Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

DAMA/LIBRA Results and Perspectives

The DAMA/LIBRA experiment (~ 250 kg sensitive mass composed by highly radio-pure NaI(Tl)) is in data taking in the underground Laboratory of Gran Sasso (LNGS). In its first phase (DAMA/LIBRA-phase1) this experiment and the former DAMA/NaI experiment (~ 100 kg of highly radio-pure NaI(Tl)) collected data for 14 independent annual cycles, exploiting the model-independent Dark Matter (DM) annual modulation signature (total exposure 1.33 ton x yr). A DM annual modulation effect has been observed at 9.3 σ C.L., supporting the presence of DM particles in the galactic halo. No systematic or side reaction able to mimic the observed DM annual modulation has been found or suggested by anyone. Recent analyses on possible diurnal effects, on the Earth shadowing effect and on possible interpretation in terms of Mirror DM will be mentioned. At present DAMA/LIBRA is running in its phase2 with increased sensitivity