AlphaFold, Artificial Intelligence (AI), and Allostery

The enigma of introns: Intronic miRNA-directed mechanisms and alternative splicing diversify alternative oxidase potential in Vitis vinifera

Alternative oxidase (AOX) transcript levels were associated with efficiently balanced respiration and validated to assist selection on multiple-resilience. Consistently, AOX has also been identified as a target for weakening the survival capabilities of parasites and microorganisms responsible for severe human diseases. Despite the unique features of AOX in Vitis vinifera, particularly intense constitutive expression of AOX2 in the presence of unusually large introns that challenge the dogma of gene expression in eukaryotes, V. vinifera has been overlooked in AOX research. This study uncovered two distinct alternative splicing variants of the AOX: AOX1a-Alternative variant attributed to unusual retention of the intron-4 in the 3´UTR, and AOX2-Alternative variant, which is intron-1-dependent, involving the skipping of exon-1. The AOX2-Alternative variant differed in that cystine-I changed to serine, which is linked to different metabolite stimulation. However, molecular docking suggested that AOX2 and the variant proteins exhibit the same catalytic activities and binding affinities for ubiquinol. The unique large introns in AOX2 exhibited 16 miRNAs, including the master regulator of development and stress responses, mir-398. Among these, nine were conserved and validated in other plant species, whereas seven were considered potential novel miRNA candidates. Transcriptome analyses revealed down- and up-regulation of AOX1a-Alternative during shrivelling and water deficiency, and up-regulation of AOX2-Alternative with increasing temperatures. Consistent with previous studies, AOX1a and AOX1d were linked to biotic and abiotic stress, whereas AOX2 showed constitutive or developmental regulation. This study encourages hypothesis-driven advanced research on early mechanisms and functionality of newly discovered alternative splicing events and intronic miRNAs. Given functional marker-assisted breeding, it strengthened the requirement to consider overall AOX transcript levels as markers for predicting multiple-resilient phenotypes.

Pioneer in Molecular Biology: Conformational Ensembles in Molecular Recognition, Allostery, and Cell Function

In 1978, for my PhD, I developed the efficient O(n3) dynamic programming algorithm for the-then open problem of RNA secondary structure prediction. This algorithm, now dubbed the "Nussinov algorithm", "Nussinov plots", and "Nussinov diagrams", is still taught across Europe and the U.S. As sequences started coming out in the 1980s, I started seeking genome-encoded functional signals, later becoming a bioinformatics trend. In the early 1990s I transited to proteins, co-developing a powerful computer vision-based docking algorithm. In the late 1990s, I proposed the foundational role of conformational ensembles in molecular recognition and allostery. At the time, conformational ensembles and free energy landscapes were viewed as physical properties of proteins but were not associated with function. The classical view of molecular recognition and binding was based on only two conformations captured by crystallography: open and closed. I proposed that all conformational states preexist. Proteins always have not one folded form-nor two-but many folded forms. Thus, rather than inducing fit, binding can work by shifting the ensembles between states, and this shifting, or redistributing the ensembles to maintain equilibrium, is the origin of the allosteric effect and protein, thus cell, function. This transformative paradigm impacted community views in allosteric drug design, catalysis, and regulation. Dynamic conformational ensemble shifts are now acknowledged as the origin of recognition, allostery, and signaling, underscoring that conformational ensembles-not proteins-are the workhorses of the cell, pioneering the fundamental idea that dynamic ensembles are the driving force behind cellular processes. Nussinov was recognized as pioneer in molecular biology by JMB.

Homozygous familial hypercholesterolemia evaluation and survival single center study in Saudi Arabia: The HESSA registry

BACKGROUND AND AIMS BACKGROUND

Homozygous Familial Hypercholesterolemia (HoFH) is a rare, life-threatening genetic disorder causing extremely high low density lipoprotein cholesterol (LDL-C) levels, leading to early cardiovascular disease (CVD) and premature death. In Saudi Arabia, where consanguinity is common, HoFH prevalence is higher with unique genetic pathogenic familial hypercholesterolemia (FH) causing variants and treatment challenges. This study aims to analyze the clinical, genetic, treatment, and cardiovascular outcomes data of Saudi pediatric and adult HoFH patients treated at King Faisal Specialist Hospital & Research Centre (KFSHRC) over 23 years.

METHODS

A retrospective review of all patients (LDL-C >8 mmol/L) at KFSHRC (2000-2023) using European Atherosclerosis Society 2023 criteria to confirm HoFH. Data from those confirmed included demographics, lipid profiles, pathogenic FH-causing variants, treatments, mortality, and cardiovascular outcomes.

RESULTS

Among 514 severe hypercholesterolemia cases, 127 had HoFH. Diagnosis occurred at an average age of 14.3 ± 9.7 years. The mortality was 16 %, and 12 % were lost to follow-up. Cardiovascular interventions were performed in 31 % (coronary interventions in 28 % and aortic valve replacement in 17 %). The most common pathogenic FH-causing variants (57 %) was the founder null mutation c.2027del p.(Gly676Alafs∗33). Statins and ezetimibe were the primary treatments (73 %), but many required LDL-apheresis (36 %) or liver transplantation (LTx) (21 %). The peri-operative mortality for LTx was 7 %, but there was no long-term mortality on average follow-up of 6.2 ± 3.6 years, with only one patient requiring percutaneous coronary intervention. Adults were more likely to receive statins/ezetimibe (94 %/91 % vs. 50 %/53 % in pediatrics, p < 0.01) and LDL-apheresis (64 % vs. 8 %, p < 0.001), while liver transplantation was more common in children (38 % vs. 7 %, p < 0.001).

CONCLUSIONS

This study highlights the burden of null LDL-R pathogenic FH-causing variants and the frequent need for invasive treatments in Saudi HoFH patients. Liver transplantation is a viable option with low peri-operative mortality and favorable long-term disease-free survival. Early diagnosis, regional genetic screening, and access to advanced therapies are essential in achieving better outcomes.

Highly versatile small virus-encoded proteins in cellular membranes: A structural perspective on how proteins' inherent conformational plasticity couples with host membranes' properties to control cellular processes

We investigated several small viral proteins that reside and function in cellular membranes. These proteins belong to the viroporin family because they assemble into ion-conducting oligomers. However, despite forming similar oligomeric structures with analogous functions, these proteins have diverse amino acid sequences. In particular, the amino acid compositions of the proposed channel-forming transmembrane (TM) helices are vastly different-some contain residues (e.g., His, Trp, Asp, Ser) that could facilitate cation transport. Still, other viroporins' TM helices encompass exclusively hydrophobic residues; therefore, it is difficult to explain their channels' activity, unless other mechanisms (e.g., involving a negative lipid headgroups and/or membrane destabilization) take place. For this study, we selected the M2, Vpu, E, p13II, p7, and 2B proteins from the influenza A, HIV-1, human T-cell leukemia, hepatitis C, and picorna viruses, respectively. We provide a brief overview of the current knowledge about these proteins' structures as well as remaining questions about more comprehensive understanding of their structures, conformational dynamics, and function. Finally, we outline strategies to utilize a multi-prong structural and computational approach to overcome current deficiencies in the knowledge about these proteins.

Synthetic Biology for Designing Allostery and Its Potential Biomedical Applications

Allosteric regulation of protein function, where a perturbation at one site induces a conformational shift or alters dynamics at a distal functional site, plays a key role in numerous biological processes. The ability to introduce allostery using synthetic biology principles holds significant potential both for biomedical and biotechnological applications, and for advancing our understanding of natural allostery. By customizing target proteins for sensing specific chemical or physical signals, including ligand binding and environmental cues, we aim to allosterically modulate the function of a target protein depending on the selected triggers. This approach, unlike active-site targeting, offers greater specificity and selectivity and can allosterically couple diverse physiological processes. Synthetic biology strategies have been developed recently for designed allosteric protein regulation, including the design of allosteric modulators such as domain insertion, generation of de novo allosteric protein switches, and application of engineered allosteric mechanisms to control cellular functions. We examine the application of artificial intelligence (AI)-based generative protein design and other important milestones, challenges and opportunities in this field, highlighting how these approaches could be applied for the development of new therapeutic strategies.

Unlocking the secrets: Structure-function dynamics of plant proteins

Plant-based proteins are becoming essential resources for sustainable food systems, pharmaceutical innovations, and functional materials. This review examines the complex structure-function relationships of plant proteins, emphasising their crucial role in defining functional properties and applications. The primary structure, consisting of amino acid sequences, along with secondary, tertiary, and quaternary structures, profoundly affects protein behaviour. External factors such as pH, ionic strength, temperature, and processing techniques like extrusion and enzymatic modification can influence protein structure, consequently modifying their functional properties. Consider rewording to "Advanced processing techniques, such as high-pressure and non-thermal methods, effectively refine protein structures while preserving their functionality.Computational modelling, employing molecular dynamics and artificial intelligence, is proposed as a revolutionary instrument for forecasting and enhancing structure-function relationships. An emerging application of plant proteins is targeted drug delivery, whose structural characteristics facilitate accurate encapsulation and release of therapeutic agents. Case studies highlight the importance of protein surface characteristics in attaining precise cellular or tissue targeting, especially for conditions related to cancer and inflammation. This review concludes by highlighting strategic avenues for harnessing the complete potential of plant proteins, placing them at the cutting edge of innovation in food science, biotechnology, and drug delivery.

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries

In today's world, with an increasing patient population, the need for medications is increasing rapidly. However, the current practice of drug development is time-consuming and requires a lot of investment by the pharmaceutical industries. Currently, it takes around 8-10 years and $3 billion of investment to develop a medication. Pharmaceutical industries and regulatory authorities are continuing to adopt new technologies to improve the efficiency of the drug development process. However, over the decades the pharmaceutical industries were not able to accelerate the drug development process. The pandemic (COVID-19) has taught the pharmaceutical industries and regulatory agencies an expensive lesson showing the need for emergency preparedness by accelerating the drug development process. Over the last few years, the pharmaceutical industries have been collaborating with artificial intelligence (AI) companies to develop algorithms and models that can be implemented at various stages of the drug development process to improve efficiency and reduce the developmental timelines significantly. In recent years, AI-screened drug candidates have entered clinical testing in human subjects which shows the interest of pharmaceutical companies and regulatory agencies. End-end integration of AI within the drug development process will benefit the industries for predicting the pharmacokinetic and pharmacodynamic profiles, toxicity, acceleration of clinical trials, study design, virtual monitoring of subjects, optimization of manufacturing process, analyzing and real-time monitoring of product quality, and regulatory preparedness. This review article discusses in detail the role of AI in various avenues of the pharmaceutical drug development process, its limitations, regulatory and future perspectives.

Electrophysiological and molecular docking analysis of 1,8-Cineole's effects on potassium currents in mouse DRG neurons

1,8-cineole (CIN) is a monoterpene widely used in traditional medicine, as it promotes biological and pharmacological effects, including the inhibition of neuronal excitability. This inhibition might be due to ion channel blockade, as reported for voltage-dependent Na+ and Ca2+ channels. However, voltage-dependent K+ channels (Kv) are also relevant proteins in neuronal excitability. Thus, this study investigated the effects of CIN on potassium current (IK+) in dissociated neurons from mouse dorsal root ganglia (DRG) using electrophysiological and molecular docking approaches. The whole-cell patch-clamp technique recorded IK+ in voltage-clamp mode. Other experiments recorded action potentials (APs) in the current-clamp mode, and molecular docking used specific software (AutoDock Vina, LigPlot+, PyMol). Consequently, CIN achieved a partial concentration-dependent inhibition of IK+. CIN at 3.0 and 6.0 mM showed similar blockade values of ∼50 % of peak and sustained IK+. IV plots from cells exposed to 3.0 mM CIN shifted by ∼15 mV toward negative values in the G/Gmax curve of sustained IK+. Peak IK + had no significant shift. Molecular docking simulations demonstrated that CIN interacts with the binding pockets of Kv2.1 and Kv3.4 channels with ΔG values of -4.7 kcal.mol-1 and -2.0 kcal.mol-1 interaction energy, respectively. This study also investigated 3 mM CIN effects on neuronal excitability. CIN blocked APs in two of eight neurons and altered several electrophysiological parameters related to excitability in the remaining six neurons. These parameters included AP amplitude, AP maximum rise slope, and AP time-to-peak without changes in resting membrane potential. This study concluded that CIN blocks total IK+ and interacts with K+ channels. Also, the changes in neuronal excitability might be due to CIN effects on K+ channels, working through a mechanism independent of resting membrane potential.

Statistical Thermodynamics of the Protein Ensemble: Mediating Function and Evolution

The growing appreciation of native state conformational fluctuations mediating protein function calls for critical reevaluation of protein evolution and adaptation. If proteins are ensembles, does nature select solely for ground state structure, or are conformational equilibria between functional states also conserved? If so, what is the mechanism and how can it be measured? Addressing these fundamental questions, we review our investigation into the role of local unfolding fluctuations in the native state ensembles of proteins. We describe the functional importance of these ubiquitous fluctuations, as revealed through studies of adenylate kinase. We then summarize elucidation of thermodynamic organizing principles, which culminate in a quantitative probe for evolutionary conservation of protein energetics. Finally, we show that these principles are predictive of sequence compatibility for multiple folds, providing a unique thermodynamic perspective on metamorphic proteins. These research areas demonstrate that the locally unfolded ensemble is an emerging, important mechanism of protein evolution.

Role of artificial intelligence in advancing immunology

Artificial intelligence (AI) has revolutionized various biomedical fields, particularly immunology, by enhancing vaccine development, immunotherapies, and allergy treatments. AI helps identify potential vaccine candidates and predict how the body reacts to different antigens based on a vast number of genomic sequences and protein structures. AI can help cancer patients by analyzing their data and offering personalized immunotherapies. AI has also advanced the field of allergy by identifying potential allergens and predicting allergic reactions based on patient genetic and environmental factors. AI could also help diagnose multiple immunological diseases, including autoimmune diseases and immunodeficiencies, by analyzing patient history and laboratory results. AI has deepened our understanding of the human genome by providing numerous amounts of data from DNA sequences previously believed to be nonfunctional. Through machine learning and deep learning, many laborious research tasks, such as screening for DNA mutations, can be efficiently performed in a short amount of time. AI and machine learning are significantly advancing biomedical science in significant areas, including research and industry. This review discusses the latest AI-based tools that can be utilized in the field of immunology. AI tools significantly advance the field of medical research and healthcare by enabling new scientific discoveries and facilitating rapid clinical diagnosis.

Sensory plasticity caused by up-down regulation encodes the information of short-term learning and memory

Learning and memory are essential for animals' well-being and survival. The underlying mechanisms are a major task of neuroscience studies. In this study, we identified a circuit consisting of ASER, RIC, RIS, and AIY, is required for short-term salt chemotaxis learning (SCL) in C. elegans. ASER NaCl-sensation possesses are remodeled by salt/food-deprivation pared conditioning. RIC integrates the sensory information of NaCl and food availability. It excites ASER and inhibits AIY by tyramine/TYRA-2 and octopamine/OCTR-1 signaling pathways, respectively. By the salt conditioning, RIC NaCl calcium response to NaCl is depressed, thus, the RIC excitation of ASER and inhibition of AIY are suppressed. ASER excites RIS by FLP-14/FRPR-10 signaling. RIS inhibits ASER via PDF-2/PDFR-1 signaling in negative feedback. ASER sensory plasticity caused by RIC plasticity and RIS negative feedback are required for both learning and memory recall. Thus, the sensation plasticity encodes the information of the short-term SCL that facilitates animal adaptation to dynamic environments.

Decoding allosteric landscapes: computational methodologies for enzyme modulation and drug discovery

Allosteric regulation is a fundamental mechanism in enzyme function, enabling dynamic modulation of activity through ligand binding at sites distal to the active site. Allosteric modulators have gained significant attention due to their unique advantages, including enhanced specificity, reduced off-target effects, and the potential for synergistic interaction with orthosteric agents. However, the inherent complexity of allosteric mechanisms has posed challenges to the systematic discovery and design of allosteric modulators. This review discusses recent advancements in computational methodologies for identifying and characterizing allosteric sites in enzymes, emphasizing techniques such as molecular dynamics (MD) simulations, enhanced sampling methods, normal mode analysis (NMA), evolutionary conservation analysis, and machine learning (ML) approaches. Advanced tools like PASSer, AlloReverse, and AlphaFold have further enhanced the understanding of allosteric mechanisms and facilitated the design of selective allosteric modulators. Case studies on enzymes such as Sirtuin 6 (SIRT6) and MAPK/ERK kinase (MEK) demonstrate the practical applications of these approaches in drug discovery. By integrating computational predictions with experimental validation, this review highlights the transformative potential of computational strategies in advancing allosteric drug discovery, offering innovative opportunities to regulate enzyme activity for therapeutic benefits.

The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives

Protein allostery is a fundamental biological regulatory mechanism that allows communication between distant locations within a protein, modifying its function in response to signals. Experimental techniques, such as NMR spectroscopy and cryo-electron microscopy (cryo-EM), are critical validation tools for computational predictions and provide valuable insights into dynamic conformational changes. Combining these approaches has greatly improved our understanding of classical conformational allostery and complex dynamic coupling mechanisms. Recent advances in machine learning and enhanced sampling methods have broadened the scope of allostery research, identifying cryptic allosteric sites and directing new drug discovery approaches. Despite progress, bridging static structural data with dynamic functional states remains challenging. This review underscores the importance of combining experimental and computational approaches to comprehensively understand protein allostery and its diverse applications in biology and medicine.

An in vivo screen for proteolytic switch domains that can mediate Notch activation by force

Notch proteins are single pass transmembrane receptors activated by sequential extracellular and intramembrane cleavages that release their cytosolic domains to function as transcription factors in the nucleus. Upon binding, Delta/Serrate/LAG-2 (DSL) ligands activate Notch by exerting a "pulling" force across the intercellular ligand/receptor bridge. This pulling force is generated by Epsin-mediated endocytosis of ligand into the signal-sending cell, and results in the extracellular cleavage of the force-sensing Negative Regulatory Region (NRR) of the receptor by an ADAM10 protease [Kuzbanian (Kuz) in Drosophila ]. Here, we have used chimeric Notch and DSL proteins to screen for other domains that can function as ligand-dependent proteolytic switches in place of the NRR in the developing Drosophila wing. While most of the tested domains are either refractory to cleavage or constitutively cleaved, we identify several that mediate Notch activation in response to ligand. These NRR analogues derive from widely divergent source proteins and have strikingly different predicted structures. Yet, almost all depend on force exerted by Epsin-mediated ligand endocytosis and cleavage catalyzed by Kuz. We posit that the sequence space of protein domains that can serve as force-sensing proteolytic switches in Notch activation is unexpectedly large, a conclusion that has implications for the mechanism of target recognition by Kuz/ADAM10 proteases and is consistent with a more general role for force dependent ADAM10 proteolysis in other cell contact-dependent signaling mechanisms. Our results also validate the screen for increasing the repertoire of proteolytic switches available for synthetic Notch (synNotch) therapies and tissue engineering.

One sentence summary

A scalable in vivo screen for Epsin and ADAM10 dependent proteolytic switches that can mediate Notch activation in response to force.

Structural Dynamics of Rho GTPases

Rho family GTPases are a part of the Ras superfamily and are signaling hubs for many cellular processes. While the detailed understanding of Ras structure and function has led to tremendous progress in oncogenic Ras-targeted drug discovery, studies of the related Rho GTPases are still catching up as the recurrent cancer-related Rho GTPase mutations have only been discovered in the last decade. Like that of Ras, an in-depth understanding of the structural basis of how Rho GTPases and their mutants behave as key oncogenic drivers benefits the development of clinically effective therapies. Recent studies of structure dynamics in Rho GTPase structure-function relationship have added new twists to the conventional wisdom of Rho GTPase signaling mechanism.

In situ characterization of amine-forming enzymes shows altered oligomeric state

Enzyme stability can be measured in a number of ways, including melting temperature, activity retention, and size analysis. However, these measurements are often conducted in an idealized storage buffer and not in the relevant enzymatic reaction media. Particularly for reactions that occur in alkaline, volatile, and high ionic strength media, typical analyses using differential scanning calorimetry, light scattering, and sodium dodecyl-sulfate polyacrylamide gel electrophoresis are not satisfactory to track the stability of these enzymes. In this work, we monitor the stability of engineered and native dehydrogenases that require a high amount of ammonia for their reaction to occur. We demonstrate the benefits of analyzing these enzymes in their reaction buffer, uncovering trends that were not observable in the typical phosphate storage buffer. This work provides a framework for analyzing the stability of many other enzymes whose reaction media is not suitable for traditional techniques. We introduce several strategies for measuring the melting temperature, oligomeric state, and activity of these enzymes in their reaction media. Further, we have identified opportunities for integration of computational tools into this workflow to engineer enzymes more effectively for solvent tolerance and improved stability.

Unleashing the future: The revolutionary role of machine learning and artificial intelligence in drug discovery

Drug discovery is a complex and multifaceted process aimed at identifying new therapeutic compounds with the potential to treat various diseases. Traditional methods of drug discovery are often time-consuming, expensive, and characterized by low success rates. Because of this, there is an urgent need to improve the drug development process using new technologies. The integration of the current state-of-art of artificial intelligence (AI) and machine learning (ML) approaches with conventional methods will enhance the efficiency and effectiveness of pharmaceutical research. This review highlights the transformative impact of AI and ML in drug discovery, discussing current applications, challenges, and future directions in harnessing these technologies to accelerate the development of innovative therapeutics. We have discussed the latest developments in AI and ML technologies to streamline several stages of drug discovery, from target identification and validation to lead optimization and preclinical studies.

Not only the top: Type I topoisomerases function in multiple tissues and organs development in plants

BACKGROUND

DNA topoisomerases (TOPs) are essential components in a diverse range of biological processes including DNA replication, transcription and genome integrity. Although the functions and mechanisms of TOPs, particularly type I TOP (TOP1s), have been extensively studied in bacteria, yeast and animals, researches on these proteins in plants have only recently commenced.

AIM OF REVIEW

In this review, the function and mechanism studies of TOP1s in plants and the structural biology of plant TOP1 are presented, providing readers with a comprehensive understanding of the current research status of this essential enzyme.The future research directions for exploring the working mechanism of plant TOP1s are also discussed.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Over the past decade, it has been discovered TOP1s play a vital role in multiphasic processes of plant development, such as maintaining meristem activity, gametogenesis, flowering time, gravitropic response and so on. Plant TOP1s affects gene transcription by modulating chromatin status, including chromatin accessibility, DNA/RNA structure, and nucleosome positioning. However, the function and mechanism of this vital enzyme is poorly summarized although it has been systematically summarized in other species. This review summarized the research progresses of plant TOP1s according to the diverse functions and working mechanism in different tissues.

An Integrated Approach to Develop a Potent Vaccine Candidate Construct Against Prostate Cancer by Utilizing Machine Learning and Bioinformatics

BACKGROUND

Prostate cancer is the most common malignancy among males. Prostaglandin G/H synthase (PGHS) is an essential enzyme in the synthesis of prostaglandins, and its activation has been linked to many malignancies, including colorectal cancer.

AIMS

Due to the limited effectiveness and specificity of existing prostate cancer therapies, this study was designed to formulate improved treatment techniques.

METHODS

Several immunoinformatic, reverse vaccinology, and molecular modeling methodologies were used to discover B- and T-cell epitopes for the glioblastoma multiforme tumor PGH2_HUMAN. This research evaluated Prostaglandin G/H synthase 2 protein as a potential vaccine candidate against the malignancy. The multi-epitope vaccine architecture is engineered to activate the immune system, with each epitope docked to its respective HLAs. Further, MD simulations analysis was performed to validate the findings.

RESULTS

A multi-epitope subunit vaccine candidate was developed by concatenating the chosen B- and T-cell epitopes. Results yield a codon adaptive index (CAI) of 0.93 and a GC content of 56.77%. Thus, it conforms to a biological requirement for effective protein expression, suggesting competent vaccine efficacy inside the Escherichia coli system. Significant interleukin and cytokine responses were seen, characterized by elevated levels of IL-2 and IFN-γ in the immune system's response to the immunization. Molecular docking demonstrated an efficient binding affinity of -278 kcal/mol, with hydrogen bonding to several residues. Furthermore, the system total root mean square deviation (RMSD) reached 3.23 Å, with a maximum of up to 5.0 Å at the 100 ns time point but remains stable till 400 ns time intervals followed by stable root mean square fluctuation (RMSF) and radius of gyration values. The hydrogen bond cloud residues are the critical sites that significantly influence the binding energies of MMPBSA and MMGBSA via substantial van der Waals interactions.

CONCLUSION

It has been determined that these in silico analyses will further augment the comprehension necessary for advancing the creation of targeted therapies for chemotherapeutic cancer treatments.

The role of artificial intelligence in drug screening, drug design, and clinical trials

The role of computational tools in drug discovery and development is becoming increasingly important due to the rapid development of computing power and advancements in computational chemistry and biology, improving research efficiency and reducing the costs and potential risks of preclinical and clinical trials. Machine learning, especially deep learning, a subfield of artificial intelligence (AI), has demonstrated significant advantages in drug discovery and development, including high-throughput and virtual screening, ab initio design of drug molecules, and solving difficult organic syntheses. This review summarizes AI technologies used in drug discovery and development, including their roles in drug screening, design, and solving the challenges of clinical trials. Finally, it discusses the challenges of drug discovery and development based on AI technologies, as well as potential future directions.

Predicting Mutation-Induced Allosteric Changes in Structures and Conformational Ensembles of the ABL Kinase Using AlphaFold2 Adaptations with Alanine Sequence Scanning

Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture the effects of single point mutations that induced significant structural changes. We examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric hotspots that correspond to state-switching mutational sites which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.

Allostery Illuminated: Harnessing AI and Machine Learning for Drug Discovery

In the past several years there has been rapid adoption of artificial intelligence (AI) and machine learning (ML) tools for drug discovery. In this Microperspective, we comment on recent AI/ML applications to the discovery of allosteric modulators, focusing on breakthroughs with AlphaFold, structure-based drug discovery (SBDD), and medicinal chemistry applications. We discuss how these technologies are facilitating drug discovery and the remaining challenges to identify allosteric binding sites and ligands.

The value of protein allostery in rational anticancer drug design: an update

INTRODUCTION

Allosteric drugs are advantageous. However, they still face hurdles, including identification of allosteric sites that will effectively alter the active site. Current strategies largely focus on identifying pockets away from the active sites into which the allosteric ligand will dock and do not account for exactly how the active site is altered. Favorable allosteric inhibitors dock into sites that are nearby the active sites and follow nature, mimicking diverse allosteric regulation strategies.

AREAS COVERED

The following article underscores the immense significance of allostery in drug design, describes current allosteric strategies, and especially offers a direction going forward. The article concludes with the authors' expert perspectives on the subject.

EXPERT OPINION

To select a productive venue in allosteric inhibitor development, we should learn from nature. Currently, useful strategies follow this route. Consider, for example, the mechanisms exploited in relieving autoinhibition and in harnessing allosteric degraders. Mimicking compensatory, or rescue mutations may also fall into such a thesis, as can molecular glues that capture features of scaffolding proteins. Capturing nature and creatively tailoring its mimicry can continue to innovate allosteric drug discovery.

AlphaFold predictions of fold-switched conformations are driven by structure memorization

Recent work suggests that AlphaFold (AF)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. We find that (1) AF is a weak predictor of fold switching and (2) some of its successes result from memorization of training-set structures rather than learned protein energetics. Combining >280,000 models from several implementations of AF2 and AF3, a 35% success rate was achieved for fold switchers likely in AF's training sets. AF2's confidence metrics selected against models consistent with experimentally determined fold-switching structures and failed to discriminate between low and high energy conformations. Further, AF captured only one out of seven experimentally confirmed fold switchers outside of its training sets despite extensive sampling of an additional ~280,000 models. Several observations indicate that AF2 has memorized structural information during training, and AF3 misassigns coevolutionary restraints. These limitations constrain the scope of successful predictions, highlighting the need for physically based methods that readily predict multiple protein conformations.

Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.

Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase

Despite the success of AlphaFold methods in predicting single protein structures, these methods showed intrinsic limitations in the characterization of multiple functional conformations of allosteric proteins. The recent NMR-based structural determination of the unbound ABL kinase in the active state and discovery of the inactive low-populated functional conformations that are unique for ABL kinase present an ideal challenge for the AlphaFold2 approaches. In the current study, we employ several adaptations of the AlphaFold2 methodology to predict protein conformational ensembles and allosteric states of the ABL kinase including randomized alanine sequence scanning combined with the multiple sequence alignment subsampling proposed in this study. We show that the proposed new AlphaFold2 adaptation combined with local frustration profiling of conformational states enables accurate prediction of the protein kinase structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AlphaFold2 predictions and detection of hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AlphaFold2. The results of this study uncovered previously unappreciated fundamental connections between local frustration profiles of the functional allosteric states and the ability of AlphaFold2 methods to predict protein structural ensembles of the active and inactive states. This study showed that integration of the randomized sequence scanning adaptation of AlphaFold2 with a robust landscape-based analysis allows for interpretable atomistic predictions and characterization of protein conformational ensembles, providing a physical basis for the successes and limitations of current AlphaFold2 methods in detecting functional allosteric states that play a significant role in protein kinase regulation.

NMR tools to detect protein allostery

Allostery is a fundamental mechanism of cellular homeostasis by intra-protein communication between distinct functional sites. It is an internal process of proteins to steer interactions not only with each other but also with other biomolecules such as ligands, lipids, and nucleic acids. In addition, allosteric regulation is particularly important in enzymatic activities. A major challenge in structural and molecular biology today is unraveling allosteric sites in proteins, to elucidate the detailed mechanism of allostery and the development of allosteric drugs. Here we summarize the recently developed tools and approaches which enable the elucidation of regulatory hotspots and correlated motion in biomolecules, focusing primarily on solution-state nuclear magnetic resonance spectroscopy (NMR). These tools open an avenue towards a rational understanding of the mechanism of allostery and provide essential information for the design of allosteric drugs.

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling

BACKGROUND

Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance.

RESULTS

In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set.

CONCLUSIONS

GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol .

A review on structure-function mechanism and signaling pathway of serine/threonine protein PIM kinases as a therapeutic target

The proviral integration for the Moloney murine leukemia virus (PIM) kinases, belonging to serine/threonine kinase family, have been found to be overexpressed in various types of cancers, such as prostate, breast, colon, endometrial, gastric, and pancreatic cancer. The three isoforms PIM kinases i.e., PIM1, PIM2, and PIM3 share a high degree of sequence and structural similarity and phosphorylate substrates controlling tumorigenic phenotypes like proliferation and cell survival. Targeting short-lived PIM kinases presents an intriguing strategy as in vivo knock-down studies result in non-lethal phenotypes, indicating that clinical inhibition of PIM might have fewer adverse effects. The ATP binding site (hinge region) possesses distinctive attributes, which led to the development of novel small molecule scaffolds that target either one or all three PIM isoforms. Machine learning and structure-based approaches have been at the forefront of developing novel and effective chemical therapeutics against PIM in preclinical and clinical settings, and none have yet received approval for cancer treatment. The stability of PIM isoforms is maintained by PIM kinase activity, which leads to resistance against PIM inhibitors and chemotherapy; thus, to overcome such effects, PIM proteolysis targeting chimeras (PROTACs) are now being developed that specifically degrade PIM proteins. In this review, we recapitulate an overview of the oncogenic functions of PIM kinases, their structure, function, and crucial signaling network in different types of cancer, and the potential of pharmacological small-molecule inhibitors. Further, our comprehensive review also provides valuable insights for developing novel antitumor drugs that specifically target PIM kinases in the future. In conclusion, we provide insights into the benefits of degrading PIM kinases as opposed to blocking their catalytic activity to address the oncogenic potential of PIM kinases.

PRUNE1 and NME/NDPK family proteins influence energy metabolism and signaling in cancer metastases

We describe here the molecular basis of the complex formation of PRUNE1 with the tumor metastasis suppressors NME1 and NME2, two isoforms appertaining to the nucleoside diphosphate kinase (NDPK) enzyme family, and how this complex regulates signaling the immune system and energy metabolism, thereby shaping the tumor microenvironment (TME). Disrupting the interaction between NME1/2 and PRUNE1, as suggested, holds the potential to be an excellent therapeutic target for the treatment of cancer and the inhibition of metastasis dissemination. Furthermore, we postulate an interaction and regulation of the other Class I NME proteins, NME3 and NME4 proteins, with PRUNE1 and discuss potential functions. Class I NME1-4 proteins are NTP/NDP transphosphorylases required for balancing the intracellular pools of nucleotide diphosphates and triphosphates. They regulate different cellular functions by interacting with a large variety of other proteins, and in cancer and metastasis processes, they can exert pro- and anti-oncogenic properties depending on the cellular context. In this review, we therefore additionally discuss general aspects of class1 NME and PRUNE1 molecular structures as well as their posttranslational modifications and subcellular localization. The current knowledge on the contributions of PRUNE1 as well as NME proteins to signaling cascades is summarized with a special regard to cancer and metastasis.

Prediction of Conformational Ensembles and Structural Effects of State-Switching Allosteric Mutants in the Protein Kinases Using Comparative Analysis of AlphaFold2 Adaptations with Sequence Masking and Shallow Subsampling

Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture of the effects of single point mutations that induced significant structural changes. We systematically examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. On the other hand, the predicted conformational ensembles for the G269E/M309L/T334I and M309L/L320I/T334I triple ABL mutants that share activating T334I gate-keeper substitution are dominated by the active ABL form. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric mediating centers that often directly correspond to state-switching mutational sites or reside in their immediate local structural proximity, which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.

Tribulations and future opportunities for artificial intelligence in precision medicine

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G protein-coupled receptors (GPCRs). GPCRs are the target of over one-third of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.

Interpretable Atomistic Prediction and Functional Analysis of Conformational Ensembles and Allosteric States in Protein Kinases Using AlphaFold2 Adaptation with Randomized Sequence Scanning and Local Frustration Profiling

The groundbreaking achievements of AlphaFold2 (AF2) approaches in protein structure modeling marked a transformative era in structural biology. Despite the success of AF2 tools in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and fold-switching systems. The recent NMR-based structural determination of the unbound ABL kinase in the active state and two inactive low-populated functional conformations that are unique for ABL kinase presents an ideal challenge for AF2 approaches. In the current study we employ several implementations of AF2 methods to predict protein conformational ensembles and allosteric states of the ABL kinase including (a) multiple sequence alignments (MSA) subsampling approach; (b) SPEACH_AF approach in which alanine scanning is performed on generated MSAs; and (c) introduced in this study randomized full sequence mutational scanning for manipulation of sequence variations combined with the MSA subsampling. We show that the proposed AF2 adaptation combined with local frustration mapping of conformational states enable accurate prediction of the ABL active and intermediate structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AF2 predictions and limitations in detecting hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AF2 methods. This study uncovered previously unappreciated, fundamental connections between distinct patterns of local frustration in functional kinase states and AF2 successes/limitations in detecting low-populated frustrated conformations, providing a better understanding of benefits and limitations of current AF2-based adaptations in modeling of conformational ensembles.

What can AlphaFold do for antimicrobial amyloids?

Amyloids, protein, and peptide assemblies in various organisms are crucial in physiological and pathological processes. Their intricate structures, however, present significant challenges, limiting our understanding of their functions, regulatory mechanisms, and potential applications in biomedicine and technology. This study evaluated the AlphaFold2 ColabFold method's structure predictions for antimicrobial amyloids, using eight antimicrobial peptides (AMPs), including those with experimentally determined structures and AMPs known for their distinct amyloidogenic morphological features. Additionally, two well-known human amyloids, amyloid-β and islet amyloid polypeptide, were included in the analysis due to their disease relevance, short sequences, and antimicrobial properties. Amyloids typically exhibit tightly mated β-strand sheets forming a cross-β configuration. However, certain amphipathic α-helical subunits can also form amyloid fibrils adopting a cross-α structure. Some AMPs in the study exhibited a combination of cross-α and cross-β amyloid fibrils, adding complexity to structure prediction. The results showed that the AlphaFold2 ColabFold models favored α-helical structures in the tested amyloids, successfully predicting the presence of α-helical mated sheets and a hydrophobic core resembling the cross-α configuration. This implies that the AI-based algorithms prefer assemblies of the monomeric state, which was frequently predicted as helical, or capture an α-helical membrane-active form of toxic peptides, which is triggered upon interaction with lipid membranes.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

Computational structure-based approach to study chimeric antigens using a new protein scaffold displaying foreign epitopes

The identification and recombinant production of functional antigens and/or epitopes of pathogens represent a crucial step for the development of an effective protein-based vaccine. Many vaccine targets are outer membrane proteins anchored into the lipidic bilayer through an extended hydrophobic portion making their recombinant production challenging. Moreover, only the extracellular loops, and not the hydrophobic regions, are naturally exposed to the immune system. In this work, the Domain 3 (D3) from Group B Streptococcus (GBS) pilus 2a backbone protein has been identified and engineered to be used as a scaffold for the display of extracellular loops of two Neisseria gonorrhoeae membrane proteins (PorB.1b and OpaB). A computational structure-based approach has been applied to the design of both the scaffold and the model antigens. Once identified the best D3 engineerable site, several different chimeric D3 displaying PorB.1b and OpaB extracellular loops were produced as soluble proteins. Each molecule has been characterized in terms of solubility, stability, and ability to correctly display the foreign epitope. This antigen dissection strategy allowed the identification of most immunogenic extracellular loops of both PorB.1b and OpaB gonococcal antigens. The crystal structure of chimeric D3 displaying PorB.1b immunodominant loop has been obtained confirming that the engineerization did not alter the predicted native structure of this epitope. Taken together, the reported data suggest that D3 is a novel protein scaffold for epitope insertion and display, and a valid alternative to the production of whole membrane protein antigens. Finally, this work describes a generalized computational structure-based approach for the identification, design, and dissection of epitopes in target antigens through chimeric proteins.

Representing structures of the multiple conformational states of proteins

Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges associated with representing single- and multiple-state models in data archives, with a particular focus on NMR structures. Establishing standardized representations and annotations will facilitate effective communication and understanding of these complex models to the broader scientific community.

Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15

For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.

Automated benchmarking of combined protein structure and ligand conformation prediction

The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.

Cell phenotypes can be predicted from propensities of protein conformations

Proteins exist as dynamic conformational ensembles. Here we suggest that the propensities of the conformations can be predictors of cell function. The conformational states that the molecules preferentially visit can be viewed as phenotypic determinants, and their mutations work by altering the relative propensities, thus the cell phenotype. Our examples include (i) inactive state variants harboring cancer driver mutations that present active state-like conformational features, as in K-Ras4BG12V compared to other K-Ras4BG12X mutations; (ii) mutants of the same protein presenting vastly different phenotypic and clinical profiles: cancer and neurodevelopmental disorders; (iii) alterations in the occupancies of the conformational (sub)states influencing enzyme reactivity. Thus, protein conformational propensities can determine cell fate. They can also suggest the allosteric drugs efficiency.

Protein conformational ensembles in function: roles and mechanisms

The sequence-structure-function paradigm has dominated twentieth century molecular biology. The paradigm tacitly stipulated that for each sequence there exists a single, well-organized protein structure. Yet, to sustain cell life, function requires (i) that there be more than a single structure, (ii) that there be switching between the structures, and (iii) that the structures be incompletely organized. These fundamental tenets called for an updated sequence-conformational ensemble-function paradigm. The powerful energy landscape idea, which is the foundation of modernized molecular biology, imported the conformational ensemble framework from physics and chemistry. This framework embraces the recognition that proteins are dynamic and are always interconverting between conformational states with varying energies. The more stable the conformation the more populated it is. The changes in the populations of the states are required for cell life. As an example, in vivo, under physiological conditions, wild type kinases commonly populate their more stable "closed", inactive, conformations. However, there are minor populations of the "open", ligand-free states. Upon their stabilization, e.g., by high affinity interactions or mutations, their ensembles shift to occupy the active states. Here we discuss the role of conformational propensities in function. We provide multiple examples of diverse systems, including protein kinases, lipid kinases, and Ras GTPases, discuss diverse conformational mechanisms, and provide a broad outlook on protein ensembles in the cell. We propose that the number of molecules in the active state (inactive for repressors), determine protein function, and that the dynamic, relative conformational propensities, rather than the rigid structures, are the hallmark of cell life.

Allosteric crosstalk in modular proteins: Function fine-tuning and drug design

Modular proteins are regulatory proteins that carry out more than one function. These proteins upregulate or downregulate a biochemical cascade to establish homeostasis in cells. To switch the function or alter the efficiency (based on cellular needs), these proteins require different facilitators that bind to a site different from the catalytic (active/orthosteric) site, aka 'allosteric site', and fine-tune their function. These facilitators (or effectors) are allosteric modulators. In this Review, we have discussed the allostery, characterized them based on their mechanisms, and discussed how allostery plays an important role in the activity modulation and function fine-tuning of proteins. Recently there is an emergence in the discovery of allosteric drugs. We have also emphasized the role, significance, and future of allostery in therapeutic applications.

Computational analysis of regulatory regions in human protein kinases

Eukaryotic proteins often feature modular domain structures comprising globular domains that are connected by linker regions and intrinsically disordered regions that may contain important functional motifs. The intramolecular interactions of globular domains and nonglobular regions can play critical roles in different aspects of protein function. However, studying these interactions and their regulatory roles can be challenging due to the flexibility of nonglobular regions, the long insertions separating interacting modules, and the transient nature of some interactions. Obtaining the experimental structures of multiple domains and functional regions is more difficult than determining the structures of individual globular domains. High-quality structural models generated by AlphaFold offer a unique opportunity to study intramolecular interactions in eukaryotic proteins. In this study, we systematically explored intramolecular interactions between human protein kinase domains (KDs) and potential regulatory regions, including globular domains, N- and C-terminal tails, long insertions, and distal nonglobular regions. Our analysis identified intramolecular interactions between human KDs and 35 different types of globular domains, exhibiting a variety of interaction modes that could contribute to orthosteric or allosteric regulation of kinase activity. We also identified prevalent interactions between human KDs and their flanking regions (N- and C-terminal tails). These interactions exhibit group-specific characteristics and can vary within each specific kinase group. Although long-range interactions between KDs and nonglobular regions are relatively rare, structural details of these interactions offer new insights into the regulation mechanisms of several kinases, such as HASPIN, MAPK7, MAPK15, and SIK1B.

In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer's Disease

Accurate protein structure prediction using computational methods remains a challenge in molecular biology. Recent advances in AI-powered algorithms provide a transformative effect in solving this problem. Even though AlphaFold's performance has improved since its release, there are still limitations that apply to its efficacy. In this study, a selection of proteins related to the pathology of Alzheimer's disease was modeled, with Presenilin-1 (PSN1) and its mutated variants in the foreground. Their structural predictions were evaluated using the ColabFold implementation of AlphaFold, which utilizes MMseqs2 for the creation of multiple sequence alignments (MSAs). A higher number of recycles than the one used in the AlphaFold DB was selected, and no templates were used. In addition, prediction by RoseTTAFold was also applied to address how structures from the two deep learning frameworks match reality. The resulting conformations were compared with the corresponding experimental structures, providing potential insights into the predictive ability of this approach in this particular group of proteins. Furthermore, a comprehensive examination was performed on features such as predicted regions of disorder and the potential effect of mutations on PSN1. Our findings consist of highly accurate superpositions with little or no deviation from experimentally determined domain-level models.

Psychrophilic enzymes: strategies for cold-adaptation

Psychrophilic organisms thriving at near-zero temperatures synthesize cold-adapted enzymes to sustain cell metabolism. These enzymes have overcome the reduced molecular kinetic energy and increased viscosity inherent to their environment and maintained high catalytic rates by development of a diverse range of structural solutions. Most commonly, they are characterized by a high flexibility coupled with an intrinsic structural instability and reduced substrate affinity. However, this paradigm for cold-adaptation is not universal as some cold-active enzymes with high stability and/or high substrate affinity and/or even an unaltered flexibility have been reported, pointing to alternative adaptation strategies. Indeed, cold-adaptation can involve any of a number of a diverse range of structural modifications, or combinations of modifications, depending on the enzyme involved, its function, structure, stability, and evolutionary history. This paper presents the challenges, properties, and adaptation strategies of these enzymes.

Modeling conformational states of proteins with AlphaFold

Many proteins exert their function by switching among different structures. Knowing the conformational ensembles affiliated with these states is critical to elucidate key mechanistic aspects that govern protein function. While experimental determination efforts are still bottlenecked by cost, time, and technical challenges, the machine-learning technology AlphaFold showed near experimental accuracy in predicting the three-dimensional structure of monomeric proteins. However, an AlphaFold ensemble of models usually represents a single conformational state with minimal structural heterogeneity. Consequently, several pipelines have been proposed to either expand the structural breadth of an ensemble or bias the prediction toward a desired conformational state. Here, we analyze how those pipelines work, what they can and cannot predict, and future directions.

Relaxation and single site multiple mutations to identify and control allosteric networks

Advances in Nuclear Magnetic Resonance (NMR) spectroscopy have allowed for the identification and characterization of movements in enzymes over the last 20 years that has also revealed the complexities of allosteric coupling. For example, many of the inherent movements of enzymes, and proteins in general, have been shown to be highly localized but nonetheless still coupled over long distances. Such partial couplings provide challenges to both identifying allosteric networks of dynamic communication and determining their roles in catalytic function. We have developed an approach to help identify and engineer enzyme function, called Relaxation And Single Site Multiple Mutations (RASSMM). This approach is a powerful extension of mutagenesis and NMR that is based on the observation that multiple mutations to a single site distal to the active site allosterically induces different effects to networks. Such an approach generates a panel of mutations that can also be subjected to functional studies in order to match catalytic effects with changes to coupled networks. In this review, the RASSMM approach is briefly outlined together with two applications that include cyclophilin-A and Biliverdin Reductase B.

State-of-the-art and novel approaches to mild solubilization of inclusion bodies

Throughout the twenty-first century, the view on inclusion bodies (IBs) has shifted from undesired by-products towards a targeted production strategy for recombinant proteins. Inclusion bodies can easily be separated from the crude extract after cell lysis and contain the product in high purity. However, additional solubilization and refolding steps are required in the processing of IBs to recover the native protein. These unit operations remain a highly empirical field of research in which processes are developed on a case-by-case basis using elaborate screening strategies. It has been shown that a reduction in denaturant concentration during protein solubilization can increase the subsequent refolding yield due to the preservation of correctly folded protein structures. Therefore, many novel solubilization techniques have been developed in the pursuit of mild solubilization conditions that avoid total protein denaturation. In this respect, ionic liquids have been investigated as promising agents, being able to solubilize amyloid-like aggregates and stabilize correctly folded protein structures at the same time. This review briefly summarizes the state-of-the-art of mild solubilization of IBs and highlights some challenges that prevent these novel techniques from being yet adopted in industry. We suggest mechanistic models based on the thermodynamics of protein unfolding with the aid of molecular dynamics simulations as a possible approach to solve these challenges in the future.