The process of evaluating and negotiating a tenure-track job offer is unstructured and highly variable, making it susceptible to bias and inequitable outcomes. We outline common aspects of and recommendations for negotiating an academic job offer in the life sciences to support equitable recruitment of diverse faculty.

Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Appraisal Skills Program (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community.

Binding kinetic parameters can be correlated with drug efficacy, which in recent years led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity, and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.

Prenylation is a post-translational modification (PTM) widely found in primary and secondary metabolism. This modification can enhance the lipophilicity of molecules, enabling them to interact with lipid membranes more effectively. The prenylation of peptides is often carried out by cyanobactin prenyltransferases (PTases) from cyanobacteria. These enzymes are of interest due to their ability to add prenyl groups to unmodified peptides, thus making them more effective therapeutics through the subsequent acquisition of increased membrane permeability and bioavailability. Herein we review the current knowledge of cyanobactin PTases, focusing on their discovery, biochemistry, and bioengineering, and highlight the potential application of them as peptide alkylation biocatalysts to generate peptide therapeutics.

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.