Jian Zhan

Energy Functions in De Novo Protein Design: Current Challenges and Future Prospects

In the past decade, a concerted effort to successfully capture specific tertiary packing interactions produced specific three-dimensional structures for many de novo designed proteins that are validated by nuclear magnetic resonance and/or X-ray crystallographic techniques. However, the success rate of computational design remains low. In this review, we provide an overview of experimentally validated, de novo designed proteins and compare four available programs, RosettaDesign, EGAD, Liang-Grishin, and RosettaDesign-SR, by assessing designed sequences computationally. Computational assessment includes the recovery of native sequences, the calculation of sizes of hydrophobic patches and total solvent-accessible surface area, and the prediction of structural properties such as intrinsic disorder, secondary structures, and three-dimensional structures. This computational assessment, together with a recent community-wide experiment in assessing scoring functions for interface design, suggests that the next-generation protein-design scoring function will come from the right balance of complementary interaction terms. Such balance may be found when more negative experimental data become available as part of a training set.

Real-time reliable determination of binding kinetics of DNA hybridization using a multi-channel graphene biosensor

Reliable determination of binding kinetics and affinity of DNA hybridization and single-base mismatches plays an essential role in systems biology, personalized and precision medicine. The standard tools are optical-based sensors that are difficult to operate in low cost and to miniaturize for high-throughput measurement. Biosensors based on nanowire field-effect transistors have been developed, but reliable and cost-effective fabrication remains a challenge. Here, we demonstrate that a graphene single-crystal domain patterned into multiple channels can measure time- and concentration-dependent DNA hybridization kinetics and affinity reliably and sensitively, with a detection limit of 10 pM for DNA. It can distinguish single-base mutations quantitatively in real time. An analytical model is developed to estimate probe density, efficiency of hybridization and the maximum sensor response. The results suggest a promising future for cost-effective, high-throughput screening of drug candidates, genetic variations and disease biomarkers by using an integrated, miniaturized, all-electrical multiplexed, graphene-based DNA array.

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

Structure‐based virtual screening usually involves docking of a library of chemical compounds onto the functional pocket of the target receptor so as to discover novel classes of ligands. However, the overall success rate remains low and screening a large library is computationally intensive. An alternative to this “ab initio” approach is virtual screening by binding homology search. In this approach, potential ligands are predicted based on similar interaction pairs (similarity in receptors and ligands). SPOT‐Ligand is an approach that integrates ligand similarity by Tanimoto coefficient and receptor similarity by protein structure alignment program SPalign. The method was found to yield a consistent performance in DUD and DUD‐E docking benchmarks even if model structures were employed. It improves over docking methods (DOCK6 and AUTODOCK Vina) and has a performance comparable to or better than other binding‐homology methods (FINDsite and PoLi) with higher computational efficiency. The server is available at http://sparks-lab.org.

B-factor profile prediction for RNA flexibility using support vector machines

Determining the flexibility of structured biomolecules is important for understanding their biological functions. One quantitative measurement of flexibility is the atomic Debye‐Waller factor or temperature B‐factor. Most existing studies are limited to temperature B‐factors of proteins and their prediction. Only one method attempted to predict temperature B‐factors of ribosomal RNA. Here, we developed and compared machine‐learning techniques in prediction of temperature B‐factors of RNAs. The best model based on Support Vector Machines yields Pearsons correction coefficient at 0.51 for fivefold cross validation and 0.50 for the independent test. Analysis of the performance indicates that the model has the best performance on rRNAs, tRNAs, and protein‐bound RNAs, for long chains in particular. The server is available at http://sparks-lab.org/server/RNAflex.

Self-derived structure-disrupting peptides targeting methionine aminopeptidase in pathogenic bacteria: a new strategy to generate antimicrobial peptides

Bacterial infection is one of the leading causes of death in young, elderly, and immune‐compromised patients. The rapid spread of multi‐drug‐resistant (MDR) bacteria is a global health emergency and there is a lack of new drugs to control MDR pathogens. We describe a heretofore‐unexplored discovery pathway for novel antibiotics that is based on self‐targeting, structure‐disrupting peptides. We show that a helical peptide, KFF‐EcH3, derived from the Escherichia coli methionine aminopeptidase can disrupt secondary and tertiary structure of this essential enzyme, thereby killing the bacterium (including MDR strains). Significantly, no detectable resistance developed against this peptide. Based on a computational analysis, our study predicted that peptide KFF‐EcH3 has the strongest interaction with the structural core of the methionine aminopeptidase. We further used our approach to identify peptide KFF‐NgH1 to target the same enzyme from Neisseria gonorrhoeae. This peptide inhibited bacterial growth and was able to treat a gonococcal infection in a human cervical epithelial cell model. These findings present an exciting new paradigm in antibiotic discovery using self‐derived peptides that can be developed to target the structures of any essential bacterial proteins.—Zhan, J., Jia, H., Semchenko, E. A., Bian, Y., Zhou, A. M., Li, Z., Yang, Y., Wang, J., Sarkar, S., Totsika, M., Blanchard, H., Jen, F. E.‐C., Ye, Q., Haselhorst, T., Jennings, M. P., Scib, K. L., Zhou, Y. Self‐derived structure‐disrupting peptides targeting methionine aminopeptidase in pathogenic bacteria: a new strategy to generate antimicrobial peptides. FASEB J. 33, 2095–2104 (2019). www.fasebj.org

YesU from Bacillus subtilis preferentially binds fucosylated glycans

The interaction of carbohydrate-binding proteins (CBPs) with their corresponding glycan ligands is challenging to study both experimentally and computationally. This is in part due to their low binding affinity, high flexibility, and the lack of a linear sequence in carbohydrates, as exists in nucleic acids and proteins. We recently described a function-prediction technique called SPOT-Struc that identifies CBPs by global structural alignment and binding-affinity prediction. Here we experimentally determined the carbohydrate specificity and binding affinity of YesU (RCSB PDB ID: 1oq1), an uncharacterized protein from Bacillus subtilis that SPOT-Struc predicted would bind high mannose-type glycans. Glycan array analyses however revealed glycan binding patterns similar to those exhibited by fucose (Fuc)-binding lectins, with SPR analysis revealing high affinity binding to Lewisx and lacto-N-fucopentaose III. Structure based alignment of YesU revealed high similarity to the legume lectins UEA-I and GS-IV, and docking of Lewisx into YesU revealed a complex structure model with predicted binding affinity of −4.3 kcal/mol. Moreover the adherence of B. subtilis to intestinal cells was significantly inhibited by Lex and Ley but by not non-fucosylated glycans, suggesting the interaction of YesU to fucosylated glycans may be involved in the adhesion of B. subtilis to the gastrointestinal tract of mammals.

Structural signatures of thermal adaptation of bacterial ribosomal RNA, transfer RNA, and messenger RNA

Temperature adaptation of bacterial RNAs is a subject of both fundamental and practical interest because it will allow a better understanding of molecular mechanism of RNA folding with potential industrial application of functional thermophilic or psychrophilic RNAs. Here, we performed a comprehensive study of rRNA, tRNA, and mRNA of more than 200 bacterial species with optimal growth temperatures (OGT) ranging from 4°C to 95°C. We investigated temperature adaptation at primary, secondary and tertiary structure levels. We showed that unlike mRNA, tRNA and rRNA were optimized for their structures at compositional levels with significant tertiary structural features even for their corresponding randomly permutated sequences. tRNA and rRNA are more exposed to solvent but remain structured for hyperthermophiles with nearly OGT-independent fluctuation of solvent accessible surface area within a single RNA chain. mRNA in hyperthermophiles is essentially the same as random sequences without tertiary structures although many mRNA in mesophiles and psychrophiles have well-defined tertiary structures based on their low overall solvent exposure with clear separation of deeply buried from partly exposed bases as in tRNA and rRNA. These results provide new insight into temperature adaptation of different RNAs.