Algorithms for Macro-Molecular Pocket Detection A-MOP UTAP-EXPL/QEQ-COM/0019/2014 Algorithms for Macro-Molecular Pocket Detection Good afternoon The purpose of this presentation is to give everybody here an overview on the AMOP project Joaquim Jorge INESC-ID

Emerging Technologies Exploratory project 12 months A-MOP Emerging Technologies Exploratory project 12 months This project is under the scope of the UT Austin | Portugal program, which promotes the scientific exchange between scholars and academics of portuguese research institutions and the University of Texas at Austin. In particular, the AMOP project resulted from the 2014 call for R&D projects in Portugal in the field of Emerging Technologies; it is an Exploratory Project, which menas that it a relatively small project both in duration and budget.

Institutional Partners As for the instituttions invloved, we have on the American side the UT Austin and on the Portuguese side INESC and Univ. Beira Interior

Project Members Chandrajit Bajaj Joaquim Jorge Daniel Simões Lopes João Madeiras Pereira Abel Gomes Tiago Simões I guess that all project members are all here participating in the workshop. From UT Austin Cbajaj From Inesc, Jjorge, myself, JAP, VCosta and a master student Hugo Fernandes. And from the Universidade de Beira Interior Agomes, Tiago Simões and a scholar awaredee.

Motivation Structure-Based Drug Design Challenges: How to correctly predict which small molecules can bind to a specific protein? How to assess their impact on protein function? Major Issues: size of proteins that current approaches can handle time required to find cavities and rendering Desiderata: More efficient algorithms for detecting pockets on the surface of large proteins (> 500K atoms)

Goals (A) to develop new and efficient geometric algorithms to determine pockets and other cavities in macromolecules (B) to develop computational methods to tackle the problem of scalability with number of atoms (e.g. millions of atoms) https://www.google.pt/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRxqFQoTCO3mzK6W7ccCFUGhFAodvCQAAQ&url=%2Furl%3Fsa%3Di%26rct%3Dj%26q%3D%26esrc%3Ds%26source%3Dimages%26cd%3D%26cad%3Drja%26uact%3D8%26ved%3D0CAcQjRxqFQoTCOuv6eyV7ccCFcI_FAodErsN2A%26url%3Dhttp%253A%252F%252Fwww.nature.com%252Fncomms%252Fjournal%252Fv3%252Fn12%252Ffig_tab%252Fncomms2304_F2.html%26psig%3DAFQjCNH804nEoBrTiH2Vs1vmj2DzarRlbg%26ust%3D1441998574319900&psig=AFQjCNH804nEoBrTiH2Vs1vmj2DzarRlbg&ust=1441998574319900

Goal (A) Geometry-based pocket prediction method: explore critical points to quickly find pockets on the protein surface, for mesh- or meshless-based methods no need to explicitly evaluate the whole surface of the molecule In order to achieve Goal A ... Gomes 2014, Comp Graph 38, 365–373

Goal (B) GPU Parallelization: Proposed methods are both localized and decoupled it is seemingly possible to take advantage of parallel computation using multiple CPU/GPU cores In order to achive Goal B ... Dias & Gomes 2007

Plan and Methods Hypothesis: by resorting on local molecular information, it is possible to develop more efficient techniques to find and classify pocket sites of large proteins Research Plan: A - Geometric modeling of molecular entities B - Location of pocket sites C - Classification of pocket sites D - Visualization of pocket sites / Molecular Visualization E - Validation of computational procedures F - Algorithm benchmarking research outlined in the six main phases of a standard pocket search algorithm

a pocket must be a critical point of the implicit function Geometric modeling of molecular entities explore implicit surface representations rely on the critical point theory that uniquely associates critical points of an implicit smooth surface to molecular biology meaning: a pocket must be a critical point of the implicit function Use Gaussian functions to represent molecular surface models electron density is always smooth)

Locating pocket sites For atoms on the periphery of the molecule find mimimization/maximation paths (of the partial derivatives) find 2-saddle points in the domain of the Gaussian function without spatial enumeration or voxelization of the domain. 2D algorithm: ACM Trans Comp Graph 38, 2014

Triangulating molecular surfaces over a LAN of GPU-enabled computers, Parallel Computing 2015

Multi-GPU-based detection of protein cavities using critical points, SE Dias, QT Nguyen, JA Jorge , AP Gomes, Future Generation Computer Systems, 2016

Output Indicators International journal Papers 5 International Conference papers 1 1PhD Thesis 1 MSc Thesis Organization of seminars and conferences 1

Ongoing / Future Work Novel protein docking methods Database databases of protein-ligand binding pockets Benchmark of pocket-finding Algorithms (CavBench) So, just to make things a bit more clear: this project is not about ...

Validation and benchmarking (CavBench) accuracy of the pocket site predictions to be validated and calibrated with specific examples of complexes found in the BindingDB database compare methods in terms of efficiency, speedup and accuracy with state of the art methods

Acknowledgements