizio Ferrè, Gabriele Ausiello, Andreas Zanzoni, and Manuela Helmer-Citterich. SURFACE: a database of protein surface regions for functional annotation. Nucleic Acids Research, 32(suppl 1):D240–D244, 2004. Cerca con Google

[116] L. Baldacci, M. Golfarelli, A. Lumini, and S. Rizzi. Clustering techniques for protein surfaces. Pattern Recognit, 39(12):2370–2382, 2006. Bioinformatics. Cerca con Google

[117] Lee Sael, Bin Li, David La, Yi Fang, Karthik Ramani, Raif Rustamov, and Daisuke Kihara. Fast protein tertiary structure retrieval based on global surface shape similarity. Proteins Struct Funct Bioinf, 72(4):1259–1273, 2008. Cerca con Google

[118] M. L. Connolly. Analytical molecular surface calculation. Journal of Applied Crystallography, 16(5):548–558, Oct 1983. Cerca con Google

[119] Michael L. Connolly. The molecular surface package. Journal of Molecular Graphics, 11(2):139–141, 1993. Cerca con Google

[120] Michel F. Sanner, Arthur J. Olson, and Jean-Claude Spehner. Fast and Robust Computation of Molecular Surfaces. In Proceedings of the Eleventh Annual Symposium on Computational Geometry, SCG ’95, pages 406–407, New York, NY, USA, 1995. ACM. Cerca con Google

[121] Kengo Kinoshita and Haruki Nakamura. Identification of the ligand binding sites on the molecular surface of proteins. Protein Science, 14(3):711–718, 2005. Cerca con Google

[122] Shuangye Yin, Elizabeth A. Proctor, Alexey A. Lugovskoy, and Nikolay V. Dokholyan. Fast screening of protein surfaces using geometric invariant fingerprints. Proceedings of the National Academy of Sciences of the United States of America, 106(39):16622–16626, 2009. Cerca con Google

[123] Laurent-Philippe Albou, Benjamin Schwarz, Olivier Poch, Jean Marie Wurtz, and Dino Moras. Defining and characterizing protein surface using alpha shapes. Proteins Struct Funct Bioinf, 76(1):1–12, 2009. Cerca con Google

[124] Zainab Abu Deeb, Donald A. Adjeroh, and Bing-Hua Jiang. Protein Surface Characterization Using an Invariant Descriptor. Int J Biomed Imaging, 2011:15, 2011. Cerca con Google

[125] Yongjie Zhang, Guoliang Xu, and Chandrajit Bajaj. Quality meshing of implicit solvation models of biomolecular structures. Comput Aided Geom Des, 23(6):510– 530, 2006. Cerca con Google

[126] MaryEllen Bock, Guido M. Cortelazzo, Carlo Ferrari, and Concettina Guerra. Identifying similar surface patches on proteins using a spin-image surface representation. In Alberto Apostolico, Maxime Crochemore, and Kunsoo Park, editors, Combinatorial Pattern Matching, volume 3537 of Lecture Notes in Computer Science, pages 417–428. Springer Berlin Heidelberg, 2005. Cerca con Google

[127] Mary Ellen Bock, Claudio Garutti, and Concettina Guerra. Cavity detection and matching for binding site recognition. Theoretical Computer Science, 408(2):151– 162, 2008. Cerca con Google

[128] Juan Esquivel-Rodriguez and Daisuke Kihara. Effect of conformation sampling strategies in genetic algorithm for multiple protein docking. BMC Proc., 6(Suppl 7):S4, 2012. Cerca con Google

[129] Juan Esquivel-Rodriguez and Daisuke Kihara. Evaluation of multiple protein docking structures using correctly predicted pairwise subunits. BMC Bioinform., 13(Suppl 2):S6, 2012. Cerca con Google

[130] Juan Esquivel-Rodriguez, Vianney Filos-Gonzalez, Bin Li, and Daisuke Kihara. Pairwise and multimeric protein–protein docking using the LZerD program suite. In Daisuke Kihara, editor, Protein Structure Prediction, volume 1137 of Methods in Molecular Biology, pages 209–234. Springer New York, 2014. Cerca con Google

[131] Scott Grandison, Carl Roberts, and Richard J. Morris. The Application of 3D Zernike Moments for the Description of “Model-Free” Molecular Structure, Functional Motion, and Structural Reliability. Journal of Computational Biology, 16(3):487–500, 2009. Cerca con Google

[132] Lee Sael, David La, Bin Li, Raif Rustamov, and Daisuke Kihara. Rapid comparison of properties on protein surface. Proteins, 73(1):1–10, 2008. Cerca con Google

[133] Bingding Huang and Michael Schroeder. LIGSITEcsc: predicting ligand binding sites using the connolly surface and degree of conservation. BMC Structural Biology, 6(1):1–11, 2006. Cerca con Google

[134] Ling Wei Lee and Andrzej Bargiela. Protein surface atoms extraction: voxels as an investigative tool. Engineering Letters, 20(3):217–228, 2012. Cerca con Google

[135] Ling Wei Lee and Andrzej Bargiela. An approximated voxel approach for the identification and modelling of ligand-binding sites. Journal of Physical Science and Application, 2(10), 2012. Cerca con Google

[136] Martin Weisel, Ewgenij Proschak, and Gisbert Schneider. PocketPicker: analysis of ligand binding-sites with shape descriptors. Chem Cent J, 1(1):7, 2007. Cerca con Google

[137] David G. Levitt and Leonard J. Banaszak. POCKET: A computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. Journal of Molecular Graphics, 10(4):229–234, 1992. Cerca con Google

[138] Manfred Hendlich, Friedrich Rippmann, and Gerhard Barnickel. LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. Journal of Molecular Graphics and Modelling, 15(6):359–363, 1997. Cerca con Google

[139] Bin Li, Srinivasan Turuvekere, Manish Agrawal, David La, Karthik Ramani, and Daisuke Kihara. Characterization of local geometry of protein surfaces with the visibility criterion. Proteins Struct Funct Bioinf, 71(2):670–683, 2008. Cerca con Google

[140] Helen M. Berman, Kim Henrick, and Haruki Nakamura. Announcing the worldwide Protein Data Bank. Nature Structural and Molecular Biology, 10(12):980, 2003. Cerca con Google

[141] Frank Desiere, Eric W. Deutsch, Nichole L. King, Alexey I. Nesvizhskii, Parag Mallick, Jimmy Eng, Sharon Chen, James Eddes, Sandra N. Loevenich, and Ruedi Aebersold. The PeptideAtlas project. Nucleic Acids Research, 34(suppl 1):D655– D658, 2006. Cerca con Google

[142] Robertson Craig, John P. Cortens, and Ronald C. Beavis. Open source system for analyzing, validating, and storing protein identification data. Journal of Proteome Research, 3(6):1234–1242, 2004. Cerca con Google

[143] Juan Antonio Vizcaíno, Richard G Côté, Attila Csordas, José A Dianes, Antonio Fabregat, Joseph M Foster, Johannes Griss, Emanuele Alpi, Melih Birim, Javier Contell, Gavin O’Kelly, Andreas Schoenegger, David Ovelleiro, Yasset PérezRiverol, Florian Reisinger, Daniel Ríos, Rui Wang, and Henning Hermjakob. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Research, 41(Database issue):D1063–9, January 2013. Cerca con Google

[144] David La, Juan Esquivel-Rodríguez, Vishwesh Venkatraman, Bin Li, Lee Sael, Stephen Ueng, Steven Ahrendt, and Daisuke Kihara. 3d-surfer: software for highthroughput protein surface comparison and analysis. Bioinformatics, 25(21):2843– 2844, 2009. Cerca con Google

[145] Jian Huang, Roni Yagel, Vassily Filippov, and Yair Kurzion. An accurate method for voxelizing polygon meshes. In Volume Visualization, 1998. IEEE Symposium on, pages 119–126. IEEE, 1998. Cerca con Google

[146] Sebastian Daberdaku and Carlo Ferrari. VoxSurf: A Voxelized Macromolecular Surface Calculation Program. In Proceedings of The 2015 International Conference on Parallel and Distributed Processing Techniques and Applications PDPTA’15, Las Vegas Nevada, USA, volume 2, pages 635–641, 2015. Cerca con Google

[147] Sebastian Daberdaku and Carlo Ferrari. A voxel-based tool for protein surface representation. In Claudia Angelini, Erik Bongcam-Rudloff, Adriano Decarli, Paola MV Rancoita, and Stefano Rovetta, editors, Twelfth international meeting on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB 2015), Naples (Italy), pages 96–101, 2015. Cerca con Google

[148] Sebastian Daberdaku and Carlo Ferrari. Computing discrete fine-grained representations of protein surfaces. In Claudia Angelini, Paola MV Rancoita, and Stefano Rovetta, editors, Computational Intelligence Methods for Bioinformatics and Biostatistics - 12th International Meeting, CIBB 2015, Naples, Italy, September 10-12, 2015, Revised Selected Papers, volume 9874 of Lecture Notes in Bioinformatics, chapter 14. Springer International Publishing, 2016 (to be published). Cerca con Google

[149] Schrödinger, LLC. The PyMOL molecular graphics system, version 1.8, November 2015. Cerca con Google

[150] Nicolas Guex and Manuel C Peitsch. SWISS-MODEL and the Swiss-Pdb Viewer: an environment for comparative protein modeling. Electrophoresis, 18(15):2714–2723, 1997. Cerca con Google

[151] Michel F Sanner, Arthur J Olson, and Jean-Claude Spehner. Reduced surface: an efficient way to compute molecular surfaces. Biopolymers, 38(3):305–320, 1996. Cerca con Google

[152] Eric F. Pettersen, Thomas D. Goddard, Conrad C. Huang, Gregory S. Couch, Daniel M. Greenblatt, Elaine C. Meng, and Thomas E. Ferrin. UCSF Chimera–a visualization system for exploratory research and analysis. Journal of computational chemistry, 25(13):1605–1612, October 2004. Cerca con Google

[153] William Humphrey, Andrew Dalke, and Klaus Schulten. VMD – Visual Molecular Dynamics. Journal of Molecular Graphics, 14:33–38, 1996. Cerca con Google

[154] Amitabh Varshney, Frederick P Brooks Jr, and William V Wright. Computing smooth molecular surfaces. Computer Graphics and Applications, IEEE, 14(5):19– 25, 1994. Cerca con Google

[155] Roger A Sayle and E James Milner-White. RASMOL: Biomolecular graphics for all. Trends in biochemical sciences, 20(9):374–376, 1995. Cerca con Google

[156] Jmol. an open-source Java viewer for chemical structures in 3D. http://www.jmol. org/. Vai! Cerca con Google

[157] Marcus D Hanwell, Donald Ephraim Curtis, David C Lonie, Tim Vandermeersch, Eva Zurek, and Geoffrey R Hutchison. Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. J. Cheminformatics, 4(1):17, 2012. Cerca con Google

[158] BIOVIA, Dassault Systèmes. Discovery Studio Modeling Environment, Release 4.5, 2015. Cerca con Google

[159] Sebastian Daberdaku and Carlo Ferrari. Parallel computation of voxelized macromolecular surfaces by spatial slicing. In Proceedings of the 13th IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA- 15), volume 3, pages 184–189, Helsinki, FI, Aug 2015. Cerca con Google

[160] Sebastian Daberdaku and Carlo Ferrari. Computing voxelised representations of macromolecular surfaces: A parallel approach. International Journal of High Performance Computing Applications, 2016. Cerca con Google

[161] P A Bash, N Pattabiraman, C Huang, T E Ferrin, and R Langridge. Van der Waals surfaces in molecular modeling: implementation with real-time computer graphics. Science, 222(4630):1325–1327, 1983. Cerca con Google

[162] B. Lee and F.M. Richards. The interpretation of protein structures: Estimation of static accessibility. Journal of Molecular Biology, 55(3):379–IN4, 1971. Cerca con Google

[163] Robert B. Corey and Linus Pauling. Molecular Models of Amino Acids, Peptides, and Proteins. Review of Scientific Instruments, 24(8):621–627, 1953. Cerca con Google

[164] Walter L. Koltun. Precision space-filling atomic models. Biopolymers, 3(6):665–679, 1965. Cerca con Google

[165] Per-Erik Danielsson. Euclidean distance mapping. Comput Graph Image Process, 14(3):227–248, 1980. Cerca con Google

[166] Gunilla Borgefors. Distance transformations in digital images. Comput Vision Graph Image Process, 34(3):344–371, jun 1986. Cerca con Google

[167] Ola Nilsson and Andreas Söderström. Euclidean Distance Transform algorithms: a comparative study. Technical report, Linköping University, Department of Science and Technology, The Institute of Technology, Digital Media, 2007. Cerca con Google

[168] George J. Grevera. Distance transform algorithms and their implementation and evaluation. In Deformable Models, Topics in Biomedical Engineering, pages 33–60. Springer New York, 2007. Cerca con Google

[169] Ricardo Fabbri, Luciano Da F. Costa, Julio C. Torelli, and Odemir M. Bruno. 2D Euclidean Distance Transform algorithms: A comparative survey. ACM Comput Surv, 40(1):2:1–2:44, feb 2008. Cerca con Google

[170] Olivier Cuisenaire. Region growing Euclidean Distance Transforms. In Alberto Bimbo, editor, Image Analysis and Processing, volume 1310 of Lecture Notes in Computer Science, pages 263–270. Springer Berlin Heidelberg, 1997. Cerca con Google

[171] A. D. MacKerell, D. Bashford, M. Bellott, R. L. Dunbrack, J. D. Evanseck, M. J. Field, S. Fischer, J. Gao, H. Guo, S. Ha, D. Joseph-McCarthy, L. Kuchnir, K. Kuczera, F. T. K. Lau, C. Mattos, S. Michnick, T. Ngo, D. T. Nguyen, B. Prodhom, W. E. Reiher, B. Roux, M. Schlenkrich, J. C. Smith, R. Stote, J. Straub, M. Watanabe, J. Wiórkiewicz-Kuczera, D. Yin, and M. Karplus. All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. Journal of Physical Chemistry B, 102(18):3586–3616, 1998. Cerca con Google

[172] J.E. Bresenham. Algorithm for computer control of a digital plotter. IBM Syst J, 4(1):25–30, 1965. Cerca con Google

[173] Wei-Wei Yu, Fei He, and Ping Xi. A rapid 3D seed-filling algorithm based on scan slice. Comput Graph, 34(4):449–459, 2010. Procedural Methods in Computer Graphics Illustrative Visualization. Cerca con Google

[174] Radu B. Rusu. The pcd (point cloud data) file format. Online: http: //pointclouds.org/documentation/tutorials/pcd_file_format.php. last accessed: 27-04-2016. Cerca con Google

[175] David Thompson, Jeff Braun, and Ray Ford. OpenDX: paths to visualization; materials used for learning OpenDX the open source derivative of IBM’s visualization Data Explorer. Visualization and Imagery Solutions, 2004. Cerca con Google

[176] W. Schroeder, K. Martin, and B. Lorensen. Visualization Toolkit: An ObjectOriented Approach to 3D Graphics, 4th Edition. Kitware, 4th edition, December 2006. Cerca con Google

[177] L.S. Avila and Inc Kitware. The VTK User’s Guide. Kitware, 2010. Cerca con Google

[178] Dong Xu and Yang Zhang. Generating triangulated macromolecular surfaces by Euclidean Distance Transform. PLoS One, 4(12):e8140, dec 2009. Cerca con Google

[179] Joint Center for Structural Genomics (JCSG). Crystal structure of Hydroperoxide resistance protein OsmC (TM0919) from Thermotoga maritima at 1.80 Å resolution. To Be Published, 2004. Cerca con Google

[180] Helen M. Berman, John Westbrook, Zukang Feng, Gary Gilliland, T. N. Bhat, Helge Weissig, Ilya N. Shindyalov, and Philip E. Bourne. The Protein Data Bank. Nucleic Acids Research, 28(1):235–242, 2000. Cerca con Google

[181] Helge Weissig and Philip E Bourne. Protein structure resources. Acta Crystallographica. Section D, Biological Crystallography, 58(6):908–915, Jun 2002. Cerca con Google

[182] Massimo Paoli, Robert Liddington, Jeremy Tame, Anthony Wilkinson, and Guy Dodson. Crystal Structure of T State Haemoglobin with Oxygen Bound At All Four Haems. Journal of Molecular Biology, 256(4):775–792, 1996. Cerca con Google

[183] Jeffrey S. Vetter and Michael O. McCracken. Statistical scalability analysis of communication operations in distributed applications. SIGPLAN Not, 36(7):123–132, June 2001. Cerca con Google

[184] T. Mitchell, M. Yuan, I. McNae, H. Morgan, and M.D. Walkinshaw. Human Pyruvate Kinase M2 Mutant C424A. To Be Published, 2014. Cerca con Google

[185] Luigi Di Costanzo, Guadalupe Sabio, Alfonso Mora, Paulo C. Rodriguez, Augusto C. Ochoa, Francisco Centeno, and David W. Christianson. Crystal structure of human arginase I at 1.29-Å resolution and exploration of inhibition in the immune response. Proceedings of the National Academy of Sciences of the United States of America, 102(37):13058–13063, 2005. Cerca con Google

[186] D.L. Scott, S.P. White, J.L. Browning, J.J. Rosa, M.H. Gelb, and P.B. Sigler. Structures of free and inhibited human secretory phospholipase A2 from inflammatory exudate. Science, 254(5034):1007–1010, 1991. Cerca con Google

[187] Michael L Connolly. Shape complementarity at the hemoglobin α1β1 subunit interface. Biopolymers, 25(7):1229–1247, 1986. Cerca con Google

[188] Raquel Norel, Shuo L Lin, Haim J Wolfson, and Ruth Nussinov. Molecular surface complementarity at protein–protein interfaces: the critical role played by surface normals at well placed, sparse, points in docking. Journal of molecular biology, 252(2):263–273, 1995. Cerca con Google

[189] Raquel Norel, Donald Petrey, Haim J Wolfson, and Ruth Nussinov. Examination of shape complementarity in docking of unbound proteins. Proteins: Structure, Function, and Bioinformatics, 36(3):307–317, 1999. Cerca con Google

[190] Dina Schneidman-Duhovny, Yuval Inbar, Vladimir Polak, Maxim Shatsky, Inbal Halperin, Hadar Benyamini, Adi Barzilai, Oranit Dror, Nurit Haspel, Ruth Nussinov, et al. Taking geometry to its edge: fast unbound rigid (and hinge-bent) docking. Proteins: Structure, Function, and Bioinformatics, 52(1):107–112, 2003. Cerca con Google

[191] Haim J Wolfson and Isidore Rigoutsos. Geometric hashing: An overview. IEEE computational science and engineering, 4(4):10–21, 1997. Cerca con Google

[192] A Mademlis, P Daras, D Tzovaras, and MG Strintzis. 3d object retrieval based on resulting fields. In 29th International Conference on EUROGRAPHICS 2008, workshop on 3D object retrieval, Crete (Greece), April 2008. Cerca con Google

[193] Vishwesh Venkatraman, Lee Sael, and Daisuke Kihara. Potential for protein surface shape analysis using spherical harmonics and 3D Zernike descriptors. Cell biochemistry and biophysics, 54(1-3):23–32, 2009. Cerca con Google

[194] Lee Sael and Daisuke Kihara. Characterization and classification of local protein surfaces using self-organizing map. International Journal of Knowledge Discovery in Bioinformatics, 1(1):32–47, 2010. Cerca con Google

[195] Lee Sael and Daisuke Kihara. Binding ligand prediction for proteins using partial matching of local surface patches. International Journal of Molecular Sciences, 11(12):5009–5026, 2010. Cerca con Google

[196] Lee Sael and Daisuke Kihara. Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison. Proteins: Structure, Function, and Bioinformatics, 80(4):1177–1195, 2012. Cerca con Google

[197] Xiaolei Zhu, Yi Xiong, and Daisuke Kihara. Large-scale binding ligand prediction by improved patch-based method Patch-Surfer 2.0. Bioinformatics, 31(5):707–713, 2015. Cerca con Google

[198] Bingjie Hu, Xiaolei Zhu, Lyman Monroe, Mark G. Bures, and Daisuke Kihara. PLPatchSurfer: A novel molecular local surface-based method for exploring protein– ligand interactions. Int J Mol Sci, 15(9):15122, 2014. Cerca con Google

[199] Woong-Hee Shin, Mark Gregory Bures, and Daisuke Kihara. PatchSurfers: Two methods for local molecular property-based binding ligand prediction. Methods, 93:41–50, 2016. Cerca con Google

[200] L Sael and D Kihara. Protein surface representation for application to comparing low-resolution protein structure data. BMC Bioinformatics, 11:S2, 2010. Cerca con Google

[201] Rayan Chikhi, Lee Sael, and Daisuke Kihara. Real-time ligand binding pocket database search using local surface descriptors. Proteins: Structure, Function, and Bioinformatics, 78(9):2007–2028, 2010. Cerca con Google

[202] Ming-Kuei Hu. Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on, 8(2):179–187, 1962. Cerca con Google

[203] Michael Reed Teague. Image analysis via the general theory of moments∗. Journal of the Optical Society of America, 70(8):920–930, Aug 1980. Cerca con Google

[204] Cho-Huak Teh and Roland T Chin. On image analysis by the methods of moments. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 10(4):496–513, 1988. Cerca con Google

[205] Xiao-Feng Wang, De-Shuang Huang, Ji-Xiang Du, Huan Xu, and Laurent Heutte. Classification of plant leaf images with complicated background. Applied mathematics and computation, 205(2):916–926, 2008. Cerca con Google

[206] Shan Li, Moon-Chuen Lee, and Chi-Man Pun. Complex Zernike moments features for shape-based image retrieval. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 39(1):227–237, 2009. Cerca con Google

[207] Chenhong Lu and Zhaoyang Lu. Zernike moment invariants based iris recognition. In Advances in Biometric Person Authentication, pages 554–561. Springer, 2004. Cerca con Google

[208] Hyoung-Joon Kim and Whoi-Yul Kim. Eye detection in facial images using zernike moments with svm. ETRI journal, 30(2):335–337, 2008. Cerca con Google

[209] Wang Liyun, Ling Hefei, Zou Fuhao, Lu Zhengding, and Wang Zhendi. Spermatogonium image recognition using zernike moments. Computer methods and programs in biomedicine, 95(1):10–22, 2009. Cerca con Google

[210] V Subbiah Bharathi and L Ganesan. Orthogonal moments based texture analysis of ct liver images. Pattern Recognition Letters, 29(13):1868–1872, 2008. Cerca con Google

[211] N. Canterakis. 3d zernike moments and zernike affine invariants for 3d image analysis and recognition. In In 11th Scandinavian Conf. on Image Analysis, pages 85–93, 1999. Cerca con Google

[212] Marcin Novotni and Reinhard Klein. Shape retrieval using 3d zernike descriptors. Computer-Aided Design, 36(11):1047–1062, 2004. Cerca con Google

[213] Khalid M Hosny and Mohamed A Hafez. An algorithm for fast computation of 3d zernike moments for volumetric images. Mathematical Problems in Engineering, 2012, 2012. Cerca con Google

[214] Thomas Funkhouser, Patrick Min, Michael Kazhdan, Joyce Chen, Alex Halderman, David Dobkin, and David Jacobs. A Search Engine for 3D Models. ACM Trans. Graph., 22(1):83–105, January 2003. Cerca con Google

[215] A. Khotanzad and Y. H. Hong. Invariant image recognition by zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5):489–497, May 1990. Cerca con Google

[216] Khalid M Hosny. Exact and fast computation of geometric moments for gray level images. Applied Mathematics and Computation, 189(2):1214–1222, 2007. Cerca con Google

[217] Todd J. Dolinsky, Jens E. Nielsen, J. Andrew McCammon, and Nathan A. Baker. PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Research, 32(suppl 2):W665–W667, 2004. Cerca con Google

[218] Todd J. Dolinsky, Paul Czodrowski, Hui Li, Jens E. Nielsen, Jan H. Jensen, Gerhard Klebe, and Nathan A. Baker. PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations. Nucleic Acids Research, 35(suppl 2):W522–W525, 2007. Cerca con Google

[219] Nathan A. Baker, David Sept, Simpson Joseph, Michael J. Holst, and J. Andrew McCammon. Electrostatics of nanosystems: Application to microtubules and the ribosome. Proceedings of the National Academy of Sciences, 98(18):10037–10041, 2001. Cerca con Google

[220] Christopher D Manning, Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Retrieval. Cambridge University Press, 1 edition, 2008. Cerca con Google

[221] Joël Janin, Kim Henrick, John Moult, Lynn Ten Eyck, Michael JE Sternberg, Sandor Vajda, Ilya Vakser, and Shoshana J Wodak. CAPRI: a Critical Assessment of PRedicted Interactions. Proteins: Structure, Function, and Bioinformatics, 52(1):2– 9, 2003. Cerca con Google

[222] Raúl Méndez, Raphaël Leplae, Leonardo De Maria, and Shoshana J Wodak. Assessment of blind predictions of protein–protein interactions: current status of docking methods. Proteins: Structure, Function, and Bioinformatics, 52(1):51–67, 2003. Cerca con Google

[223] Joël Janin. Assessing predictions of protein–protein interaction: the CAPRI experiment. Protein science, 14(2):278–283, 2005. Cerca con Google

[224] Rong Chen and Zhiping Weng. A novel shape complementarity scoring function for protein–protein docking. Proteins: Structure, Function, and Bioinformatics, 51(3):397–408, 2003. Cerca con Google

[225] David W Ritchie, Dima Kozakov, and Sandor Vajda. Accelerating and focusing protein–protein docking correlations using multi-dimensional rotational FFT generating functions. Bioinformatics, 24(17):1865–1873, 2008. Cerca con Google

[226] HaimJ Wolfson and Ruth Nussinov. From computer vision to protein structure and association. New Comprehensive Biochemistry, 32:313–334, 1998. Cerca con Google

[227] Florian Krull, Gerrit Korff, Nadia Elghobashi-Meinhardt, and Ernst-Walter Knapp. ProPairs: A Data Set for Protein–Protein Docking. Journal of chemical information and modeling, 55(7):1495–1507, 2015. Cerca con Google

[228] Thom Vreven, Iain H Moal, Anna Vangone, Brian G Pierce, Panagiotis L Kastritis, Mieczyslaw Torchala, Raphael Chaleil, Brian Jiménez-García, Paul A Bates, Juan Fernandez-Recio, et al. Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. Journal of molecular biology, 427(19):3031–3041, 2015. Cerca con Google

[229] E Romero, O Cuisenaire, J.F Denef, J Delbeke, B Macq, and C Veraart. Automatic morphometry of nerve histological sections. Journal of Neuroscience Methods, 97(2):111–122, 2000. Cerca con Google

[230] O. Cuisenaire, J. Thiran, B. Macq, C. Michel, and A. De Volder. Automatic registration of 3D MR images with a computerized brain atlas. In SPIE Medical Imaging, volume 2710 of Lecture Notes in Computer Science, pages 438–448. IEEE, 1996. Cerca con Google

[231] Q. Noirhomme, M. Ferrant, Y. Vandermeeren, E. Olivier, B. Macq, and O. Cuisenaire. Registration and real-time visualization of transcranial magnetic stimulation with 3-D MR images. IEEE Transactions on Biomedical Engineering, 51(11):1994– 2005, Nov 2004 Cerca con Google

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