Zhixiu Li

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.

DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations employing sequence and structural properties at nucleotide and protein levels

MOTIVATION Frameshifting (FS) indels and nonsense (NS) variants disrupt the protein-coding sequence downstream of the mutation site by changing the reading frame or introducing a premature termination codon, respectively. Despite such drastic changes to the protein sequence, FS indels and NS variants have been discovered in healthy individuals. How to discriminate disease-causing from neutral FS indels and NS variants is an understudied problem. RESULTS We have built a machine learning method called DDIG-in (FS) based on real human genetic variations from the Human Gene Mutation Database (inherited disease-causing) and the 1000 Genomes Project (GP) (putatively neutral). The method incorporates both sequence and predicted structural features and yields a robust performance by 10-fold cross-validation and independent tests on both FS indels and NS variants. We showed that human-derived NS variants and FS indels derived from animal orthologs can be effectively employed for independent testing of our method trained on human-derived FS indels. DDIG-in (FS) achieves a Matthews correlation coefficient (MCC) of 0.59, a sensitivity of 86%, and a specificity of 72% for FS indels. Application of DDIG-in (FS) to NS variants yields essentially the same performance (MCC of 0.43) as a method that was specifically trained for NS variants. DDIG-in (FS) was shown to make a significant improvement over existing techniques.

Small Open Reading Frames: Current Prediction Techniques and Future Prospect

Evidence is accumulating that small open reading frames (sORF, <100 codons) play key roles in many important biological processes. Yet, they are generally ignored in gene annotation despite they are far more abundant than the genes with more than 100 codons. Here, we demonstrate that popular homolog search and codon-index techniques perform poorly for small genes relative to that for larger genes, while a method dedicated to sORF discovery has a similar level of accuracy as homology search. The result is largely due to the small dataset of experimentally verified sORF available for homology search and for training ab initio techniques. It highlights the urgent need for both experimental and computational studies in order to further advance the accuracy of sORF prediction.

Genetic spectrum of dyschromatosis symmetrica hereditaria in Chinese patients including a novel nonstop mutation in ADAR1 gene

BackgroundDyschromatosis symmetrica hereditaria (DSH) is a rare autosomal dominant cutaneous disorder caused by the mutations of adenosine deaminase acting on RNA1 (ADAR1) gene. We present a clinical and genetic study of seven unrelated families and two sporadic cases with DSH for mutations in the full coding sequence of ADAR1 gene.MethodsADAR1 gene was sequenced in seven unrelated families and two sporadic cases with DSH and 120 controls. Functional significance of the observed ADAR1 mutations was analyzed using PolyPhen 2, SIFT and DDIG-in.ResultsWe describe six novel mutations of the ADAR1 gene in Chinese patients with DSH including a nonstop mutation p.Stop1227R, which was firstly reported in ADAR1 gene. In silico analysis proves that all the mutations reported here are pathogenic.ConclusionThis study is useful for functional studies of the protein and to define a diagnostic strategy for mutation screening of the ADAR1 gene. A three-generation family exhibiting phenotypic variability with a single germline ADAR1 mutation suggests that chilblain might aggravate the clinical phenotypes of DSH.

Progress of genome-wide association studies of ankylosing spondylitis

Ankylosing spondylitis (AS) is an immune‐mediated arthritis which primarily affects the spine and sacroiliac joints. Significant progress has been made in discovery of genetic associations with AS by genome‐wide association studies (GWAS) over past decade. These findings have uncovered novel pathways involved pathogenesis of the disease and have led to introduction of novel therapeutic treatments for AS. In this Review, we discuss the genetic variations associated with AS identified by GWAS, the major pathways revealed by these AS‐associated variations and critical cell types involved in AS development.

Investigation the Possibility of Using Peptides with a Helical Repeating Pattern of Hydro-Phobic and Hydrophilic Residues to Inhibit IL-10

Blockade of IL-10 signalling clears chronic viral and bacterial infections. Immunization together with blockade of IL-10 signalling or relatively low level of IL-10 further enhances viral and bacterial clearance. IL-10 functions through binding to interleukin 10 receptor (IL-10R). Here we showed that peptides P1 and P2 with the hydrophobic and hydrophilic pattern of the IL10R-binding helix in IL-10 could bind with either IL-10R1 or IL-10, and inhibit inflammatory signals with long duration and negligible cytotoxicity in vitro. Furthermore, P2 can enhance antigen specific CD8+ T cell responses in mice induced by the vaccine based on a long peptide of protein E7 in a human papillomavirus type 16.

Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment‐based local and energy‐based nonlocal profiles

Locating sequences compatible with a protein structural fold is the well‐known inverse protein‐folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy‐optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment‐derived sequence profiles and structure‐derived energy profiles. SPIN improves over the fragment‐derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild‐type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single‐body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks‐lab.org. Proteins 2014; 82:2565–2573.

Intrinsic Disorder and Semi-disorder Prediction by SPINE-D.

Over the past decade, it has become evident that a large proportion of proteins contain intrinsically disordered regions, which play important roles in pivotal cellular functions. Many computational tools have been developed with the aim of identifying the level and location of disorder within a protein. In this chapter, we describe a neural network based technique called SPINE-D that employs a unique three-state design and can accurately capture disordered residues in both short and long disordered regions. SPINE-D was trained on a large database of 4229 non-redundant proteins, and yielded an AUC of 0.86 on a cross-validation test and 0.89 on an independent test. SPINE-D can also detect a semi-disordered state that is associated with induced folders and aggregation-prone regions in disordered proteins and weakly stable or locally unfolded regions in structured proteins. We implement an online web service and an offline stand-alone program for SPINE-D, they are freely available at http://sparks-lab.org/SPINE-D/ . We then walk you through how to use the online and offline SPINE-D in making disorder predictions, and examine the disorder and semi-disorder prediction in a case study on the p53 protein.

Epigenetic and gene expression analysis of ankylosing spondylitis-associated loci implicate immune cells and the gut in the disease pathogenesis

Ankylosing spondylitis (AS) is a common immune-mediated arthropathy primarily affecting the spine and pelvis. Most AS patients have subclinical intestinal inflammation, suggesting the gut microbiome and the immune response play a role in pathogenesis. Susceptibility to AS is primarily genetic, and at least 114 susceptibility variants have been identified to date. We applied bioinformatic methods utilizing epigenetic and gene and protein expression data to identify the cell types through which AS-associated variants operate. Variants were enriched in transcriptionally regulated regions in monocytes, CD4+ and CD8+ T cells, natural killer cells, regulatory T cells and B cells and mucosa from the small intestine, sigmoid colon and rectum. Weak signals were detected in bone cells, consistent with bone disease being a secondary manifestation. RNA sequencing of blood cells from AS patients and controls identified differentially expressed genes. Interrogation of expression databases showed that the upregulated genes were enriched in monocytes and downregulated genes were enriched in CD8+ T cells and natural killer cells. Gene Ontology term enrichment analysis identified microbes and the gut in the aetiology of AS. These findings identify the key immune cell types that drive the disease, and further highlight the involvement of the gut microbiome in the pathogenesis of AS.

Transcriptome analysis of ankylosing spondylitis patients before and after TNF-α inhibitor therapy reveals the pathways affected

Tumor necrosis factor-α (TNF-α) inhibitors are highly effective in suppressing inflammation in ankylosing spondylitis (AS) patients, and operate by suppression of TFN-α and downstream immunological pathways. To determine the mechanisms of action of TNF-α inhibitors in AS patients, we used transcriptomic and bioinformatic approaches on peripheral blood mononuclear cells from AS patients pre and post treatment. We found 656 differentially expressed genes, including the genome-wide significant AS-associated genes, IL6R, NOTCH1, IL10, CXCR2 and TNFRSF1A. A distinctive gene expression profile was found between male and female patients, mainly because of sex chromosome-linked genes and interleukin 17 receptor C, potentially accounting for the differences in clinical manifestation and treatment response between the genders. In addition to immune and inflammation regulatory pathways, like intestinal immune network for IgA production, cytokine–cytokine receptor interaction, Ras signaling pathway, allograft rejection and hematopoietic cell lineage, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses revealed that infection-associated pathways (influenza A and toxoplasmosis) and metabolism-associated pathways were involved in response to TNF-α inhibitor treatment, providing insight into the mechanism of TNF-α inhibitors.