Fold Recognition Research Articles

Decoding of novel missense TSC2 gene variants using in-silico methods

BackgroundMutations in TSC1 or TSC2 gene cause tuberous sclerosis complex (TSC), an autosomal dominant disorder characterized by the formation of non-malignant hamartomas in multiple vital organs. TSC1 and TSC2 gene products form TSC heterodimer that senses specific cell growth conditions to control mTORC1 signalling.MethodsIn the present study 98 TSC patients were tested for variants in TSC1 and TSC2 genes and 14 novel missense variations were identified. The pathogenecity of these novel variations was determined by applying different bioinformatics tools involving computer aided protein modeling.ResultsProtein modelling could be done only for ten variants which were within the functional part of the protein. Homology modeling is the most reliable method for structure prediction of a protein. Since no sequence homology structure was available for the tuberin protein, three dimensional structure was modeled by a combination of homology modeling and the predictive fold recognition and threading method using Phyre2 threading server. The best template structures for model building of the TSC1 interacting domain, tuberin domain and GAP domain are the crystal structures of clathrin adaptor core protein, Rap1GAP catalytic domain and Ser/Thr kinase Tor protein respectively.ConclusionsIn this study, an attempt has been made to assess the impact of each novel missense variant based on their TSC1-TSC2 hydrophobic interactions and its effect on protein function.

Protein Sequences Features Extraction for Predicting Beta- Turns and their Types: A Review

Beta-turns are considered to be important secondary structure types that have essential role in molecular recognition, protein folding and stability. They represent 25% of protein structures, therefore they are the most common type of non-repetitive or tight turns structures. Their prediction is considered to be an important issue in bioinformatics and molecular biology, because it provides valuable information and inputs for the fold recognition and drug design. There are many machine learning and statistical based approaches that were designed to predict beta-turns. Among the successful approaches that are based on machine learning are the approaches that used Neural Networks (NNs) and Support Vector Machines (SVMs) methods. These approaches used different features and features organizations. Among the most usable features in beta-turns prediction are the Position Specific Scoring Matrices (PSSMs) and the predicted secondary structure. This work gives a review of the most successful methods that are used for beta-turns prediction and the features as well as the organizations of these features that they used.

Crystal Structure of Poly(Aspartic Acid) Hydrolase‐1

Poly(aspartic acid) (PAA) is a biodegradable synthetic polymer that is easily produced through heating aspartic acid followed by the subsequent addition of sodium hydroxide. Polymer applications range from drug delivery and biomimetics to hygiene products. Partial biodegradation of PAA is accomplished using poly(aspartic acid) hydrolase‐1 (PAAH‐1) which selectively cleaves a specific cross‐link. E. coli expressions of recombinant PAAH‐1 showed the production of significant amounts of protein which could be purified using a single Ni‐NTA column. Analysis of the PAAH‐1 sequence using the Phyre2 Protein Fold Recognition Server resulted in a 33% similarity to a “putative” poly(3‐hydroxybutyrate) depolymerase belonging to a family of enzymes that hydrolyze carboxylic ester bonds; however, no known structure of PAAH‐1 has been reported. Protein crystallization screening at the Hauptman‐Woodward Research Institute resulted in numerous protein crystallization hits being identified. Crystallization experiments were optimized in‐house and the structure was determined at 2.45Å using molecular replacement. The progress of PAAH‐1 characterization and its structure will be discussed.Support or Funding InformationNSF‐IUSE Grant: DUE 1611988This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Protein tertiary structure prediction using hidden Markov model based on lattice.

The prediction of protein structure from its amino acid sequence is one of the most prominent problems in computational biology. The biological function of a protein depends on its tertiary structure which is determined by its amino acid sequence via the process of protein folding. We propose a novel fold recognition method for protein tertiary structure prediction based on a hidden Markov model and 3D coordinates of amino acid residues. The method introduces states based on the basis vectors in Bravais cubic lattices to learn the path of amino acids of the proteins of each fold. Three hidden Markov models are considered based on simple cubic, body-centered cubic (BCC) and face-centered cubic (FCC) lattices. A 10-fold cross validation was performed on a set of 42 fold SCOP dataset. The proposed composite methodology is compared to fold recognition methods which have HMM as base of their algorithms having approaches on only amino acid sequence or secondary structure. The accuracy of proposed model based on face-centered cubic lattices is quite better in comparison with SAM, 3-HMM optimized and Markov chain optimized in overall experiment. The huge data of 3D space help the model to have greater performance in comparison to methods which use only primary structures or only secondary structures.

Computer-aided Drug Designing for the Identification of High-Affinity Small Molecule Targeting CD20 for the Clinical Treatment of Chronic Lymphocytic Leukemia (CLL).

Chronic lymphocytic leukemia (CLL) is a B-lineage lymphoid malignancy of self-reactive cells that are focused to produce polyreactive natural autoantibodies. Its surface protein marker CD20 plays an important role in the humoral immune response targeting which has emerged as an attractive therapeutic option for the treatment of CLL. The present study explains the interaction of the CD20 with its established inhibitors and to discover the compound having high binding affinity against the target protein receptor. Technically, during the development of new compound through docking studies, best drug among all pre-exist drugs got filtered, hence in reference to docked best drug study moved ahead. The 3D structure of CD20 was built using homology base fold recognition method using Smith waterman's Local alignment and standalone Delta Blast algorithms. 23 established inhibitors towards CD20 were selected in this present investigation. Among these inhibitors, etoposide (RMSD value -96.6481) showed high binding capacity with the receptor CD20 which was further subjected to virtual screening. The said screening presented 380 possible drugs having structural similarity to etoposide. The docking studies of the screened drugs separated the compound having PubChem CID: 11753896 (RMSD value -98.5416). Toxicity and interaction profile validated this compound for having a better affinity with the target protein. Conclusively, this research study says that according to ADMET profile and BOILED-Egg plot, the compound (PubChem CID: 11753896) obtained from Virtual Screen could be the best drug in future during the prevention of Chronic Lymphocytic Leukemia. The compound identified in the present investigation can be subjected further for in vitro and in vivo studies for ADMET properties and it could optimize a good profile in the field of pharmacy and bioavailable for suppressing cancer. The pharmacophore study revealed that the drug CID11753896 is a non-inhibitor of CYP450 microsomal enzymes and was found to be non-toxic, similar to the established compound CID36462. It has a lower LD50 value of 2.5423mol/kg as compared to the established compound whose LD50 value is 2.9588mol/kg. Also, the compound was found to be non-carcinogenic.

Protein fold recognition based on multi-view modeling.

Protein fold recognition has attracted increasing attention because it is critical for studies of the 3D structures of proteins and drug design. Researchers have been extensively studying this important task, and several features with high discriminative power have been proposed. However, the development of methods that efficiently combine these features to improve the predictive performance remains a challenging problem. In this study, we proposed two algorithms: MV-fold and MT-fold. MV-fold is a new computational predictor based on the multi-view learning model for fold recognition. Different features of proteins were treated as different views of proteins, including the evolutionary information, secondary structure information and physicochemical properties. These different views constituted the latent space. The ε-dragging technique was employed to enlarge the margins between different protein folds, improving the predictive performance of MV-fold. Then, MV-fold was combined with two template-based methods: HHblits and HMMER. The ensemble method is called MT-fold incorporating the advantages of both discriminative methods and template-based methods. Experimental results on five widely used benchmark datasets (DD, RDD, EDD, TG and LE) showed that the proposed methods outperformed some state-of-the-art methods in this field, indicating that MV-fold and MT-fold are useful computational tools for protein fold recognition and protein homology detection and would be efficient tools for protein sequence analysis. Finally, we constructed an update and rigorous benchmark dataset based on SCOPe (version 2.07) to fairly evaluate the performance of the proposed method, and our method achieved stable performance on this new dataset. This new benchmark dataset will become a widely used benchmark dataset to fairly evaluate the performance of different methods for fold recognition. Supplementary data are available at Bioinformatics online.

A Composite Approach to Protein Tertiary Structure Prediction: Hidden Markov Model Based on Lattice.

The biological function of protein depends mainly on its tertiary structure which is determined by its amino acid sequence via the process of protein folding. Prediction of protein structure from its amino acid sequence is one of the most prominent problems in computational biology. Two basic methodologies on protein structure prediction are combined: ab initio method (3-D space lattice) and fold recognition method (hidden Markov model). The primary structure of proteins and 3-D coordinates of amino acid residues are put together in one hidden Markov model to learn the path of amino acid residues in 3-D space from the first atom to the last atom of each protein of each fold. Therefore, each model has the information of 3-D path of amino acids of each fold. The proposed method is compared to fold recognition methods which have hidden Markov model as a base of their algorithms having approaches on only amino acid sequence or secondary structure. To validate the proposed method, the models are assessed with three datasets. Results show that the proposed models outperform 7-HMM and 3-HMM in the same dataset. The face-centered cubic lattice which is the most compacted 3-D lattice reached the maximum classification accuracy in all experiments in comparison with the performance of the most effective version of optimized 3-HMM as well as the performance of the latest version of SAM 3.5. Results show that 3-D coordinates of atoms of amino acids in proteins have an important role in prediction. It also has great hidden information as compared to secondary structure of proteins in fold classification.

G-quadruplexes formation in the 5'UTRs of mRNAs associated with colorectal cancer pathways.

RNA G-quadruplexes (rG4) are stable non-canonical secondary structures composed of G-rich sequences. Many rG4 structures located in the 5’UTRs of mRNAs act as translation repressors due to their high stability which is thought to impede ribosomal scanning. That said, it is not known if these are mRNA-specific examples, or if they are indicative of a global expression regulation mechanism of the mRNAs involved in a common pathway based on structure folding recognition. Gene-ontology analysis of mRNAs bearing a predicted rG4 motif in their 5’UTRs revealed an enrichment for mRNAs associated with the colorectal cancer pathway. Bioinformatic tools for rG4 prediction, and experimental in vitro validations were used to confirm and compare the folding of the predicted rG4s of the mRNAs associated with dysregulated pathways in colorectal cancer. The rG4 folding was confirmed for the first time for 9 mRNAs. A repressive effect of 3 rG4 candidates on the expression of a reporter gene was also measured in colorectal cancer cell lines. This work highlights the fact that rG4 prediction is not yet accurate, and that experimental characterization is still essential in order to identify the precise rG4 folding sequences and the possible common features shared between the rG4 overrepresented in important biological pathways.

U32 collagenase from Pseudoalteromonas agarivorans NW4327: Activity, structure, substrate interactions and molecular dynamics simulations

A protease of the primary pathogen (Pseudoalteromonas agarivorans NW4327) of the disease affecting the Great Barrier Reef sponge Rhopaloeides odorabile was purified. Zymography demonstrated calcium-dependent collagenase and gelatinase activity of the purified protein. This metalloprotease was identified by matrix assisted laser desorption ionization time-of-flight mass spectrophotometry as a 52,509 Da U32 collagenase. Predicted tertiary structure of U32 collagenase (by Phyre2 fold recognition server) demonstrated 13% identity with known hydrolases establishing novelty of the enzyme. Molecular docking conceived two interacting loops of the collagenase that bound with collagen triple helices and two calcium ions remained centered between the loops. According to ConSurf multiple sequence alignment, the residues of loop1 of the collagenase were mostly conserved while variations among residues of loop2 were comparatively higher than loop1. Asp262, Glu263 of loop1 and Thr363, Lys364, Gln365 of loop2 participated in the interaction with Ca2+ and collagen. Root mean square deviation and root mean square fluctuation values signified higher stability of the collagen-Ca2+-collagenase complex and greater structural stability of the residues of the loops in the complex compared to apocollagenase. Observed properties of NW4327 U32 collagenase and its interaction with collagen were different from similar enzymes of thermophilic bacteria and terrestrial pathogens.

Human Otopathologic Findings in Cases of Folded Cochlear Implant Electrodes.

We hypothesize that human cases of cochlear implantation (CI) with folding of the electrode array will demonstrate greater degrees of intracochlear ossification, lower spiral ganglion neuron (SGN) counts, and poorer audiometric outcomes. CI electrode array folding, such folding of the proximal array, is a relatively common surgical complication that can occur with forceful electrode insertion and may be an important and avoidable factor affecting implant outcomes. However, otopathologic findings and audiologic outcomes of human cases where folding of the implant electrode array is observed remain undefined. Specimens from a human temporal bone repository having undergone CI during life were evaluated. Specimens with folding of the electrode array on histological analysis constituted study cases. Electrode-matched specimens without array folding constituted controls. All specimens were examined by light microscopy and histopathologically described. Intracochlear fibrosis and osseous tissue, and SGN counts were measured. Pre- and postoperative word recognition scores were also compared. Cases with folded electrodes showed greater volumes of intracochlear osseous tissue than controls, which was most prominent in areas adjacent to array folding. Both cases and controls demonstrated similar amounts of fibrous tissue. Folded cases showed decreased SGNs when compared with the contralateral ear, whereas controls showed stable SGN populations between ears. In this small cohort, postoperative hearing outcomes were similar between groups. Atypical fibro-osseous changes and lower SGN counts are observed in cases of CI electrode folding. Future studies are necessary to determine if recognition and correction of folding can prevent long-term intracochlear changes.

Protein engineering by chemical methods: Incorporation of nonnatural amino acids as a tool for studying protein folding, stability, and function

AbstractProteins are large complex biomolecules that act as the effectors of essentially all cell functions. Due to the intrinsic complexity of protein architecture at the microscopic level and the inadequacy of theoretical methods to predict protein reactivity (ie, folding, stability, and function), protein engineering has emerged as a valuable tool to investigate structure–stability–activity relationships in proteins and nowadays recombinant DNA technologies are the “gold standard” for site‐specifically manipulating a given protein chain. The usefulness of current mutagenesis techniques, however, is limited by the relatively poor chemical diversity of the 20 DNA‐coded amino acids, such that it is difficult to precisely assign the observed change of protein stability or function to the variation of a single physicochemical property at a protein site (ie, hydrophobicity, conformational propensity, polarizability, hydrogen bonding, etc). In this article, we report relevant examples from our laboratory showing that chemical methods, that is, enzyme‐catalyzed semisynthesis and stepwise solid‐phase synthesis, allow to conveniently incorporate non‐natural amino acids with “tailored” side chains into small proteins and thus effectively transfer the structure–activity relationship methodology, typical of the medicinal chemistry approach on small molecules, to the study of folding, stability, and molecular recognition in macromolecular protein systems.

Enhanced Artificial Neural Network for Protein Fold Recognition and Structural Class Prediction

In Bioinformatics Protein Fold Recognition (PFR) and Structural Class Prediction (SCP) is a significant problem in predicting protein with a three dimensional structure. Extraction of valuable features of protein that consists of 20 amino acids to acquire more desirable classifiers is fundamental to this PFR and SCP. Feature extraction technique predominantly exploits Forward Consecutive Search Scheme (FCS) that supplements syntactical-based, evolutionary-based and physicochemical-based information. In this research work, a classifier known as Enhanced Artificial Neural Network (ANN) is employed as it is more efficient than Forward Consecutive Search scheme in order to improve the performance of PFR and SCP. The Enhanced ANN algorithm is an improved version of Artificial Neural Network when compared with various existing algorithms such as Support Vector Machine (SVM), ANN, K-Nearest Neighbor (KNN) and the Bayesian. The experiments are conducted on four datasets namely DD, EDD, TG and RDD. Ultimately, the statistical imputation of Enhanced ANN algorithm hypothesizes gives better results than other algorithms to improve the performance of PFR and SCP.

Tight Turns of Outer Membrane Proteins: An Analysis of Sequence, Structure, and Hydrogen Bonding

As a structural class, tight turns can control molecular recognition, enzymatic activity, and nucleation of folding. They have been extensively characterized in soluble proteins but have not been characterized in outer membrane proteins (OMPs), where they also support critical functions. We clustered the 4 to 6 residue tight turns of 110 OMPs to characterize the phi/psi angles, sequence, and hydrogen bonding of these structures. We find significant differences between reports of soluble protein tight turns and OMP tight turns. Since OMP strands are less twisted than soluble strands, they favor different turn structures types. Moreover, the membrane localization of OMPs yields different sequence hallmarks for their tight turns relative to soluble protein turns. We also characterize the differences in phi/psi angles, sequence, and hydrogen bonding between OMP extracellular loops and OMP periplasmic turns. As previously noted, the extracellular loops tend to be much longer than the periplasmic turns. We find that this difference in length is due to the broader distribution of lengths of the extracellular loops not a large difference in the median length. Extracellular loops also tend to have more charged residues as predicted by the charge-out rule. Finally, in all OMP tight turns, hydrogen bonding between the side chain and backbone 2 to 4 residues away from that side chain plays an important role. These bonds preferentially use an Asp, Asn, Ser, or Thr residue in a beta or pro phi/psi conformation. We anticipate that this study will be applicable to future design and structure prediction of OMPs.

Metal cofactor modulated folding and target recognition of HIV-1 NCp7.

The HIV-1 nucleocapsid 7 (NCp7) plays crucial roles in multiple stages of HIV-1 life cycle, and its biological functions rely on the binding of zinc ions. Understanding the molecular mechanism of how the zinc ions modulate the conformational dynamics and functions of the NCp7 is essential for the drug development and HIV-1 treatment. In this work, using a structure-based coarse-grained model, we studied the effects of zinc cofactors on the folding and target RNA(SL3) recognition of the NCp7 by molecular dynamics simulations. After reproducing some key properties of the zinc binding and folding of the NCp7 observed in previous experiments, our simulations revealed several interesting features in the metal ion modulated folding and target recognition. Firstly, we showed that the zinc binding makes the folding transition states of the two zinc fingers less structured, which is in line with the Hammond effect observed typically in mutation, temperature or denaturant induced perturbations to protein structure and stability. Secondly, We showed that there exists mutual interplay between the zinc ion binding and NCp7-target recognition. Binding of zinc ions enhances the affinity between the NCp7 and the target RNA, whereas the formation of the NCp7-RNA complex reshapes the intrinsic energy landscape of the NCp7 and increases the stability and zinc affinity of the two zinc fingers. Thirdly, by characterizing the effects of salt concentrations on the target RNA recognition, we showed that the NCp7 achieves optimal balance between the affinity and binding kinetics near the physiologically relevant salt concentrations. In addition, the effects of zinc binding on the inter-domain conformational flexibility and folding cooperativity of the NCp7 were also discussed.

Self-organization, entropy and allostery.

Allostery is a fundamental regulatory mechanism in biology. Although generally accepted that it is a dynamics-driven process, the exact molecular mechanism of allosteric signal transmission is hotly debated. We argue that allostery is as a part of a bigger picture that also includes fractal-like properties of protein interior, hierarchical protein folding and entropy-driven molecular recognition. Although so far all these phenomena were studied separately, they stem from the same common root: self-organization of polypeptide chains and, thus, has to be studied collectively. This merge will allow the cross-referencing of a broad spectrum of multi-disciplinary data facilitating progress in all these fields.

Hydrophobic residues can identify native protein structures.

Evaluation of protein structures needs a trustworthy potential function. Although several knowledge-based potential functions exist, the impact of different types of amino acids in the scoring functions has not been studied yet. Previously, we have reported the importance of nonlocal interactions in scoring function (based on Delaunay tessellation) in discrimination of native structures. Then, we have questioned the structural impact of hydrophobic amino acids in protein fold recognition. Therefore, a Hydrophobic Reduced Model (HRM) was designed to reduce protein structure of FS (Full Structure) into RS (Reduced Structure). RS is considered as a reduced structure of only seven hydrophobic amino acids (L, V, F, I, A, W, Y) and all their interactions. The presented model was evaluated via four different performance metrics including the number of correctly identified natives, the Z-score of the native energy, the RMSD of the minimum score, and the Pearson correlation coefficient between the energy and the model quality. Results indicated that only nonlocal interactions between hydrophobic amino acids could be sufficient and accurate enough for protein fold recognition. Interestingly, the results of HRM is significantly close to the model that considers all amino acids (20-amino acid model) to discriminate the native structure of the proteins on eleven decoy sets. This indicates that the power of knowledge-based potential functions in protein fold recognition is mostly due to hydrophobic interactions. Hence, we suggest combining a different well-designed scoring function for non-hydrophobic interactions with HRM to achieve better performance in fold recognition.

Protein Fold Recognition by Circular Dichroism Spectroscopy

Protein Fold Recognition by Circular Dichroism Spectroscopy

Protein fold recognition using Deep Kernelized Extreme Learning Machine and linear discriminant analysis

Protein fold recognition is considered as an essential step in determining the tertiary structure of proteins in bioinformatics. The most complex challenge in the protein folding problem is the high dimensionality of feature vectors and the diversity of the protein fold classes. In this paper, two frameworks are proposed to solve this problem. The main components in the first two-level framework are Deep Kernelized Extreme Learning Machine (DKELM) and linear discriminant analysis. Second framework consists of three levels. In the first level, the dataset is initialized to be used in the next level. In the second level, OVADKELM and OVODKELM are independently employed to extract four and six new features, respectively, which are added into the basic datasets in the third level. DKELM is applied in the third level as a final classifier to classify the instances into folds. The proposed frameworks are implemented on DD and TG datasets which are considered as SCOP datasets. The experimental results indicate that proposed methods could improve the classification accuracy in both datasets.

Toward a unified scoring function for native state discrimination and drug-binding pocket recognition.

Protein folding and receptor-ligand recognition are fundamental processes for any living organism. Although folding and ligand recognition are based on the same chemistry, the existing empirical scoring functions target just one problem: predicting the correct fold or the correct binding pose. We here introduce a statistical potential which considers moieties as fundamental units. The scoring function is able to deal with both folding and ligand pocket recognition problems with a performance comparable to the scoring functions specifically tailored for one of the two tasks. We foresee that the capability of the new scoring function to tackle both problems in a unified framework will be a key to deal with the induced fit phenomena, in which a target protein changes significantly its conformation upon binding. Moreover, the new scoring function might be useful in docking protocols towards intrinsically disordered proteins, whose flexibility cannot be handled with the available docking software.

DeepSF: deep convolutional neural network for mapping protein sequences to folds.

MotivationProtein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice.ResultsWe develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein sequence into one of 1195 known folds, which is useful for both fold recognition and the study of sequence–structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and maps it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding an average classification accuracy of 75.3%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 73.0%. We compare our method with a top profile–profile alignment method—HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 12.63–26.32% higher than HHSearch on template-free modeling targets and 3.39–17.09% higher on hard template-based modeling targets for top 1, 5 and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking.Availability and implementationThe DeepSF server is publicly available at: http://iris.rnet.missouri.edu/DeepSF/.Supplementary information Supplementary data are available at Bioinformatics online.