Protein Structure Prediction Research Articles

AlphaFold-Multimer accurately captures interactions and dynamics of intrinsically disordered protein regions

Interactions mediated by intrinsically disordered protein regions (IDRs) pose formidable challenges in structural characterization. IDRs are highly versatile, capable of adopting diverse structures and engagement modes. Motivated by recent strides in protein structure prediction, we embarked on exploring the extent to which AlphaFold-Multimer can faithfully reproduce the intricacies of interactions involving IDRs. To this end, we gathered multiple datasets covering the versatile spectrum of IDR binding modes and used them to probe AlphaFold-Multimer's prediction of IDR interactions and their dynamics. Our analyses revealed that AlphaFold-Multimer is not only capable of predicting various types of bound IDR structures with high success rate, but that distinguishing true interactions from decoys, and unreliable predictions from accurate ones is achievable by appropriate use of AlphaFold-Multimer's intrinsic scores. We found that the quality of predictions drops for more heterogeneous, fuzzy interaction types, most likely due to lower interface hydrophobicity and higher coil content. Notably though, certain AlphaFold-Multimer scores, such as the Predicted Aligned Error and residue-ipTM, are highly correlated with structural heterogeneity of the bound IDR, enabling clear distinctions between predictions of fuzzy and more homogeneous binding modes. Finally, our benchmarking revealed that predictions of IDR interactions can also be successful when using full-length proteins, but not as accurate as with cognate IDRs. To facilitate identification of the cognate IDR of a given partner, we established "minD," which pinpoints potential interaction sites in a full-length protein. Our study demonstrates that AlphaFold-Multimer can correctly identify interacting IDRs and predict their mode of engagement with a given partner.

A new framework for SubtiWiki, the database for the model organism Bacillus subtilis.

Bacillus subtilis is a Gram-positive model bacterium and one of the most-studied and best understood organisms. The complex information resulting from its investigation is compiled in the database SubtiWiki (https://subtiwiki.uni-goettingen.de/v5) in an integrated and intuitive manner. To enhance the utility of SubtiWiki, we have added novel features such as a viewer to interrogate conserved genomic organization, a widget that shows mutant fitness data for all non-essential genes, and a widget showing protein structures, structure predictions and complex structures. Moreover, we have integrated metabolites as new entities. The new framework also includes a documented API, enabling programmatic access to data for computational tasks. Here we present the recent developments of SubtiWiki and the current state of the data for this organism.

The Present State and Impact of AI-Driven Computational Tools for Predicting Plant Protein Structures.

Several key functions of plants, such as photosynthesis, nutrient transport, disease resistance, and abiotic tolerance, are manifested by several classes of proteins. Prediction of 3- dimensional (3-D) structures of proteins and their working mechanisms can have a profound impact on plant proteomics research and could help improve key agricultural traits in crop plants. This review aims to present the current status of plant protein structure determination and discuss the way forward. Most experimentally proven protein structures are available only for the model plant Arabidopsis thaliana. Most of the key crop plants have only a few hundred or fewer experimentally proven 3-D structures. Fewer than 1% of the protein sequences in the majority of plants have had their 3D structures experimentally determined, and A. thaliana is the sole plant with the highest percentage of 1.4 % of protein sequences with experimentally determined structures. AI-based protein structure prediction tool AlphaFold has predicted models of several thousand proteins for many crop plants. In AlphaFold predicted protein models, soybean has the highest percentage (65%) of its UniProt protein sequences with predicted models, and a few other crop plants have also a considerable percentage of its UniProt sequences with AlphaFold predicted models. AlphaFold might help predict models and bridge the gap in plant structure determination studies. Protein structure information might lead to engineering key residues to improve the agronomical performance of crop plants.

Phylogenetic analysis of selected species of Asteraceae on the basis of RPS 11 Gene

The Asteraceae family is a prominent group of flowering plants found across the globe, with the exception of Antarctica. The Asteraceae family is a largest flowering family pivotal group in plant evolution and diversification. Despite its importance, the genetic diversity within this family remains understudied. We focused on the rps-11 gene, a chloroplast marker previously utilized in phylogenetic studies, to investigate its potential in resolving Asteraceae relationships. The focus was on examining genetic diversity within sixteen specifically chosen species from the Asteraceae family. This assessment was based on an analysis of a chloroplast gene responsible for encoding the ribosomal protein of the smaller subunit 11 (rps 11). Nearly 417 bp of rps 11 gene was amplified, sequenced, computationally translated into amino acid sequence and the data was used for phylogenetic analysis as well as for rps 11 protein structure predictions. Based on nucleotide and amino acid sequences phylograms were drawn with the help of Molecular Evolutionary Genetic Analysis (MEGA 6), which exhibited clear genetic relationship among species under investigation. The observed genetic distance was 0.02 for Maximum likelihood tree based on nucleotide sequences whereas it was 0.05 for phylogram based on amino acid sequences. These values revealed that amino acid-based tree has demonstrated greater diversity among selected species in comparison to nucleotides-based tree. On the basis of pair wise distance calculations, genetic divergence values were found within the range of 0.015–0.309. Moreover, 3D protein modeling for rps 11 protein of sixteen selected species was also carried out by iterative threading assembly refinement (I-Tasser) software. The models exhibiting the highest C-score were picked with satisfactory plot statistics (> 90%) and structurally validated by PROCHECK. Furthermore, Ramachandran plots displayed that the rps 11 protein structures of Tagetes minuta, Xanthium strumarium, Lactuca sativa and Chrysanthemum indicum have best feature models with > 90% of residues in the allowed region and ≤ 2% in the disallowed region. The research is not enough to stand alone to validate the viability of the rps11 gene as a prospective contender for phylogenetic analysis. İn future we will focus on the maximum genetic diversity theory for phylogenetic analysis of this family.

Structural and functional insights from the sequences and complex domain architecture of adhesin-like proteins from Methanobrevibacter smithii and Methanosphaera stadtmanae

Methanogenic archaea, or methanogens, are crucial in guts and rumens, consuming hydrogen, carbon dioxide, and other fermentation products. While their molecular interactions with other microorganisms are not fully understood, genomic sequences provide information. The first genome sequences of human gut methanogens, Methanosphaera stadtmanae and Methanobrevibacter smithii, revealed genes encoding adhesin-like proteins (ALPs). These proteins were also found in other gut and rumen methanogens, but their characteristics and functions remain largely unknown. This study analyzes the ALP repertoire of M. stadtmanae and M. smithii using AI-guided protein structure predictions of unique ALP domains. Both genomes encode more than 40 ALPs each, comprising over 10% of their genomes. ALPs contain repetitive sequences, many of which are unmatched in protein domain databases. We present unique sequence signatures of conserved ABD repeats in ALPs and propose a classification based on domain architecture. Our study offers insights into ALP features and how methanogens may interact with other microorganisms.

How the technologies behind self-driving cars, social networks, ChatGPT, and DALL-E2 are changing structural biology.

The performance of deep Neural Networks (NNs) in the text (ChatGPT) and image (DALL-E2) domains has attracted worldwide attention. Convolutional NNs (CNNs), Large Language Models (LLMs), Denoising Diffusion Probabilistic Models (DDPMs)/Noise Conditional Score Networks (NCSNs), and Graph NNs (GNNs) have impacted computer vision, language editing and translation, automated conversation, image generation, and social network management. Proteins can be viewed as texts written with the alphabet of amino acids, as images, or as graphs of interacting residues. Each of these perspectives suggests the use of tools from a different area of deep learning for protein structural biology. Here, I review how CNNs, LLMs, DDPMs/NCSNs, and GNNs have led to major advances in protein structure prediction, inverse folding, protein design, and small molecule design. This review is primarily intended as a deep learning primer for practicing experimental structural biologists. However, extensive references to the deep learning literature should also make it relevant to readers who have a background in machine learning, physics or statistics, and an interest in protein structural biology.

Development of Tertiary Structure of Laccase from Geobacillus uzenensis using Homology Modelling

Laccases are a versatile enzyme with immense potential in various biotechnological applications. In this study, the protein structure prediction of laccase from Geobacillus uzenensis, a bacteria known for its robust enzymatic activity is focused. The protein structural elucidation of this laccase is crucial to understand its substrate binding and overall functionality. Protein structure can be determined by computational prediction, which is commonly known as homology modelling. The three-dimensional (3D) model was built based on the available crystal structures of related laccase enzymes and aligned with the primary sequence of the target protein. The resulting model exhibited a high degree of structural similarity and conservation of key catalytic residues, supporting its reliability for further analyses. Firstly, the suitable template was identified with 85.61% of sequence identity determined by sequence alignment. Then, MODELLER program was used to predict the model using the method of satisfaction of spatial restraints. The model was then analyzed for its quality by computational analysis tools such as Ramachandran's Plot. Finally, the binding site of the protein was identified using the GRaSP computational tool. These findings provide significant insight on comprehensive analysis of the laccase from G. uzenensis, by providing a structural framework to unravel its functional properties and substrate specificity.

Easy and accurate protein structure prediction using ColabFold.

Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological questions, by using predicted protein structures of single monomers or full complexes, a common practice. ColabFold-AF2 is an open-source Jupyter Notebook inside Google Colaboratory and a command-line tool that makes it easy to use AF2 while exposing its advanced options. ColabFold-AF2 shortens turnaround times of experiments because of its optimized usage of AF2's models. In this protocol, we guide the reader through ColabFold best practices by using three scenarios: (i) monomer prediction, (ii) complex prediction and (iii) conformation sampling. The first two scenarios cover classic static structure prediction and are demonstrated on the human glycosylphosphatidylinositol transamidase protein. The third scenario demonstrates an alternative use case of the AF2 models by predicting two conformations of the human alanine serine transporter 2. Users can run the protocol without computational expertise via Google Colaboratory or in a command-line environment for advanced users. Using Google Colaboratory, it takes <2 h to run each procedure. The data and code for this protocol are available at https://protocol.colabfold.com .

Restoring protein glycosylation with GlycoShape.

Despite ground-breaking innovations in experimental structural biology and protein structure prediction techniques, capturing the structure of the glycans that functionalize proteins remains a challenge. Here we introduce GlycoShape ( https://glycoshape.org ), an open-access glycan structure database and toolbox designed to restore glycoproteins to their native and functional form in seconds. The GlycoShape database counts over 500 unique glycans so far, covering the human glycome and augmented by elements from a wide range of organisms, obtained from 1 ms of cumulative sampling from molecular dynamics simulations. These structures can be linked to proteins with a robust algorithm named Re-Glyco, directly compatible with structural data in open-access repositories, such as the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and AlphaFold Protein Structure Database, or own. The quality, performance and broad applicability of GlycoShape is demonstrated by its ability to predict N-glycosylation occupancy, scoring a 93% agreement with experiment, based on screening all proteins in the PDB with a corresponding glycoproteomics profile, for a total of 4,259 N-glycosylation sequons.

Unmasking AlphaFold to integrate experiments and predictions in multimeric complexes.

Since the release of AlphaFold, researchers have actively refined its predictions and attempted to integrate it into existing pipelines for determining protein structures. These efforts have introduced a number of functionalities and optimisations at the latest Critical Assessment of protein Structure Prediction edition (CASP15), resulting in a marked improvement in the prediction of multimeric protein structures. However, AlphaFold's capability of predicting large protein complexes is still limited and integrating experimental data in the prediction pipeline is not straightforward. In this study, we introduce AF_unmasked to overcome these limitations. Our results demonstrate that AF_unmasked can integrate experimental information to build larger or hard to predict protein assemblies with high confidence. The resulting predictions can help interpret and augment experimental data. This approach generates high quality (DockQ score > 0.8) structures even when little to no evolutionary information is available and imperfect experimental structures are used as a starting point. AF_unmasked is developed and optimised to fill incomplete experimental structures (structural inpainting), which may provide insights into protein dynamics. In summary, AF_unmasked provides an easy-to-use method that efficiently integrates experiments to predict large protein complexes more confidently.

Covalent modification of protein by chemical probe in living cells for structural and interaction studies.

Cellular activities are predominantly carried out by proteins that can dynamically adopt different structural conformations and differentially interact with other biomolecules according to cellular needs. Chemical probes are small molecules used to selectively interact and modulate the activities of specific proteins to study their functions such as the validation of potential drug targets. The remarkable performance of AlphaFold algorithms in the prediction of protein structures has pivoted interest toward elucidating the intracellular dynamics of protein structural conformation where covalent modification of proteins by chemical probes could be used to shed light upon. However, due to the barrier to entry by cell membrane and the general unfavorable reactive conditions of the intracellular environment, most studies using reactive chemical probes are still conducted on purified proteins and cell lysates. Nevertheless, recent progresses have been made in designing chemical probes with improved membrane permeability, stability and reactivity. This paper surveys the literature on recent advancements in membrane-permeable chemical probes and their applications with protein mass spectrometry for the intracellular studies of protein structural conformations and biomolecular interactions.

Deep Learning in Hematology: From Molecules to Patients.

Deep learning (DL), a subfield of machine learning, has made remarkable strides across various aspects of medicine. This review examines DL's applications in hematology, spanning from molecular insights to patient care. The review begins by providing a straightforward introduction to the basics of DL tailored for those without prior knowledge, touching on essential concepts, principal architectures, and prevalent training methods. It then discusses the applications of DL in hematology, concentrating on elucidating the models' architecture, their applications, performance metrics, and inherent limitations. For example, at the molecular level, DL has improved the analysis of multi-omics data and protein structure prediction. For cells and tissues, DL enables the automation of cytomorphology analysis, interpretation of flow cytometry data, and diagnosis from whole slide images. At the patient level, DL's utility extends to analyzing curated clinical data, electronic health records, and clinical notes through large language models. While DL has shown promising results in various hematology applications, challenges remain in model generalizability and explainability. Moreover, the integration of novel DL architectures into hematology has been relatively slow in comparison to that in other medical fields.

In vivo thrombin activity in the diatom Phaeodactylum tricornutum: biotechnological insights.

Diatoms are responsible for 20% of global carbon dioxide fixation and have significant potential in various biotechnological and industrial applications. Recently, the pennate diatom Phaeodactylum tricornutum has emerged as a prominent platform organism for metabolic engineering and synthetic biology. The availability of its genome sequence has facilitated the development of new bioengineering tools. In this study, we used in silico analyses to identify sequences potentially encoding thrombin-like proteins, which are involved in recognizing and cleaving the thrombin sequence LVPRGS in P. tricornutum. Protein structure prediction and docking studies indicated a similar active site and ligand positioning compared to characterized human and bovine thrombin. The evidence and efficiency of the cleavage were determined in vivo using two fusion-protein constructs that included YFP to measure expression, protein accumulation, and cleavage. Western blot analysis revealed 50-100% cleavage between YFP and N-terminal fusion proteins. Our findings suggest the existence of a novel thrombin-like protease in P. tricornutum. This study advances the application of diatoms for the synthesis and production of complex proteins and enhances our understanding of the functional role of these putative thrombin sequences in diatom physiology. KEY POINTS: • Protein structure predictions reveal thrombin-like active sites in P. tricornutum. • Validated cleavage efficiency of thrombin-like protease on fusion proteins in vivo. • Study advances bioengineering tools for diatom-based biotechnological applications.

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop.

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. We also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Large language models and their applications in bioinformatics

Recent advancements in Natural Language Processing (NLP) have been significantly driven by the development of Large Language Models (LLMs), representing a substantial leap in language-based technology capabilities. These models, built on sophisticated deep learning architectures, typically transformers, are characterized by billions of parameters and extensive training data, enabling them to achieve high accuracy across various tasks. The transformer architecture of LLMs allows them to effectively handle context and sequential information, which is crucial for understanding and generating human language. Beyond traditional NLP applications, LLMs have shown significant promise in bioinformatics, transforming the field by addressing challenges associated with large and complex biological datasets. In genomics, proteomics, and personalized medicine, LLMs facilitate identifying patterns, predicting protein structures, or understanding genetic variations. This capability is crucial, e.g., for advancing drug discovery, where accurate prediction of molecular interactions is essential. This review discusses the current trends in LLMs research and their potential to revolutionize the field of bioinformatics and accelerate novel discoveries in the life sciences.

Conditioned Protein Structure Prediction

Deep-learning-based protein structure prediction has facilitated major breakthroughs in biological sciences. However, current methods struggle with alternative conformation prediction and offer limited integration of expert knowledge on protein dynamics. We introduce AFEXplorer, a generic approach that tailors AlphaFold predictions to user-defined constraints in coarse coordinate spaces by optimizing embedding features. Its effectiveness in generating functional protein conformations in accordance with predefined conditions is demonstrated through comprehensive examples. AFEXplorer serves as a versatile platform for conditioned protein structure prediction, bridging the gap between automated models and domain-specific insights. Published by the American Physical Society 2024

CoNglyPred: Accurate Prediction of N-Linked Glycosylation Sites Using ESM-2 and Structural Features With Graph Network and Co-Attention.

N-Linked glycosylation is crucial for various biological processes such as protein folding, immune response, and cellular transport. Traditional experimental methods for determining N-linked glycosylation sites entail substantial time and labor investment, which has led to the development of computational approaches as a more efficient alternative. However, due to the limited availability of 3D structural data, existing prediction methods often struggle to fully utilize structural information and fall short in integrating sequence and structural information effectively. Motivated by the progress of protein pretrained language models (pLMs) and the breakthrough in protein structure prediction, we introduced a high-accuracy model called CoNglyPred. Having compared various pLMs, we opt for the large-scale pLM ESM-2 to extract sequence embeddings, thus mitigating certain limitations associated with manual feature extraction. Meanwhile, our approach employs a graph transformer network to process the 3D protein structures predicted by AlphaFold2. The final graph output and ESM-2 embedding are intricately integrated through a co-attention mechanism. Among a series of comprehensive experiments on the independent test dataset, CoNglyPred outperforms state-of-the-art models and demonstrates exceptional performance in case study. In addition, we are the first to report the uncertainty of N-linked glycosylation predictors using expected calibration error and expected uncertainty calibration error.

The success rate of processed predicted models in molecular replacement: implications for experimental phasing in the AlphaFold era.

The availability of highly accurate protein structure predictions from AlphaFold2 (AF2) and similar tools has hugely expanded the applicability of molecular replacement (MR) for crystal structure solution. Many structures can be solved routinely using raw models, structures processed to remove unreliable parts or models split into distinct structural units. There is therefore an open question around how many and which cases still require experimental phasing methods such as single-wavelength anomalous diffraction (SAD). Here, this question is addressed using a large set of PDB depositions that were solved by SAD. A large majority (87%) could be solved using unedited or minimally edited AF2 predictions. A further 18 (4%) yield straightforwardly to MR after splitting of the AF2 prediction using Slice'N'Dice, although different splitting methods succeeded on slightly different sets of cases. It is also found that further unique targets can be solved by alternative modelling approaches such as ESMFold (four cases), alternative MR approaches such as ARCIMBOLDO and AMPLE (two cases each), and multimeric model building with AlphaFold-Multimer or UniFold (three cases). Ultimately, only 12 cases, or 3% of the SAD-phased set, did not yield to any form of MR tested here, offering valuable hints as to the number and the characteristics of cases where experimental phasing remains essential for macromolecular structure solution.

Exploring the energetic and conformational properties of the sequence space connecting naturally occurring RNA tetraloop receptor motifs.

Folded RNAs contain tertiary contact motifs whose structures and energetics are conserved across different RNAs. The transferable properties of RNA motifs simplify the RNA folding problem, but measuring energetic and conformational properties of many motifs remains a challenge. Here, we use a high-throughput thermodynamic approach to investigate how sequence changes alter the binding properties of naturally-occurring motifs, the GAAA tetraloop•tetraloop receptor (TLR) interactions. We measured the binding energies and conformational preferences of TLR sequences that span mutational pathways from the canonical 11ntR to two other natural TLRs, the IC3R and Vc2R. While the IC3R and Vc2R share highly similar energetic and conformational properties, the landscapes that map the sequence changes for their conversion from the 11ntR to changes in these properties differ dramatically. Differences in the energetic landscapes stem from the mutations needed to convert the 11ntR to the IC3R and Vc2R rather than a difference in the intrinsic energetic architectures of these TLRs. The conformational landscapes feature several non-native TLR variants with conformational preferences that differ from both the initial and final TLRs; these species represent potential branching points along the multidimensional sequence space to sequences with greater fitness in other RNA contexts with alternative conformational preferences. Our high-throughput, quantitative approach reveals the complex nature of sequence-fitness landscapes and leads to models for their molecular origins. Systematic and quantitative molecular approaches provide critical insights into understanding the evolution of natural RNAs as they traverse complex landscapes in response to selective pressures.

Structural and functional characterization of haemoglobin genes in Labeo catla: Insights into hypoxic adaptation and survival

Haemoglobin (HB) protein comprises four subunits: two identical α-subunits (HBA) and two identical β-subunits (HBB), encoded by the HBA and HBB genes. In this investigation, 5′/3′ RACE PCR (Rapid Amplification of cDNA Ends) was used to obtain complete coding sequences (CDSs) of both the genes from farmed Labeo catla. The resulting CDSs were 432 base pairs and 447 base pairs for HBA and HBB, respectively, corresponding to 143 and 148 amino acids. Phylogenetic analysis revealed close relationships with other cyprinids, with Labeo rohita being the closest relative. Functional analysis and protein structure prediction were conducted using bioinformatics tools. Expression profiling of both genes was checked in various tissues under control (C) and hypoxic (H) conditions. Notably, under hypoxia, HBA and HBB genes were significantly upregulated (P < 0.05) initially, followed by a return to normal expression levels. Similar trends were observed for Hif1α (Hypoxia-inducible factor one alpha) and EPO (Erythropoietin) genes. Additionally, haematological indices also significantly increased corresponding to the gene expressions. However, with the decrease in the expression of these genes an onset of mortality was observed in the hypoxia (H) treated groups. The results of the current study explored the role of haemoglobin genes in adaptation to the hypoxic condition.