The emergence of man: information from protein systems

Protein amino acid sequences are not directly informative about the emergence of man from his immediate primate ancestry. But this could be because too little attention has been given to protein systems most relevant to the progress of hominization, those that, through their cell-surface action or enzymatic control of biosynthesis of other key proteins or hormones, can modulate the course of cell and tissue development, and so determine changes in tooth architecture, bone and tissue structure, brain and nerve cell development, etc. There are also the histones and other proteins associated with DNA in the genetic material, which have some modulating influence on gene expression. The whole process of carrying information from the genetic material to a species-reproducible morphology is through a cascade of multiple interlocking systems involving proteins at every turn. It is through changes in balance within these systems, and subsequent selection pressures, that the modulations of primate morphology and behaviour that constitute hominization have proceeded, and the process must be understood in terms of cytobiochemistry to give a fully detailed information of the evolving human genome.     

Protein aggregation in silico

Protein aggregation is a challenge to the successful manufacture of protein therapeutics; it can impose severe limitations on purification yields and compromise formulation stability. Advances in computer power, and the wealth of computational studies pertaining to protein folding, have facilitated the development of molecular simulation as a tool to investigate protein misfolding and aggregation. Here, we highlight the successes of protein aggregation studies carried out in silico, with a particular emphasis on studies related to biotechnology. To conclude, we discuss future prospects for the field, and identify several biotechnology-related problems that would benefit from molecular simulation.     

DynaMIT: the dynamic motif integration toolkit

De-novo motif search is a frequently applied bioinformatics procedure to identify and prioritize recurrent elements in sequences sets for biological investigation, such as the ones derived from high-throughput differential expression experiments. Several algorithms have been developed to perform motif search, employing widely different approaches and often giving divergent results. In order to maximize the power of these investigations and ultimately be able to draft solid biological hypotheses, there is the need for applying multiple tools on the same sequences and merge the obtained results. However, motif reporting formats and statistical evaluation methods currently make such an integration task difficult to perform and mostly restricted to specific scenarios. We thus introduce here the Dynamic Motif Integration Toolkit (DynaMIT), an extremely flexible platform allowing to identify motifs employing multiple algorithms, integrate them by means of a user-selected strategy and visualize results in several ways; furthermore, the platform is user-extendible in all its aspects. DynaMIT is freely available at http://cibioltg.bitbucket.org.     

Protein aggregation: in silico algorithms and applications

Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.     

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.     

Protein engineering of Bacillus acidopullulyticus pullulanase for enhanced thermostability using in silico data driven rational design methods

Thermostability has been considered as a requirement in the starch processing industry to maintain high catalytic activity of pullulanase under high temperatures. Four data driven rational design methods (B-FITTER, proline theory, PoPMuSiC-2.1, and sequence consensus approach) were adopted to identify the key residue potential links with thermostability, and 39 residues of Bacillus acidopullulyticus pullulanase were chosen as mutagenesis targets. Single mutagenesis followed by combined mutagenesis resulted in the best mutant E518I-S662R-Q706P, which exhibited an 11-fold half-life improvement at 60 °C and a 9.5 °C increase in T_m. The optimum temperature of the mutant increased from 60 to 65 °C. Fluorescence spectroscopy results demonstrated that the tertiary structure of the mutant enzyme was more compact than that of the wild-type (WT) enzyme. Structural change analysis revealed that the increase in thermostability was most probably caused by a combination of lower stability free-energy and higher hydrophobicity of E518I, more hydrogen bonds of S662R, and higher rigidity of Q706P compared with the WT. The findings demonstrated the effectiveness of combined data-driven rational design approaches in engineering an industrial enzyme to improve thermostability.     

Experimental design in phylogenetics: testing predictions from expected information

Taxon and character sampling are central to phylogenetic experimental design; yet, we lack general rules. Goldman introduced a method to construct efficient sampling designs in phylogenetics, based on the calculation of expected Fisher information given a probabilistic model of sequence evolution. The considerable potential of this approach remains largely unexplored. In an earlier study, we applied Goldman's method to a problem in the phylogenetics of caecilian amphibians and made an a priori evaluation and testable predictions of which taxon additions would increase information about a particular weakly supported branch of the caecilian phylogeny by the greatest amount. We have now gathered mitogenomic and rag1 sequences (some newly determined for this study) from additional caecilian species and studied how information (both expected and observed) and bootstrap support vary as each new taxon is individually added to our previous data set. This provides the first empirical test of specific predictions made using Goldman's method for phylogenetic experimental design. Our results empirically validate the top 3 (more intuitive) taxon addition predictions made in our previous study, but only information results validate unambiguously the 4th (less intuitive) prediction. This highlights a complex relationship between information and support, reflecting that each measures different things: Information is related to the ability to estimate branch length accurately and support to the ability to estimate the tree topology accurately. Thus, an increase in information may be correlated with but does not necessitate an increase in support. Our results also provide the first empirical validation of the widely held intuition that additional taxa that join the tree proximal to poorly supported internal branches are more informative and enhance support more than additional taxa that join the tree more distally. Our work supports the view that adding more data for a single (well chosen) taxon may increase phylogenetic resolution and support in weakly supported parts of the tree without adding more characters/genes. Altogether our results corroborate that, although still underexplored, Goldman's method offers a powerful tool for experimental design in molecular phylogenetic studies. However, there are still several drawbacks to overcome, and further assessment of the method is needed in order to make it better understood, more accessible, and able to assess the addition of multiple taxa.     

Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the “where and when”) and then allow for empirical testing of alternative network models of brain function that link information to behavior (the “how”). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach—dynamic activity flow modeling—then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory–motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

How is cognitive task behavior generated by brain network interactions? This study describes a novel network modeling approach and applies it to source electroencephalography data. The model accurately predicts future information dynamics underlying behavior and (via simulated lesioning) suggests a role for cognitive control networks as key drivers of response information flow.     

Protein structures and information extraction from biological texts: the PASTA system

MOTIVATION: The rapid increase in volume of protein structure literature means useful information may be hidden or lost in the published literature and the process of finding relevant material, sometimes the rate-determining factor in new research, may be arduous and slow. RESULTS: We describe the Protein Active Site Template Acquisition (PASTA) system, which addresses these problems by performing automatic extraction of information relating to the roles of specific amino acid residues in protein molecules from online scientific articles and abstracts. Both the terminology recognition and extraction capabilities of the system have been extensively evaluated against manually annotated data and the results compare favourably with state-of-the-art results obtained in less challenging domains. PASTA is the first information extraction (IE) system developed for the protein structure domain and one of the most thoroughly evaluated IE system operating on biological scientific text to date. AVAILABILITY: PASTA makes its extraction results available via a browser-based front end: http://www.dcs.shef.ac.uk/nlp/pasta/. The evaluation resources (manually annotated corpora) are also available through the website: http://www.dcs.shef.ac.uk/nlp/pasta/results.html.     

蛋白质工程：从定向进化到计算设计

定向进化通过建立突变体文库与高通量筛选方法,快速提升蛋白的特定性质,是目前蛋白质工程最为常用的蛋白质设计改造策略。近十年随着计算机运算能力大幅提升以及先进算法不断涌现,计算机辅助蛋白质设计改造得到了极大的重视和发展,成为蛋白质工程新开辟的重要方向。以结构模拟与能量计算为基础的蛋白质计算设计不但能改造酶的底物特异性与热稳定性,还可从头设计具有特定功能的人工酶。近年来机器学习等人工智能技术也被应用于计算机辅助蛋白质设计改造,并取得瞩目的成绩。文中介绍了蛋白质工程的发展历程,重点评述当前计算机辅助蛋白质设计改造方面的进展与应用,并展望其未来发展方向。     

Locomotif: from graphical motif description to RNA motif search

MOTIVATION AND RESULTS: Motivated by the recent rise of interest in small regulatory RNAs, we present Locomotif--a new approach for locating RNA motifs that goes beyond the previous ones in three ways: (1) motif search is based on efficient dynamic programming algorithms, incorporating the established thermodynamic model of RNA secondary structure formation. (2) motifs are described graphically, using a Java-based editor, and search algorithms are derived from the graphics in a fully automatic way. The editor allows us to draw secondary structures, annotated with size and sequence information. They closely resemble the established, but informal way in which RNA motifs are communicated in the literature. Thus, the learning effort for Locomotif users is minimal. (3) Locomotif employs a client-server approach. Motifs are designed by the user locally. Search programs are generated and compiled on a bioinformatics server. They are made available both for execution on the server, and for download as C source code plus an appropriate makefile. AVAILABILITY: Locomotif is available at http://bibiserv.techfak.uni-bielefeld.de/locomotif.     

MoiRNAiFold: a novel tool for complex in silico RNA design

Novel tools for in silico design of RNA constructs such as riboregulators are required in order to reduce time and cost to production for the development of diagnostic and therapeutic advances. Here, we present MoiRNAiFold, a versatile and user-friendly tool for de novo synthetic RNA design. MoiRNAiFold is based on Constraint Programming and it includes novel variable types, heuristics and restart strategies for Large Neighborhood Search. Moreover, this software can handle dozens of design constraints and quality measures and improves features for RNA regulation control of gene expression, such as Translation Efficiency calculation. We demonstrate that MoiRNAiFold outperforms any previous software in benchmarking structural RNA puzzles from EteRNA. Importantly, with regard to biologically relevant RNA designs, we focus on RNA riboregulators, demonstrating that the designed RNA sequences are functional both in vitro and in vivo. Overall, we have generated a powerful tool for de novo complex RNA design that we make freely available as a web server (https://moiraibiodesign.com/design/).     

Protein motif by computer: the perfect Greek key jell yroll designer

A program that generates amino acid sequences that are compatiblewith the Greek key protein motif is presented. Using statisticaldata derived from the structures of molecules from the proteindatabank, the novel algorithm generates amino acid sequencescompatible with an 8-stranded perfect Greek key jellyroll motifIn this motif, all hydrogen bonds present in the theoreticaloriginating (ß-hairpin stay in register as the whole8-stranded domain fokis at once in an anti clockwise swirl.Eight residues are generated per strand and 32 residues persheet making 64 residues in the antiparallel (ß-barrel.The seven loops between ß-stands contain an additional27 residues. All recognized features of (ß-sheetsand ß-strands such as alternating hydrophobic, hydrophilicresidues with hydrophobics on the narrow-hydrogen-bond-pair,concave side of the theoretical originating (ß-ribbon;sheet twist, strand twist, side chain rotation about the strands;the theories of side chain packing between the sheets; an average30° rotation between (ß-sheets; the theoreticalanti complementary patch residues of each sheet; and the anticomplementaty,isotropically stressed hyperbolic parabloid shape of each sheet,are taken into account in the program. The sequences of theloops between strands are designed by turn type and strand twistis considered in the design of the motif's single (ß-hairpinturn. Secondary structure parameters and between-strand aminoacid pair correlations also figure importantly in the novelalgorithm.     

In silico design of robust bolalipid membranes

The robustness of microorganisms used in industrial fermentations is essential for the efficiency and yield of the production process. A viable tool to increase the robustness is through engineering of the cell membrane and especially by incorporating lipids from species that survive under harsh conditions. Bolalipids are tetraether lipids found in Archaea bacteria, conferring stability to these bacteria by spanning across the cytoplasmic membrane. Here we report on in silico experiments to characterize and design optimal bolalipid membranes in terms of robustness. We use coarse-grained molecular dynamics simulations to study the structure, dynamics, and stability of membranes composed of model bolalipids, consisting of two dipalmitoylphosphatidylcholine (DPPC) lipids covalently linked together at either one or both tail ends. We find that bolalipid membranes differ substantially from a normal lipid membrane, with an increase in thickness and tail order, an increase in the gel-to-liquid crystalline phase transition temperature, and a decrease in diffusivity of the lipids. By changing the flexibility of the linker between the lipid tails, we furthermore show how the membrane properties can be controlled. A stiffer linker increases the ratio between spanning and looping conformations, rendering the membrane more rigid. Our study may help in designing artificial membranes, with tunable properties, able to function under extreme conditions. As an example, we show that incorporation of bolalipids makes the membrane more tolerant toward butanol.