Macromolecular structure comparison and docking: an algorithmic review

The comparison of macromolecular structures, in terms of functionalities, is a crucial step when aiming to identify potential docking sites. Drug designers require the identification of such docking sites for the binding of two proteins, in order to form a stable complex. This paper presents a review of current approaches to macromolecular structure comparison and docking, following an algorithmic approach. We describe techniques based on the Bayesian framework, kernel-based methods, projection-based techniques and spectral approaches. We introduce the use of quantum particle swarm optimization, in order to aid us to find the most appropriate docking sites. We discuss the importance of the heat and Schr\uf6dinger equations to address the non-rigid nature of proteins and highlight the strengths and limitations of the various methods.Peer reviewed: YesNRC publication: Ye

Addressing the Docking Problem: Finding Similar 3-D Protein Envelopes for Computer-Aided Drug Design

Consider a protein (PX) that has been identified, during drug design, to constitute a new breakthrough in the design of a drug for treating a terminal illness. That is, this protein has the ability to dock on active sites and mask the subsequent docking of harmful foreign proteins. Unfortunately, protein X has serious side-effects and is therefore not suitable for use in drug design. Suppose another protein (PY) with similar outer structure (or envelope) and functionality, but without these side-effects, exists. Locating, and using, such an alternative protein has obvious benefits. This paper introduces an approach to locate such similar protein envelopes by considering their three-dimensional (3-D) shapes. We present a system which indexes and searches a large 3-D protein database and illustrate its effectiveness against a very large protein repository.Imaginons que, gr\ue2ce \ue0 une prot\ue9ine (PX) d\ue9couverte au cours de la synth\ue8se de m\ue9dicaments, nous puissions faire un grand pas en avant vers la mise au point d'un m\ue9dicament capable de traiter une maladie terminale. En clair, cette prot\ue9ine peut s\u2019ancrer sur des sites actifs et inhiber l'ancrage ult\ue9rieur de prot\ue9ines \ue9trang\ue8res nocives. Malheureusement, cette prot\ue9ine X provoque des effets secondaires graves et son utilisation dans la conception de m\ue9dicaments est donc contre-indiqu\ue9e. Supposons qu\u2019il existe une autre prot\ue9ine (PY) poss\ue9dant une structure externe (ou enveloppe) et des fonctions similaires, mais pas les effets secondaires. Pouvoir rep\ue9rer et utiliser une telle prot\ue9ine offre des avantages \ue9vidents. Nous pr\ue9sentons dans cet article une m\ue9thode permettant de d\ue9tecter des enveloppes prot\ue9iques similaires en fonction de leur organisation tridimensionnelle (3D). Nous d\ue9crivons un syst\ue8me capable d\u2019interroger et d\u2019indexer une vaste base de donn\ue9es de structures 3D de prot\ue9ines et illustrons son efficacit\ue9 envers une imposante banque de prot\ue9ines.Peer reviewed: YesNRC publication: Ye

Multimodal representations, indexing, unexpectedness and proteins

Complex systems, such as proteins, are inherently difficult to describe, analyse and interpret. A multimodal methodology which utilizes various diverse representations is needed to further our understanding of such intrinsically multifaceted systems. This paper presents a multimodal system designed to describe and interpret the content of the Protein Data Bank, a repository which contains tens of thousands of known proteins. We describe how complimentary modalities based on the amino acid sequence, the protein's backbone (or topology) and the envelope (the outer shape), are used when furthering our understanding of proteins' functionalities, behaviours and interactions. We illustrate our methodology against the human haemoglobin and show that the interplay between modalities allows our system to find unexpected, complimentary results with different domains of validity. \ua9 2011 Springer-Verlag.Peer reviewed: YesNRC publication: Ye

Visualization Techniques for Data Mining

Peer reviewed: YesNRC publication: Ye

Probability Distributions from Riemannian Geometry, Generalized Hybrid Monte Carlo Sampling and Path Integrals

When considering probabilistic pattern recognition methods, especially methods based on Bayesian analysis, the probabilistic distribution is of the utmost importance. However, despite the fact that the geometry associated with the probability distribution constitutes essential background information, it is often not ascertained. This paper discusses how the standard Euclidian geometry should be generalized to the Riemannian geometry when a curvature is observed in the distribution. To this end, the probability distribution is defined for curved geometry. In order to calculate the probability distribution, a Lagrangian and a Hamiltonian constructed from curvature invariants are associated with the Riemannian geometry and a generalized hybrid Monte Carlo sampling is introduced. Finally, we consider the calculation of the probability distribution and the expectation in Riemannian space with path integrals, which allows a direct extension of the concept of probability to curved space.Lorsqu\u2019il s\u2019agit de consid\ue9rer des m\ue9thodes probabilistes pour la reconnaissance de formes, particuli\ue8rement les m\ue9thodes fond\ue9es sur l\u2019analyse bay\ue9sienne, la distribution probabiliste est cruciale. Toutefois, bien que la g\ue9om\ue9trie associ\ue9e \ue0 la distribution de la probabilit\ue9 repr\ue9sente une information de base essentielle, sa v\ue9rification est souvent escamot\ue9e. Dans le pr\ue9sent article, nous expliquons en quoi la g\ue9om\ue9trie euclidienne standard devrait \ueatre g\ue9n\ue9ralis\ue9e \ue0 une g\ue9om\ue9trie de Riemann lorsqu\u2019il y a une courbure dans la distribution. \uc0 cette fin, nous d\ue9finissons la distribution de la probabilit\ue9 pour une g\ue9om\ue9trie courb\ue9e. Pour calculer la distribution de la probabilit\ue9, nous associons un lagrangien et un hamiltonien (b\ue2tis \ue0 partir d\u2019invariants de courbure) \ue0 la g\ue9om\ue9trie de Riemann, et nous introduisons un \ue9chantillonnage de Monte Carlo hybride g\ue9n\ue9ralis\ue9. Enfin, nous examinons le calcul de la distribution de la probabilit\ue9 et l\u2019esp\ue9rance de l\u2019espace de Riemann calcul\ue9e \ue0 partir d\u2019int\ue9grales de chemin, qui permet une extension directe du concept de probabilit\ue9 \ue0 un espace courb\ue9.Peer reviewed: YesNRC publication: Ye

Indexing and Retrieval of Multiple 3D Protein Structure Representations for the Protein Data Bank

Peer reviewed: YesNRC publication: Ye

Addressing Climo-Skepticism: Towards an Integrated Repository of Climate Change Research Findings

Peer reviewed: YesNRC publication: Ye