To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one.

An accurate potential energy model is crucial for biomolecular simulations. Despite many recent improvements of classical protein force fields, there are remaining key issues: much weaker temperature dependence of folding/unfolding equilibrium and overly collapsed unfolded or disordered states. For the latter problem, a new water model (TIP4P-D) has been proposed to correct the significantly underestimated water dispersion interactions. Here, using TIP4P-D, we reveal problems in current force fields through failures in folding model systems (a polyalanine peptide, Trp-cage, and the GB1 hairpin). By using residue-specific parameters to achieve better match between amino acid sequences and native structures and adding a small H-bond correction to partially compensate the missing many-body effects in α-helix formation, the new RSFF2+ force field with the TIP4P-D water model can excellently reproduce experimental melting curves of both α-helical and β-hairpin systems. The RSFF2+/TIP4P-D method also gives less collapsed unfolded structures and describes well folded proteins simultaneously.

While 1,2-addition represents the most common mode of alkyne hydroboration, herein we describe a new 1,1-hydroboration mode. It is the first demonstration of gem-(H,B) addition to an alkyne triple bond. With the superior [CpRu­(MeCN)3]­PF6 catalyst, a range of silyl alkynes reacted efficiently with HBpin under mild conditions to form various synthetically useful silyl vinyl boronates with complete stereoselectivity and broad functional group compatibility. An extension to germanyl alkynes and the hydrosilylation of alkynyl boronates toward the same type of products were also achieved. Mechanistically, this process features a new pathway featuring gem-(H,B) addition to form the key α-boryl-α-silyl Ru-carbene intermediate followed by silyl migration. It is believed that the orbital interaction between boron and Cβ in the coplanar relationship between the boron atom and the ruthenacyclopropene ring preceding boron migration is responsible for the new reactivity. Control experiments and DFT (including molecular dynamics) calculations provided important insights into the mechanism, which excluded the involvement of a metal vinylidene intermediate. This study represents a new step forward not only for alkyne hydroboration but also for other geminal additions of alkynes.

In the yeast, meiotic recombination is initiated by double-strand DNA breaks (DSBs) which occur at relatively high frequencies in some genomic regions (hotspots) and relatively low frequencies in others (coldspots). Although observations concerning individual hot/cold spots have given clues as to the mechanism of recombination initiation, the prediction of hot/cold spots from DNA sequence information is a challenging task. In this article, we introduce a random forest (RF) prediction model to detect recombination hot/cold spots from yeast genome. The out-of-bag (OOB) estimation of the model indicated that the RF classifier achieved high prediction performance with 82.05% total accuracy and 0.638 Mattew's correlation coefficient (MCC) value. Compared with an alternative machine-learning algorithm, support vector machine (SVM), the RF method outperforms it in both sensitivity and specificity. The prediction model is implemented as a web server (RF-DYMHC) and it is freely available at http://www.bioinf.seu.edu.cn/Recombination/rf_dymhc.htm. Given a yeast genome and prediction parameters (RI-value and non-overlapping window scan size), the program reports the predicted hot/cold spots and marks them in color.