Improved protein structure prediction using potentials from deep learning

Protein subcellular localization prediction tools

Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.

Rhodopsin-based light-harvesting system for sustainable synthetic biology

Rhodopsins, a diverse class of light-sensitive proteins found in various life domains, have attracted considerable interest for their potential applications in sustainable synthetic biology. These proteins exhibit remarkable photochemical properties, undergoing conformational changes upon light absorption that drive a variety of biological processes. Exploiting rhodopsin's natural properties could pave the way for creating sustainable and energy-efficient technologies. Rhodopsin-based light-harvesting systems offer innovative solutions to a few key challenges in sustainable engineering, from bioproduction to renewable energy conversion. In this opinion article, we explore the recent advancements and future possibilities of employing rhodopsins for sustainable engineering, underscoring the transformative potential of these biomolecules.

PhosAF: An integrated deep learning architecture for predicting protein phosphorylation sites with AlphaFold2 predicted structures

Phosphorylation is indispensable in comprehending biological processes, while biological experimental methods for identifying phosphorylation sites are tedious and arduous. With the rapid growth of biotechnology, deep learning methods have made significant progress in site prediction tasks. Nevertheless, most existing predictors only consider protein sequence information, that limits the capture of protein spatial information. Building upon the latest advancement in protein structure prediction by AlphaFold2, a novel integrated deep learning architecture PhosAF is developed to predict phosphorylation sites in human proteins by integrating CMA-Net and MFC-Net, which considers sequence and structure information predicted by AlphaFold2. Here, CMA-Net module is composed of multiple convolutional neural network layers and multi-head attention is appended to obtaining the local and long-term dependencies of sequence features. Meanwhile, the MFC-Net module composed of deep neural network layers is used to capture the complex representations of evolutionary and structure features. Furthermore, different features are combined to predict the final phosphorylation sites. In addition, we put forward a new strategy to construct reliable negative samples via protein secondary structures. Experimental results on independent test data and case study indicate that our model PhosAF surpasses the current most advanced methods in phosphorylation site prediction.

A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?

Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality.

Level of Evidence

Level V.

Assessing the functional roles of coevolving PHD finger residues

Although in silico folding based on coevolving residue constraints in the deep-learning era has transformed protein structure prediction, the contributions of coevolving residues to protein folding, stability, and other functions in physical contexts remain to be clarified and experimentally validated. Herein, the PHD finger module, a well-known histone reader with distinct subtypes containing subtype-specific coevolving residues, was used as a model to experimentally assess the contributions of coevolving residues and to clarify their specific roles. The results of the assessment, including proteolysis and thermal unfolding of wildtype and mutant proteins, suggested that coevolving residues have varying contributions, despite their large in silico constraints. Residue positions with large constraints were found to contribute to stability in one subtype but not others. Computational sequence design and generative model-based energy estimates of individual structures were also implemented to complement the experimental assessment. Sequence design and energy estimates distinguish coevolving residues that contribute to folding from those that do not. The results of proteolytic analysis of mutations at positions contributing to folding were consistent with those suggested by sequence design and energy estimation. Thus, we report a comprehensive assessment of the contributions of coevolving residues, as well as a strategy based on a combination of approaches that should enable detailed understanding of the residue contributions in other large protein families.

Variety of Plant Oils: Species-Specific Lipid Biosynthesis

Plant oils represent a large group of neutral lipids with important applications in food, feed and oleochemical industries. Most plants accumulate oils in the form of triacylglycerol within seeds and their surrounding tissues, which comprises three fatty acids attached to a glycerol backbone. Different plant species accumulate unique fatty acids in their oils, serving a range of applications in pharmaceuticals and oleochemicals. To enable the production of these distinctive oils, select plant species have adapted specialized oil metabolism pathways, involving differential gene co-expression networks and structurally divergent enzymes/proteins. Here, we summarize some of the recent advances in our understanding of oil biosynthesis in plants. We compare expression patterns of oil metabolism genes from representative species, including Arabidopsis thaliana, Ricinus communis (castor bean), Linum usitatissimum L. (flax) and Elaeis guineensis (oil palm) to showcase the co-expression networks of relevant genes for acyl metabolism. We also review several divergent enzymes/proteins associated with key catalytic steps of unique oil accumulation, including fatty acid desaturases, diacylglycerol acyltransferases and oleosins, highlighting their structural features and preference toward unique lipid substrates. Lastly, we briefly discuss protein interactomes and substrate channeling for oil biosynthesis and the complex regulation of these processes.

Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information

Predicting the effects of one or more mutations to the in vivo or in vitro properties of a wild-type protein is a major computational challenge, due to the presence of epistasis, that is, of interactions between amino acids in the sequence. We introduce a computationally efficient procedure to build minimal epistatic models to predict mutational effects by combining evolutionary (homologous sequence) and few mutational-scan data. Mutagenesis measurements guide the selection of links in a sparse graphical model, while the parameters on the nodes and the edges are inferred from sequence data. We show, on 10 mutational scans, that our pipeline exhibits performances comparable to state-of-the-art deep networks trained on many more data, while requiring much less parameters and being hence more interpretable. In particular, the identified interactions adapt to the wild-type protein and to the fitness or biochemical property experimentally measured, mostly focus on key functional sites, and are not necessarily related to structural contacts. Therefore, our method is able to extract information relevant for one mutational experiment from homologous sequence data reflecting the multitude of structural and functional constraints acting on proteins throughout evolution.

Harnessing conformational dynamics in enzyme catalysis to achieve nature-like catalytic efficiencies: the shortest path map tool for computational enzyme redesign

Enzymes exhibit diverse conformations, as represented in the free energy landscape (FEL). Such conformational diversity provides enzymes with the ability to evolve towards novel functions. The challenge lies in identifying mutations that enhance specific conformational changes, especially if located in distal sites from the active site cavity. The shortest path map (SPM) method, which we developed to address this challenge, constructs a graph based on the distances and correlated motions of residues observed in nanosecond timescale molecular dynamics (MD) simulations. We recently introduced a template based AlphaFold2 (tAF2) approach coupled with 10 nanosecond MD simulations to quickly estimate the conformational landscape of enzymes and assess how the FEL is shifted after mutation. In this study, we evaluate the potential of SPM when coupled with tAF2-MD in estimating conformational heterogeneity and identifying key conformationally-relevant positions. The selected model system is the beta subunit of tryptophan synthase (TrpB). We compare how the SPM pathways differ when integrating tAF2 with different MD simulation lengths from as short as 10 ns until 50 ns and considering two distinct Amber forcefield and water models (ff14SB/TIP3P versus ff19SB/OPC). The new methodology can more effectively capture the distal mutations found in laboratory evolution, thus showcasing the efficacy of tAF2-MD-SPM in rapidly estimating enzyme dynamics and identifying the key conformationally relevant hotspots for computational enzyme engineering.

Predicting therapeutic and side effects from drug binding affinities to human proteome structures

Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.

A guide to genetically-encoded redox biosensors: state of the art and opportunities

Genetically-encoded redox biosensors have become invaluable tools for monitoring cellular redox processes with high spatiotemporal resolution, coupling the presence of the redox-active analyte with a change in fluorescence signal that can be easily recorded. This review summarizes the available fluorescence recording methods and presents an in-depth classification of the redox biosensors, organized by the analytes they respond to. In addition to the fluorescent protein-based architectures, this review also describes the recent advances on fluorescent, chemigenetic-based redox biosensors and other emerging chemigenetic strategies. This review examines how these biosensors are designed, the biosensors sensing mechanism, and their practical advantages and disadvantages.

Phylogeny, envenomation syndrome, and membrane permeabilising venom produced by Australia's electric caterpillar Comana monomorpha

Zygaenoidea is a superfamily of lepidopterans containing many venomous species, including the Limacodidae (nettle caterpillars) and Megalopygidae (asp caterpillars). Venom proteomes have been recently documented for several species from each of these families, but further data are required to understand the evolution of venom in Zygaenoidea. In this study, we examined the 'electric' caterpillar from North-Eastern Australia, a limacodid caterpillar densely covered in venomous spines. We used DNA barcoding to identify this caterpillar as the larva of the moth Comana monomorpha (Turner, 1904). We report the clinical symptoms of C. monomorpha envenomation, which include acute pain, and erythema and oedema lasting for more than a week. Combining transcriptomics of venom spines with proteomics of venom harvested from the spine tips revealed a venom markedly different in composition from previously examined limacodid venoms that are rich in peptides. In contrast, the venom of C. monomorpha is rich in aerolysin-like proteins similar to those found in venoms of asp caterpillars (Megalopygidae). Consistent with this composition, the venom potently permeabilises sensory neurons and human neuroblastoma cells. This study highlights the diversity of venom composition in Limacodidae.

Three-Dimensional Interaction Homology: Deconstructing Residue-Residue and Residue-Lipid Interactions in Membrane Proteins

A method is described to deconstruct the network of hydropathic interactions within and between a protein's sidechain and its environment into residue-based three-dimensional maps. These maps encode favorable and unfavorable hydrophobic and polar interactions, in terms of spatial positions for optimal interactions, relative interaction strength, as well as character. In addition, these maps are backbone angle-dependent. After map calculation and clustering, a finite number of unique residue sidechain interaction maps exist for each backbone conformation, with the number related to the residue's size and interaction complexity. Structures for soluble proteins (~749,000 residues) and membrane proteins (~387,000 residues) were analyzed, with the latter group being subdivided into three subsets related to the residue's position in the membrane protein: soluble domain, core-facing transmembrane domain, and lipid-facing transmembrane domain. This work suggests that maps representing residue types and their backbone conformation can be reassembled to optimize the medium-to-high resolution details of a protein structure. In particular, the information encoded in maps constructed from the lipid-facing transmembrane residues appears to paint a clear picture of the protein-lipid interactions that are difficult to obtain experimentally.

Accurate Protein pKa Prediction with Physical Organic Chemistry Guided 3D Protein Representation

Protein pKa is a fundamental physicochemical parameter that dictates protein structure and function. However, accurately determining protein site-pKa values remains a substantial challenge, both experimentally and theoretically. In this study, we introduce a physical organic approach, leveraging a protein structural and physical-organic-parameter-based representation (P-SPOC), to develop a rapid and intuitive model for protein pKa prediction. Our P-SPOC model achieves state-of-the-art predictive accuracy, with a mean absolute error (MAE) of 0.33 pKa units. Furthermore, we have incorporated advanced protein structure prediction models, like AlphaFold2, to approximate structures for proteins lacking three-dimensional representations, which enhances the applicability of our model in the context of structure-undetermined protein research. To promote broader accessibility within the research community, an online prediction interface was also established at isyn.luoszgroup.com.

Protein Nanoparticles as Vaccine Platforms for Human and Zoonotic Viruses

Vaccines are one of the most effective medical interventions, playing a pivotal role in treating infectious diseases. Although traditional vaccines comprise killed, inactivated, or live-attenuated pathogens that have resulted in protective immune responses, the negative consequences of their administration have been well appreciated. Modern vaccines have evolved to contain purified antigenic subunits, epitopes, or antigen-encoding mRNAs, rendering them relatively safe. However, reduced humoral and cellular responses pose major challenges to these subunit vaccines. Protein nanoparticle (PNP)-based vaccines have garnered substantial interest in recent years for their ability to present a repetitive array of antigens for improving immunogenicity and enhancing protective responses. Discovery and characterisation of naturally occurring PNPs from various living organisms such as bacteria, archaea, viruses, insects, and eukaryotes, as well as computationally designed structures and approaches to link antigens to the PNPs, have paved the way for unprecedented advances in the field of vaccine technology. In this review, we focus on some of the widely used naturally occurring and optimally designed PNPs for their suitability as promising vaccine platforms for displaying native-like antigens from human viral pathogens for protective immune responses. Such platforms hold great promise in combating emerging and re-emerging infectious viral diseases and enhancing vaccine efficacy and safety.

LGC-DBP: the method of DNA-binding protein identification based on PSSM and deep learning

The recognition of DNA Binding Proteins (DBPs) plays a crucial role in understanding biological functions such as replication, transcription, and repair. Although current sequence-based methods have shown some effectiveness, they often fail to fully utilize the potential of deep learning in capturing complex patterns. This study introduces a novel model, LGC-DBP, which integrates Long Short-Term Memory (LSTM), Gated Inception Convolution, and Improved Channel Attention mechanisms to enhance the prediction of DBPs. Initially, the model transforms protein sequences into Position Specific Scoring Matrices (PSSM), then processed through our deep learning framework. Within this framework, Gated Inception Convolution merges the concepts of gating units with the advantages of Graph Convolutional Network (GCN) and Dilated Convolution, significantly surpassing traditional convolution methods. The Improved Channel Attention mechanism substantially enhances the model's responsiveness and accuracy by shifting from a single input to three inputs and integrating three sigmoid functions along with an additional layer output. These innovative combinations have significantly improved model performance, enabling LGC-DBP to recognize and interpret the complex relationships within DBP features more accurately. The evaluation results show that LGC-DBP achieves an accuracy of 88.26% and a Matthews correlation coefficient of 0.701, both surpassing existing methods. These achievements demonstrate the model's strong capability in integrating and analyzing multi-dimensional data and mark a significant advancement over traditional methods by capturing deeper, nonlinear interactions within the data.

Insights into AlphaFold's breakthrough in neurodegenerative diseases

Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons' functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer's disease (AD) and Parkinson's disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington's disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-β (Aβ), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases' advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins-a beneficial approach for drug designing.

Predicting binding events in very flexible, allosteric, multi-domain proteins

Knowledge of the structures formed by proteins and small ligands is of fundamental importance for understanding molecular principles of chemotherapy and for designing new and more effective drugs. Due to the still high costs and to the several limitations of experimental techniques, it is most often desirable to predict these ligand-protein complexes in silico, particularly when screening for new putative drugs from databases of millions of compounds. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology aimed at generating bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites. Validation was performed on the enzyme adenylate kinase (ADK), a paradigmatic example of proteins that undergo very large conformational changes upon ligand binding. By only exploiting the unbound structure and the putative binding sites of the protein, we generated a significant fraction of bound-like structures, which employed in ensemble-docking calculations allowed to find native-like poses of substrates, inhibitors, and catalytically incompetent binders. Our protocol provides a general framework for the generation of bound-like conformations of flexible proteins that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening for difficult targets, prediction of the impact of amino acid mutations on structure and dynamics, and protein engineering.

Protein Editing using a Concerted Transposition Reaction

Protein engineering through the chemical or enzymatic ligation of polypeptide fragments has proven enormously powerful for studying countless biochemical processes in vitro. In general, this strategy necessitates a protein folding step following ligation of the unstructured fragments, a requirement that constrains the types of systems amenable to the approach. Here, we report an in vitro strategy that allows internal regions of target proteins to be replaced in a single operation. Conceptually, our system is analogous to a DNA transposition reaction, but employs orthogonal pairs of split inteins to swap out a designated region of a host protein with an exogenous molecular cassette. We show using isotopic labeling experiments that this 'protein transposition' reaction is concerted when the kinetics for the embedded intein pairs are suitably matched. Critically, this feature allows for efficient manipulation of protein primary structure in the context of a native fold. The utility of this method is illustrated using several protein systems including the multisubunit chromatin remodeling complex, ACF, where we also show protein transposition can occur in situ within the cell nucleus. By carrying out a molecular 'cut and paste' on a protein or protein complex under native folding conditions, our approach dramatically expands the scope of protein semisynthesis.

Apprehensions and emerging solutions in ML-based protein structure prediction

The three-dimensional structure of proteins determines their function in vital biological processes. Thus, when the structure is known, the molecular mechanism of protein function can be understood in more detail and obtained information utilized in biotechnological, diagnostics, and therapeutic applications. Over the past five years, machine learning (ML)-based modeling has pushed protein structure prediction to the next level with AlphaFold in the front line, predicting the structure for hundreds of millions of proteins. Further advances recently report promising ML-based approaches for solving remaining challenges by incorporating functionally important metals, co-factors, post-translational modifications, structural dynamics, and interdomain and multimer interactions in the structure prediction process.

Protein remote homology detection and structural alignment using deep learning

Exploiting sequence-structure-function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec and DeepBLAST. TM-Vec allows searching for structure-structure similarities in large sequence databases. It is trained to accurately predict TM-scores as a metric of structural similarity directly from sequence pairs without the need for intermediate computation or solution of structures. Once structurally similar proteins have been identified, DeepBLAST can structurally align proteins using only sequence information by identifying structurally homologous regions between proteins. It outperforms traditional sequence alignment methods and performs similarly to structure-based alignment methods. We show the merits of TM-Vec and DeepBLAST on a variety of datasets, including better identification of remotely homologous proteins compared with state-of-the-art sequence alignment and structure prediction methods.

Litchi aspartic protease LcAP1 enhances plant resistance via suppressing cell death triggered by the pectate lyase PlPeL8 from Peronophythora litchii

Plant cell death is regulated in plant-pathogen interactions. While some aspartic proteases (APs) participate in regulating programmed cell death or defense responses, the defense functions of most APs remain largely unknown. Here, we report on a virulence factor, PlPeL8, which is a pectate lyase found in the hemibiotrophic pathogen Peronophythora litchii. Through in vivo and in vitro assays, we confirmed the interaction between PlPeL8 and LcAP1 from litchi, and identified LcAP1 as a positive regulator of plant immunity. PlPeL8 induced cell death associated with NbSOBIR1 and NbMEK2. The 11 conserved residues of PlPeL8 were essential for inducing cell death and enhancing plant susceptibility. Twenty-three LcAPs suppressed cell death induced by PlPeL8 in Nicotiana benthamiana depending on their interaction with PlPeL8. The N-terminus of LcAP1 was required for inhibiting PlPeL8-triggered cell death and susceptibility. Furthermore, PlPeL8 led to higher susceptibility in NbAPs-silenced N. benthamiana than the GUS-control. Our results indicate the crucial roles of LcAP1 and its homologs in enhancing plant resistance via suppression of cell death triggered by PlPeL8, and LcAP1 represents a promising target for engineering disease resistance. Our study provides new insights into the role of plant cell death in the arms race between plants and hemibiotrophic pathogens.

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

DIProT: A deep learning based interactive toolkit for efficient and effective Protein design

The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets, with promising sequence recovery and inference time. Case studies further illustrate how DIProT can facilitate user-guided protein design.

State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction

RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/.

PRUNE1 and NME/NDPK family proteins influence energy metabolism and signaling in cancer metastases

We describe here the molecular basis of the complex formation of PRUNE1 with the tumor metastasis suppressors NME1 and NME2, two isoforms appertaining to the nucleoside diphosphate kinase (NDPK) enzyme family, and how this complex regulates signaling the immune system and energy metabolism, thereby shaping the tumor microenvironment (TME). Disrupting the interaction between NME1/2 and PRUNE1, as suggested, holds the potential to be an excellent therapeutic target for the treatment of cancer and the inhibition of metastasis dissemination. Furthermore, we postulate an interaction and regulation of the other Class I NME proteins, NME3 and NME4 proteins, with PRUNE1 and discuss potential functions. Class I NME1-4 proteins are NTP/NDP transphosphorylases required for balancing the intracellular pools of nucleotide diphosphates and triphosphates. They regulate different cellular functions by interacting with a large variety of other proteins, and in cancer and metastasis processes, they can exert pro- and anti-oncogenic properties depending on the cellular context. In this review, we therefore additionally discuss general aspects of class1 NME and PRUNE1 molecular structures as well as their posttranslational modifications and subcellular localization. The current knowledge on the contributions of PRUNE1 as well as NME proteins to signaling cascades is summarized with a special regard to cancer and metastasis.

Engineered Imine Reductase for Asymmetric Synthesis of Dextromethorphan Key Intermediate

(S)-1-(4-Methoxybenzyl)-1,2,3,4,5,6,7,8-octahydroisoquinoline ((S)-1-(4-methoxybenzyl)-OHIQ) is the key intermediate of the nonopioid antitussive dextromethorphan. In this study, (S)-IR61-V69Y/P123A/W179G/F182I/L212V (M4) was identified with a 766-fold improvement in catalytic efficiency compared with wide-type IR61 through enzyme engineering. M4 could completely convert 200 mM of 1-(4-methoxybenzyl)-3,4,5,6,7,8-hexahydroisoquinoline into (S)-1-(4-methoxybenzyl)-OHIQ in 77% isolated yield, with >99% enantiomeric excess and a high space-time yield of 542 g L-1 day-1, demonstrating a great potential for the synthesis of dextromethorphan intermediate in industrial applications.

Display of porcine epidemic diarrhea virus spike protein B-cell linear epitope on Lactobacillus mucosae G01 S-layer surface induce a robust immunogenicity in mice

The Porcine epidemic diarrhea virus (PEDV) presents a substantial risk to the domestic pig industry, resulting in extensive and fatal viral diarrhea among piglets. Recognizing the mucosal stimulation triggered by PEDV and harnessing the regulatory impact of lactobacilli on intestinal function, we have developed a lactobacillus-based vaccine that is carefully designed to elicit a strong mucosal immune response. Through bioinformatics analysis, we examined PEDV S proteins to identify B-cell linear epitopes that meet the criteria of being non-toxic, soluble, antigenic, and capable of neutralizing the virus. In this study, a genetically modified strain of Lactobacillus mucosae G01 (L.mucosae G01) was created by utilizing the S layer protein (SLP) as a scaffold for surface presentation. Chimeric immunodominant epitopes with neutralizing activity were incorporated at various sites on SLP. The successful expression of SLP chimeric immunodominant epitope 1 on the surface of L.mucosae G01 was confirmed through indirect immunofluorescence and transmission electron microscopy, revealing the formation of a transparent membrane. The findings demonstrate that the oral administration of L.mucosae G01, which expresses the SLP chimeric immunodominant gene epitope1, induces the production of secreted IgA in the intestine and feces of mice. Additionally, there is an elevation in IgG levels in the serum. Moreover, the levels of cytokines IL-2, IL-4, IFN-γ, and IL-17 are significantly increased compared to the negative control group. These results suggest that L. mucosae G01 has the ability to deliver exogenous antigens and elicit a specific mucosal immune response against PEDV. This investigation presents new possibilities for immunoprophylaxis against PEDV-induced diarrhea.

NMR Structure of Retinal Guanylate Cyclase Activating Protein 5 (GCAP5) with R22A Mutation That Abolishes Dimerization and Enhances Cyclase Activation

Guanylate cyclase activating protein-5 (GCAP5) in zebrafish photoreceptors promotes the activation of membrane receptor retinal guanylate cyclase (GC-E). Previously, we showed the R22A mutation in GCAP5 (GCAP5R22A) abolishes dimerization of GCAP5 and activates GC-E by more than 3-fold compared to that of wild-type GCAP5 (GCAP5WT). Here, we present ITC, NMR, and functional analysis of GCAP5R22A to understand how R22A causes a decreased dimerization affinity and increased cyclase activation. ITC experiments reveal GCAP5R22A binds a total of 3 Ca2+, including two sites in the nanomolar range followed by a single micromolar site. The two nanomolar sites in GCAP5WT were not detected by ITC, suggesting that R22A may affect the binding of Ca2+ to these sites. The NMR-derived structure of GCAP5R22A is overall similar to that of GCAP5WT (RMSD = 2.3 Å), except for local differences near R22A (Q19, W20, Y21, and K23) and an altered orientation of the C-terminal helix near the N-terminal myristate. GCAP5R22A lacks an intermolecular salt bridge between R22 and D71 that may explain the weakened dimerization. We present a structural model of GCAP5 bound to GC-E in which the R22 side-chain contacts exposed hydrophobic residues in GC-E. Cyclase assays suggest that GC-E binds to GCAP5R22A with ∼25% higher affinity compared to GCAP5WT, consistent with more favorable hydrophobic contact by R22A that may help explain the increased cyclase activation.

Rethinking therapeutic strategies of dual-target drugs: An update on pharmacological small-molecule compounds in cancer

Oncogenes and tumor suppressors are well-known to orchestrate several signaling cascades, regulate extracellular and intracellular stimuli, and ultimately control the fate of cancer cells. Accumulating evidence has recently revealed that perturbation of these key modulators by mutations or abnormal protein expressions are closely associated with drug resistance in cancer therapy; however, the inherent drug resistance or compensatory mechanism remains to be clarified for targeted drug discovery. Thus, dual-target drug development has been widely reported to be a promising therapeutic strategy for improving drug efficiency or overcoming resistance mechanisms. In this review, we provide an overview of the therapeutic strategies of dual-target drugs, especially focusing on pharmacological small-molecule compounds in cancer, including small molecules targeting mutation resistance, compensatory mechanisms, synthetic lethality, synergistic effects, and other new emerging strategies. Together, these therapeutic strategies of dual-target drugs would shed light on discovering more novel candidate small-molecule drugs for the future cancer treatment.

Parvalbumin interneuron cell-to-network plasticity: mechanisms and therapeutic avenues

Alzheimer's disease (AD) and schizophrenia (SCZ) represent two major neuropathological conditions with a high disease burden. Despite their distinct etiologies, patients suffering from AD or SCZ share a common burden of disrupted memory functions unattended by current therapies. Recent preclinical analyses highlight cell-type-specific contributions of parvalbumin interneurons (PVIs), particularly the plasticity of their cellular excitability, towards intact neuronal network function (cell-to-network plasticity) and memory performance. Here we argue that deficits of PVI cell-to-network plasticity may underlie memory deficits in AD and SCZ, and we explore two therapeutic avenues: the targeting of PVI-specific neuromodulation, including by neuropeptides, and the recruitment of network synchrony in the gamma frequency range (40 Hz) by external stimulation. We finally propose that these approaches be merged under consideration of recent insights into human brain physiology.

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.

COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework

The fast and accurate conformation space modeling is an essential part of computational approaches for solving ligand and structure-based drug discovery problems. Recent state-of-the-art diffusion models for molecular conformation generation show promising distribution coverage and physical plausibility metrics but suffer from a slow sampling procedure. We propose a novel adversarial generative framework, COSMIC, that shows comparable generative performance but provides a time-efficient sampling and training procedure. Given a molecular graph and random noise, the generator produces a conformation in two stages. First, it constructs a conformation in a rotation and translation invariant representation─internal coordinates. In the second step, the model predicts the distances between neighboring atoms and performs a few fast optimization steps to refine the initial conformation. The proposed model considers conformation energy, achieving comparable space coverage, and diversity metrics results.

Distance plus attention for binding affinity prediction

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π -stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap. Scientific Contribution StatementThis study innovatively introduces distance-based features to predict protein-ligand binding affinity, capitalizing on unique molecular interactions. Furthermore, the incorporation of protein sequence features of specific residues enhances the model's proficiency in capturing intricate binding patterns. The predictive capabilities are further strengthened through the use of a deep learning architecture with attention mechanisms, and an ensemble approach, averaging the outputs of five models, is implemented to ensure robust and reliable predictions.

Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features

Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2's predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments.

Identifying the Quantitative Trait Locus and Candidate Genes of Traits Related to Milling Quality in Rice via a Genome-Wide Association Study

Milling quality directly affects production efficiency in rice, which is closely related to the brown rice recovery (BRR), the milled rice recovery (MRR) and the head milled rice recovery (HMRR). The present study investigated these three traits in 173 germplasms in two environments, finding abundant phenotypic variation. Three QTLs for BRR, two for MRR, and three for HMRR were identified in a genome-wide association study, five of these were identified in previously reported QTLs and three were newly identified. By combining the linkage disequilibrium (LD) analyses, the candidate gene LOC_Os05g08350 was identified. It had two haplotypes with significant differences and Hap 2 increased the BRR by 4.40%. The results of the qRT-PCR showed that the expression of LOC_Os05g08350 in small-BRR accessions was significantly higher than that in large-BRR accessions at Stages 4-5 of young panicle development, reaching the maximum value at Stage 5. The increase in thickness of the spikelet hulls of the accession carrying LOC_Os05g08350TT occurred due to an increase in the cell width and the cell numbers in cross-sections of spikelet hulls. These results help to further clarify the molecular genetic mechanism of milling-quality-related traits and provide genetic germplasm materials for high-quality breeding in rice.

Mechanisms of ligand recognition and activation of melanin-concentrating hormone receptors

Melanin-concentrating hormone (MCH) is a cyclic neuropeptide that regulates food intake, energy balance, and other physiological functions by stimulating MCHR1 and MCHR2 receptors, both of which are class A G protein-coupled receptors. MCHR1 predominately couples to inhibitory G protein, Gi/o, and MCHR2 can only couple to Gq/11. Here we present cryo-electron microscopy structures of MCH-activated MCHR1 with Gi and MCH-activated MCHR2 with Gq at the global resolutions of 3.01 Å and 2.40 Å, respectively. These structures reveal that MCH adopts a consistent cysteine-mediated hairpin loop configuration when bound to both receptors. A central arginine from the LGRVY core motif between the two cysteines of MCH penetrates deeply into the transmembrane pocket, triggering receptor activation. Integrated with mutational and functional insights, our findings elucidate the molecular underpinnings of ligand recognition and MCH receptor activation and offer a structural foundation for targeted drug design.

Accelerated Engineering of ELP-Based Materials through Hybrid Biomimetic-De Novo Predictive Molecular Design

Efforts to engineer high-performance protein-based materials inspired by nature have mostly focused on altering naturally occurring sequences to confer the desired functionalities, whereas de novo design lags significantly behind and calls for unconventional innovative approaches. Here, using partially disordered elastin-like polypeptides (ELPs) as initial building blocks this work shows that de novo engineering of protein materials can be accelerated through hybrid biomimetic design, which this work achieves by integrating computational modeling, deep neural network, and recombinant DNA technology. This generalizable approach involves incorporating a series of de novo-designed sequences with α-helical conformation and genetically encoding them into biologically inspired intrinsically disordered repeating motifs. The new ELP variants maintain structural conformation and showed tunable supramolecular self-assembly out of thermal equilibrium with phase behavior in vitro. This work illustrates the effective translation of the predicted molecular designs in structural and functional materials. The proposed methodology can be applied to a broad range of partially disordered biomacromolecules and potentially pave the way toward the discovery of novel structural proteins.

Downregulation of PIK3IP1/TrIP on T cells is controlled by TCR signal strength, PKC, and metalloprotease-mediated cleavage

The protein known as PI3K-interacting protein (PIK3IP1), or transmembrane inhibitor of PI3K (TrIP), is highly expressed by T cells and can modulate PI3K activity in these cells. Several studies have also revealed that TrIP is rapidly downregulated following T cell activation. However, it is unclear as to how this downregulation is controlled. Using a novel monoclonal antibody that robustly stains cell-surface TrIP, we demonstrate that TrIP is lost from the surface of activated T cells in a manner dependent on the strength of signaling through the T cell receptor (TCR) and specific downstream signaling pathways. In addition, TrIP expression returns after 24 hours, suggesting that it may play a role in resetting TCR signaling at later time points. Finally, by expressing truncated forms of TrIP in cells, we identify the region in the extracellular stalk domain of TrIP that is targeted for proteolytic cleavage by metalloprotease ADAM17.

Key residues in the VDAC2-BAK complex can be targeted to modulate apoptosis

BAK and BAX execute intrinsic apoptosis by permeabilising the mitochondrial outer membrane. Their activity is regulated through interactions with pro-survival BCL-2 family proteins and with non-BCL-2 proteins including the mitochondrial channel protein VDAC2. VDAC2 is important for bringing both BAK and BAX to mitochondria where they execute their apoptotic function. Despite this important function in apoptosis, while interactions with pro-survival family members are well characterised and have culminated in the development of drugs that target these interfaces to induce cancer cell apoptosis, the interaction between BAK and VDAC2 remains largely undefined. Deep scanning mutagenesis coupled with cysteine linkage identified key residues in the interaction between BAK and VDAC2. Obstructive labelling of specific residues in the BH3 domain or hydrophobic groove of BAK disrupted this interaction. Conversely, mutating specific residues in a cytosol-exposed region of VDAC2 stabilised the interaction with BAK and inhibited BAK apoptotic activity. Thus, this VDAC2-BAK interaction site can potentially be targeted to either inhibit BAK-mediated apoptosis in scenarios where excessive apoptosis contributes to disease or to promote BAK-mediated apoptosis for cancer therapy.

The structural landscape and diversity of Pyricularia oryzae MAX effectors revisited

Magnaporthe AVRs and ToxB-like (MAX) effectors constitute a family of secreted virulence proteins in the fungus Pyricularia oryzae (syn. Magnaporthe oryzae), which causes blast disease on numerous cereals and grasses. In spite of high sequence divergence, MAX effectors share a common fold characterized by a ß-sandwich core stabilized by a conserved disulfide bond. In this study, we investigated the structural landscape and diversity within the MAX effector repertoire of P. oryzae. Combining experimental protein structure determination and in silico structure modeling we validated the presence of the conserved MAX effector core domain in 77 out of 94 groups of orthologs (OG) identified in a previous population genomic study. Four novel MAX effector structures determined by NMR were in remarkably good agreement with AlphaFold2 (AF2) predictions. Based on the comparison of the AF2-generated 3D models we propose a classification of the MAX effectors superfamily in 20 structural groups that vary in the canonical MAX fold, disulfide bond patterns, and additional secondary structures in N- and C-terminal extensions. About one-third of the MAX family members remain singletons, without strong structural relationship to other MAX effectors. Analysis of the surface properties of the AF2 MAX models also highlights the high variability within the MAX family at the structural level, potentially reflecting the wide diversity of their virulence functions and host targets.

MFTrans: A multi-feature transformer network for protein secondary structure prediction

In the rapidly evolving field of computational biology, accurate prediction of protein secondary structures is crucial for understanding protein functions, facilitating drug discovery, and advancing disease diagnostics. In this paper, we propose MFTrans, a deep learning-based multi-feature fusion network aimed at enhancing the precision and efficiency of Protein Secondary Structure Prediction (PSSP). This model employs a Multiple Sequence Alignment (MSA) Transformer in combination with a multi-view deep learning architecture to effectively capture both global and local features of protein sequences. MFTrans integrates diverse features generated by protein sequences, including MSA, sequence information, evolutionary information, and hidden state information, using a multi-feature fusion strategy. The MSA Transformer is utilized to interleave row and column attention across the input MSA, while a Transformer encoder and decoder are introduced to enhance the extracted high-level features. A hybrid network architecture, combining a convolutional neural network with a bidirectional Gated Recurrent Unit (BiGRU) network, is used to further extract high-level features after feature fusion. In independent tests, our experimental results show that MFTrans has superior generalization ability, outperforming other state-of-the-art PSSP models by 3 % on average on public benchmarks including CASP12, CASP13, CASP14, TEST2016, TEST2018, and CB513. Case studies further highlight its advanced performance in predicting mutation sites. MFTrans contributes significantly to the protein science field, opening new avenues for drug discovery, disease diagnosis, and protein.

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

Targeted inhibition of the HNF1A/SHH axis by triptolide overcomes paclitaxel resistance in non-small cell lung cancer

Paclitaxel resistance is associated with a poor prognosis in non-small cell lung cancer (NSCLC) patients, and currently, there is no promising drug for paclitaxel resistance. In this study, we investigated the molecular mechanisms underlying the chemoresistance in human NSCLC-derived cell lines. We constructed paclitaxel-resistant NSCLC cell lines (A549/PR and H460/PR) by long-term exposure to paclitaxel. We found that triptolide, a diterpenoid epoxide isolated from the Chinese medicinal herb Tripterygium wilfordii Hook F, effectively enhanced the sensitivity of paclitaxel-resistant cells to paclitaxel by reducing ABCB1 expression in vivo and in vitro. Through high-throughput sequencing, we identified the SHH-initiated Hedgehog signaling pathway playing an important role in this process. We demonstrated that triptolide directly bound to HNF1A, one of the transcription factors of SHH, and inhibited HNF1A/SHH expression, ensuing in attenuation of Hedgehog signaling. In NSCLC tumor tissue microarrays and cancer network databases, we found a positive correlation between HNF1A and SHH expression. Our results illuminate a novel molecular mechanism through which triptolide targets and inhibits HNF1A, thereby impeding the activation of the Hedgehog signaling pathway and reducing the expression of ABCB1. This study suggests the potential clinical application of triptolide and provides promising prospects in targeting the HNF1A/SHH pathway as a therapeutic strategy for NSCLC patients with paclitaxel resistance. Schematic diagram showing that triptolide overcomes paclitaxel resistance by mediating inhibition of the HNF1A/SHH/ABCB1 axis.

Full-length single-molecule protein fingerprinting

Proteins are the primary functional actors of the cell. While proteoform diversity is known to be highly biologically relevant, current protein analysis methods are of limited use for distinguishing proteoforms. Mass spectrometric methods, in particular, often provide only ambiguous information on post-translational modification sites, and sequences of co-existing modifications may not be resolved. Here we demonstrate fluorescence resonance energy transfer (FRET)-based single-molecule protein fingerprinting to map the location of individual amino acids and post-translational modifications within single full-length protein molecules. Our data show that both intrinsically disordered proteins and folded globular proteins can be fingerprinted with a subnanometer resolution, achieved by probing the amino acids one by one using single-molecule FRET via DNA exchange. This capability was demonstrated through the analysis of alpha-synuclein, an intrinsically disordered protein, by accurately quantifying isoforms in mixtures using a machine learning classifier, and by determining the locations of two O-GlcNAc moieties. Furthermore, we demonstrate fingerprinting of the globular proteins Bcl-2-like protein 1, procalcitonin and S100A9. We anticipate that our ability to perform proteoform identification with the ultimate sensitivity may unlock exciting new venues in proteomics research and biomarker-based diagnosis.

A comprehensive analysis of SARS-CoV-2 missense mutations indicates that all possible amino acid replacements in the viral proteins occurred within the first two-and-a-half years of the pandemic

The surveillance of COVID-19 pandemic has led to the determination of millions of genome sequences of the SARS-CoV-2 virus, with the accumulation of a wealth of information never collected before for an infectious disease. Exploring the information retrieved from the GISAID database reporting at that time >13 million genome sequences, we classified the 141,639 unique missense mutations detected in the first two-and-a-half years (up to October 2022) of the pandemic. Notably, our analysis indicates that 98.2 % of all possible conservative amino acid replacements occurred. Even non-conservative mutations were highly represented (73.9 %). For a significant number of residues (3 %), all possible replacements with the other nineteen amino acids have been observed. These observations strongly indicate that, in this time interval, the virus explored all possible alternatives in terms of missense mutations for all sites of its polypeptide chain and that those that are not observed severely affect SARS-CoV-2 integrity. The implications of the present findings go well beyond the structural biology of SARS-CoV-2 as the huge amount of information here collected and classified may be valuable for the elucidation of the sequence-structure-function relationships in proteins.

Automated preparation of nanoscopic structures: Graph-based sequence analysis, mismatch detection, and pH-consistent protonation with uncertainty estimates

Structure and function in nanoscale atomistic assemblies are tightly coupled, and every atom with its specific position and even every electron will have a decisive effect on the electronic structure, and hence, on the molecular properties. Molecular simulations of nanoscopic atomistic structures therefore require accurately resolved three-dimensional input structures. If extracted from experiment, these structures often suffer from severe uncertainties, of which the lack of information on hydrogen atoms is a prominent example. Hence, experimental structures require careful review and curation, which is a time-consuming and error-prone process. Here, we present a fast and robust protocol for the automated structure analysis and pH-consistent protonation, in short, ASAP. For biomolecules as a target, the ASAP protocol integrates sequence analysis and error assessment of a given input structure. ASAP allows for pK a  prediction from reference data through Gaussian process regression including uncertainty estimation and connects to system-focused atomistic modeling described in Brunken and Reiher (J. Chem. Theory Comput. 16, 2020, 1646). Although focused on biomolecules, ASAP can be extended to other nanoscopic objects, because most of its design elements rely on a general graph-based foundation guaranteeing transferability. The modular character of the underlying pipeline supports different degrees of automation, which allows for (i) efficient feedback loops for human-machine interaction with a low entrance barrier and for (ii) integration into autonomous procedures such as automated force field parametrizations. This facilitates fast switching of the pH-state through on-the-fly system-focused reparametrization during a molecular simulation at virtually no extra computational cost.

A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method

Efficient drug discovery relies on drug repurposing, an important and open research field. This work presents a novel factorization method and a practical comparison of different approaches for drug repurposing. First, we propose a novel tensor-matrix-tensor (TMT) formulation as a new data array method with a gradient-based factorization procedure. Additionally, this paper examines and contrasts four computational drug repurposing approaches-factorization-based methods, machine learning methods, deep learning methods, and graph neural networks-to fulfill the second purpose. We test the strategies on two datasets and assess each approach's performance, drawbacks, problems, and benefits based on results. The results demonstrate that deep learning techniques work better than other strategies and that their results might be more reliable. Ultimately, graph neural methods need to be in an inductive manner to have a reliable prediction.

Computational tools for plant genomics and breeding

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.