Three-dimensional protein structure prediction: Methods and computational strategies

Quantum Mechanical and Molecular Mechanics Modeling of Membrane-Embedded Rhodopsins

Computational chemistry provides versatile methods for studying the properties and functioning of biological systems at different levels of precision and at different time scales. The aim of this article is to review the computational methodologies that are applicable to rhodopsins as archetypes for photoactive membrane proteins that are of great importance both in nature and in modern technologies. For each class of computational techniques, from methods that use quantum mechanics for simulating rhodopsin photophysics to less-accurate coarse-grained methodologies used for long-scale protein dynamics, we consider possible applications and the main directions for improvement.

Trehalose synthesis inhibitor: A molecular in silico drug design

Infectious diseases are serious public health problems, affecting a large portion of the world's population. A molecule that plays a key role in pathogenic organisms is trehalose and recently has been an interest in the metabolism of this molecule for drug development. The trehalose-6-phosphate synthase (TPS1) is an enzyme responsible for the biosynthesis of trehalose-6-phosphate (T6P) in the TPS1/TPS2 pathway, which results in the formation of trehalose. Studies carried out by our group demonstrated the inhibitory capacity of T6P in the TPS1 enzyme from Saccharomyces cerevisiae, preventing the synthesis of trehalose. By in silico techniques, we compiled sequences and experimentally determined structures of TPS1. Sequence alignments and molecular modeling were performed. The generated structures were submitted in validation of algorithms, aligned structurally and analyzed evolutionarily. Molecular docking methodology was applied to analyze the interaction between T6P and TPS1 and ADMET properties of T6P were analyzed. The results demonstrated the models created presented sequence and structural similarities with experimentally determined structures. With the molecular docking, a cavity in the protein surface was identified and the molecule T6P was interacting with the residues TYR-40, ALA-41, MET-42, and PHE-372, indicating the possible uncompetitive inhibition mechanism provided by this ligand, which can be useful in directing the molecular design of inhibitors. In ADMET analyses, T6P had acceptable risk values compared with other compounds from World Drug Index. Therefore, these results may present a promising strategy to explore to develop a broad-spectrum antibiotic of this specific target with selectivity, potency, and reduced side effects, leading to a new way to treat infectious diseases like tuberculosis and candidiasis.

Loop Enhanced Conformational Resampling Method for Protein Structure Prediction

Protein structure prediction has been a long-standing problem for the past decades. In particular, the loop region structure remains an obstacle in forming an accurate protein tertiary structure because of its flexibility. In this study, Rama torsion angle and secondary structure feature-guided differential evolution named RSDE is proposed to predict three-dimensional structure with the exploitation on the loop region structure. In RSDE, the structure of the loop region is improved by the following: loop-based cross operator, which interchanges configuration of a randomly selected loop region between individuals, and loop-based mutate operator, which considers torsion angle feature into conformational sampling. A stochastic ranking selective strategy is designed to select conformations with low energy and near-native structure. Moreover, the conformational resampling method, which uses previously learned knowledge to guide subsequent sampling, is proposed to improve the sampling efficiency. Experiments on a total of 28 test proteins reveals that the proposed RSDE is effective and can obtain native-like models.

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.

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.

Comparison between Echinococcus granulosus sensu stricto (G1) and E. canadensis (G6) mitochondrial genes (cox1 and nad1) and their related protein models using experimental and bioinformatics analysis

BACKGROUND

Cystic echinococcosis (CE) as a zoonotic parasitic disease, remains a health challenge in many parts of the world. There are different species of Echinococcus granulosus sensu lato with different pathogenicity and host preferences.Different procedures have been applied for characterization of Echinococcus taxa in which two mitochondrial genes, cox1 and nad1 have been used more common. They have been able to differentiate E. granulosus sensu stricto and E. canadensis species in different hosts. The affinity of E. granulosus sensu stricto and E. canadensis species for localizing different organs seems to be different. To what such affinity and related pathogenicity could be related, is not known, so far. Bioinformatics analysis may be helpful to interpret such difference by investigating the genes and their related protein models between different species infecting human and animals. The current work was designed to study the differences between E. granulosus s.s. and E. canadensis species mitochondrial genes (cox1 and nad1) and related protein models of CE cysts by experimental and bioinformatics analysis.

MATERIALS AND METHODS

Different human and animal CE cysts were collected and their DNA was extracted and sequenced based on their cox1 and nad1 genes. In order to determine the E. granulosus s.s. and E. canadensis species of the samples, BLAST analysis was performed on sequenced genes. Three sequences were selected for analysis and were deposited in GenBank. Moreover, the sequence number of KT988116.1 which belonged to E. canadensis from our already deposited in GenBank was also selected. Alignment and phylogenetic analysis were performed on the sequences using BioEdit and MEGA7 software. The raw sequences of translated proteins belonged to the mentioned genes were obtained from Protein database in NCBI. The secondary structure was determined by PSIPRED Protein Sequence Analysis Workbench. The tertiary models of COX1 and NAD1 proteins in both genotypes were constructed using Modeler 9.12 software and their physicochemical features were computed using ProtParam tool in ExPASY server.

RESULTS

BLAST analysis on sequenced genes showed that the samples belonged to E. granulosus s.s. and E. canadensis species. These sequences were deposited in GenBank with accession numbers: JN579173.1, KF437811.1, and KY924632.1. The results showed that proteins of COX1 of E. granulosus s.s., COX1of E. canadensis, NAD1of E. granulosus s.s. and NAD1of E. canadensis species, consisted of 135, 122, 120 and 124 amino acids, respectively. The aligned sequences of translated proteins belonged to COX1 and NAD1 enzymes in E. granulosus s.s. and E. canadensis species were different; such that alignment COX1 sequence between E. granulosus s.s. and E. canadensis species showed that amino acids were different in 6 positions. This difference for NAD1 sequences were different in 19 positions. The secondary structure determined by PSIPRED showed differences in coil, strand and helix chains in COX1 and NAD1 proteins in E. granulosus s.s. and E. canadensis species. Comparison between three-dimensional structures (3D) of COX1 protein model in E. granulosus s.s. and E. canadensis species demonstrated an additional helix with two conserved iron binding sites in the COX1 protein of E. granulosus s.s. species.

CONCLUSION

E. granulosus s.s. and E. canadensis species differences are reflected in two important proteins: COX1 and NAD1. These differences are demonstrable in the 3D structure of proteins of both strains. So, the present study is adding to our understanding of the difference in molecular sequences between the E. granulosus s.s. (G1) and E. canadensis (G6) which may be used for interpreting the difference between the pathogenicity and localization affinity in these two important helminthic zoonosis.

Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction

Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have resulted in considerable efforts in the development of computational methods to accurately model protein structures from sequences. Sequence-based prediction of super-secondary structure has direct application in protein structure prediction, and there have been significant efforts in the prediction of super-secondary structure in the last decade. In this chapter, we first introduce the protein structure prediction problem and highlight some of the important progress in the field of protein structure prediction. Next, we discuss recent methods for the prediction of super-secondary structures. Finally, we discuss applications of super-secondary structure prediction in structure prediction/analysis of proteins. We also discuss prediction of protein structures that are composed of simple super-secondary structure repeats and protein structures that are composed of complex super-secondary structure repeats. Finally, we also discuss the recent trends in the field.

Mycobacterial genomics and structural bioinformatics: opportunities and challenges in drug discovery

Of the more than 190 distinct species of Mycobacterium genus, many are economically and clinically important pathogens of humans or animals. Among those mycobacteria that infect humans, three species namely Mycobacterium tuberculosis (causative agent of tuberculosis), Mycobacterium leprae (causative agent of leprosy) and Mycobacterium abscessus (causative agent of chronic pulmonary infections) pose concern to global public health. Although antibiotics have been successfully developed to combat each of these, the emergence of drug-resistant strains is an increasing challenge for treatment and drug discovery. Here we describe the impact of the rapid expansion of genome sequencing and genome/pathway annotations that have greatly improved the progress of structure-guided drug discovery. We focus on the applications of comparative genomics, metabolomics, evolutionary bioinformatics and structural proteomics to identify potential drug targets. The opportunities and challenges for the design of drugs for M. tuberculosis, M. leprae and M. abscessus to combat resistance are discussed.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

BACKGROUND

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts.

OBJECTIVE

A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools.

METHODS

Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data.

RESULTS

Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields.

CONCLUSION

This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Perspectives and applications of machine learning for evolutionary developmental biology

Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand how development was modulated by the evolutionary process. In this sense, "omic" studies emerged as a powerful ally to unravel the molecular mechanisms underlying development. In this scenario, bioinformatics tools become necessary to analyze the growing amount of information. Among computational approaches, machine learning stands out as a promising field to generate knowledge and trace new research perspectives for bioinformatics. In this review, we aim to expose the current advances of machine learning applied to evolution and development. We draw clear perspectives and argue how evolution impacted machine learning techniques.

Comparison of Peptide Ion Conformers Arising from Non-Helical and Helical Peptides Using Ion Mobility Spectrometry and Gas-Phase Hydrogen/Deuterium Exchange

The dominant gas-phase conformer of [M+3H]³⁺ ions of the model peptide acetyl-PSSSSKSSSSKSSSSKSSSSK has been examined with ion mobility spectrometry (IMS), gas-phase hydrogen deuterium exchange (HDX), and mass spectrometry (MS) techniques. The [M+3H]³⁺ peptide ions are observed predominantly as a relatively compact conformer type. Upon subjecting these ions to electron transfer dissociation (ETD), the level of protection for each amino acid residue in the peptide sequence is assessed. The overall per-residue deuterium uptake is observed to be relatively more efficient for the neutral residues than for the model peptide acetyl-PAAAAKAAAAKAAAAKAAAAK. In comparison, the N-terminal and C-terminal regions of the serine peptide show greater relative protection compared with interior residues. Molecular dynamics (MD) simulations have been used to generate candidate structures for collision cross section and HDX reactivity matching. Hydrogen accessibility scoring (HAS) for select structural candidates from MD simulations has been used to suggest conformer types that could contribute to the observed HDX patterns. The results are discussed with respect to recent studies employing extensive MD simulations of gas-phase structure establishment of a peptide system. Graphical Abstract ᅟ.

Predicting the Structures of Glycans, Glycoproteins, and Their Complexes

Complex carbohydrates are ubiquitous in nature, and together with proteins and nucleic acids they comprise the building blocks of life. But unlike proteins and nucleic acids, carbohydrates form nonlinear polymers, and they are not characterized by robust secondary or tertiary structures but rather by distributions of well-defined conformational states. Their molecular flexibility means that oligosaccharides are often refractory to crystallization, and nuclear magnetic resonance (NMR) spectroscopy augmented by molecular dynamics (MD) simulation is the leading method for their characterization in solution. The biological importance of carbohydrate-protein interactions, in organismal development as well as in disease, places urgency on the creation of innovative experimental and theoretical methods that can predict the specificity of such interactions and quantify their strengths. Additionally, the emerging realization that protein glycosylation impacts protein function and immunogenicity places the ability to define the mechanisms by which glycosylation impacts these features at the forefront of carbohydrate modeling. This review will discuss the relevant theoretical approaches to studying the three-dimensional structures of this fascinating class of molecules and interactions, with reference to the relevant experimental data and techniques that are key for validation of the theoretical predictions.

A brief history of bioinformatics

It is easy for today’s students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of ‘Big Data’ has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology.

A Comparative Study of Modern Homology Modeling Algorithms for Rhodopsin Structure Prediction

Rhodopsins are seven α-helical membrane proteins that are of great importance in chemistry, biology, and modern biotechnology. Any in silico study on rhodopsin properties and functioning requires a high-quality three-dimensional structure. Due to particular difficulties with obtaining membrane protein structures from the experiment, in silico prediction of the three-dimensional rhodopsin structure based only on its primary sequence is an especially important task. For the last few years, significant progress was made in the field of protein structure prediction, especially for methods based on comparative modeling. However, the majority of this progress was made for soluble proteins and further investigations are needed to achieve similar progress for membrane proteins. In this paper, we evaluate the performance of modern protein structure prediction methodologies (implemented in the Medeller, I-TASSER, and Rosetta packages) for their ability to predict rhodopsin structures. Three widely used methodologies were considered: two general methodologies that are commonly applied to soluble proteins and a methodology that uses constraints that are specific for membrane proteins. The test pool consisted of 36 target-template pairs with different sequence similarities that was constructed on the basis of 24 experimental rhodopsin structures taken from the RCSB database. As a result, we showed that all three considered methodologies allow obtaining rhodopsin structures with the quality that is close to the crystallographic one (root mean square deviation (RMSD) of the predicted structure from the corresponding X-ray structure up to 1.5 Å) if the target-template sequence identity is higher than 40%. Moreover, all considered methodologies provided structures of average quality (RMSD < 4.0 Å) if the target-template sequence identity is higher than 20%. Such structures can be subsequently used for further investigation of molecular mechanisms of protein functioning and for the development of modern protein-based biotechnologies.

Incorporation of Solvent Effect into Multi-Objective Evolutionary Algorithm for Improved Protein Structure Prediction

The problem of predicting the three-dimensional (3-D) structure of a protein from its one-dimensional sequence has been called the "holy grail of molecular biology", and it has become an important part of structural genomics projects. Despite the rapid developments in computer technology and computational intelligence, it remains challenging and fascinating. In this paper, to solve it we propose a multi-objective evolutionary algorithm. We decompose the protein energy function Chemistry at HARvard Macromolecular Mechanics force fields into bond and non-bond energies as the first and second objectives. Considering the effect of solvent, we innovatively adopt a solvent-accessible surface area as the third objective. We use 66 benchmark proteins to verify the proposed method and obtain better or competitive results in comparison with the existing methods. The results suggest the necessity to incorporate the effect of solvent into a multi-objective evolutionary algorithm to improve protein structure prediction in terms of accuracy and efficiency.

An improved stochastic fractal search algorithm for 3D protein structure prediction

Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

A Memetic Algorithm for 3-D Protein Structure Prediction Problem

Memetic Algorithms are population-based metaheuristics intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature of the Memetic Algorithms. In this paper, we present a Memetic Algorithm to tackle the three-dimensional protein structure prediction problem. The method uses a structured population and incorporates a Simulated Annealing algorithm as a local search strategy, as well as ad-hoc crossover and mutation operators to deal with the problem. It takes advantage of structural knowledge stored in the Protein Data Bank, by using an Angle Probability List that helps to reduce the search space and to guide the search strategy. The proposed algorithm was tested on nineteen protein sequences of amino acid residues, and the results show the ability of the algorithm to find native-like protein structures. Experimental results have revealed that the proposed algorithm can find good solutions regarding root-mean-square deviation and global distance total score test in comparison with the experimental protein structures. We also show that our results are comparable in terms of folding organization with state-of-the-art prediction methods, corroborating the effectiveness of our proposal.

A ternary complex model of Sirtuin4-NAD+-Glutamate dehydrogenase

Sirtuin4 (Sirt4) is one of the mammalian homologues of Silent information regulator 2 (Sir2), which promotes the longevity of yeast, C. elegans, fruit flies and mice. Sirt4 is localized in the mitochondria, where it contributes to preventing the development of cancers and ischemic heart disease through regulating energy metabolism. The ADP-ribosylation of glutamate dehydrogenase (GDH), which is catalyzed by Sirt4, downregulates the TCA cycle. However, this reaction mechanism is obscure, because the structure of Sirt4 is unknown. We here constructed structural models of Sirt4 by homology modeling and threading, and docked nicotinamide adenine dinucleotide⁺ (NAD⁺) to Sirt4. In addition, a partial GDH structure was docked to the Sirt4-NAD⁺ complex model. In the ternary complex model of Sirt4-NAD⁺-GDH, the acetylated lysine 171 of GDH is located close to NAD⁺. This suggests a possible mechanism underlying the ADP-ribosylation at cysteine 172, which may occur through a transient intermediate with ADP-ribosylation at the acetylated lysine 171. These results may be useful in designing drugs for the treatment of cancers and ischemic heart disease.

Coiled coil protein origami: from modular design principles towards biotechnological applications

This review illustrates the current state in designing coiled-coil-based proteins with an emphasis on coiled coil protein origami structures and their potential.

Everyone Is a Protagonist: Residue Conformational Preferences in High-Resolution Protein Structures

In many structural bioinformatics problems, there is a broad range of unanswered questions about protein dynamics and amino acid properties. Proteins are not strictly static objects, but rather populate ensembles of conformations. One way to understand these particularities is to analyze the information available in experimental databases. The Ramachandran plot, despite being more than half a century old, remains an utterly useful tool in the study of protein conformation. Based on its assumptions, we inspected a large data set (11,130 protein structures, amounting to 5,255,768 residues) and discriminated the conformational preferences of each residue type regarding their secondary structure participation. These data were studied for phi [Formula: see text], psi [Formula: see text], and side chain chi [Formula: see text] angles, being presented in non-Ramachandranian plots. In the largest analysis of protein conformation made so far, we propose an original plot to depict conformational preferences in relation to different secondary structure elements. Despite confirming previous observations, our results strongly support a unique character for each residue type, whereas also reinforcing the observation that side chains have a major contribution to secondary structure and, by consequence, on protein conformation. This information can be further used in the development of more robust methods and computational strategies for structural bioinformatics problems.

How cells engulf: a review of theoretical approaches to phagocytosis

Phagocytosis is a fascinating process whereby a cell surrounds and engulfs particles such as bacteria and dead cells. This is crucial both for single-cell organisms (as a way of acquiring nutrients) and as part of the immune system (to destroy foreign invaders). This whole process is hugely complex and involves multiple coordinated events such as membrane remodelling, receptor motion, cytoskeleton reorganisation and intracellular signalling. Because of this, phagocytosis is an excellent system for theoretical study, benefiting from biophysical approaches combined with mathematical modelling. Here, we review these theoretical approaches and discuss the recent mathematical and computational models, including models based on receptors, models focusing on the forces involved, and models employing energetic considerations. Along the way, we highlight a beautiful connection to the physics of phase transitions, consider the role of stochasticity, and examine links between phagocytosis and other types of endocytosis. We cover the recently discovered multistage nature of phagocytosis, showing that the size of the phagocytic cup grows in distinct stages, with an initial slow stage followed by a much quicker second stage starting around half engulfment. We also address the issue of target shape dependence, which is relevant to both pathogen infection and drug delivery, covering both one-dimensional and two-dimensional results. Throughout, we pay particular attention to recent experimental techniques that continue to inform the theoretical studies and provide a means to test model predictions. Finally, we discuss population models, connections to other biological processes, and how physics and modelling will continue to play a key role in future work in this area.

A novel in silico minigene vaccine based on CD4+ T-helper and B-cell epitopes of EG95 isolates for vaccination against cystic echinococcosis

EG95 oncospheral antigen plays a crucial role in Echinococcus granulosus pathogenicity. Considering the diversity of antigen among different EG95 isolates, it seems to be an ideal antigen for designing a universal multivalent minigene vaccine, so-called multi-epitope vaccine. This is the first in silico study to design a construct for the development of global EG95-based hydatid vaccine against E. granulosus in intermediate hosts. After antigen sequence selection, the three-dimensional structure of EG95 was modeled and multilaterally validated. The preliminary parameters for B-cell epitope prediction were implemented such as the possible transmembrane helix, signal peptide, post-translational modifications and allergenicity. The high ranked linear and conformational B-cell epitopes derived from several online web-servers (e.g., ElliPro, BepiPred v1.0, BcePred, ABCpred, SVMTrip, IEDB algorithms, SEPPA v2.0 and Discotope v2.0) were utilized for multiple sequence alignment and then for engineering the vaccine construct. T-helper based epitopes were predicted by molecular docking between the high frequent ovar class II allele (Ovar-DRB1*1202) and hexadecamer fragments of the EG95 protein. Having used the immune-informatics tools, we formulated the first EG95-based minigene vaccine based on T-helper epitope with high-binding affinity to the ovar MHC allele. This designed construct was analyzed for different physicochemical properties. It was also codon-optimized for high-level expression in Escherichia coli k12. Taken all, we propose the present in silico vaccine constructs as a promising platform for the generation of broadly protective vaccines for species and genus-specific immunization of the natural hosts of the parasite.

Enhancing Protein Conformational Space Sampling Using Distance Profile-Guided Differential Evolution

De novo protein structure prediction aims to search for low-energy conformations as it follows the thermodynamics hypothesis that places native conformations at the global minimum of the protein energy surface. However, the native conformation is not necessarily located in the lowest-energy regions owing to the inaccuracies of the energy model. This study presents a differential evolution algorithm using distance profile-based selection strategy to sample conformations with reasonable structure effectively. In the proposed algorithm, besides energy, the residue-residue distance is considered another measure of the conformation. The average distance errors of decoys between the distance of each residue pair and the corresponding distance in the distance profiles are first calculated when the trial conformation yields a larger energy value than that of the target. Then, the distance acceptance probability of the trial conformation is designed based on distance profiles if the trial conformation obtains a lower average distance error compared with that of the target conformation. The trial conformation is accepted to the next generation in accordance with its distance acceptance probability. By using the dual constraints of energy and distance in guiding sampling, the algorithm can sample conformations with lower energies and more reasonable structures. Experimental results of 28 benchmark proteins show that the proposed algorithm can effectively predict near-native protein structures.

The unexpected structures of hepatitis C virus envelope proteins

Hepatitis C virus (HCV) envelope proteins are essential not only for maintaining the viral life cycle, but also for evading the host's immune response and in clinical intervention. A thorough understanding of HCV envelope proteins depends on the availability of detailed structural information. Two crystal structures of the E2 core portion and of the E2 ectodomain, and one structure of the N-terminus of E1 ectodomain have shed new light on the complexity of HCV envelope proteins. In addition, the full-length E1-E2 complex has recently been modeled. The present review focuses on these advancements, introduces the recently solved structures and their biological implications and proposes novel ideas for studying the full-length E1-E2 complex.

NEAT-FLEX: Predicting the conformational flexibility of amino acids using neuroevolution of augmenting topologies

The development of computational methods to accurately model three-dimensional protein structures from sequences of amino acid residues is becoming increasingly important to the structural biology field. This paper addresses the challenge of predicting the tertiary structure of a given amino acid sequence, which has been reported to belong to the NP-Complete class of problems. We present a new method, namely NEAT–FLEX, based on NeuroEvolution of Augmenting Topologies (NEAT) to extract structural features from (ABS) proteins that are determined experimentally. The proposed method manipulates structural information from the Protein Data Bank (PDB) and predicts the conformational flexibility (FLEX) of residues of a target amino acid sequence. This information may be used in three-dimensional structure prediction approaches as a way to reduce the conformational search space. The proposed method was tested with 24 different amino acid sequences. Evolving neural networks were compared against a traditional error back-propagation algorithm; results show that the proposed method is a powerful way to extract and represent structural information from protein molecules that are determined experimentally.

Structural impact analysis of missense SNPs present in the uroguanylin gene by long-term molecular dynamics simulations

The guanylate cyclase activator 2B, also known as uroguanylin, is part of the guanylin peptide family, which includes peptides such as guanylin and lymphoguanylin. The guanylin peptides could be related to sodium absorption inhibition and water secretion induction and their dysfunction may be related to various pathologies such as chronic renal failure, congestive heart failure and nephrotic syndrome. Besides, uroguanylin point mutations have been associated with essential hypertension. However, currently there are no studies on the impact of missense SNPs on uroguanylin structure. This study applied in silico SNP impact prediction tools to evaluate the impact of uroguanylin missense SNPs and to filter those considered as convergent deleterious, which were then further analyzed through long-term molecular dynamics simulations of 1μs of duration. The simulations suggested that all missense SNPs considered as convergent deleterious caused some kind of structural change to the uroguanylin peptide. Additionally, four of these SNPs were also shown to cause modifications in peptide flexibility, possibly resulting in functional changes.

The 3-D Structural Basis for the Pgi Genotypic Differences in the Performance of the Butterfly Melitaea cinxia at Different Temperatures

Although genotype-by-environment interaction has long been used to unveil the genetic variation that affects Darwinian fitness, the mechanisms underlying the interaction usually remain unknown. Genetic variation at the dimeric glycolytic enzyme phosphoglucoisomerase (Pgi) has been observed to interact with temperature to explain the variation in the individual performance of the butterfly Melitaea cinxia. At relatively high temperature, individuals with Pgi-non-f genotypes generally surpass those with Pgi-f genotypes, while the opposite applies at relatively low temperature. In this study, we did protein structure predictions and BlastP homology searches with the aim to understand the structural basis for this temperature-dependent difference in the performance of M. cinxia. Our results show that, at amino acid (AA) site 372, one of the two sites that distinguish Pgi-f (the translated polypeptide of the Pgi-f allele) from Pgi-non-f (the translated polypeptide of the Pgi-non-f allele), the Pgi-non-f-related residue strengthens an electrostatic attraction between a pair of residues (Glu373-Lys472) that are from different monomers, compared to the Pgi-f-related residue. Further, BlastP searches of animal protein sequences reveal a dramatic excess of electrostatically attractive combinations of the residues at the Pgi AA sites equivalent to sites 373 and 472 in M. cinxia. This suggests that factors enhancing the inter-monomer interaction between these two sites, and therefore helping the tight association of two Pgi monomers, are favourable. Our homology-modelling results also show that, at the second AA site that distinguishes Pgi-f from Pgi-non-f in M. cinxia, the Pgi-non-f-related residue is more entropy-favourable (leading to higher structural stability) than the Pgi-f-related residue. To sum up, this study suggests a higher structural stability of the protein products of the Pgi-non-f genotypes than those of the Pgi-f genotypes, which may explain why individuals carrying Pgi-non-f genotypes outperform those carrying Pgi-f genotypes at stressful high temerature.

Amyotrophic Lateral Sclerosis Type 20 - In Silico Analysis and Molecular Dynamics Simulation of hnRNPA1

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease that affects the upper and lower motor neurons. 5-10% of cases are genetically inherited, including ALS type 20, which is caused by mutations in the hnRNPA1 gene. The goals of this work are to analyze the effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on hnRNPA1 protein function, to model the complete tridimensional structure of the protein using computational methods and to assess structural and functional differences between the wild type and its variants through Molecular Dynamics simulations. nsSNP, PhD-SNP, Polyphen2, SIFT, SNAP, SNPs&GO, SNPeffect and PROVEAN were used to predict the functional effects of nsSNPs. Ab initio modeling of hnRNPA1 was made using Rosetta and refined using KoBaMIN. The structure was validated by PROCHECK, Rampage, ERRAT, Verify3D, ProSA and Qmean. TM-align was used for the structural alignment. FoldIndex, DICHOT, ELM, D2P2, Disopred and DisEMBL were used to predict disordered regions within the protein. Amino acid conservation analysis was assessed by Consurf, and the molecular dynamics simulations were performed using GROMACS. Mutations D314V and D314N were predicted to increase amyloid propensity, and predicted as deleterious by at least three algorithms, while mutation N73S was predicted as neutral by all the algorithms. D314N and D314V occur in a highly conserved amino acid. The Molecular Dynamics results indicate that all mutations increase protein stability when compared to the wild type. Mutants D314N and N319S showed higher overall dimensions and accessible surface when compared to the wild type. The flexibility level of the C-terminal residues of hnRNPA1 is affected by all mutations, which may affect protein function, especially regarding the protein ability to interact with other proteins.

Homology modeling and virtual screening studies of FGF-7 protein-a structure-based approach to design new molecules against tumor angiogenesis

Keratinocyte growth factor (KGF) protein is a member of the fibroblast growth factor (FGF) family, which is also known as FGF-7. The FGF-7 plays an important role in tumor angiogenesis. In the present work, FGF-7 is treated as a potential therapeutic target to prevent angiogenesis in cancerous tissue. Computational techniques are applied to evaluate and validate the 3D structure of FGF-7 protein. The active site region of the FGF-7 protein is identified based on hydrophobicity calculations using CASTp and Q-site Finder active site prediction tools. The protein-protein docking study of FGF-7 with its natural receptor FGFR2b is carried out to confirm the active site region in FGF-7. The amino acid residues Asp34, Arg67, Glu116, and Thr194 in FGF-7 interact with the receptor protein (FGFR2b). A grid is generated at the active site region of FGF-7 using Glide module of Schrödinger suite. Subsequently, a virtual screening study is carried out at the active site using small molecular structural databases to identify the ligand molecules. The binding interactions of the ligand molecules, with piperazine moiety as a pharmacophore, are observed at Arg67 and Glu149 residues of the FGF-7 protein. The identified ligand molecules against the FGF-7 protein show permissible pharmacokinetic properties (ADME). The ligand molecules with good docking scores and satisfactory pharmacokinetic properties are prioritized and identified as novel ligands for the FGF-7 protein in cancer therapy.

The problems of biological information

The discovery of genetic encoding in the DNA molecule, and its mode of translation into protein structures, secured the modern view of biology as an information science. But it remains unclear what kind of information science it is. The all-too-ready analogy with computer programs stored on spools of magnetic tape has been hard to relinquish, even while the complexity of information storage and flow in the cell has become ever more apparent. To understand how life is sustained and evolves through encoding and processing of information, new ideas are now required, within which genetic encoding in DNA seems likely to provide only one part of a much broader and more profound puzzle. In particular, it seems likely that the emerging picture will need to take a more subtle view of causation, context and meaning in the orchestrated, hierarchical processes that make life possible.

Protein thermostability engineering

Using structure and sequence based analysis we can engineer proteins to increase their thermal stability.

Molecular dynamics simulations: advances and applications

Molecular dynamics simulations have evolved into a mature technique that can be used effectively to understand macromolecular structure-to-function relationships. Present simulation times are close to biologically relevant ones. Information gathered about the dynamic properties of macromolecules is rich enough to shift the usual paradigm of structural bioinformatics from studying single structures to analyze conformational ensembles. Here, we describe the foundations of molecular dynamics and the improvements made in the direction of getting such ensemble. Specific application of the technique to three main issues (allosteric regulation, docking, and structure refinement) is discussed.

A balance-evolution artificial bee colony algorithm for protein structure optimization based on a three-dimensional AB off-lattice model

Protein structure prediction is a fundamental issue in the field of computational molecular biology. In this paper, the AB off-lattice model is adopted to transform the original protein structure prediction scheme into a numerical optimization problem. We present a balance-evolution artificial bee colony (BE-ABC) algorithm to address the problem, with the aim of finding the structure for a given protein sequence with the minimal free-energy value. This is achieved through the use of convergence information during the optimization process to adaptively manipulate the search intensity. Besides that, an overall degradation procedure is introduced as part of the BE-ABC algorithm to prevent premature convergence. Comprehensive simulation experiments based on the well-known artificial Fibonacci sequence set and several real sequences from the database of Protein Data Bank have been carried out to compare the performance of BE-ABC against other algorithms. Our numerical results show that the BE-ABC algorithm is able to outperform many state-of-the-art approaches and can be effectively employed for protein structure optimization.