The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences.In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.     

Capabilities of bioinformatics tools for optimizing physicochemical features of proteins used in Nano biosensors: A short overview of the tools related to bioinformatics

Protein-protein ligand is one of the most detection methods used in Nano biosensors. Based on the advantage of specific docking between two special 3D structures, they have become a potent candidate in bioanalysis and Nanodiagnostic tools. These tools lease users to do a simple, fast, cost-effective, sensitive, and specific detection of molecular biomarkers in real samples. Recent advantages of using protein-protein ligand Nano-biosensors application is remarkable due to its special docking that refers to each protein unique 3D conformation. However, it challenges different problems such as low rate of docking and hard process for fixation on the basic layer. These challenges make developers to optimize the structure and functions of proteins. The process has different Nano scale calculation that could be done with algorithms and solutions are available as bioinformatics tools. This article aimed to have a short overview of the abilities of bioinformatics tools for modeling and optimization of physiochemical features of proteins in Nano scale.     

Preparation of Giant Myelin Vesicles and Proteoliposomes to Register Ionic Channels

Abstract: Myelin vesicles, reconstituted liposomes with proteolipid protein (PLP), the main protein component of myelin, and electrophysiological patch-clamp are potentially powerful tools to study the role of myelin in functional ionic channels. However, technical difficulties in the vesiculation of myelin and the small size of the vesicles obtained do not permit the application of micropipettes for current recordings. From a suspension of purified myelin we have prepared oligolamellar vesicles (mean diameter of 144 nm) using the so-called French pressure system. From this preparation we obtained giant myelin vesicles ∼10 µm in mean diameter, using a dehydration-rehydration procedure. Qualitative analysis of proteins by sodium dodecyl sulfate-polyacrylamide gel electrophoresis revealed no significant loss of any component in these vesicles due to pressure, in comparison with non-vesiculated myelin. A way of preparing giant liposomes of ∼80–100 µm and proteoliposomes of ∼30 µm in mean diameter, using the same dehydration-rehydration procedure, is also reported. Reconstitution of purified PLP in giant liposomes was confirmed by fluorescent labeling of PLP and by fluorescence microscopy. The current recordings from these vesicles prove the validity of these methods and provide significant evidence of the existence of ionic channels in myelin membranes and the possibility that PLP functions as a channel. The physiological significance and characterization of these channels remain yet unresolved. These results have a special significance for elucidating the molecular role of myelin in the regulation of neural activity and in the brain ion microenvironment.     

Chemical strategies for controlling protein folding and elucidating the molecular mechanisms of amyloid formation and toxicity

It has been more than a century since the first evidence linking the process of amyloid formation to the pathogenesis of Alzheimer's disease. During the last three decades in particular, increasing evidence from various sources (pathology, genetics, cell culture studies, biochemistry, and biophysics) continues to point to a central role for the pathogenesis of several incurable neurodegenerative and systemic diseases. This is in part driven by our improved understanding of the molecular mechanisms of protein misfolding and aggregation and the structural properties of the different aggregates in the amyloid pathway and the emergence of new tools and experimental approaches that permit better characterization of amyloid formation in vivo. Despite these advances, detailed mechanistic understanding of protein aggregation and amyloid formation in vitro and in vivo presents several challenges that remain to be addressed and several fundamental questions about the molecular and structural determinants of amyloid formation and toxicity and the mechanisms of amyloid-induced toxicity remain unanswered. To address this knowledge gap and technical challenges, there is a critical need for developing novel tools and experimental approaches that will not only permit the detection and monitoring of molecular events that underlie this process but also allow for the manipulation of these events in a spatial and temporal fashion both in and out of the cell. This review is primarily dedicated in highlighting recent results that illustrate how advances in chemistry and chemical biology have been and can be used to address some of the questions and technical challenges mentioned above. We believe that combining recent advances in the development of new fluorescent probes, imaging tools that enabled the visualization and tracking of molecular events with advances in organic synthesis, and novel approaches for protein synthesis and engineering provide unique opportunities to gain a molecular-level understanding of the process of amyloid formation. We hope that this review will stimulate further research in this area and catalyze increased collaboration at the interface of chemistry and biology to decipher the mechanisms and roles of protein folding, misfolding, and aggregation in health and disease.     

细胞外基质蛋白质预测工具研究进展

细胞外基质蛋白质在细胞的一系列生物过程中发挥着重要作用,它的异常调节会导致很多重大疾病。理论细胞外基质蛋白质参考数据是实现细胞外基质蛋白质高效鉴定的基础,研究者们已经基于机器学习的方法开发出一系列的细胞外基质蛋白质预测工具。文中首先阐述了基于机器学习模型构建细胞外基质蛋白质预测工具的基本流程,之后以工具为单位总结了已有细胞外基质蛋白质预测工具的研究成果,最后提出了细胞外基质蛋白质预测工具目前面临的问题和可能的优化方法。     

Protein complex prediction: A survey

Protein complexes are one of the most important functional units for deriving biological processes within the cell. Experimental methods have provided valuable data to infer protein complexes. However, these methods have inherent limitations. Considering these limitations, many computational methods have been proposed to predict protein complexes, in the last decade. Almost all of these in-silico methods predict protein complexes from the ever-increasing protein–protein interaction (PPI) data. These computational approaches usually use the PPI data in the format of a huge protein–protein interaction network (PPIN) as input and output various sub-networks of the given PPIN as the predicted protein complexes. Some of these methods have already reached a promising efficiency in protein complex detection. Nonetheless, there are challenges in prediction of other types of protein complexes, specially sparse and small ones. New methods should further incorporate the knowledge of biological properties of proteins to improve the performance. Additionally, there are several challenges that should be considered more effectively in designing the new complex prediction algorithms in the future. This article not only reviews the history of computational protein complex prediction but also provides new insight for improvement of new methodologies. In this article, most important computational methods for protein complex prediction are evaluated and compared. In addition, some of the challenges in the reconstruction of the protein complexes are discussed. Finally, various tools for protein complex prediction and PPIN analysis as well as the current high-throughput databases are reviewed.     

Recent breakthroughs in computational structural biology harnessing the power of sequences and structures

Recent advances in computational approaches and their integration into structural biology enable tackling increasingly complex questions. Here, we discuss several key areas, highlighting breakthroughs and remaining challenges. Theoretical modeling has provided tools to accurately predict and design protein structures on a scale currently difficult to achieve using experimental approaches. Molecular Dynamics simulations have become faster and more precise, delivering actionable information inaccessible by current experimental methods. Virtual screening workflows allow a high-throughput approach to discover ligands that bind and modulate protein function, while Machine Learning methods enable the design of proteins with new functionalities. Integrative structural biology combines several of these approaches, pushing the frontiers of structural and functional characterization to ever larger systems, advancing towards a complete understanding of the living cell. These breakthroughs will accelerate and significantly impact diverse areas of science.     

Lysozyme binding to tethered bilayer lipid membranes prepared by rapid solvent exchange and vesicle fusion methods

Tethered bilayer lipid membranes (tBLMs) are important tools for studying protein–lipid interactions. The widely used methodology for the preparation of these membranes is the fusion of phospholipid vesicles from an aqueous medium onto an anchored phospholipid layer. The preparation of phospholipid vesicles is a long and tedious procedure. There is another simple method, rapid solvent exchange, for preparing lipid membranes. However, there is a lack of information on the effects of the preparation method of tBLMs on their interactions with proteins. Therefore, we present in this paper a comparative study on the binding of lysozyme onto tBLMs prepared by the abovementioned methods. The prepared tBLMs have either zwitterionic or anionic characteristics. The results show that lysozyme binding onto the prepared tBLMs is unaffected by the preparation method of the tBLMs, suggesting that the tedious fusion method might be replaced by the simple rapid solvent exchange method without altering the level of protein–lipid interactions.     

Modern-day challenges in therapeutic protein production

The market for therapeutic proteins is on the rise, plagued by several challenges related to production amounts and costs. Solutions to these problems are widely thought to come from academia, governments and production companies. This conference aimed to bring experts in the industry together under one roof, in order to demystify several novel technologies in therapeutic protein development. Key topics included analytical tools for protein stability and ligand interactions, measurement of protein aggregates as small as 30 nm and reducing production costs, just to name a few. The need to eliminate protein aggregates early during bioprocessing was emphasized. Finally, several companies presented novel technologies related to therapeutic protein development.     

Ontological analysis of gene expression data: current tools, limitations, and open problems

Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.     

Theoretical analysis of alternative splice forms using computational methods

Nowadays understanding alternative splicing is one of the greatest challenges in biology, because it is a genetic process much more important than thought at the time of its discovery. In this paper, we explain the approach of using the different available databases and software tools to start a large scale investigation of alternative splice forms. To collect information about alternative splicing we investigated known data in the databases using different computational methods. The investigations proceeded from the genomic sequence data to structural protein data. Then, we interpreted those data to find the relationship between alternative splice forms and protein function and structure. We found some interesting features of alternative splicing which are presented here. We discuss the results of one chosen example. They concern the coverage quality of the protein sequence of a known structure, an EST analysis, the validation of splice variants, the determination of the alternative splice type, and finally the link between alternative splicing and disease.     

Web Resources for Metagenomics Studies

The development of next-generation sequencing(NGS) platforms spawned an enormous volume of data. This explosion in data has unearthed new scalability challenges for existing bioinformatics tools. The analysis of metagenomic sequences using bioinformatics pipelines is complicated by the substantial complexity of these data. In this article, we review several commonly-used online tools for metagenomics data analysis with respect to their quality and detail of analysis using simulated metagenomics data. There are at least a dozen such software tools presently available in the public domain. Among them, MGRAST, IMG/M, and METAVIR are the most well-known tools according to the number of citations by peer-reviewed scientific media up to mid-2015. Here, we describe 12 online tools with respect to their web link, annotation pipelines, clustering methods, online user support, and availability of data storage. We have also done the rating for each tool to screen more potential and preferential tools and evaluated five best tools using synthetic metagenome. The article comprehensively deals with the contemporary problems and the prospects of metagenomics from a bioinformatics viewpoint.     

Understanding organellar protein folding capacities and assessing their pharmacological modulation by small molecules

Dysfunctional organellar protein quality control machinery leads to protein misfolding associated cardiovascular, neurodegenerative, metabolic and secretory disorders. To understand organellar homeostasis, suitable tools are required which can sense changes in their respective protein folding capacity upon exposure to environmental and pharmacological perturbations. Herein, we have assessed protein folding capacity of cellular organelles using a metastable sensor selectively targeted to cytosol, nucleus, mitochondria, endoplasmic reticulum, golgi and peroxisomes. Microscopy and biochemical data revealed that these sensors report both acute and organelle-specific cellular insults. It also provided insights into contrasting refolding capacities of cellular organelles to recover from proteotoxic challenges. Further, we used these metastable sensors to evaluate pharmacological modulation of organellar protein folding capacity by small molecules. We observed pyrazole based scaffolds increased organellar protein folding capacity through upregulation of chaperones, mainly HSP90 and its co-chaperone HOP which coordinate refolding of misfolded/aggregated species. Overall, our data highlights the potential use of organelle-specific metastable sensors to understand protein folding capacity of sub-cellular compartments and assess pharmacological correction of their proteostasis imbalance. This study also provides additional avenue for use of these organelle-specific metastable sensors in drug discovery programs for identification of novel pharmacophores and drug repositioning of promising scaffolds for protein conformational diseases associated with different cellular organelles.     

CRISPR–Cas9 gRNA efficiency prediction: an overview of predictive tools and the role of deep learning

The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a successful and promising technology for gene-editing. To facilitate its effective application, various computational tools have been developed. These tools can assist researchers in the guide RNA (gRNA) design process by predicting cleavage efficiency and specificity and excluding undesirable targets. However, while many tools are available, assessment of their application scenarios and performance benchmarks are limited. Moreover, new deep learning tools have been explored lately for gRNA efficiency prediction, but have not been systematically evaluated. Here, we discuss the approaches that pertain to the on-target activity problem, focusing mainly on the features and computational methods they utilize. Furthermore, we evaluate these tools on independent datasets and give some suggestions for their usage. We conclude with some challenges and perspectives about future directions for CRISPR–Cas9 guide design.     

Optimisation of signal peptide for recombinant protein secretion in bacterial hosts

Escherichia coli—the powerhouse for recombinant protein production—is rapidly gaining status as a reliable and efficient host for secretory expression. An improved understanding of protein translocation processes and its mechanisms has inspired and accelerated the development of new tools and applications in this field and, in particular, a more efficient secretion signal. Several important characteristics and requirements are summarised for the design of a more efficient signal peptide for the production of recombinant proteins in E. coli. General approaches and strategies to optimise the signal peptide, including the selection and modification of the signal peptide components, are included. Several challenges in the secretory production of recombinant proteins are discussed, and research approaches designed to meet these challenges are proposed.     

Elucidating the mechanisms of coupled electron transfer and catalytic reactions by protein film voltammetry

Protein film voltammetry, the direct electrochemistry of redox enzymes and proteins, provides precise and comprehensive information on complicated reaction mechanisms. By controlling the driving force for a reaction (using the applied potential) and monitoring the reaction in real time (using the current), it allows thermodynamic and kinetic information to be determined simultaneously. Two challenges are inherent to protein film voltammetry: (i) to adsorb the protein or enzyme in a native and active configuration on the electrode surface, and (ii) to understand and interpret voltammetric results on both a qualitative and quantitative level, allowing mechanistic models to be proposed and rigorous experiments to test these models to be devised. This review focuses on the second of these two challenges. It describes how to use protein film voltammetry to derive mechanistic and biochemically relevant information about redox proteins and enzymes, and how to evaluate and interpret voltammetric results. Selected key studies are described in detail, to illustrate their underlying principles, strategies and physical interpretations.     

The value of applying molecular biology tools in environmental engineering: Academic and industry perspective in the USA

The integration of molecular biology tools in environmental engineering is a challenge. We discuss our views on the following four critical issues: (i) faculty career development, (ii) tool standardization, (iii) teaching, and (iv) the application of molecular biology tools in practice. For (i), we suggest that administrators and faculty need to understand the special challenges inherent to research and teaching within this highly interdisciplinary area. Furthermore, we suggest preparing two white papers aimed at educating administrators in universities and agencies. For (ii), we conclude that, because molecular biology tools are still in a state of rapid development, proposing standards at this time is premature. In the future, standards for widely applied tools should be in an on-line, peer-reviewed format. Concerning (iii), we believe that molecular biology should be taught only to the degree needed to achieve program goals. For example, environmental engineering practitioners only need to know the vocabulary and basic concepts of molecular biology tools, not be experts at doing them hands on. To help engineering students gain the right level and type of information, learning modules should be developed for them. Finally, although engineering successes applying molecular biology tools are available (iv), the biggest value will come when the tools are fully integrated with practice. Therefore, we encourage the creation of a demonstration project to document the value of applying molecular biology tools in environmental engineering. This revised version was published online in June 2006 with corrections to the Cover Date.     

Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions

Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models. Looking into the future, more advanced deep learning models of effectively integrating cryo-EM data with other sources of complementary data such as protein sequences and AlphaFold-predicted structures need to be developed to further advance the field.     

A practical guide to MaxEnt for modeling species’ distributions: what it does,and why inputs and settings matter

The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt’s calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt’s outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.     

Elucidating the mechanisms of coupled electron transfer and catalytic reactions by protein film voltammetry

Protein film voltammetry, the direct electrochemistry of redox enzymes and proteins, provides precise and comprehensive information on complicated reaction mechanisms. By controlling the driving force for a reaction (using the applied potential) and monitoring the reaction in real time (using the current), it allows thermodynamic and kinetic information to be determined simultaneously. Two challenges are inherent to protein film voltammetry: (i) to adsorb the protein or enzyme in a native and active configuration on the electrode surface, and (ii) to understand and interpret voltammetric results on both a qualitative and quantitative level, allowing mechanistic models to be proposed and rigorous experiments to test these models to be devised. This review focuses on the second of these two challenges. It describes how to use protein film voltammetry to derive mechanistic and biochemically relevant information about redox proteins and enzymes, and how to evaluate and interpret voltammetric results. Selected key studies are described in detail, to illustrate their underlying principles, strategies and physical interpretations.