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Determining the atomic-level structure of a protein has been a decades-long challenge. However, recent advances in transformers and related neural network architectures have enabled researchers to significantly improve solutions to this problem. These methods use large datasets of sequence information and corresponding known protein template structures, if available. Yet, such methods only focus on sequence information. Other available prior knowledge could also be utilized, such as constructs derived from x-ray crystallography experiments and the known structures of the most common conformations of amino acid residues, which we refer to as partial structures. To the best of our knowledge, we propose the first transformer-based model that directly utilizes experimental protein crystallographic data and partial structure information to calculate electron density maps of proteins. In particular, we use Patterson maps, which can be directly obtained from x-ray crystallography experimental data, thus bypassing the well-known crystallographic phase problem. We demonstrate that our method, CrysFormer, achieves precise predictions on two synthetic datasets of peptide fragments in crystalline forms, one with two residues per unit cell and the other with fifteen. These predictions can then be used to generate accurate atomic models using established crystallographic refinement programs.

The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallography, based on a synthetic dataset of small fragments derived from a large well curated subset of solved structures in the Protein Data Bank (PDB). In particular, electron-density estimates of simple artificial systems are produced directly from corresponding Patterson maps using a convolutional neural network architecture as a proof of concept.

Adenylate kinase is a ubiquitous enzyme in living systems and undergoes dramatic conformational changes during its catalytic cycle. For these reasons, it is widely studied by genetic, biochemical, and biophysical methods, both experimental and theoretical. We have determined the basic crystal structures of three differently liganded states of adenylate kinase from Methanotorrus igneus, a hyperthermophilic organism whose adenylate kinase is a homotrimeric oligomer. The multiple copies of each protomer in the asymmetric unit of the crystal provide a unique opportunity to study the variation in the structure and were further analyzed using advanced crystallographic refinement methods and analysis tools to reveal conformational heterogeneity and, thus, implied dynamic behaviors in the catalytic cycle.

Background New approaches are needed to improve and destigmatize remediation in undergraduate medical education (UME). The COVID-19 pandemic magnified the need to support struggling learners to ensure competency and readiness for graduate medical education (GME). Clinical skills (CS) coaching is an underutilized approach that may mitigate the stigma of remedial learning. Methods A six-month CS coaching pilot was conducted at Harvard Medical School (HMS) as a destigmatized remedial learning environment for clerkship and post-clerkship students identified as ‘at risk’ based on objective structured clinical examinations (OSCE). The pilot entailed individual and group coaching with five faculty, direct bedside observation of CS, and standardized patient encounters with video review. Strengths-based coaching principles and appreciative inquiry were emphasized. Results Twenty-three students participated in the pilot: 14 clerkship students (cohort 1) and 9 post-clerkship students (cohort 2). All clerkship students (cohort 1) demonstrated sustained improvement in CS across three OSCEs compared to baseline: at pilot close, at 6-months post pilot, and at 21-24 months post-pilot all currently graduating students (10/10, 100%) passed the summative OSCE, an HMS graduation requirement. All post-clerkship students (cohort 2) passed the HMS graduation OSCE (9/9,100%). Feedback survey results included clerkship students (9/14; 64%) and post-clerkship students (7/9; 78%); all respondents unanimously agreed that individual coaching was “impactful to my clinical learning and practice”. Faculty and leadership fully supported the pilot as a destigmatized and effective approach to remediation. Conclusion Remediation has an essential and growing role in medical schools. CS coaching for remedial learning can reduce stigma, foster a growth mindset, and support sustained progress for ‘at risk’ early clerkship through final year students. An “implementation template” with suggested tools and timelines can be locally adapted to guide CS coaching for UME remediation. The CS coaching pilot model is feasible and can be generalized to many UME programs.

Here, we illustrate what happens inside the catalytic cleft of an enzyme when substrate or ligand binds on single-millisecond timescales. The initial phase of the enzymatic cycle is observed with near-atomic resolution using the most advanced X-ray source currently available: the European XFEL (EuXFEL). The high repetition rate of the EuXFEL combined with our mix-and-inject technology enables the initial phase of ceftriaxone binding to the Mycobacterium tuberculosis β-lactamase to be followed using time-resolved crystallography in real time. It is shown how a diffusion coefficient in enzyme crystals can be derived directly from the X-ray data, enabling the determination of ligand and enzyme–ligand concentrations at any position in the crystal volume as a function of time. In addition, the structure of the irreversible inhibitor sulbactam bound to the enzyme at a 66 ms time delay after mixing is described. This demonstrates that the EuXFEL can be used as an important tool for biomedically relevant research.

Determining the atomic-level structure of a protein has been a decades-long challenge. However, recent advances in transformers and related neural network architectures have enabled researchers to significantly improve solutions to this problem. These methods use large datasets of sequence information and corresponding known protein template structures, if available. Yet, such methods only focus on sequence information. Other available prior knowledge could also be utilized, such as constructs derived from x-ray crystallography experiments and the known structures of the most common conformations of amino acid residues, which we refer to as partial structures. To the best of our knowledge, we propose the first transformer-based model that directly utilizes experimental protein crystallographic data and partial structure information to calculate electron density maps of proteins. In particular, we use Patterson maps, which can be directly obtained from x-ray crystallography experimental data, thus bypassing the well-known crystallographic phase problem. We demonstrate that our method, CrysFormer, achieves precise predictions on two synthetic datasets of peptide fragments in crystalline forms, one with two residues per unit cell and the other with fifteen. These predictions can then be used to generate accurate atomic models using established crystallographic refinement programs. [ABSTRACT FROM AUTHOR]

SYNOPSIS: Accounting firms increasingly provide remote or hybrid work options to attract qualified professionals, but they must also effectively socialize new employees to develop and retain them. This study explores the effect of remote work on newcomer socialization. Rather than surveying remote newcomers who have less understanding of what "successful" socialization means, we ask 122 experienced Big 4 professionals about the impact of remote work during the COVID-19 pandemic on socialization. Respondents report that every aspect of socialization is impacted by remote work. Although participants perceive that newcomers are able to learn technical skills (e.g., how to complete work tasks) remotely, they are less able to learn about firm expectations (e.g., client interactions). Participants believe remote newcomers are less able to form bonds with coworkers and clients and are less committed to their firm. Best practices for remote socialization are discussed (e.g., virtual audit rooms) to assist accounting firms. [ABSTRACT FROM AUTHOR]

The phase problem in X-ray crystallography arises from the fact that only the intensities, and not the phases, of the diffracting electromagnetic waves are measured directly. Molecular replacement can often estimate the relative phases of reflections starting with those derived from a template structure, which is usually a previously solved structure of a similar protein. The key factor in the success of molecular replacement is finding a good template structure. When no good solved template exists, predicted structures based partially on templates can sometimes be used to generate models for molecular replacement, thereby extending the lower bound of structural and sequence similarity required for successful structure determination. Here, the effectiveness is examined of structures predicted by a state-of-the-art prediction algorithm, the Associative memory, Water-mediated, Structure and Energy Model Suite (AWSEM-Suite), which has been shown to perform well in predicting protein structures in CASP13 when there is no significant sequence similarity to a solved protein or only very low sequence similarity to known templates. The performance of AWSEM-Suite structures in molecular replacement is discussed and the results show that AWSEM-Suite performs well in providing useful phase information, often performing better than I-TASSER-MR and the previous algorithm AWSEM-Template.

Adenylate kinase is a ubiquitous enzyme in living systems and undergoes dramatic conformational changes during its catalytic cycle. For these reasons, it is widely studied by genetic, biochemical, and biophysical methods, both experimental and theoretical. We have determined the basic crystal structures of three differently liganded states of adenylate kinase from Methanotorrus igneus, a hyperthermophilic organism whose adenylate kinase is a homotrimeric oligomer. The multiple copies of each protomer in the asymmetric unit of the crystal provide a unique opportunity to study the variation in the structure and were further analyzed using advanced crystallographic refinement methods and analysis tools to reveal conformational heterogeneity and, thus, implied dynamic behaviors in the catalytic cycle. [ABSTRACT FROM AUTHOR]