Capturing native/native like structures with a physico-chemical metric (pcSM) in protein folding

Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling

Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The threshold value was long assumed to be around 20-30%. Below this level, obtained sequence identity was getting dangerously close to values that can be obtained by chance, after aligning any random, unrelated sequences. In these cases, other approaches, including ab initio folding simulations or fragment assembly, were usually employed. The most recent editions of the CASP and CAMEO community-wide modeling methods assessment have brought some surprising outcomes, proving that much more clues can be inferred from protein sequence analyses than previously thought. In this chapter, we focus on recent advances in the field of difficult protein modeling, pushing the threshold deep into the "twilight zone", with particular attention devoted to improvements in applications of machine learning and model evaluation.

Comparing protein structures and inferring functions with a novel three-dimensional Yau-Hausdorff method

Structures and functions of proteins play various essential roles in biological processes. The functions of newly discovered proteins can be predicted by comparing their structures with that of known-functional proteins. Many approaches have been proposed for measuring the protein structure similarity, such as the template-modeling (TM)-score method, GRaphlet (GR)-Align method as well as the commonly used root-mean-square deviation (RMSD) measures. However, the alignment comparisons between the similarity of protein structure cost much time on large dataset, and the accuracy still have room to improve. In this study, we introduce a new three-dimensional (3D) Yau-Hausdorff distance between any two 3D objects. The (3D) Yau-Hausdorff distance can be used in particular to measure the similarity/dissimilarity of two proteins of any size and does not need aligning and superimposing two structures. We apply structural similarity to study function similarity and perform phylogenetic analysis on several datasets. The results show that (3D) Yau-Hausdorff distance could serve as a more precise and effective method to discover biological relationships between proteins than other methods on structure comparison. Communicated by Ramaswamy H. Sarma.

Role of solvent accessibility for aggregation-prone patches in protein folding

The arrangement of amino acids in a protein sequence encodes its native folding. However, the same arrangement in aggregation-prone regions may cause misfolding as a result of local environmental stress. Under normal physiological conditions, such regions congregate in the protein’s interior to avoid aggregation and attain the native fold. We have used solvent accessibility of aggregation patches (SAAP_p) to determine the packing of aggregation-prone residues. Our results showed that SAAP_p has low values for native crystal structures, consistent with protein folding as a mechanism to minimize the solvent accessibility of aggregation-prone residues. SAAP_p also shows an average correlation of 0.76 with the global distance test (GDT) score on CASP12 template-based protein models. Using SAAP_p scores and five structural features, a random forest machine learning quality assessment tool, SAAP-QA, showed 2.32 average GDT loss between best model predicted and actual best based on GDT score on independent CASP test data, with the ability to discriminate native-like folds having an AUC of 0.94. Overall, the Pearson correlation coefficient (PCC) between true and predicted GDT scores on independent CASP data was 0.86 while on the external CAMEO dataset, comprising high quality protein structures, PCC and average GDT loss were 0.71 and 4.46 respectively. SAAP-QA can be used to detect the quality of models and iteratively improve them to native or near-native structures.

Dihedral angle preferences of DNA and RNA binding amino acid residues in proteins

A protein can interact with DNA or RNA molecules to perform various cellular processes. Identifying or analyzing DNA/RNA binding site amino acid residues is important to understand molecular recognition process. It is quite possible to accurately model DNA/RNA binding amino acid residues in experimental protein-DNA/RNA complex by using the electron density map whereas, locating/modeling the binding site amino acid residues in the predicted three dimensional structures of DNA/RNA binding proteins is still a difficult task. Considering the above facts, in the present work, we have carried out a comprehensive analysis of dihedral angle preferences of DNA and RNA binding site amino acid residues by using a classical Ramachandran map. We have computed backbone dihedral angles of non-DNA/RNA binding residues and used as control dataset to make a comparative study. The dihedral angle preference of DNA and RNA binding site residues of twenty amino acid type is presented. Our analysis clearly revealed that the dihedral angles (φ, ψ) of DNA/RNA binding amino acid residues prefer to occupy (-89° to -60°, -59° to -30°) bins. The results presented in this paper will help to model/locate DNA/RNA binding amino acid residues with better accuracy.

DeepQA: improving the estimation of single protein model quality with deep belief networks

BACKGROUND

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem.

RESULTS

We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods.

CONCLUSION

DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Protein single-model quality assessment by feature-based probability density functions

Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method–Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

Large-scale model quality assessment for improving protein tertiary structure prediction

Motivation: Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well.

Results: Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM’s outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling.

Availability and implementation: The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/.

Contact: chengji@missouri.edu

ProTSAV: A protein tertiary structure analysis and validation server

Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. The server is validated on ~64,446 protein structures including experimental structures from RCSB and predicted model structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures and achieves a specificity of 88%and a sensitivity of 91% on predicted protein structures of CASP11 targets under 2Å.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp.

Bhageerath-H: a homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins

BACKGROUND

The advent of human genome sequencing project has led to a spurt in the number of protein sequences in the databanks. Success of structure based drug discovery severely hinges on the availability of structures. Despite significant progresses in the area of experimental protein structure determination, the sequence-structure gap is continually widening. Data driven homology based computational methods have proved successful in predicting tertiary structures for sequences sharing medium to high sequence similarities. With dwindling similarities of query sequences, advanced homology/ ab initio hybrid approaches are being explored to solve structure prediction problem. Here we describe Bhageerath-H, a homology/ ab initio hybrid software/server for predicting protein tertiary structures with advancing drug design attempts as one of the goals.

RESULTS

Bhageerath-H web-server was validated on 75 CASP10 targets which showed TM-scores ≥ 0.5 in 91% of the cases and Cα RMSDs ≤ 5 Å from the native in 58% of the targets, which is well above the CASP10 water mark. Comparison with some leading servers demonstrated the uniqueness of the hybrid methodology in effectively sampling conformational space, scoring best decoys and refining low resolution models to high and medium resolution.

CONCLUSION

Bhageerath-H methodology is web enabled for the scientific community as a freely accessible web server. The methodology is fielded in the on-going CASP11 experiment.

Computational and functional characterization of Angiogenin mutations, and correlation with amyotrophic lateral sclerosis

The Angiogenin (ANG) gene is frequently mutated in patients suffering from the neurodegenerative disease--amyotrophic lateral sclerosis (ALS). Most of the ALS-causing mutations in Angiogenin affect either its ribonucleolytic or nuclear translocation activity. Here we report the functional characterization of two previously uncharacterized missense mutations in Angiogenin--D22G and L35P. We predict the nature of loss-of-function(s) in these mutants through our previously established Molecular Dynamics (MD) simulation extended to 100 ns, and show that the predictions are entirely validated through biochemical studies with wild-type and mutated proteins. Based on our studies, we provide a biological explanation for the loss-of-function of D22G-Angiogenin leading to ALS, and suggest that the L35P-Angiogenin mutation would probably cause ALS symptoms in individuals harboring this mutation. Our study thus highlights the strength of MD simulation-based predictions, and suggests that this method can be used for correlating mutations in Angiogenin or other effector proteins with ALS symptoms.

Bio/chemoinformatics in India: an outlook

With the advent of significant establishment and development of Internet facilities and computational infrastructure, an overview on bio/chemoinformatics is presented along with its multidisciplinary facts, promises and challenges. The Government of India has paved the way for more profound research in biological field with the use of computational facilities and schemes/projects to collaborate with scientists from different disciplines. Simultaneously, the growth of available biomedical data has provided fresh insight into the nature of redundant and compensatory data. Today, bioinformatics research in India is characterized by a powerful grid computing systems, great variety of biological questions addressed and the close collaborations between scientists and clinicians, with a full spectrum of focuses ranging from database building and methods development to biological discoveries. In fact, this outlook provides a resourceful platform highlighting the funding agencies, institutes and industries working in this direction, which would certainly be of great help to students seeking their career in bioinformatics. Thus, in short, this review highlights the current bio/chemoinformatics trend, educations, status, diverse applicability and demands for further development.

The VP4 peptide of hepatitis A virus ruptures membranes through formation of discrete pores

Membrane-active peptides, components of capsid structural proteins, assist viruses in overcoming the host membrane barrier in the initial stages of infection. Several such peptides have been identified, and their roles in membrane fusion or disruption have been characterized through biophysical studies. In several members of the Picornaviridae family, the role of the VP4 structural peptide in cellular-membrane penetration is well established. However, there is not much information on the membrane-penetrating capsid components of hepatitis A virus (HAV), an unusual member of this family. The VP4 peptide of HAV differs from its analogues in other picornaviruses in being significantly shorter in length and in lacking a signal for myristoylation, thought to be a critical requisite for VP4-mediated membrane penetration. Here we report, for the first time, that the atypical VP4 in HAV contains significant membrane-penetrating activity. Using a combination of biophysical assays and molecular dynamics simulation studies, we show that VP4 integrates into membrane vesicles through its N-terminal region to finally form discrete pores of 5- to 9-nm diameter, which induces leakage in the vesicles without altering their overall size or shape. We further demonstrate that the membrane activity of VP4 is specific toward vesicles mimicking the lipid content of late endosomes at acidic pH. Taken together, our data indicate that VP4 might be essential for the penetration of host endosomal membranes and release of the viral genome during HAV entry.

IMPORTANCE

Hepatitis A virus causes acute hepatitis in humans through the fecal-oral route and is particularly prevalent in underdeveloped regions with poor hygienic conditions. Although a vaccine for HAV exists, its high cost makes it unsuitable for universal application in developing countries. Studies on host-virus interaction for HAV have been hampered due to a lack of starting material, since the virus is extremely slow growing in culture. Among the unknown aspects of the HAV life cycle is its manner of host membrane penetration, which is one of the most important initial steps in viral infection. Here, we present data to suggest that a small peptide, VP4, a component of the HAV structural polyprotein, might be essential in helping the viral genome cross cell membranes during entry. It is hoped that this work might help in elucidating the manner of initial host cell interaction by HAV.

Genomes to hits in silico - a country path today, a highway tomorrow: a case study of chikungunya

These are exciting times for bioinformaticians, computational biologists and drug designers with the genome and proteome sequences and related structural databases growing at an accelerated pace. The post-genomic era has triggered high expectations for a rapid and successful treatment of diseases. However, in this biological information rich and functional knowledge poor scenario, the challenges are indeed grand, no less than the assembly of the genome of the whole organism. These include functional annotation of genes, identification of druggable targets, prediction of three-dimensional structures of protein targets from their amino acid sequences, arriving at lead compounds for these targets followed by a transition from bench to bedside. We propose here a "Genome to Hits In Silico" strategy (called Dhanvantari) and illustrate it on Chikungunya virus (CHIKV). "Genome to hits" is a novel pathway incorporating a series of steps such as gene prediction, protein tertiary structure determination, active site identification, hit molecule generation, docking and scoring of hits to arrive at lead compounds. The current state of the art for each of the steps in the pathway is high-lighted and the feasibility of creating an automated genome to hits assembly line is discussed.