Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions

Conjugation with the Carrier Helped to Reveal acidification-Induced Structural Shift in the Peptide from Phospholipase Domain of Parvovirus B19

Spectroscopic studies on domains and peptides of large proteins are complicated because of the tendency of short peptides to form oligomers in aquatic buffers, but conjugation of a peptide with a carrier protein may be helpful. In this study we approved that a fragment of SK30 peptide from phospholipase A2 domain of VP1 Parvovirus B19 capsid protein (residues: 144-159; 164; 171-183; sequence: SAVDSAARIHDFRYSQLAKLGINPYTHWTVADEELLKNIK) turns from random coil to alpha helix in the acidic medium only in case if it had been conjugated with BSA (through additional N-terminal Cys residue, turning it into CSK31 peptide, and SMCC linker) according to CD-spectroscopy results. In contrast, unconjugated SK30 peptide does not undergo such shift because it forms stable oligomers connected by intermolecular antiparallel beta sheet, according to IR-spectroscopy, CD-spectroscopy, blue native gel electrophoresis and centrifugal ultrafiltration, as, probably, the whole isolated phospholipase domain of VP1 protein does. However, being a part of the long VP1 capsid protein, phospholipase domain may change its fold during the acidification of the medium in the endolysosome by the way of the formation of contacts between protonated His153 and Asp175, promoting the shift from random coil to alpha helix in its N-terminal part. This study opens up a perspective of vaccine development, since rabbit polyclonal antibodies against the conjugate of CSK31 peptide with BSA, in which the structure of the second alpha helix from the phospholipase A2 domain should be reproduced, can bind epitopes of the complete recombinant unique part of VP1 Parvovirus B19 capsid (residues: 1-227).

An integrated multitool analysis contributes elements to interpreting unclassified factor IX missense variants associated with hemophilia B

BACKGROUND

Dissection of genotype-phenotype relationships in hemophilia B (HB) is particularly relevant for challenging (mild HB) or for HB-associated but unclassified factor (F)IX missense variants.

OBJECTIVE

To contribute elements to interpret unclassified HB-associated FIX missense variants by a multiple-level approach upon identification of a reported, but uncharacterized, FIX missense variant associated with mild HB.

METHODS

Molecular modeling of wild-type and V92A FIX variants, expression studies in HEK293 cells with evaluation of protein (ELISA, western blotting) and activity (activated partial thromboplastin time-based/chromogenic assays) levels after recombinant expression, and multiple prediction tools.

RESULTS

The F9(NM_000133.4):c.275T>C (p.V92A) variant was found in a mild HB patient (antigen, 45.4 U/dL; coagulant activity, 23.6 IU/dL; specific activity, 0.52). Newly generated molecular models showed alterations in Gla/EGF1-EGF2 domain conformation impacting Ca++ affinity and protein-protein interactions with activated factor XI (FXIa). Multitool analysis indicated a moderate impact on protein structure/function of the valine-to-alanine substitution, in accordance with patient and modeling data. Expression studies on the V92A variant showed a specific activity (0.49 ± 0.07; wild-type, 1.0 ± 0.1) recapitulating that of the natural variant, and pointed toward a moderate activation impairment as the main determinant underlying the p.V92A defect. The validated multitool approach, integrated with evidence-based data, was challenged on a panel (n = 9) of unclassified FIX missense variants, which resulted in inferred protein (secretion/function) outputs and HB severity.

CONCLUSION

The rational integration of multitool and multiparameter analyses contributed elements to interpret genotype/phenotype relationships of unclassified FIX missense variants, with implications for diagnosis, management, and treatment of HB patients, and potentially translatable into other human disorders.

Clinical and Molecular Profiling in GNAO1 Permits Phenotype-Genotype Correlation

BACKGROUND

Defects in GNAO1, the gene encoding the major neuronal G-protein Gαo, are related to neurodevelopmental disorders, epilepsy, and movement disorders. Nevertheless, there is a poor understanding of how molecular mechanisms explain the different phenotypes.

OBJECTIVES

We aimed to analyze the clinical phenotype and the molecular characterization of GNAO1-related disorders.

METHODS

Patients were recruited in collaboration with the Spanish GNAO1 Association. For patient phenotyping, direct clinical evaluation, analysis of homemade-videos, and an online questionnaire completed by families were analyzed. We studied Gαo cellular expression, the interactions of the partner proteins, and binding to guanosine triphosphate (GTP) and G-protein-coupled receptors (GPCRs).

RESULTS

Eighteen patients with GNAO1 genetic defects had a complex neurodevelopmental disorder, epilepsy, central hypotonia, and movement disorders. Eleven patients showed neurological deterioration, recurrent hyperkinetic crisis with partial recovery, and secondary complications leading to death in three cases. Deep brain stimulation improved hyperkinetic crisis, but had inconsistent benefits in dystonia. The molecular defects caused by pathogenic Gαo were aberrant GTP binding and hydrolysis activities, an inability to interact with cellular binding partners, and reduced coupling to GPCRs. Decreased localization of Gαo in the plasma membrane was correlated with the phenotype of "developmental and epileptic encephalopathy 17." We observed a genotype-phenotype correlation, pathogenic variants in position 203 were related to developmental and epileptic encephalopathy, whereas those in position 209 were related to neurodevelopmental disorder with involuntary movements. Milder phenotypes were associated with other molecular defects such as del.16q12.2q21 and I344del.

CONCLUSION

We highlight the complexity of the motor phenotype, which is characterized by fluctuations throughout the day, and hyperkinetic crisis with a distinct post-hyperkinetic crisis state. We confirm a molecular-based genotype-phenotype correlation for specific variants. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features

Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.

Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.

Bioinformatics approach for prediction and analysis of the Non-Structural Protein 4B (NSP4B) of the Zika virus

BACKGROUND

The Nonstructural Protein (NSP) 4B of Zika virus of 251 amino acids from (ZIKV/Human/POLG_ZIKVF) with accession number (A0A024B7W1), Induces the production of Endoplasmic Reticulum ER-derived membrane vesicles, which are the sites of viral replication. To understand the physical basis of how proteins fold in nature and to solve the challenge of protein structure prediction, Ab-initio and comparative modeling are crucial tools.

RESULTS

The systematic in silico technique, ThreaDom, had only predicted one domain (4 - 190) of NSP4B. I-TASSER, and Alphafold were ranked as the best servers for full-length 3-D protein structure predictions of NSP4B, where the predicted models were evaluated quantitatively using benchmarked metrics including C-score (-3.43), TM-score (0.77949), RMSD (2.73), and Z-score (1.561). The functional and protein binding motifs were realized using motif databases, secondary and surface accessibility predictions combined with Post-Translational Modification Sites (PTMs) prediction. Two highly conserved protein-binding motifs (Flavi NS4B and Bacillus papRprotein), together with three (PTMs) (Casein Kinase II, Myristyl site, and ASN-Glycosylation site) were predicted utilizing the Motif scan and Scanprosite servers. These patterns and PTMs were associated with NSP4B's role in triggering the development of the viral replication complex and its participation in the localization of NS3 and NS5 on the membrane. Only one hit from Structural Classification of Protein (SCOP) matched the protein sequence at positions 10 to 397 and was categorized six-hairpin glycosidases superfamily according to CATH (Class, Architecture, Topology, and Homology). Integrating this NSP4B information with the templates' SCOP and CATH annotations achieves it easier to attribute structure-function/evolution links to both previously known and recently discovered protein structures.

It is theoretically possible to avoid misfolding into non-covalent lasso entanglements using small molecule drugs

A novel class of protein misfolding characterized by either the formation of non-native noncovalent lasso entanglements in the misfolded structure or loss of native entanglements has been predicted to exist and found circumstantial support through biochemical assays and limited-proteolysis mass spectrometry data. Here, we examine whether it is possible to design small molecule compounds that can bind to specific folding intermediates and thereby avoid these misfolded states in computer simulations under idealized conditions (perfect drug-binding specificity, zero promiscuity, and a smooth energy landscape). Studying two proteins, type III chloramphenicol acetyltransferase (CAT-III) and D-alanyl-D-alanine ligase B (DDLB), that were previously suggested to form soluble misfolded states through a mechanism involving a failure-to-form of native entanglements, we explore two different drug design strategies using coarse-grained structure-based models. The first strategy, in which the native entanglement is stabilized by drug binding, failed to decrease misfolding because it formed an alternative entanglement at a nearby region. The second strategy, in which a small molecule was designed to bind to a non-native tertiary structure and thereby destabilize the native entanglement, succeeded in decreasing misfolding and increasing the native state population. This strategy worked because destabilizing the entanglement loop provided more time for the threading segment to position itself correctly to be wrapped by the loop to form the native entanglement. Further, we computationally identified several FDA-approved drugs with the potential to bind these intermediate states and rescue misfolding in these proteins. This study suggests it is possible for small molecule drugs to prevent protein misfolding of this type.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

An intramolecular cross-talk in D29 mycobacteriophage endolysin governs the lytic cycle and phage-host population dynamics

D29 mycobacteriophage encodes LysA endolysin, which mediates mycobacterial host cell lysis by targeting its peptidoglycan layer, thus projecting itself as a potential therapeutic. However, the regulatory mechanism of LysA during the phage lytic cycle remains ill defined. Here, we show that during D29 lytic cycle, structural and functional regulation of LysA not only orchestrates host cell lysis but also is critical for maintaining phage-host population dynamics by governing various phases of lytic cycle. We report that LysA exists in two conformations, of which only one is active, and the protein undergoes a host peptidoglycan-dependent conformational switch to become active for carrying out endogenous host cell lysis. D29 maintains a pool of inactive LysA, allowing complete assembly of phage progeny, thus helping avoid premature host lysis. In addition, we show that the switch reverses after lysis, thus preventing exogenous targeting of bystanders, which otherwise negatively affects phage propagation in the environment.

Discovery and Identification of a Novel Tag of HlyA60 for Protein Active Aggregate Formation in Escherichia coli

The strategy of active aggregation tag fusion expression with target proteins can solve the problems of restricted expression, inefficient purification, and laborious immobilization faced in the production of recombinant proteins in Escherichia coli. We localized a novel active aggregation peptide HlyA60 from the hemolysin A secretion system, which can effectively induce aggregate formation with satisfactory protein activities in E. coli after fusion expression with the protein of interest. Based on structural prediction and surface properties, the process of active aggregation of HlyA60 through electrostatic interactions and hydrophobic interactions was analyzed. To investigate the potential application of HlyA60 as an efficient aggregation tag, it was fused with acetyl xylan esterase and lipase A, separately. The resulting fusion proteins demonstrated active aggregation rates of 97.6 and 66.7%, respectively, leading to 1.9-fold and 1.7-fold increases in bacterial density at the end of fermentation. The AXE-HlyA60 fusion protein, which exhibited superior performance, was subjected to purification and immobilization. It was able to achieve column-free purification with an impressive 98.8% recovery and in situ immobilization; the immobilization enabled 30 cycles of reactions to take place with 85% residual activity maintained. Our findings provide a novel tool for efficiently producing recombinant proteins in E. coli.

Recent Advances and Challenges in Protein Structure Prediction

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality

Plant virus genomes encode proteins that are involved in replication, encapsidation, cell-to-cell, and long-distance movement, avoidance of host detection, counter-defense, and transmission from host to host, among other functions. Even though the multifunctionality of plant viral proteins is well documented, contemporary functional repertoires of individual proteins are incomplete. However, these can be enhanced by modeling tools. Here, predictive modeling of proteins encoded by the two genomic RNAs, i.e., RNA1 and RNA2, of grapevine fanleaf virus (GFLV) and their satellite RNAs by a suite of protein prediction software confirmed not only previously validated functions (suppressor of RNA silencing [VSR], viral genome-linked protein [VPg], protease [Pro], symptom determinant [Sd], homing protein [HP], movement protein [MP], coat protein [CP], and transmission determinant [Td]) and previously identified putative functions (helicase [Hel] and RNA-dependent RNA polymerase [Pol]), but also predicted novel functions with varying levels of confidence. These include a T3/T7-like RNA polymerase domain for protein 1AVSR, a short-chain reductase for protein 1BHel/VSR, a parathyroid hormone family domain for protein 1EPol/Sd, overlapping domains of unknown function and an ABC transporter domain for protein 2BMP, and DNA topoisomerase domains, transcription factor FBXO25 domain, or DNA Pol subunit cdc27 domain for the satellite RNA protein. Structural predictions for proteins 2AHP/Sd, 2BMP, and 3A? had low confidence, while predictions for proteins 1AVSR, 1BHel*/VSR, 1CVPg, 1DPro, 1EPol*/Sd, and 2CCP/Td retained higher confidence in at least one prediction. This research provided new insights into the structure and functions of GFLV proteins and their satellite protein. Future work is needed to validate these findings.

Structural Shifts of the Parvovirus B19 Capsid Receptor-binding Domain: A Peptide Study

BACKGROUND

Binding appropriate cellular receptors is a crucial step of a lifecycle for any virus. Structure of receptor-binding domain for a viral surface protein has to be determined before the start of future drug design projects.

OBJECTIVES

Investigation of pH-induced changes in the secondary structure for a capsid peptide with loss of function mutation can shed some light on the mechanism of entrance.

METHODS

Spectroscopic methods were accompanied by electrophoresis, ultrafiltration, and computational biochemistry.

RESULTS

In this study, we showed that a peptide from the receptor-binding domain of Parvovirus B19 VP1 capsid (residues 13-31) is beta-structural at pH=7.4 in 0.01 M phosphate buffer, but alpha- helical at pH=5.0, according to the circular dichroism (CD) spectroscopy results. Results of infra- red (IR) spectroscopy showed that the same peptide exists in both alpha-helical and beta-structural conformations in partial dehydration conditions both at pH=7.4 and pH=5.0. In contrast, the peptide with Y20W mutation, which is known to block the internalization of the virus, forms mostly alpha-helical conformation in partial dehydration conditions at pH=7.4. According to our hypothesis, an intermolecular antiparallel beta structure formed by the wild-type peptide in its tetramers at pH=7.4 is the prototype of the similar intermolecular antiparallel beta structure formed by the corresponding part of Parvovirus B19 receptor-binding domain with its cellular receptor (AXL).

CONCLUSION

Loss of function Y20W substitution in VP1 capsid protein prevents the shift into the beta-structural state by the way of alpha helix stabilization and the decrease of its ability to turn into the disordered state.

Partial desensitization of MYC2 transcription factor alters the interaction with jasmonate signaling components and affects specialized metabolism

The activity of bHLH transcription factor MYC2, a key regulator in jasmonate signaling and plant specialized metabolism, is sensitive to repression by JASMONATE-ZIM-domain (JAZ) proteins and co-activation by the mediator subunit MED25. The substitution of a conserved aspartic acid (D) to asparagine (N) in the JAZ-interacting domain (JID) of Arabidopsis MYC2 affects interaction with JAZ, although the mechanism remained unclear. The effects of the conserved residue MYC2D128 on interaction with MED25 have not been investigated. Using tobacco as a model, we generated all possible substitutions of aspartic acid 128 (D128) in NtMYC2a. NtMYC2aD128N partially desensitized the repression by JAZ proteins, while strongly interacting with MED25, resulting in increased expression of nicotine pathway genes and nicotine accumulation in tobacco hairy roots overexpressing NtMYC2aD128N compared to those overexpressing NtMYC2a. The proline substitution, NtMYC2aD128P, negatively affected transactivation and abolished the interaction with JAZ proteins and MED25. Structural modeling and simulation suggest that the overall stability of the JID binding pocket is a predominant cause for the observed effects of substitutions at D128. The D128N substitution has an overall stabilizing effect on the binding pocket, which is destabilized by D128P. Our study offers an innovative tool to increase the production of plant natural products.

Integrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15

We report the results of the "UM-TBM" and "Zheng" groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D-I-TASSER and DMFold-Multimer algorithms, respectively. For monomer structure prediction, D-I-TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi-source MSA searching and a structural modeling-based MSA ranker; (ii) attention-network based spatial restraints; (iii) a multi-domain module containing domain partition and arrangement for domain-level templates and spatial restraints; (iv) an optimized I-TASSER-based folding simulation system for full-length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge-based potentials. For 47 free modeling targets in CASP15, the final models predicted by D-I-TASSER showed average TM-score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo-based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end-to-end deep learning methods alone. For protein complex structure prediction, DMFold-Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end-to-end modeling module from AlphaFold2-Multimer. For the 38 complex targets, DMFold-Multimer generated models with an average TM-score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking.

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

Understanding the impact of structural modifications at the NNAT gene's post-translational acetylation site: in silico approach for predicting its drug-interaction role in anorexia nervosa

PURPOSE

Anorexia nervosa (AN) is a neuropsychological public health concern with a socially disabling routine and affects a person's healthy relationship with food. The role of the NNAT (Neuronatin) gene in AN is well established. The impact of mutation at the protein's post-translational modification (PTM) site has been exclusively associated with the worsening of the protein's biochemical dynamics.

METHODS

To understand the relationship between genotype and phenotype, it is essential to investigate the appropriate molecular stability of protein required for proper biological functioning. In this regard, we investigated the PTM-acetylation site of the NNAT gene in terms of 19 other specific amino acid probabilities in place of wild type (WT) through various in silico algorithms. Based on the highest pathogenic impact computed through the consensus classifier tool, we generated 3 residue-specific (K59D, P, W) structurally modified 3D models of NNAT. These models were further tested through the AutoDock Vina tool to compute the molecular drug binding affinities and inhibition constant (Ki) of structural variants and WT 3D models.

RESULTS

With trained in silico machine learning algorithms and consensus classifier; the three structural modifications (K59D, P, W), which were also the most deleterious substitution at the acetylation site of the NNAT gene, showed the highest structural destabilization and decreased molecular flexibility. The validation and quality assessment of the 3D model of these structural modifications and WT were performed. They were further docked with drugs used to manage AN, it was found that the ΔGbind (kcal/mol) values and the inhibition constants (Ki) were relatively lower in structurally modified models as compared to WT.

CONCLUSION

We concluded that any future structural variation(s) at the PTM-acetylation site of the NNAT gene due to possible mutational consequences, will serve as a basis to explore its relationship with the propensity of developing AN.

LEVEL OF EVIDENCE

No level of evidence-open access bioinformatics research.

Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes

Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness.

ModelCIF: An Extension of PDBx/mmCIF Data Representation for Computed Structure Models

ModelCIF (github.com/ihmwg/ModelCIF) is a data information framework developed for and by computational structural biologists to enable delivery of Findable, Accessible, Interoperable, and Reusable (FAIR) data to users worldwide. ModelCIF describes the specific set of attributes and metadata associated with macromolecular structures modeled by solely computational methods and provides an extensible data representation for deposition, archiving, and public dissemination of predicted three-dimensional (3D) models of macromolecules. It is an extension of the Protein Data Bank Exchange / macromolecular Crystallographic Information Framework (PDBx/mmCIF), which is the global data standard for representing experimentally-determined 3D structures of macromolecules and associated metadata. The PDBx/mmCIF framework and its extensions (e.g., ModelCIF) are managed by the Worldwide Protein Data Bank partnership (wwPDB, wwpdb.org) in collaboration with relevant community stakeholders such as the wwPDB ModelCIF Working Group (wwpdb.org/task/modelcif). This semantically rich and extensible data framework for representing computed structure models (CSMs) accelerates the pace of scientific discovery. Herein, we describe the architecture, contents, and governance of ModelCIF, and tools and processes for maintaining and extending the data standard. Community tools and software libraries that support ModelCIF are also described.

Interaction of Pexiganan (MSI-78)-Derived Analogues Reduces Inflammation and TLR4-Mediated Cytokine Secretion: A Comparative Study

Antibiotic-resistant bacterial infections have increased the prevalence of sepsis and septic shock mortality worldwide and have become a global concern. Antimicrobial peptides (AMPs) show remarkable properties for developing new antimicrobial agents and host response modulatory therapies. A new series of AMPs derived from pexiganan (MSI-78) were synthesized. The positively charged amino acids were segregated at their N- and C-termini, and the rest of the amino acids created a hydrophobic core surrounded by positive charges and were modified to simulate the lipopolysaccharide (LPS). The peptides were investigated for their antimicrobial activity and LPS-induced cytokine release inhibition profile. Various biochemical and biophysical methods were used, including attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, microscale thermophoresis (MST), and electron microscopy. Two new AMPs, MSI-Seg-F2F and MSI-N7K, preserved their neutralizing endotoxin activity while reducing toxicity and hemolytic activity. Combining all of these properties makes the designed peptides potential candidates to eradicate bacterial infection and detoxify LPS, which might be useful for sepsis treatment.

In silico analysis of the possible crosstalk between O-linked β-GlcNAcylation and phosphorylation sites of Disabled 1 adaptor protein in vertebrates

Disabled 1 (Dab1) is an adaptor protein with essential functions regulated by reelin signaling and affects many biological processes in the nervous system, including cell motility, adhesion, cortical development, maturation, and synaptic plasticity. Posttranslational modifications directly guide the fates of cytoplasmic proteins to complete their functions correctly. Reciprocal crosstalk between O-GlcNAcylation and phosphorylation is a dynamic modification in cytoplasmic proteins. It modulates the functions of the proteins by regulating their interactions with other molecules in response to the continuously changeable cell microenvironment. Although Dab1 contains conserved recognition sites for phosphorylation in their N-terminal protein interaction domain, the O-β-GlcNAcylation and phosphorylation sites of human Dab1 sequence, their reciprocal crosstalk, and potential kinases catalyzing the phosphorylation remain unknown. In this study, we determined potential thirty-seven O-β-GlcNAcylation and sixty-seven phosphorylation sites. Conserved twenty-one residues of these glycosylated sites were also phosphorylated with various kinases, including ATM, CKI, DNAPK, GSK3, PKC, PKG, RSK, cdc2, cdk5, and p38MAPK. In addition, we analyzed these conserved sites at our constructed two- and three-dimensional structures of human Dab1 protein. Dab1 protein models were frequently composed of coil structures as well as α-helix and β-strands. Many of these conserved crosstalk sites between O-β-GlcNAcylation and phosphorylation were localized at the coil region of the protein model. These findings may guide biochemical, genetic, and glyco-biology based on further experiments about the Dab1 signaling process. Understanding these modifications might change the point of view of the Dab1 signaling process and treatment for pathological conditions in neurodegenerative diseases such as Alzheimer's disease.

Identification and Characterization of a Novel Cathelicidin from Hydrophis cyanocinctus with Antimicrobial and Anti-Inflammatory Activity

The abuse of antibiotics and lack of new antibacterial drugs has led to the emergence of superbugs that raise fears of untreatable infections. The Cathelicidin family of antimicrobial peptide (AMP) with varying antibacterial activities and safety is considered to be a promising alternative to conventional antibiotics. In this study, we investigated a novel Cathelicidin peptide named Hydrostatin-AMP2 from the sea snake Hydrophis cyanocinctus. The peptide was identified based on gene functional annotation of the H. cyanocinctus genome and bioinformatic prediction. Hydrostatin-AMP2 showed excellent antimicrobial activity against both Gram-positive and Gram-negative bacteria, including standard and clinical Ampicillin-resistant strains. The results of the bacterial killing kinetic assay demonstrated that Hydrostatin-AMP2 had faster antimicrobial action than Ampicillin. Meanwhile, Hydrostatin-AMP2 exhibited significant anti-biofilm activity including inhibition and eradication. It also showed a low propensity to induce resistance as well as low cytotoxicity and hemolytic activity. Notably, Hydrostatin-AMP2 apparently decreased the production of pro-inflammatory cytokines in the LPS-induced RAW264.7 cell model. To sum up, these findings indicate that Hydrostatin-AMP2 is a potential peptide candidate for the development of new-generation antimicrobial drugs fighting against antibiotic-resistant bacterial infections.

Quality assessment of VHH models

Heavy Chain Only Antibodies are specific to Camelid species. Despite the lack of the light chain variable domain, their heavy chain variable domain (VH) domain, named VHH or nanobody, has promising potential applications in research and therapeutic fields. The structural study of VHH is therefore of great interest. Unfortunately, considering the huge amount of sequences that might be produced, only about one thousand of VHH experimental structures are publicly available in the Protein Data Bank, implying that structural model prediction of VHH is a necessary alternative to obtaining 3D information besides its sequence. The present study aims to assess and compare the quality of predictions from different modelling methodologies. Established comparative & homology modelling approaches to recent Deep Learning-based modelling strategies were applied, i.e. Modeller using single or multiple structural templates, ModWeb, SwissModel (with two evaluation schema), RoseTTAfold, AlphaFold 2 and NanoNet. The prediction accuracy was evaluated using RMSD, TM-score, GDT-TS, GDT-HA and Protein Blocks distance metrics. Besides the global structure assessment, we performed specific analyses of Frameworks and CDRs structures. We observed that AlphaFold 2 and especially NanoNet performed better than the other evaluated softwares. Importantly, we performed molecular dynamics simulations of an experimental structure and a NanoNet predicted model of a VHH in order to compare the global structural flexibility and local conformations using Protein Blocks. Despite rather similar structures, substantial differences in dynamical properties were observed, which underlies the complexity of the task of model evaluation.Communicated by Ramaswamy H. Sarma.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Structure of the divergent human astrovirus MLB capsid spike

Despite their worldwide prevalence and association with human disease, the molecular bases of human astrovirus (HAstV) infection and evolution remain poorly characterized. Here, we report the structure of the capsid protein spike of the divergent HAstV MLB clade (HAstV MLB). While the structure shares a similar folding topology with that of classical-clade HAstV spikes, it is otherwise strikingly different. We find no evidence of a conserved receptor-binding site between the MLB and classical HAstV spikes, suggesting that MLB and classical HAstVs utilize different receptors for host-cell attachment. We provide evidence for this hypothesis using a novel HAstV infection competition assay. Comparisons of the HAstV MLB spike structure with structures predicted from its sequence reveal poor matches, but template-based predictions were surprisingly accurate relative to machine-learning-based predictions. Our data provide a foundation for understanding the mechanisms of infection by diverse HAstVs and can support structure determination in similarly unstudied systems.

In Silico Analysis on the Interaction of Haloacid Dehalogenase from Bacillus cereus IndB1 with 2-Chloroalkanoic Acid Substrates

Recently, haloacid dehalogenases have gained a lot of interest because of their potential applications in bioremediation and synthesis of chemical products. The haloacid dehalogenase gene from Bacillus cereus IndB1 (bcfd1) has been isolated, expressed, and Bcfd1 enzyme activity towards monochloroacetic acid has been successfully studied. However, the structure, enantioselectivity, substrate range, and essential residues of Bcfd1 have not been elucidated. This research performed computational studies to predict the Bcfd1 protein structure and analyse the interaction of Bcfd1 towards several haloacid substrates to comprehend their enantioselectivity and substrates' range. Structure prediction revealed that Bcfd1 protein consist of two domains. The main domain consists of seven β-sheets connected by six α-helices and four 3₁₀-helices forming a Rossmannoid fold. On the other hand, the cap domain consists of five β-sheets connected by five α-helices. The docking simulation showed that 2-chloroalkanoic acids bind to the active site of Bcfd1 with docking energy decreases as the length of their alkyl chain increases. The docking simulation also indicated that the docking energy differences of two enantiomers of 2-chloroalkanoic acids substrates were not significant. Further analysis revealed the role of Met1, Asp2, Cys33, and Lys204 residues in orienting the carboxylic group of 2-chloroalkanoic acids in the active site of this enzyme through hydrogen bonds. This research proved that computational studies could be used to figure out the effect of substrates enantiomer and length of carbon skeleton to Bcfd1 affinity toward 2-chloroalkanoic acids.

In silico identification of the rare-coding pathogenic mutations and structural modeling of human NNAT gene associated with anorexia nervosa

PURPOSE

Increased susceptibility towards anorexia nervosa (AN) was reported with reduced levels of neuronatin (NNAT) gene. We sought to investigate the most pathogenic rare-coding missense mutations, non-synonymous single-nucleotide polymorphisms (nsSNPs) of NNAT and their potential damaging impact on protein function through transcript level sequence and structure based in silico approaches.

METHODS

Gene sequence, single nucleotide polymorphisms (SNPs) of NNAT was retrieved from public databases and the putative post-translational modification (PTM) sites were analyzed. Distinctive in silico algorithms were recruited for transcript level SNPs analyses and to characterized high-risk rare-coding nsSNPs along with their impact on protein stability function. Ab initio 3D-modeling of wild-type, alternate model prediction for most deleterious nsSNP, validation and recognition of druggable binding pockets were also performed. AN 3D therapeutic compounds that followed rule of drug-likeness were docked with most pathogenic variant of NNAT to estimate the drugs' binding free energies.

RESULTS

Conclusively, 10 transcript (201-205)-based nsSNPs from 3 rare-coding missense variants, i.e., rs539681368, rs542858994, rs560845323 out of 840 exonic SNPs were identified. Transcript-based functional impact analyses predicted rs539681368 (C30Y) from NNAT-204 as the high-risk rare-coding pathogenic nsSNP, deviating protein functions. The 3D-modeling analysis of AN drugs' binding energies indicated lowest binding free energy (ΔG) and significant inhibition constant (K_i) with mutant models C30Y.

CONCLUSIONS

Mutant model (C30Y) exhibiting significant drug binding affinity and the commonest interaction observed at the acetylation site K59. Thus, based on these findings, we concluded that the identified nsSNP may serve as potential targets for various studies, diagnosis and therapeutic interventions.

LEVEL OF EVIDENCE

No level of evidence-open access bioinformatics research.

Simulation Study of the Effect of Antimicrobial Peptide Associations on the Mechanism of Action with Bacterial and Eukaryotic Membranes

Antimicrobial peptides (AMPs) can be directed to specific membranes based on differences in lipid composition. In this study, we performed atomistic and coarse-grained simulations of different numbers of the designed AMP adepantin-1 with a eukaryotic membrane, cytoplasmic Gram-positive and Gram-negative membranes, and an outer Gram-negative membrane. At the core of adepantin-1's behavior is its amphipathic α-helical structure, which was implemented in its design. The amphipathic structure promotes rapid self-association of peptide in water or upon binding to bacterial membranes. Aggregates initially make contact with the membrane via positively charged residues, but with insertion, the hydrophobic residues are exposed to the membrane's hydrophobic core. This adaptation alters the aggregate's stability, causing the peptides to diffuse in the polar region of the membrane, mostly remaining as a single peptide or pairing up to form an antiparallel dimer. Thus, the aggregate's proposed role is to aid in positioning the peptide into a favorable conformation for insertion. Simulations revealed the molecular basics of adepantin-1 binding to various membranes, and highlighted peptide aggregation as an important factor. These findings contribute to the development of novel anti-infective agents to combat the rapidly growing problem of bacterial resistance to antibiotics.

I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction

Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-domain protein structure assembly, mainly due to the complexity of modeling multi-domain proteins, which involves higher degrees of freedom in domain-orientation space and various levels of continuous and discontinuous domain assembly and linker refinement. To meet the challenge and the high demand of the community, we developed I-TASSER-MTD to model the structures and functions of multi-domain proteins through a progressive protocol that combines sequence-based domain parsing, single-domain structure folding, inter-domain structure assembly and structure-based function annotation in a fully automated pipeline. Advanced deep-learning models have been incorporated into each of the steps to enhance both the domain modeling and inter-domain assembly accuracy. The protocol allows for the incorporation of experimental cross-linking data and cryo-electron microscopy density maps to guide the multi-domain structure assembly simulations. I-TASSER-MTD is built on I-TASSER but substantially extends its ability and accuracy in modeling large multi-domain protein structures and provides meaningful functional insights for the targets at both the domain- and full-chain levels from the amino acid sequence alone.

DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments with deep-learning inter-domain restraint prediction

Most proteins in nature contain multiple folding units (or domains). The revolutionary success of AlphaFold2 in single-domain structure prediction showed potential to extend deep-learning techniques for multi-domain structure modeling. This work presents a significantly improved method, DEMO2, which integrates analogous template structural alignments with deep-learning techniques for high-accuracy domain structure assembly. Starting from individual domain models, inter-domain spatial restraints are first predicted with deep residual convolutional networks, where full-length structure models are assembled using L-BFGS simulations under the guidance of a hybrid energy function combining deep-learning restraints and analogous multi-domain template alignments searched from the PDB. The output of DEMO2 contains deep-learning inter-domain restraints, top-ranked multi-domain structure templates, and up to five full-length structure models. DEMO2 was tested on a large-scale benchmark and the blind CASP14 experiment, where DEMO2 was shown to significantly outperform its predecessor and the state-of-the-art protein structure prediction methods. By integrating with new deep-learning techniques, DEMO2 should help fill the rapidly increasing gap between the improved ability of tertiary structure determination and the high demand for the high-quality multi-domain protein structures. The DEMO2 server is available at https://zhanggroup.org/DEMO/.

Deep learning geometrical potential for high-accuracy ab initio protein structure prediction

Ab initio protein structure prediction has been vastly boosted by the modeling of inter-residue contact/distance maps in recent years. We developed a new deep learning model, DeepPotential, which accurately predicts the distribution of a complementary set of geometric descriptors including a novel hydrogen-bonding potential defined by C-alpha atom coordinates. On 154 Free-Modeling/Hard targets from the CASP and CAMEO experiments, DeepPotential demonstrated significant advantage on both geometrical feature prediction and full-length structure construction, with Top-L/5 contact accuracy and TM-score of full-length models 4.1% and 6.7% higher than the best of other deep-learning restraint prediction approaches. Detail analyses showed that the major contributions to the TM-score/contact-map improvements come from the employment of multi-tasking network architecture and metagenome-based MSA collection assisted with confidence-based MSA selection, where hydrogen-bonding and inter-residue orientation predictions help improve hydrogen-bonding network and secondary structure packing. These results demonstrated new progress in the deep-learning restraint-guided ab initio protein structure prediction.

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Multi-tasking network architecture for multiple inter-residue geometries

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Novel deep learning model for improved hydrogen-bonding modeling

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Rapid and high-accuracy Ab initio protein structure prediction

Discovery of the ATPase Activity of a Cobalt-Type Nitrile Hydratase Activator and Its Promoting Effect on Enzyme Maturation

An activator protein and a metal ion are two factors known to be indispensable for the maturation of nitrile hydratase (NHase). Here, the third key factor, adenosine triphosphate (ATP), was identified to play an important role in the activation of Co-type NHase. Free phosphate measurements revealed that the Co-type activator protein can hydrolyze ATP/GTP with appreciable performance and that such catalytic performance is related to NHase activity. Computational analysis and site-directed mutagenesis identified several potential hot spot residues involved in the binding of ATP to Co-type activator protein, and an E60A/W61A/D62A/I139A/T141A combinatorial variant reduced the ATPase activity to 18% of its original level. Further NHase activation studies using the combinatorial variant demonstrated that although the ATPase activity of the Co-type activator protein correlated with NHase activity, a low ATP concentration of 0.5 mmol/L was optimal for NHase activation, with higher ATP concentrations potentially inhibiting NHase activity.

LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation

Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep learning threading methods. For the first time, we have extended LOMETS3 to handle multi-domain proteins and to construct full-length models with gradient-based optimizations. Starting from a FASTA-formatted sequence, LOMETS3 performs four steps of domain boundary prediction, domain-level template identification, full-length template/model assembly and structure-based function prediction. The output of LOMETS3 contains (i) top-ranked templates from LOMETS3 and its component threading programs, (ii) up to 5 full-length structure models constructed by L-BFGS (limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm) optimization, (iii) the 10 closest Protein Data Bank (PDB) structures to the target, (iv) structure-based functional predictions, (v) domain partition and assembly results, and (vi) the domain-level threading results, including items (i)–(iii) for each identified domain. LOMETS3 was tested in large-scale benchmarks and the blind CASP14 (14th Critical Assessment of Structure Prediction) experiment, where the overall template recognition and function prediction accuracy is significantly beyond its predecessors and other state-of-the-art threading approaches, especially for hard targets without homologous templates in the PDB. Based on the improved developments, LOMETS3 should help significantly advance the capability of broader biomedical community for template-based protein structure and function modelling.

Progressive assembly of multi-domain protein structures from cryo-EM density maps

Progress in cryo-electron microscopy has provided the potential for large-size protein structure determination. However, the success rate for solving multi-domain proteins remains low because of the difficulty in modelling inter-domain orientations. Here we developed domain enhanced modeling using cryo-electron microscopy (DEMO-EM), an automatic method to assemble multi-domain structures from cryo-electron microscopy maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep-neural-network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to 12 continuous and discontinuous domains with medium- to low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (template modeling score (TM-score) >0.5) for 97% of cases and outperformed state-of-the-art methods. DEMO-EM was applied to the severe acute respiratory syndrome coronavirus 2 genome and generated models with average TM-score and root-mean-square deviation of 0.97 and 1.3 Å, respectively, with respect to the deposited structures. These results demonstrate an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modelling from cryo-electron microscopy maps.

VHH Structural Modelling Approaches: A Critical Review

VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.

Accurate flexible refinement for atomic-level protein structure using cryo-EM density maps and deep learning

With the rapid progress of deep learning in cryo-electron microscopy and protein structure prediction, improving the accuracy of the protein structure model by using a density map and predicted contact/distance map through deep learning has become an urgent need for robust methods. Thus, designing an effective protein structure optimization strategy based on the density map and predicted contact/distance map is critical to improving the accuracy of structure refinement. In this article, a protein structure optimization method based on the density map and predicted contact/distance map by deep-learning technology was proposed in accordance with the result of matching between the density map and the initial model. Physics- and knowledge-based energy functions, integrated with Cryo-EM density map data and deep-learning data, were used to optimize the protein structure in the simulation. The dynamic confidence score was introduced to the iterative process for choosing whether it is a density map or a contact/distance map to dominate the movement in the simulation to improve the accuracy of refinement. The protocol was tested on a large set of 224 non-homologous membrane proteins and generated 214 structural models with correct folds, where 4.5% of structural models were generated from structural models with incorrect folds. Compared with other state-of-the-art methods, the major advantage of the proposed methods lies in the skills for using density map and contact/distance map in the simulation, as well as the new energy function in the re-assembly simulations. Overall, the results demonstrated that this strategy is a valuable approach and ready to use for atomic-level structure refinement using cryo-EM density map and predicted contact/distance map.

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds

Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient. We propose a machine learning protocol, the inductive transfer learning force field (ITLFF), to construct protein force fields in seconds with any level of accuracy from a small dataset. This process is achieved by incorporating an inductive transfer learning algorithm into deep neural networks, which learn knowledge of any high-level calculations from a large dataset of low-level method. Here, we use a double-hybrid density functional theory (DFT) as a case functional, but ITLFF is suitable for any high-precision functional. The performance of the selected 18 proteins indicates that compared with the fragment-based double-hybrid DFT algorithm, the force field constructed by ITLFF achieves considerable accuracy with a mean absolute error of 0.0039 kcal/mol/atom for energy and a root mean square error of 2.57 $\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more than 30 000 times faster and obtains more significant efficiency benefits as the system increases. The outstanding performance of ITLFF provides promising prospects for accurate and efficient protein dynamic simulations and makes an important step toward protein folding simulation. Due to the ability of ITLFF to utilize the knowledge acquired in one task to solve related problems, it is also applicable for various problems in biology, chemistry and material science.

Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14

In this article, we report 3D structure prediction results by two of our best server groups ("Zhang-Server" and "QUARK") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (a) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (b) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (c) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (d) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues.

Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set of sequence segments in 1D, a set of amino acid contact pairs in 2D, and a set of traces in 3D at the secondary structure level. A test of fourteen cases shows that the accuracy of predicted secondary structures is critical for deriving topologies. The use of significant long-range contact pairs is most effective at enriching the rank of the maximum-match topology for proteins with a large number of secondary structures, if the secondary structure prediction is fairly accurate. It was observed that the enrichment depends on the quality of initial topology candidates in this approach. We provide detailed analysis in various cases to show the potential and challenge when combining three sources of information.

Current Approaches in Supersecondary Structures Investigation

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.