1. 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

2. 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.

3. 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

4. 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.

5. 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)

6. 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)

7. 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

8. 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

9. 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

10. 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)

11. 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

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

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

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

15. 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 ...

16. 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

17. Acknowledgements