Surveying molecular interactions with DOT
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Deflation Techniques for an Implicitly Restarted Arnoldi Iteration
SIAM Journal on Matrix Analysis and Applications
Dimension reduction by local principal component analysis
Neural Computation
GTM: the generative topographic mapping
Neural Computation
NAMD2: greater scalability for parallel molecular dynamics
Journal of Computational Physics - Special issue on computational molecular biophysics
The Mathematica book (4th edition)
The Mathematica book (4th edition)
A discriminant analysis for undersampled data
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
A method for improving protein localization prediction from datasets with outliers
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Protein conformational flexibility analysis with noisy data
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Linear dimensionality reduction in random motion planning
International Journal of Robotics Research
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Proteins are involved either directly or indirectly in all biological processes in living organisms. It is now widely accepted that conformational changes of proteins can critically affect their ability to bind other molecules and that any progress in modeling protein motion and flexibility will contribute to the understanding of key biological functions. However, modeling protein flexibility has proven a very difficult task. Experimental laboratory methods such as X-ray crystallography produce rather few structures, while computational methods such as Molecular Dynamics are too slow for routine use with large systems. A medium sized protein typically has a few thousands of degrees of freedom. This paper shows how to obtain a reduced basis representation of protein flexibility. We use the Principal Component Analysis method, a dimensionality reduction technique, to transform the original high dimensional representation of protein motion into a lower dimensional representation that captures the dominant modes of motions of the protein. Although there is inevitably some loss in accuracy, we show that we can obtain conformations that have been observed in laboratory experiments, starting from different initial conformations and working in a drastically reduced search space.