Potential function of simplified protein models for discriminating native proteins from decoys: Combining contact interaction and local sequence-dependent geometry

An effective potential function is critical for protein structure prediction and folding simulation. For simplified models of proteins where coordinates of only $C_\alpha$ atoms need to be specified, an accurate potential function is important. Such a simplified model is essential for efficient search of conformational space. In this work, we present a formulation of potential function for simplified representations of protein structures. It is based on the combination of descriptors derived from residue-residue contact and sequence-dependent local geometry. The optimal weight coefficients for contact and local geometry is obtained through optimization by maximizing margins among native and decoy structures. The latter are generated by chain growth and by gapless threading. The performance of the potential function in blind test of discriminating native protein structures from decoys is evaluated using several benchmark decoy sets. This potential function have comparable or better performance than several residue-based potential functions that require in addition coordinates of side chain centers or coordinates of all side chain atoms.Comment: 4 pages, 2 figures, Accepted by 26th IEEE-EMBS Conference, San Francisc

Genome-Wide Association Mapping and Genomic Prediction for Enhancing FHB Resistance in Hard Winter Wheat

Wheat is one of the most important staple crops providing 20% of energy for 35% of the world population. Fusarium head blight (FHB), primarily caused by the fugal Fusarium graminearum Schwabe, is a damaging disease in wheat that affects global wheat production every year and causes food safety issues. The disease not only reduces the grain yield and quality but also produces mycotoxin in the diseased kernels making them unsuitable for human consumption or as livestock feeds. Breeding FHB resistant cultivar is the most effective and economical approach to managing the disease. This study combines genome-wide association study (GWAS) and genomic approaches (GS) to identify resistance loci/markers and evaluate the efficiency of genomic prediction (GP) in hard winter wheat breeding lines in the South Dakota State University (SDSU) winter wheat breeding program. In the first study, we conducted a multi-locus genome-wide association study (ML-GWAS) with 9,321 high-quality single nucleotide polymorphisms (SNPs) and a panel of 257 elite breeding lines from the South Dakota State University (SDSU) breeding program to uncover the genetic basis of native FHB resistance in the US hard winter wheat. Marker-trait associations (MTAs) were identified with eight different ML-GWAS models, the most appropriate being Fixed and random model Wheat is one of the most important staple crops providing 20% of energy for 35% of the world population. Fusarium head blight (FHB), primarily caused by the fugal Fusarium graminearum Schwabe, is a damaging disease in wheat that affects global wheat production every year and causes food safety issues. The disease not only reduces the grain yield and quality but also produces mycotoxin in the diseased kernels making them unsuitable for human consumption or as livestock feeds. Breeding FHB resistant cultivar is the most effective and economical approach to managing the disease. This study combines genome-wide association study (GWAS) and genomic approaches (GS) to identify resistance loci/markers and evaluate the efficiency of genomic prediction (GP) in hard winter wheat breeding lines in the South Dakota State University (SDSU) winter wheat breeding program. In the first study, we conducted a multi-locus genome-wide association study (ML-GWAS) with 9,321 high-quality single nucleotide polymorphisms (SNPs) and a panel of 257 elite breeding lines from the South Dakota State University (SDSU) breeding program to uncover the genetic basis of native FHB resistance in the US hard winter wheat. Marker-trait associations (MTAs) were identified with eight different ML-GWAS models, the most appropriate being Fixed and random model lines. Our results demonstrate the potential of integrating genomic selection in hard winter wheat breeding to improve FHB resistance