In silico-driven identification and experimental confirmation of antifungal proteins (AFPs) against Candida albicans.

Mycoses infect millions of people annually across the world. The most common mycosis agent, Candida albicans is responsible for a great deal of illness and death. C. albicans infection is becoming more widespread and the current antifungals polyenes, triazoles, and echinocandins are less efficient against it. Investigating antifungal peptides (AFPs) as therapeutic is gaining momentum. Therefore, we used MALDI-TOF/MS analysis to identify AFPs and protein-protein docking to analyze their interactions with the C. albicans target protein. Some microorganisms with strong antifungal action against C. albicans were selected for the isolation of AFPs. Using MALDI-TOF/MS, we identified 3 AFPs Chitin binding protein (ACW83017.1; Bacillus licheniformis), the bifunctional protein GlmU (BBQ13478.1; Stenotrophomonas maltophilia), and zinc metalloproteinase aureolysin (BBA25172.1; Staphylococcus aureus). These AFPs showed robust interactions with C. albicans target protein Sap5. We deciphered some important residues in identified APFs and highlighted interaction with Sap5 through hydrogen bonds, protein-protein interactions, and salt bridges using protein-protein docking and MD simulations. The three discovered AFPs-Sap5 complexes exhibit different levels of stability, as seen by the RMSD analysis and interaction patterns. Among protein-protein interactions, the remarkable stability of the BBQ25172.1-2QZX complex highlights the role of salt bridges and hydrogen bonds. Identified AFPs could be further studied for developing successful antifungal candidates and peptide-based new antifungal therapeutic strategies as fresh insights into addressing antifungal resistance also.

Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta.

The oligomerization of protein macromolecules on cell membranes plays a fundamental role in regulating cellular function. From modulating signal transduction to directing immune response, membrane proteins (MPs) play a crucial role in biological processes and are often the target of many pharmaceutical drugs. Despite their biological relevance, the challenges in experimental determination have hampered the structural availability of membrane proteins and their complexes. Computational docking provides a promising alternative to model membrane protein complex structures. Here, we present Rosetta-MPDock, a flexible transmembrane (TM) protein docking protocol that captures binding-induced conformational changes. Rosetta-MPDock samples large conformational ensembles of flexible monomers and docks them within an implicit membrane environment. We benchmarked this method on 29 TM-protein complexes of variable backbone flexibility. These complexes are classified based on the root-mean-square deviation between the unbound and bound states (RMSDUB) as: rigid (RMSDUB <1.2 Å), moderately-flexible (RMSDUB ∈ [1.2, 2.2) Å), and flexible targets (RMSDUB > 2.2 Å). In a local docking scenario, i.e. with membrane protein partners starting ≈10 Å apart embedded in the membrane in their unbound conformations, Rosetta-MPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked models) for 67% moderately flexible targets and 60% of the highly flexible targets, a substantial improvement from the existing membrane protein docking methods. Further, by integrating AlphaFold2-multimer for structure determination and using Rosetta-MPDock for docking and refinement, we demonstrate improved success rates over the benchmark targets from 64% to 73%. Rosetta-MPDock advances the capabilities for membrane protein complex structure prediction and modeling to tackle key biological questions and elucidate functional mechanisms in the membrane environment. The benchmark set and the code is available for public use at github.com/Graylab/MPDock.

Improving Fertility in Non-obstructive Azoospermia: Results from an Autologous Bone Mar-row-Derived Mesenchymal Stromal/Stem Cell Phase I Clinical Trial.

BACKGROUND: In this phase I clinical trial, our primary objective was to develop an innovative therapeutic approach utilizing autologous bone marrow-derived mesenchymal stromal/stem cells (BM-MSCs) for the treatment of nonobstructive azoospermia (NOA). Additionally, we aimed to assess the feasibility and safety of this approach. MATERIALS AND METHODS: We recruited 80 participants in this non-randomized, open-label clinical trial, including patients undergoing NOA treatment using autologous BM-MSCs (n=40) and those receiving hormone therapy as a control group (n=40). Detailed participant characteristics, such as age, baseline hormonal profiles, etiology of NOA, and medical history, were thoroughly documented. Autotransplantation of BM-MSCs into the testicular network was achieved using microsurgical testicular sperm extraction (microTESE). Semen analysis and hormonal assessments were performed both before and six months after treatment. Additionally, we conducted an in-silico analysis to explore potential protein-protein interactions between exosomes secreted from BM-MSCs and receptors present in human seminiferous tubule cells. RESULTS: Our results revealed significant improvements following treatment, including increased testosterone and inhibin B levels, elevated sperm concentration, and reduced levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), and prolactin. Notably, in nine patients (22.5%) previously diagnosed with secondary infertility and exhibiting azoospermia before treatment, the proposed approach yielded successful outcomes, as indicated by hormonal profile changes over six months. Importantly, these improvements were achieved without complications. Additionally, our in-silico analysis identified potential binding interactions between the protein content of BM-MSC-derived exosomes and receptors integral to spermatogenesis. CONCLUSION: Autotransplantation of BM-MSCs into the testicular network using microTESE in NOA patients led to the regeneration of seminiferous tubules and the regulation of hormonal profiles governing spermatogenesis. Our findings support the safety and effectiveness of autologous BM-MSCs as a promising treatment modality for NOA, with a particular focus on the achieved outcomes in patients with secondary infertility (registration number: IRCT20190519043634N1).

Assessment of Protein-Protein Docking Models Using Deep Learning.

Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.

Phosphorylation of GCN2 by mTOR confers adaptation to conditions of hyper-mTOR activation under stress.

Adaptation to the shortage in free amino acids (AA) is mediated by 2 pathways, the integrated stress response (ISR) and the mechanistic target of rapamycin (mTOR). In response to reduced levels, primarily of leucine or arginine, mTOR in its complex 1 configuration (mTORC1) is suppressed leading to a decrease in translation initiation and elongation. The eIF2α kinase general control nonderepressible 2 (GCN2) is activated by uncharged tRNAs, leading to induction of the ISR in response to a broader range of AA shortage. ISR confers a reduced translation initiation, while promoting the selective synthesis of stress proteins, such as ATF4. To efficiently adapt to AA starvation, the 2 pathways are cross-regulated at multiple levels. Here we identified a new mechanism of ISR/mTORC1 crosstalk that optimizes survival under AA starvation, when mTORC1 is forced to remain active. mTORC1 activation during acute AA shortage, augmented ATF4 expression in a GCN2-dependent manner. Under these conditions, enhanced GCN2 activity was not dependent on tRNA sensing, inferring a different activation mechanism. We identified a labile physical interaction between GCN2 and mTOR that results in a phosphorylation of GCN2 on serine 230 by mTOR, which promotes GCN2 activity. When examined under prolonged AA starvation, GCN2 phosphorylation by mTOR promoted survival. Our data unveils an adaptive mechanism to AA starvation, when mTORC1 evades inhibition.

Approaches to Backbone Flexibility in Protein-Protein Docking.

Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.

Sampling and Scoring in Protein-Protein Docking.

Protein-protein docking is considered one of the most important techniques supporting experimental proteomics. Recent developments in the field of computer science helped to improve this computational technique so that it better handles the complexity of protein nature. Sampling algorithms are responsible for the generation of numerous protein-protein ensembles. Unfortunately, a primary docking output comprises a set of both near-native poses and decoys. Application of the efficient scoring function helps to differentiate poses with the most favorable properties from those that are very unlikely to represent a natural state of the complex. This chapter explains the importance of sampling and scoring in the process of protein-protein docking. Moreover, it summarizes advances in the field.

From Multiple Protein Docking to Protein-Protein Docking at Interactome Level.

Molecular docking is used to anticipate the optimal orientation of a particular molecule to a target to form a stable complex. It makes predictions about the 3D structure of any complex based on the binding characteristics of the ligand and the target receptor usually a protein. It is an exceptionally useful tool, which is used as a model to study how ligands attach to proteins. Docking can also be used for studying the interaction of ligands and proteins to analyze inhibitory efficacy. The ligand may also be a protein, making it possible to study interactions between two different proteins using the numerous docking tools available for basic research on protein interactions. The protein-protein docking is a crucial approach to understanding the protein interactions and predicting the structure of protein complexes that have not yet been experimentally determined. Moreover, the protein-protein interactions can predict the function of target proteins and the drug-like properties of molecules. Therefore, protein docking assists in uncovering insights into protein interactions and also aids in a better understanding of molecular pathways/mechanisms. This chapter comprehends the various tools for protein-protein docking (pairwise and multiple), including their methodologies and analysis of output as results.

Docking Foundations: From Rigid to Flexible Docking.

Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods. The latter derive from techniques originally published and applied almost half a century ago and are based on interdisciplinary knowledge from the nexus of the fields of biology, chemistry, and physics about protein sequences, structures, and their folding. Protein-protein docking, the main protagonist of this chapter, is routinely treated as an integral part of protein research. Herein, we describe the basic foundations of the whole process in general terms, but step by step from protein representations through docking methods and evaluation of complexes to their final validation.

Software and Databases for Protein-Protein Docking.

Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking.

Machine Learning Methods in Protein-Protein Docking.

An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field. However, despite the rapid development of technology and the progress in developing new approaches, many aspects remain challenging to solve, such as predicting conformational changes in proteins, or more "trivial" issues as high-quality data in huge quantities.Therefore, this chapter focuses on a short introduction to various AI approaches to study protein-protein interactions, followed by a description of the most up-to-date algorithms and programs used for this purpose. Yet, given the considerable pace of development in this hot area of computational science, at the time you read this chapter, the development of the algorithms described, or the emergence of new (and better) ones should come as no surprise.

Molecular Dynamics Simulations in Protein-Protein Docking.

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

Challenges of Protein-Protein Docking of the Membrane Proteins.

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

Computational Methods to Target Protein-Protein Interactions.

The chapter emphasizes the importance of understanding protein-protein interactions in cellular mechanisms and highlights the role of computational modeling in predicting these interactions. It discusses sequence-based approaches such as evolutionary trace (ET), correlated mutation analysis (CMA), and subtractive correlated mutation (SCM) for identifying crucial amino acid residues, considering interface conservation or evolutionary changes. The chapter also explores methods like differential ET, hidden-site class model, and spatial cluster detection (SCD) for interface specificity and spatial clustering. Furthermore, it examines approaches combining structural and sequential methodologies and evaluates modeled predictions through initiatives like critical assessment of prediction of interactions (CAPRI). Additionally, the chapter provides an overview of various software programs used for molecular docking, detailing their search, sampling, refinement and scoring stages, along with innovative techniques and tools like normal mode analysis (NMA) and adaptive Poisson-Boltzmann solver (APBS) for electrostatic calculations. These computational and experimental approaches are crucial for unraveling protein-protein interactions and aid in developing potential therapeutics for various diseases.

Refinement of Docked Protein-Protein Complexes Using Repulsive Scaling Replica Exchange Simulations.

Accurate prediction and evaluation of protein-protein complex structures is of major importance to understand the cellular interactome. Predicted complex structures based on deep learning approaches or traditional docking methods require often structural refinement and rescoring for realistic evaluation. Standard molecular dynamics (MD) simulations are time-consuming and often do not structurally improve docking solutions. Better refinement can be achieved with our recently developed replica-exchange-based scheme employing different levels of repulsive biasing between proteins in each replica simulation (RS-REMD). The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii without changing interactions within each protein or with the solvent. It allows for an improvement of the predicted protein-protein complex structure and simultaneous realistic free energy scoring of protein-protein complexes. The setup of RS-REMD simulations is described in detail including the application on two examples (all necessary scripts and input files can be obtained from https://gitlab.com/TillCyrill/mmib ).

Protein-Protein Docking Approach to GPCR Oligomerization.

G-protein-coupled receptors (GPCRs), the largest family of human membrane proteins, play a crucial role in cellular control and are the target of approximately one-third of all drugs on the market. Targeting these complexes with selectivity or formulating small molecules capable of modulating receptor-receptor interactions could potentially offer novel avenues for drug discovery, fostering the development of more refined and safer pharmacotherapies. Due to the lack of experimentally derived X-ray crystallography spectra of GPCR oligomers, there is growing evidence supporting the development of new in silico approaches for predicting GPCR self-assembling structures. The significance of GPCR oligomerization, the challenges in modeling these structures, and the potential of protein-protein docking algorithms to address these challenges are discussed. The study also underscores the use of various software solutions for modeling GPCR oligomeric structures and presents practical cases where these techniques have been successfully applied.

LC-MS/MS profiling and analysis of Bacillus licheniformis extracellular proteins for antifungal potential against Candida albicans.

Candida albicans, a significant human pathogenic fungus, employs hydrolytic proteases for host invasion. Conventional antifungal agents are reported with resistance issues from around the world. This study investigates the role of Bacillus licheniformis extracellular proteins (ECP) as effective antifungal peptides (AFPs). The aim was to identify and characterize the ECP of B. licheniformis through LC-MS/MS and bioinformatics analysis. LC-MS/MS analysis identified 326 proteins with 69 putative ECP, further analyzed in silico. Of these, 21 peptides exhibited antifungal properties revealed by classAMP tool and are predominantly anionic. Peptide-protein docking revealed interactions between AFPs like Peptide chain release factor 1 (Q65DV1_Seq1: SASEQLSDAK) and Putative carboxy peptidase (Q65IF0_Seq7: SDSSLEDQDFILESK) with C. albicans virulent SAP5 proteins (PDB ID 2QZX), forming hydrogen bonds and significant Pi-Pi interactions. The identification of B. licheniformis ECP is the novelty of the study that sheds light on their antifungal potential. The identified AFPs, particularly those interacting with bonafide pharmaceutical targets SAP5 of C. albicans represent promising avenues for the development of antifungal treatments with AFPs that could be the pursuit of a novel therapeutic strategy against C. albicans. SIGNIFICANCE OF STUDY: The purpose of this work was to carry out proteomic profiling of the secretome of B. licheniformis. Previously, the efficacy of Bacillus licheniformis extracellular proteins against Candida albicans was investigated and documented in a recently communicated manuscript, showcasing the antifungal activity of these proteins. In order to achieve high-throughput identification of ES (Excretory-secretory) proteins, the utilization of liquid chromatography tandem mass spectrometry (LC-MS) was utilized. There was a lack of comprehensive research on AFPs in B. licheniformis, nevertheless. The proteins secreted by B. licheniformis in liquid medium were initially discovered using liquid chromatography-tandem mass spectrometry (LC-MS) analysis and identification in order to immediately characterize the unidentified active metabolites in fermentation broth.

Molecular docking analysis of α-Synuclein aggregation with Anle138b.

α-Synuclein aggregation into toxic oligomeric species is central to Parkinson's disease pathogenesis. Anle138b is a recently identified inhibitor of α-synuclein oligomerization showing promise in preclinical studies. This study employed computational approaches to elucidate Anle138b's mechanism of oligomer-specific action. The inhibitory potential of Anle138b against α-synuclein oligomers was evaluated by performing molecular docking studies using AutoDock Tools, followed by their binding pocket analysis. Further, protein-protein docking studies were performed using Hex8.0 to validate the aggregation inhibitory potential of Anle138b. Molecular docking revealed increasing binding affinity of Anle138b against higher order α-synuclein oligomers (dimer to decamer). Anle138b occupied oligomeric cavity and interacted with residues Thr54, Gly73, Val74 and Thr75 across several oligomers. Protein-protein docking showed that Anle138b interferes with α-synuclein decamer formation. These results highlight the oligomer-directed inhibitory mechanism of Anle138b, without hindering the monomeric forms and provide molecular insights to advance its therapeutic development for Parkinson's and related synucleinopathies.

SurfPro-NN: A 3D point cloud neural network for the scoring of protein-protein docking models based on surfaces features and protein language models.

Protein-protein interactions (PPI) play a crucial role in numerous key biological processes, and the structure of protein complexes provides valuable clues for in-depth exploration of molecular-level biological processes. Protein-protein docking technology is widely used to simulate the spatial structure of proteins. However, there are still challenges in selecting candidate decoys that closely resemble the native structure from protein-protein docking simulations. In this study, we introduce a docking evaluation method based on three-dimensional point cloud neural networks named SurfPro-NN, which represents protein structures as point clouds and learns interaction information from protein interfaces by applying a point cloud neural network. With the continuous advancement of deep learning in the field of biology, a series of knowledge-rich pre-trained models have emerged. We incorporate protein surface representation models and language models into our approach, greatly enhancing feature representation capabilities and achieving superior performance in protein docking model scoring tasks. Through comprehensive testing on public datasets, we find that our method outperforms state-of-the-art deep learning approaches in protein-protein docking model scoring. Not only does it significantly improve performance, but it also greatly accelerates training speed. This study demonstrates the potential of our approach in addressing protein interaction assessment problems, providing strong support for future research and applications in the field of biology.

The mitochondrial carrier homolog 2 is involved in down-regulation of influenza A virus replication.

BACKGROUND: The mitochondrial carrier homolog 2 (MTCH2) is a mitochondrial outer membrane protein regulating mitochondrial metabolism and functions in lipid homeostasis and apoptosis. Experimental data on the interaction of MTCH2 with viral proteins in virus-infected cells are very limited. Here, the interaction of MTCH2 with PA subunit of influenza A virus RdRp and its effects on viral replication was investigated. METHODS: The human MTCH2 protein was identified as the influenza A virus PA-related cellular factor with the Y2H assay. The interaction between GST.MTCH2 and PA protein co-expressed in transfected HEK293 cells was evaluated by GST-pull down. The effect of MTCH2 on virus replication was determined by quantification of viral transcript and/or viral proteins in the cells transfected with MTCH2-encoding plasmid or MTCH2-siRNA. An interaction model of MTCH2 and PA was predicted with protein modeling/docking algorithms. RESULTS: It was observed that PA and GST.MTCH2 proteins expressed in HEK293 cells were co-precipitated by glutathione-agarose beads. The influenza A virus replication was stimulated in HeLa cells whose MTCH2 expression was suppressed with specific siRNA, whereas the increase of MTCH2 in transiently transfected HEK293 cells inhibited viral RdRp activity. The results of a Y2H assay and protein-protein docking analysis suggested that the amino terminal part of the viral PA (nPA) can bind to the cytoplasmic domain comprising amino acid residues 253 to 282 of the MTCH2. CONCLUSION: It is suggested that the host mitochondrial MTCH2 protein is probably involved in the interaction with the viral polymerase protein PA to cause negative regulatory effect on influenza A virus replication in infected cells.