后基因组研究中蛋白结构与功能的预测

阐述蛋白质结构建模和功能预测的基本方法以及最新研究进展,展望了蛋白质预测技术的前景。     

简单的一致性方法预测蛋白质二级结构

蛋白质二级结构预测对于我们了解蛋白质空间结构是至关重要的一步。文章提出了一种简单的二级结构预测方法,该方法采用多数投票法将现有的3种较好的二级结构预测方法的预测结果汇集形成一致性预测结果。从PDB数据库中随机选取近两年新测定结构的57条相似性小于30%的蛋白质,对该方法的预测结果进行测试,其Q3准确率比3种独立的方法提高了1.12—2.29%,相关系数及SOV准确率也有相应的提高。并且各项准确率均比同样采用一致性方法的Jpred二级结构预测程序准确率要高。这种预测方法虽然原理简单,但无须使用额外的参数,计算量小,易于实现,最重要的前提就是必须选用目前准确性比较出色的蛋白质二级结构预测方法。     

Sequence Representation and Prediction of Protein Secondary Structure for Structural Motifs in Twilight Zone Proteins

Characterizing and classifying regularities in protein structure is an important element in uncovering the mechanisms that regulate protein structure, function and evolution. Recent research concentrates on analysis of structural motifs that can be used to describe larger, fold-sized structures based on homologous primary sequences. At the same time, accuracy of secondary protein structure prediction based on multiple sequence alignment drops significantly when low homology (twilight zone) sequences are considered. To this end, this paper addresses a problem of providing an alternative sequences representation that would improve ability to distinguish secondary structures for the twilight zone sequences without using alignment. We consider a novel classification problem, in which, structural motifs, referred to as structural fragments (SFs) are defined as uniform strand, helix and coil fragments. Classification of SFs allows to design novel sequence representations, and to investigate which other factors and prediction algorithms may result in the improved discrimination. Comprehensive experimental results show that statistically significant improvement in classification accuracy can be achieved by: (1) improving sequence representations, and (2) removing possible noise on the terminal residues in the SFs. Combining these two approaches reduces the error rate on average by 15% when compared to classification using standard representation and noisy information on the terminal residues, bringing the classification accuracy to over 70%. Finally, we show that certain prediction algorithms, such as neural networks and boosted decision trees, are superior to other algorithms.This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).     

PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity,Functional Association,and Machine Learning

Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naïve Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of non-interactions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions.     

蛋白质结构预测进展

蛋白质的序列决定结构,结构决定功能。新一代准确的蛋白质结构预测工具为结构生物学、结构生物信息学、药物研发和生命科学等许多领域带来了全新的机遇与挑战,单链蛋白质结构预测的准确率达到与试验方法相媲美的水平。本综述概述了蛋白质结构预测领域的理论基础、发展历程与最新进展,讨论了大量预测的蛋白质结构和基于人工智能的方法如何影响实验结构生物学,最后,分析了当前蛋白质结构预测领域仍未解决的问题以及未来的研究方向。     

The Evaluation of Protein Structure Prediction Results

Methods for protein structure prediction are flourishing and becoming widely available to both experimentalists and computational biologists. However, how good are they? What is their range of applicability and how can we know which method is better suited for the task at hand? These are the questions that this review tries to address, by describing the worldwide Critical Assessment of techniques for protein Structure Prediction (CASP) initiative and focusing on the specific problems of assessing the quality of a protein 3D model.     

ISFOLD: Structure Prediction of Base Pairs in Non-Helical RNA Motifs from Isostericity Signatures in Their Sequence Alignments

Abstract

The existence and identity of non-Watson-Crick base pairs (bps) within RNA bulges, internal loops, and hairpin loops cannot reliably be predicted by existing algorithms. We have developed the Isfold (Isosteric Folding) program as a tool to examine patterns of nucleotide substitutions from sequence alignments or mutation experiments and identify plausible bp interactions. We infer these interactions based on the observation that each non-Watson-Crick bp has a signature pattern of isosteric substitutions where mutations can be made that preserve the 3D structure. Isfold produces a dynamic representation of predicted bps within defined motifs in order of their probabilities. The software was developed under Windows XP, and is capable of running on PC and MAC with Matlab 7.1 (SP3) or higher. A PC standalone version that does not require Matlab also is available. This software and a user manual are freely available at www.ucsf.edu/frankel/isfold.     

Prediction of Protein Structure by Simulating Coarse-grained Folding Pathways: A Preliminary Report

Abstract

A set of software tools designed to study protein structure and kinetics has been developed. The core of these tools is a program called Folding Machine (FM) which is able to generate low resolution folding pathways using modest computational resources. The FM is based on a coarse-grained kinetic ab initio Monte-Carlo sampler that can optionally use information extracted from secondary structure prediction servers or from fragment libraries of local structure. The model underpinning this algorithm contains two novel elements: (a) the conformational space is discretized using the Ramachandran basins defined in the local φ-ψ energy maps; and (b) the solvent is treated implicitly by rescaling the pairwise terms of the non-bonded energy function according to the local solvent environments. The purpose of this hybrid ab initio/knowledge-based approach is threefold: to cover the long time scales of folding, to generate useful 3-dimensional models of protein structures, and to gain insight on the protein folding kinetics. Even though the algorithm is not yet fully developed, it has been used in a recent blind test of protein structure prediction (CASP5). The FM generated models within 6 Å backbone rmsd for fragments of about 60–70 residues of a-helical proteins. For a CASP5 target that turned out to be natively unfolded, the trajectory obtained for this sequence uniquely failed to converge. Also, a new measure to evaluate structure predictions is presented and used along the standard CASP assessment methods. Finally, recent improvements in the prediction of β-sheet structures are briefly described.     

RRM RNA结合蛋白的结构与功能

RRM RNA结合蛋白是一类含一个或数个RRM结构域及附属结构域的RNA结合蛋白,参与RNA前体的剪接、RNA的细胞定位、RNA的稳定性等多种转录后调控过程.在RRM基序中含有许多保守的氨基酸以保证对RNA的结合活性,但是这一家族的不同蛋白质却能特异地结合各种不同的RNA分子.RRM RNA结合蛋白与某些人类遗传性疾病及肿瘤相关.     

蛋白质结构与功能中的结构域

结构域是蛋白质亚基结构中的紧密球状区域.结构域作为蛋白质结构中介于二级与三级结构之间的又一结构层次,在蛋白质中起着独立的结构单位、功能单位与折叠单位的作用.在复杂蛋白质中,结构域具有结构与功能组件与遗传单位的作用.结构域层次的研究将会促进蛋白质结构与功能关系、蛋白质折叠机制以及蛋白质设计的研究.     

Prediction of the Secondary Structure of Myelin Basic Protein

An investigation into the probable secondary structure of the myelin basic protein was carried out by the application of three procedures currently in use to predict the secondary structures of proteins from knowledge of their amino acid sequences. In order to increase the accuracy of the predictions, the amino acid substitutions that occur in the basic protein from different species were incorporated into the predictive algorithms. It was possible to locate regions of probable alpha-helix, beta-structure, beta-turn, and unordered conformation (coil) in the protein. One of the predictive methods introduces a bias into the algorithm to maximize or minimize the amounts of alpha-helix and/or beta-structure present; this made it possible to assess how conditions such as pH and protein concentration or the presence of anionic amphiphilic molecules could influence the protein's secondary structure. The predictions made by the three methods were in reasonably good agreement with one another. They were consistent with experimental data, provided that the stabilizing or destabilizing effects of the environment were taken into account. According to the predictions, the extent of possible alpha-helix and beta-structure formation in the protein s severely restricted by the low frequency and extensive scattering of hydrophobic residues, along with a high frequency and extensive scattering of residues that favor the formation of beta-turns and coils. Neither prolyl residues nor cationic residues per se are responsible for the low content of alpha-helix predicted in the protein. The principal ordered conformation predicted is the beta-turn. Many of the predicted beta-turns overlap extensively, involving in some cases up to 10 residues. In some of these structures it is possible for the peptide backbone to oscillate in a sinusoidal manner, generating a flat, pleated sheetlike structure. Cationic residues located in these structures would appear to be ideally oriented for interaction with lipid phosphate groups located at the cytoplasmic surface of the myelin membrane. An analysis of possible and probable conformations that the triproline sequence could assume questions the popular notion that this sequence produces a hairpin turn in the basic protein.     

Protein Function Prediction: From Traditional Classifier to Deep Learning

Deep learning demonstrates greater competence over traditional machine learning techniques for many tasks. In last several years, deep learning has been applied to protein function prediction and a series of good achievements has been obtained. These findings extensively advanced our understanding of protein function. However, the accuracy of protein function prediction based upon deep learning still has yet to be improved. In article number 1900019, Issue 12, Zhang et al. construct DeepFunc, a deep learning framework using derived feature information of protein sequence and protein interactions network. They find that implementing DeepFunc for protein function prediction is more accurate than using DeepGO, a similar method reported previously. Meanwhile, they find that the method of combining multiple derived feature information in DeepFunc is much better than the method of using only single derived feature information. Due to its fully exploiting feature representation learning ability, deep learning with more derived feature information will enable it to be a promising method for solving more complicated protein function prediction problems and other bioinformatics challenges. Recent researches have provided some major insights into the value for using deep learning to protein function prediction problem.     

Protein Structure Evaluation using an All-Atom Energy Based Empirical Scoring Function

Abstract

Arriving at the native conformation of a polypeptide chain characterized by minimum most free energy is a problem of long standing interest in protein structure prediction endeavors. Owing to the computational requirements in developing free energy estimates, scoring functions—energy based or statistical—have received considerable renewed attention in recent years for distinguishing native structures of proteins from non-native like structures. Several cleverly designed decoy sets, CASP (Critical Assessment of Techniques for Protein Structure Prediction) structures and homology based internet accessible three dimensional model builders are now available for validating the scoring functions. We describe here an all-atom energy based empirical scoring function and examine its performance on a wide series of publicly available decoys. Barring two protein sequences where native structure is ranked second and seventh, native is identified as the lowest energy structure in 67 protein sequences from among 61,659 decoys belonging to 12 different decoy sets. We further illustrate a potential application of the scoring function in bracketing native-like structures of two small mixed alpha/beta globular proteins starting from sequence and secondary structural information. The scoring function has been web enabled at www.scfbio-iitd.res.in/utility/proteomics/energy.jsp     

蛋白质结构研究现状与展望

本文综述了近年来蛋白质结构的确定、预测、比较、分类和功能五个问题的研究概况,并分析了解决每个问题的方法,最后指出蛋白质结构研究的发展前景。     

BTB家族蛋白结构与功能的研究进展

BTB(BR-C,ttk,and bab)家族是类Kuppel锌指蛋白家族中的一种,广泛存在于从酵母到人类的各种物种中,它最主要的特征是在N端至少含有一个BTB结构域.BTB结构域是在进化上高度保守的结构域,约含有115个氨基酸.越来越多的研究表明BTB蛋白在转录和调控的过程中起着重要的作用.     

X-连锁肾上腺-脑白质营养不良蛋白的结构与功能

X-连锁肾上腺 脑白质营养不良基因（ALD基因）编码的ALD蛋白（ALDP）是4种人类ABCD转运蛋白之一,为一种半ABC转运蛋白,既有ABC(ATP binding cassette)转运蛋白的共有特征,又有过氧化物酶体膜蛋白的特点. 其功能可能是将胞浆中极长链饱和脂肪酸（VLCFA）或其衍生物转运到过氧化物酶体内,并在其中进行β氧化. 已报道的ALD基因突变有900多个,其后果多种多样,但最终都使VLCFA或其衍生物无法进入过氧化物酶体,从而使VLCFA在体内蓄积. 作者认为,ALDP是研究ABCD转运蛋白,乃至所有ABC转运蛋白的一个极好模型.     

Homepeptide Repeats:Implications for Protein Structure,Function and Evolution

Analysis of protein sequences from Mycobacterium tuberculosis H37Rv(Mtb H37Rv) was performed to identify homopeptide repeatcontaining proteins(HRCPs).Functional annotation of the HRCPs showed that they are preferentially involved in cellular metabolism.Furthermore,these homopeptide repeats might play some specific roles in protein-protein interaction.Repeat length differences among Bacteria,Archaea and Eukaryotes were calculated in order to identify the conservation of the repeats in these divergent kingdoms.From the results,it was evident that these repeats have a higher degree of conservation in Bacteria and Archaea than in Eukaryotes.In addition,there seems to be a direct correlation between the repeat length difference and the degree of divergence between the species.Our study supports the hypothesis that the presence of homopeptide repeats influences the rate of evolution of the protein sequences in which they are embedded.Thus,homopeptide repeat may have structural,functional and evolutionary implications on proteins.     

含有补体控制蛋白结构域的蛋白质的结构和功能

补体控制蛋白(CCP)结构域分布广泛,含有CCP结构域的蛋白质在补体调节、机体排异和抵御微生物侵袭,甚至肿瘤发生发展等方面具有重要的功能。现在发现的含CCP结构域的蛋白质大约有100多种。我们综述了CCP结构域的基本特征,简要介绍了有代表性的含CCP结构域的蛋白质的功能。     

Next Generation Protein Structure Predictions and Genetic Variant Interpretation

The need to make sense of the thousands of genetic variants uncovered every day in terms of pathology or biological mechanism is acute. Many insights into how genetic changes impact protein function can be gleaned if three-dimensional structures of the associated proteins are available. The availability of a highly accurate method of predicting structures from amino acid sequences (e.g. Alphafold2) is thus potentially a great boost to those wanting to understand genetic changes. In this paper we discuss the current state of protein structures known for the human and other proteomes and how Alphafold2 might impact on variant interpretation efforts. For the human proteome in particular, the state of the available structural data suggests that the impact on variant interpretation might be less than anticipated. We also discuss additional efforts in structure prediction that could further aid the understanding of genetic variants.     

蛋白激酶Cα相互作用蛋白的结构与功能

蛋白激酶Cα相互作用蛋白（protein interacting with Cα kinase,PICK1）是蛋白激酶Cox（protein kinase Cα,PKCα）的靶蛋白之一,也是在PKCα和突触后膜受体蛋白间起重要作用的衔接蛋白。PICK1分别由PDZ结构域、BAR结构域以及卷曲螺旋区和酸性氨基酸区组成。PICK1中的PDZ结构域和受体蛋白、转运蛋白、衔接蛋白的相互作用报道较多,BAR结构域则与支架蛋白、质膜等相互作用。PICK1在突触可塑性、神经递质传递、外周神经感觉、细胞生长和黏连等方面发挥重要作用。本文对PICK1的结构和功能进行综述。