Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm.

The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations.

Inter-domain distance prediction based on deep learning for domain assembly.

AlphaFold2 achieved a breakthrough in protein structure prediction through the end-to-end deep learning method, which can predict nearly all single-domain proteins at experimental resolution. However, the prediction accuracy of full-chain proteins is generally lower than that of single-domain proteins because of the incorrect interactions between domains. In this work, we develop an inter-domain distance prediction method, named DeepIDDP.

Structural analogue-based protein structure domain assembly assisted by deep learning.

Motivation: With the breakthrough of AlphaFold2, the protein structure prediction problem has made remarkable progress through deep learning end-to-end techniques, in which correct folds could be built for nearly all single-domain proteins. However, the full-chain modelling appears to be lower on average accuracy than that for the constituent domains and requires higher demand on computing hardware, indicating the performance of full-chain modelling still needs to be improved. In this study, we investigate whether the predicted accuracy of the full-chain model can be further improved by domain assembly assisted by deep learning.

DomBpred: Protein Domain Boundary Prediction Based on Domain-Residue Clustering Using Inter-Residue Distance.

Domain boundary prediction is one of the most important problems in the study of protein structure and function, especially for large proteins. At present, most domain boundary prediction methods have low accuracy and limitations in dealing with multi-domain proteins. In this study, we develop a sequence-based protein domain boundary prediction, named DomBpred.

DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments with deep-learning inter-domain restraint prediction.

Most proteins in nature contain multiple folding units (or domains). The revolutionary success of AlphaFold2 in single-domain structure prediction showed potential to extend deep-learning techniques for multi-domain structure modeling. This work presents a significantly improved method, DEMO2, which integrates analogous template structural alignments with deep-learning techniques for high-accuracy domain structure assembly.

Multi contact-based folding method for de novo protein structure prediction.

Meta contact, which combines different contact maps into one to improve contact prediction accuracy and effectively reduce the noise from a single contact map, is a widely used method. However, protein structure prediction using meta contact cannot fully exploit the information carried by original contact maps. In this work, a multi contact-based folding method under the evolutionary algorithm framework, MultiCFold, is proposed.

A sequential niche multimodal conformational sampling algorithm for protein structure prediction.

Motivation: Massive local minima on the protein energy landscape often cause traditional conformational sampling algorithms to be easily trapped in local basin regions, because they find it difficult to overcome high-energy barriers. Also, the lowest energy conformation may not correspond to the native structure due to the inaccuracy of energy models. This study investigates whether these two problems can be alleviated by a sequential niche technique without loss of accuracy.

Underestimation-Assisted Global-Local Cooperative Differential Evolution and the Application to Protein Structure Prediction.

Various mutation strategies show distinct advantages in differential evolution (DE). The cooperation of multiple strategies in the evolutionary process may be effective. This paper presents an underestimation-assisted global and local cooperative DE to simultaneously enhance the effectiveness and efficiency.

De novo Protein Structure Prediction by Coupling Contact With Distance Profile.

De novo protein structure prediction is a challenging problem that requires both an accurate energy function and an efficient conformation sampling method. In this study, a de novo structure prediction method, named CoDiFold, is proposed. In CoDiFold, contacts and distance profiles are organically combined into the Rosetta low-resolution energy function to improve the accuracy of energy function.

[Sequence comparison of the hemagglutinin gene of the duck-origin H9N2 subtype avian influenza viruses].

To demonstrate the phylogenetic evolution, the molecular characteristics of the motif of HA protein cleavage site and the varieties at the receptor binding sites of the hemagglutinin gene of the duck-origin H9N2 subtype avian influenza viruses, sequence alignment and phylogenetic analysis were performed by MEGA 4.1 Neighbor-Joining method..

Epidemiological investigation and genome analysis of duck circovirus in Southern China.

Duck circovirus (DuCV), a potential immunosuppressive virus, was investigated in Southern China from March 2006 to December 2009 by using a polymerase chain reaction (PCR) based method. In this study, a total of 138 sick or dead duck samples from 18 different farms were examined with an average DuCV infection rate of ∼35%. It was found that ducks between the ages of 40∼60 days were more susceptible to DuCV.

The development of a rapid SYBR Green I-based quantitative PCR for detection of Duck circovirus.

This report describes a one-step real-time polymerase chain reaction assay based on SYBR Green I for detection of a broad range of duck circovirus (DuCV). Align with all DuCV complete genome sequences and other Genus Circovirus download from the GenBank (such as goose circovirus, pigeon circovirus), the primers targets to the replicate gene of DuCV were designed. The detection assay was linear in the range of 1.

Genetic diversity and genotype analysis of duck circovirus.

To investigate the genetic diversity and genotype of duck circovirus (DuCV), nine full-length DuCV genomes were determined from clinical samples. Multiple sequence alignment and phylogenetic analyses were performed on the nine viral genome sequences as well as on 27 genome sequences retrieved from the GenBank database. Pairwise analysis showed that the determined genome sequences have a genome organization identical to the 27 sequences and share 83.

[Genome cloning and sequence analysis of duck circovirus].

To reveal the molecular biological characteristics of genome of circovirus in infected ducks, two nucleotide fragments were amplified by overlapping PCRs using DNA extracted from various tissues of ducks. After they had been assembled together, the nucleotide components, the genome organization and the phylogenetic scale of the sequence were analyzed. The results showed that the obtained sequence is a circular DNA with a total length of 1995nt.