Search

Background: Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categorized as sequence-based or 3D-structure-based methods. All these methods are based on traditional machine learning. In a series of binding residue prediction tasks, 3D-structure-based methods are widely superior to sequence-based methods. However, due to the great number of proteins with known amino acid sequences, sequence-based methods have considerable room for improvement with the development of deep learning. Therefore, prediction of protein-ligand binding residues with deep learning requires study. Results: In this study, we propose a new sequence-based approach called DeepCSeqSite for ab initio protein-ligand binding residue prediction. DeepCSeqSite includes a standard edition and an enhanced edition. The classifier of DeepCSeqSite is based on a deep convolutional neural network. Several convolutional layers are stacked on top of each other to extract hierarchical features. The size of the effective context scope is expanded as the number of convolutional layers increases. The long-distance dependencies between residues can be captured by the large effective context scope, and stacking several layers enables the maximum length of dependencies to be precisely controlled. The extracted features are ultimately combined through one-by-one convolution kernels and softmax to predict whether the residues are binding residues. The state-of-the-art ligand-binding method COACH and some of its submethods are selected as baselines. The methods are tested on a set of 151 nonredundant proteins and three extended test sets. Experiments show that the improvement of the Matthews correlation coefficient (MCC) is no less than 0.05. In addition, a training data augmentation method that slightly improves the performance is discussed in this study. Conclusions: Without using any templates that include 3D-structure data, DeepCSeqSite significantlyoutperforms existing sequence-based and 3D-structure-based methods, including COACH. Augmentation of the training sets slightly improves the performance. The model, code and datasets are available at https://github.com/yfCuiFaith/DeepCSeqSite. [ABSTRACT FROM AUTHOR]

Motivation. To assist efforts in determining and exploring the functional properties of proteins, it is desirable to characterize and predict protein flexibilities. Results. In this study, the conformational entropy is used as an indicator of the protein flexibility. We first explore whether the conformational change can capture the protein flexibility. The well-defined decoy structures are converted into one-dimensional series of letters from a structural alphabet. Four different structure alphabets, including the secondary structure in 3-class and 8-class, the PB structure alphabet (16-letter), and the DW structure alphabet (28-letter), are investigated. The conformational entropy is then calculated from the structure alphabet letters. Some of the proteins show high correlation between the conformation entropy and the protein flexibility. We then predict the protein flexibility from basic amino acid sequence. The local structures are predicted by the dual-layer model and the conformational entropy of the predicted class distribution is then calculated. The results show that the conformational entropy is a good indicator of the protein flexibility, but false positives remain a problem. The DW structure alphabet performs the best, which means that more subtle local structures can be captured by large number of structure alphabet letters. Overall this study provides a simple and efficient method for the characterization and prediction of the protein flexibility. [ABSTRACT FROM AUTHOR]

Model quality assessment is an important task in protein structure prediction, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction methods. A new prediction category that evaluates the quality of protein models has been implemented since CASP7. In this study, a machine learning-based method for protein global model quality assessment (GMQA) is presented and the web server can be assessed at http://www.iipl.fudan.edu.cn/gmqa/index.html. The GMQA method takes a protein model as input and outputs the predicted MaxSub score that indicates the absolute quality of the protein model. The proposed method extracts the structural features from the 3D coordinates and assigns an absolute quality score to a model by support vector regression. Three types of features are extracted, including secondary structure, relative solvent accessibility, and contact. The closed test is performed on the CASP7 data set using cross validation. An open testing is performed on the LKF data set. In both tests, good correlations between the predicted and true scores are observed. Furthermore, our method is able to discriminate the native or near-native structures from a set of decoys. We also demonstrate that the GMQA method outperforms two existing methods, i.e. ProQ and Victor/FRST. Our results show that GMQA is a useful tool for model quality assessment and ranking. [ABSTRACT FROM AUTHOR]

Abstract: Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multi-domain proteins but also for the experimental structure determination. In this work, a novel index at the profile level is presented, namely, the profile domain linker propensity index (PDLI), which uses the evolutionary information of profiles for domain linker prediction. The frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. PDLI is then obtained by the frequencies of binary profiles in domain linkers as compared to those in domains. A smooth and normalized numeric profile is generated for any amino acid sequences from which the domain linkers can be predicted. Testing on the Structural Classification of Proteins (SCOP) database and CASP6 targets shows that PDLI outperforms other indexes at the amino acid level. [Copyright &y& Elsevier]

• CSMD3 mutations occur frequently in patients with NDDs. • Csmd3-/- mice exhibited growth retardation and NDD-related behaviors. • Csmd3 deficiency impaired synaptogenesis and neuronal development. • Csmd3 deficiency perturbed functional networks in synaptogenesis and immunity. Recent studies have reported that complement-related proteins modulate brain development through regulating synapse processes in the cortex. CSMD3 belongs to a group of putative complement control proteins. However, its role in the central nervous system and synaptogenesis remains largely unknown. Here we report that CSMD3 deleterious mutations occur frequently in patients with neurodevelopmental disorders (NDDs). Csmd3 is predominantly expressed in cortical neurons of the developing cortex. In mice, Csmd3 disruption induced retarded development and NDD-related behaviors. Csmd3 deficiency impaired synaptogenesis and neurogenesis, allowing fewer neurons reaching the cortical plate. Csmd3 deficiency also induced perturbed functional networks in the developing cortex, involving a number of downregulated synapse-associated genes that influence early synaptic organization and upregulated genes related to immune activity. Our study provides mechanistic insights into the endogenous regulation of complement-related proteins in synaptic development and supports the pathological role of CSMD3 in NDDs. [ABSTRACT FROM AUTHOR]

Abstract: Protein remote homology detection is a critical step toward annotating its structure and function. Supervised learning algorithms such as support vector machine are currently the most accurate methods. The position-specific score matrices (PSSMs) contain wealthy information about the evolutionary relationship of proteins. However, the PSSMs often have different lengths, which are difficult to be used by machine-learning methods. In this study, a simple, fast and powerful method is presented for protein remote homology detection, which combines support vector machine with auto-cross covariance transformation. The PSSMs are converted into a series of fixed-length vectors by auto-cross covariance transformation and these vectors are then input to a support vector machine classifier for remote homology detection. The sequence-order effects can be effectively captured by this scheme. Experiments are performed on well-established datasets, and the remote homology is simulated at the superfamily and the fold level, respectively. The results show that the proposed method, referred to as ACCRe, is comparable or even better than the state-of-the-art methods in terms of detection performance, and its time complexity is superior to those of other profile-based SVM methods. The auto-cross covariance transformation provides a novel way for the usage of evolutionary information, which can be widely used for protein-level studies. [Copyright &y& Elsevier]

Hesperetin (HES) is a dihydroflavone with the molecular formula of C16H14O6. It has been reported that Hesperetin has antioxidant and anticancer effects. Recent studies showed that it can also regulate immune responses. To assess its potential function as a vaccine adjuvant, we formulated HES with inactivated B16F10 melanoma cells and determined whether it would enhance the activation of antigen‐presenting cells by experiments in vivo and in vitro. We found that HES activated the PI3K‐Akt signalling pathway in antigen‐presenting cells (APCs), enhanced cytotoxic T lymphocyte (CTL) responses and deactivated tolerogenic T cells. We also observed that inactivated B16F10 cells in combination with HES vaccine inhibited the growth of mice tumours, resulting in improved overall survival compared to the effects of inactivated B16F10 cell vaccine. To verify that CD8+T cells play a key role in inhibiting the development of melanoma, we transferred the sorted CD8+T cells from immunized mice to B16F10 challenged models and found that the survival rate of tumour‐bearing mice was significantly prolonged. Taken together, these results suggest that hesperetin can be used as a potential adjuvant to improve tumour immune responses and antigen immunogenicity. [ABSTRACT FROM AUTHOR]

Uracil DNA glycosylase (UNG) removes mutagenic uracil base from DNA to initiate base excision repair (BER). The result is an abasic site (AP site) that is further processed by the high-fidelity BER pathway to complete repair and maintain genome integrity. The gammaherpesviruses (GHVs), human Kaposi sarcoma herpesvirus (KSHV), Epstein-Barr virus (EBV), and murine gammaherpesvirus 68 (MHV68) encode functional UNGs that have a role in viral genome replication. Mammalian and GHVs UNG share overall structure and sequence similarity except for a divergent amino-terminal domain and a leucine loop motif in the DNA binding domain that varies in sequence and length. To determine if divergent domains contribute to functional differences between GHV and mammalian UNGs, we analyzed their roles in DNA interaction and catalysis. By utilizing chimeric UNGs with swapped domains we found that the leucine loop in GHV, but not mammalian UNGs facilitates interaction with AP sites and that the amino-terminal domain modulates this interaction. We also found that the leucine loop structure contributes to differential UDGase activity on uracil in single- versus double-stranded DNA. Taken together we demonstrate that the GHV UNGs evolved divergent domains from their mammalian counterparts that contribute to differential biochemical properties from their mammalian counterparts. • Gammaherpesvirus UNG binds abasic sites while mammalian UNG does not. • Divergent structures in the leucine loop of gammaherpesvirus UNG mediate abasic site binding. • The N-terminus of UNG modulates abasic site binding. • UNG leucine loop structures mediate differential UDGase activity on uracil in ss- versus dsDNA. [ABSTRACT FROM AUTHOR]

Motivation: Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles.Results: Here, we propose a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well.Availability and implementation: For users’ convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided athttp://bioinformatics.hitsz.edu.cn/main/∼binliu/remote/Contact: bliu@insun.hit.edu.cn or bliu@gordonlifescience.orgSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Abstract: Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. In this study, we present a building block of proteins called order profiles to use the evolutionary information of the protein sequence frequency profiles and apply this building block to produce a class of propensities called order profile interface propensities. For comparisons, we revisit the usage of residue interface propensities and binary profile interface propensities for protein binding site prediction. Each kind of propensities combined with sequence profiles and accessible surface areas are inputted into SVM. When tested on four types of complexes (hetero-permanent complexes, hetero-transient complexes, homo-permanent complexes and homo-transient complexes), experimental results show that the order profile interface propensities are better than residue interface propensities and binary profile interface propensities. Therefore, order profile is a suitable profile-level building block of the protein sequences and can be widely used in many tasks of computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the protein remote homology detection. [Copyright &y& Elsevier]

The current study aimed to analyze the genotype-phenotype relationship in patients with variants of zinc finger E box-binding homeobox 2 (ZEB2), which is a gene encoding a homeobox transcription factor known to be mutated in Mowat Wilson syndrome (MWS). Whole genome sequencing (WGS) was performed in 530 children, of whom 333 had epilepsy with or without developmental delay and 197 developmental delay alone. Pathogenic variants were identified and verified using Sanger sequencing, and the disease phenotypes of the corresponding patients were analyzed for features of MWS. WGS was performed in 333 children with epilepsy, with or without developmental delays or intellectual disability and 197 children with developmental delay alone. A total of 4 unrelated patients were indicated to be heterozygous for truncating mutations in ZEB2. A total of three of these were nonsense mutations (novel Gln1072X and recurrent Trp97X and Arg921X), and one was a frameshift mutation (novel Val357Aspfs*15). The mutations have occurred de novo as confirmed by Sanger sequence comparisons in patients and their parents. All 4 patients exhibited signs of MWS, whereby the severity increased the closer a mutation was located to the amino terminus of the protein. The results suggest that the clinical outcome in MWS depends on the relative position of the truncation in the ZEB2 gene. A number of interpretations of this genotype/phenotype association are discussed in the present study. [ABSTRACT FROM AUTHOR]