RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence–structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction.

CoinFold (http://raptorx2.uchicago.edu/ContactMap/) is a web server for protein contact prediction and contact-assisted de novo structure prediction. CoinFold predicts contacts by integrating joint multi-family evolutionary coupling (EC) analysis and supervised machine learning. This joint EC analysis is unique in that it not only uses residue coevolution information in the target protein family, but also that in the related families which may have divergent sequences but similar folds. The supervised learning further improves contact prediction accuracy by making use of sequence profile, contact (distance) potential and other information. Finally, this server predicts tertiary structure of a sequence by feeding its predicted contacts and secondary structure to the CNS suite. Tested on the CASP and CAMEO targets, this server shows significant advantages over existing ones of similar category in both contact and tertiary structure prediction.

Thiophenic compounds are the refractory organosulfur compounds remaining in the transportation fuels, and their removal from liquid fuels has become increasingly important. In this work, adsorption isotherms of thiophene (T), benzothiophene (BT), and dibenzothiophene (DBT) in isooctane onto a metal–organic framework (Cu-BTC) were measured for (293.15 to 313.15) K and equilibrium sulfur concentrations up to 370 ppmw-S (ppmw of sulfur). The adsorption capacity followed the order of BT < T < DBT under the investigated sulfur concentrations and temperatures. The adsorption isotherms of T and DBT are highly favorable. The isotherm data were well-correlated using a multitemperature Langmuir model, and three parameters were extracted for each thiophenic compound. The multitemperature Langmuir model has predictive ability for the adsorption of T, BT, and DBT within the sulfur concentration range and the temperature range studied. The heat of adsorption (ΔH) of T, BT, and DBT is (−21.99, −14.23, and −37.34) kJ·mol–1, respectively. The order of ΔH is in agreement with the adsorption affinities.

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