Most aquatic photoautotrophs depend on CO2-concentrating mechanisms (CCMs) to maintain productivity at ambient concentrations of CO2, and carbonic anhydrase (CA) plays a key role in these processes. Here we present different lines of evidence showing that the protein LCIP63, identified in the marine diatom Thalassiosira pseudonana, is a CA. However, sequence analysis showed that it has a low identity with any known CA and therefore belongs to a new subclass that we designate as iota-CA. Moreover, LCIP63 unusually prefers Mn2+ to Zn2+ as a cofactor, which is potentially of ecological relevance since Mn2+ is more abundant than Zn2+ in the ocean. LCIP63 is located in the chloroplast and only expressed at low concentrations of CO2. When overexpressed using biolistic transformation, the rate of photosynthesis at limiting concentrations of dissolved inorganic carbon increased, confirming its role in the CCM. LCIP63 homologs are present in the five other sequenced diatoms and in other algae, bacteria, and archaea. Thus LCIP63 is phylogenetically widespread but overlooked. Analysis of the Tara Oceans database confirmed this and showed that LCIP63 is widely distributed in marine environments and is therefore likely to play an important role in global biogeochemical carbon cycling.

/pdf/a-new-widespread-subclass-of-carbonic-anhydrase-in-marine-2qcsliz8rc.pdf

A new widespread subclass of carbonic anhydrase in marine phytoplankton.

We assess the progress in biomolecular modeling and simulation, focusing on structure prediction and dynamics, by presenting the field's history, metrics for its rise in popularity, early expressed expectations, and current significant applications. The increases in computational power combined with improvements in algorithms and force fields have led to considerable success, especially in protein folding, specificity of ligand/biomolecule interactions, and interpretation of complex experimental phenomena (e.g. NMR relaxation, protein-folding kinetics and multiple conformational states) through the generation of structural hypotheses and pathway mechanisms. Although far from a general automated tool, structure prediction is notable for proteins and RNA that preceded the experiment, especially by knowledge-based approaches. Thus, despite early unrealistic expectations and the realization that computer technology alone will not quickly bridge the gap between experimental and theoretical time frames, ongoing improvements to enhance the accuracy and scope of modeling and simulation are propelling the field onto a productive trajectory to become full partner with experiment and a field on its own right.

/pdf/biomolecular-modeling-and-simulation-a-field-coming-of-age-372iw83zzu.pdf

Biomolecular modeling and simulation: a field coming of age

Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between proteins from sequence are unable to recognize and exploit structural patterns when sequences have diverged too far, limiting our ability to transfer knowledge between structurally related proteins. We newly approach this problem through the lens of representation learning. We introduce a framework that maps any protein sequence to a sequence of vector embeddings --- one per amino acid position --- that encode structural information. We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism that incorporates information from (i) global structural similarity between proteins and (ii) pairwise residue contact maps for individual proteins. To enable learning from structural similarity information, we define a novel similarity measure between arbitrary-length sequences of vector embeddings based on a soft symmetric alignment (SSA) between them. Our method is able to learn useful position-specific embeddings despite lacking direct observations of position-level correspondence between sequences. We show empirically that our multi-task framework outperforms other sequence-based methods and even a top-performing structure-based alignment method when predicting structural similarity, our goal. Finally, we demonstrate that our learned embeddings can be transferred to other protein sequence problems, improving the state-of-the-art in transmembrane domain prediction.

Learning protein sequence embeddings using information from structure

C4-dicarboxylates sensing mechanism revealed by the crystal structures of DctB sensor domain.

http://www.ks.uiuc.edu/Training/TutorialsOverview/science/evolution/evolution-tutorial-files/papers/odonoghue_JMB_2005.pdf

Evolutionary profiles derived from the QR factorization of multiple structural alignments gives an economy of information.

http://binf.gmu.edu/vaisman/binf731/biophysj02-gan-seq-str.pdf

Analysis of protein sequence/structure similarity relationships.

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Analysis of protein sequence/structure similarity relationships.