Protein Secondary Structure Elements Research Articles

Method for Recognizing the Characteristic Elements of Protein Secondary Structure From the Llm of Its Amino Acid Sequence

The spatial structure of a protein determines its biochemical properties and, consequently, its function. The same applies to elements of secondary structure, which adopt shapes of helices, coiled coils, strands, sheets and other formations in three-dimensional space. Automatic detection of such formations based on their corresponding amino acid sequences in the protein will enable the cataloging of these sequence fragments, examining and systematizing their correspondence to spatial protein formations. This, in turn, should simplify the task of searching for complementary and functional similarities among different proteins. For this purpose, a method based on covariance, autocorrelation, and spatial-spectral analysis of embeddings of their amino acid sequences has been developed and tested.

Dynamic Implications of Noncovalent Interactions in Amphiphilic Single-Chain Polymer Nanoparticles.

Single-chain polymer nanoparticles (SCNPs) combine the chemical diversity of synthetic polymers with the intricate structure of biopolymers, generating versatile biomimetic materials. The mobility of polymer chain segments at length scales similar to secondary structural elements in proteins is critical to SCNP structure and thus function. However, the influence of noncovalent interactions used to form SCNPs (e.g., hydrogen-bonding and biomimetic secondary-like structure) on these conformational dynamics is challenging to quantitatively assess. To isolate the effects of noncovalent interactions on SCNP structure and conformational dynamics, we synthesized a series of amphiphilic copolymers containing dimethylacrylamide and monomers capable of forming these different interactions: (1) di(phenylalanine) acrylamide that forms intramolecular β-sheet-like cross-links, (2) phenylalanine acrylamide that forms hydrogen-bonds but lacks a defined local structure, and (3) benzyl acrylamide that has the lowest propensity for hydrogen-bonding. Each SCNP formed folded structures comparable to those of intrinsically disordered proteins, as observed by size exclusion chromatography and small angle neutron scattering. The dynamics of these polymers, as characterized by a combination of dynamic light scattering and neutron spin echo spectroscopy, was well described using the Zimm with internal friction (ZIF) model, highlighting the role of each noncovalent interaction to additively restrict the internal relaxations of SCNPs. These results demonstrate the utility of local scale interactions to control SCNP polymer dynamics, guiding the design of functional biomimetic materials with refined binding sites and tunable kinetics.

Determining Protein Secondary Structures in Heterogeneous Medium-Resolution Cryo-EM Images Using CryoSSESeg.

While the acquisition of cryo-electron microscopy (cryo-EM) at near-atomic resolution is becoming more prevalent, a considerable number of density maps are still resolved only at intermediate resolutions (5-10 Å). Due to the large variation in quality among these medium-resolution density maps, extracting structural information from them remains a challenging task. This study introduces a convolutional neural network (CNN)-based framework, cryoSSESeg, to determine the organization of protein secondary structure elements in medium-resolution cryo-EM images. CryoSSESeg is trained on approximately 1300 protein chains derived from around 500 experimental cryo-EM density maps of varied quality. It demonstrates strong performance with residue-level F 1 scores of 0.76 for helix detection and 0.60 for β-sheet detection on average across a set of testing chains. In comparison to traditional image processing tools like SSETracer, which demand significant manual intervention and preprocessing steps, cryoSSESeg demonstrates comparable or superior performance. Additionally, it demonstrates competitive performance alongside another deep learning-based model, Emap2sec. Furthermore, this study underscores the importance of secondary structure quality, particularly adherence to expected shapes, in detection performance, emphasizing the necessity for careful evaluation of the data quality.

A unique approach for protein secondary structure comparison under TOPS representation

To unravel the intricate connection between protein function and protein structure, it is imperative to comprehensively evaluate protein secondary structure similarity from various perspectives. While numerous techniques have been suggested for comparing protein secondary structure elements (SSE), there continues to be a substantial need for finding alternative ways of comparing the same. In this paper, Topology of Protein Structure (TOPS) representations of protein secondary structures are considered to offer a new alignment-free method for evaluating similarities/dissimilarities of protein secondary structures. Initially, a two-dimensional numerical representation of the SSE is created, associating each point with a mass reflecting its frequency of occurrence. Then the means of coordinate values are determined by averaging weighted sums, and these mean values are subsequently used to calculate moments-of-inertia. Next, a four-component descriptor is generated out of the eigenvalues of the matrix and the mean values of the represented coordinates. Thereafter, Manhattan distance measure is used to obtain the distance matrix. This is finally applied to obtain the phylogenetic trees under the use of NJ method. SSE considered in the proposed method comprises 36-elements from the Chew-Kedem database giving five different taxa: globin, alpha-beta, tim-barrel, beta, and alpha. Phylogenetic trees were created for these SSE through the application of various methods: Clustal-Omega, LZ-Complexity, SED, TOPS + and TOC, to facilitate comparative analysis. Phylogenetic tree of the proposed method outperformed results of the previous methods when applied to the same SSE. Therefore, the method effectively constructs phylogenetic tree for analyzing protein secondary structure comparison. Communicated by Ramaswamy H. Sarma

The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement.

We present fast and simple-to-implement measures of the entanglement of protein tertiary structures which are appropriate for highly flexible structure comparison. These are performed using the SKMT algorithm, a novel method of smoothing the Cα backbone to achieve a minimal complexity curve representation of the manner in which the protein's secondary structure elements fold to form its tertiary structure. Its subsequent complexity is characterised using measures based on the writhe and crossing number quantities heavily utilised in DNA topology studies, and which have shown promising results when applied to proteins recently. The SKMT smoothing is used to derive empirical bounds on a protein's entanglement relative to its number of secondary structure elements. We show that large scale helical geometries dominantly account for the maximum growth in entanglement of protein monomers, and further that this large scale helical geometry is present in a large array of proteins, consistent across a number of different protein structure types and sequences. We also show how these bounds can be used to constrain the search space of protein structure prediction from small angle x-ray scattering experiments, a method highly suited to determining the likely structure of proteins in solution where crystal structure or machine learning based predictions often fail to match experimental data. Finally we develop a structural comparison metric based on the SKMT smoothing which is used in one specific case to demonstrate significant structural similarity between Rossmann fold and TIM Barrel proteins, a link which is potentially significant as attempts to engineer the latter have in the past produced the former. We provide the SWRITHE interactive python notebook to calculate these metrics.

Properties and structural features of native and modified proteins of concentrates from white and brown rice

The physicochemical properties and structural features of proteins of concentrates (PC) from white and brown rice are described. Differences in a degree of proteolysis and the relationship between the functional and technological properties of proteins and their structure were established. Proteins from white rice had lower molecular weights (MW) than proteins from brown rice: 1.3–199.5 kDa versus 1.5–251.1 kDa. Most proteins were grouped in three low-molecular-weight fractions (МW 11.7–27.5 kDa) in PC from white rice and in one fraction with the high molecular weight (182–251.1 kDa) in PC from brown rice. In the process of hydrolysis, the MW of proteins (high-molecular-weight) of PC from white rice decreased from 199.5 to 120.2 kDa with generation of peptides with a molecular weight of <1.3 kDa, while the MW remained unchanged upon hydrolysis of proteins of PC from brown rice. In the composition of PC from white rice, flavonoids interacted with protein fractions with molecular weights of 131, 10, and 4.0 kDa; while in PC from brown rice, they interacted with only one fraction with a molecular weight of 216 kDa. The quantity of flavonoids in PC from white rice was 2.3 times less than that in PC from brown rice. Elements of protein secondary structure were established for PC: α–helix, 310 — helix, β-structure, β-bends, and irregular shape. Proteolysis of proteins was accompanied by a decrease in the number of α-helices, increase in the proportion of β-structures and irregular regions, weakening of the hydrophobic properties of proteins and an increase in the number of S–S bonds; PC from brown rice was characterized by a higher content of –СН2 groups from the flavonoid family, as well as by a higher degree of unsaturation of groups of benzene nuclei, ketone and ester groups. A negative correlation was found between the foaming ability of PC and the upper boundaries of molecular weights (r = — 0.95), quantity of high-molecular weight proteins (r = — 0.80) and aggregation constants, and a positive correlation (r = + 0.8) with the number of S–S bonds. The high foaming ability is interrelated with proteins with a molecular weight of no more than 120 kDa. The results are intended to regulate the functional properties of protein products based on the characteristics of the physicochemical properties of native and modified proteins.

Design and evaluation of α-helix-based peptide inhibitors for blocking PD-1/PD-L1 interaction

The current research in tumor immunotherapy indicates that blocking the protein-protein interaction (PPI) between PD-1 and its ligand, PD-L1, may be one of the most effective treatments for cancer patients. The α-helix is a common elements of protein secondary structure and is often involved in protein interaction. Thus, α-helix-based peptides could mimic proteins involved in such interactions and are also capable of modulating PPI in vivo. In this study, starting from a potential α-helix-rich protein, we designed a series of α-helix-based peptide candidates to block PD-1/PD-L1 interaction. These candidates were first screened using molecular docking and molecular dynamics simulations, and then their capacities to inhibit PD-1/PD-L1 interactions and to restore antitumor immune activities were investigated using the HTRF assay, SPR assay, cellular co-culture experiments and animal model experiments. Two peptides exhibited the best anti-tumor effects and the strong ability to restore the immunity of tumor-infiltrating T-cells. Further D-amino acid substitution was employed to improve the serum stability of peptide candidate, making the intravenous administration easier while maintaining the therapeutic efficacy. The resultant peptides showed promise as checkpoint inhibitors for application in tumor immunotherapy. These findings suggested that our strategy for developing peptides starting from an α-helical structure could be used in the design of bioactive inhibitors to potential block protein-protein interactions.

Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates.

Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Cα atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO.

Nanozyme mediated SOD1 strategy linked to nanochaperone facilitate aggregation inhibition in amyotrophic lateral sclerosis.

Unlike natural enzymes, nanomaterial based ‘nanozyme’ offers higher stability and easy storage to address a steady function even in the extreme conditions of pH, temperature and molecular resistance to digestion from proteases. Nanozyme scavenging surplus reactive oxygen species (ROS) can serve as an effective tool to treat various neurodegenerative diseases. Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder characterized by the loss of the upper motor neuron and lower motor neuron leading to destruction of motor neurons and extra motor symptoms. Thus, loss/inefficiency of SOD1 enzymatic activity and its aggregation, both are known to be associated with ALS disease pathology. Here, nanoparticle (NP) functionalization strategy is used to develop a histidine (His) based superoxide dismutase-I (SOD1) therapeutic substitute with a dual role of nanozyme and nanochaperone activity. Zinc and Copper are an essential micronutrient of biological milieu in humans and act as an integral part of several enzymes active sites. His-functionalized nanozyme was found significant for scavenging excess ROS and targeting mitochondria with cytoprotective effect in the ALS model in-vitro. The nanochaperone role was characterized by its ability to reduce the aggregation of human apoSOD1WT and apoSOD1G93A mutant. In particular, a critical analysis of Amide I and III Raman band, specified to protein secondary structural elements suggested the decrease of a-helix and the early predominant b-sheets in human SOD1 in the absence of nanochaperone. But a reverse phenomenon was observed in the presence of nanochaperons. The dual role of nanozyme-nanochaperone provides a promising insightful into ALS pathology and may open new avenues for future therapeutics.

Circular Dichroism Spectra of α-Chymotrypsin-SDS Solutions Depend on the Procedure of Their Preparation.

We recorded the far- and near-UV circular dichroism (CD) spectra of solutions of α-chymotrypsin and sodium dodecyl sulfate (SDS) with the final surfactant concentration significantly above the critical micellization concentration. Solutions were prepared using three different procedures. The reference procedure was to mix the chymotrypsin solution with the SDS solution once, immediately achieving the final SDS concentration. In alternative procedures, the protein solutions initially contained some SDS and were mixed with pure SDS solutions at a concentration to provide the same final surfactant as the reference mixing. We demonstrate that the supplementation to the selected final concentration of SDS of the pure chymotrypsin solution leads to different CD spectra than the supplementation to this final concentration of SDS in the chymotrypsin solution containing a small concentration of a few millimolar SDS. These differences disappear when the initial concentration of SDS in the protein solution, which we then supplement to the indicated final concentration, is higher. This suggests the irreversibility of the processes caused by the addition of SDS to chymotrypsin and the influence of the initial amount of this surfactant on the processes occurring with its further addition to the solution. For quantitative analysis of far-UV CD spectra in terms of populations of protein secondary structure elements, we used four well-established software packages. All programs consistently indicate that the observed differences in the far-UV CD spectra can be explained by the differences in the increase in the population of helical forms in chymotrypsin under the influence of SDS.

Antiviral phytocompounds "ellagic acid" and "(+)-sesamin" of Bridelia retusa identified as potential inhibitors of SARS-CoV-2 3CL pro using extensive molecular docking, molecular dynamics simulation studies, binding free energy calculations, and bioactivity prediction.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected billions and has killed millions to date. Studies are being carried out to find therapeutic molecules that can potentially inhibit the replication of SARS-CoV-2. 3-chymotrypsin-like protease (3CL pro) involved in the polyprotein cleavage process is believed to be the key target for viral replication, and hence is an attractive target for the discovery of antiviral molecules. In the present study, we aimed to identify natural phytocompounds from Bridelia retusa as potential inhibitors of SARS-CoV-2 3CL pro (PDB ID: 6M2N) using in silico techniques. Molecular docking studies conducted with three different tools in triplicates revealed that ellagic acid (BR6) and (+)-sesamin (BR13) has better binding affinity than the co-crystal inhibitor “3WL” of 6M2N. BR6 and BR13 were found to have a high LD50 value with good bioavailability. 3WL, BR6, and BR13 bind to the same active binding site and interacted with the HIS41-CYS145 catalytic dyad including other crucial amino acids. Molecular dynamics simulation studies revealed stability of protein–ligand complexes as evidenced from root-mean-square deviations, root-mean-square fluctuations (RMSF), protein secondary structure elements, ligand-RMSF, protein–ligand contacts, ligand torsions, and ligand properties. BR6 (−22.3064 kcal/mol) and BR13 (−19.1274 kcal/mol) showed a low binding free energy value. The Bayesian statistical model revealed BR6 and BR13 as better protease inhibitors than 3WL. Moreover, BR6 and BR13 had already been reported to elicit antiviral activities. Therefore, we conclude that ellagic acid and (+)-sesamin as natural antiviral phytocompounds with inhibitory potential against SARS-CoV-2 3CL pro.Supplementary informationThe online version contains supplementary material available at 10.1007/s11224-022-01959-3.

Computational screening and analysis of deleterious nsSNPs in human p14ARF (CDKN2A gene) protein using molecular dynamic simulation approach

Cyclin-dependent kinase inhibitor 2 A (CDKN2A) gene belongs to the cyclin-dependent kinase family that code for two transcripts (p16INK4A and p14ARF), both work as tumor suppressors proteins. The mutation that occurs in the p14ARF protein can lead to different types of cancers. Single nucleotide polymorphisms (SNPs) are an important type of genetic alteration that can lead to different types of diseases. In this study, we applied the computational strategy on human p14ARF protein to identify the potential deleterious nsSNPs and check their impact on the structure, function, and protein stability. We applied more than ten prediction tools to screen the retrieved 288 nsSNPs, consequently extracting four deleterious nsSNPs i.e., rs139725688 (R10G), rs139725688 (R21W), rs374360796 (F23L) and rs747717236 (L124R). Homology modeling, conservation and conformational analysis of mutant models were performed to examine the divergence of these variants from the native p14ARF structure. All-atom molecular dynamics simulation revealed a significant impact of these mutations on protein stability, compactness, globularity, solvent accessibility and secondary structure elements. Protein-protein interactions indicated that p14ARF operates as a hub linking clusters of different proteins and that changes in p14ARF may result in the disassociation of numerous signal cascades. Our current study is the first survey of computational analysis on p14ARF protein that determines the association of these nsSNPs with the altered function of p14ARF protein and leads to the development of various types of cancers. This research proposes the described functional SNPs as possible targets for proteomic investigations, diagnostic procedures, and treatments. Communicated by Ramaswamy H. Sarma.

An End-to-End Deep Learning Pipeline for Assigning Secondary Structure in Proteins

Protein secondary structure assignment, a subdiscipline of computational chemistry, is yet to be explored using deep learning techniques. Protein secondary structure elements are assigned to support structural analysis and prediction. Algorithms like DSSP, generally regarded as the gold standard for assigning the secondary structure of proteins, need full atom information to label protein coordinates. The PDB database has been the major repository for data on the 3D structures of proteins, nucleic acids, and other complex assemblies since 1971. However, a significant fraction of protein structures contains missing atoms. As a result, new approaches to reliably and consistently assigning secondary structures based on coarse-grained atomic coordinates are needed. While deep learning architectures have an unparalleled track record in applications such as protein structure prediction, there are only a few known deep learning solutions for structure assignment problems. While the gold standard methods are based on bonding information and other geometric characteristics, deep learning methods extract features themselves without human intervention. The benefit that standardised datasets provide to the effectiveness of deep learning systems in multiple domains motivated us to create a labeled dataset for protein structure assignment tasks. Additionally, a deep learning model, named PSSADL, solely based on [Formula: see text] coordinates was trained on the generated dataset to validate its potency. The proposed method, which combines Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)/Bidirectional LSTM networks, has been compared to the established standards and more recent techniques. The model achieved an accuracy of [Formula: see text] on the benchmark and individual test sets. The results show that deep learning techniques have a promising future in protein structure analysis, implying that the dataset developed as part of our work will be a valuable resource for further protein structure research.

Assigning secondary structure in proteins using AI.

Knowledge about protein structure assignment enriches the structural and functional understanding of proteins. Accurate and reliable structure assignment data is crucial for secondary structure prediction systems. Since the 1980s, various methods based on hydrogen bond analysis and atomic coordinate geometry, followed by machine learning, have been employed in protein structure assignment. However, the assignment process becomes challenging when missing atoms are present in the protein files. Our method proposed a multi-class classifier program named DLFSA for assigning protein secondary structure elements (SSE) using convolutional neural networks (CNNs). A fast and efficient GPU-based parallel procedure extracts fragments from protein files. The model implemented in this work is trained with a subset of the protein fragments and achieves 88.1% and 82.5% train and test accuracy, respectively. The model uses only Cα coordinates for secondary structure assignments. The model has been successfully tested on a few full-length proteins also. Results from the fragment-based studies demonstrate the feasibility of applying deep learning solutions for structure assignment problems.

Dynamics of Germination Behaviour, Protein Secondary Structure, Technofunctional Properties, Antinutrients, Antioxidant Capacity and Mineral Elements in Germinated Dhaincha.

SUMMARYResearch backgroundDhaincha (Sesbania aculeata) is a forage legume primarily used for green manuring and animal feed. Good nutritional profile of dhaincha makes it a potential alternative legume in human nutrition. However, the presence of high amount of antinutrients poses a problem in its utilisation for food applications. The present investigation intends to germinate dhaincha seeds at different time-temperature regimes and to evaluate the process of germination to ascertain optimal conditions and improve its potential for utilisation.Experimental approachDhaincha seeds were germinated at 24, 28 and 32 °C for 24, 48 and 72 h. Germination characteristics and germination loss, spectral characteristics, technofunctionality, antinutrients, bioactive constituents, antioxidant capacity and mineral element content of germinated dhaincha were evaluated. Optimal balance of technobiofunctionality of germinated dhaincha seeds was validated by principal component analysis.Results and conclusionsSprout length and germination loss increased with the higher germination temperature and prolonged germination time. Seeds showed similar germination rate at 28 and 32 °C and it was markedly higher than at 24 °C. Germination for 24 h resulted in mild conformational changes in the secondary structure of proteins, whereas germination for 48 and 72 h exhibited major conformational changes in the β-sheets, resulting in the improvement in the hydration and foaming properties. Progression of germination (72 h) caused the decrease of tannin (24.47%), phytic acid (16.38%) and saponin (24.58%) mass fractions, and of trypsin inhibitor (40.33%) and lectin activity (62.50%). Slight decrease of DPPH˙ (3.7%) and ABTS˙+ (18.5%) values was also observed, whereas total flavonoid content (36.14%) and metal chelating activity (26.76%) increased. Total phenolics, FRAP, and reducing power decreased after 24 h, followed by a gradual increase. Zinc extractability increased drastically with germination. Germination at 28 °C for 72 h resulted in higher reduction of antinutrients with optimal retention of antioxidant activity and better functional characteristics, as validated by principal component analysis.Novelty and scientific contributionDhaincha is an unknown crop in Europe, and even in Asia it is predominantly used as green manure and animal feed. This research demonstrated that the intervention in germination can transform dhaincha into a promising crop for food industry. Germinated dhaincha exhibited enhanced technobiofunctionality for utilisation in various food formulations.

Protein secondary structure assignment using pc-polyline and convolutional neural network.

The assignment of protein secondary structure elements (SSEs) underpins structural analysis and prediction. The backbone of a protein could be adequately represented using a pc-polyline that passes through the centers of its peptide planes. One salient feature of pc-polyline representation is that the secondary structure of a protein becomes recognizable in a matrix whose elements are the pairwise distances between two peptide plane centers. Thus, a pc-polyline could in turn be used to assign SSEs. Using convolutional neural network (CNN) here we confirm that a pc-polyline indeed contains enough information for it to be used for the accurate assignments of the six SSE types: α-helix, β-sheet, β-bulge, 310 -helix, turn and loop. The applications to three large data sets show that the assignments by our CNN-based p2psse program agree very well with those by dssp, stride and quite well with those by five other programs. The analyses of their SSE assignments raise some general questions about the characterizations of protein secondary structure. In particular the analyses illustrate the difficulty with giving a quantitative and consistent definition for each of the six SSE types especially for 310 -helix, β-bulge, turn or loop in terms of either backbone H-bond patterns, or backbone dihedral angles, or Cα -polyline or pc-polyline. The difficulty suggests that the SSE space though being dominated by the regions for the six SSE types is to a certain degree continuous. The program is available at https://github.com/wlincong/p2pSSE.

ISSEC: inferring contacts among protein secondary structure elements using deep object detection

BackgroundThe formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them.ResultsWe here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions.ConclusionsComprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.

Loops linking secondary structure elements affect the stability of the molten globule intermediate state of apomyoglobin.

Apomyoglobin is a widely used model for studying the molecular mechanisms of globular protein folding. This work aimed to analyze the effects of rigidity and length of loops linking protein secondary structure elements on the stability of the molten globule intermediate state. For this purpose, we studied folding/unfolding of mutant apomyoglobin forms with substitutions of loop-located proline residues to glycine and with loop extension by three or six glycine residues. The kinetic and equilibrium experiments performed gave an opportunity to calculate free energies of different apomyoglobin states. Our analysis revealed that the mutations introduced into the apomyoglobin loops have a noticeable effect on the stability of the intermediate state compared to the unfolded state.

Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Maps.

In recent years, three‐dimensional density maps reconstructed from single particle images obtained by electron cryo‐microscopy (cryo‐EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de‐novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo‐EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main‐chain placement. Due to its high recall and precision rates of 95.1 % and 80.3 %, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP‐EM suite.

Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo‐Electron Microscopy Maps

AbstractIn recent years, three‐dimensional density maps reconstructed from single particle images obtained by electron cryo‐microscopy (cryo‐EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de‐novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo‐EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main‐chain placement. Due to its high recall and precision rates of 95.1 % and 80.3 %, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP‐EM suite.