Computational engineering of water-soluble human potassium ion channels through QTY transformation.

Transmembrane potassium ion channels are crucial for ion transport, metabolism, and signaling, and serve as promising targets for anti-cancer therapies. However, their hydrophobic transmembrane nature requires detergents, posing a major bottleneck for experimental handling. In this paper, we present a structural bioinformatics study of six experimentally determined and twelve modeled potassium channel structures, in which hydrophobic amino acids (L, I/V, and F) were systematically replaced with neutral hydrophilic ones (Q, T, and Y), making the proteins more water-soluble.

Assessing AF2's ability to predict structural ensembles of proteins.

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors.

Structural bioinformatics studies of glutamate transporters and their AlphaFold2 predicted water-soluble QTY variants and uncovering the natural mutations of L->Q, I->T, F->Y and Q->L, T->I and Y->F.

Glutamate transporters play key roles in nervous physiology by modulating excitatory neurotransmitter levels, when malfunctioning, involving in a wide range of neurological and physiological disorders. However, integral transmembrane proteins including the glutamate transporters remain notoriously difficult to study, due to their localization within the cell membrane. Here we present the structural bioinformatics studies of glutamate transporters and their water-soluble variants generated through QTY-code, a protein design strategy based on systematic amino acid substitutions.

Structural bioinformatics studies of six human ABC transporters and their AlphaFold2-predicted water-soluble QTY variants.

Human ATP-binding cassette (ABC) transporters are one of the largest families of membrane proteins and perform diverse functions. Many of them are associated with multidrug resistance that often results in cancer treatment with poor outcomes. Here, we present the structural bioinformatics study of six human ABC membrane transporters with experimentally determined cryo-electron microscopy (CryoEM) structures including ABCB7, ABCC8, ABCD1, ABCD4, ABCG1, ABCG5, and their AlphaFold2-predicted water-soluble QTY variants.

Corrigendum: Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.

[This corrects the article DOI: 10.3389/fimmu.2023.

Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.

During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots.

Effects of ion type and concentration on the structure and aggregation of the amyloid peptide A .

Among the various factors controlling the amyloid aggregation process, the influences of ions on the aggregation rate and the resulting structures are important aspects to consider, which can be studied by molecular simulations. There is a wide variety of protein force fields and ion models, raising the question of which model to use in such studies. To address this question, we perform molecular dynamics simulations of Aβ , a fragment of the Alzheimer's amyloid β peptide, using different protein force fields, AMBER99SB-disp (A99-d) and CHARMM36m (C36m), and different ion parameters.

Structural bioinformatics studies of bacterial outer membrane beta-barrel transporters and their AlphaFold2 predicted water-soluble QTY variants.

Beta-barrel outer membrane proteins (OMP) are integral components of Gram-negative bacteria, eukaryotic mitochondria, and chloroplasts. They play essential roles in various cellular processes including nutrient transport, membrane stability, host-pathogen interactions, antibiotic resistance and more. The advent of AlphaFold2 for accurate protein structure predictions transformed structural bioinformatic studies.

Erratum: Comparing 2 crystal structures and 12 AlphaFold2-predicted human membrane glucose transporters and their water-soluble glutamine, threonine and tyrosine variants - CORRIGENDUM.

[This corrects the article DOI: 10.1017/qrd.2022.

Scalable biomimetic sensing system with membrane receptor dual-monolayer probe and graphene transistor arrays.

Affinity-based biosensing can enable point-of-care diagnostics and continuous health monitoring, which commonly follows bottom-up approaches and is inherently constrained by bioprobes' intrinsic properties, batch-to-batch consistency, and stability in biofluids. We present a biomimetic top-down platform to circumvent such difficulties by combining a "dual-monolayer" biorecognition construct with graphene-based field-effect-transistor arrays. The construct adopts redesigned water-soluble membrane receptors as specific sensing units, positioned by two-dimensional crystalline S-layer proteins as dense antifouling linkers guiding their orientations.

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction.

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity.

Structural informatic study of determined and AlphaFold2 predicted molecular structures of 13 human solute carrier transporters and their water-soluble QTY variants.

Solute carrier transporters are integral membrane proteins, and are important for diverse cellular nutrient transports, metabolism, energy demand, and other vital biological activities. They have recently been implicated in pancreatic cancer and other cancer metastasis, angiogenesis, programmed cell death and proliferation, cell metabolism and chemo-sensitivity. Here we report the study of 13 human solute carrier membrane transporters using the highly accurate AlphaFold2 predictions of 3D protein structures.

Development of a High-Affinity Antibody against the Tumor-Specific and Hyperactive 611-p95HER2 Isoform.

The expression of human epidermal growth factor receptor 2 (HER2) is a key classification factor in breast cancer. Many breast cancers express isoforms of HER2 with truncated carboxy-terminal fragments (CTF), collectively known as p95HER2. A common p95HER2 isoform, 611-CTF, is a biomarker for aggressive disease and confers resistance to therapy.

Protein Design: From the Aspect of Water Solubility and Stability.

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications.

Comparing 2 crystal structures and 12 AlphaFold2-predicted human membrane glucose transporters and their water-soluble glutamine, threonine and tyrosine variants.

Membrane transporters including glucose transporters (GLUTs) are involved in cellular energy supplies, cell metabolism and other vital biological activities. They have also been implicated in cancer proliferation and metastasis, thus they represent an important target in combatting cancer. However, membrane transporters are very difficult to study due to their multispan transmembrane properties.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven mAb sequence synthesis.

Comparing Native Crystal Structures and AlphaFold2 Predicted Water-Soluble G Protein-Coupled Receptor QTY Variants.

Accurate predictions of 3-dimensional protein structures by AlphaFold2 is a game-changer for biology, especially for structural biology. Here we present the studies of several native chemokine receptors including CCR5, CCR9, CXCR2 and CXCR4 determined by X-ray crystallography, and their water-soluble QTY counter parts predicted by AlphaFold2. In the native structures, there are hydrophobic amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F) in the transmembrane helices.