PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality

Enhancement of thermostability and expression level of Rasamsonia emersonii lipase in Pichia pastoris and its application in biodiesel production in a continuous flow reactor

The acidic lipase from Rasamsonia emersonii named LIPR has great potential for biodiesel synthesis due to its strong methanol tolerance. Nonetheless, the limited thermostability of LIPR and low expression level in Escherichia coli remain major obstacles to its use in biodiesel synthesis. To enhance the thermostability, the mutant LIPR harboring mutations A126C-P238C for the formation of a new disulfide bond and amino acid substitution D214L was obtained through rational design. To our delight, the thermostability of LIPR mutant was greatly improved. Moreover, a comprehensive optimization strategy, such as employing the Mss signal peptide, co-expressing the molecular chaperone protein disulfide isomerase (PDI), knocking out the vacuolar sorting receptor gene VPS10-01, and overexpressing the dihydroxyacetone synthase gene DAS2, was adopted to obtain the combination-optimized mutant Pichia pastoris strain GS54. Furthermore, the biodiesel synthetic capability with the mutant GS54-LIPR was verified and the production yield was 52.2 % after 24 h in a shake flask. Subsequently, a continuous flow system was adopted to increase the biodiesel yield to 73.6 % within 3 h, demonstrating its efficacy in enhancing enzyme biocatalysis. The engineered GS54-LIPR mutant lipase is an efficient and reusable biocatalyst for the sustained production of biodiesel in a continuous flow reaction.

Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations

Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies.

Enhancing predictions of protein stability changes induced by single mutations using MSA-based Language Models

MOTIVATION

Protein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a Protein Language Model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data pre-processing to mitigate overfitting.

RESULTS

We ensure comprehensive comparisons by fine-tuning various pre-trained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations.

AVAILABILITY AND IMPLEMENTATION

Code and data at https://github.com/RitAreaSciencePark/PLM4Muts.

SUPPLEMENTARY INFORMATION

Supplementary Information is available at Bioinformatics online.

Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers

Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.

Deep mutational scanning reveals sequence to function constraints for SWEET family transporters

Protein science is entering a transformative phase enabled by deep mutational scans that provide an unbiased view of the residue level interactions that mediate function. However, it has yet to be extensively used to characterize the mutational and evolutionary landscapes of plant proteins. Here, we apply the method to explore sequence-function relationships within the sugar transporter AtSWEET13. DMS results describe how mutational interrogation throughout different regions of the protein affects AtSWEET13 abundance and transport function. Our results identify novel transport-enhancing mutations that are validated using the FRET sensor assays. Extending DMS results to phylogenetic analyses reveal the role of transmembrane helix 4 (TM4) which makes the SWEET family transporters distinct from prokaryotic SemiSWEETs. We show that transmembrane helix 4 is intolerant to motif swapping with other clade-specific SWEET TM4 compositions, despite accommodating single point-mutations towards aromatic and charged polar amino acids. We further show that the transfer learning approaches based on physics and ML based In silico variant prediction tools have limited utility for engineering plant proteins as they were unable to reproduce our experimental results. We conclude that DMS can produce datasets which, when combined with the right predictive computational frameworks, can direct plant engineering efforts through derivative phenotype selection and evolutionary insights.

Protein stability prediction by fine-tuning a protein language model on a mega-scale dataset

Protein stability plays a crucial role in a variety of applications, such as food processing, therapeutics, and the identification of pathogenic mutations. Engineering campaigns commonly seek to improve protein stability, and there is a strong interest in streamlining these processes to enable rapid optimization of highly stabilized proteins with fewer iterations. In this work, we explore utilizing a mega-scale dataset to develop a protein language model optimized for stability prediction. ESMtherm is trained on the folding stability of 528k natural and de novo sequences derived from 461 protein domains and can accommodate deletions, insertions, and multiple-point mutations. We show that a protein language model can be fine-tuned to predict folding stability. ESMtherm performs reasonably on small protein domains and generalizes to sequences distal from the training set. Lastly, we discuss our model's limitations compared to other state-of-the-art methods in generalizing to larger protein scaffolds. Our results highlight the need for large-scale stability measurements on a diverse dataset that mirrors the distribution of sequence lengths commonly observed in nature.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Review of predicting protein stability changes upon variations

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.

Computer-aided rational design strategy based on protein surface charge to improve the thermal stability of a novel esterase from Geobacillus jurassicus

OBJECTIVES

Although Geobacillus are significant thermophilic bacteria source, there are no reports of thermostable esterase gene in Geobacillus jurassicus or rational design strategies to increase the thermal stability of esterases.

RESULTS

Gene gju768 showed a highest similarity of 15.20% to esterases from Geobacillus sp. with detail enzymatic properties. Using a combination of Gibbs Unfolding Free Energy (∆∆G) calculator and the distance from the mutation site to the catalytic site (DsCα-Cα) to screen suitable mutation sites with elimination of negative surface charge, the mutants (D24N, E221Q, and E253Q) displayed stable mutants with higher thermal stability than the wild-type (WT). Mutant E253Q exhibited the best thermal stability, with a half-life (T1/2) at 65 °C of 32.4 min, which was 1.8-fold of the WT (17.9 min).

CONCLUSION

Cloning of gene gju768 and rational design based on surface charge engineering contributed to the identification of thermostable esterase from Geobacillus sp. and the exploration of evolutionary strategies for thermal stability.

Identification, Characterization, and Computer-Aided Rational Design of a Novel Thermophilic Esterase from Geobacillus subterraneus, and Application in the Synthesis of Cinnamyl Acetate

Investigation of a novel thermophilic esterase gene from Geobacillus subterraneus DSMZ 13552 indicated a high amino acid sequence similarity of 25.9% to a reported esterase from Geobacillus sp. A strategy that integrated computer-aided rational design tools was developed to select mutation sites. Six mutants were selected from four criteria based on the simulated saturation mutation (including 19 amino acid residues) results. Of these, the mutants Q78Y and G119A were found to retain 87% and 27% activity after incubation at 70 °C for 20 min, compared with the 19% activity for the wild type. Subsequently, a double-point mutant (Q78Y/G119A) was obtained and identified with optimal temperature increase from 65 to 70 °C and a 41.51% decrease in Km. The obtained T1/2 values of 42.2 min (70 °C) and 16.9 min (75 °C) for Q78Y/G119A showed increases of 340% and 412% compared with that in the wild type. Q78Y/G119A was then employed as a biocatalyst to synthesize cinnamyl acetate, for which the conversion rate reached 99.40% with 0.3 M cinnamyl alcohol at 60 °C. The results validated the enhanced enzymatic properties of the mutant and indicated better prospects for industrial application as compared to that in the wild type. This study reported a method by which an enzyme could evolve to achieve enhanced thermostability, thereby increasing its potential for industrial applications, which could also be expanded to other esterases.

Improvement of thermostability by increasing rigidity in the finger regions and flexibility in the catalytic pocket area of Pseudoalteromonas porphyrae κ-carrageenase

Poor thermostability reduces the industrial application value of κ-carrageenase. In this study, the PoPMuSiC algorithm combined with site-directed mutagenesis was applied to improve the thermostability of the alkaline κ-carrageenase from Pseudoalteromonas porphyrae. The mutant E154A with improved thermal stability was successfully obtained using this strategy after screening seven rationally designed mutants. Compared with the wild-type κ-carrageenase (WT), E154A improved the activity by 29.4% and the residual activity by 51.6% after treatment at 50 °C for 30 min. The melting temperature (Tm) values determined by circular dichroism were 66.4 °C and 64.6 °C for E154A and WT, respectively. Molecular dynamics simulation analysis of κ-carrageenase showed that the flexibility decreased within the finger regions (including F1, F2, F3, F5 and F6) and the flexibility improved in the catalytic pocket area of the mutant E154A. The catalytic tunnel dynamic simulation analysis revealed that E154A led to enlarged catalytic tunnel volume and increased rigidity of the enzyme-substrate complex. The increasing rigidity within the finger regions and more flexible catalytic pocket of P. porphyrae κ-carrageenase might be a significant factor for improvement of the thermostability of the mutant κ-carrageenase E154A. The proposed rational design strategy could be applied to improve the enzyme kinetic stability of other industrial enzymes. Moreover, the hydrolysates of κ-carrageenan digested by the mutant E154A demonstrated increased scavenging activities against hydroxyl (OH) radicals and 2,2'-azinobis(3-ethylbenzothiazoline)-6-sulfonic acid (ABTS) radicals compared with the undigested κ-carrageenan.

Premature Activation of the HIV-1 Protease Is Influenced by Polymorphisms in the Hinge Region

HIV-1 protease inhibitors are an essential component of antiretroviral therapy. However, drug resistance is a pervasive issue motivating a persistent search for novel therapies. Recent reports found that when protease activates within the host cell's cytosol, it facilitates the pyroptotic killing of infected cells. This has led to speculation that promoting protease activation, rather than inhibiting it, could help to eradicate infected cells and potentially cure HIV-1 infection. Here, we used a nanoscale flow cytometry-based assay to characterize protease resistance mutations and polymorphisms. We quantified protease activity, viral concentration, and premature protease activation and confirmed previous findings that major resistance mutations generally destabilize the protease structure. Intriguingly, we found evidence that common polymorphisms in the hinge domain of protease can influence its susceptibility to premature activation. This suggests that viral heterogeneity could pose a considerable challenge for therapeutic strategies aimed at inducing premature protease activation in the future.

Ago2/CAV1 interaction potentiates metastasis via controlling Ago2 localization and miRNA action

Ago2 differentially regulates oncogenic and tumor-suppressive miRNAs in cancer cells. This discrepancy suggests a secondary event regulating Ago2/miRNA action in a context-dependent manner. We show here that a positive charge of Ago2 K212, that is preserved by SIR2-mediated Ago2 deacetylation in cancer cells, is responsible for the direct interaction between Ago2 and Caveolin-1 (CAV1). Through this interaction, CAV1 sequesters Ago2 on the plasma membranes and regulates miRNA-mediated translational repression in a compartment-dependent manner. Ago2/CAV1 interaction plays a role in miRNA-mediated mRNA suppression and in miRNA release via extracellular vesicles (EVs) from tumors into the circulation, which can be used as a biomarker of tumor progression. Increased Ago2/CAV1 interaction with tumor progression promotes aggressive cancer behaviors, including metastasis. Ago2/CAV1 interaction acts as a secondary event in miRNA-mediated suppression and increases the complexity of miRNA actions in cancer.

The polymorphism analysis for CD36 among platelet donors

CD36 may defect on platelets and/or monocytes in healthy individuals, which was defined as CD36 deficiency. However, we did not know the correlation between the molecular and protein levels completely. Here, we aim to determine the polymorphisms of the CD36 gene, RNA level, and CD36 on platelets and in plasma. The individuals were sequenced by Sanger sequencing. Bioinformational analysis was used by the HotMuSiC, CUPSAT, SAAFEC-SEQ, and FoldX. RNA analysis and CD36 protein detection were performed by qPCR, flow cytometry, and ELISA. In this study, we found c.1228_1239delATTGTGCCTATT (allele frequency = 0.0072) with the highest frequency among our cohort, and one mutation (c.1329_1354dupGATAGAAATGATCTTACTCAGTGTTG) was not present in the dbSNP database. 5 mutations located in the extracellular domain sequencing region with confirmation in deficient individuals, of which c.284T>C, c.512A>G, c.572C>T, and c.869T>C were found to have a deleterious impact on CD36 protein stability. Furthermore, the MFI of CD36 expression on platelets in the mutation-carry, deleterious-effect, and deficiency group was significantly lower than the no-mutation group (P < 0.0500). In addition, sCD36 levels in type II individuals were significantly lower compared with positive controls (P = 0.0060). Nevertheless, we found the presence of sCD36 in a type I individual. RNA analysis showed CD36 RNA levels in platelets of type II individuals were significantly lower than the positive individuals (P = 0.0065). However, no significant difference was observed in monocytes (P = 0.7500). We identified the most prevalent mutation (c.1228_1239delATTGTGCCTATT) among Kunming donors. Besides, our results suggested RNA level alterations could potentially underlie type II deficiency. Furthermore, sCD36 may hold promise for assessing immune reaction risk in CD36-deficient individuals, but more studies should be conducted to validate this hypothesis.

Enhancing thermostability of tryptophan hydroxylase via protein engineering and its application in 5-hydroxytryptophan production

5-Hydroxytryptophan (5-HTP), as the precursor of serotonin and melatonin in animals, can regulate mood, sleep, and behavior, which is widely used in pharmaceutical and health products industry. The enzymatic production of 5-hydroxytryptophan (5-HTP) from L-tryptophan (L-Trp) using tryptophan hydroxylase (TPH) show huge potential in application due to its advantages, such as mild reaction conditions, avoidance of protection/deprotection processes, excellent regioselectivity and considerable catalytic efficiency, compared with chemical synthesis and natural extraction. However, the low thermostability of TPH restricted its hydroxylation efficiency toward L-Trp. In this study, we aimed to improve the thermostability of TPH via semi-rational design guided by (folding free energy) ΔΔG fold calculation. After two rounds of evolution, two beneficial mutants M1 (S422V) and M30 (V275L/I412K) were obtained. Thermostability evaluation showed that M1 and M30 possessed 5.66-fold and 6.32-fold half-lives (t1/2) at 37 °C, and 4.2 °C and 6.0 °C higher melting temperature (Tm) than the WT, respectively. The mechanism behind thermostability improvement was elucidated with molecular dynamics simulation. Furthermore, biotransformation of 5-HTP from L-Trp was performed, M1 and M30 displayed 1.80-fold and 2.30-fold than that of WT, respectively. This work provides important insights into the thermostability enhancement of TPH and generate key mutants that could be robust candidates for practical production of 5-HTP.

Iterative In Silico Screening for Optimizing Stable Conformation of Anti-SARS-CoV-2 Nanobodies

Nanobodies (Nbs or VHHs) are single-domain antibodies (sdAbs) derived from camelid heavy-chain antibodies. Nbs have special and unique characteristics, such as small size, good tissue penetration, and cost-effective production, making Nbs a good candidate for the diagnosis and treatment of viruses and other pathologies. Identifying effective Nbs against COVID-19 would help us control this dangerous virus or other unknown variants in the future. Herein, we introduce an in silico screening strategy for optimizing stable conformation of anti-SARS-CoV-2 Nbs. Firstly, various complexes containing nanobodies were downloaded from the RCSB database, which were identified from immunized llamas. The primary docking between Nbs and the SARS-CoV-2 spike protein receptor-binding domain was performed through the ClusPro program, with the manual screening leaving the reasonable conformation to the next step. Then, the binding distances of atoms between the antigen-antibody interfaces were measured through the NeighborSearch algorithm. Finally, filtered nanobodies were acquired according to HADDOCK scores through HADDOCK docking the COVID-19 spike protein with nanobodies under restrictions of calculated molecular distance between active residues and antigenic epitopes less than 4.5 Å. In this way, those nanobodies with more reasonable conformation and stronger neutralizing efficacy were acquired. To validate the efficacy ranking of the nanobodies we obtained, we calculated the binding affinities (∆G) and dissociation constants (Kd) of all screened nanobodies using the PRODIGY web tool and predicted the stability changes induced by all possible point mutations in nanobodies using the MAESTROWeb server. Furthermore, we examined the performance of the relationship between nanobodies' ranking and their number of mutation-sensitive sites (Spearman correlation > 0.68); the results revealed a robust correlation, indicating that the superior nanobodies identified through our screening process exhibited fewer mutation hotspots and higher stability. This correlation analysis demonstrates the validity of our screening criteria, underscoring the suitability of these nanobodies for future development and practical implementation. In conclusion, this three-step screening strategy iteratively in silico greatly improved the accuracy of screening desired nanobodies compared to using only ClusPro docking or default HADDOCK docking settings. It provides new ideas for the screening of novel antibodies and computer-aided screening methods.

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.

Cancer-Related Mutations in the Sam Domains of EphA2 Receptor and Ship2 Lipid Phosphatase: A Computational Study

The lipid phosphatase Ship2 interacts with the EphA2 receptor by forming a heterotypic Sam (sterile alpha motif)-Sam complex. Ship2 works as a negative regulator of receptor endocytosis and consequent degradation, and anti-oncogenic effects in cancer cells should be induced by hindering its association with EphA2. Herein, a computational approach is presented to investigate the relationship between Ship2-Sam/EphA2-Sam interaction and cancer onset and further progression. A search was first conducted through the COSMIC (Catalogue of Somatic Mutations in Cancer) database to identify cancer-related missense mutations positioned inside or close to the EphA2-Sam and Ship2-Sam reciprocal binding interfaces. Next, potential differences in the chemical-physical properties of mutant and wild-type Sam domains were evaluated by bioinformatics tools based on analyses of primary sequences. Three-dimensional (3D) structural models of mutated EphA2-Sam and Ship2-Sam domains were built as well and deeply analysed with diverse computational instruments, including molecular dynamics, to classify potentially stabilizing and destabilizing mutations. In the end, the influence of mutations on the EphA2-Sam/Ship2-Sam interaction was studied through docking techniques. This in silico approach contributes to understanding, at the molecular level, the mutation/cancer relationship by predicting if amino acid substitutions could modulate EphA2 receptor endocytosis.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Energy, water, and protein folding: A molecular dynamics-based quantitative inventory of molecular interactions and forces that make proteins stable

Protein folding energetics can be determined experimentally on a case-by-case basis but it is not understood in sufficient detail to provide deep control in protein design. The fundamentals of protein stability have been outlined by calorimetry, protein engineering, and biophysical modeling, but these approaches still face great difficulty in elucidating the specific contributions of the intervening molecules and physical interactions. Recently, we have shown that the enthalpy and heat capacity changes associated to the protein folding reaction can be calculated within experimental error using molecular dynamics simulations of native protein structures and their corresponding unfolded ensembles. Analyzing in depth molecular dynamics simulations of four model proteins (CI2, barnase, SNase, and apoflavodoxin), we dissect here the energy contributions to ΔH (a key component of protein stability) made by the molecular players (polypeptide and solvent molecules) and physical interactions (electrostatic, van der Waals, and bonded) involved. Although the proteins analyzed differ in length, isoelectric point and fold class, their folding energetics is governed by the same quantitative pattern. Relative to the unfolded ensemble, the native conformations are enthalpically stabilized by comparable contributions from protein-protein and solvent-solvent interactions, and almost equally destabilized by interactions between protein and solvent molecules. The native protein surface seems to interact better with water than the unfolded one, but this is outweighed by the unfolded surface being larger. From the perspective of physical interactions, the native conformations are stabilized by van de Waals and Coulomb interactions and destabilized by conformational strain arising from bonded interactions. Also common to the four proteins, the sign of the heat capacity change is set by interactions between protein and solvent molecules or, from the alternative perspective, by Coulomb interactions.

The genomic landscape of CYP2D6 variation in the Indian population

Aim: The CYP2D6 gene is highly polymorphic, causing large interindividual variability in the metabolism of several clinically important drugs. Materials & methods: The authors investigated the diversity and distribution of CYP2D6 alleles in Indians using whole genome sequences (N = 1518). Functional consequences were assessed using pathogenicity scores and molecular dynamics simulations. Results: The analysis revealed population-specific CYP2D6 alleles (*86, *7, *111, *112, *113, *99) and remarkable differences in variant and phenotype frequencies with global populations. The authors observed that one in three Indians could benefit from a dose alteration for psychiatric drugs with accurate CYP2D6 phenotyping. Molecular dynamics simulations revealed large conformational fluctuations, confirming the predicted reduced function of *86 and *113 alleles. Conclusion: The findings emphasize the utility of comprehensive CYP2D6 profiling for aiding precision public health.

Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges

Missense mutations are known contributors to diverse genetic disorders, due to their subtle, single amino acid changes imparted on the resultant protein. Because of this, understanding the impact of these mutations on protein stability and function is crucial for unravelling disease mechanisms and developing targeted therapies. The Critical Assessment of Genome Interpretation (CAGI) provides a valuable platform for benchmarking state-of-the-art computational methods in predicting the impact of disease-related mutations on protein thermodynamics. Here we report the performance of our comprehensive platform of structure-based computational approaches to evaluate mutations impacting protein structure and function on 3 challenges from CAGI6: Calmodulin, MAPK1 and MAPK3. Our stability predictors have achieved correlations of up to 0.74 and AUCs of 1 when predicting changes in ΔΔG for MAPK1 and MAPK3, respectively, and AUC of up to 0.75 in the Calmodulin challenge. Overall, our study highlights the importance of structure-based approaches in understanding the effects of missense mutations on protein thermodynamics. The results obtained from the CAGI6 challenges contribute to the ongoing efforts to enhance our understanding of disease mechanisms and facilitate the development of personalised medicine approaches.

OmeDDG: Improved Protein Mutation Stability Prediction Based on Predicted 3D Structures

Determining changes in the protein's thermal stability following mutations is critical in protein engineering and understanding pathogenic missense mutations. Despite the development of various computational methods to predict the effects of single-point mutations, their accuracy remains limited. In this study, we propose a new computational method, OmeDDG, that more accurately predicts mutation-induced Gibbs free energy changes in protein folding (ΔΔG). OmeDDG takes the sequences of wild-type and mutant proteins as input, utilizes OmegaFold to obtain the 3D structure, employs a convolutional neural network to extract structural features, and combines them with protein mutation features and pretraining features to predict the stability of single-point mutations in proteins. We performed a comprehensive comparison between OmeDDG and other available prediction methods on four blind test datasets, confirming that OmeDDG can effectively enhance protein mutation prediction performance. Notably, on the antisymmetric dataset Ssym, OmeDDG achieves the best performance, demonstrating favorable antisymmetry with PCC = 0.79 and RMSE = 0.96 for forward mutations and PCC = 0.77 and RMSE = 0.97 for reverse mutant types.

Empirical validation of ProteinMPNN's efficiency in enhancing protein fitness

Introduction: Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range. Methods: We collected three biological experimental datasets and compared the design results of ProteinMPNN for wild-type proteins with the experimental datasets to verify the ability of ProteinMPNN in improving protein fitness. Results: The validation on biological experimental datasets shows that ProteinMPNN has the ability to design mutation types with higher fitness in single and multi-point mutations. We have verified the high accuracy of ProteinMPNN in protein engineering tasks from both positive and negative perspectives. Discussion: Our research indicates that using large-scale pre trained models to design protein mutants provides a new approach for protein engineering, providing strong support for guiding biological experiments and applications in biotechnology.

Precision enzyme discovery through targeted mining of metagenomic data

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.

Rational design engineering of a more thermostable Sulfurihydrogenibium yellowstonense carbonic anhydrase for potential application in carbon dioxide capture technologies

Implementing hyperthermostable carbonic anhydrases into CO2 capture and storage technologies in order to increase the rate of CO2 absorption from the industrial flue gases is of great importance from technical and economical points of view. The present study employed a combination of in silico tools to further improve thermostability of a known thermostable carbonic anhydrase from Sulfurihydrogenibium yellowstonense. Experimental results showed that our rationally engineered K100G mutant not only retained the overall structure and catalytic efficiency but also showed a 3 °C increase in the melting temperature and a two-fold improvement in the enzyme half-life at 85 °C. Based on the molecular dynamics simulation results, rearrangement of salt bridges and hydrogen interactions network causes a reduction in local flexibility of the K100G variant. In conclusion, our study demonstrated that thermostability can be improved through imposing local structural rigidity by engineering a single-point mutation on the surface of the enzyme.

Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset

Insight into how mutations affect protein stability is crucial for protein engineering, understanding genetic diseases, and exploring protein evolution. Numerous computational methods have been developed to predict the impact of amino acid substitutions on protein stability. Nevertheless, comparing these methods poses challenges due to variations in their training data. Moreover, it is observed that they tend to perform better at predicting destabilizing mutations than stabilizing ones. Here, we meticulously compiled a new dataset from three recently published databases: ThermoMutDB, FireProtDB, and ProThermDB. This dataset, which does not overlap with the well-established S2648 dataset, consists of 4038 single-point mutations, including over 1000 stabilizing mutations. We assessed these mutations using 27 computational methods, including the latest ones utilizing mega-scale stability datasets and transfer learning. We excluded entries with overlap or similarity to training datasets to ensure fairness. Pearson correlation coefficients for the tested tools ranged from 0.20 to 0.53 on unseen data, and none of the methods could accurately predict stabilizing mutations, even those performing well in anti-symmetric property analysis. While most methods present consistent trends for predicting destabilizing mutations across various properties such as solvent exposure and secondary conformation, stabilizing mutations do not exhibit a clear pattern. Our study also suggests that solely addressing training dataset bias may not significantly enhance accuracy of predicting stabilizing mutations. These findings emphasize the importance of developing precise predictive methods for stabilizing mutations.

Lipid and cholesterols modulate the dynamics of SARS-CoV-2 viral ion channel ORF3a and its pathogenic variants

SARS-CoV-2 accessory protein, ORF3a is a putative ion channel which immensely contributes to viral pathogenicity by modulating host immune responses and virus-host interactions. Relatively high expression of ORF3a in diseased individuals and implication with inflammasome activation, apoptosis and autophagy inhibition, ratifies as an effective target for developing vaccines and therapeutics. Herein, we present the elusive dynamics of ORF3a-dimeric state using all-atoms molecular dynamics (MD) simulations at μ-seconds scale in a heterogeneous lipid-mimetic system in multiple replicates. Additionally, we also explore the effect of non-synonymous pathogenic mutations on ORF3a ion channel activity and viral pathogenicity in different SARS-CoV-2 variants using various structure-based protein stability (ΔΔG) tools and computational saturation mutagenesis. Our study ascertains the role of phosphatidylcholines and cholesterol in modulating the structure of ORF3a, which perturbs the size and flexibility of the polar cavity that allows permeation of large cations. Discrete trend in ion channel pore radius and area per lipid arises the premise that presence of lipids might also affect the overall conformation of ORF3a. MD structural-ensembles, in some replicates rationalize the crucial role of TM2 in maintaining the native structure of ORF3a. We also infer that loss of structural stability primarily grounds for pathogenicity in more than half of the pathogenic variants of ORF3a. Overall, the effect of mutation on alteration of ion permeability of ORF3a, proposed in this study brings mechanistic insights into variant consequences on viral membrane proteins of SARS-CoV-2, which can be utilized for the development of novel therapeutics to treat COVID-19 and other coronavirus diseases.

Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations

Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions.

A cytosolic mutp53(E285K) variant confers chemoresistance of malignant melanoma

Malignant melanoma (MM) is known to be intrinsically chemoresistant, even though only ~20% of MM carry mutations of the tumor suppressor p53. Despite improvement of systemic therapy the mortality rate of patients suffering from metastatic MM is still ~70%, highlighting the need for alternative treatment options or for the re-establishment of conventional therapeutic approaches, including chemotherapy. Screening the p53 mutation status in a cohort of 19 patient-derived melanoma samples, we identified one rarely described missense mutation of p53 leading to E285K amino acid exchange (mutp53(E285K)). Employing structural and computational analysis we revealed a major role of E285 residue in maintaining stable conformation of wild-type p53 (wtp53). E285K mutation was predicted to cause interruption of a salt-bridge network affecting the conformation of the C-terminal helix of the DNA-binding domain (DBD) thereby preventing DNA interaction. In this context, a cluster of frequently mutated amino acid residues in cancer was identified to putatively lead to similar structural effects as E285K substitution (E285 cluster). Functional analysis, including knockdown of endogenous p53 and reconstitution with diverse p53 missense mutants confirmed mutp53(E285K) to have lost transcriptional activity, to be localized in the cytosol of cancer cells, by both means conferring chemoresistance. Re-sensitization to cisplatin-induced cell death was achieved using clinically approved compounds aiming to restore p53 wild-type function (PRIMA1-Met), or inhibition of AKT-driven MAPK survival pathways (afuresertib), in both cases being partially due to ferroptosis induction. Consequently, active ferroptosis induction using the GPX4 inhibitor RSL3 proved superior in tumorselectively fighting MM cells. Due to high prevalence of the E285-cluster mutations in MM as well as in a variety of other tumor types, we conclude this cluster to serve an important function in tumor development and therapy and suggest new implications for ferroptosis induction in therapeutic applications fighting MM in particular and cancer in general.

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.

Comprehensive analysis of non-synonymous missense SNPs of human galactose mutarotase (GALM) gene: an integrated computational approach

Missense Non-synonymous single nucleotide polymorphisms (nsSNPs) of Galactose Mutarotase (GALM) are associated with the Novel type of Galactosemia (Galactosemia type 4) together with symptoms such as high blood galactose levels and eye cataracts. The objective of the present study was to identify deleterious nsSNPs of GALM recorded on the dbSNP database through comprehensive insilico analysis. Among the 319 missense nsSNPs reported, various insilco tools predicted R78S, R82G, A163E, P210S, Y281C, E307G and F339C as the most deleterious mutations. Structural analysis, PTM analysis and molecular dynamics simulations (MDS) were carried out to understand the effect of these mutations on the structural and physicochemical properties of the GALM protein. The residues R82G and E307G were found to be part of the binding site that resulted in decreased surface accessibility. Replacing the charged wild-type residue with a neutral mutant type affected its substrate binding. All 7 mutations were found to increase the rigidity of the protein structure, which is unfavorable during ligand binding. The mutation F339E made the protein structure more rigid than all the other mutations. Y281 is a phosphorylated site, and therefore, less significant structural changes were observed when compared to other mutations; however, it may have significant differences in the usual functioning of the protein. In summary, the structural and functional analysis of missense SNPs of GALM is important to reduce the number of potential mutations to be evaluated in vitro to understand the association with some genetic diseases.Communicated by Ramaswamy H. Sarma.

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks

Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exhibit anti-symmetric bias toward destabilizing situations, whereas others struggle with generalization to unseen examples. To address these issues, we propose a gated graph neural network-based approach to predict changes in protein stability upon mutation. The model uses message passing to encode the links between the molecular structure and property after eliminating the non-mutant structure and creating input feature vectors. While doing so, it also incorporates the coordinates of the raw atoms to provide spatial insights into the chemical systems. We test the model on the Ssym, Myoglobin, Broom, and p53 datasets to demonstrate the generalization performance. Compared to existing approaches, our proposed method achieves improved linearity with symmetry in less time. The code for this study is available at: https://github.com/HongzhouTang/Pros-GNN.

Comparing Supervised Learning and Rigorous Approach for Predicting Protein Stability upon Point Mutations in Difficult Targets

Accurate prediction of protein stability upon a point mutation has important applications in drug discovery and personalized medicine. It remains a challenging issue in computational biology. Existing computational prediction methods, which range from mechanistic to supervised learning approaches, have experienced limited progress over the last few decades. This stagnation is largely due to their heavy reliance on both the quantity and quality of the training data. This is evident in recent state-of-the-art methods that continue to yield substantial errors on two challenging blind test sets: frataxin and p53, with average root-mean-square errors exceeding 3 and 1.5 kcal/mol, respectively, which is still above the theoretical 1 kcal/mol prediction barrier. Rigorous approaches, on the other hand, offer greater potential for accuracy without relying on training data but are computationally demanding and require both wild-type and mutant structure information. Although they showed high accuracy for conserving mutations, their performance is still limited for charge-changing mutation cases. This might be due to the lack of an available mutant structure, often represented by a simplified capped peptide. The recent advances in protein structure prediction methods now make it possible to obtain structures comparable to experimental ones, including complete mutant structure information. In this work, we compare the performance of supervised learning-based methods and rigorous approaches for predicting protein stability on point mutations in difficult targets: frataxin and p53. The rigorous alchemical method significantly surpasses state-of-the-art techniques in terms of both the root-mean-squared error and Pearson correlation coefficient in these two challenging blind test sets. Additionally, we propose an improved alchemical method that employs the pmx double-system/single-box approach to accurately predict the folding free energy change upon both conserving and charge-changing mutations. The enhanced protocol can accurately predict both types of mutations, thereby outperforming existing state-of-the-art methods in overall performance.

Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability

BACKGROUND

Computational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability.

RESULTS

We show that multiple linear regression models can be used to quantify the uncertainty associated with FoldX prediction for individual mutations. Comparing the performance among models with varying degrees of complexity, we find that the model precision improves significantly when we utilize molecular dynamics simulation as part of the FoldX workflow. Based on the model that incorporates information from molecular dynamics, biochemical properties, as well as FoldX energy terms, we can generally expect upper bounds on the uncertainty of folding stability predictions of ± 2.9 kcal/mol and ± 3.5 kcal/mol for binding stability predictions. The uncertainty for individual mutations varies; our model estimates it using FoldX energy terms, biochemical properties of the mutated residue, as well as the variability among snapshots from molecular dynamics simulation.

CONCLUSIONS

Using a linear regression framework, we construct models to predict the uncertainty associated with FoldX prediction of stability changes upon mutation. This technique is straightforward and can be extended to other computational methods as well.

Computational saturation mutagenesis to explore the effect of pathogenic mutations on extra-cellular domains of TREM2 associated with Alzheimer's and Nasu-Hakola disease

CONTEXT

The specialised family of triggering receptors expressed on myeloid cells (TREMs) plays a pivotal role in causing neurodegenerative disorders and activating microglial anti-inflammatory responses. Nasu-Hakola disease (NHD), a rare autosomal recessive disorder, has been associated with mutations in TREM2, which is also responsible for raising the risk of Alzheimer's disease (AD). Herein, we have made an endeavour to differentiate the confirmed pathogenic variants in TREM2 extra-cellular domain (ECD) linked with NHD and AD using mutation-induced fold stability change (∆∆G), with the computation of 12distinct structure-based methods through saturation mutagenesis. Correlation analysis between relative solvent accessibility (RSA) and ∆∆G expresses the discrete distributive behaviour of mutants associated with TREM2 in AD (R2 = 0.061) and NHD (R2 = 0.601). Our findings put an emphasis on W50 and V126 as major players in maintaining V-like domain in TREM2. Interestingly, we discern that both of them interact with a common residue Y108, which is dissolved upon mutation. This Y108 could have structural or functional role for TREM2 which can be an ideal candidate for further study. Furthermore, the residual interaction network highlights the importance of R47 and R62 in maintaining the CDR loops that are crucial for ligand binding. Future studies using biophysical characterisation of ligand interactions in TREM2-ECD would be helpful for the development of novel therapeutics for AD and NHD.

METHODS

ConSurf algorithm and ENDscript were used to determine the position and conservation of each residue in the wild-type ECD of TREM2. The mutation-induced fold stability change (∆∆G) of confirmed pathogenic mutants associated with NHD and AD was estimated using 12 state-of-the-art structure-based protein stability tools. Furthermore, we also computed the effect of random mutation on these sites using computational saturation mutagenesis. Linear regression analysis was performed using mutants ∆∆G and RSA through GraphPad software. In addition, a comprehensive non-bonded residual interaction network (RIN) of wild type and its mutants of TREM2-ECD was enumerated using RING3.0.

PROSTATA: a framework for protein stability assessment using transformers

MOTIVATION

Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning.

RESULTS

In this work, we introduce PROSTATA, a predictive model built in a knowledge-transfer fashion on a new curated dataset. PROSTATA demonstrates advantage over existing solutions based on neural networks. We show that the large improvement margin is due to both the architecture of the model and the quality of the new training dataset. This work opens up opportunities to develop new lightweight and accurate models for protein stability assessment.

AVAILABILITY AND IMPLEMENTATION

PROSTATA is available at https://github.com/AIRI-Institute/PROSTATA and https://prostata.airi.net.

Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization

Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.

Bioinformatic analysis of the effect of SNPs in the pig TERT gene on the structural and functional characteristics of the enzyme to develop new genetic markers of productivity traits

BACKGROUND

Telomerase reverse transcriptase (TERT) plays a crucial role in synthesizing telomeric repeats that safeguard chromosomes from damage and fusion, thereby maintaining genome stability. Mutations in the TERT gene can lead to a deviation in gene expression, impaired enzyme activity, and, as a result, abnormal telomere shortening. Genetic markers of productivity traits in livestock can be developed based on the TERT gene polymorphism for use in marker-associated selection (MAS). In this study, a bioinformatic-based approach is proposed to evaluate the effect of missense single-nucleotide polymorphisms (SNPs) in the pig TERT gene on enzyme function and structure, with the prospect of developing genetic markers.

RESULTS

A comparative analysis of the coding and amino acid sequences of the pig TERT was performed with corresponding sequences of other species. The distribution of polymorphisms in the pig TERT gene, with respect to the enzyme's structural-functional domains, was established. A three-dimensional model of the pig TERT structure was obtained through homological modeling. The potential impact of each of the 23 missense SNPs in the pig TERT gene on telomerase function and stability was assessed using predictive bioinformatic tools utilizing data on the amino acid sequence and structure of pig TERT.

CONCLUSIONS

According to bioinformatic analysis of 23 missense SNPs of the pig TERT gene, a predictive effect of rs789641834 (TEN domain), rs706045634 (TEN domain), rs325294961 (TRBD domain) and rs705602819 (RTD domain) on the structural and functional parameters of the enzyme was established. These SNPs hold the potential to serve as genetic markers of productivity traits. Therefore, the possibility of their application in MAS should be further evaluated in associative analysis studies.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Computer-Aided Lipase Engineering for Improving Their Stability and Activity in the Food Industry: State of the Art

As some of the most widely used biocatalysts, lipases have exhibited extreme advantages in many processes, such as esterification, amidation, and transesterification reactions, which causes them to be widely used in food industrial production. However, natural lipases have drawbacks in terms of organic solvent resistance, thermostability, selectivity, etc., which limits some of their applications in the field of foods. In this systematic review, the application of lipases in various food processes was summarized. Moreover, the general structure of lipases is discussed in-depth, and the engineering strategies that can be used in lipase engineering are also summarized. The protocols of some classical methods are compared and discussed, which can provide some information about how to choose methods of lipase engineering. Thermostability engineering and solvent tolerance engineering are highlighted in this review, and the basic principles for improving thermostability and solvent tolerance are summarized. In the future, comput er-aided technology should be more emphasized in the investigation of the mechanisms of reactions catalyzed by lipases and guide the engineering of lipases. The engineering of lipase tunnels to improve the diffusion of substrates is also a promising prospect for further enhanced lipase activity and selectivity.

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations

The development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein interaction, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variants (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases, and the distribution of the free energy changes is not the same. It is shown that the Pearson correlation coefficients (PCCs) of folding and binding free energy changes obtained in cases involving SNVs are smaller than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. Furthermore, it is demonstrated that some methods are sensitive to the chemical nature of the mutations, resulting in PCCs that differ by a factor of four across chemically different mutations. All methods are found to underestimate the energy changes by roughly a factor of 2.

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

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.

Simultaneous enhancement of thermostability and catalytic activity of κ-carrageenase from Pseudoalteromonas tetraodonis by rational design

κ-Carrageenase provides an attractive enzymatic approach to preparation of κ-carrageenan oligosaccharides. Pseudoalteromonas tetraodonis κ-carrageenase is active at the alkaline conditions but displays low thermostability. To further improve its enzymatic performance, two mutants of Q42V and I51H exhibiting both improved thermostability and enzyme activity were screened by the PoPMuSiC algorithm. Compared with the wild-type κ-carrageenase (WT), Q42V and I51H increased the enzyme activity by 20.9% and 25.4%, respectively. After treatment at 50 ℃ for 40 min, Q42V and I51H enhanced the residual activity by 31.1% and 25.9%, respectively. The T_m values of Q42V, I51H, and WT determined by differential scanning calorimetry were 58.2 ℃, 54.8 ℃, and 51.2 ℃, respectively. Compared with untreated and HCl-treated κ-carrageenans, Q42V-treated κ-carrageenan exhibited higher pancreatic lipase inhibitory activity. Molecular docking analysis indicated that the additional pi-sigma force and hydrophobic interaction in the enzyme-substrate complex could account for the increased catalytic activity of Q42V and I51H, respectively. Molecular dynamics analysis indicated that the improved thermostability of mutants Q42V and I51H could be attributed to the less structural deviation and the flexible changes of enzyme conformation at high temperature. This study provides new insight into κ-carrageenase performance improvement and identifies good candidates for their industrial applications.

Site-directed mutagenesis improves the practical application of L-glutamic acid decarboxylase in Escherichia coli

γ-Aminobutyric acid (GABA) is a kind of non-proteinogenic amino acid which is highly soluble in water and widely used in the food and pharmaceutical industries. Enzymatic conversion is an efficient method to produce GABA, whereby glutamic acid decarboxylase (GAD) is the key enzyme that catalyzes the process. The activity of wild-type GAD is usually limited by temperature, pH or biotin concentration, and hence directional modification is applied to improve its catalytic properties and practical application. GABA was produced using whole cell transformation of the recombinant strains Escherichia coli BL21(DE3)-Gad B, E. coli BL21(DE3)-Gad B-T62S and E. coli BL21(DE3)-Gad B-Q309A. The corresponding GABA concentrations in the fermentation broth were 219.09, 238.42, and 276.66 g/L, and the transformation rates were 78.02%, 85.04%, and 98.58%, respectively. The results showed that Gad B-T62S and Gad B-Q309A are two effective mutation sites. These findings may contribute to ideas for constructing potent recombinant strains for GABA production. Practical Application : Enzymatic properties of the GAD from Escherichia coli and GAD site-specific mutants were examined by analyzing their conserved sequences, substrate contacts, contact between GAD amino acid residues and mutation energy (ΔΔG) of the GAD mutants. The enzyme activity and stability of Gad B-T62S and Gad B-Q309A mutants were improved compared to Gad B. The kinetic parameters K_m and V_max of Gad B, Gad B-T62S, and Gad B-Q309A mutants were 11.3 ± 2.1 mM and 32.1 ± 2.4 U/mg, 7.3 ± 2.5 mM and 76.1 ± 3.1 U/mg, and 7.2 ± 3.8 mM and 87.3 ± 1.1 U/mg, respectively. GABA was produced using whole cell transformation of the recombinant strains E. coli BL21(DE3)-Gad B, E. coli BL21(DE3)-Gad B-T62S, and E. coli BL21(DE3)-Gad B-Q309A. The corresponding GABA concentrations in the fermentation broth were 219.09, 238.42, and 276.66 g/L, and the transformation rates were 78.02%, 85.04%, and 98.58%, respectively.

Recent advances in simultaneous thermostability-activity improvement of industrial enzymes through structure modification

Engineered thermostable microbial enzymes are widely employed to catalyze chemical reactions in numerous industrial sectors. Although high thermostability is a prerequisite of industrial applications, enzyme activity is usually sacrificed during thermostability improvement. Therefore, it is vital to select the common and compatible strategies between thermostability and activity improvement to reduce mutants̕ libraries and screening time. Three functional protein engineering approaches, including directed evolution, rational design, and semi-rational design, are employed to manipulate protein structure on a genetic basis. From a structural standpoint, integrative strategies such as increasing substrate affinity; introducing electrostatic interaction; removing steric hindrance; increasing flexibility of the active site; N- and C-terminal engineering; and increasing intramolecular and intermolecular hydrophobic interactions are well-known to improve simultaneous activity and thermostability. The current review aims to analyze relevant strategies to improve thermostability and activity simultaneously to circumvent the thermostability and activity trade-off of industrial enzymes.

Biochemical Studies of Systemic Lupus Erythematosus-Associated Mutations in Nonreceptor Tyrosine Kinases Ack1 and Brk

Tyrosine kinases (TKs) play essential roles in signaling processes that regulate cell survival, migration, and proliferation. Dysregulation of tyrosine kinases underlies many disorders, including cancer, cardiovascular and developmental diseases, as well as pathologies of the immune system. Ack1 and Brk are nonreceptor tyrosine kinases (NRTKs) best known for their roles in cancer. Here, we have biochemically characterized novel Ack1 and Brk mutations identified in patients with systemic lupus erythematosus (SLE). These mutations are the first SLE-linked polymorphisms found among NRTKs. We show that two of the mutants are catalytically inactive, while the other three have reduced activity. To understand the structural changes associated with the loss-of-function phenotype, we solved the crystal structure of one of the Ack1 kinase mutants, K161Q. Furthermore, two of the mutated residues (Ack1 A156 and K161) critical for catalytic activity are highly conserved among other TKs, and their substitution in other members of the kinase family could have implications in cancer. In contrast to canonical gain-of-function mutations in TKs observed in many cancers, we report loss-of-function mutations in Ack1 and Brk, highlighting the complexity of TK involvement in human diseases.

Role of distal sites in enzyme engineering

The limitations associated with natural enzyme catalysis have triggered the rise of the field of protein engineering. Traditional rational design was based on the analysis of protein structural information and catalytic mechanisms to identify key active sites or ligand binding sites to reshape the substrate pocket. The role and significance of functional sites in the active center have been studied extensively. With a deeper understanding of the structure-catalysis relationship map, the entire protein molecule can be filled with residues that play a substantial role in its structure and function. However, the catalytic mechanism underlying distal mutations remains unclear. The aim of this review was to highlight the criticality of the distal site in enzyme engineering based on the following three aspects: What can distal mutations exert on function from mutability landscape? How do distal sites influence enzyme function? How to predict and design distal mutations? This review provides insights into the catalytic mechanism of enzymes from the global interaction network, knowledge from sequence-structure-dynamics-function relationships, and strategies for distal mutation-based protein engineering.