3D Protein Structure Prediction Research Articles

PredMP: a web server for de novo prediction and visualization of membrane proteins

PredMP is the first web service, to our knowledge, that aims at de novo prediction of the membrane protein (MP) 3D structure followed by the embedding of the MP into the lipid bilayer for visualization. Our approach is based on a high-throughput Deep Transfer Learning (DTL) method that first predicts MP contacts by learning from non-MPs and then predicts the 3D model of the MP using the predicted contacts as distance restraints. This algorithm is derived from our previous Deep Learning (DL) method originally developed for soluble protein contact prediction, which has been officially ranked No. 1 in CASP12. The DTL framework in our approach overcomes the challenge that there are only a limited number of solved MP structures for training the deep learning model. There are three modules in the PredMP server: (i) The DTL framework followed by the contact-assisted folding protocol has already been implemented in RaptorX-Contact, which serves as the key module for 3D model generation; (ii) The 1D annotation module, implemented in RaptorX-Property, is used to predict the secondary structure and disordered regions; and (iii) the visualization module to display the predicted MPs embedded in the lipid bilayer guided by the predicted transmembrane topology. Tested on 510 non-redundant MPs, our server predicts correct folds for ∼290 MPs, which significantly outperforms existing methods. Tested on a blind and live benchmark CAMEO from September 2016 to January 2018, PredMP can successfully model all 10 MPs belonging to the hard category. PredMP is freely accessed on the web at http://www.predmp.com. Supplementary data are available at Bioinformatics online.

Protein threading using residue co-variation and deep learning.

MotivationTemplate-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain very challenging.ResultsWe present a new method called DeepThreader to improve protein threading, including both alignment generation and template selection, by making use of deep learning (DL) and residue co-variation information. Our method first employs DL to predict inter-residue distance distribution from residue co-variation and sequential information (e.g. sequence profile and predicted secondary structure), and then builds sequence-template alignment by integrating predicted distance information and sequential features through an ADMM algorithm. Experimental results suggest that predicted inter-residue distance is helpful to both protein alignment and template selection especially for protein sequences without very close templates, and that our method outperforms currently popular homology modeling method HHpred and threading method CNFpred by a large margin and greatly outperforms the latest contact-assisted protein threading method EigenTHREADER.Availability and implementation http://raptorx.uchicago.edu/ Supplementary information Supplementary data are available at Bioinformatics online.

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html.

Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling.

Homology modeling predicts the 3D structure of a query protein based on the sequence alignment with one or more template proteins of known structure. Its great importance for biological research is owed to its speed, simplicity, reliability and wide applicability, covering more than half of the residues in protein sequence space. Although multiple templates have been shown to generally increase model quality over single templates, the information from multiple templates has so far been combined using empirically motivated, heuristic approaches.We present here a rigorous statistical framework for multi-template homology modeling. First, we find that the query proteins’ atomic distance restraints can be accurately described by two-component Gaussian mixtures. This insight allowed us to apply the standard laws of probability theory to combine restraints from multiple templates. Second, we derive theoretically optimal weights to correct for the redundancy among related templates. Third, a heuristic template selection strategy is proposed.We improve the average GDT-ha model quality score by 11% over single template modeling and by 6.5% over a conventional multi-template approach on a set of 1000 query proteins. Robustness with respect to wrong constraints is likewise improved. We have integrated our multi-template modeling approach with the popular MODELLER homology modeling software in our free HHpred server http://toolkit.tuebingen.mpg.de/hhpred and also offer open source software for running MODELLER with the new restraints at https://bitbucket.org/soedinglab/hh-suite.

An efficient and flexible scanning of databases of protein secondary structures

Protein secondary structure describe protein construction in terms of regular spatial shapes, including alpha-helices, beta-strands, and loops, which protein amino acid chain can adopt in some of its regions. This information is supportive for protein classification, functional annotation, and 3D structure prediction. The relevance of this information and the scope of its practical applications cause the requirement for its effective storage and processing. Relational databases, widely-used in commercial systems in recent years, are one of the serious alternatives honed by years of experience, enriched with developed technologies, equipped with the declarative SQL query language, and accepted by the large community of programmers. Unfortunately, relational database management systems are not designed for efficient storage and processing of biological data, such as protein secondary structures. In this paper, we present a new search method implemented in the search engine of the PSS-SQL language. The PSS-SQL allows formulation of queries against a relational database in order to find proteins having secondary structures similar to the structural pattern specified by a user. In the paper, we will show how the search process can be accelerated by multiple scanning of the Segment Index and parallel implementation of the alignment procedure using multiple threads working on multiple-core CPUs.

SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity.

Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure and function and as a component of protein 3D structure prediction pipelines. Most predictors use a combination of machine learning and profiles, and thus must be retrained and assessed periodically as the number of available protein sequences and structures continues to grow. We present newly trained modular versions of the SSpro and ACCpro predictors of secondary structure and relative solvent accessibility together with their multi-class variants SSpro8 and ACCpro20. We introduce a sharp distinction between the use of sequence similarity alone, typically in the form of sequence profiles at the input level, and the additional use of sequence-based structural similarity, which uses similarity to sequences in the Protein Data Bank to infer annotations at the output level, and study their relative contributions to modern predictors. Using sequence similarity alone, SSpro's accuracy is between 79 and 80% (79% for ACCpro) and no other predictor seems to exceed 82%. However, when sequence-based structural similarity is added, the accuracy of SSpro rises to 92.9% (90% for ACCpro). Thus, by combining both approaches, these problems appear now to be essentially solved, as an accuracy of 100% cannot be expected for several well-known reasons. These results point also to several open technical challenges, including (i) achieving on the order of ≥ 80% accuracy, without using any similarity with known proteins and (ii) achieving on the order of ≥ 85% accuracy, using sequence similarity alone. SSpro, SSpro8, ACCpro and ACCpro20 programs, data and web servers are available through the SCRATCH suite of protein structure predictors at http://scratch.proteomics.ics.uci.edu.

Protein threading using context-specific alignment potential

Motivation: Template-based modeling, including homology modeling and protein threading, is the most reliable method for protein 3D structure prediction. However, alignment errors and template selection are still the main bottleneck for current template-base modeling methods, especially when proteins under consideration are distantly related.Results: We present a novel context-specific alignment potential for protein threading, including alignment and template selection. Our alignment potential measures the log-odds ratio of one alignment being generated from two related proteins to being generated from two unrelated proteins, by integrating both local and global context-specific information. The local alignment potential quantifies how well one sequence residue can be aligned to one template residue based on context-specific information of the residues. The global alignment potential quantifies how well two sequence residues can be placed into two template positions at a given distance, again based on context-specific information. By accounting for correlation among a variety of protein features and making use of context-specific information, our alignment potential is much more sensitive than the widely used context-independent or profile-based scoring function. Experimental results confirm that our method generates significantly better alignments and threading results than the best profile-based methods on several large benchmarks. Our method works particularly well for distantly related proteins or proteins with sparse sequence profiles because of the effective integration of context-specific, structure and global information.Availability: http://raptorx.uchicago.edu/download/.Contact: jinboxu@gmail.com

Predicting protein contact map using evolutionary and physical constraints by integer programming

Motivation: Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains challenging to predict contact map using only sequence information. Most existing methods predict the contact map matrix element-by-element, ignoring correlation among contacts and physical feasibility of the whole-contact map. A couple of recent methods predict contact map by using mutual information, taking into consideration contact correlation and enforcing a sparsity restraint, but these methods demand for a very large number of sequence homologs for the protein under consideration and the resultant contact map may be still physically infeasible.Results: This article presents a novel method PhyCMAP for contact map prediction, integrating both evolutionary and physical restraints by machine learning and integer linear programming. The evolutionary restraints are much more informative than mutual information, and the physical restraints specify more concrete relationship among contacts than the sparsity restraint. As such, our method greatly reduces the solution space of the contact map matrix and, thus, significantly improves prediction accuracy. Experimental results confirm that PhyCMAP outperforms currently popular methods no matter how many sequence homologs are available for the protein under consideration.Availability: http://raptorx.uchicago.edu.Contact: jinboxu@gmail.com

Quality assessment of protein model-structures based on structural and functional similarities

BackgroundExperimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology.ResultsGOBA - Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests.ConclusionsThe validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and one of CASP9, compared to the contest participants. Consequently, GOBA offers a novel single model quality assessment program that addresses the practical needs of biologists. In conjunction with other Model Quality Assessment Programs (MQAPs), it would prove useful for the evaluation of single protein models.

Glocal: Reconstructing Protein 3D Structure from 2D Contact Map by Combining Global and Local Optimization Schemes

Prediction of protein 3D structure from solely its amino acid sequence is one of the most challenging problems in structural bioinformatics, where the 3D structure reconstruction from observed constraints is the key step. In this paper, we propose a novel protocol called Glocal to recover a protein’s 3D coordinates based on a given 2D contact map by combining both global and local optimization schemes achieved by the swarm intelligence of Particle Swarm Optimization (PSO) and the Simulated Annealing (SA) techniques respectively. Our results demonstrate that Glocal can recover the 3D structures with the average RMSD less than 2 Å from the native contact map. Further analysis also shows that Glocal is powerful for handling with noisy contact map with the proposed combination optimization approaches. Keywords: Glocal, particle swarm optimization, protein contact map, protein structure reconstruction, simulated annealing, Simulated Annealing, protein, Threshold, resolutions

PROTEIN TERTIARY STRUCTURE PREDICTION BASED ON MAIN CHAIN ANGLE USING A HYBRID BEES COLONY OPTIMIZATION ALGORITHM

Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

RNA and protein 3D structure modeling: similarities and differences

In analogy to proteins, the function of RNA depends on its structure and dynamics, which are encoded in the linear sequence. While there are numerous methods for computational prediction of protein 3D structure from sequence, there have been very few such methods for RNA. This review discusses template-based and template-free approaches for macromolecular structure prediction, with special emphasis on comparison between the already tried-and-tested methods for protein structure modeling and the very recently developed “protein-like” modeling methods for RNA. We highlight analogies between many successful methods for modeling of these two types of biological macromolecules and argue that RNA 3D structure can be modeled using “protein-like” methodology. We also highlight the areas where the differences between RNA and proteins require the development of RNA-specific solutions. FigureApproaches for predicting RNA structure. Top: Template-free modeling. Bottom: Template-based modeling

Three Non-Aspartate Amino Acid Mutations in the ComA Response Regulator Receiver Motif Severely Decrease Surfactin Production, Competence Development and Spore Formation in Bacillus subtilis

Bacillus subtilis strains produce a broad spectrum of bioactive peptides. The lipopeptide surfactin belongs to one wellknown class, which includes amphiphilic membrane-active biosurfactants and peptide antibiotics. Both the srfA promoter and the ComP-ComA signal transduction system are an important part of the factor that results in the production of surfactin. Bs-M49, obtained by means of low-energy ion implantation in wild-type Bs-916, produced significantly lower levels of surfactin, and had no obvious effects against R. solani. Occasionally, we found strain Bs- M49 decreased spore formation and the development of competence. Blast comparison of the sequences from Bs- 916 and M49 indicate that there is no difference in the srfA operon promoter PsrfA, but there are differences in the coding sequence of the comA gene. These differences result in three missense mutations within the M49 ComA protein. RT-PCR analyses results showed that the expression levels of selected genes involved in competence and sporulation in both the wild-type Bs-916 and mutant M49 strains were significantly different. When we integrated the comA ORF into the chromosome of M49 at the amyE locus, M49 restored hemolytic activity and antifungal activity. Then, HPLC analyses results also showed the comA-complemented strain had a similar ability to produce surfactin with wild-type strain Bs-916. These data suggested that the mutation of three key amino acids in ComA greatly affected the biological activity of Bacillus subtilis. ComA protein 3D structure prediction and motif search prediction indicated that ComA has two obvious motifs common to response regulator proteins, which are the Nterminal response regulator receiver motif and the Cterminal helix-turn-helix motif. The three residues in the ComA N-terminal portion may be involved in phosphorylation activation mechanism. These structural prediction results implicate that three mutated residues in the ComA protein may play an important role in the formation of a saltbridge to the phosphoryl group keeping active conformation to subsequent regulation of the expression of downstream genes.

MUFOLD: A new solution for protein 3D structure prediction

There have been steady improvements in protein structure prediction during the past 2 decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. Toward achieving more accurate and efficient structure prediction, we developed a number of novel methods and integrated them into a software package, MUFOLD. First, a systematic protocol was developed to identify useful templates and fragments from Protein Data Bank for a given target protein. Then, an efficient process was applied for iterative coarse-grain model generation and evaluation at the Calpha or backbone level. In this process, we construct models using interresidue spatial restraints derived from alignments by multidimensional scaling, evaluate and select models through clustering and static scoring functions, and iteratively improve the selected models by integrating spatial restraints and previous models. Finally, the full-atom models were evaluated using molecular dynamics simulations based on structural changes under simulated heating. We have continuously improved the performance of MUFOLD by using a benchmark of 200 proteins from the Astral database, where no template with >25% sequence identity to any target protein is included. The average root-mean-square deviation of the best models from the native structures is 4.28 A, which shows significant and systematic improvement over our previous methods. The computing time of MUFOLD is much shorter than many other tools, such as Rosetta. MUFOLD demonstrated some success in the 2008 community-wide experiment for protein structure prediction CASP8.

NNcon: improved protein contact map prediction using 2D-recursive neural networks.

Protein contact map prediction is useful for protein folding rate prediction, model selection and 3D structure prediction. Here we describe NNcon, a fast and reliable contact map prediction server and software. NNcon was ranked among the most accurate residue contact predictors in the Eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008. Both NNcon server and software are available at http://casp.rnet.missouri.edu/nncon.html.

Homology Modelling: Eine Übersicht über die Methode am Beispiel der Strukturbestimmung vom Diabetes Antigen GAD 65

This introductory paper describes the basic principles and clinical applications of the protein 3D structure prediction by homology modelling. The paper mainly addresses physicians and medical chemists. Because many proteins are of immediate clinical importance, the determination of their structures is crucial for molecular medicine. In homology modelling, a protein sequence with unknown structure is aligned with sequences of known protein structures. By exploiting structural information from the known configurations, the new structure can be predicted. The necessary condition for successful homology modelling is a sufficient similarity between the protein sequences. Because in the near future for every protein family at least one member with a known structure will be available, the importance and applicability of homology modelling is steadily increasing. We demonstrate the principles of homology modelling on hand of the Glutamic Acid Decarboxylase (GAD 65) structure prediction, which is a typical autoantigen involved in Diabetes Mellitus Type 1.

Structural Bioinformatics: From the Sequence to Structure and Function

Proteins are the molecules of life which are involved in cellular processes. The functional specificity of a protein is linked to its structure. A great section of bioinformatics deals with the prediction, analysis and visualization of protein 3D structures. High-throughput methods for the determination of protein structures provide the information needed to build structure-activity relationships. The accessibility of these structural data together with genomic and clinical data is of crucial importance for the application of bioinformatics in medical research. The experimental methods are supplemented by homology modelling, where new protein structures are predicted by exploiting structural information from known configurations. Computer visualization of protein models provide insights into biological processes which can not be adequately explained otherwise. For the analysis of protein-protein interactions, Voronoi tessellations are used to quantify the macromolecular interfaces. Details at the atomic and electronic levels of the protein molecules, needed for a deeper understanding of properties that remain unrevealed after structural elucidation, are provided by methods based on quantum theoretical calculations. Many proteins are of immediate medical and pharmacological relevance. The structural analysis is therefore of special interest to understand diseases at a molecular level, which is the prerequisite for new developments in diagnosis and therapy. Keywords: Protein structures, protein data base, protein visualization, homology modelling, voronoi tessellation, density functional theory, molecular medicine

FAMSD: A Powerful Protein Modeling Platform that Combines Alignment Methods, Homology Modeling, 3D Structure Quality Estimation and Molecular Dynamics

The prediction of a protein three-dimensional (3D) structure is one of the most important challenges in computational structural biology. We have developed an automatic protein 3D structure prediction method called FAMSD. FAMSD is based on a comparative modeling method which consists of the following four steps: (1) generating and selecting sequence alignments between target and template proteins; (2) constructing 3D structure models based on each selected alignment; (3) selecting the best 3D structure model and (4) refining the selected model. In the FAMSD method, sequence alignment programs such as a series of BLAST programs, SP3 and SPARKS2 programs, the homology modeling program FAMS (Full Automatic Modeling System), the model quality estimation program CIRCLE and the molecular dynamics program APRICOT were used in combination to construct high quality protein models. To assess the FAMSD method we have participated in the 8th Critical Assessment of Techniques for Protein Structure Prediction (CASP8) experiment. The results of our original assessment indicate that the FAMSD method offers excellent capability in packing side-chains with the correct torsion angles while avoiding the formation of atom-atom collisions. Since side-chain packing plays a significant role in defining the biological function of proteins, this method is a valuable resource in biological, pharmaceutical and medicinal research efforts.

Improving Protein 3D Structure Prediction Accuracy using Dense Regions Areas of Secondary Structures in the Contact Map

Problem Statement: The protein folding problem is a fundamental problem in structural molecular biology. This problem describes how a protein is transformed from its primary sequence (i.e., amino acid sequence) into the three dimensional structure (3D structure) for this sequence that determines the function of the protein. The 3D structure of a protein can be represented using a square symmetrical binary matrix called contact map. The concept of contact map facilitates the transformation of the folding problem into a computational one, so various computational approaches use the contact map to predict protein secondary structures. Correlation mutation analysis is an approach that tries to study the mutated patterns that appear in the multiple sequence alignments, this approach predicts every pair of protein residues to be in contact or not independently of the other pairs. Approach: This study proposed an improvement over correlation mutation analysis to predict the secondary structures that exist in the contact map. The proposed method uses regions of the secondary structures instead of independent pairs as in the typical correlation mutation analysis; also it applies the analysis on the dense regions rather than the whole contact map. Results: The proposed method was implemented on proteins related to different classes (i.e., mainly alpha, mainly beta, mixed alpha beta and low secondary structures). The test proteins are extracted from the Protein Data Bank (PDB) of solved structures. The results show improvements of dense regions accuracy over correlation mutation accuracy and random accuracy. Conclusion: According to the amount of wrongly predicted contacts, the results show a large decrease in the wrongly predicted contacts in the dense regions analysis over correlation mutation analysis.

Offlattice model in the prediction of protein 3D structure

3-dimension HPNX offlattice model is developed from the 2-dimension HP offlattice model. In the HP model, 20 types of amino acid monomers are divided into two classes, H (non-polar monomer) and P (polar monomer). In the HPNX model, polar monomers are split into positively charged (P), negatively charged (N) and neutral (X) monomers. A new evolutionary algorithm is applied to study long chains of the HPNX offlattice protein model. This method successfully predict the structures of several proteins in the 3-dimension space that are similar to the structures gotten by X-Ray Crystallography and NMR and published in the PDB(Protein Data Bank).