Computational methods for protein interaction and structural prediction

Protein complex prediction based on maximum matching with domain–domain interaction

Most computational protein structure prediction methods are designed for either template based or template-free (ab initio) structure prediction. The approaches that integrate the prediction capabilities of both template-based modeling and template-free modeling are needed to synergistically combine the two kinds of methods to improve protein structure prediction. In this work, we develop a new method to integrate several protein structure prediction methods including our template-based MULTICOM server, our ab initio contact-based protein structure prediction method CONFOLD, our multi-template-based model generation tool MTMG, and locally installed external Rosetta, I-TASSER and RaptorX protein structure prediction tools to improve protein structure prediction of a fullspectrum difficulty ranging from easy, to medium and to hard. Our method participated in the 11(th) community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014 as MULTICOM-NOVEL server. It was ranked among top 10 methods for protein tertiary structure prediction according to the official CASP11 assessment, which demonstrates that integrating complementary modeling methods is useful for advancing protein structure prediction.

Most of the protein structure prediction methods use a multi-step process, which often includes secondary structure prediction, contact prediction, fragment generation, clustering, etc. For many years, secondary structure prediction has been the workhorse for numerous methods aimed at predicting protein structure and function. This paper presents a new mixed integer linear optimization (MILP)-based consensus method: a Consensus scheme based On a mixed integer liNear optimization method for seCOndary stRucture preDiction (CONCORD). Based on seven secondary structure prediction methods, SSpro, DSC, PROF, PROFphd, PSIPRED, Predator and GorIV, the MILP-based consensus method combines the strengths of different methods, maximizes the number of correctly predicted amino acids and achieves a better prediction accuracy. The method is shown to perform well compared with the seven individual methods when tested on the PDBselect25 training protein set using sixfold cross validation. It also performs well compared with another set of 10 online secondary structure prediction servers (including several recent ones) when tested on the CASP9 targets ( http://predictioncenter.org/casp9/ ). The average Q3 prediction accuracy is 83.04 per cent for the sixfold cross validation of the PDBselect25 set and 82.3 per cent for the CASP9 targets. We have developed a MILP-based consensus method for protein secondary structure prediction. A web server, CONCORD, is available to the scientific community at http://helios.princeton.edu/CONCORD .

BackgroundIn recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results.We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction methods. For each local sequence window of a protein sequence the method predicts probability estimates for the sequence to attain particular local structures from a set of predefined local structure candidates.The first step is to define a set of local structure representatives based on clustering recurrent local structures. In the second step a discriminative model is trained to predict the local structure representative given local sequence information.ResultsThe step of clustering local structures yields an average RMSD quantization error of 1.19 Å for 27 structural representatives (for a fragment length of 7 residues). In the prediction step the area under the ROC curve for detection of the 27 classes ranges from 0.68 to 0.88.ConclusionThe described method yields probability estimates for local protein structure candidates, giving signals for all kinds of local structure. These local structure predictions can be incorporated either into fold recognition algorithms to improve alignment quality and the overall prediction accuracy or into new fold prediction methods.

Ribonucleic acid is vital in numerous stages of protein synthesis; it also possesses important functional and structural roles within the cell. The function of an RNA molecule within a particular organic system is principally determined by its structure. The current physical methods available for structure determination are time-consuming and expensive. Hence, computational methods for structure prediction are sought after. The energies involved by the formation of secondary structure elements are significantly greater than those of tertiary elements. Therefore, RNA structure prediction focuses on secondary structure. We present P-RnaPredict, a parallel evolutionary algorithm for RNA secondary structure prediction. The speedup provided by parallelization is investigated with five sequences, and a dramatic improvement in speedup is demonstrated, especially with longer sequences. An evaluation of the performance of P-RnaPredict in terms of prediction accuracy is made through comparison with 10 individual known structures from 3 RNA classes (5S rRNA, Group I intron 16S rRNA and 16S rRNA) and the mfold dynamic programming algorithm. P-RnaPredict is able to predict structures with higher true positive base pair counts and lower false positives than mfold on certain sequences. P-RnaPredict is available for non-commercial usage. Interested parties should contact Kay C. Wiese (wiese@cs.sfu.ca).

A force field for (η3-allyl)palladium complexes with phosphorus and/or nitrogen ligands has been developed, based upon both X-ray and quantum chemical reference data. The quality of the force field in structure prediction is checked by comparison to X-ray structures, including several not used in the parametrization. The force field is also compared to alternative computational methods for structure prediction, including earlier force fields and two quantum chemical methods (PM3(tm) and B3LYP/LANL2DZ).

Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. We develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method-AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method-AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA. The source code is available at https://github.com/BioinfoMachineLearning/EnQA. Supplementary data are available at Bioinformatics online.

Efforts to use computers in predicting the secondary structure of proteins based only on primary structure information started over a quarter century ago [1-3]. Although the results were encouraging initially, the accuracy of the pioneering methods generally did not attain the level required for using predictions of secondary structures reliably in modelling the three-dimensional topology of proteins. During the last decade, however, the introduction of new computational techniques as well as the use of multiple sequence information has lead to a dramatic increase in the success rate of prediction methods, such that successful 3D modelling based on predicted secondary structure has become feasible [e.g., Ref 4]. This review is aimed at presenting an overview of the scale of the secondary structure prediction problem and associated pitfalls, as well as the history of the development of computational prediction methods. As recent successful strategies for secondary structure prediction all rely on multiple sequence information, some methods for accurate protein multiple sequence alignments will also be described. While the main focus is on prediction methods for globular proteins, also the prediction of trans-membrane segments within membrane proteins will be briefly summarised. Finally, an integrated iterative approach tying secondary structure prediction and multiple alignment will be introduced [5].

Background: The Mycobacterium tuberculosis complex (MTBC) bacteria include the slowly growing, host-associated bacteria Mycobacterium tuberculosis, Mycobacterium Bovis, Mycobacterium microti, Mycobacterium africanum, Mycobacterium pinnipedii. Aim: Comparative Functional Genomics Studies for understanding the Hypothetical Proteins in Mycobacterium tuberculosis variant microti 12. Objective: A computational genomics study was performed to understand the 247 hypothetical protein genes. Functional annotation of virtual proteins was performed on different servers to maximize confidence level. Methods: Sequence Retrieval. The whole genome sequences for the Mycobacterium tuberculosis micro variant 12 were retrieved from the KEGG database ( http://www.genome.jp/kegg/) and were used for screening 247 hypothetical proteins (Fig. 1 ). Functional Annotation and Sub-cellular localization. The Mycobacterium tuberculosis micro variant 12 hypothetical proteins were screened and sorted out from the genome and were individually analyzed for the presence of conserved functional domains by using computational biology tools like CDD-BLAST ( https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) ;Pfam ( http://pfam.xfam.org/ncbiseq/398365647); The subcellular localization of hypothetical proteins was determined by CELLO2GO ( http://cello.life.nctu.edu.tw). These web tools can search the defined conserved domains in the sequences available in the online servers or databases and assist in the classification of proteins in the appropriate families. Protein Structure Prediction. The in-silico structure predictions of the hypothetical protein sequences showing functional properties were carried out by using the PS2 Protein Structure Prediction Server ( http://www.ps2.life.nctu.edu.tw/). The online server helps to generate the 3D structures of the hypothetical proteins. The server accepts the sequences in FASTA format as a query to generate resultant proteins 3D structures. The structure determination is completely based on the conserved template regions detected during functional annotations. Protein-protein interaction through String database: The interaction of each hypothetical protein analyzed for functional characteristics was subjected to a protein-protein interaction server for the prediction of a possible functional role in interaction amongst the available known proteins ( https://string-db.org/). This information can help us to further validated the functional role of such hypothetical proteins and their possible role in the Mycobacterium Tuberculosis micro variant. Protein secondary structure prediction through JPred4: The secondary structure prediction of all the hypothetical proteins was determined through JPred4 ( http://www.compbio.dundee.ac.uk/jpred4/index.html) and served to identify the available secondary structures in the unknown hypothetical protein sequences. These further help us to understand the available templates in the uncharacterized protein sequences for the prediction of novel functions associated with these proteins. The predictions were further characterized by the Phyre2 server for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Protein modeling, prediction, and analysis through Phyre2. The hypothetical proteins which were identified to have functional properties were further characterized by the Phyre2 server ( http://www.sbg.bio.ic.ac.uk/phyre2) for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Results: A computational genomics study was performed to understand the 247 hypothetical protein genes Functional annotation of virtual proteins, and was performed on different servers to maximize confidence level. The functional prediction was performed by CDD-Blast and Pfam. The gene sequences of proteins have probably been successfully functionally annotated, characterized, and their subcellular localization and 3-D structural predictions have been predicted computationally. Online automated bioinformatics tools such as CDD-Blast, Pfam, CELLO2GO and PS2-Server were used for the structural and functional characterization of screened hypothetical proteins. The structure, function, and subcellular localization of a hypothetical protein from Mycobacterium tuberculosis variant microti 12 have been obtained and presented (Fig. 2 ). Also, the three-dimensional structure generated after using the template with the highest score was displayed as the template ID in the structure column of the respective hypothetical protein. However, as systems biology denies hypothetical protein functions, the structures of such proteins can be tested through biological processes and experiments, making them suitable for understanding their role in the life cycle, pathogenesis, and drug development. We can further explore these predictive possibilities in pharmaceuticals, and other clinically relevant studies. This study by HP helped find structure-function relationships in Mycobacterium tuberculosis variant microti 12 using a variety of bioinformatics tools. The string database made predictions about protein-protein interactions and the template helped us predict a hypothetical protein structure and even helped us find its 3D protein structure. Protein profiling can be performed on structures retrieved from these servers. This is useful for proteomics studies, including protein-protein interactions, protein expression of specific hypothetical proteins, and post-translational modifications of protein-coding genes. Further understanding of these hypothetical proteins can help us to know more about the Mycobacterium tuberculosis complex (MTBC) and may assist in Drugs and inhibitors against different pathogens within this complex. Conclusion: The all-inclusive bioinformatic study has helped to functionally elucidate 247 hypothetical proteins, which have resulted and made it easier to understand many functional proteins available in the Mycobacterium tuberculosis micro variant 12. The subcellular localization of the 247 sorted hypothetical proteins was also carried & which further helped us understand the localization of identified enzymes or proteins. We have successfully characterized the 247 unknown proteins of hypothetical protein sequences from Mycobacterium tuberculosis micro variant 12 to validate their structure and functions of the gene products. These predicted functions and three-dimensional structures may lead to establishing their role in the life cycle of the bacterium. This computationally generated data can also be further used for developing new protocols for new vaccines against Mycobacterium tuberculosis micro variant 12 that are essential for preventing infection, diseases, and transmission. This complete result of Hypothetical Protein is needed for further studies of the whole genomic of the Mycobacterium Tuberculosis micro variant 12 for their function interpretation which further help in the understanding of its functions as well as structure. Moreover, this interpretation would help us to study the evolution of Mycobacterium Tuberculosis micro variant 12 which further helps in the process of discovering the drugs to inhibit the causes of diseases.

Computationally generated models of protein structures bridge the gap between the practically negligible price tag of sequencing and the high cost of experimental structure determination. By providing a low-cost (and often free) partial alternative to experimentally determined structures, these models help biologists design and interpret their experiments. Obviously, the more accurate the models the more useful they are. However, methods for protein structure prediction generate many structural models of various qualities, necessitating means for the estimation of their accuracy. In this work we present MESHI_consensus, a new method for the estimation of model accuracy. The method uses a tree-based regressor and a set of structural, target-based, and consensus-based features. The new method achieved high performance in the EMA (Estimation of Model Accuracy) track of the recent CASP14 community-wide experiment (https://predictioncenter.org/casp14/index.cgi). The tertiary structure prediction track of that experiment revealed an unprecedented leap in prediction performance by a single prediction group/method, namely AlphaFold2. This achievement would inevitably have a profound impact on the field of protein structure prediction, including the accuracy estimation sub-task. We conclude this manuscript with some speculations regarding the future role of accuracy estimation in a new era of accurate protein structure prediction.

Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data, including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features, thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of nonproteolytic and noninhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task and thereby highlight limitations of computational interaction prediction methods.

Metal-organic frameworks (MOFs) have emerged as highly versatile materials with applications in gas storage and separation, solar light energy harvesting and photocatalysis. The design of new MOFs, however, has been hampered by the lack of computational methods for ab initio MOF structure prediction, which could be used to inspire and direct experimental synthesis. Here, we report the first ab initio method for the prediction of MOF structures and test it against a diverse set of known MOFs that were chosen for their differences in topology, metal coordination geometry, and ligand binding sites. In all cases, our calculations produced structures that match experiment using only the target composition and ligand molecular structure, proving the versatility of our procedure. The herein presented methodology utilizes the point group symmetry of ligands to enable, for the first time, prediction of MOF structures from first principles, without having to resort to empirical guidelines based on rigid connectivity of nodes and linkers, or to previously determined crystal structures and topologies of known MOFs. This advance provides the first tool to change MOF design from an empirically based process that is based on chemistʼs intuition rooted in literature- or database-established knowledge of node-and-linker connectivity to a more general and theory-driven materials development. This ab initio MOF structure prediction approach, which is here validated on a range of known MOF classes, provides a unique opportunity to explore the phase landscape of MOFs computationally and enables MOF research and development even in case of limited access to laboratory resources, as for example in case of a global pandemic.

The current work has deduced the novel method for tertiary structure prediction of various important unpredicted proteins through machine learning technique neural network. Multi-layer perceptron architecture has been developed to predict the tertiary structure (Phi/Psi) of proteins. A novel binary codification system has been devised for input and output processing. Twenty physiochemical properties representation scheme for each amino acid and binary output discretization of real valued torsion angle for each angle of residues has been adopted. The proposed system has been tested with different number of neural networks, training set sizes and training epochs. The overall successful prediction of residues for tertiary structure prediction (Phi/Psi) of protein has been reported according to window size as 9(52.4% / 56.2%), 13(56.5% / 61.3%), 17(53.7% / 57.2%), 21(53.2% / 57.4%). This study demonstrated the prospect of implementing fast and efficient structure prediction of peptide sequences using neural network.

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality.

BackgroundVarious forms of the so-called loop closure problem are crucial to protein structure prediction methods. Given an N- and a C-terminal end, the problem consists of finding a suitable segment of a certain length that bridges the ends seamlessly.In homology modelling, the problem arises in predicting loop regions. In de novo protein structure prediction, the problem is encountered when implementing local moves for Markov Chain Monte Carlo simulations.Most loop closure algorithms keep the bond angles fixed or semi-fixed, and only vary the dihedral angles. This is appropriate for a full-atom protein backbone, since the bond angles can be considered as fixed, while the (φ, ψ) dihedral angles are variable. However, many de novo structure prediction methods use protein models that only consist of Cα atoms, or otherwise do not make use of all backbone atoms. These methods require a method that alters both bond and dihedral angles, since the pseudo bond angle between three consecutive Cα atoms also varies considerably.ResultsHere we present a method that solves the loop closure problem for Cα only protein models. We developed a variant of Cyclic Coordinate Descent (CCD), an inverse kinematics method from the field of robotics, which was recently applied to the loop closure problem. Since the method alters both bond and dihedral angles, which is equivalent to applying a full rotation matrix, we call our method Full CCD (FCDD). FCCD replaces CCD's vector-based optimization of a rotation around an axis with a singular value decomposition-based optimization of a general rotation matrix. The method is easy to implement and numerically stable.ConclusionWe tested the method's performance on sets of random protein Cα segments between 5 and 30 amino acids long, and a number of loops of length 4, 8 and 12. FCCD is fast, has a high success rate and readily generates conformations close to those of real loops. The presence of constraints on the angles only has a small effect on the performance. A reference implementation of FCCD in Python is available as supplementary information.