Protein–protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.

The HAP3 gene encodes a subunit of the CCAAT-box-binding factor (CBF), a highly conserved trimeric activator that recognizes and binds the ubiquitous CCAAT promoter element with high affinity. Two types of HAP3 gene have been identified in plant genomes. The LEAFY COTYLEDON1 (LEC1)–type HAP3 genes encode a functionally specialized subunit of CBF, which is expressed specifically in developing seeds. In contrast, most non–LEC1-type HAP3 genes are expressed in various tissues. It has been proposed that the LEC1-type HAP3 genes originated from the duplication and functional divergence of non–LEC1-type HAP3 genes. However, it is not yet known when this duplication event took place or whether the LEC1-type HAP3 genes appeared at the same time as the origin of seed plants. Here we describe a comprehensive comparison of the duplication patterns of HAP3 genes in different plant genomes. We recognize a major expansion of the HAP3 gene family accompanying the origin and early diversification of land plants and postulate that retrotransposition and other mechanisms of gene duplication have been involved in the expansion of the plant HAP3 gene family. We provide evidence that the LEC1-type HAP3 genes originated in nonseed vascular plant genomes and demonstrate that they are inductively expressed under drought stress in nonseed plants. These genes, however, were recruited to a novel regulatory network in the early stages of seed plant evolution and steadily expressed during seed development and maturation.

NF-Y is a ubiquitous CCAAT-binding factor composed of NF-YA, NF-YB and NF-YC. Multiple genes encoding NF-Y subunits have been identified in plant genomes. It remains unclear whether the duplicate genes underwent different evolutionary patterns. Likelihood-ratio tests were used to examine whether the amino acid substitution rates are the same between duplicate genes. The influences of selection on evolution were evaluated by comparing the conservative and radical amino acid substitution rates, as well as maximum-likelihood analysis. Some NF-YB and NF-YC duplicates showed significant evidence of asymmetric evolution but not the NF-YA duplicates. Most amino acid replacements in the NF-YB and NF-YC duplicates result in changes in hydropathy, polar requirement and polarity. The physicochemical changes in the sequences of NF-YB seem to be coupled to asymmetric divergence in gene function. Plant NF-Y genes have evolved in different patterns. Relaxed selective constraints following gene duplication are most likely responsible for the unequal evolutionary rates and distinct divergence patterns of duplicate NF-Y genes. Positive selection may have promoted amino acid hydropathy changes in the NF-YC duplicates.

Depression as a common complication of brain tumors. Is there a possible common pathogenesis for depression and glioma? The most serious major depressive disorder (MDD) and glioblastoma (GBM) in both diseases are studied, to explore the common pathogenesis between the two diseases. In this article, we first rely on transcriptome data to obtain reliable and useful differentially expressed genes (DEGs) by differential expression analysis. Then, we used the transcriptomics of DEGs to find out and analyze the common pathway of MDD and GBM from three directions. Finally, we determine the important biological pathways that are common to MDD and GBM by statistical knowledge. Our findings provide the first direct transcriptomic evidence that common pathway in two diseases for the common pathogenesis of the human MDD and GBM. Our results provide a new reference methods and values for the study of the pathogenesis of depression and glioblastoma.

Bipolar disorder is a common and severe mental illness with unsolved pathophysiology. A genome-wide association study (GWAS) has been used to find a number of risk genes, but it is difficult for a GWAS to find genes indirectly associated with a disease. To find core hub genes, we introduce a network analysis after the GWAS was conducted. Six thousand four hundred fifty eight single nucleotide polymorphisms (SNPs) with p < 0.01 were sifted out from Wellcome Trust Case Control Consortium (WTCCC) dataset and mapped to 2045 genes, which are then compared with the protein–protein network. One hundred twelve genes with a degree >17 were chosen as hub genes from which five significant modules and four core hub genes (FBXL13, WDFY2, bFGF, and MTHFD1L) were found. These core hub genes have not been reported to be directly associated with BD but may function by interacting with genes directly related to BD. Our method engenders new thoughts on finding genes indirectly associated with, but important for, complex diseases.

SARS-CoV-2 has led to a global pandemic of new crown pneumonia, which has had a tremendous impact on human society. Antibody drug therapy is one of the most effective way of combating SARS-CoV-2. In order to design potential antibody drugs with high affinity, we used antibody S309 from patients with SARS-CoV as the target antibody and RBD of S protein as the target antigen. Systems with RBD glycosylated and non-glycosylated were constructed to study the influence of glycosylation. From the results of molecular dynamics simulations, the steric effects of glycans on the surface of RBD plays a role of “wedge”, which makes the L335-E340 region of RBD close to the CDR3 region of the heavy chain of antibody and increases the contact area between antigen and antibody. By mutating the key residues of antibody at the interaction interface, we found that the binding affinities of antibody mutants G103A, P28W and Y100W were all stronger than that of the wild-type, especially for the G103A mutant. G103A significantly reduces the distance between the binding region of L335-K356 in the antigen and P28-Y32 of heavy chain in the antibody through structural transition. Taken together, the antibody design method described in this work can provide theoretical guidance and a time-saving method for antibody drug design.