Target Protein Sequence Research Articles

Multimodal Fusion-Based Lightweight Model for Enhanced Generalization in Drug-Target Interaction Prediction.

Predicting drug-target interactions (DTIs) with precision is a crucial challenge in the quest for efficient and cost-effective drug discovery. Existing DTI prediction models often require significant computational resources because of the intricate and exceptionally lengthy protein target sequences. This study introduces MMF-DTI, a lightweight model that uses multimodal fusion, to improve the generalizability of DTI predictions without sacrificing computational efficiency. The MMF-DTI model combines four distinct modalities: molecular sequence, molecular properties, target sequence, and target function description. This approach is noteworthy because it is the first to use natural language-based target function descriptions in predicting DTIs. The effectiveness of MMF-DTI has been confirmed through benchmark data sets, demonstrating its comparable performance in terms of generalizability, especially in scenarios with limited information about the drug or target. Remarkably, MMF-DTI accomplishes this using only half of the parameters and 17% of the VRAM compared with previous state-of-the-art models. This allows it to function even in constrained computational environments. The combination of performance and efficiency highlights the potential of multimodal data fusion in improving the overall applicability of models, providing promising opportunities for future drug discovery endeavors.

A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.

Deep generative models are gaining attention in the field of de novo drug design. However, the rational design of ligand molecules for novel targets remains challenging, particularly in controlling the properties of the generated molecules. Here, inspired by the DNA-encoded compound library technique, we introduce DeepBlock, a deep learning approach for block-based ligand generation tailored to target protein sequences while enabling precise property control. DeepBlock neatly divides the generation process into two steps: building blocks generation and molecule reconstruction, accomplished by a neural network and a rule-based reconstruction algorithm we proposed, respectively. Furthermore, DeepBlock synergizes the optimization algorithm and deep learning to regulate the properties of the generated molecules. Experiments show that DeepBlock outperforms existing methods in generating ligands with affinity, synthetic accessibility and drug likeness. Moreover, when integrated with simulated annealing or Bayesian optimization using toxicity as the optimization objective, DeepBlock successfully generates ligands with low toxicity while preserving affinity with the target.

Innovative Mamba and graph transformer framework for superior protein-ligand affinity prediction

Accurate measurement of the affinity between drug targets is of great importance in the field of drug discovery, as it provides significant information about drug action. However, the diverse representations of compounds and proteins increase the difficulty of this task. Recently, numerous studies have explored the application of various deep learning methods and architectures in Drug-Target Affinity (DTA) prediction tasks, continuously improving performance. In this work, we present a multimodal late-stage fusion prediction model called MGDTA that combines the pioneering Mamba architecture with a hybrid model architecture based on graph transformers and transformers (GTT). For molecular compounds, structural characterisation is extracted using GTT layers. Additionally, a feature integration module is utilized to obtain molecular features from derived molecular fingerprints, culminating in a unified representation of drug molecules through a feature integration module. For proteins, we adopt the state-space model (SSM) module Mamba to learn feature representations from target protein sequences. This represents a preliminary exploration of the Mamba module in protein sequence feature extraction in DTA tasks. Finally, we predict the results by integrating the feature representations of both molecules and proteins. Our results indicate that the proposed model achieves superior performance on benchmark datasets, validating the feasibility of using the Mamba architecture for DTA tasks. Furthermore, sequence feature visualization demonstrates the capability of the Mamba block to focus on binding site residues within drug targets.

MoPPIt: De Novo Generation of Motif-Specific Binders with Protein Language Models.

The ability to precisely target specific motifs on disease-related proteins, whether conserved epitopes on viral proteins, intrinsically disordered regions within transcription factors, or breakpoint junctions in fusion oncoproteins, is essential for modulating their function while minimizing off-target effects. Current methods struggle to achieve this specificity without reliable structural information. In this work, we introduce a motif-specific PPI targeting algorithm, moPPIt, for de novo generation of motif-specific peptide binders from the target protein sequence alone. At the core of moPPIt is BindEvaluator, a transformer-based model that interpolates protein language model embeddings of two proteins via a series of multi-headed self-attention blocks, with a key focus on local motif features. Trained on over 510,000 annotated PPIs, BindEvaluator accurately predicts target binding sites given protein-protein sequence pairs with a test AUC > 0.94, improving to AUC > 0.96 when fine-tuned on peptide-protein pairs. By combining BindEvaluator with our PepMLM peptide generator and genetic algorithm-based optimization, moPPIt generates peptides that bind specifically to user-defined residues on target proteins. We demonstrate moPPIt's efficacy in computationally designing binders to specific motifs, first on targets with known binding peptides and then extending to structured and disordered targets with no known binders. In total, moPPIt serves as a powerful tool for developing highly specific peptide therapeutics without relying on target structure or structure-dependent latent spaces.

Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model

Deep learning models adapted from natural language processing offer new opportunities for the prediction of active compounds via machine translation of sequential molecular data representations. For example, chemical language models are often derived for compound string transformation. Moreover, given the principal versatility of language models for translating different types of textual representations, off-the-beaten-path design tasks might be explored. In this work, we have investigated generative design of active compounds with desired potency from target sequence embeddings, representing a rather provoking prediction task. Therefore, a dual-component conditional language model was designed for learning from multimodal data. It comprised a protein language model component for generating target sequence embeddings and a conditional transformer for predicting new active compounds with desired potency. To this end, the designated “biochemical” language model was trained to learn mappings of combined protein sequence and compound potency value embeddings to corresponding compounds, fine-tuned on individual activity classes not encountered during model derivation, and evaluated on compound test sets that were structurally distinct from training sets. The biochemical language model correctly reproduced known compounds with different potency for all activity classes, providing proof-of-concept for the approach. Furthermore, the conditional model consistently reproduced larger numbers of known compounds as well as more potent compounds than an unconditional model, revealing a substantial effect of potency conditioning. The biochemical language model also generated structurally diverse candidate compounds departing from both fine-tuning and test compounds. Overall, generative compound design based on potency value-conditioned target sequence embeddings yielded promising results, rendering the approach attractive for further exploration and practical applications.Scientific contributionThe approach introduced herein combines protein language model and chemical language model components, representing an advanced architecture, and is the first methodology for predicting compounds with desired potency from conditioned protein sequence data.

DERNA Enables Pareto Optimal RNA Design.

The design of an RNA sequence that encodes an input target protein sequence is a crucial aspect of messenger RNA (mRNA) vaccine development. There are an exponential number of possible RNA sequences for a single target protein due to codon degeneracy. These potential RNA sequences can assume various secondary structure conformations, each with distinct minimum free energy (MFE), impacting thermodynamic stability and mRNA half-life. Furthermore, the presence of species-specific codon usage bias, quantified by the codon adaptation index (CAI), plays a vital role in translation efficiency. While earlier studies focused on optimizing either MFE or CAI, recent research has underscored the advantages of simultaneously optimizing both objectives. However, optimizing one objective comes at the expense of the other. In this work, we present the Pareto Optimal RNA Design problem, aiming to identify the set of Pareto optimal solutions for which no alternative solutions exist that exhibit better MFE and CAI values. Our algorithm DEsign RNA (DERNA) uses the weighted sum method to enumerate the Pareto front by optimizing convex combinations of both objectives. We use dynamic programming to solve each convex combination in time and space. Compared with a CDSfold, previous approach that only optimizes MFE, we show on a benchmark data set that DERNA obtains solutions with identical MFE but superior CAI. Moreover, we show that DERNA matches the performance in terms of solution quality of LinearDesign, a recent approach that similarly seeks to balance MFE and CAI. We conclude by demonstrating our method's potential for mRNA vaccine design for the SARS-CoV-2 spike protein.

Mirror-Image Phage Display for the Selection of D-Amino Acid Peptide Ligands as Potential Therapeutics.

In neurodegenerative diseases like Alzheimer's disease (AD), endogenous proteins or peptides aggregate with themselves. These proteins may lose their function or aggregates and/or oligomers can obtain toxicity, causing injury or death to cells. Aggregation of two major proteins characterizes AD. Amyloid-β peptide (Aβ) is deposited in amyloid plaques within the extracellular space of the brain and Tau in so-called neurofibrillary tangles in neurons. Finding peptide ligands to halt protein aggregation is a promising therapeutical approach. Using mirror-image phage display with a commercially available, randomized 12-mer peptide library, we have selected D-amino acid peptides, which bind to the Tau protein and modulate its aggregation in vitro. Peptides can bind specifically and selectively to a target molecule, but natural L-amino acid peptides may have crucial disadvantages for in vivo applications, as they are sensitive to protease degradation and may elicit immune responses. One strategy to circumvent these disadvantages is the use of non-naturally occurring D-amino acid peptides as they exhibit increased protease resistance and generally do not activate the immune system. To perform mirror-image phage display, the target protein needs to be synthesized as D-amino acid version. If the target protein sequence is too long to be synthesized properly, smaller peptides derived from the full length protein can be used for the selection process. This also offers the possibility to influence the binding region of the selected D-peptides in the full-length target protein. Here we provide the protocols for mirror-image phage display selection on the PHF6* peptide of Tau, based on the commercially available Ph.D.™-12 Phage Display Peptide Library Kit, leading to D-peptides that also bind the full length Tau protein (Tau441), next to PHF6*. In addition, we provide protocols and data for the first characterization of those D-peptides that inhibit Tau aggregation in vitro. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Mirror image phage display selection against D-PHF6* fibrils Support Protocol 1: Single phage ELISA Basic Protocol 2: Sequencing and D-peptide generation Basic Protocol 3: Thioflavin-T (ThT) test to control inhibition of Tau aggregation Support Protocol 2: Purification of full-length Tau protein Basic Protocol 4: ELISA to demonstrate the binding of the generated D-peptides to PHF6* and full-length Tau fibrils.

Phenolic Substitution in Fidaxomicin: A Semisynthetic Approach to Antibiotic Activity Across Species.

Fidaxomicin (Fdx) is a natural product antibiotic with potent activity against Clostridioides difficile and other Gram-positive bacteria such as Mycobacterium tuberculosis. Only a few Fdx derivatives have been synthesized and examined for their biological activity in the 50 years since its discovery. Fdx has a well-studied mechanism of action, namely inhibition of the bacterial RNA polymerase. Yet, the targeted organisms harbor different target protein sequences, which poses a challenge for the rational development of new semisynthetic Fdx derivatives. We introduced substituents on the two phenolic hydroxy groups of Fdx and evaluated the resulting trends in antibiotic activity against M. tuberculosis, C. difficile, and the Gram-negative model organism Caulobacter crescentus. As suggested by the target protein structures, we identified the preferable derivatisation site for each organism. The derivative ortho-methyl Fdx also exhibited activity against the Gram-negative C. crescentus wild type, a first for fidaxomicin antibiotics. These insights will guide the synthesis of next-generation fidaxomicin antibiotics.

Molecular Mechanisms of Resistance to Direct-Acting Antiviral (DAA) Drugs for the Treatment of Hepatitis C Virus Infections.

Hepatitis C virus (HCV) is a hepatotropic virus that affects millions of human lives worldwide. Direct-acting antiviral (DAA) regimens are the most effective HCV treatment option. However, amino acid substitution-dependent resistance to DAAs has been a major challenge. This study aimed to determine the increasing risk of DAA resistance due to substitutions in DAA target non-structural proteins (NS3/4A, NS5A, and NS5B). Using a Sequence Retrieval System (SRS) at the virus pathogen resource (ViPR/BV-BRC), n = 32763 target protein sequences were retrieved and analyzed for resistance-associated amino acid substitutions (RAASs) by the Sequence Feature Variant Type (SFVT) antiviral-resistance assessment modeling tool. Reference target protein sequences with 100% identity were retried from UniProt following NCBI BLAST. The types and locations of RAASs were identified and visualized by AlphaFold and PyMol. Linux-r-base/R-studio was used for the data presentation. Multi-drug-resistant variants of NS3/4A in genotype 1 (n = 9) and genotype 5 (n = 5) along with DAA-specific NS3/4A, NS5A, and NS5B variants were identified pan-genotypically. A total of 27 variants (RAASs) of all the targets were identified. Fourteen genotype 1-specific substitutions: V1196A, V1158I, D1194A/T/G, R1181K, T1080S, Q1106R, V1062A, S1148G, A1182V, Y2065N, M2000T, and L2003V were identified. The most frequent substitutions were V1062L and L2003M, followed by Q2002H. L2003V, Q2002H, M2000T, Y2065N, and NL2003M of NS5A and L2003M of NS5B conferred resistance to daclatasvir. S2702T NS5B was the sofosbuvir-resistant variant. D1194A NS3/4A was triple DAA (simeprevir, faldaprevir, and asunaprevir) resistant. The double-drug resistant variants R1181K (faldaprevir and asunaprevir), A1182V and Q1106K/R (faldaprevir and simeprevir), T1080S (faldaprevir and telaprevir), and single drug-resistant variants V1062L (telaprevir), D1194E/T (simeprevir), D1194G (asunaprevir), S1148A/G (simeprevir), and Q1106L (Boceprevir) of NS3/4A were determined. The molecular phenomenon of DAA resistance is paramount in the development of HCV drug candidates. RAASs in NS3, NS5A, and NS5B reduce the susceptibility to DAAs; therefore, continuous RAAS-dependent resistance profiling in HCV is recommended to minimize the probability of DAA therapeutic failure.

VIRTUAL SCREENING OF NATURAL ALPHA-GLUCOSIDASE INHIBITOR FROM ALPINIA GALANGA BIOACTIVE COMPOUNDS AS ANTI-DIABETIC CANDIDATE

Glucose toxicity become serious risk factor of type 2 diabetes mellitus. Multiple complication can be caused of the abnormal condition of glucose level in blood. Thus, managing the plasma glucose is one of the strategy to minimize the negative effects of type 2 diabetes mellitus. In this present study, we aimed to virtually evaluate the bioactive compounds from Alpinia galanga as inhibitor agent against alpha-glucosidase, which was known as carbohydrate catabolic enzyme. The 2D structure of ligands were retrieved from PubChem database (pubchem.ncbi.nlm.nih.gov/) and were evaluated based on Lipinski rule of five for ensured the druglike-ness standard, while the sequence and 3D structure of target protein, alpha-glucosidase, was retrieved from UniProt database (https://www.uniprot.org/) and was modelled through SWISS-MODEL (swissmodel.expasy.org/). All ligands used in this study then underwent optimization prior to molecular docking and analyzing procedures. According to our prediction results, we found that galangin and 1's-1'-acetoxychavicol acetate which known as bioactive compounds found in A. galanga have significant potency as inhibitor agents against the alpha-glucosidase due to its binding affinity scores and other physicochemical properties compared to the anti-diabetic drug, mignitol. Thus, from this finding, we have recent starting point to expand the potency of these bioactive compounds, especially for inhibiting the alpha-glucosidase as the novel strategy to reduce the poor prognosis of type 2 diabetes mellitus.

Interpretable bilinear attention network with domain adaptation improves drug–target prediction

Predicting drug–target interaction is key for drug discovery. Recent deep learning-based methods show promising performance, but two challenges remain: how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation and how to optimize generalization performance of predictions on novel drug–target pairs. Here, we present DrugBAN, a deep bilinear attention network (BAN) framework with domain adaptation to explicitly learn pairwise local interactions between drugs and targets, and adapt in response to out-of-distribution data. DrugBAN works on drug molecular graphs and target protein sequences to perform prediction, with conditional domain adversarial learning to align learned interaction representations across different distributions for better generalization on novel drug–target pairs. Experiments on three benchmark datasets under both in-domain and cross-domain settings show that DrugBAN achieves the best overall performance against five state-of-the-art baseline models. Moreover, visualizing the learned bilinear attention map provides interpretable insights from prediction results.

MSALigMap-A Tool for Mapping Active-Site Amino Acids in PDB Structures onto Known and Novel Unannotated Homologous Sequences with Similar Function.

MSALigMap (Multiple Sequence Alignment Ligand Mapping) is a tool for mapping active-site amino-acid residues that bind selected ligands on to target protein sequences of interest. Users can also provide novel sequences (unavailable in public databases) for analysis. MSALigMap is written in Python. There are several tools and servers available for comparing and mapping active-site amino-acid residues among protein structures. However, there has not previously been a tool for mapping ligand binding amino-acid residues onto protein sequences of interest. Using MSALigMap, users can compare multiple protein sequences, such as those from different organisms or clinical strains, with sequences of proteins with crystal structures in PDB that are bound with the ligand/drug and DNA of interest. This allows users to easily map the binding residues and to predict the consequences of different mutations observed in the binding site. The MSALigMap server can be accessed at https://albiorix.bioenv.gu.se/MSALigMap/HomePage.py.

The metal chaperone protein MtmA plays important roles in antifungal drug susceptibility in Aspergillus fumigatus.

Drug-resistant fungal infections are emerging as an important clinical problem. In general, antifungal resistance results from increased target expression or mutations within the target protein sequence. However, the molecular mechanisms of non-drug target mutations of antifungal resistance in fungal pathogens remain to be explored. Previous studies indicated that the metal chaperone protein Mtm1 is required for mitochondrial Sod2 activation and responses to oxidative stress in yeast and in the fungal pathogen Aspergillus fumigatus, but there is no report of MtmA-related antifungal resistance. In this study, we found that repressed expression of MtmA (only 10% expression) using a conditional promoter resulted in significantly enhanced itraconazole resistance, which was not the result of highly expressed drug targets Erg11A and Erg11B. Furthermore, we demonstrated that repressed expression of MtmA results in upregulation of a series of multidrug resistance-associated transport genes, which may cause multidrug resistance. Further mechanistic studies revealed that inhibition of MtmA expression led to abnormal activation of the calcium signaling system and prompted persistent nucleation of the calcium signaling transcription factor CrzA. Our findings suggest that the metal chaperone protein MtmA is able to negatively regulate fungal resistance via affecting calcium signaling pathway.

DeepLPI: a novel deep learning-based model for protein–ligand interaction prediction for drug repurposing

The substantial cost of new drug research and development has consistently posed a huge burden for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs by identifying interactions between drug molecules and target proteins based on computational methods have gained growing attention. Here, we propose the DeepLPI, a novel deep learning-based model that mainly consists of ResNet-based 1-dimensional convolutional neural network (1D CNN) and bi-directional long short term memory network (biLSTM), to establish an end-to-end framework for protein–ligand interaction prediction. We first encode the raw drug molecular sequences and target protein sequences into dense vector representations, which go through two ResNet-based 1D CNN modules to derive features, respectively. The extracted feature vectors are concatenated and further fed into the biLSTM network, followed by the MLP module to finally predict protein–ligand interaction. We downloaded the well-known BindingDB and Davis dataset for training and testing our DeepLPI model. We also applied DeepLPI on a COVID-19 dataset for externally evaluating the prediction ability of DeepLPI. To benchmark our model, we compared our DeepLPI with the baseline methods of DeepCDA and DeepDTA, and observed that our DeepLPI outperformed these methods, suggesting the high accuracy of the DeepLPI towards protein–ligand interaction prediction. The high prediction performance of DeepLPI on the different datasets displayed its high capability of protein–ligand interaction in generalization, demonstrating that the DeepLPI has the potential to pinpoint new drug-target interactions and to find better destinations for proven drugs.

ICAN: Interpretable cross-attention network for identifying drug and target protein interactions.

Drug–target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. However, improvements are needed for enhancing prediction performance and DTI mechanism elucidation. To address these problems, we developed an attention-based method designated the interpretable cross-attention network (ICAN), which predicts DTIs using the Simplified Molecular Input Line Entry System of drugs and amino acid sequences of target proteins. We optimized the attention mechanism architecture by exploring the cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention mechanism. We found that a plain attention mechanism that decodes drug-related protein context features without any protein-related drug context features effectively achieved high performance. The ICAN outperformed state-of-the-art methods in several metrics on the DAVIS dataset and first revealed with statistical significance that some weighted sites in the cross-attention weight matrix represent experimental binding sites, thus demonstrating the high interpretability of the results. The program is freely available at https://github.com/kuratahiroyuki/ICAN.

AI-based prediction of new binding site and virtual screening for the discovery of novel P2X3 receptor antagonists

Artificial intelligence (AI) has been recognized as a powerful technique that can accelerate drug discovery during the hit compound identification step. However, most simple deep learning models have been used for naive pre-filtering as the prediction result cannot be interpreted. Recently, our group developed a new deep learning model (Highlight on Target Sequence; HoTS) that can predict binding regions in a target protein sequence based on patterns learned from interactions between a target protein sequence and a ligand. In this study, we searched for new binding regions of the P2X3 receptor (P2X3R) using HoTS, and suggested a novel putative binding site of P2X3R by a cavity search on the predicted binding regions. The novel putative binding site was employed to generate pharmacophore features, and combinations of pharmacophore features were validated as queries. Two separate virtual screenings using the optimized pharmacophore query Q12 with docking-based scoring and HoTS-based prediction of ligand interactions enabled the initial selection of the compound library for in vitro screening. The screening of each set of 500 compounds from the two approaches (HoTS interaction prediction and Pharmacophore-LibDock cascade) resulted in the identification of 10 (HoTS-1 – 10) and 6 compounds (PD-1 – 6) with low micromolar IC50 values. Remarkably, the hit rate was 10-fold higher than that from the previous random screening of 8364 compound library, and the chemical structures of all identified hit compounds were distinct from those of known P2X3R antagonists, indicating that novel chemical entities could be developed for P2X3R antagonists by targeting the binding site. Overall, this study suggests the discovery of a novel putative binding site for P2X3R using the AI deep learning protocol along with in silico MD simulation and experimental screening of targeted library compounds to successfully identify 16 unique and novel hit compounds. These results may accelerate the discovery of novel chemical-class drugs for P2X3R antagonists.

Detecting Drug-Target Interactions with Feature Similarity Fusion and Molecular Graphs.

Simple SummaryAccurate identification of potential targets for drugs to interact with can accelerate drug development. The identification of drug–target interactions can provide insights into hidden drug efficacy. This paper presents a prediction model based on feature similarity fusion that can identify crucial features of drugs and targets to help predict drug–target interactions.The key to drug discovery is the identification of a target and a corresponding drug compound. Effective identification of drug–target interactions facilitates the development of drug discovery. In this paper, drug similarity and target similarity are considered, and graphical representations are used to extract internal structural information and intermolecular interaction information about drugs and targets. First, drug similarity and target similarity are fused using the similarity network fusion (SNF) method. Then, the graph isomorphic network (GIN) is used to extract the features with information about the internal structure of drug molecules. For target proteins, feature extraction is carried out using TextCNN to efficiently capture the features of target protein sequences. Three different divisions (CVD, CVP, CVT) are used on the standard dataset, and experiments are carried out separately to validate the performance of the model for drug–target interaction prediction. The experimental results show that our method achieves better results on AUC and AUPR. The docking results also show the superiority of the proposed model in predicting drug–target interactions.

Accurate discrimination of leucine and isoleucine residues by combining continuous digestion with multiple MS3 spectra integration in protein sequence

Protein de novo sequencing based on tandem mass spectrometry is a crucial technology that enables the identification of peptides without searching databases and assembling unknown sequence proteins, especially for monoclonal antibodies (mAbs). However, the discrimination of leucine (Leu) and isoleucine (Ile) residues in the target protein sequence is still challenging. Herein, we developed an accurate method by continuous digestion with MS3-based fragmentation and multiple spectra integration (evaluated by combined verification score, CVS) to distinguish Leu and Ile residues. Continuous digestion promotes the diversity of peptides in order to expose more Leu and Ile at the N-terminal. CVS integrates multiple MS3 spectra to reduce the interference from noise and co-fragmented ions and improve accuracy. This method successfully resolved all 75 Leu/Ile in bovine serum albumin, especially 3 consecutive Leu/Ile. We further applied the method to analyze trastuzumab and 67 out of the 68 Leu/Ile from the light chain and heavy chain were accurately discriminated, demonstrating the great potential in mAbs sequencing.

ROCker Models for Reliable Detection and Typing of Short-Read Sequences Carrying β-Lactamase Genes.

ABSTRACTIdentification of genes encoding β-lactamases (BLs) from short-read sequences remains challenging due to the high frequency of shared amino acid functional domains and motifs in proteins encoded by BL genes and related non-BL gene sequences. Divergent BL homologs can be frequently missed during similarity searches, which has important practical consequences for monitoring antibiotic resistance. To address this limitation, we built ROCker models that targeted broad classes (e.g., class A, B, C, and D) and individual families (e.g., TEM) of BLs and challenged them with mock 150-bp- and 250-bp-read data sets of known composition. ROCker identifies most-discriminant bit score thresholds in sliding windows along the sequence of the target protein sequence and hence can account for nondiscriminative domains shared by unrelated proteins. BL ROCker models showed a 0% false-positive rate (FPR), a 0% to 4% false-negative rate (FNR), and an up-to-50-fold-higher F1 score [2 × precision × recall/(precision + recall)] compared to alternative methods, such as similarity searches using BLASTx with various e-value thresholds and BL hidden Markov models, or tools like DeepARG, ShortBRED, and AMRFinder. The ROCker models and the underlying protein sequence reference data sets and phylogenetic trees for read placement are freely available through http://enve-omics.ce.gatech.edu/data/rocker-bla. Application of these BL ROCker models to metagenomics, metatranscriptomics, and high-throughput PCR gene amplicon data should facilitate the reliable detection and quantification of BL variants encoded by environmental or clinical isolates and microbiomes and more accurate assessment of the associated public health risk, compared to the current practice.IMPORTANCE Resistance genes encoding β-lactamases (BLs) confer resistance to the widely prescribed antibiotic class β-lactams. Therefore, it is important to assess the prevalence of BL genes in clinical or environmental samples for monitoring the spreading of these genes into pathogens and estimating public health risk. However, detecting BLs in short-read sequence data is technically challenging. Our ROCker model-based bioinformatics approach showcases the reliable detection and typing of BLs in complex data sets and thus contributes toward solving an important problem in antibiotic resistance surveillance. The ROCker models developed substantially expand the toolbox for monitoring antibiotic resistance in clinical or environmental settings.

Pemodelan Calponin Ikan Gabus (Channa striata) dengan Phyre2 dan Interaksi dengan Protein Lain

Knowledge of the three-dimensional structure of proteins is very important to understand their character and function at the molecular level. Determination of protein structure in the laboratory is expensive and relatively difficult because it requires sophisticated instruments and takes a long time. As an alternative, an in silico approach can be used to predict the three-dimensional structure of proteins, namely the fold recognition method. The purpose of this study was to create a three-dimensional structure model of snakehead fish (Channa striata) calponin protein using the Phyre2 web server. The target protein sequence was obtained from UniProt KB with code K9LH64. The results of the study showed that the template for building the model was the c1wynA code. The results of the model (MP) evaluation obtained a coverage value was 45%, a confidence value was 100%, and an identity value was 69%. Validation of the model (MP) using the PROCHECK program showed the model was in the most favoured residue plot of 82.4%. This model deserves to be used as a model for calponin protein of snakehead fish, because the disallowed area was below 15%. The results of the analysis of interactions with other proteins using STRING-DB obtained the interaction model (MP) with Actin assembly-inducing protein (ACTA2) was 0.791; and Tropomyosin (TPM1) was 0.786. The results of molecular docking using PatchDock obtained the value of Atomic Contact Energy (ACE) for the calponin protein model (MP)-ACTA2 was 372.76 kJ/mol; Calponin model protein (MP)-TPM1 was 406.93 kJ/mol. The interactions that occured in the calponin model (MP)-ACTA2 were hydrogen bonds at residues Gly-41, Glu-60, Arg-64, Arg-66, Pro-67, Gly-68, Lys-71 and hydrophobic bonds at Arg-64, Lys- 71, Lys-72, Ile-73, Va-138. The interactions model of calponin model (MP)-TPM1 were hydrogen bonding at residues Ser-13, Lys-71, Ala-95, Tyr-96, Ser-136, Arg-137, Arg-150 and hydrophobic bonds at residues Leu-12, Ala-14, Lys-71, Ala-95, Val-138, Arg-150, Phe-152. Keywords: fold recognition, Phyre2 , 3D structure protein, calponin, protein-protein interaction