Combining Experimental Data and Computational Methods for the Non-Computer Specialist

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction

Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularly with the development of AlphaFold2 (AF2), which can analyze sequences of amino acids and predict 3D structures with near experimental accuracy. Since the release of AF2, numerous studies have been conducted, either using AF2 directly for large-scale modeling or building upon the software for other use cases. Many reviews have been published discussing the impact of AF2 in the field of protein bioinformatics, particularly in relation to neural networks, which have highlighted what AF2 can and cannot do. It is evident that AF2 and similar approaches are open to further development and several new approaches have emerged, in addition to older refinement approaches, for improving the quality of predictions. Here we provide a brief overview, aimed at the general biologist, of how machine learning techniques have been used for improvement of 3D models of proteins following AF2, and we highlight the impacts of these approaches. In the most recent experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP15), the most successful groups all developed their own tools for protein structure modeling that were based at least in some part on AF2. This improvement involved employing techniques such as generative modeling, changing parameters such as dropout to generate more AF2 structures, and data-driven approaches including using alternative templates and MSAs.

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.

Analysing the stability of He-filled hydrates: how many He atoms fit in the sII crystal?

Clathrate hydrates have the ability to encapsulate atoms and molecules within their cavities, and thus they could be potentially large storage capacity materials. The present work studies the multiple cage occupancy effects in the recently discovered He@sII crystal. On the basis of previous theoretical and experimental findings, the stability of He(1/1)@sII, He(1/4)@sII and He(2/4)@sII crystals was analysed in terms of structural, mechanical and energetic properties. For this purpose, first-principles DFT/DFT-D computations were performed by using both semi-local and non-local functionals, not only to elucidate which configuration is the most energetically favoured, but also to scrutinize the relevance of the long-range dispersion interactions in these kinds of compounds. We have encountered that dispersion interactions play a fundamental role in describing the underlying interactions, and different tendencies were observed depending on the choice of the functional. We found that PW86PBE-XDM shows the best performance, while the non-local functionals tested here were not able to correctly account for them. The present results reveal that the most stable configuration is the one presenting singly occupied D cages and tetrahedrally occupied H cages (He(1/4)@sII) in line with the experimental observation.

Computational approaches for modeling and structural design of biological systems: A comprehensive review

The convergence of biology and computational science has ushered in a revolutionary era, revolutionizing our understanding of biological systems and providing novel solutions to global problems. The field of genetic engineering has facilitated the manipulation of genetic codes, thus providing opportunities for the advancement of innovative disease therapies and environmental enhancements. The emergence of bio-molecular simulation represents a significant advancement in this particular field, as it offers the ability to gain microscopic insights into molecular-level biological processes over extended periods. Biomolecular simulation plays a crucial role in advancing our comprehension of organismal mechanisms by establishing connections between molecular structures, interactions, and biological functions. The field of computational biology has demonstrated its significance in deciphering intricate biological enigmas through the utilization of mathematical models and algorithms. The process of decoding the human genome has resulted in the advancement of therapies for a wide range of genetic disorders, while the simulation of biological systems contributes to the identification of novel pharmaceutical compounds. The potential of biomolecular simulation and computational biology is vast and limitless. As the exploration of the underlying principles that govern living organisms progresses, the potential impact of this understanding on cancer treatment, environmental restoration, and other domains is anticipated to be transformative. This review examines the notable advancements achieved in the field of computational biology, emphasizing its potential to revolutionize the comprehension and enhancement of biological systems.

Characterization of Phosphorylation-Dependent Antibody Binding to Cancer-Mutated Linkers of C2H2 Zinc Finger Proteins by Intact Transition Epitope Mapping-Thermodynamic Weak-Force Order Analysis

With Intact Transition Epitope Mapping-Thermodynamic Weak-force Order (ITEM-TWO) analysis in combination with molecular modeling, the phosphorylation-dependent molecular recognition motif of the anti-HpTGEKP antibody has been investigated with binary and ternary component mixtures consisting of antibody and (phospho-) peptides. Amino acid sequences have been selected to match either the antibody's recognition motif or the cancer-related zinc finger protein mutations and phosphorylations of the respective amino acid residues. Upon electrospraying of all the components of the mixtures, that is, hexapeptides, antibody, and intact immune complexes, the produced ions were subjected to mass spectrometric mass filtering. The antibody ions as well as the immune complex ions traversed into the mass spectrometer's collision chamber, whereas paths of unbound peptide ions were blocked prior to entering the collision cell. After dissociation of the multiply charged immune complexes in the gas phase, the complex-released peptide ions were recorded after having traversed the second mass filter. Complex-released peptides were unambiguously identified by their masses using mass analysis with isotope resolution. From the results of our studies with seven (phospho-) peptides with distinct amino acid sequences, which resembled either the antibody's binding motif or mutations, we conclude the following: (i) A negatively charged phospho group, located near the peptide's N-terminus is mandatory for antibody binding when placed on the peptide surface at a precise distance to the C-terminally located positively charged ε-amino group of a lysinyl residue. (ii) A bulky amino acid residue, such as the tyrosinyl residue at the N-terminal position of the (phospho-) threoninyl residue, abolishes antibody binding. (iii) Two closely spaced phospho groups negatively interfere with the surface polarity pattern and abolish antibody binding as well. (iv) Non-phosphorylated peptides are not binding partners of the anti-HpTGEKP antibody.

New Horizons in Structural Biology of Membrane Proteins: Experimental Evaluation of the Role of Conformational Dynamics and Intrinsic Flexibility

A plethora of membrane proteins are found along the cell surface and on the convoluted labyrinth of membranes surrounding organelles. Since the advent of various structural biology techniques, a sub-population of these proteins has become accessible to investigation at near-atomic resolutions. The predominant bona fide methods for structure solution, X-ray crystallography and cryo-EM, provide high resolution in three-dimensional space at the cost of neglecting protein motions through time. Though structures provide various rigid snapshots, only an amorphous mechanistic understanding can be inferred from interpolations between these different static states. In this review, we discuss various techniques that have been utilized in observing dynamic conformational intermediaries that remain elusive from rigid structures. More specifically we discuss the application of structural techniques such as NMR, cryo-EM and X-ray crystallography in studying protein dynamics along with complementation by conformational trapping by specific binders such as antibodies. We finally showcase the strength of various biophysical techniques including FRET, EPR and computational approaches using a multitude of succinct examples from GPCRs, transporters and ion channels.

Characterizing conformational ensembles of multi-domain proteins using anisotropic paramagnetic NMR restraints

It has been over two decades since paramagnetic NMR started to form part of the essential techniques for structural analysis of proteins under physiological conditions. Paramagnetic NMR has significantly expanded our understanding of the inherent flexibility of proteins, in particular, those that are formed by combinations of two or more domains. Here, we present a brief overview of techniques to characterize conformational ensembles of such multi-domain proteins using paramagnetic NMR restraints produced through anisotropic metals, with a focus on the basics of anisotropic paramagnetic effects, the general procedures of conformational ensemble reconstruction, and some representative reweighting approaches.

Order and disorder-An integrative structure of the full-length human growth hormone receptor

Because of its small size (70 kilodalton) and large content of structural disorder (>50%), the human growth hormone receptor (hGHR) falls between the cracks of conventional high-resolution structural biology methods. Here, we study the structure of the full-length hGHR in nanodiscs with small-angle x-ray scattering (SAXS) as the foundation. We develop an approach that combines SAXS, x-ray diffraction, and NMR spectroscopy data obtained on individual domains and integrate these through molecular dynamics simulations to interpret SAXS data on the full-length hGHR in nanodiscs. The hGHR domains reorient freely, resulting in a broad structural ensemble, emphasizing the need to take an ensemble view on signaling of relevance to disease states. The structure provides the first experimental model of any full-length cytokine receptor in a lipid membrane and exemplifies how integrating experimental data from several techniques computationally may access structures of membrane proteins with long, disordered regions, a widespread phenomenon in biology.