dSCOPE: a software to detect sequences critical for liquid-liquid phase separation

Membrane-based cells are the fundamental structural and functional units of organisms, while evidences demonstrate that liquid-liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation (PS), but these studies lacked an effective tool to identify the sequence segments that critical for LLPS. In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the PS-driving regions. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted PS-driving regions enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted PS-driving regions enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS-related research.

COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization

Protein contact prediction helps reconstruct the tertiary structure that greatly determines a protein’s function; therefore, contact prediction from the sequence is an important problem. Recently there has been exciting progress on this problem, but many of the existing methods are still low quality of prediction accuracy. In this paper, we present a new mixed integer linear programming (MILP)-based consensus method: a Consensus scheme based On a Mixed integer linear opTimization method for prOtein contact Prediction (COMTOP). The MILP-based consensus method combines the strengths of seven selected protein contact prediction methods, including CCMpred, EVfold, DeepCov, NNcon, PconsC4, plmDCA, and PSICOV, by optimizing the number of correctly predicted contacts and achieving a better prediction accuracy. The proposed hybrid protein residue–residue contact prediction scheme was tested in four independent test sets. For 239 highly non-redundant proteins, the method showed a prediction accuracy of 59.68%, 70.79%, 78.86%, 89.04%, 94.51%, and 97.35% for top-5L, top-3L, top-2L, top-L, top-L/2, and top-L/5 contacts, respectively. When tested on the CASP13 and CASP14 test sets, the proposed method obtained accuracies of 75.91% and 77.49% for top-L/5 predictions, respectively. COMTOP was further tested on 57 non-redundant α-helical transmembrane proteins and achieved prediction accuracies of 64.34% and 73.91% for top-L/2 and top-L/5 predictions, respectively. For all test datasets, the improvement of COMTOP in accuracy over the seven individual methods increased with the increasing number of predicted contacts. For example, COMTOP performed much better for large number of contact predictions (such as top-5L and top-3L) than for small number of contact predictions such as top-L/2 and top-L/5. The results and analysis demonstrate that COMTOP can significantly improve the performance of the individual methods; therefore, COMTOP is more robust against different types of test sets. COMTOP also showed better/comparable predictions when compared with the state-of-the-art predictors.

A protein structural study based on the centrality analysis of protein sequence feature networks

In this paper, we use network approaches to analyze the relations between protein sequence features for the top hierarchical classes of CATH and SCOP. We use fundamental connectivity measures such as correlation (CR), normalized mutual information rate (nMIR), and transfer entropy (TE) to analyze the pairwise-relationships between the protein sequence features, and use centrality measures to analyze weighted networks constructed from the relationship matrices. In the centrality analysis, we find both commonalities and differences between the different protein 3D structural classes. Results show that all top hierarchical classes of CATH and SCOP present strong non-deterministic interactions for the composition and arrangement features of Cystine (C), Methionine (M), Tryptophan (W), and also for the arrangement features of Histidine (H). The different protein 3D structural classes present different preferences in terms of their centrality distributions and significant features.

Reconstitution and optimisation of the biosynthesis of bacterial sugar pseudaminic acid (Pse5Ac7Ac) enables preparative enzymatic synthesis of CMP-Pse5Ac7Ac

Pseudaminic acids present on the surface of pathogenic bacteria, including gut pathogens Campylobacter jejuni and Helicobacter pylori, are postulated to play influential roles in the etiology of associated infectious diseases through modulating flagella assembly and recognition of bacteria by the human immune system. Yet they are underexplored compared to other areas of glycoscience, in particular enzymes responsible for the glycosyltransfer of these sugars in bacteria are still to be unambiguously characterised. This can be largely attributed to a lack of access to nucleotide-activated pseudaminic acid glycosyl donors, such as CMP-Pse5Ac7Ac. Herein we reconstitute the biosynthesis of Pse5Ac7Ac in vitro using enzymes from C. jejuni (PseBCHGI) in the process optimising coupled turnover with PseBC using deuterium wash in experiments, and establishing a method for co-factor regeneration in PseH tunover. Furthermore we establish conditions for purification of a soluble CMP-Pse5Ac7Ac synthetase enzyme PseF from Aeromonas caviae and utilise it in combination with the C. jejuni enzymes to achieve practical preparative synthesis of CMP-Pse5Ac7Ac in vitro, facilitating future biological studies.

Computational approaches for inferring 3D conformations of chromatin from chromosome conformation capture data

Chromosome conformation capture (3C) and its variants are powerful experimental techniques for probing intra- and inter-chromosomal interactions within cell nuclei at high resolution and in a high-throughput, quantitative manner. The contact maps derived from such experiments provide an avenue for inferring the 3D spatial organization of the genome. This review provides an overview of the various computational methods developed in the past decade for addressing the very important but challenging problem of deducing the detailed 3D structure or structure population of chromosomal domains, chromosomes, and even entire genomes from 3C contact maps.

Investigation of machine learning techniques on proteomics: A comprehensive survey

Proteomics is the extensive investigation of proteins which has empowered the recognizable proof of consistently expanding quantities of protein. Proteins are necessary part of living life form, with numerous capacities. The proteome is the complete arrangement of proteins that are created or altered by a life form or framework of the organism. Proteome fluctuates with time and unambiguous prerequisites, or stresses, that a cell or organism experiences. Proteomics is an interdisciplinary area that has derived from the hereditary data of different genome ventures. Much proteomics information is gathered with the assistance of high throughput techniques, for example, mass spectrometry and microarray. It would regularly take weeks or months to analyze the information and perform examinations by hand. Therefore, scholars and scientific experts are teaming up with computer science researchers and mathematicians to make projects and pipeline to computationally examine the protein information. Utilizing bioinformatics procedures, scientists are prepared to do quicker investigation and protein information storing. The goal of this paper is to brief about the review of machine learning procedures and its application in the field of proteomics.

In silico analysis of the V66M variant of human BDNF in psychiatric disorders: An approach to precision medicine

Brain-derived neurotrophic factor (BDNF) plays an important role in neurogenesis and synapse formation. The V66M is the most prevalent BDNF mutation in humans and impairs the function and distribution of BDNF. This mutation is related to several psychiatric disorders. The pro-region of BDNF, particularly position 66 and its adjacent residues, are determinant for the intracellular sorting and activity-dependent secretion of BDNF. However, it has not yet been fully elucidated. The present study aims to analyze the effects of the V66M mutation on BDNF structure and function. Here, we applied nine algorithms, including SIFT and PolyPhen-2, for functional and stability prediction of the V66M mutation. The complete theoretical model of BNDF was generated by Rosetta and validated by PROCHECK, RAMPAGE, ProSa, QMEAN and Verify-3D algorithms. Structural alignment was performed using TM-align. Phylogenetic analysis was performed using the ConSurf server. Molecular dynamics (MD) simulations were performed and analyzed using the GROMACS 2018.2 package. The V66M mutation was predicted as deleterious by PolyPhen-2 and SIFT in addition to being predicted as destabilizing by I-Mutant. According to SNPeffect, the V66M mutation does not affect protein aggregation, amyloid propensity, and chaperone binding. The complete theoretical structure of BDNF proved to be a reliable model. Phylogenetic analysis indicated that the V66M mutation of BDNF occurs at a non-conserved position of the protein. MD analyses indicated that the V66M mutation does not affect the BDNF flexibility and surface-to-volume ratio, but affects the BDNF essential motions, hydrogen-bonding and secondary structure particularly at its pre and pro-domain, which are crucial for its activity and distribution. Thus, considering that these parameters are determinant for protein interactions and, consequently, protein function; the alterations observed throughout the MD analyses may be related to the functional impairment of BDNF upon V66M mutation, as well as its involvement in psychiatric disorders.

A membrane-inserted structural model of the yeast mitofusin Fzo1

Mitofusins are large transmembrane GTPases of the dynamin-related protein family, and are required for the tethering and fusion of mitochondrial outer membranes. Their full-length structures remain unknown, which is a limiting factor in the study of outer membrane fusion. We investigated the structure and dynamics of the yeast mitofusin Fzo1 through a hybrid computational and experimental approach, combining molecular modelling and all-atom molecular dynamics simulations in a lipid bilayer with site-directed mutagenesis and in vivo functional assays. The predicted architecture of Fzo1 improves upon the current domain annotation, with a precise description of the helical spans linked by flexible hinges, which are likely of functional significance. In vivo site-directed mutagenesis validates salient aspects of this model, notably, the long-distance contacts and residues participating in hinges. GDP is predicted to interact with Fzo1 through the G1 and G4 motifs of the GTPase domain. The model reveals structural determinants critical for protein function, including regions that may be involved in GTPase domain-dependent rearrangements.

Ribonucleotide Reductases from Bifidobacteria Contain Multiple Conserved Indels Distinguishing Them from All Other Organisms: In Silico Analysis of the Possible Role of a 43 aa Bifidobacteria-Specific Insert in the Class III RNR Homolog

Bifidobacteria comprises an important group/order of bacteria whose members have widespread usage in the food and health industry due to their health-promoting activity in the human gastrointestinal tract. However, little is known about the underlying molecular properties that are responsible for the probiotic effects of these bacteria. The enzyme ribonucleotide reductase (RNR) plays a key role in all organisms by reducing nucleoside di- or tri- phosphates into corresponding deoxyribose derivatives required for DNA synthesis, and RNR homologs belonging to classes I and III are present in either most or all Bifidobacteriales. Comparative analyses of these RNR homologs have identified several novel sequence features in the forms of conserved signature indels (CSIs) that are exclusively found in bifidobacterial RNRs. Specifically, in the large subunit of the aerobic class Ib RNR, three CSIs have been identified that are uniquely found in the Bifidobacteriales homologs. Similarly, the large subunit of the anaerobic class III RNR contains five CSIs that are also distinctive characteristics of bifidobacteria. Phylogenetic analyses indicate that these CSIs were introduced in a common ancestor of the Bifidobacteriales and retained by all descendants, likely due to their conferring advantageous functional roles. The identified CSIs in the bifidobacterial RNR homologs provide useful tools for further exploration of the novel functional aspects of these important enzymes that are exclusive to these bacteria. We also report here the results of homology modeling studies, which indicate that most of the bifidobacteria-specific CSIs are located within the surface loops of the RNRs, and of these, a large 43 amino acid insert in the class III RNR homolog forms an extension of the allosteric regulatory site known to be essential for protein function. Preliminary docking studies suggest that this large CSI may be playing a role in enhancing the stability of the RNR dimer complex. The possible significances of the identified CSIs, as well as the distribution of RNR homologs in the Bifidobacteriales, are discussed.

An Insight Into Structure, Function, and Expression Analysis of 3-Hydroxy-3-Methylglutaryl-CoA Reductase of Cymbopogon winterianus

Citronella (Cymbopogon winterianus) is one of the richest sources of high-value isoprenoid aromatic compounds used as flavour, fragrance, and therapeutic elements. These isoprenoid compounds are synthesized by 2 independent pathways: mevalonate pathway and 2-C-methyl-d-erythritol-4-phosphate pathway. Evidence suggests that 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) is a rate-controlling enzyme for the synthesis of variety of isoprenoids. This study reports the isolation, characterization, and tissue-specific expression analysis of HMGR from citronella. The modelled HMGR is a class I type of HMGR enzyme with 3-domain architecture. The active site comprises a cofactor (nicotinamide adenine dinucleotide phosphate) and the substrate-binding motifs. The real-time and quantitative reverse transcription-polymerase chain reaction results revealed equal expression level in both leaf sheath and root tissue. The results from our study shall be a valuable resource for future molecular intervention to alter the metabolic flux towards improvement of key active ingredient in this important medicinal plant.

Structural dynamics of Casein Kinase I (CKI) from malarial parasite Plasmodium falciparum (Isolate 3D7): Insights from theoretical modelling and molecular simulations

The protein kinases (PKs), belonging to serine/threonine kinase (STKs), are important drug targets for a wide spectrum of diseases in human. Among protein kinases, the Casein Kinases (CKs) are vastly expanded in various organisms, where, the malarial parasite Plasmodium falciparum possesses a single member i.e., PfCKI, which can phosphorylate various proteins in parasite extracts in vitro condition. But, the structure-function relationship of PfCKI and dynamics of ATP binding is yet to be understood. Henceforth, an attempt was made to study the dynamics, stability, and ATP binding mechanisms of PfCKI through computational modelling, docking, molecular dynamics (MD) simulations, and MM/PBSA binding free energy estimation. Bi-lobed catalytic domain of PfCKI shares a high degree of secondary structure topology with CKI domains of rice, human, and mouse indicating co-evolution of these kinases. Molecular docking study revealed that ATP binds to the active site where the glycine-rich ATP-binding motif (G16-X-G18-X-X-G21) along with few conserved residues plays a crucial role maintaining stability of the complex. Structural superposition of PfCKI with close structural homologs depicted that the location and length of important loops are different, indicating the dynamic properties of these loops among CKIs, which is consistent with principal component analysis (PCA). PCA displayed that the overall global motion of ATP-bound form is comparatively higher than that of apo form. The present study provides insights into the structural features of PfCKI, which could contribute towards further understanding of related protein structures, dynamics of catalysis and phosphorylation mechanism in these important STKs from malarial parasite in near future.

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/.

The role and significance of potential lipid-binding regions in the mitochondrial protein import motor: an in-depth in silico study

Over the last two decades, an impressive progress has been made in the identification of novel factors in the translocation machineries of the mitochondrial protein import and their possible roles. The role of lipids and possible protein–lipids interactions remains a relatively unexplored territory. Investigating the role of potential lipid-binding regions in the sub-units of the mitochondrial motor might help to shed some more light in our understanding of protein–lipid interactions mechanistically. Bioinformatics results seem to indicate multiple potential lipid-binding regions in each of the sub-units. The subsequent characterization of some of those regions in silico provides insight into the mechanistic functioning of this intriguing and essential part of the protein translocation machinery. Details about the way the regions interact with phospholipids were found by the use of Monte Carlo simulations. For example, Pam18 contains one possible transmembrane region and two tilted surface bound conformations upon interaction with phospholipids. The results demonstrate that the presented bioinformatics approach might be useful in an attempt to expand the knowledge of the possible role of protein–lipid interactions in the mitochondrial protein translocation process.

Assessment of the utility of contact-based restraints in accelerating the prediction of protein structure using molecular dynamics simulations

Molecular dynamics (MD) simulation is a well-established tool for the computational study of protein structure and dynamics, but its application to the important problem of protein structure prediction remains challenging, in part because extremely long timescales can be required to reach the native structure. Here, we examine the extent to which the use of low-resolution information in the form of residue-residue contacts, which can often be inferred from bioinformatics or experimental studies, can accelerate the determination of protein structure in simulation. We incorporated sets of 62, 31, or 15 contact-based restraints in MD simulations of ubiquitin, a benchmark system known to fold to the native state on the millisecond timescale in unrestrained simulations. One-third of the restrained simulations folded to the native state within a few tens of microseconds-a speedup of over an order of magnitude compared with unrestrained simulations and a demonstration of the potential for limited amounts of structural information to accelerate structure determination. Almost all of the remaining ubiquitin simulations reached near-native conformations within a few tens of microseconds, but remained trapped there, apparently due to the restraints. We discuss potential methodological improvements that would facilitate escape from these near-native traps and allow more simulations to quickly reach the native state. Finally, using a target from the Critical Assessment of protein Structure Prediction (CASP) experiment, we show that distance restraints can improve simulation accuracy: In our simulations, restraints stabilized the native state of the protein, enabling a reasonable structural model to be inferred.

Structural comparison, substrate specificity, and inhibitor binding of AGPase small subunit from monocot and dicot: present insight and future potential

ADP-glucose pyrophosphorylase (AGPase) is the first rate limiting enzyme of starch biosynthesis pathway and has been exploited as the target for greater starch yield in several plants. The structure-function analysis and substrate binding specificity of AGPase have provided enormous potential for understanding the role of specific amino acid or motifs responsible for allosteric regulation and catalytic mechanisms, which facilitate the engineering of AGPases. We report the three-dimensional structure, substrate, and inhibitor binding specificity of AGPase small subunit from different monocot and dicot crop plants. Both monocot and dicot subunits were found to exploit similar interactions with the substrate and inhibitor molecule as in the case of their closest homologue potato tuber AGPase small subunit. Comparative sequence and structural analysis followed by molecular docking and electrostatic surface potential analysis reveal that rearrangements of secondary structure elements, substrate, and inhibitor binding residues are strongly conserved and follow common folding pattern and orientation within monocot and dicot displaying a similar mode of allosteric regulation and catalytic mechanism. The results from this study along with site-directed mutagenesis complemented by molecular dynamics simulation will shed more light on increasing the starch content of crop plants to ensure the food security worldwide.

WeFold: a coopetition for protein structure prediction

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.

Transcriptome-wide identification and characterization of Ornithogalum saundersiae phenylalanine ammonia lyase gene family

Transcriptome-wide identification and characterization ofOrnithogalum saundersiaephenylalanine ammonia lyase gene family.

Binding partners for curcumin in human schwannoma cells: biologic implications

Curcumin (diferuloylmethane) is a potent anti-inflammatory and anti-tumorigenic agent that has shown preclinical activity in diverse cancers. Curcumin up-regulates heat shock protein 70 (hsp70) mRNA in several different cancer cell lines. Hsp70 contributes to an escape from the apoptotic effects of curcumin by several different mechanisms including prevention of the release of apoptosis inducing factor from the mitochondria and inhibition of caspases 3 and 9. Previously we showed that the combination of curcumin plus a heat shock protein inhibitor was synergistic in its down-regulation of the proliferation of a human schwannoma cell line (HEI-193) harboring an NF2 mutation, possibly because curcumin up-regulated hsp70, which also binds merlin, the NF2 gene product. In order to determine if curcumin also interacts directly with hsp70 and to discover other binding partners of curcumin, we synthesized biotinylated curcumin (bio-curcumin) and treated HEI-193 schwannoma cells. Cell lysates were prepared and incubated with avidin-coated beads. Peptides pulled down from this reaction were sequenced and it was determined that biotinylated curcumin bound hsp70, hsp90, 3-phosphoglycerate dehydrogenase, and a β-actin variant. These binding partners may serve to further elucidate the underlying mechanisms of curcumin's actions.

Building a knowledge-based statistical potential by capturing high-order inter-residue interactions and its applications in protein secondary structure assessment

The rapidly increasing number of protein crystal structures available in the Protein Data Bank (PDB) has naturally made statistical analyses feasible in studying complex high-order inter-residue correlations. In this paper, we report a context-based secondary structure potential (CSSP) for assessing the quality of predicted protein secondary structures generated by various prediction servers. CSSP is a sequence-position-specific knowledge-based potential generated based on the potentials of mean force approach, where high-order inter-residue interactions are taken into consideration. The CSSP potential is effective in identifying secondary structure predictions with good quality. In 56% of the targets in the CB513 benchmark, the optimal CSSP potential is able to recognize the native secondary structure or a prediction with Q3 accuracy higher than 90% as best scored in the predicted secondary structures generated by 10 popularly used secondary structure prediction servers. In more than 80% of the CB513 targets, the predicted secondary structures with the lowest CSSP potential values yield higher than 80% Q3 accuracy. Similar performance of CSSP is found on the CASP9 targets as well. Moreover, our computational results also show that the CSSP potential using triplets outperforms the CSSP potential using doublets and is currently better than the CSSP potential using quartets.