Over the past 10 years, anion–π interaction has been recognized as an important weak force that may occur between anionic systems and electron-deficient aromatics. Lately, this supramolecular contact has experienced a rapidly growing interest, as reflected by numerous recent literature reports. The present paper highlights the tremendous progress achieved in the field by emphasizing three important studies involving anion–π interactions published in 2010. In addition, a pioneering search of the Protein Data Bank (PDB) reveals short anion–π contacts in some protein structures.

Background: Machine learning methods are nowadays used for many biological prediction problems involving drugs, ligands or polypeptide segments of a protein. In order to build a prediction model a so called training data set of molecules with measured target properties is needed. For many such problems the size of the training data set is limited as measurements have to be performed in a wet lab. Furthermore, the considered problems are often complex, such that it is not clear which molecular descriptors (features) may be suitable to establish a strong correlation with the target property. In many applications all available descriptors are used. This can lead to difficult machine learning problems, when thousands of descriptors are considered and only few (e.g. below hundred) molecules are available for training.Results: The CoEPrA contest provides four data sets, which are typical for biological regression problems (few molecules in the training data set and thousands of descriptors). We applied the same two-step training procedure for all four regression tasks. In the first stage, we used optimized L1 regularization to select the most relevant features. Thus, the initial set of more than 6,000 features was reduced to about 50. In the second stage, we used only the selected features from the preceding stage applying a milder L2 regularization, which generally yielded further improvement of prediction performance. Our linear model employed a soft loss function which minimizes the influence of outliers.Conclusions: The proposed two-step method showed good results on all four CoEPrA regression tasks. Thus, it may be useful for many other biological prediction problems where for training only a small number of molecules are available, which are described by thousands of descriptors.

At the heart of photosynthetic reaction centers (RCs) are pairs of chlorophyll a (Chl a ), P700 in photosystem I (PSI) and P680 in photosystem II (PSII) of cyanobacteria, algae, or plants, and a pair of bacteriochlorophyll a (BChl a ), P870 in purple bacterial RCs (PbRCs). These pairs differ greatly in their redox potentials for one-electron oxidation, E _m . For P680, E _m is 1,100–1,200 mV, but for P700 and P870, E _m is only 500 mV. Calculations with the linearized Poisson–Boltzmann equation reproduce these measured E _m differences successfully. Analyzing the origin for these differences, we found as major factors in PSII the unique Mn ₄ Ca cluster (relative to PSI and PbRC), the position of P680 close to the luminal edge of transmembrane α-helix d (relative to PSI), local variations in the cd loop (relative to PbRC), and the intrinsically higher E _m of Chl a compared with BChl a (relative to PbRC).