Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning

Identification of deleterious non-synonymous single nucleotide polymorphisms in the mRNA decay activator ZFP36L2

More than 4,000 single nucleotide polymorphisms (SNP) variants have been identified in the human ZFP36L2 gene, however only a few have been studied in the context of protein function. The tandem zinc finger domain of ZFP36L2, an RNA binding protein, is the functional domain that binds to its target mRNAs. This protein/RNA interaction triggers mRNA degradation, controlling gene expression. We identified 32 non-synonymous SNPs (nsSNPs) in the tandem zinc finger domain of ZFP36L2 that could have possible deleterious impacts in humans. Using different bioinformatic strategies, we prioritized five among these 32 nsSNPs, namely rs375096815, rs1183688047, rs1214015428, rs1215671792 and rs920398592 to be validated. When we experimentally tested the functionality of these protein variants using gel shift assays, all five (Y154H, R160W, R184C, G204D, and C206F) resulted in a dramatic reduction in RNA binding compared to the WT protein. To understand the mechanistic effect of these variants on the protein/RNA interaction, we employed DUET, DynaMut and PyMOL to investigate structural changes in the protein. Additionally, we conducted Molecular Docking and Molecular Dynamics Simulations to fine tune the active behaviour of this biomolecular system at an atomic level. Our results propose atomic explanations for the impact of each of these five genetic variants identified.

Computational analyses of drug resistance mutations in katG and emb complexes in Mycobacterium tuberculosis

The number of antibiotic resistant pathogens is increasing rapidly, and with this comes a substantial socioeconomic cost that threatens much of the world. To alleviate this problem, we must use antibiotics in a more responsible and informed way, further our understanding of the molecular basis of drug resistance, and design new antibiotics. Here, we focus on a key drug-resistant pathogen, Mycobacterium tuberculosis, and computationally analyze trends in drug-resistant mutations in genes of the proteins embA, embB, embC, and katG, which play essential roles in the action of the first-line drugs ethambutol and isoniazid. We use docking to predict binding modes of isoniazid to katG that agree with suggested binding sites found in our laboratory using cryo-EM. Using mutant stability predictions, we recapitulate the idea that resistance occurs when katG's heme cofactor is destabilized rather than due to a decrease in affinity to isoniazid. Conversely, we have identified resistance mutations that affect the affinity of ethambutol more drastically than the affinity of the natural substrate of embB. With this, we illustrate that we can distinguish between the two types of drug resistance-cofactor destabilization and drug affinity reduction-suggesting potential uses in the prediction of novel drug-resistant mutations.

Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning

The fast evolution of SARS-CoV-2 and other infectious viruses poses a grand challenge to the rapid response in terms of viral tracking, diagnostics, and design and manufacture of monoclonal antibodies (mAbs) and vaccines, which are both time-consuming and costly. This underscores the need for efficient computational approaches. Recent advancements, like topological deep learning (TDL), have introduced powerful tools for forecasting emerging dominant variants, yet they require deep mutational scanning (DMS) of viral surface proteins and associated three-dimensional (3D) protein-protein interaction (PPI) complex structures. We propose an AlphaFold 3 (AF3)-assisted multi-task topological Laplacian (MT-TopLap) strategy to address this need. MT-TopLap combines deep learning with topological data analysis (TDA) models, such as persistent Laplacians (PL) to extract detailed topological and geometric characteristics of PPIs, thereby enhancing the prediction of DMS and binding free energy (BFE) changes upon virus mutations. Validation with four experimental DMS datasets of SARS-CoV-2 spike receptor-binding domain (RBD) and the human angiotensin-converting enzyme-2 (ACE2) complexes indicates that our AF3 assisted MT-TopLap strategy maintains robust performance, with only an average 1.1% decrease in Pearson correlation coefficients (PCC) and an average 9.3% increase in root mean square errors (RMSE), compared with the use of experimental structures. Additionally, AF3-assisted MT-TopLap achieved a PCC of 0.81 when tested with a SARS-CoV-2 HK.3 variant DMS dataset, confirming its capability to accurately predict BFE changes and adapt to new experimental data, thereby showcasing its potential for rapid and effective response to fast viral evolution.

Highly multiplexed design of an allosteric transcription factor to sense new ligands

Allosteric transcription factors (aTF) regulate gene expression through conformational changes induced by small molecule binding. Although widely used as biosensors, aTFs have proven challenging to design for detecting new molecules because mutation of ligand-binding residues often disrupts allostery. Here, we develop Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screen a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identifies biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design shows shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we develop cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid and scalable design of new biosensors, overcoming constraints of natural biosensors.

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.

The power of computational proteomics platforms to decipher protein-protein interactions

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.

Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method

BACKGROUND

Thermostability is a fundamental property of proteins to maintain their biological functions. Predicting protein stability changes upon mutation is important for our understanding protein structure-function relationship, and is also of great interest in protein engineering and pharmaceutical design.

RESULTS

Here we present mutDDG-SSM, a deep learning-based framework that uses the geometric representations encoded in protein structure to predict the mutation-induced protein stability changes. mutDDG-SSM consists of two parts: a graph attention network-based protein structural feature extractor that is trained with a self-supervised learning scheme using large-scale high-resolution protein structures, and an eXtreme Gradient Boosting model-based stability change predictor with an advantage of alleviating overfitting problem. The performance of mutDDG-SSM was tested on several widely-used independent datasets. Then, myoglobin and p53 were used as case studies to illustrate the effectiveness of the model in predicting protein stability changes upon mutations. Our results show that mutDDG-SSM achieved high performance in estimating the effects of mutations on protein stability. In addition, mutDDG-SSM exhibited good unbiasedness, where the prediction accuracy on the inverse mutations is as well as that on the direct mutations.

CONCLUSION

Meaningful features can be extracted from our pre-trained model to build downstream tasks and our model may serve as a valuable tool for protein engineering and drug design.

Analysis of proteins in the light of mutations

Proteins have evolved through mutations-amino acid substitutions-since life appeared on Earth, some 109 years ago. The study of these phenomena has been of particular significance because of their impact on protein stability, function, and structure. This study offers a new viewpoint on how the most recent findings in these areas can be used to explore the impact of mutations on protein sequence, stability, and evolvability. Preliminary results indicate that: (1) mutations can be viewed as sensitive probes to identify 'typos' in the amino-acid sequence, and also to assess the resistance of naturally occurring proteins to unwanted sequence alterations; (2) the presence of 'typos' in the amino acid sequence, rather than being an evolutionary obstacle, could promote faster evolvability and, in turn, increase the likelihood of higher protein stability; (3) the mutation site is far more important than the substituted amino acid in terms of the marginal stability changes of the protein, and (4) the unpredictability of protein evolution at the molecular level-by mutations-exists even in the absence of epistasis effects. Finally, the Darwinian concept of evolution "descent with modification" and experimental evidence endorse one of the results of this study, which suggests that some regions of any protein sequence are susceptible to mutations while others are not. This work contributes to our general understanding of protein responses to mutations and may spur significant progress in our efforts to develop methods to accurately forecast changes in protein stability, their propensity for metamorphism, and their ability to evolve.

Expanding the phenotypic spectrum of LHCGR signal peptide insertion variant: novel clinical and allelic findings causing Leydig cell hypoplasia type II

PURPOSE

Leydig cell hypoplasia (LCH) type II is a rare disease with only a few cases reported. Patients presented with hypospadias, micropenis, undescended testes, or infertility. In this study, we report a new patient with compound heterozygous variants in the LHCGR gene and LCH type II phenotype.

METHODS

Whole exome sequencing (WES) was performed followed by Sanger sequencing to confirm the detected variants in the patient and his parents.

RESULTS

A novel missense variant (p.Phe444Cys) was identified in a highly conserved site and is verified to be in trans with the signal peptide's 33-bases insertion variant.

CONCLUSION

Our research provides a more comprehensive clinical and genetic spectrum of Leydig cell hypoplasia type II. It highlighted the importance of WES in the diagnosis of this uncommon genetic disorder as well as the expansion of the genotype of LCH type II.

Highly multiplexed design of an allosteric transcription factor to sense novel ligands

Allosteric transcription factors (aTF), widely used as biosensors, have proven challenging to design for detecting novel molecules because mutation of ligand-binding residues often disrupts allostery. We developed Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screened a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identified novel biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design showed shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we developed cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid, scalable design of new biosensors, overcoming constraints of natural biosensors.

Predicting Mutational Effects on Ca2+-Activated Chloride Conduction of TMEM16A Based on a Simulation Study

The dysfunction and defects of ion channels are associated with many human diseases, especially for loss-of-function mutations in ion channels such as cystic fibrosis transmembrane conductance regulator mutations in cystic fibrosis. Understanding ion channels is of great current importance for both medical and fundamental purposes. Such an understanding should include the ability to predict mutational effects and describe functional and mechanistic effects. In this work, we introduce an approach to predict mutational effects based on kinetic information (including reaction barriers and transition state locations) obtained by studying the working mechanism of target proteins. Specifically, we take the Ca2+-activated chloride channel TMEM16A as an example and utilize the computational biology model to predict the mutational effects of key residues. Encouragingly, we verified our predictions through electrophysiological experiments, demonstrating a 94% prediction accuracy regarding mutational directions. The mutational strength assessed by Pearson's correlation coefficient is -0.80 between our calculations and the experimental results. These findings suggest that the proposed methodology is reliable and can provide valuable guidance for revealing functional mechanisms and identifying key residues of the TMEM16A channel. The proposed approach can be extended to a broad scope of biophysical systems.

Proposal of pharmacophore model for HIV reverse transcriptase inhibitors: Combined mutational effect analysis, molecular dynamics, molecular docking and pharmacophore modeling study

OBJECTIVES

Antiretroviral therapy (ART) efficacy is jeopardized by the emergence of drug resistance mutations in HIV, compromising treatment effectiveness. This study aims to propose novel analogs of Effavirenz (EFV) as potential direct inhibitors of HIV reverse transcriptase, employing computer-aided drug design methodologies.

METHODS

Three key approaches were applied: a mutational profile study, molecular dynamics simulations, and pharmacophore development. The impact of mutations on the stability, flexibility, function, and affinity of target proteins, especially those associated with NRTI, was assessed. Molecular dynamics analysis identified G190E as a mutation significantly altering protein properties, potentially leading to therapeutic failure. Comparative analysis revealed that among six first-line antiretroviral drugs, EFV exhibited notably low affinity with viral reverse transcriptase, further reduced by the G190E mutation. Subsequently, a search for EFV-similar inhibitors yielded 12 promising molecules based on their affinity, forming the basis for generating a pharmacophore model.

RESULTS

Mutational analysis pinpointed G190E as a crucial mutation impacting protein properties, potentially undermining therapeutic efficacy. EFV demonstrated diminished affinity with viral reverse transcriptase, exacerbated by the G190E mutation. The search for EFV analogs identified 12 high-affinity molecules, culminating in a pharmacophore model elucidating key structural features crucial for potent inhibition.

CONCLUSION

This study underscores the significance of EFV analogs as potential inhibitors of HIV reverse transcriptase. The findings highlight the impact of mutations on drug efficacy, particularly the detrimental effect of G190E. The generated pharmacophore model serves as a pivotal reference for future drug development efforts targeting HIV, providing essential structural insights for the design of potent inhibitors based on EFV analogs identified in vitro.

Rendering protein mutation movies with MutAmore

BACKGROUND

The success of AlphaFold2 in reliable protein three-dimensional (3D) structure prediction, assists the move of structural biology toward studies of protein dynamics and mutational impact on structure and function. This transition needs tools that qualitatively assess alternative 3D conformations.

RESULTS

We introduce MutAmore, a bioinformatics tool that renders individual images of protein 3D structures for, e.g., sequence mutations into a visually intuitive movie format. MutAmore streamlines a pipeline casting single amino-acid variations (SAVs) into a dynamic 3D mutation movie providing a qualitative perspective on the mutational landscape of a protein. By default, the tool first generates all possible variants of the sequence reachable through SAVs (L*19 for proteins with L residues). Next, it predicts the structural conformation for all L*19 variants using state-of-the-art models. Finally, it visualizes the mutation matrix and produces a color-coded 3D animation. Alternatively, users can input other types of variants, e.g., from experimental structures.

CONCLUSION

MutAmore samples alternative protein configurations to study the dynamical space accessible from SAVs in the post-AlphaFold2 era of structural biology. As the field shifts towards the exploration of alternative conformations of proteins, MutAmore aids in the understanding of the structural impact of mutations by providing a flexible pipeline for the generation of protein mutation movies using current and future structure prediction models.

ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations

Background

Genomic variations may cause deleterious effects on protein functionality and perturb biological processes. Elucidating the effects of variations is critical for developing novel treatment strategies for diseases of genetic origin. Computational approaches have been aiding the work in this field by modeling and analyzing the mutational landscape. However, new approaches are required, especially for accurate representation and data-centric analysis of sequence variations.

Method

In this study, we propose ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations), a method for the featurization (i.e., quantitative representation) of single amino acid variations (SAVs), which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. ASCARIS utilizes the direct and spatial correspondence between the location of the SAV on the sequence/structure and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.), along with structural features and physicochemical properties. The main novelty of this method lies in constructing reusable numerical representations of SAVs via functional annotations.

Results

We statistically analyzed the relationship between these features and the consequences of variations and found that each carries information in this regard. To investigate potential applications of ASCARIS, we trained variant effect prediction models that utilize our SAV representations as input. We carried out an ablation study and a comparison against the state-of-the-art methods and observed that ASCARIS has a competing and complementary performance against widely-used predictors. ASCARIS can be used alone or in combination with other approaches to represent SAVs from a functional perspective. ASCARIS is available as a programmatic tool at https://github.com/HUBioDataLab/ASCARIS and as a web-service at https://huggingface.co/spaces/HUBioDataLab/ASCARIS.

Genetic investigation of Nordic patients with complement-mediated kidney diseases

Background

Complement activation in atypical hemolytic uremic syndrome (aHUS), C3 glomerulonephropathy (C3G) and immune complex-mediated membranoproliferative glomerulonephritis (IC-MPGN) may be associated with rare genetic variants. Here we describe gene variants in the Swedish and Norwegian populations.

Methods

Patients with these diagnoses (N=141) were referred for genetic screening. Sanger or next-generation sequencing were performed to identify genetic variants in 16 genes associated with these conditions. Nonsynonymous genetic variants are described when they have a minor allele frequency of <1% or were previously reported as being disease-associated.

Results

In patients with aHUS (n=94, one also had IC-MPGN) 68 different genetic variants or deletions were identified in 60 patients, of which 18 were novel. Thirty-two patients had more than one genetic variant. In patients with C3G (n=40) 29 genetic variants, deletions or duplications were identified in 15 patients, of which 9 were novel. Eight patients had more than one variant. In patients with IC-MPGN (n=7) five genetic variants were identified in five patients. Factor H variants were the most frequent in aHUS and C3 variants in C3G. Seventeen variants occurred in more than one condition.

Conclusion

Genetic screening of patients with aHUS, C3G and IC-MPGN is of paramount importance for diagnostics and treatment. In this study, we describe genetic assessment of Nordic patients in which 26 novel variants were found.

In silico resources help combat cancer drug resistance mediated by target mutations

Drug resistance causes catastrophic cancer treatment failures. Mutations in target proteins with altered drug binding indicate a main mechanism of cancer drug resistance (CDR). Global research has generated considerable CDR-related data and well-established knowledge bases and predictive tools. Unfortunately, these resources are fragmented and underutilized. Here, we examine computational resources for exploring CDR caused by target mutations, analyzing these tools based on their functional characteristics, data capacity, data sources, methodologies and performance. We also discuss their disadvantages and provide examples of how potential inhibitors of CDR have been discovered using these resources. This toolkit is designed to help specialists explore resistance occurrence effectively and to explain resistance prediction to non-specialists easily.

Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions

Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

Topological data analysis of protein structure and inter/intra-molecular interaction changes attributable to amino acid mutations

The presence of some amino acid mutations in the amino acid sequence that determines a protein's structure can significantly affect that 3D structure and its biological function. However, the effects upon structural and functional changes differ for each displaced amino acid, and it is very difficult to predict these changes in advance. Although computer simulations are very effective at predicting conformational changes, they struggle to determine whether the amino acid mutation of interest induces sufficient conformational changes, unless the researcher is a specialist in molecular structure calculations. Therefore, we created a framework that efficiently utilizes molecular dynamics and persistent homology methods to identify amino acid mutations that induce structural changes. We show that this framework can be used not only to predict conformational changes produced by amino acid mutations but also to extract groups of mutations that significantly alter similar molecular interactions, by capturing the resultant protein-protein interaction changes.

Protein structure-based evaluation of missense variants: Resources, challenges and future directions

We provide an overview of the methods that can be used for protein structure-based evaluation of missense variants. The algorithms can be broadly divided into those that calculate the difference in free energy (ΔΔG) between the wild type and variant structures and those that use structural features to predict the damaging effect of a variant without providing a ΔΔG. A wide range of machine learning approaches have been employed to develop those algorithms. We also discuss challenges and opportunities for variant interpretation in view of the recent breakthrough in three-dimensional structural modelling using deep learning.

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning

Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification of driver mutations from experiments is expensive and time intensive. In the present study, we collected the data for experimentally known driver mutations in 22 different cancer types and classified them into six categories: breast cancer (BRCA), acute myeloid leukaemia (LAML), endometrial carcinoma (EC), stomach cancer (STAD), skin cancer (SKCM), and other cancer types which contains 5747 disease prone and 5514 neutral sites in 516 proteins. The analysis of amino acid distribution along mutant sites revealed that the motifs AAA and LR are preferred in disease-prone sites whereas QPP and QF are dominant in neutral sites. Further, we developed a method using deep neural networks to predict disease-prone sites with amino acid sequence-based features such as physicochemical properties, secondary structure, tri-peptide motifs and conservation scores. We obtained an average AUC of 0.97 in five cancer types BRCA, LAML, EC, STAD and SKCM in a test dataset and 0.72 in all other cancer types together. Our method showed excellent performance for identifying cancer-specific mutations with an average sensitivity, specificity, and accuracy of 96.56 %, 97.39 %, and 97.64 %, respectively. We developed a web server for identifying cancer-prone sites, and it is available at https://web.iitm.ac.in/bioinfo2/MutBLESS/index.html. We suggest that our method can serve as an effective method to identify disease-prone sites and assist to develop therapeutic strategies.

Exploring the Transferability of Replica Exchange Structure Reservoirs to Accelerate Generation of Ensembles for Alternate Hamiltonians or Protein Mutations

Generating precise ensembles is commonly a prerequisite to understand the energetics of biological processes using Molecular Dynamics (MD) simulations. Previously, we have shown how unweighted reservoirs built from high temperature MD simulations can accelerate convergence of Boltzmann-weighted ensembles by at least 10× with the Reservoir Replica Exchange MD (RREMD) method. Therefore, in this work, we explore whether an unweighted structure reservoir generated with one Hamiltonian (solute force field plus solvent model) can be reused to quickly generate accurately weighted ensembles for Hamiltonians other than the one that was used to generate the reservoir. We also extended this methodology to rapidly estimate the effects of mutations on peptide stability by using a reservoir of diverse structures obtained from wild-type simulations. These results suggest that structures generated via fast methods such as coarse-grained models or structures predicted by Rosetta or deep learning approaches could be integrated into a reservoir to accelerate generation of ensembles using more accurate representations.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Delineation of functionally essential protein regions for 242 neurodevelopmental genes

Neurodevelopmental disorders (NDDs), including severe paediatric epilepsy, autism and intellectual disabilities are heterogeneous conditions in which clinical genetic testing can often identify a pathogenic variant. For many of them, genetic therapies will be tested in this or the coming years in clinical trials. In contrast to first-generation symptomatic treatments, the new disease-modifying precision medicines require a genetic test-informed diagnosis before a patient can be enrolled in a clinical trial. However, even in 2022, most identified genetic variants in NDD genes are 'variants of uncertain significance'. To safely enrol patients in precision medicine clinical trials, it is important to increase our knowledge about which regions in NDD-associated proteins can 'tolerate' missense variants and which ones are 'essential' and will cause a NDD when mutated. In addition, knowledge about functionally indispensable regions in the 3D structure context of proteins can also provide insights into the molecular mechanisms of disease variants. We developed a novel consensus approach that overlays evolutionary, and population based genomic scores to identify 3D essential sites (Essential3D) on protein structures. After extensive benchmarking of AlphaFold predicted and experimentally solved protein structures, we generated the currently largest expert curated protein structure set for 242 NDDs and identified 14 377 Essential3D sites across 189 gene disorders associated proteins. We demonstrate that the consensus annotation of Essential3D sites improves prioritization of disease mutations over single annotations. The identified Essential3D sites were enriched for functional features such as intermembrane regions or active sites and discovered key inter-molecule interactions in protein complexes that were otherwise not annotated. Using the currently largest autism, developmental disorders, and epilepsies exome sequencing studies including >360 000 NDD patients and population controls, we found that missense variants at Essential3D sites are 8-fold enriched in patients. In summary, we developed a comprehensive protein structure set for 242 NDDs and identified 14 377 Essential3D sites in these. All data are available at https://es-ndd.broadinstitute.org for interactive visual inspection to enhance variant interpretation and development of mechanistic hypotheses for 242 NDDs genes. The provided resources will enhance clinical variant interpretation and in silico drug target development for NDD-associated genes and encoded proteins.

Predicting mutational function using machine learning

Genetic variations are one of the major causes of phenotypic variations between human individuals. Although beneficial as being the substrate of evolution, germline mutations may cause diseases, including Mendelian diseases and complex diseases such as diabetes and heart diseases. Mutations occurring in somatic cells are a main cause of cancer and likely cause age-related phenotypes and other age-related diseases. Because of the high abundance of genetic variations in the human genome, i.e., millions of germline variations per human subject and thousands of additional somatic mutations per cell, it is technically challenging to experimentally verify the function of every possible mutation and their interactions. Significant progress has been made to solve this problem using computational approaches, especially machine learning (ML). Here, we review the progress and achievements made in recent years in this field of research. We classify the computational models in two ways: one according to their prediction goals including protein structural alterations, gene expression changes, and disease risks, and the other according to their methodologies, including non-machine learning methods, classical machine learning methods, and deep neural network methods. For models in each category, we discuss their architecture, prediction accuracy, and potential limitations. This review provides new insights into the applications and future directions of computational approaches in understanding the role of mutations in aging and disease.

Analyzing PKC Gamma (+ 19,506 A/G) polymorphism as a promising genetic marker for HCV-induced hepatocellular carcinoma

BACKGROUND

HCC is a major health concern worldwide. PKC gamma, a member of the conventional PKC subclass, is involved in many cancer types, but the protein has received little attention in the context of single nucleotide polymorphisms and HCC. Therefore, the study aims to investigate the association of PKC gamma missense SNP with HCV-induced hepatocellular carcinoma.

METHODS

The PKC gamma nsSNPs were retrieved from the ENSEMBL genome browser and the deleterious nsSNPs were filtered out through involvingPredictSNP2, CADD, DANN, FATHMM, FunSeq2 and GWAVA. Among the filtered nsSNPs, nsSNP rs1331262028 was identified to be the most pathogenic one. Through involving I-TASSER, ProjectHOPE, I-Mutant, MUpro, mCSM, SDM, DynaMut and MutPred, the influence of SNP rs1331262028 on protein structure, function and stability was estimated. A molecular Dynamic simulation was run to determine the conformational changes in mutant protein structure compared to wild. The blood samples were collected for genotyping analysis and for assessing ALT levels in the blood.

RESULTS

The study identified for the first time an SNP (rs1331262028) of PRKCG to strongly decrease protein stability and induce HCC. The RMSD, RMSF, and Rg values of mutant and wild types found were significantly different. Based on OR and RR values of 5.194 and 2.287, respectively, genotype analysis revealed a higher correlation between the SNP homozygous wild Typeform, AA, and the disease while patients with genotype AG have higher viral load.

CONCLUSION

Outcomes of the current study delineated PKC gamma SNP rs1331262028 as a genetic marker for HCV-induced HCC that could facilitate disease management after further validation.

PSP-GNM: Predicting Protein Stability Changes upon Point Mutations with a Gaussian Network Model

Understanding the effects of missense mutations on protein stability is a widely acknowledged significant biological problem. Genomic missense mutations may alter one or more amino acids, leading to increased or decreased stability of the encoded proteins. In this study, we describe a novel approach—Protein Stability Prediction with a Gaussian Network Model (PSP-GNM)—to measure the unfolding Gibbs free energy change (ΔΔG) and evaluate the effects of single amino acid substitutions on protein stability. Specifically, PSP-GNM employs a coarse-grained Gaussian Network Model (GNM) that has interactions between amino acids weighted by the Miyazawa–Jernigan statistical potential. We used PSP-GNM to simulate partial unfolding of the wildtype and mutant protein structures, and then used the difference in the energies and entropies of the unfolded wildtype and mutant proteins to calculate ΔΔG. The extent of the agreement between the ΔΔG calculated by PSP-GNM and the experimental ΔΔG was evaluated on three benchmark datasets: 350 forward mutations (S350 dataset), 669 forward and reverse mutations (S669 dataset) and 611 forward and reverse mutations (S611 dataset). We observed a Pearson correlation coefficient as high as 0.61, which is comparable to many of the existing state-of-the-art methods. The agreement with experimental ΔΔG further increased when we considered only those measurements made close to 25 °C and neutral pH, suggesting dependence on experimental conditions. We also assessed for the antisymmetry (ΔΔG_reverse = −ΔΔG_forward) between the forward and reverse mutations on the Ssym+ dataset, which has 352 forward and reverse mutations. While most available methods do not display significant antisymmetry, PSP-GNM demonstrated near-perfect antisymmetry, with a Pearson correlation of −0.97. PSP-GNM is written in Python and can be downloaded as a stand-alone code.

A Study of Combined Genotype Effects of SHCBP1 on Wool Quality Traits in Chinese Merino

SHCBP1 (Shc SH2-domain binding protein 1) is a member of the Src and collagen homolog (Shc) protein family and is closely associated with multiple signaling pathways that play important roles during hair follicle induction, morphogenesis, and cycling. The purpose of this study was to investigate SHCBP1 gene expression, polymorphisms, and the association between SHCBP1 and wool quality traits in Chinese Merino sheep. The SHCBP1 gene was shown, by qPCR, to be ubiquitously expressed in sheep tissues and differentially expressed in the adult skin of Chinese Merino and Suffolk sheep. Four SNPs (termed SHCBP1SNPs 1-4) were identified by Sanger sequencing and were located in exon 2, intron 9, intron 12, and exon 13 of the sheep SHCBP1 gene, respectively. SHCBP1SNPs 3 and 4 (rs411176240 and rs160910635) were significantly associated with wool crimp (P < 0.05). The combined polymorphism (SHCBP1SNP3-SHCBP1SNP4) was significantly associated with wool crimp (P < 0.05). Bioinformatics analysis showed that the SNPs associated with wool crimp (SHCBP1SNPs 3 and 4) might affect the pre-mRNA splicing by creating binding sites for serine-arginine-rich proteins and that SHCBP1SNP4 might alter the SHCBP1 mRNA and protein secondary structure. Our results suggest that SHCBP1 influences wool crimp and SHCBP1SNPs 3 and 4 might be useful markers for marker-assisted selection and sheep breeding.

PROST: AlphaFold2-aware Sequence-Based Predictor to Estimate Protein Stability Changes upon Missense Mutations

An essential step in engineering proteins and understanding disease-causing missense mutations is to accurately model protein stability changes when such mutations occur. Here, we developed a new sequence-based predictor for the protein stability (PROST) change (Gibb's free energy change, ΔΔG) upon a single-point missense mutation. PROST extracts multiple descriptors from the most promising sequence-based predictors, such as BoostDDG, SAAFEC-SEQ, and DDGun. RPOST also extracts descriptors from iFeature and AlphaFold2. The extracted descriptors include sequence-based features, physicochemical properties, evolutionary information, evolutionary-based physicochemical properties, and predicted structural features. The PROST predictor is a weighted average ensemble model based on extreme gradient boosting (XGBoost) decision trees and an extra-trees regressor; PROST is trained on both direct and hypothetical reverse mutations using the S5294 (S2647 direct mutations + S2647 inverse mutations). The parameters for the PROST model are optimized using grid searching with 5-fold cross-validation, and feature importance analysis unveils the most relevant features. The performance of PROST is evaluated in a blinded manner, employing nine distinct data sets and existing state-of-the-art sequence-based and structure-based predictors. This method consistently performs well on frataxin, S217, S349, Ssym, S669, Myoglobin, and CAGI5 data sets in blind tests and similarly to the state-of-the-art predictors for p53 and S276 data sets. When the performance of PROST is compared with the latest predictors such as BoostDDG, SAAFEC-SEQ, ACDC-NN-seq, and DDGun, PROST dominates these predictors. A case study of mutation scanning of the frataxin protein for nine wild-type residues demonstrates the utility of PROST. Taken together, these findings indicate that PROST is a well-suited predictor when no protein structural information is available. The source code of PROST, data sets, examples, and pretrained models along with how to use PROST are available at https://github.com/ShahidIqb/PROST and https://prost.erc.monash.edu/seq.

Mutation-Specific Differences in Kv7.1 (KCNQ1) and Kv11.1 (KCNH2) Channel Dysfunction and Long QT Syndrome Phenotypes

The electrocardiogram (ECG) empowered clinician scientists to measure the electrical activity of the heart noninvasively to identify arrhythmias and heart disease. Shortly after the standardization of the 12-lead ECG for the diagnosis of heart disease, several families with autosomal recessive (Jervell and Lange-Nielsen Syndrome) and dominant (Romano-Ward Syndrome) forms of long QT syndrome (LQTS) were identified. An abnormally long heart rate-corrected QT-interval was established as a biomarker for the risk of sudden cardiac death. Since then, the International LQTS Registry was established; a phenotypic scoring system to identify LQTS patients was developed; the major genes that associate with typical forms of LQTS were identified; and guidelines for the successful management of patients advanced. In this review, we discuss the molecular and cellular mechanisms for LQTS associated with missense variants in KCNQ1 (LQT1) and KCNH2 (LQT2). We move beyond the "benign" to a "pathogenic" binary classification scheme for different KCNQ1 and KCNH2 missense variants and discuss gene- and mutation-specific differences in K+ channel dysfunction, which can predispose people to distinct clinical phenotypes (e.g., concealed, pleiotropic, severe, etc.). We conclude by discussing the emerging computational structural modeling strategies that will distinguish between dysfunctional subtypes of KCNQ1 and KCNH2 variants, with the goal of realizing a layered precision medicine approach focused on individuals.

Variants of the SCD gene and their association with fatty acid composition in Awassi sheep

BACKGROUND

Genetic factors affect the variability of fatty acid composition in ruminant products. Thus, this study aimed to investigate the association between the variations of the SCD gene and fatty acid composition in Awassi sheep.

METHODS AND RESULTS

A total of 100 Awassi rams between the ages of one and two and a half years old were used in this study. Blood samples were taken at abattoirs in Babylon, and from each animal, longissimus dorsi (LD) muscle samples were taken to measure the fatty acid composition. DNA samples were isolated from each blood sample, then PCR-single strand conformation polymorphism (PCR-SSCP) experiments were conducted for genotyping followed by sequencing reactions. The study identified two genotypes (TT and TA) of the SCD gene (exon 3). Several novel variants were discovered in the amplified fragments of the SCD gene.

CONCLUSIONS

The TA genotype resulted in increased intramuscular fat and monounsaturated fatty acids compared to the TT genotype. Breeding for the TA genotype could be used for producing meat containing less saturated fatty acids and more monounsaturated fatty acids, making meat more favorable for human consumption.

Viral hijacking mechanism in humans through protein-protein interactions

Numerous viruses have evolved mechanisms to inhibit or alter the host cell's apoptotic response as part of their coevolution with their hosts. The analysis of virus-host protein interactions require an in-depth understanding of both the viral and host protein structures and repertoires, as well as evolutionary mechanisms and pertinent biological facts. Throughout the course of a viral infection, there is constant battle for binding between virus and cellular proteins. Exogenous interfaces facilitating viral-host interactions are well known for constantly targeting and suppressing endogenous interfaces mediating intraspecific interactions, such as viral-viral and host-host connections. In these interactions, the protein-protein interactions (PPIs), are mostly shown as networks (protein interaction networks, PINs), with proteins represented as nodes and their interactions represented as edges. Host proteins with a higher degree of connectivity are more likely to interact with viral proteins. Due to technical advancements, three-dimensional interactions may now be visualized computationally utilizing molecular modeling and cryo-EM approaches. The uniqueness of viral domain repertoires, their evolution, and their activities during viral infection make viruses fascinating models for research. This chapter aims to provide readers a complete picture of the viral hijacking mechanism in protein-protein interactions.

Genetic and Biological Effects of SLC12A3, a Sodium-Chloride Cotransporter, in Gitelman Syndrome and Diabetic Kidney Disease

The SLC12A3 (Solute carrier family 12 member 3) gene encodes a sodium-chloride cotransporter and mediates Na+ and Cl− reabsorption in the distal convoluted tubule of kidneys. An experimental study has previously showed that with knockdown of zebrafish ortholog, slc12a3 led to structural abnormality of kidney pronephric distal duct at 1-cell stage, suggesting that SLC12A3 may have genetic effects in renal disorders. Many clinical reports have demonstrated that the function-loss mutations in the SLC12A3 gene, mainly including Thr60Met, Asp486Asn, Gly741Arg, Leu859Pro, Arg861Cys, Arg913Gln, Arg928Cys and Cys994Tyr, play the pathogenic effects in Gitelman syndrome. This kidney disease is inherited as an autosomal recessive trait. In addition, several population genetic association studies have indicated that the single nucleotide variant Arg913Gln in the SLC12A3 gene is associated with diabetic kidney disease in type 2 diabetes subjects. In this review, we first summarized bioinformatics of the SLC12A3 gene and its genetic variation. We then described the different genetic and biological effects of SLC12A3 in Gitelman syndrome and diabetic kidney disease. We also discussed about further genetic and biological analyses of SLC12A3 as pharmacokinetic targets of diuretics.

Using Structure-guided Fragment-Based Drug Discovery to Target Pseudomonas aeruginosa Infections in Cystic Fibrosis

Cystic fibrosis (CF) is progressive genetic disease that predisposes lungs and other organs to multiple long-lasting microbial infections. Pseudomonas aeruginosa is the most prevalent and deadly pathogen among these microbes. Lung function of CF patients worsens following chronic infections with P. aeruginosa and is associated with increased mortality and morbidity. Emergence of multidrug-resistant, extensively drug-resistant and pandrug-resistant strains of P. aeruginosa due to intrinsic and adaptive antibiotic resistance mechanisms has failed the current anti-pseudomonal antibiotics. Hence new antibacterials are urgently needed to treat P. aeruginosa infections. Structure-guided fragment-based drug discovery (FBDD) is a powerful approach in the field of drug development that has succeeded in delivering six FDA approved drugs over the past 20 years targeting a variety of biological molecules. However, FBDD has not been widely used in the development of anti-pseudomonal molecules. In this review, we first give a brief overview of our structure-guided FBDD pipeline and then give a detailed account of FBDD campaigns to combat P. aeruginosa infections by developing small molecules having either bactericidal or anti-virulence properties. We conclude with a brief overview of the FBDD efforts in our lab at the University of Cambridge towards targeting P. aeruginosa infections.

Oncological drug discovery: AI meets structure-based computational research

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.

SWAAT Bioinformatics Workflow for Protein Structure-Based Annotation of ADME Gene Variants

Recent genomic studies have revealed the critical impact of genetic diversity within small population groups in determining the way individuals respond to drugs. One of the biggest challenges is to accurately predict the effect of single nucleotide variants and to get the relevant information that allows for a better functional interpretation of genetic data. Different conformational scenarios upon the changing in amino acid sequences of pharmacologically important proteins might impact their stability and plasticity, which in turn might alter the interaction with the drug. Current sequence-based annotation methods have limited power to access this type of information. Motivated by these calls, we have developed the Structural Workflow for Annotating ADME Targets (SWAAT) that allows for the prediction of the variant effect based on structural properties. SWAAT annotates a panel of 36 ADME genes including 22 out of the 23 clinically important members identified by the PharmVar consortium. The workflow consists of a set of Python codes of which the execution is managed within Nextflow to annotate coding variants based on 37 criteria. SWAAT also includes an auxiliary workflow allowing a versatile use for genes other than ADME members. Our tool also includes a machine learning random forest binary classifier that showed an accuracy of 73%. Moreover, SWAAT outperformed six commonly used sequence-based variant prediction tools (PROVEAN, SIFT, PolyPhen-2, CADD, MetaSVM, and FATHMM) in terms of sensitivity and has comparable specificity. SWAAT is available as an open-source tool.

Structure-function relationships of the disease-linked A218T oxytocin receptor variant

Various single nucleotide polymorphisms (SNPs) in the oxytocin receptor (OXTR) gene have been associated with behavioral traits, autism spectrum disorder (ASD) and other diseases. The non-synonymous SNP rs4686302 results in the OXTR variant A218T and has been linked to core characteristics of ASD, trait empathy and preterm birth. However, the molecular and intracellular mechanisms underlying those associations are still elusive. Here, we uncovered the molecular and intracellular consequences of this mutation that may affect the psychological or behavioral outcome of oxytocin (OXT)-treatment regimens in clinical studies, and provide a mechanistic explanation for an altered receptor function. We created two monoclonal HEK293 cell lines, stably expressing either the wild-type or A218T OXTR. We detected an increased OXTR protein stability, accompanied by a shift in Ca²⁺ dynamics and reduced MAPK pathway activation in the A218T cells. Combined whole-genome and RNA sequencing analyses in OXT-treated cells revealed 7823 differentially regulated genes in A218T compared to wild-type cells, including 429 genes being associated with ASD. Furthermore, computational modeling provided a molecular basis for the observed change in OXTR stability suggesting that the OXTR mutation affects downstream events by altering receptor activation and signaling, in agreement with our in vitro results. In summary, our study provides the cellular mechanism that links the OXTR rs4686302 SNP with genetic dysregulations associated with aspects of ASD.

Organism-specific training improves performance of linear B-cell epitope prediction

MOTIVATION

In silico identification of linear B-cell epitopes represents an important step in the development of diagnostic tests and vaccine candidates, by providing potential high-probability targets for experimental investigation. Current predictive tools were developed under a generalist approach, training models with heterogeneous datasets to develop predictors that can be deployed for a wide variety of pathogens. However, continuous advances in processing power and the increasing amount of epitope data for a broad range of pathogens indicate that training organism or taxon-specific models may become a feasible alternative, with unexplored potential gains in predictive performance.

RESULTS

This article shows how organism-specific training of epitope prediction models can yield substantial performance gains across several quality metrics when compared to models trained with heterogeneous and hybrid data, and with a variety of widely used predictors from the literature. These results suggest a promising alternative for the development of custom-tailored predictive models with high predictive power, which can be easily implemented and deployed for the investigation of specific pathogens.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article, as well as the full reproducibility scripts, are available at https://github.com/fcampelo/OrgSpec-paper. The R package that implements the organism-specific pipeline functions is available at https://github.com/fcampelo/epitopes.

SUPPLEMENTARY INFORMATION

Supplementary materials are available at Bioinformatics online.

Expansion of the phenotypic and mutational spectrum of Carpenter syndrome

Carpenter syndrome 1 (CRPT1) is an acrocephalopolysyndactyly (ACPS) disorder characterized by craniosynostosis, polysyndactyly, obesity, and other malformations. It is caused by mutations in the gene RAB23. We are reporting on two patients from two unrelated consanguineous Egyptian families. Patient 1 presented with an atypical clinical presentation of Carpenter syndrome including overgrowth with advanced bone age, epileptogenic changes on electroencephalogram and autistic features. Patient 2 presented with typical clinical features suggestive of Carpenter syndrome. Therefore, Patient 1 was subjected to whole exome sequencing (WES) to find an explanation for his unusual features and Patient 2 was subjected to Sanger sequencing of the coding exons of theRAB23 gene to confirm the diagnosis. We identified a novel homozygous missense RAB23 variant (NM_001278668:c.T416C:p.Leu139Pro) in Patient 1 and a novel homozygous splicing variant (NM_016277.5:c.398+1G > A) in Patient 2. We suggest that the overgrowth with advanced bone age, electroencephalogram epileptogenic changes, and autistic features seen in Patient 1 are an expansion of the Carpenter phenotype and could be due to the novel missense RAB23 variant. Additionally, the novel identified RAB23 variants in Patient 1 and 2 broaden the spectrum of variants associated with Carpenter syndrome.

Application of Computational Methods in Understanding Mutations in Mycobacterium tuberculosis Drug Resistance

The emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb) impedes the End TB Strategy by the World Health Organization aiming for zero deaths, disease, and suffering at the hands of tuberculosis (TB). Mutations within anti-TB drug targets play a major role in conferring drug resistance within Mtb; hence, computational methods and tools are being used to understand the mechanisms by which they facilitate drug resistance. In this article, computational techniques such as molecular docking and molecular dynamics are applied to explore point mutations and their roles in affecting binding affinities for anti-TB drugs, often times lowering the protein’s affinity for the drug. Advances and adoption of computational techniques, chemoinformatics, and bioinformatics in molecular biosciences and resources supporting machine learning techniques are in abundance, and this has seen a spike in its use to predict mutations in Mtb. This article highlights the importance of molecular modeling in deducing how point mutations in proteins confer resistance through destabilizing binding sites of drugs and effectively inhibiting the drug action.

Biallelic Mutations in ACACA Cause a Disruption in Lipid Homeostasis That Is Associated With Global Developmental Delay, Microcephaly, and Dysmorphic Facial Features

Objective

We proposed that the deficit of ACC1 is the cause of patient symptoms including global developmental delay, microcephaly, hypotonia, and dysmorphic facial features. We evaluated the possible disease-causing role of the ACACA gene in developmental delay and investigated the pathogenesis of ACC1 deficiency.

Methods

A patient who presented with global developmental delay with unknown cause was recruited. Detailed medical records were collected and reviewed. Whole exome sequencing found two variants of ACACA with unknown significance. ACC1 mRNA expression level, protein expression level, and enzyme activity level were detected in patient-derived cells. Lipidomic analysis, and in vitro functional studies including cell proliferation, apoptosis, and the migratory ability of patient-derived cells were evaluated to investigate the possible pathogenic mechanism of ACC1 deficiency. RNAi-induced ACC1 deficiency fibroblasts were established to assess the causative role of ACC1 deficit in cell migratory disability in patient-derived cells. Palmitate supplementation assays were performed to assess the effect of palmitic acid on ACC1 deficiency-induced cell motility deficit.

Results

The patient presented with global developmental delay, microcephaly, hypotonia, and dysmorphic facial features. A decreased level of ACC1 and ACC1 enzyme activity were detected in patient-derived lymphocytes. Lipidomic profiles revealed a disruption in the lipid homeostasis of the patient-derived cell lines. In vitro functional studies revealed a deficit of cell motility in patient-derived cells and the phenotype was further recapitulated in ACC1-knockdown (KD) fibroblasts. The cell motility deficit in both patient-derived cells and ACC1-KD were attenuated by palmitate.

Conclusion

We report an individual with biallelic mutations in ACACA, presenting global development delay. In vitro studies revealed a disruption of lipid homeostasis in patient-derived lymphocytes, further inducing the deficit of cell motility capacity and that the deficiency could be partly attenuated by palmitate.

Investigating the effect of an identified mutation within a critical site of PAS domain of WalK protein in a vancomycin-intermediate resistant Staphylococcus aureus by computational approaches

Background

Vancomycin-intermediate resistant Staphylococcus aureus (VISA) is becoming a common cause of nosocomial infections worldwide. VISA isolates are developed by unclear molecular mechanisms via mutations in several genes, including walKR. Although studies have verified some of these mutations, there are a few studies that pay attention to the importance of molecular modelling of mutations.

Method

For genomic and transcriptomic comparisons in a laboratory-derived VISA strain and its parental strain, Sanger sequencing and reverse transcriptase quantitative PCR (RT-qPCR) methods were used, respectively. After structural protein mapping of the detected mutation, mutation effects were analyzed using molecular computational approaches and crystal structures of related proteins.

Results

A mutation WalK-H364R was occurred in a functional zinc ion coordinating residue within the PAS domain in the VISA strain. WalK-H364R was predicted to destabilize protein and decrease WalK interactions with proteins and nucleic acids. The RT-qPCR method showed downregulation of walKR, WalKR-regulated autolysins, and agr locus.

Conclusion

Overall, WalK-H364R mutation within a critical metal-coordinating site was presumably related to the VISA development. We assume that the WalK-H364R mutation resulted in deleterious effects on protein, which was verified by walKR gene expression changes.. Therefore, molecular modelling provides detailed insight into the molecular mechanism of VISA development, in particular, where allelic replacement experiments are not readily available.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02298-9.

Evolutionary selectivity of amino acid is inspired from the enhanced structural stability and flexibility of the folded protein

AIM

The present study attempts to decipher the site-specific amino acid alterations at certain positions experiencing preferential selectivity and their effect on proteins' stability and flexibility. The study examines the selection preferences by considering pair-wise non-bonded interaction energies of adjacent and interacting amino acids present at the interacting site, along with their evolutionary history.

MATERIALS AND METHODS

For the study, variations in the interacting residues of spike protein (S-Protein) receptor-binding domain (RBD) of different coronaviruses were examined. The MD simulation trajectory analysis revealed that, though all the variants studied were structurally stable at their native and bound confirmations, the RBD of 2019-nCoV/SARS-CoV-2 was found to be more flexible and more dynamic. Furthermore, a noticeable change observed in the non-bonded interaction energies of the amino acids interacting with the receptor corroborated their selection at respective positions.

KEY FINDINGS

The conformational changes exerted by the altered amino acids could be the reason for a broader range of interacting receptors among the selected proteins.

SIGNIFICANCE

The results envisage a strong indication that the residue selection at certain positions is governed by a well-orchestrated feedback mechanism, which follows increased stability and flexibility in the folded structure compared to its evolutionary predecessor.

Genetic variation at the Cyp6m2 putative insecticide resistance locus in Anopheles gambiae and Anopheles coluzzii

Background

The emergence of insecticide resistance is a major threat to malaria control programmes in Africa, with many different factors contributing to insecticide resistance in its vectors, Anopheles mosquitoes. CYP6M2 has previously been recognized as an important candidate in cytochrome P450-mediated detoxification in Anopheles. As it has been implicated in resistance against pyrethroids, organochlorines and carbamates, its broad metabolic activity makes it a potential agent in insecticide cross-resistance. Currently, allelic variation within the Cyp6m2 gene remains unknown.

Methods

Here, Illumina whole-genome sequence data from Phase 2 of the Anopheles gambiae 1000 Genomes Project (Ag1000G) was used to examine genetic variation in the Cyp6m2 gene across 16 populations in 13 countries comprising Anopheles gambiae and Anopheles coluzzii mosquitoes. To identify whether these alleles show evidence of selection either through potentially modified enzymatic function or by being linked to variants that change the transcriptional profile of the gene, hierarchical clustering of haplotypes, linkage disequilibrium, median joining networks and extended haplotype homozygosity analyses were performed.

Results

Fifteen missense biallelic substitutions at high frequency (defined as > 5% frequency in one or more populations) are found, which fall into five distinct haplotype groups that carry the main high frequency variants: A13T, D65A, E328Q, Y347F, I359V and A468S. Despite consistent reports of Cyp6m2 upregulation and metabolic activity in insecticide resistant Anophelines, no evidence of directional selection is found occurring on these variants or on the haplotype clusters in which they are found.

Conclusion

These results imply that emerging resistance associated with Cyp6m2 is potentially driven by distant regulatory loci such as transcriptional factors rather than by its missense variants, or that other genes are playing a more significant role in conferring metabolic resistance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03757-4.

Global Dynamics Renders Protein Sites with High Functional Response

Deep mutational scanning enables examination of the effects of many mutations at each amino acid position in a query protein, readily disclosing positions that are particularly sensitive. Mutations in these positions alter protein function the most. Here, on the premise that dynamics underlie function, we explore to what extent the measured sensitivity to mutations could be linked to-perhaps be explained by-the structural dynamics of the protein. We employ a minimalist perturbation-response approach based on the Gaussian Network Model (GNM) on a data set of seven proteins with deep mutational scanning data. The analysis shows that the mutation-sensitive positions are often of capacity to modulate the global dynamics and to intermediate allosteric interactions in the structure. With that, upon strain perturbation, these positions decrease residue fluctuations the most, affecting function via entropy changes. This is particularly relevant for positions that are distant from binding sites or other functional regions of the protein and are sensitive to mutations, nevertheless. Our results indicate that mutations in these positions allosterically manipulate protein function.

Structure-Function Analyses of New SARS-CoV-2 Variants B.1.1.7, B.1.351 and B.1.1.28.1: Clinical, Diagnostic, Therapeutic and Public Health Implications

SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus 2) has accumulated multiple mutations during its global circulation. Recently, three SARS-CoV-2 lineages, B.1.1.7 (501Y.V1), B.1.351 (501Y.V2) and B.1.1.28.1 (P.1), have emerged in the United Kingdom, South Africa and Brazil, respectively. Here, we have presented global viewpoint on implications of emerging SARS-CoV-2 variants based on structural-function impact of crucial mutations occurring in its spike (S), ORF8 and nucleocapsid (N) proteins. While the N501Y mutation was observed in all three lineages, the 501Y.V1 and P.1 accumulated a different set of mutations in the S protein. The missense mutational effects were predicted through a COVID-19 dedicated resource followed by atomistic molecular dynamics simulations. Current findings indicate that some mutations in the S protein might lead to higher affinity with host receptors and resistance against antibodies, but not all are due to different antibody binding (epitope) regions. Mutations may, however, result in diagnostic tests failures and possible interference with binding of newly identified anti-viral candidates against SARS-CoV-2, likely necessitating roll out of recurring "flu-like shots" annually for tackling COVID-19. The functional relevance of these mutations has been described in terms of modulation of host tropism, antibody resistance, diagnostic sensitivity and therapeutic candidates. Besides global economic losses, post-vaccine reinfections with emerging variants can have significant clinical, therapeutic and public health impacts.

MPTherm-pred: Analysis and Prediction of Thermal Stability Changes upon Mutations in Transmembrane Proteins

The stability of membrane proteins differs from globular proteins due to the presence of nonpolar membrane-spanning regions. Using a dataset of 929 membrane protein mutations whose effects on thermal stability (ΔT_m) were experimentally determined, we found that the average ΔT_m due to 190 stabilizing and 232 destabilizing mutations occurring in membrane-spanning regions are 2.43(3.1) °C and -5.48(5.5) °C, respectively. The ΔT_m values for mutations occurring in solvent-exposed regions are 2.56(2.82) and - 6.8(7.2) °C. We have systematically analyzed the factors influencing the stability of mutants and observed that changes in hydrophobicity, number of contacts between Cα atoms and frequency of aliphatic residues are important determinants of the stability change induced by mutations occurring in membrane-spanning regions. We have developed structure- and sequence-based machine learning predictors of ΔT_m due to mutations specifically for membrane proteins. They showed a correlation and mean absolute error (MAE) of 0.72 and 2.85 °C, respectively, between experimental and predicted ΔT_m for mutations in membrane-spanning regions on 10-fold group-wise cross-validation. The average correlation and MAE for mutations in aqueous regions are 0.73 and 3.7 °C, respectively. These MAE values are about 50% lower than standard deviations from the mean ΔT_m values. The reliability of the method was affirmed on a test set of mutations occurring in evolutionary independent protein sequences. The developed MPTherm-pred server for predicting thermal stability changes upon mutations in membrane proteins is available at https://web.iitm.ac.in/bioinfo2/mpthermpred/. Our results provide insights into factors influencing the stability of membrane proteins and can aid in designing mutants that are more resistant to thermal stress.