Automated structure prediction of weakly homologous proteins on a genomic scale

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

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.

AI-driven pan-proteome analyses reveal insights into the biohydrometallurgical properties of Acidithiobacillia

Microorganism-mediated biohydrometallurgy, a sustainable approach for metal recovery from ores, relies on the metabolic activity of acidophilic bacteria. Acidithiobacillia with sulfur/iron-oxidizing capacities are extensively studied and applied in biohydrometallurgy-related processes. However, only 14 distinct proteins from Acidithiobacillia have experimentally determined structures currently available. This significantly hampers in-depth investigations of Acidithiobacillia's structure-based biological mechanisms pertaining to its relevant biohydrometallurgical processes. To address this issue, we employed a state-of-the-art artificial intelligence (AI)-driven approach, with a median model confidence of 0.80, to perform high-quality full-chain structure predictions on the pan-proteome (10,458 proteins) of the type strain Acidithiobacillia. Additionally, we conducted various case studies on de novo protein structural prediction, including sulfate transporter and iron oxidase, to demonstrate how accurate structure predictions and gene co-occurrence networks can contribute to the development of mechanistic insights and hypotheses regarding sulfur and iron utilization proteins. Furthermore, for the unannotated proteins that constitute 35.8% of the Acidithiobacillia proteome, we employed the deep-learning algorithm DeepFRI to make structure-based functional predictions. As a result, we successfully obtained gene ontology (GO) terms for 93.6% of these previously unknown proteins. This study has a significant impact on improving protein structure and function predictions, as well as developing state-of-the-art techniques for high-throughput analysis of large proteomic data.

De novo protein fold design through sequence-independent fragment assembly simulations

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

Computational investigation of phytoalexins as potential antiviral RAP-1 and RAP-2 (Replication Associated Proteins) inhibitor for the management of cucumber mosaic virus (CMV): a molecular modeling, in silico docking and MM-GBSA study

The Replication Associated Proteins (RAP-1 and RAP-2) encoded by CMV ORF 1a and ORF 2a are required for the different stages of the viral replication cycle; being multi-functional, they are good inhibitory targets for anti-CMV compounds. As a new perspective for sustainable crop improvement, we investigated the natural plant-based antimicrobial phytoalexins for their anti-CMV potential. Here, we modeled and predicted the functional domains of RAP-1 and RAP-2, docked with a ligand library comprising 128 phytoalexins reported with broad-spectrum activity, determined their binding energies (BEs), molecular interactions, and inhibition constant (Ki), and compared with the reference plant antiviral compounds ribavirin, ningnanmycin, and benzothiadiazole (BTH). Further, the change in Gibb's free energy of binding (ΔG) and the per residue contribution of the selected top-scored ligand molecules was assessed by the prime MM-GBSA approach. Our results revealed RAP-1 as a discontinuous two-domain and RAP-2 as a multi-domain protein. The compounds glyceollidin (9.8 kcal/mol) and moracin D (7.8 kcal/mol) topped the list for RAP-1 and RAP-2 protein targets respectively and also, the lead molecules had energetically more favorable and comparative ΔG values than the top-scored plant antiviral agent ningnanmycin. The evaluation of in vitro toxicity and agrochemical-like properties showed the least toxicity of these anti-CMV compounds. Taken together, our results provide new insights in understanding the inhibitory effects of phytoalexins towards the RAP proteins and could be employed as new promising anti-CMV candidate compounds for their application in agriculture as biopesticides to combat the CMV disease incidence.Communicated by Ramaswamy H. Sarma.

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

A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.

Toward the solution of the protein structure prediction problem

Since Anfinsen demonstrated that the information encoded in a protein's amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.

Deep learning techniques have significantly impacted protein structure prediction and protein design

Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constraint prediction and end-to-end model training has significantly improved the accuracy of protein structure prediction, largely solving the problem at the fold level for single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples have shown that information stored in neural-network models can be used to advance functional protein design. Thus, incorporation of deep learning techniques into different steps of protein folding and design approaches represents an exciting future direction and should continue to have a transformative impact on both fields.

Protein Structure Modeling with MODELLER

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.

Antimalarial Peptide and Polyketide Natural Products from the Fijian Marine Cyanobacterium Moorea producens

A new cyclic peptide, kakeromamide B (1), and previously described cytotoxic cyanobacterial natural products ulongamide A (2), lyngbyabellin A (3), 18E-lyngbyaloside C (4), and lyngbyaloside (5) were identified from an antimalarial extract of the Fijian marine cyanobacterium Moorea producens. Compounds 1 and 1 exhibited moderate activity against Plasmodium falciparum blood-stages with EC50 values of 0.89 and 0.99 µM, respectively, whereas 3 was more potent with an EC50 value of 0.15 nM, respectively. Compounds 1, 4, and 5 displayed moderate liver-stage antimalarial activity against P. berghei liver schizonts with EC50 values of 1.1, 0.71, and 0.45 µM, respectively. The threading-based computational method FINDSITEcomb2.0 predicted the binding of 1 and 2 to potentially druggable proteins of Plasmodiumfalciparum, prompting formulation of hypotheses about possible mechanisms of action. Kakeromamide B (1) was predicted to bind to several Plasmodium actin-like proteins and a sortilin protein suggesting possible interference with parasite invasion of host cells. When 1 was tested in a mammalian actin polymerization assay, it stimulated actin polymerization in a dose-dependent manner, suggesting that 1 does, in fact, interact with actin.

I-TASSER gateway: A protein structure and function prediction server powered by XSEDE

There is an increasing gap between the number of known protein sequences and the number of proteins with experimentally characterized structure and function. To alleviate this issue, we have developed the I-TASSER gateway, an online server for automated and reliable protein structure and function prediction. For a given sequence, I-TASSER starts with template recognition from a known structure library, followed by full-length atomic model construction by iterative assembly simulations of the continuous structural fragments excised from the template alignments. Functional insights are then derived from comparative matching of the predicted model with a library of proteins with known function. The I-TASSER pipeline has been recently integrated with the XSEDE Gateway system to accommodate pressing demand from the user community and increasing computing costs. This report summarizes the configuration of the I-TASSER Gateway with the XSEDE-Comet supercomputer cluster, together with an overview of the I-TASSER method and milestones of its development.

DESTINI: A deep-learning approach to contact-driven protein structure prediction

The amino acid sequence of a protein encodes the blueprint of its native structure. To predict the corresponding structural fold from the protein’s sequence is one of most challenging problems in computational biology. In this work, we introduce DESTINI (deep structural inference for proteins), a novel computational approach that combines a deep-learning algorithm for protein residue/residue contact prediction with template-based structural modelling. For the first time, the significantly improved predictive ability is demonstrated in the large-scale tertiary structure prediction of over 1,200 single-domain proteins. DESTINI successfully predicts the tertiary structure of four times the number of “hard” targets (those with poor quality templates) that were previously intractable, viz, a “glass-ceiling” for previous template-based approaches, and also improves model quality for “easy” targets (those with good quality templates). The significantly better performance by DESTINI is largely due to the incorporation of better contact prediction into template modelling. To understand why deep-learning accomplishes more accurate contact prediction, systematic clustering reveals that deep-learning predicts coherent, native-like contact patterns compared to co-evolutionary analysis. Taken together, this work presents a promising strategy towards solving the protein structure prediction problem.

Selecting Near-Native Protein Structures from Predicted Decoy Sets Using Ordered Graphlet Degree Similarity

Effective prediction of protein tertiary structure from sequence is an important and challenging problem in computational structural biology. Ab initio protein structure prediction is based on amino acid sequence alone, thus, it has a wide application area. With the ab initio method, a large number of candidate protein structures called decoy set can be predicted, however, it is a difficult problem to select a good near-native structure from the predicted decoy set. In this work we propose a new method for selecting the near-native structure from the decoy set based on both contact map overlap (CMO) and graphlets. By generalizing graphlets to ordered graphs, and using a dynamic programming to select the optimal alignment with an introduced gap penalty, a GR_score is defined for calculating the similarity between the three-dimensional (3D) decoy structures. The proposed method was applied to all 54 single-domain targets in CASP11 and all 43 targets in CASP10, and ensemble clustering was used to cluster the protein decoy structures based on the computed CR_scores. The most popular centroid structure was selected as the near-native structure. The experiments showed that compared to the SPICKER method, which is used in I-TASSER, the proposed method can usually select better near-native structures in terms of the similarity between the selected structure and the true native structure.

Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction

Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have resulted in considerable efforts in the development of computational methods to accurately model protein structures from sequences. Sequence-based prediction of super-secondary structure has direct application in protein structure prediction, and there have been significant efforts in the prediction of super-secondary structure in the last decade. In this chapter, we first introduce the protein structure prediction problem and highlight some of the important progress in the field of protein structure prediction. Next, we discuss recent methods for the prediction of super-secondary structures. Finally, we discuss applications of super-secondary structure prediction in structure prediction/analysis of proteins. We also discuss prediction of protein structures that are composed of simple super-secondary structure repeats and protein structures that are composed of complex super-secondary structure repeats. Finally, we also discuss the recent trends in the field.

Recent advances in sequence-based protein structure prediction

The most accurate characterizations of the structure of proteins are provided by structural biology experiments. However, because of the high cost and labor-intensive nature of the structural experiments, the gap between the number of protein sequences and solved structures is widening rapidly. Development of computational methods to accurately model protein structures from sequences is becoming increasingly important to the biological community. In this article, we highlight some important progress in the field of protein structure prediction, especially those related to free modeling (FM) methods that generate structure models without using homologous templates. We also provide a short synopsis of some of the recent advances in FM approaches as demonstrated in the recent Computational Assessment of Structure Prediction competition as well as recent trends and outlook for FM approaches in protein structure prediction.

Structure refinement of membrane proteins via molecular dynamics simulations

A refinement protocol based on physics-based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge-based or implicit membrane-based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid-facing residues. Scoring with knowledge-based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane-based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models.

Leishmania infantum 5'-Methylthioadenosine Phosphorylase presents relevant structural divergence to constitute a potential drug target

BACKGROUND

The 5'-methylthioadenosine phosphorylase (MTAP), an enzyme involved in purine and polyamine metabolism and in the methionine salvage pathway, is considered as a potential drug target against cancer and trypanosomiasis. In fact, Trypanosoma and Leishmania parasites lack de novo purine pathways and rely on purine salvage pathways to meet their requirements. Herein, we propose the first comprehensive bioinformatic and structural characterization of the putative Leishmania infantum MTAP (LiMTAP), using a comparative computational approach.

RESULTS

Sequence analysis showed that LiMTAP shared higher identity rates with the Trypanosoma brucei (TbMTAP) and the human (huMTAP) homologs as compared to the human purine nucleoside phosphorylase (huPNP). Motifs search using MEME identified more common patterns and higher relatedness of the parasite proteins to the huMTAP than to the huPNP. The 3D structures of LiMTAP and TbMTAP were predicted by homology modeling and compared to the crystal structure of the huMTAP. These models presented conserved secondary structures compared to the huMTAP, with a similar topology corresponding to the Rossmann fold. This confirmed that both LiMTAP and TbMTAP are members of the NP-I family. In comparison to the huMTAP, the 3D model of LiMTAP showed an additional α-helix, at the C terminal extremity. One peptide located in this specific region was used to generate a specific antibody to LiMTAP. In comparison with the active site (AS) of huMTAP, the parasite ASs presented significant differences in the shape and the electrostatic potentials (EPs). Molecular docking of 5'-methylthioadenosine (MTA) and 5'-hydroxyethylthio-adenosine (HETA) on the ASs on the three proteins predicted differential binding modes and interactions when comparing the parasite proteins to the human orthologue.

CONCLUSIONS

This study highlighted significant structural peculiarities, corresponding to functionally relevant sequence divergence in LiMTAP, making of it a potential drug target against Leishmania.

Validation of Molecular Dynamics Simulations for Prediction of Three-Dimensional Structures of Small Proteins

Although various higher-order protein structure prediction methods have been developed, almost all of them were developed based on the three-dimensional (3D) structure information of known proteins. Here we predicted the short protein structures by molecular dynamics (MD) simulations in which only Newton’s equations of motion were used and 3D structural information of known proteins was not required. To evaluate the ability of MD simulationto predict protein structures, we calculated seven short test protein (10–46 residues) in the denatured state and compared their predicted and experimental structures. The predicted structure for Trp-cage (20 residues) was close to the experimental structure by 200-ns MD simulation. For proteins shorter or longer than Trp-cage, root-mean square deviation values were larger than those for Trp-cage. However, secondary structures could be reproduced by MD simulations for proteins with 10–34 residues. Simulations by replica exchange MD were performed, but the results were similar to those from normal MD simulations. These results suggest that normal MD simulations can roughly predict short protein structures and 200-ns simulations are frequently sufficient for estimating the secondary structures of protein (approximately 20 residues). Structural prediction method using only fundamental physical laws are useful for investigating non-natural proteins, such as primitive proteins and artificial proteins for peptide-based drug delivery systems.

3D-QSAR and Molecular Docking Studies on the TcPMCA1-Mediated Detoxification of Scopoletin and Coumarin Derivatives

The carmine spider mite, Tetranychus cinnabarinus (Boisduval), is an economically important agricultural pest that is difficult to prevent and control. Scopoletin is a botanical coumarin derivative that targets Ca2+-ATPase to exert a strong acaricidal effect on carmine spider mites. In this study, the full-length cDNA sequence of a plasma membrane Ca2+-ATPase 1 gene (TcPMCA1) was cloned. The sequence contains an open reading frame of 3750 bp and encodes a putative protein of 1249 amino acids. The effects of scopoletin on TcPMCA1 expression were investigated. TcPMCA1 was significantly upregulated after it was exposed to 10%, 30%, and 50% of the lethal concentration of scopoletin. Homology modeling, molecular docking, and three-dimensional quantitative structure-activity relationships were then studied to explore the relationship between scopoletin structure and TcPMCA1-inhibiting activity of scopoletin and other 30 coumarin derivatives. Results showed that scopoletin inserts into the binding cavity and interacts with amino acid residues at the binding site of the TcPMCA1 protein through the driving forces of hydrogen bonds. Furthermore, CoMFA (comparative molecular field analysis)- and CoMSIA (comparative molecular similarity index analysis)-derived models showed that the steric and H-bond fields of these compounds exert important influences on the activities of the coumarin compounds.Notably, the C3, C6, and C7 positions in the skeletal structure of the coumarins are the most suitable active sites. This work provides insights into the mechanism underlying the interaction of scopoletin with TcPMCA1. The present results can improve the understanding on plasma membrane Ca2+-ATPase-mediated (PMCA-mediated) detoxification of scopoletin and coumarin derivatives in T. cinnabarinus, as well as provide valuable information for the design of novel PMCA-inhibiting acaricides.

Protein structural motifs in prediction and design

The Protein Data Bank (PDB) has been an integral resource for shaping our fundamental understanding of protein structure and for the advancement of such applications as protein design and structure prediction. Over the years, information from the PDB has been used to generate models ranging from specific structural mechanisms to general statistical potentials. With accumulating structural data, it has become possible to mine for more complete and complex structural observations, deducing more accurate generalizations. Motif libraries, which capture recurring structural features along with their sequence preferences, have exposed modularity in the structural universe and found successful application in various problems of structural biology. Here we summarize recent achievements in this arena, focusing on subdomain level structural patterns and their applications to protein design and structure prediction, and suggest promising future directions as the structural database continues to grow.

Accelerating physical simulations of proteins by leveraging external knowledge

It is challenging to compute structure-function relationships of proteins using molecular physics. The problem arises from the exponential scaling of the computational searching and sampling of large conformational spaces. This scaling challenge is not met by today's methods, such as Monte Carlo, simulated annealing, genetic algorithms, or molecular dynamics (MD) or its variants such as replica exchange. Such methods of searching for optimal states on complex probabalistic landscapes are referred to more broadly as Explore-and-Exploit (EE), including in contexts such as computational learning, games, industrial planning and modeling military strategies. Here we describe a Bayesian method, called MELD, that 'melds' together explore-and-exploit approaches with externally added information that can be vague, combinatoric, noisy, intuitive, heuristic, or from experimental data. MELD is shown to accelerate physical MD simulations when using experimental data to determine protein structures; for predicting protein structures by using heuristic directives; and when predicting binding affinities of proteins from limited information about the binding site. Such Guided Explore-and-Exploit approaches might also be useful beyond proteins and beyond molecular science.

Virtual Screening for Potential Inhibitors of CTX-M-15 Protein of Klebsiella pneumoniae

The Gram-negative bacterium Klebsiella pneumoniae, responsible for a wide variety of nosocomial infections in immuno-deficient patients, involves the respiratory, urinary and gastrointestinal tract infections and septicemia. Extended spectrum β-lactamases (ESBL) belong to β-lactamases capable of conferring antibiotic resistance in Gram-negative bacteria. CTX-M-15, a prevalent ESBL reported from Enterobacteriaceae including K. pneumoniae, was selected as a potent anti-bacterial target. To identify the novel drug-like compounds, structure-based screening procedure was employed against downloaded drug-like compounds from ZINC database. An acronym for "ZINC" is not commercial. The docking free energy values were investigated and compared to the known inhibitor Avibactam. Six best novel drug-like compounds were selected and their hydrogen bindings with the receptor were determined. Based on the binding efficiency mode, three among these six identified most potential inhibitors, ZINC21811621, ZINC93091917 and ZINC19488569, were predicted as potential competitive inhibitors against CTX-M-15 compared to Avibactam. These three inhibitors may provide a framework for the experimental studies to develop anti-Klebsiella novel drug candidates targeting CTX-M-15.

Optimized protocol for soluble prokaryotic expression, purification and structural analysis of human placenta specific-1(PLAC1)

Placenta specific -1 (PLAC1) has been recently introduced as a small membrane-associated protein mainly involved in placental development. Expression of PLAC1 transcript has been documented in almost one hundred cancer cell lines standing for fourteen distinct cancer types. The presence of two disulfide bridges makes difficult to produce functional recombinant PLAC1 in soluble form with high yield. This limitation also complicates the structural studies of PLAC1, which is important for prediction of its physiological roles. To address this issue, we employed an expression matrix consisting of two expression vectors, five different E. coli hosts and five solubilization conditions to optimize production of full and truncated forms of human PLAC1. The recombinant proteins were then characterized using an anti-PLAC1-specific antibody in Western blotting (WB) and enzyme linked immunosorbent assay (ELISA). Structure of full length protein was also investigated using circular dichroism (CD). We demonstrated the combination of Origami™ and pCold expression vector to yield substantial amount of soluble truncated PLAC1 without further need for solubilization step. Full length PLAC1, however, expressed mostly as inclusion bodies with higher yield in Origami™ and Rosetta2. Among solubilization buffers examined, buffer containing Urea 2 M, pH 12 was found to be more effective. Recombinant proteins exhibited excellent reactivity as detected by ELISA and WB. The secondary structure of full length PLAC1 was considered by CD spectroscopy. Taken together, we introduced here a simple, affordable and efficient expression system for soluble PLAC1 production.

A database assisted protein structure prediction method via a swarm intelligence algorithm

A swarm-intelligence-based protein structure prediction method holds promise for narrowing the sequence-structure gap of proteins.

Protein Structure Modeling with MODELLER

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.

Repurposing FDA-approved drugs for anti-aging therapies

There is great interest in drugs that are capable of modulating multiple aging pathways, thereby delaying the onset and progression of aging. Effective strategies for drug development include the repurposing of existing drugs already approved by the FDA for human therapy. FDA approved drugs have known mechanisms of action and have been thoroughly screened for safety. Although there has been extensive scientific activity in repurposing drugs for disease therapy, there has been little testing of these drugs for their effects on aging. The pool of FDA approved drugs therefore represents a large reservoir of drug candidates with substantial potential for anti-aging therapy. In this paper we employ FINDSITEcomb, a powerful ligand homology modeling program, to identify binding partners for proteins produced by temperature sensing genes that have been implicated in aging. This list of drugs with potential to modulate aging rates was then tested experimentally for lifespan and healthspan extension using a small invertebrate model. Three protein targets of the rotifer Brachionus manjavacas corresponding to products of the transient receptor potential gene 7, ribosomal protein S6 polypeptide 2 gene, or forkhead box C gene, were screened against a compound library consisting of DrugBank drugs including 1347 FDA approved, non-nutraceutical molecules. Twenty nine drugs ranked in the top 1 % for binding to each target were subsequently included in our experimental analysis. Continuous exposure of rotifers to 1 µM naproxen significantly extended rotifer mean lifespan by 14 %. We used three endpoints to estimate rotifer health: swimming speed (mobility proxy), reproduction (overall vitality), and mitochondria activity (cellular senescence proxy). The natural decline in swimming speed with aging was more gradual when rotifers were exposed to three drugs, so that on day 6, mean swimming speed of females was 1.19 mm/s for naproxen (P = 0.038), 1.20 for fludarabine (P = 0.040), 1.35 for hydralazine (P = 0.038), as compared to 0.88 mm/s in the control. The average reproduction of control females in the second half of their reproductive lifespan was 1.08 per day. In contrast, females treated with 1 µM naproxen produced 1.4 offspring per day (P = 0.027) and females treated with 10 µM fludarabine or 1 µM hydralazine produced 1.72 (P = <0.001) and 1.66 (P = 0.001) offspring per day, respectively. Mitochondrial activity naturally declines with rotifer aging, but B. manjavacas treated with 1 µM hydralazine or 10 µM fludarabine retained 49 % (P = 0.038) and 89 % (P = 0.002) greater mitochondria activity, respectively, than untreated controls. Our results demonstrate that coupling computation to experimentation can quickly identify new drug candidates with anti-aging potential. Screening drugs for anti-aging effects using a rotifer bioassay is a powerful first step in identifying compounds worthy of follow-up in vertebrate models. Even if lifespan extension is not observed, certain drugs could improve healthspan, slowing age-dependent losses in mobility and vitality.

Novel small molecule binders of human N-glycanase 1, a key player in the endoplasmic reticulum associated degradation pathway

Peptide:N-glycanase (NGLY1) is an enzyme responsible for cleaving oligosaccharide moieties from misfolded glycoproteins to enable their proper degradation. Deletion and truncation mutations in this gene are responsible for an inherited disorder of the endoplasmic reticulum-associated degradation pathway. However, the literature is unclear whether the disorder is a result of mutations leading to loss-of-function, loss of substrate specificity, loss of protein stability or a combination of these factors. In this communication, without burdening ourselves with the mechanistic underpinning of disease causation because of mutations on the NGLY1 protein, we demonstrate the successful application of virtual ligand screening (VLS) combined with experimental high-throughput validation to the discovery of novel small-molecules that show binding to the transglutaminase domain of NGLY1. Attempts at recombinant expression and purification of six different constructs led to successful expression of five, with three constructs purified to homogeneity. Most mutant variants failed to purify possibly because of misfolding and the resultant exposure of surface hydrophobicity that led to protein aggregation. For the purified constructs, our threading/structure-based VLS algorithm, FINDSITE(comb), was employed to predict ligands that may bind to the protein. Then, the predictions were assessed by high-throughput differential scanning fluorimetry. This led to the identification of nine different ligands that bind to the protein of interest and provide clues to the nature of pharmacophore that facilitates binding. This is the first study that has identified novel ligands that bind to the NGLY1 protein as a possible starting point in the discovery of ligands with potential therapeutic applications in the treatment of the disorder caused by NGLY1 mutants.

Are protein-protein interfaces special regions on a protein's surface?

Protein-protein interactions (PPIs) are involved in many cellular processes. Experimentally obtained protein quaternary structures provide the location of protein-protein interfaces, the surface region of a given protein that interacts with another. These regions are termed half-interfaces (HIs). Canonical HIs cover roughly one third of a protein's surface and were found to have more hydrophobic residues than the non-interface surface region. In addition, the classical view of protein HIs was that there are a few (if not one) HIs per protein that are structurally and chemically unique. However, on average, a given protein interacts with at least a dozen others. This raises the question of whether they use the same or other HIs. By copying HIs from monomers with the same folds in solved quaternary structures, we introduce the concept of geometric HIs (HIs whose geometry has a significant match to other known interfaces) and show that on average they cover three quarters of a protein's surface. We then demonstrate that in some cases, these geometric HI could result in real physical interactions (which may or may not be biologically relevant). The composition of the new HIs is on average more charged compared to most known ones, suggesting that the current protein interface database is biased towards more hydrophobic, possibly more obligate, complexes. Finally, our results provide evidence for interface fuzziness and PPI promiscuity. Thus, the classical view of unique, well defined HIs needs to be revisited as HIs are another example of coarse-graining that is used by nature.

A comparison of different functions for predicted protein model quality assessment

In protein structure prediction, a considerable number of models are usually produced by either the Template-Based Method (TBM) or the ab initio prediction. The purpose of this study is to find the critical parameter in assessing the quality of the predicted models. A non-redundant template library was developed and 138 target sequences were modeled. The target sequences were all distant from the proteins in the template library and were aligned with template library proteins on the basis of the transformation matrix. The quality of each model was first assessed with QMEAN and its six parameters, which are C_β interaction energy (C_beta), all-atom pairwise energy (PE), solvation energy (SE), torsion angle energy (TAE), secondary structure agreement (SSA), and solvent accessibility agreement (SAE). Finally, the alignment score (score) was also used to assess the quality of model. Hence, a total of eight parameters (i.e., QMEAN, C_beta, PE, SE, TAE, SSA, SAE, score) were independently used to assess the quality of each model. The results indicate that SSA is the best parameter to estimate the quality of the model.

Improved cryoEM-Guided Iterative Molecular Dynamics--Rosetta Protein Structure Refinement Protocol for High Precision Protein Structure Prediction

Many excellent methods exist that incorporate cryo-electron microscopy (cryoEM) data to constrain computational protein structure prediction and refinement. Previously, it was shown that iteration of two such orthogonal sampling and scoring methods – Rosetta and molecular dynamics (MD) simulations – facilitated exploration of conformational space in principle. Here, we go beyond a proof-of-concept study and address significant remaining limitations of the iterative MD–Rosetta protein structure refinement protocol. Specifically, all parts of the iterative refinement protocol are now guided by medium-resolution cryoEM density maps, and previous knowledge about the native structure of the protein is no longer necessary. Models are identified solely based on score or simulation time. All four benchmark proteins showed substantial improvement through three rounds of the iterative refinement protocol. The best-scoring final models of two proteins had sub-Ångstrom RMSD to the native structure over residues in secondary structure elements. Molecular dynamics was most efficient in refining secondary structure elements and was thus highly complementary to the Rosetta refinement which is most powerful in refining side chains and loop regions.

ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein Structures

The advance of next-generation sequencing technologies has made exome sequencing rapid and relatively inexpensive. A major application of exome sequencing is the identification of genetic variations likely to cause Mendelian diseases. This requires processing large amounts of sequence information and therefore computational approaches that can accurately and efficiently identify the subset of disease-associated variations are needed. The accuracy and high false positive rates of existing computational tools leave much room for improvement. Here, we develop a boosted tree regression machine-learning approach to predict human disease-associated amino acid variations by utilizing a comprehensive combination of protein sequence and structure features. On comparing our method, ENTPRISE, to the state-of-the-art methods SIFT, PolyPhen-2, MUTATIONASSESSOR, MUTATIONTASTER, FATHMM, ENTPRISE exhibits significant improvement. In particular, on a testing dataset consisting of only proteins with balanced disease-associated and neutral variations defined as having the ratio of neutral/disease-associated variations between 0.3 and 3, the Mathews Correlation Coefficient by ENTPRISE is 0.493 as compared to 0.432 by PPH2-HumVar, 0.406 by SIFT, 0.403 by MUTATIONASSESSOR, 0.402 by PPH2-HumDiv, 0.305 by MUTATIONTASTER, and 0.181 by FATHMM. ENTPRISE is then applied to nucleic acid binding proteins in the human proteome. Disease-associated predictions are shown to be highly correlated with the number of protein-protein interactions. Both these predictions and the ENTPRISE server are freely available for academic users as a web service at http://cssb.biology.gatech.edu/entprise/.

Protein Structure and Function Prediction Using I-TASSER

I-TASSER is a hierarchical protocol for automated protein structure prediction and structure-based function annotation. Starting from the amino acid sequence of target proteins, I-TASSER first generates full-length atomic structural models from multiple threading alignments and iterative structural assembly simulations followed by atomic-level structure refinement. The biological functions of the protein, including ligand-binding sites, enzyme commission number, and gene ontology terms, are then inferred from known protein function databases based on sequence and structure profile comparisons. I-TASSER is freely available as both an on-line server and a stand-alone package. This unit describes how to use the I-TASSER protocol to generate structure and function prediction and how to interpret the prediction results, as well as alternative approaches for further improving the I-TASSER modeling quality for distant-homologous and multi-domain protein targets.

In silico drug re-purposing against African sleeping sickness using GlcNAc-PI de-N-acetylase as an experimental target

Trypanosoma brucei is a protozoan that causes African sleeping sickness in humans. Many glycoconjugate compounds are present on the entire cell surface of Trypanosoma brucei to control the infectivity and survival of this pathogen. These gycoconjugates are anchored to the plasma membrane with the help of glycosyl phosphatidyl inositol (GPI) anchors. This type of anchor is much more common in protozoans than in other eukaryotes. The second step of glycosyl phosphatidyl inositol (GPI) anchor biosynthesis is catalyzed by an enzyme, which is GlcNAc-PI de-N-acetylase. GlcNAc-PI de-N-acetylase has a conserved GPI domain, which is responsible for the functionality of this enzyme. In this study, the three-dimensional structure of the target is modelled by I-TASSER and the ligand is modelled by PRODRG server. It is found that the predicted active site residues of the GPI domain are ultra-conserved for the Trypanosomatidae family. The predicted active site residues are His41, Pro42, Asp43, Asp44, Met47, Phe48, Ser74, Arg80, His103, Val144, Ser145, His147 and His150. Two hydrogen bond acceptors and four hydrogen bond donors are found in the modelled pharmacophore. All compounds of the Drugbank database and twenty three known inhibitors have been considered for structure based virtual screening. This work is focused on approved drugs because they are already tested for safety and effectiveness in humans. After the structure-based virtual screening, seventeen approved drugs and two inhibitors are found, which interact with the ligand on the basis of the designed pharmacophore. The docking has been performed for the resultant seventeen approved drugs and two known inhibitors. Two approved drugs have negative binding energy and their pKa values are similar to the selected known inhibitors. The result of this study suggests that the approved drugs Ethambutol (DB00330) and Metaraminol (DB00610) may prove useful in the treatment of African sleeping sickness.

Template-based protein structure prediction in CASP11 and retrospect of I-TASSER in the last decade

We report the structure prediction results of a new composite pipeline for template-based modeling (TBM) in the 11th CASP experiment. Starting from multiple structure templates identified by LOMETS based meta-threading programs, the QUARK ab initio folding program is extended to generate initial full-length models under strong constraints from template alignments. The final atomic models are then constructed by I-TASSER based fragment reassembly simulations, followed by the fragment-guided molecular dynamic simulation and the MQAP-based model selection. It was found that the inclusion of QUARK-TBM simulations as an intermediate modeling step could help improve the quality of the I-TASSER models for both Easy and Hard TBM targets. Overall, the average TM-score of the first I-TASSER model is 12% higher than that of the best LOMETS templates, with the RMSD in the same threading-aligned regions reduced from 5.8 to 4.7 Å. Nevertheless, there are nearly 18% of TBM domains with the templates deteriorated by the structure assembly pipeline, which may be attributed to the errors of secondary structure and domain orientation predictions that propagate through and degrade the procedures of template identification and final model selections. To examine the record of progress, we made a retrospective report of the I-TASSER pipeline in the last five CASP experiments (CASP7-11). The data show no clear progress of the LOMETS threading programs over PSI-BLAST; but obvious progress on structural improvement relative to threading templates was witnessed in recent CASP experiments, which is probably attributed to the integration of the extended ab initio folding simulation with the threading assembly pipeline and the introduction of atomic-level structure refinements following the reduced modeling simulations. Proteins 2016; 84(Suppl 1):233-246. © 2015 Wiley Periodicals, Inc.

General overview on structure prediction of twilight-zone proteins

Protein structure prediction from amino acid sequence has been one of the most challenging aspects in computational structural biology despite significant progress in recent years showed by critical assessment of protein structure prediction (CASP) experiments. When experimentally determined structures are unavailable, the predictive structures may serve as starting points to study a protein. If the target protein consists of homologous region, high-resolution (typically <1.5 Å) model can be built via comparative modelling. However, when confronted with low sequence similarity of the target protein (also known as twilight-zone protein, sequence identity with available templates is less than 30%), the protein structure prediction has to be initiated from scratch. Traditionally, twilight-zone proteins can be predicted via threading or ab initio method. Based on the current trend, combination of different methods brings an improved success in the prediction of twilight-zone proteins. In this mini review, the methods, progresses and challenges for the prediction of twilight-zone proteins were discussed.

Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

MOTIVATION

Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g., >3 bonds, is too low to effectively assist structure assembly simulations.

RESULTS

We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins.

AVAILABILITY AND IMPLEMENTATION

http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/

CONTACT

zhng@umich.edu or hbshen@sjtu.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

GPCR-I-TASSER: A Hybrid Approach to G Protein-Coupled Receptor Structure Modeling and the Application to the Human Genome

Experimental structure determination remains difficult for G protein-coupled receptors (GPCRs). We propose a new hybrid protocol to construct GPCR structure models that integrates experimental mutagenesis data with ab initio transmembrane (TM) helix assembly simulations. The method was tested on 24 known GPCRs where the ab initio TM-helix assembly procedure constructed the correct fold for 20 cases. When combined with weak homology and sparse mutagenesis restraints, the method generated correct folds for all the tested cases with an average Cα root-mean-square deviation 2.4 Å in the TM regions. The new hybrid protocol was applied to model all 1,026 GPCRs in the human genome, where 923 have a high confidence score and are expected to have correct folds; these contain many pharmaceutically important families with no previously solved structures, including Trace amine, Prostanoids, Releasing hormones, Melanocortins, Vasopressin, and Neuropeptide Y receptors. The results demonstrate new progress on genome-wide structure modeling of TM proteins.

Comprehensive prediction of drug-protein interactions and side effects for the human proteome

Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITE(comb), whose average precision is ~30% and recall ~27%, is employed. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome, and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds.

Cytotoxic effect of the immunotoxin constructed of the ribosome-inactivating protein curcin and the monoclonal antibody against Her2 receptor on tumor cells

The toxicity of the curcin on cancer cells allows to consider this protein as the toxic component of an immunotoxin directed to Her2, which is associated with cancer. Reductive amination was proposed to conjugate curcin and an anti-Her2; the binding was tested using Polyacrylamide gel electrophoresis, western blot, and immunocytochemistry. The in vitro cytotoxicity of curcin and the immunotoxin was assessed on breast cancer cell lines SK-BR-3 (Her2(+)) and MDA-MB-231 (Her2(-)). IC50 values for curcin were 15.5 ± 8.3 and 18.6 ± 2.4 μg/mL, respectively, statistically equivalent (p < 0.05). While to the immunotoxin was 2.2 ± 0.08 for SK-BR-3 and 147.6 ± 2.5 μg/mL for MDA-MB-231. These values showed that the immunotoxin was seven times more toxic to the SK-BR-3 than curcin and eight times less toxic to the MDA-MB-231. The immunotoxin composed of curcin and an antibody against Her2 and constructed by reductive amination could be a therapeutic candidate against Her2(+) cancer.

Implications of the small number of distinct ligand binding pockets in proteins for drug discovery, evolution and biochemical function

Coincidence of the properties of ligand binding pockets in native proteins with those in proteins generated by computer simulations without selection for function shows that pockets are a generic protein feature and the number of distinct pockets is small. Similar pockets occur in unrelated protein structures, an observation successfully employed in pocket-based virtual ligand screening. The small number of pockets suggests that off-target interactions among diverse proteins are inherent; kinases, proteases and phosphatases show this prototypical behavior. The ability to repurpose FDA approved drugs is general, and minor side effects cannot be avoided. Finally, the implications to drug discovery are explored.

RNA folding: structure prediction, folding kinetics and ion electrostatics

Beyond the "traditional" functions such as gene storage, transport and protein synthesis, recent discoveries reveal that RNAs have important "new" biological functions including the RNA silence and gene regulation of riboswitch. Such functions of noncoding RNAs are strongly coupled to the RNA structures and proper structure change, which naturally leads to the RNA folding problem including structure prediction and folding kinetics. Due to the polyanionic nature of RNAs, RNA folding structure, stability and kinetics are strongly coupled to the ion condition of solution. The main focus of this chapter is to review the recent progress in the three major aspects in RNA folding problem: structure prediction, folding kinetics and ion electrostatics. This chapter will introduce both the recent experimental and theoretical progress, while emphasize the theoretical modelling on the three aspects in RNA folding.