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Biomedical research benefits from the rapid growth and diversity of experimentally detected protein–protein interactions (PPIs) by gaining important biological insights. However, increasingly dense PPI networks can be challenging to interpret and apply. The 2025 update of the Integrated Interactions Database (IID) enhances accessibility and utility through several new features. We identify and incorporate network structural components from co-purified protein sets, as well as curated and predicted complexes, enabling users to explore network organization Show less

Proteomics has matured into a discipline capable of quantifying nearly every protein encoded by the genome, yet it remains largely blind to the true operational units of physiology: proteoforms. Each proteoform—defined by a specific sequence and post-translationally modified state—represents a unique molecular identity with distinct chemical, functional, and structural properties. This review proposes the proteoform functor: a mathematical map between the abstract proteoform state space and the realised physiological space of biological function—and ultimately complex phenotypes. Show less

Transcription-coupled repair (TCR) is a vital nucleotide excision repair sub-pathway that removes DNA lesions from actively transcribed DNA strands. Binding of CSB to lesion-stalled RNA Polymerase II (Pol II) initiates TCR by triggering the recruitment of downstream repair factors. Yet it remains unknown how transcription factor IIH (TFIIH) is recruited to the intact TCR complex. Combining existing structural data with AlphaFold predictions, we build an integrative model of the initial TFIIH-bound TCR complex. We show how TFIIH can be first recruited in an open repair-inhibited conformation, which requires subsequent CAK module removal and conformational closure to process damaged DNA. In our model, CSB, CSA, UVSSA, elongation factor 1 (ELOF1), and specific Pol II and UVSSA-bound ubiquitin moieties come together to provide interaction interfaces needed for TFIIH recruitment. STK19 acts as a linchpin of the assembly, orienting the incoming TFIIH and bridging Pol II to core TCR factors and DNA. Molecular simulations of the TCR-associated CRL4CSA ubiquitin ligase complex unveil the interplay of segmental DDB1 flexibility, continuous Cullin4A flexibility, and the key role of ELOF1 for Pol II ubiquitination that enables TCR. Collectively, these findings elucidate the coordinated assembly of repair proteins in early TCR. Show less

Deep learning, a cornerstone of artificial intelligence, is driving rapid advancements in computational biology. Protein-protein interactions (PPIs) are fundamental regulators of biological functions. With the inclusion of deep learning in PPI research, the field is undergoing transformative changes. Therefore, there is an urgent need for a comprehensive review and assessment of recent developments to improve analytical methods and open up a wider range of biomedical applications. This review meticulously assesses deep learning progress in PPI prediction from 2021 Show less

Motivation: Proteins are of great significance in living organisms. However, understanding their functions encounters numerous challenges, such as insufficient integration of multimodal information, a large number of training parameters, limited flexibility of classification-based methods, and the lack of systematic evaluation metrics for protein question answering systems. To tackle these issues, we propose the Prot2Chat framework. Results: We modified ProteinMPNN to encode protein sequence and structural information in a unified way. We used a large language model Show less

Claudin (CLDN) proteins are extensively studied due to their critical role in maintaining tissue barriers and cell polarity. However, significant gaps remain in understanding the functional mechanisms of their sequence motifs and the molecular mechanisms of their interactions with other tight junction proteins. This review systematically examines the multifunctional properties of the CLDN protein family from the perspectives of sequence and structure. During evolution, CLDN family members have developed highly conserved structural features, particularly key conserved sites within the first Show less

Predicting protein‒protein interactions (PPIs) plays a crucial role in understanding biological processes. Although biological experimental methods can identify PPIs, they are costly, time-consuming, labor-intensive, and often lack stability. In contrast, computational approaches for PPI prediction, particularly deep learning methods, can efficiently learn representations from protein sequences. However, the generalizability, robustness, and stability of computational PPI prediction models still need improvement, especially for species with limited verified PPI Show less

Computational drug discovery is essential for screening potential treatments and reducing the costs and time associated with proposing or combining drugs for disease management. Despite the extensive research conducted in this field, it remains an emerging area, particularly with the advent of machine learning, deep learning, and large language models (LLMs). This systematic review examines the integration of machine learning and deep learning techniques in drug discovery, concentrating on three critical areas: drug−drug interactions (DDIs), drug-target interactions (DTIs), and adverse drug reactions (ADRs). The review analyzes over 100 papers published between 2020 and 2025, categorizing the methods into deep learning, machine learning, graph learning, and hybrid models. It highlights the transformative impact of natural language processing (NLP) and LLMs in extracting meaningful insights from biomedical literature and chemical data. Furthermore, this work introduces key databases and data sets widely utilized in drug discovery. Additionally, this review identifies gaps in the existing research, such as the lack of comprehensive studies that simultaneously address DDI, DTI, and ADR extraction, and it proposes a more holistic approach to fill these gaps. The paper concludes by thoroughly evaluating various models, underscoring their performance metrics. Show less

One of the major challenges in precision oncology is the identification of pathogenic, actionable variants and the selection of personalized treatments. We present Onkopus, a variant interpretation framework based on a modular architecture, for interpreting and prioritizing genetic alterations in cancer patients. A multitude of tools and databases are integrated into Onkopus to provide a comprehensive overview about the consequences of a variant, each with its own semantic, including pathogenicity predictions, allele frequency, biochemical and protein features, Show less

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases. Show less

Motivation: Drug–target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could Show less

Background: Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. ARTICLE HISTORY Objective: This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. Received: June 07, 2024 Revised: August 11, 2024 Accepted: August 19, 2024 Methods: We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literaDOI: 10.2174/0113894501330963240905083020 ture. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. Results: The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. Conclusion: The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process. Show less

Hydrogen sulfide (H2S) played a pivotal role in the early evolution of life on Earth before the predominance of atmospheric oxygen. The legacy of a persistent role for H2S in life's processes recently emerged through its discovery in modern biochemistry as an endogenous cellular signalling modulator involved in numerous biological processes. One major mechanism through which H2S signals is protein cysteine persulfidation, an oxidative post-translational modification. In recent years, chemoproteomic technologies have been developed to allow the global scanning of protein persulfidation targets in mammalian cells and tissues, providing a powerful tool to elucidate the broader impact of altered H2S in organismal physiological health and human disease states. While hundreds of proteins were confirmed to be persulfidated by global persulfidome methodologies, the targeting of specific proteins of interest and the investigation of further mechanistic studies are still underdeveloped due to a lack of stringent specificity of the methods and the inherent instability of persulfides. This review provides an overview of the processes of endogenous H2S production, oxidation, and signalling and highlights the application and limitations of current persulfidation labelling approaches for investigation of this important evolutionarily conserved biological switch for protein function. Show less

Authors Di Zhou, Qing Yu, Roel C. Janssens, Jurgen A. Marteijn Correspondence J.Marteijn@erasmusmc.nl In brief Zhou et al. generate cells with knockin fluorescent labeling of transcriptioncoupled repair proteins CSB and UVSSA. These tools enable fluorescence recovery after photobleaching (FRAP) studies to quantify transcription-blocking DNA damage and its repair in living cells. Highlights d CRISPR-mediated, fluorescent tagging of endogenous TCNER pathway proteins d CSB mobility determined by FRAP is a sensitive marker for Show less

Nucleophosmin (NPM1) is a key nucleolar protein released from the nucleolus in response to stress stimuli. NPM1 functions as a stress regulator with nucleic acid and protein chaperone activities, rapidly shuttling between the nucleus and cytoplasm. NPM1 is ubiquitously expressed in tissues and can be found in the nucleolus, nucleoplasm, cytoplasm, and extracellular environment. It plays a central role in various biological processes such as ribosome biogenesis, cell cycle regulation, cell proliferation, DNA damage repair, and apoptosis. In addition, it is highly expressed in cancer cells and solid tumors, and its mutation is a major cause of acute myeloid leukemia (AML). This review focuses on NPM1's structural features, functional diversity, subcellular distribution, and role in stress modulation. Show less

Motivation: Thousands of genomes are publicly available, however, most genes in those genomes have poorly defined functions. This is partly due to a gap between previously published, experimentally characterized protein activities and activities deposited in databases. This activity de­ position is bottlenecked by the time-consuming biocuration process. The emergence of large language models presents an opportunity to speed up the text-mining of protein activities for biocuration. Results: We developed FuncFetch—a workflow that integrates NCBI E-Utilities, OpenAI’s GPT-4, and Zotero—to screen thousands of manu­ Show less

Human Replication Protein A (RPA) was historically discovered as one of the six components needed to reconstitute simian virus 40 DNA replication from purified components. RPA is now known to be involved in all DNA metabolism pathways that involve single-stranded DNA (ssDNA). Heterotrimeric RPA comprises several domains connected by flexible linkers and is heavily regulated by post-translational modifications (PTMs). The structure of RPA has been challenging to obtain. Various structural methods have been applied, but a complete understanding of RPA's flexible structure, its function, and how it is regulated by PTMs has yet to be obtained. This review will summarize recent literature concerning how RPA is phosphorylated in the cell cycle, the structural analysis of RPA, DNA and protein interactions involving RPA, and how PTMs regulate RPA activity and complex formation in double-strand break repair. There are many holes in our understanding of this research area. We will conclude with perspectives for future research on how RPA PTMs control double-strand break repair in the cell cycle. Show less

Most cancer patients ultimately die from the consequences of distant metastases. As metastasis formation consumes energy mitochondria play an important role during this process as they are the most important cellular organelle to synthesise the energy rich substrate ATP, which provides the necessary energy to enable distant metastasis forma‑ tion. However, mitochondria are also important for the execution of apoptosis, a process which limits metastasis formation. We therefore wanted to investigate the mitochondrial content in ovarian cancer cells and link its pres‑ Show less

RNA-binding proteins (RBPs) play crucial roles in the functions and homoeostasis of various tissues by regulating multiple events of RNA processing including RNA splicing, intracellular RNA transport, and mRNA translation. The Drosophila behavior and human splicing (DBHS) family proteins including PSF/ SFPQ, NONO, and PSPC1 are ubiquitously expressed RBPs that contribute to the physiology of several tissues. In mammals, DBHS proteins have been reported to contribute to neurological diseases and play Show less

Proteins and their assemblies are fundamental for living cells to function. Their complex three-dimensional architecture and its stability are attributed to the combined effect of various noncovalent interactions. It is critical to scrutinize these noncovalent interactions to understand their role in the energy landscape in folding, catalysis, and molecular recognition. This Review presents a comprehensive summary of unconventional noncovalent interactions, beyond conventional hydrogen bonds and hydrophobic interactions, which have gained prominence over the past decade. The noncovalent interactions discussed include low-barrier hydrogen bonds, C5 hydrogen bonds, C-H···π interactions, sulfur-mediated hydrogen bonds, n → π* interactions, London dispersion interactions, halogen bonds, chalcogen bonds, and tetrel bonds. This Review focuses on their chemical nature, interaction strength, and geometrical parameters obtained from X-ray crystallography, spectroscopy, bioinformatics, and computational chemistry. Also highlighted are their occurrence in proteins or their complexes and recent advances made toward understanding their role in biomolecular structure and function. Probing the chemical diversity of these interactions, we determined that the variable frequency of occurrence in proteins and the ability to synergize with one another are important not only for ab initio structure prediction but also to design proteins with new functionalities. A better understanding of these interactions will promote their utilization in designing and engineering ligands with potential therapeutic value. Show less

Apoptosis is a form of regulated cell death (RCD) that involves proteases of the caspase family. Pharmacological and genetic strategies that experimentally inhibit or delay apoptosis in mammalian systems have elucidated the key contribution of this process not only to (post-)embryonic development and adult tissue homeostasis, but also to the etiology of multiple human disorders. Consistent with this notion, while defects in the molecular machinery for apoptotic cell death impair organismal development and promote oncogenesis, the unwarranted activation of apoptosis promotes cell loss and tissue damage in the context of various neurological, cardiovascular, renal, hepatic, infectious, neoplastic and inflammatory conditions. Here, the Nomenclature Committee on Cell Death (NCCD) gathered to critically summarize an abundant pre-clinical literature mechanistically linking the core apoptotic apparatus to organismal homeostasis in the context of disease. Show less

Screening new drug-target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based-DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework. Show less

Motivation: Screening new drug–target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based-DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework. Results: We developed MDTips, a multimodal-data-based DTI prediction system, by fusing the knowledge graphs, gene expression profiles, and Show less

Transition metal elements, such as copper, play diverse and pivotal roles in oncology. They act as constituents of metalloenzymes involved in cellular metabolism, function as signaling molecules to regulate the proliferation and metastasis of tumors, and are integral components of metal-based anticancer drugs. Notably, recent research reveals that excessive copper can also modulate the occurrence of programmed cell death (PCD), known as cuprotosis, in cancer cells. This modulation occurs through the disruption of tumor cell metabolism and the induction of proteotoxic stress. This discovery uncovers a mode of interaction between transition metals and proteins, emphasizing the intricate link between copper homeostasis and tumor metabolism. Moreover, they provide innovative therapeutic strategies for the precise diagnosis and treatment of malignant tumors. At the crossroads of chemistry and oncology, we undertake a comprehensive review of copper homeostasis in tumors, elucidating the molecular mechanisms underpinning cuproptosis. Additionally, we summarize current nanotherapeutic approaches that target cuproptosis and provide an overview of the available laboratory and clinical methods for monitoring this process. In the context of emerging concepts, challenges, and opportunities, we emphasize the significant potential of nanotechnology in the advancement of this field. Show less

During the COVID-19 pandemic, the structural biology community swung into action quickly and efficiently, and many urgent questions were solved by macromolecular structure determination. The Coronavirus Structural Task Force evaluated all structures from SARS-CoV-1 and SARS-CoV-2, but errors in measurement, data processing and modelling are present beyond these structures and throughout the structures deposited in the Protein Data Bank. Identifying them is only the first step; in order to minimize the impact that errors have in structural biology, error culture needs to change. It should be emphasized that the atomic model which is published is an interpretation of the measurement. Furthermore, risks should be minimized by addressing issues early and by investigating the source of a given problem, so that it may be avoided in the future. If we as a community can do this, it will greatly benefit experimental structural biologists as well as downstream users who are using structural models to deduce new biological and medical answers in the future. Show less

The robustness of NMR coherence transfer in proximity of a paramagnetic center depends on the relaxation properties of the nuclei involved. In the case of Iron-Sulfur Proteins, different pulse schemes or different parameter sets often provide complementary results. Tailored versions of HCACO and CACO experiments significantly increase the number of observed ­Cα/C’ connectivities in highly paramagnetic systems, by recovering many resonances that were lost due to paramagnetic relaxation. Optimized 13C direct detected experiments can significantly extend the available assignments, improving the Show less

Cells tend to disintegrate themselves or are forced to undergo such destructive processes in critical circumstances. This complex cellular function necessitates various mechanisms and molecular pathways in order to be executed. The very nature of cell death is essentially important and vital for maintaining homeostasis, thus any type of disturbing occurrence might lead to different sorts of diseases and dysfunctions. Cell death has various modalities and yet, every now and then, a new type of this elegant procedure gets to be discovered. The diversity of cell death compels the need for a universal organizing system in order to facilitate further studies, therapeutic strategies and the invention of new methods of research. Considering all that, we attempted to review most of the known cell death mechanisms and sort them all into one arranging system that operates under a simple but subtle decision-making (If \ Else) order as a sorting algorithm, in which it decides to place and sort an input data (a type of cell death) into its proper set, then a subset and finally a group of cell death. By proposing this algorithm, the authors hope it may solve the problems regarding newer and/or undiscovered types of cell death and facilitate research and therapeutic applications of cell death. Show less

Ligand substitution at the metal center is common in catalysis and signal transduction of metalloproteins. Understanding the effects of particular ligands, as well as the polypeptide surrounding, is critical for uncovering mechanisms of these biological processes and exploiting them in the design of bioinspired catalysts and molecular devices. A series of switchable K79G/M80X/F82C (X = Met, His, or Lys) variants of cytochrome (cyt) c was employed to directly compare the stability of differently ligated proteins and activation barriers for Met, His, and Lys replacement at the ferric heme iron. Studies of these variants and their nonswitchable counterparts K79G/M80X have revealed stability trends Met < Lys < His and Lys < His < Met for the protein Fe^III-X and Fe^II-X species, respectively. The differences in the hydrogen-bonding interactions in folded proteins and in solvation of unbound X in the unfolded proteins explain these trends. Calculations of free energy of ligand dissociation in small heme model complexes reveal that the ease of the Fe^III-X bond breaking increases in the series amine < imidazole < thioether, mirroring trends in hardness of these ligands. Experimental rate constants for X dissociation in differently ligated cyt c variants are consistent with this sequence, but the differences between Met and His dissociation rates are attenuated because the former process is limited by the heme crevice opening. Analyses of activation parameters and comparisons to those for the Lys-to-Met ligand switch in the alkaline transition suggest that ligand dissociation is entropically driven in all the variants and accompanied by Lys protonation at neutral pH. The described thiolate redox-linked switches have offered a wealth of new information about interactions of different protein-derived ligands with the heme iron in cyt c model proteins, and we anticipate that the strategy of employing these switches could benefit studies of other redox metalloproteins and model complexes. Show less

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug–target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models. Academic Editors: Kyriakos Kachrimanis, David Barlow, Jakub Show less