A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.     

Conformational dynamics of nonsynonymous variants at protein interfaces reveals disease association

Recent studies have shown that the protein interface sites between individual monomeric units in biological assemblies are enriched in disease‐associated non‐synonymous single nucleotide variants (nsSNVs). To elucidate the mechanistic underpinning of this observation, we investigated the conformational dynamic properties of protein interface sites through a site‐specific structural dynamic flexibility metric (dfi) for 333 multimeric protein assemblies. dfi measures the dynamic resilience of a single residue to perturbations that occurred in the rest of the protein structure and identifies sites contributing the most to functionally critical dynamics. Analysis of dfi profiles of over a thousand positions harboring variation revealed that amino acid residues at interfaces have lower average dfi (31%) than those present at non‐interfaces (50%), which means that protein interfaces have less dynamic flexibility. Interestingly, interface sites with disease‐associated nsSNVs have significantly lower average dfi (23%) as compared to those of neutral nsSNVs (42%), which directly relates structural dynamics to functional importance. We found that less conserved interface positions show much lower dfi for disease nsSNVs as compared to neutral nsSNVs. In this case, dfi is better as compared to the accessible surface area metric, which is based on the static protein structure. Overall, our proteome‐wide conformational dynamic analysis indicates that certain interface sites play a critical role in functionally related dynamics (i.e., those with low dfi values), therefore mutations at those sites are more likely to be associated with disease. Proteins 2015; 83:428–435. © 2014 Wiley Periodicals, Inc.     

GGIP: Structure and sequence‐based GPCR–GPCR interaction pair predictor

G Protein‐Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo‐ or hetero‐dimers or higher‐order molecular complexes. Many GPCRs exert a wide variety of molecular functions by forming specific combinations of GPCR subtypes. In addition, some GPCRs are reportedly associated with diseases. GPCR oligomerization is now recognized as an important event in various biological phenomena, and many researchers are investigating this subject. We have developed a support vector machine (SVM)‐based method to predict interacting pairs for GPCR oligomerization, by integrating the structure and sequence information of GPCRs. The performance of our method was evaluated by the Receiver Operating Characteristic (ROC) curve. The corresponding area under the curve was 0.938. As far as we know, this is the only prediction method for interacting pairs among GPCRs. Our method could accelerate the analyses of these interactions, and contribute to the elucidation of the global structures of the GPCR networks in membranes. Proteins 2016; 84:1224–1233. © 2016 Wiley Periodicals, Inc.     

Protein structure prediction assisted with sparse NMR data in CASP13

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and ¹⁵N-¹H residual dipolar coupling data, typical of that obtained for ¹⁵N,¹³C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.     

Partner-specific prediction of RNA-binding residues in proteins: A critical assessment

RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are “specific”, that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are “non-RNA specific.” Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.     

Updating the proteome of the uncultivable hemotrophic Mycoplasma suis in experimentally infected pigs

Mycoplasma suis belongs to the hemotrophic mycoplasmas that are associated with acute and chronic anemia in a wide range of livestock and wild animals. The inability to culture M. suis in vitro has hindered its characterization at the molecular level. Since the publication of M. suis genome sequences in 2011 only one proteome study has been published. Aim of the presented study was to significantly extend the proteome coverage of M. suis strain KI_3806 during acute infection by applying three different protein extraction methods followed by 1D SDS‐PAGE and LC‐MS/MS. A total of 404 of 795 M. suis KI_3806 proteins (50.8%) were identified. Data analysis revealed the expression of 83.7% of the predicted ORFs with assigned functions but also highlights the expression of 179 of 523 (34.2%) hypothetical proteins with unknown functions. Computational analyses identified expressed membrane‐associated hypothetical proteins that might be involved in adhesion or host–pathogen interaction. Furthermore, analyses of the expressed proteins indicated the existence of a hexose‐6‐phosphate‐transporter and an ECF transporter. In conclusion, our proteome study provides a further step toward the elucidation of the unique life cycle of M. suis and the establishment of an in vitro culture. All MS data have been deposited in the ProteomeXchange with identifier PXD002294 ( http://proteomecentral.proteomexchange.org/dataset/PXD002294 ).     

Sann: solvent accessibility prediction of proteins by nearest neighbor method

We present a method to predict the solvent accessibility of proteins which is based on a nearest neighbor method applied to the sequence profiles. Using the method, continuous real-value prediction as well as two-state and three-state discrete predictions can be obtained. The method utilizes the z-score value of the distance measure in the feature vector space to estimate the relative contribution among the k-nearest neighbors for prediction of the discrete and continuous solvent accessibility. The Solvent accessibility database is constructed from 5717 proteins extracted from PISCES culling server with the cutoff of 25% sequence identities. Using optimal parameters, the prediction accuracies (for discrete predictions) of 78.38% (two-state prediction with the threshold of 25%), 65.1% (three-state prediction with the thresholds of 9 and 36%), and the Pearson correlation coefficient (between the predicted and true RSA's for continuous prediction) of 0.676 are achieved An independent benchmark test was performed with the CASP8 targets where we find that the proposed method outperforms existing methods. The prediction accuracies are 80.89% (for two state prediction with the threshold of 25%), 67.58% (three-state prediction), and the Pearson correlation coefficient of 0.727 (for continuous prediction) with mean absolute error of 0.148. We have also investigated the effect of increasing database sizes on the prediction accuracy, where additional improvement in the accuracy is observed as the database size increases. The SANN web server is available at http://lee.kias.re.kr/~newton/sann/.     

外来木本植物入侵的生态预测与风险评价综述

外来植物引种导致的入侵已经成为当前生物多样性保育和引种工作面临的一个紧要研究课题。综述了木本植物入侵的生态预测和生态风险评价方面的国内外相关研究进展。首先介绍了目前国内外木本植物引种的概况,对木本植物入侵的生态预测基本原理做了较为详细的总结。目前比较被认可的生态预测途径主要包括编辑入侵植物名录利用入侵历史纪录预测、物种特征作为入侵的预测指标、繁殖体压力作为建群概率的决定性因素、环境匹配作为入侵潜力的预测工具及专家意见等,并对物种特征进行了归类和分析。物种特征指标主要包括物种的繁殖和快速生长性状指标、对入侵地区局部条件和干扰体系的适应性指标、生物地理分布指标等,并指出在生态预测中单独使用这些指标是不严谨的,而应当多途径互相结合验证。同时还简介了WRA等几个应用较为广泛的实用性植物入侵风险评价系统。分析了目前国内外在木本植物入侵的生态预测方面面临的一些困难,包括入侵机理的复杂性导致的预测难度增大和可信性下降,所用数据库标准的不统一和更新的困难等,指出在进行木本植物引种的生态预测和风险评价研究的同时,必须加强相关法律法规建设,重视入侵机理研究,完善相关的数据库。出于实际情况的限制,可以借鉴国际上实用性杂草风险分析和有害生物风险分析的方法,逐步建立我国的入侵风险评价系统,以满足目前对木本植物入侵的预测和风险评价的需求。