蛋白质三级结构预测方法：
1. 同源模建 homology modeling
2. 折叠识别 fold recognition
3. 从头计算 ab initio method
同源模建基本步骤：
1. 模板的选择；
2.待测序列与模板序列的比对；
3. 模型的建立：①待测蛋白的主链构建；②loop区的模建；③侧链安装
#目前较为流行的侧链安装程序SCWRL(http://dunbrack.fccc.edu/scwrl4/index.php)
4. 模型的评估和循环精修。
#常见的模型评估手段是：
(1)、利用软件自动绘制蛋白质主链二面角 Ranmachandran 图，判断处于Ranmachandran 图中许可区域的氨基酸的比例是否高于85%，检查分子中的键长、键角和过近接触等，通过判断这些立体化学性参数的异常来判断所建模型的好坏；
(2)、统计实验测定的蛋白质结构，得到打分函数，再比较预测模型的打分，从而实现对预测模型的评估。
#模型评估的软件：PROCHECK/Verify3D/ModFold/MetaMQAP/ProQ/......
常用的蛋白质结构预测方法的网址：
1. 预测方法---同源模建
1）Modeller
网址： http://salilab.org/modeller/
简介： MODELLER is used for homology or comparative modeling of protein three-dimensional structures. The user provides an alignment of a sequence to be modeled with known related structures and MODELLER automatically calculates a model containing all non-hydrogen atoms. MODELLER implements comparative protein structure modeling by satisfaction of spatial restraints, and can perform many additional tasks, including de novo modeling of loops in protein structures, optimization of various models of protein structure with respect to a flexibly defined objective function, multiple alignment of protein sequences and/or structures, clustering, searching of sequence databases, comparison of protein structures, etc.
2）Swiss-Model
网址： https://swissmodel.expasy.org/
简介：SWISS-MODEL is a fully automated protein structure homology-modelling server, accessible via the ExPASy web server, or from the program DeepView (Swiss Pdb-Viewer). The purpose of this server is to make Protein Modelling accessible to all biochemists and molecular biologists worldwide.
3）3D-JIGSAW
网址： http://bmm.crick.ac.uk/~3djigsaw/
简介：3D-JIGSAW is an automated system to build three-dimensional models for proteins based on homologues of known structure.
4）EsyPred3D
网址： http://www.unamur.be/sciences/biologie/urbm/bioinfo/esypred/
简介：ESyPred3D is a new automated homology modeling program. The method gets benefit of the increased alignment performances of a new alignment strategy using neural networks. Alignments are obtained by combining, weighting and screening the results of several multiple alignment programs.
参考文献：http://www.ncbi.nlm.nih.gov/pubmed/12217917
ESyPred3D: Prediction of proteins 3D structures.
Lambert C, Leonard N, De Bolle X, Depiereux E.
Bioinformatics. 2002 Sep;18(9):1250-1256
5）CPHmodels
网址： http://www.cbs.dtu.dk/services/CPHmodels/
简介：CPHmodels 3.2 is a protein homology modeling server. The template recognition is based on profile-profile alignment guided by secondary structure and exposure predictions.
6）RaptorX
网址： http://raptorx.uchicago.edu/
简介：RaptorX is a Web Portal for Protein Structure and Function Prediction.This web portal for protein structure and function prediction is developed by Xu group, excelling at secondary, tertiary and contact prediction for protein sequences without close homologs in the Protein Data Bank (PDB). Given a protein sequence, RaptorX predicts its secondary and tertiary structures as well as contact map, solvent accessibility, disordered regions and binding sites. RaptorX assigns the following confidence scores to indicate the quality of a predicted 3D model: P-value for the relative global quality, GDT (global distance test) and uGDT (un-normalized GDT) for the absolute global quality, and RMSD for the absolute local quality of each residue in the model. RaptorX-Binding predicts the binding sites of a protein sequence, based upon the predicted 3D model by RaptorX.
7）HHpred
网址： https://toolkit.tuebingen.mpg.de/hhpred#
简介：HHpred- Homology detection & structure prediction by HMM-HMM comparison
2. 预测方法---折叠识别
1）DescFold
网址： http://protein.cau.edu.cn/DescFold/
简介：DescFold(Descriptor-based Fold Recognition System) is a web server for protein fold recognition,which can predict a protein's fold type from its amino acid sequence. The server combines six effictive descriptors : a profile-sequence-alignment-based descriptor using Psi-blaste-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blaste-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), a descriptor based on the occurrence of PROSITE functional motifs, a descriptor based on profile-profile-alignment(PPA) and a descriptor based on Profile-structural-profile-alignment (PSPA) .
2）pGenThreader
网址： http://bioinf.cs.ucl.ac.uk/psipred/
简介：Highly sensitive fold recognition using profile-profile comparison (whole chain library).
3）pGenThreader
网址： http://ffas.burnham.org/ffas-cgi/cgi/ffas.pl
简介：
4）FFAS03
网址： http://ffas.burnham.org/ffas-cgi/cgi/ffas.pl
参考文献：http://www.ncbi.nlm.nih.gov/pubmed/15980471
Nucleic Acids Res.2005 Jul 1;33(Web Server issue):W284-8.
FFAS03: a server for profile--profile sequence alignments.
Jaroszewski L1,Rychlewski L,Li Z,Li W,Godzik A.
5）Phyre2
网址：http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index
参考文献：
http://www.nature.com/nprot/journal/v10/n6/full/nprot.2015.053.html
NATURE PROTOCOLS|PROTOCOL
The Phyre2 web portal for protein modeling, prediction and analysis
Lawrence A Kelley
3. 预测方法---从头计算法
1）Rosetta
网址：https://www.rosettacommons.org/software
简介：The Rosetta software suite includes algorithms for computational modeling and analysis of protein structures. It has enabled notable scientific advances in computational biology, including de novo protein design, enzyme design, ligand docking, and structure prediction of biological macromolecules and macromolecular complexes.
2）QUARK
网址：http://zhanglab.ccmb.med.umich.edu/QUARK/
简介：QUARK is a computer algorithm for ab initio protein folding and protein structure prediction, which aims to construct the correct protein 3D model from amino acid sequence only. QUARK models are built from small fragments (1-20 residues long) by replica-exchange Monte Carlo simulation under the guide of an atomic-level knowledge-based force field.
4. 预测方法---综合法
1）I-TASSER
网址：http://zhanglab.ccmb.med.umich.edu/I-TASSER/
简介：I-TASSER (Iterative Threading ASSEmbly Refinement) is a hierarchical approach to protein structure and function prediction. Structural templates are first identified from the PDB by multiple threading approach LOMETS; full-length atomic models are then constructed by iterative template fragment assembly simulations. Finally, function insights of the target are derived by threading the 3D models through protein function database BioLiP
5. 蛋白质二级结构预测
1) NetSurfP
网址： http://www.cbs.dtu.dk/services/NetSurfP/
简介：NetSurfP server predicts the surface accessibility and secondary structure of amino acids in an amino acid sequence. The method also simultaneously predicts the reliability for each prediction, in the form of a Z-score. The Z-score is related to the surface prediction, and not the secondary structure.
2) SSpro
网址： http://scratch.proteomics.ics.uci.edu/
简介：SSpro is a server for protein secondary structure prediction based on protein evolutionary information (sequence homology) and homologous protein's secondary structure (structure homology).
SSpro currently achieves a performance exceeding 79% correctly classified residues on proteins with no homologs in the PDB and exceeding 92% correctly classified residues on proteins where homologs can be found in the PDB, ranking on top of the tested prediction servers.
3) PredictProtein
网址： https://www.predictprotein.org/
4) JPred
网址： http://www.compbio.dundee.ac.uk/jpred/
5) PREDATOR
网址：http://mobyle.pasteur.fr/cgi-bin/portal.py?%20#forms::predator
简介：Protein secondary structure prediction from a single sequence or a set of sequences
6) PSSpred
网址：http://zhanglab.ccmb.med.umich.edu/PSSpred/
简介：PSSpred (ProteinSecondaryStructurePREDiction) is a simple neural network training algorithm for accurate protein secondary structure prediction. It first collects multiple sequence alignments using PSI-BLAST. Amino-acid frequence and log-odds data with Henikoff weights are then used to train secondary structure, separately, based on the Rumelhart error backpropagation method. The final secondary structure prediction result is a combination of 7 neural network predictors from different profile data and parameters.
7) PSSpred
网址：http://bioinf.cs.ucl.ac.uk/psipred/
声明：本文是整合前人的智慧，欢迎补充讨论！