We report in the present work a new method for exploring conformational energy landscapes. The method, called T-RRT, combines ideas from statistical physics and robot path planning algorithms. A search tree is constructed on the conformational space starting from a given state. The tree expansion is driven by a double strategy: on the one hand, it is naturally biased towards yet unexplored regions of the space; on the other, a Monte Carlo-like transition test guides the expansion toward energetically favorable regions. The balance between these two strategies is automatically achieved thanks to a self-tuning mechanism. The method is able to efficiently find both, energy minima and transition paths between them. As a proof of concept, the method is applied to two academic benchmarks and to the alanine dipeptide.

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The most important natural sources of new leads are plant extracts, bacterial broths, animal venoms and peptides isolated from living organisms. However, only the three first have been used extensively in the development of new therapeutic agents. This is probably due to the low pharmacological profile exhibited by peptides, that requires a lengthy transformation to make them suitable as new leads. In contrast, bioactive compounds isolated from the other sources are regularly closer to be used as lead compounds. Nevertheless, the sources for compounds of this category are nowadays scarce. In contrast, there are new bioactive peptides discovered quite often and reported as ligands for different receptors. Under these circumstances peptides appear as an attractive source of prospective new leads. In order to reduce the time involved in the design of a potential lead from a peptide, molecular modeling tools have been developed in the last few years. The purpose of the present work is to review the different techniques available and to report various successful examples of design of new peptidomimetics published in the literature.

This paper presents a numerical method to compute all possible conformations of distance-constrained molecular loops, i.e., loops where some interatomic distances are held fixed, while others can vary. The method is general (it can be applied to single or multiple intermingled loops of arbitrary topology) and complete (it isolates all solutions, even if they form positive-dimensional sets). Generality is achieved by reducing the problem to finding all embeddings of a set of points constrained by pairwise distances, which can be formulated as computing the roots of a system of Cayley-Menger determinants. Completeness is achieved by expressing these determinants in Bernstein form and using a numerical algorithm that exploits such form to bound all root locations at any desired precision. The method is readily parallelizable, and the current implementation can be run on single-or multiprocessor machines. Experiments are included that show the method's performance on rigid loops, mobile loops, and multiloop molecules. In all cases, complete maps including all possible conformations are obtained, thus allowing an exhaustive analysis and visualization of all pseudo-rotation paths between different conformations satisfying loop closure.

The present study describes an extensive conformational search of substance P using two different computational methods. On the one hand, the peptide was studied using the iterative simulated annealing, and on the other, molecular dynamics simulations at 300 and 400 K. With the former method, the peptide was studied in vacuo with a dielectric constant of 80, whereas using the latter study the peptide was studied in a box of TIP3P water molecules. Analysis of the results obtained using both methodologies was carried out using an in-house methodology using a cluster analysis method based on information theory. Comparison of the two sampling methodologies and the different environment used in the calculations is also analyzed. Finally, the conformational motifs that are characteristic of substance P in a hydrophilic environment are presented and compared with the experimental results available in the literature.