Engineering of CRISPR-Cas PAM recognition using deep learning of vast evolutionary data.

CRISPR-Cas enzymes must recognize a protospacer-adjacent motif (PAM) to edit a genomic site, significantly limiting the range of targetable sequences in a genome. Machine learning-based protein engineering provides a powerful solution to efficiently generate Cas protein variants tailored to recognize specific PAMs. Here, we present Protein2PAM, an evolution-informed deep learning model trained on a dataset of over 45,000 CRISPR-Cas PAMs.

Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen.

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and antibody docking methods are still unreliable.

Flexible protein-protein docking with a multitrack iterative transformer.

Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking).

ProGen2: Exploring the boundaries of protein language models.

Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial-intelligence-driven protein design. However, we lack a sufficient understanding of how very large-scale models and data play a role in effective protein model development. We introduce a suite of protein language models, named ProGen2, that are scaled up to 6.

IgLM: Infilling language modeling for antibody sequence design.

Discovery and optimization of monoclonal antibodies for therapeutic applications relies on large sequence libraries but is hindered by developability issues such as low solubility, high aggregation, and high immunogenicity. Generative language models, trained on millions of protein sequences, are a powerful tool for the on-demand generation of realistic, diverse sequences. We present the Immunoglobulin Language Model (IgLM), a deep generative language model for creating synthetic antibody libraries.

SARS-CoV-2 infection establishes a stable and age-independent CD8 T cell response against a dominant nucleocapsid epitope using restricted T cell receptors.

The resolution of SARS-CoV-2 replication hinges on cell-mediated immunity, wherein CD8 T cells play a vital role. Nonetheless, the characterization of the specificity and TCR composition of CD8 T cells targeting non-spike protein of SARS-CoV-2 before and after infection remains incomplete. Here, we analyzed CD8 T cells recognizing six epitopes from the SARS-CoV-2 nucleocapsid (N) protein and found that SARS-CoV-2 infection slightly increased the frequencies of N-recognizing CD8 T cells but significantly enhanced activation-induced proliferation compared to that of the uninfected donors.

Contextual protein and antibody encodings from equivariant graph transformers.

The optimal residue identity at each position in a protein is determined by its structural, evolutionary, and functional context. We seek to learn the representation space of the optimal amino-acid residue in different structural contexts in proteins. Inspired by masked language modeling (MLM), our training aims to transduce learning of amino-acid labels from non-masked residues to masked residues in their structural environments and from general (e.

Flexible Protein-Protein Docking with a Multi-Track Iterative Transformer.

Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and re-ranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, e.g., structure-based virtual screening.

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.

Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge.

Hallucinating structure-conditioned antibody libraries for target-specific binders.

Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space.

The Relationship Between Cannabis, Cognition, and Schizophrenia: It's Complicated.

The consequences of cannabis use, especially in the context of schizophrenia, have gained increased importance with the legalization of cannabis in North America and across the globe. Cannabis use has multifaceted impacts on cognition in schizophrenia patients and healthy subjects. Healthy subjects, particularly those who initiated cannabis use at earlier ages and used high-potency cannabis for longer durations, exhibited poorer cognition mainly in working memory and attention.

A versatile design platform for glycoengineering therapeutic antibodies.

Manipulation of glycosylation patterns, i.e., glycoengineering, is incorporated in the therapeutic antibody development workflow to ensure clinical safety, and this approach has also been used to modulate the biological activities, functions, or pharmacological properties of antibody drugs.

Simultaneous prediction of antibody backbone and side-chain conformations with deep learning.

Antibody engineering is becoming increasingly popular in medicine for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design.

Antibody structure prediction using interpretable deep learning.

Therapeutic antibodies make up a rapidly growing segment of the biologics market. However, rational design of antibodies is hindered by reliance on experimental methods for determining antibody structures. Here, we present DeepAb, a deep learning method for predicting accurate antibody F structures from sequence.

Alcohol and Vaporized Nicotine Co-exposure During Adolescence Contribute Differentially to Sex-Specific Behavioral Effects in Adulthood.

Introduction: Co-occurrence of e-cigarette use and alcohol consumption during adolescence is frequent. Here, we examined whether adolescent co-exposure to alcohol drinking and vaporized nicotine would impact reward- and cognition-related behaviors in adult male and female rats during adulthood.

Aims And Methods: Four groups of male and female Sprague Dawley rats (n = 8-11/group/sex) received either nicotine (JUUL 5% nicotine pods) or vehicle vapor for 10 minutes daily between postnatal days 30-46, while having continuous voluntary access to ethanol and water during this time in a two-bottle preference design.

Modeling of lamprey reticulospinal neurons: multiple distinct parameter sets yield realistic simulations.

For the lamprey and other vertebrates, reticulospinal (RS) neurons project descending axons to the spinal cord and activate motor networks to initiate locomotion and other behaviors. In the present study, a biophysically detailed computer model of lamprey RS neurons was constructed consisting of three compartments: dendritic, somatic, and axon initial segment (AIS). All compartments included passive channels.

Geometric potentials from deep learning improve prediction of CDR H3 loop structures.

Motivation: Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure.

Microtubule S-glutathionylation as a potential approach for antimitotic agents.

Background: Microtubules have been one of the most effective targets for the development of anticancer agents. Cancer cells treated by these agents are characterized by cell arrest at G2/M phase. Microtubule-targeting drugs are, therefore, referred to as antimitotic agents.

Comorbidity of body dysmorphic disorder and eating disorders: severity of psychopathology and body image disturbance.

Objective: The current study examined comorbidity and clinical correlates of eating disorders in a large sample of individuals with body dysmorphic disorder (BDD).

Method: Two hundred individuals with DSM-IV (4th ed. of the Diagnostic and Statistical Manual of Mental Disorders.

Assessing olfaction in the neuropsychological exam: the relationship between odor identification and cognition in older adults.

The relationship between odor identification and cognition has not been previously well characterized. The neuroanatomy of the olfactory system and the frequent finding of olfactory dysfunction in neurodegenerative diseases suggest a likely relationship between odor identification and memory, language, and executive functioning, though previous studies have often failed to demonstrate the expected relationship. The current study examined this relationship in across a continuum of ability levels (N=100).

Assessment of mood state in dementia by use of the visual analog mood scales (VAMS).

Objective: The assessment of mood states in individuals with dementia is a challenging yet clinically useful task. The purpose of the present study was to examine the validity of the Visual Analog Mood Scales (VAMS) in individuals with dementia.

Methods: Thirty-one patients who met diagnostic criteria for dementia completed the VAMS and a modified Profile of Mood States.

A comparison of administration procedures for the Rey-Osterrieth Complex Figure: flowcharts versus pen switching.

The Rey-Osterrieth Complex Figure (ROCF) is commonly used to assess visuospatial skills, visuoconstruction, visual memory, and executive functioning. Two different methods are traditionally used to record the order in which the figure is drawn: the flowchart method and the pen-switching method. Although it has been suggested that pen switching may interfere with performance, to date no research has been conducted to assess whether ROCF performance significantly differs due to administration method.