Applications of Biomolecular Nanostructures for Anti-Angiogenic Theranostics.

Angiogenesis is a physiological process of forming new blood vessels that has pathological importance in seemingly unrelated illnesses like cancer, diabetes, and various inflammatory diseases. Treatment targeting angiogenesis has shown promise for these types of diseases, but current anti-angiogenic agents have critical limitations in delivery and side-effects. This necessitates exploration of alternative approaches like biomolecule-based drugs. Proteins, lipids, and oligonucleotides have recently become popular in biomedicine, specifically as biocompatible components of therapeutic drugs. Their excellent bioavailability and potential bioactive and immunogenic properties make them prime candidates for drug discovery or drug delivery systems. Lipid-based liposomes have become standard vehicles for targeted nanoparticle (NP) delivery, while protein and nucleotide NPs show promise for environment-sensitive delivery as smart NPs. Their therapeutic applications have initially been hampered by short circulation times and difficulty of fabrication but recent developments in nanofabrication and NP engineering have found ways to circumvent these disadvantages, vastly improving the practicality of biomolecular NPs. In this review, we are going to briefly discuss how biomolecule-based NPs have improved anti-angiogenesis-based therapy.

Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning.

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.

Comprehensive encoding of conformational and compositional protein structural ensembles through the mmCIF data structure.

In the folded state, biomolecules exchange between multiple conformational states crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo-electron microscopy. Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the macromolecular crystallographic information file (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable the capture of macromolecular ensembles in a human and machine-interpretable way, potentially catalyzing breakthroughs for ensemble-function predictions, analogous to the achievements of AlphaFold with single-structure prediction.

In some cases more complicated approaches to refinement of macromolecular structures are not necessary.

The manuscript `Modeling a unit cell: crystallographic refinement procedure using the biomolecular MD simulation platform Amber' presents a novel protein structure refinement method claimed to offer improvements over traditional techniques like Refmac5 and Phenix. Our re-evaluation suggests that while the new method provides improvements, traditional methods achieve comparable results with less computational effort.

Protein Gas Vesicles of Bacillus megaterium as Enhancers of Ultrasound-Induced Transcriptional Regulation.

Gas vesicles (GVs) are large cylindrical gas-filled protein assemblies found in diverse aquatic bacteria that enable their adaptation of buoyancy. GVs have already been used as ultrasound contrasting agents. Here, we investigate GVs derived from Bacillus megaterium, aiming to minimize the number of accessory Gvps within the GV gene cluster and demonstrate the use of GVs as enhancers of acoustic radiation force administered by ultrasound. Three (GvpR, GvpT, and GvpU) out of 11 genes in the cluster were found to be dispensable for functional GV formation, and their omission resulted in narrower GVs. Two essential proteins GvpJ and GvpN were absent from recently determined GV structures, but GvpJ was nevertheless found to be tightly bound to the cylindrical part of GVs in this study. Additionally, the N-terminus of GvpN was observed to play an important role in the formation of mature GVs. The binding of engineered GvpC fromAnabaena flos-aquae to HEK293 cells via integrins enhanced the acoustic force delivered by ultrasound and resulted in an increased Ca2+ influx into cells. Coupling with a synthetic Ca2+-dependent signaling pathway GVs efficiently enhanced cell stimulation by ultrasound, which expands the potentials of noninvasive sonogenetics cell stimulation.

Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment.

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid-base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

AI-Assisted Processing Pipeline to Boost Protein Isoform Detection.

Proteomics, the study of proteins within biological systems, has seen remarkable advancements in recent years, with protein isoform detection emerging as one of the next major frontiers. One of the primary challenges is achieving the necessary peptide and protein coverage to confidently differentiate isoforms as a result of the protein inference problem and protein false discovery rate estimation challenge in large data. In this chapter, we describe the application of artificial intelligence-assisted peptide property prediction for database search engine rescoring by Oktoberfest, an approach that has proven effective, particularly for complex samples and extensive search spaces, which can greatly increase peptide coverage. Further, it illustrates a method for increasing isoform coverage by the PickedGroupFDR approach that is designed to excel when applied on large data. Real-world examples are provided to illustrate the utility of the tools in the context of rescoring, protein grouping, and false discovery rate estimation. By implementing these cutting-edge techniques, researchers can achieve a substantial increase in both peptide and isoform coverage, thus unlocking the potential of protein isoform detection in their studies and shedding light on their roles and functions in biological processes.

Demystifying PTM Identification Using MODplus: Best Practices and Pitfalls.

Tandem mass spectrometry (MS/MS) facilitates the rapid identification of posttranslational modifications (PTMs), which play a pivotal role in regulating numerous biological processes. This chapter explores recent advancements that expand the types of detectable PTMs and enhance the speed of the PTM searches. We also delve into computational challenges associated with searching for a multitude of PTMs simultaneously. The latter section introduces an automated procedure to identify an extensive range of PTMs using MODplus, a free PTM analysis software tool. We guide the reader through the preparation of the modification search, the determination of optional search parameters, the execution of the search, and the analysis of results, exemplified by a case study using specific MS/MS dataset.

Detection and Analysis of Short Linear Motif-Based Protein-Protein Interactions with SLiMAn2 Web Server.

Interactomics is bringing a deluge of data regarding protein-protein interactions (PPIs) which are involved in various molecular processes in all types of cells. However, this information does not easily translate into direct and precise molecular interfaces. This limits our understanding of each interaction network and prevents their efficient modulation. A lot of the detected interactions involve recognition of short linear motifs (SLiMs) by a folded domain while others rely on domain-domain interactions. Functional SLiMs hide among a lot of spurious ones, making deeper analysis of interactomes tedious. Hence, actual contacts and direct interactions are difficult to identify.Consequently, there is a need for user-friendly bioinformatic tools, enabling rapid molecular and structural analysis of SLiM-based PPIs in a protein network. In this chapter, we describe the use of the new webserver SLiMAn to help digging into SLiM-based PPIs in an interactive fashion.

Analysis and Visualization of Protein Channels, Tunnels, and Pores with MOLEonline and ChannelsDB 2.0.

Channels, tunnels, and pores serve as pathways for the transport of molecules and ions through protein structures, thus participating to their functions. MOLEonline ( https://mole.upol.cz ) is an interactive web-based tool with enhanced capabilities for detecting and characterizing channels, tunnels, and pores within protein structures. MOLEonline has two distinct calculation modes for analysis of channel and tunnels or transmembrane pores. This application gives researchers rich analytical insights into channel detection, structural characterization, and physicochemical properties. ChannelsDB 2.0 ( https://channelsdb2.biodata.ceitec.cz/ ) is a comprehensive database that offers information on the location, geometry, and physicochemical characteristics of tunnels and pores within macromolecular structures deposited in Protein Data Bank and AlphaFill databases. These tunnels are sourced from manual deposition from literature and automatic detection using software tools MOLE and CAVER. MOLEonline and ChannelsDB visualization is powered by the LiteMol Viewer and Mol* viewer, ensuring a user-friendly workspace. This chapter provides an overview of user applications and usage.

Mutually Orthogonal Bioorthogonal Reactions: Selective Chemistries for Labeling Multiple Biomolecules Simultaneously.

Bioorthogonal click chemistry has played a transformative role in many research fields, including chemistry, biology, and medicine. Click reactions are crucial to produce increasingly complex bioconjugates, to visualize and manipulate biomolecules in living systems and for various applications in bioengineering and drug delivery. As biological (model) systems grow more complex, researchers have an increasing need for using multiple orthogonal click reactions simultaneously. In this review, we will introduce the most common bioorthogonal reactions and discuss their orthogonal use on the basis of their mechanism and electronic or steric tuning. We provide an overview of strategies to create reaction orthogonality and show recent examples of mutual orthogonal chemistry used for simultaneous biomolecule labeling. We end by discussing some considerations for the type of chemistry needed for labeling biomolecules in a system of choice.

Efficient CRISPR-mediated C-to-T base editing in Komagataella phaffii.

The nonconventional methylotrophic yeast Komagataella phaffii is widely applied in the production of industrial enzymes, pharmaceutical proteins, and various high-value chemicals. The development of robust and versatile genome editing tools for K. phaffii is crucial for the design of increasingly advanced cell factories. Here, we first developed a base editing method for K. phaffii based on the CRISPR-nCas9 system. We engineered 24 different base editor constructs, using a variety of promoters and cytidine deaminases (CDAs). The optimal base editor (PAOX2*-KpA3A-nCas9-KpUGI-DAS1TT) comprised a truncated AOX2 promoter (PAOX2*), a K. phaffii codon-optimized human APOBEC3A CDA (KpA3A), human codon-optimized nCas9 (D10A), and a K. phaffii codon-optimized uracil glycosylase inhibitor (KpUGI). This optimal base editor efficiently performed C-to-T editing in K. phaffii, with single-, double-, and triple-locus editing efficiencies of up to 96.0%, 65.0%, and 5.0%, respectively, within a 7-nucleotide window from C-18 to C-12. To expand the targetable genomic region, we also replaced nCas9 in the optimal base editor with nSpG and nSpRy, and achieved 50.0%-60.0% C-to-T editing efficiency for NGN-protospacer adjacent motif (PAM) sites and 20.0%-93.2% C-to-T editing efficiency for NRN-PAM sites, respectively. Therefore, these constructed base editors have emerged as powerful tools for gene function research, metabolic engineering, genetic improvement, and functional genomics research in K. phaffii.

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.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking.

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants.

Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.